Das Data4UrbanMobility Paper “Crosstown traffic – supervised prediction of impact of planned special events on urban traffic” wurde im renommierten GeoInformatica Journal veröffentlicht. Das Paper beschreibt, wie Auswirkungen von Veranstaltungen auf den Straßenverkehr gemessen und prognostiziert werden können. Den vollen Artikel finden Sie hier:

Crosstown Traffic – Supervised Prediction of Impact of Planned Special Events on Urban Traffic. Tempelmeier, Nicolas; Dietze, Stefan; Demidova, Elena (2019). URL

• Die Kämpfe der Zukunft: Gleichheit und Gerechtigkeit im 21. Jahrhundert Piketty, Thomas; Sandel, Michael J. (2025). (2. Auflage ) C.H. Beck, München.

  Wir leben in einer Zeit tiefer politischer Instabilität und schwerer Umweltkrisen. Was ist zu tun, um gegenzusteuern? Piketty und Sandel stimmen in vielen Punkten überein: Wir brauchen mehr Investitionen in inklusive Gesundheit und Ausbildung, höhere progressive Steuern, klare Grenzen für die Macht des Reichtums und der Märkte. Aber wie kommen wir dahin? Und sollen wir materiellen Wohlstand oder sozialen Wandel priorisieren? Schließlich: Wie ist es um all diese Themen bestellt, wenn überall auf der Welt ein neuer radikaler Nationalismus auf dem Vormarsch ist?
• A SURVEY ON A MODEL FOR PESTICIDE RECOMMENDATION USING MACHINE LEARNING. lakshmi S Abbigeri, Vijaya; Devanagavi, Geetha D (2025).
• Female moths call in vain: Streetlights diminish the promise of mating. Storms, Mona; Degen, Tobias; Degen, Jacqueline (2025). n/a(n/a)

  Abstract Artificial light at night has increased significantly in recent decades, profoundly impacting moths, which are key contributors to pollination networks. The global shift to light-emitting diode (LED) streetlights has further transformed the nocturnal light environment, especially because of their high variability in spectrum and intensity. However, the impact on moths' mating success remains poorly understood, highlighting the urgent need to investigate their behavioural responses. We recorded the flight behaviour of male moths (Sphinx ligustri L.) using a symmetrical flight tunnel. Two different light environments, homogeneous and heterogeneous, with a female positioned on one side of the tunnel, were used to test in different combinations the effect of LEDs (Amber, Warm white and Neutral white) and intensities (0.05, 150, 370 and 590 lux) on arrival location, flight duration and direction changes of males. In a homogeneous light environment, all tested LEDs significantly reduced the arrival frequency of males at the females compared with a dim light condition, demonstrating that streetlight negatively affects encounter rates. However, neither flight duration nor direction changes were significantly affected. In a heterogeneous light environment, the highest proportion of males was able to reach the females in the presence of Warm white (2200 K), indicating that Amber (1800 K) may not be a universal solution for mitigating the negative effects of artificial light. Both flight duration and direction changes were significantly reduced for Neutral white (3900 K) compared with Warm white, concluding that this LED type induced fast and directed flights towards the light source. Interestingly, light intensity did not significantly affect any of the analysed parameters. Statement of significance: The presence of light drastically reduced the probability of males reaching the females. Furthermore, LEDs with the lowest correlated colour temperature (CCT) were not the ones leading to the highest promise of mating success, indicating that the spectrum of an LED might have an optimum. As LED efficiency decreases with lower CCT values this finding is of crucial importance when considering the indirect effects of light pollution, such as the CO2 footprint, when developing lighting strategies as minimally disruptive as possible.
• VeriBin: Adaptive Verification of Patches at the Binary Level. Wu, Hongwei; Wu, Jianliang; Wu, Ruoyu; Sharma, Ayushi; Machiry, Aravind; Bianchi, Antonio (2025).
• An asymmetric nautilus-like HflK/C assembly controls FtsH proteolysis of membrane proteins. Ghanbarpour, Alireza; Telusma, Bertina; Powell, Barrett M; Zhang, Jia Jia; Bolstad, Isabella; Vargas, Carolyn; Keller, Sandro; Baker, Tania A; Sauer, Robert T; Davis, Joseph H (2025).
• Structures of ΔD421 Truncated Tau Fibrils. El Mammeri, Nadia; Duan, Pu; Hong, Mei (2025). 437(10)
• Deadline-Aware Task Scheduling Strategy for Reducing Network Contention in NoC-Based Multicore Systems. Farooq, Mohd; Zafar, Aasim; Samad, Abdus N. Meghanathan (ed.) (2025). (Vol. 17) 19–34.

  Network on Chip (NoC) has revolutionized on-chip communication in multicore systems, establishing itself as a critical design paradigm for modern multicore processors and System-on-Chip (SoC) architectures. In contrast to standard bus-based interconnects, NoC employs a network-like structure that enables scalable and efficient communication between several processing components. This technique has addressed the issues raised by the rising complexity of integrated circuits, providing higher performance, reduced latency, and increased power efficiency. NoC has played a critical role in enabling the development of high-performance computing systems and sophisticated electrical devices by facilitating robust communication channels between components, marking a substantial shift from earlier interconnect technologies. Mapping tasks to the Network on Chip (NoC) is a critical challenge in multicore systems, as it can substantially impact throughput due to communication congestion. Poor mapping decisions can lead to an increase in total makespan, increase in task missing deadlines, and underutilization of cores. The proposed algorithm schedules tasks to cores while considering network congestion through various links and availability of processing elements. The experimental results demonstrate that the proposed algorithm improves task deadline satisfaction and minimize makespan by 23.83% and 22.83%, respectively, when compared to other dynamic task allocation algorithms.
• StoichLife: A Global Dataset of Plant and Animal Elemental Content. González, Angélica L.; Merder, Julian; Andraczek, Karl; Brose, Ulrich; Filipiak, Michal; Harpole, W. Stanley; Hillebrand, Helmut; Jackson, Michelle C.; Jochum, Malte; Leroux, Shawn J.; Nessel, Mark P.; Onstein, Renske E.; Paseka, Rachel; Perry, George L. W.; Rugenski, Amanda; Sitters, Judith; Sperfeld, Erik; Striebel, Maren; Zandona, Eugenia; Aymes, Jean-Christophe; Blanckaert, Alice; Bluhm, Sarah L.; Doi, Hideyuki; Eisenhauer, Nico; Farjalla, Vinicius F.; Hood, James; Kratina, Pavel; Labonne, Jacques; Lovelock, Catherine E.; Moody, Eric K.; Mozsár, Attila; Nash, Liam; Pollierer, Melanie M.; Potapov, Anton; Romero, Gustavo Q.; Roussel, Jean-Marc; Scheu, Stefan; Scheunemann, Nicole; Seeber, Julia; Steinwandter, Michael; Susanti, Winda Ika; Tiunov, Alexei; Dézerald, Olivier (2025). 12(1) 569.

  The elemental content of life is a key trait shaping ecology and evolution, yet organismal stoichiometry has largely been studied on a case-by-case basis. This limitation has hindered our ability to identify broad patterns and mechanisms across taxa and ecosystems. To address this, we present StoichLife, a global dataset of 28,049 records from 5,876 species spanning terrestrial, freshwater, and marine realms. Compiled from published and unpublished sources, StoichLife documents elemental content and stoichiometric ratios (\%C, \%N, \%P, C:N, C:P, and N:P) for individual plants and animals. The dataset is standardized and, where available, includes information on taxonomy, habitat, body mass (for animals), geography, and environmental conditions such as temperature, solar radiation, and nutrient availability. By providing an unprecedented breadth of organismal stoichiometry, StoichLife enables the exploration of global patterns, ecological and evolutionary drivers, and context-dependent variations. This resource advances our understanding of the chemical makeup of life and its responses to environmental change, supporting progress in ecological stoichiometry and related fields.
• Enhancing Public Reputation Systems: Trust Scaling to Mitigate Voter Subjectivity. Nguyen, Manh Hung (2025). 142–146.
• The Complexity of Counting Turns in the Line-Based Dial-a-Ride Problem. Lauerbach, Antonio; Reiter, Kendra; Schmidt, Marie R. Královič, V. Kůrková (eds.) (2025). 85–98.
• A digital twin of the Biograph Vision Quadra long axial field of view PET/CT: Monte Carlo simulation and image reconstruction framework. Pommranz, Christian M.; Elmoujarkach, Ezzat A.; Lan, Wenhong; Cabello, Jorge; Linder, Pia M.; Vo, Hong Phuc; Mannheim, Julia G.; Santangelo, Andrea; Conti, Maurizio; la Fougère, Christian; Rafecas, Magdalena; Schmidt, Fabian P. (2025). 12(1)
• Towards Cognitive Situational Awareness in Maritime Traffic Using Federated Evidential Learning. Gao, Shang; Huang, Zhixin; Al-Falouji, Ghassan; Sick, Bernhard; Tomforde, Sven (2025).

  The increasing complexity of maritime navigation and the shift towards (semi)autonomous systems necessitate enhanced situational awareness (SA) to ensure maritime safety. This transition introduces new requirements for situation mod- elling and SA, particularly in busy waterways. To address these challenges, we present the Federated Evidential Learning for Anomaly Detection of Ship Trajectories (FEAST) framework, which integrates Federated Learning and Evidential Learning to provide a privacy-preserving, collaborative, and uncertainty- aware approach to out-of-distribution (OOD) anomaly detection in maritime traffic. FEAST uses data from the Automatic Identification System from the Kiel region, Germany, which exhibits unique characteristics of dynamic and heterogeneous maritime activity due to its connection with the traffic-dense Kiel Canal. Our extensive evaluations demonstrate that FEAST improves OOD anomaly detection by leveraging epistemic and aleatoric uncertainty estimates, outperforming baseline methods such as Denoise AutoEncoders and Variational AutoEncoders. Consequently, FEAST forms a solution to reliable and inter- pretable maritime traffic anomaly detection, supporting enhanced SA in maritime operations.
• Unify: Uncertainty incorporated Federated Learning for Object Detection. Gao, Shang; Sick, Bernhard; Götz-Hahn, Franz (2025).

  Federated Learning (FL) has demonstrated promise for collaborative training on decentralized data while preserving privacy. However, applying FL to object detection remains challenging due to non-IID data distributions and domain shifts that can degrade performance with standard aggregation methods. To address these issues, we propose the Uncertainty Incorporated Federated Learning for Object Detection (Unify) framework, a novel FL framework that leverages Evidential Learning (EL) to estimate both epistemic and aleatoric uncertainties. By integrating these uncertainty estimates into the aggregation process, Unify down-weights unreliable client updates, leading to improved cross-domain generalization and enhanced stability. Built upon a lightweight YOLOX detector, our approach achieves detection performance that closely approaches centralized training while significantly outperforming conventional FedAvg under heterogeneous conditions. Evaluations on the KITTI and nuImages datasets demonstrate that Unify improves mean Average Precision (mAP) and yields uncertainties. The findings highlight the potential of uncertainty-incorporated FL in developing resilient, adaptive, and self-organizing computing systems.
• Dynamic Multimodal Cyclist Behaviour Modelling: From Representation Insights to Federated Collaboration. Gao, Shang S. Tomforde, C. Krupitzer (eds.) (2025).

  In the field of intelligent transportation systems, accurately modelling cyclist behaviour is crucial to enhancing traffic safety and efficiency, particularly as road environments are increasingly shared with automated vehicles. Modelling cyclist behaviour is intrinsically complex due to its dynamic, context-dependent nature, shaped by diverse environmental and individual factors. These complexities pose significant challenges for traditional centralized learning approaches, particularly regarding data privacy concerns and heterogeneity across distributed data sources. Federated learning, as an emerging framework, offers the potential to address these challenges by enabling collaborative model training without sharing raw data. However, in real-world traffic scenarios, federated learning must tackle the complexities of non-independent and identically distributed data and the need for real-time adaptability in dynamic traffic environments. This work explores an uncertainty-aware federated learning framework, incorporating multimodal data fusion, to support modelling cyclist behaviour in traffic environments. This research addresses key challenges in federated learning for cyclist behaviour modelling and explores potential solutions, offering both theoretical and practical insights for future applications in intelligent transportation sys- tems.
• Boundedness of polarized log Calabi-Yau fibrations with bounded bases. Jiang, Xiaowei; Jiao, Junpeng; Zhu, Minzhe (2025).
• „Die Sonne fühlt sich an wie der Lauf einer Pistole, die auf dich gerichtet ist“. Schrader, Christopher (2025).

  Der Klimawandel steigert die Gefahr durch Hitzewellen und UV-Strahlung – und ist deswegen ein Thema für den gesetzlichen Arbeitsschutz. Teil 1 unserer Serie #BetriebsKlima
• Simplification of polyline bundles of graphs and trees. Bosch, Yannick; Schäfer, Peter; Spoerhase, Joachim; Storandt, Sabine; Zink, Johannes (2025). 16(1) 203–252.
• Empirische Rekonstruktion von Kompetenzen einer BNE. Just, Janina; Siller, Hans-Stefan; Vorhölter, Katrin (2025).
• The flip side of digital technologies in internal party elections: a cybersecurity dilemma?. Paulis, Emilien; Kins, Lucas; Barberà, Oscar; Vittori, Davide (2025). 1–26.

  The digitalization of party organizations has garnered increasing scholarly attention in recent years. This study examines an underexplored area: how individuals working for political parties perceive the cybersecurity threats associated with using digital technologies for intra-party voting procedures. Drawing on survey data collected online in 2021 from three European democracies (Belgium, Italy, and Spain), our analysis reveals that cybersecurity concerns are more pronounced among party actors who (1) are non-elected staffers born before the 1980s, (2) had negative experiences with digital technologies during the COVID-19 crisis, or (3) work for right-wing parties. Our findings highlight the need to avoid technological determinism and call for a more systematic study of cybersecurity challenges in the context of party digitalization.
• What it takes to solve the Hubble tension through scale-dependent modifications of the primordial power spectrum. Lee, Nanoom; Braglia, Matteo; Ali-Haïmoud, Yacine (2025).
• Nuclear phase retrieval spectroscopy using resonant x-ray scattering. Yuan, Ziyang; Wang, Hongxia; Li, Zhiwei; Wang, Tao; Wang, Hui; Huang, Xinchao; Li, Tianjun; Ma, Ziru; Zhu, Linfan; Xu, Wei; Zhang, Yujun; Chen, Yu; Masuda, Ryo; Yoda, Yoshitaka; Yuan, Jianmin; Pálffy, Adriana; Kong, Xiangjin (2025). 16(1) 3096.

  Light-matter interaction is exploited in spectroscopic techniques to access information about molecular, atomic or nuclear constituents of a sample. While scattered light carries both amplitude and phase information of the electromagnetic field, the latter is lost in intensity measurements. However, often the phase information is paramount to reconstruct the desired information of the target, as it is well known from coherent x-ray imaging. Here we introduce a phase retrieval method which allows us to reconstruct the field phase information from two-dimensional time- and energy-resolved spectra. We apply this method to the case of x-ray scattering off Mössbauer nuclei at a synchrotron radiation source. Knowledge of the phase allows also for the reconstruction of energy spectra from two-dimensional experimental data sets with excellent precision, without theoretical modelling of the sample. Our approach provides an efficient and accurate data analysis tool which will benefit x-ray quantum optics and Mössbauer spectroscopy with synchrotron radiation alike.
• Creative Commons Public License (CCPL): Kommentar und Handbuch für die Rechtspraxis Boehm, Franziska; Euler, Ellen; Klimpel, Paul; Rack, Fabian; Weitzmann, John (2025). Carl Grossmann Publishers, Berlin.

  Creative Commons-Lizenzen werden seit rund 20 Jahren eingesetzt und sind als freies Lizenzmodell weltweit etabliert. Mit ihnen können Kreative der Allgemeinheit die Nutzung urheberrechtlich geschützter Materialien unter bestimmten Bedingungen erlauben.Das Buch kommentiert zum ersten Mal den Lizenztext der Creative Commons Public License und leistet somit einen wichtigen Beitrag zur Rechtspraxis mit Creative Commons-Lizenzen.Der Kommentar bietet damit die erste umfassende juristische Analyse der Creative Commons-Lizenzen. Der Handbuchteil stellt Spezifika unter anderem für die Wissenschaft, die öffentliche Verwaltung und die Rechtsdurchsetzung dar.
• Beware of "Explanations" of AI Martens, David; Shmueli, Galit; Evgeniou, Theodoros; Bauer, Kevin; Janiesch, Christian; Feuerriegel, Stefan; Gabel, Sebastian; Goethals, Sofie; Greene, Travis; Klein, Nadja; Kraus, Mathias; Kühl, Niklas; Perlich, Claudia; Verbeke, Wouter; Zharova, Alona; Zschech, Patrick; Provost, Foster (2025).
• Cross-ecosystem effects of light pollution and invasive signal crayfish on riparian spiders. Manfrin, Alessandro; Schirmel, Jens; Arias, Marina; Entling, Martin H.; Hoffmann, Julian; Huszarik, Maike; Kolb, Sebastian; Schrimpf, Anne; Schmidt, Thomas; Bundschuh, Mirco; Schulz, Ralf (2025). e03577.
• Testing the soundscape response to silvicultural interventions in a controlled before-and-after experiment. Rothacher, Julia; Mitesser, Oliver; Müller, Sandra; Scherer-Lorenzen, Michael; Buřivalová, Zuzana; Müller, Jörg (2025). 306 111116.
• AudioProtoPNet: An interpretable deep learning model for bird sound classification. Heinrich, René; Rauch, Lukas; Sick, Bernhard; Scholz, Christoph (2025). 87 103081.

  Deep learning models have significantly advanced acoustic bird monitoring by recognizing numerous bird species based on their vocalizations. However, traditional deep learning models are black boxes that provide no insight into their underlying computations, limiting their usefulness to ornithologists and machine learning engineers. Explainable models could facilitate debugging, knowledge discovery, trust, and interdisciplinary collaboration. We introduce AudioProtoPNet, an adaptation of the Prototypical Part Network (ProtoPNet) for multi-label bird sound classification. It is inherently interpretable, leveraging a ConvNeXt backbone to extract embeddings and a prototype learning classifier trained on these embeddings. The classifier learns prototypical patterns of each bird species’ vocalizations from spectrograms of instances in the training data. During inference, recordings are classified by comparing them to learned prototypes in the embedding space, providing explanations for the model’s decisions and insights into the most informative embeddings of each bird species. The model was trained on the BirdSet training dataset, which consists of 9734 bird species and over 6800 h of recordings. Its performance was evaluated on the seven BirdSet test datasets, covering different geographical regions. AudioProtoPNet outperformed the state-of-the-art bird sound classification model Perch, which is superior to the more popular BirdNet, achieving an average AUROC of 0.90 and a cmAP of 0.42, with relative improvements of 7.1% and 16.7% over Perch, respectively. These results demonstrate that even for the challenging task of multi-label bird sound classification, it is possible to develop powerful yet interpretable deep learning models that provide valuable insights for professionals in ornithology and machine learning.
• CHaOS: A Persistent Lightweight Cache Hybridization-aware OS. Wilbert, Nils; Szymanski, Matthias; Wildermann, Stefan; Herzog, Henriette; Hönig, Timo; Teich, Jürgen (2025). (Vol. 38)

  Computer systems are prone to crashes and power outages, particularly in intermittent computing scenarios where embedded systems rely on unreliable energy-harvesting sources. Hybrid caches, which integrate volatile and non-volatile memory (NVM) sections, offer opportunities for efficient operating system (OS) services to ensure data persistence over power interruptions. However, the lack of OS awareness of hybrid cache structures can result in inefficient backups, data loss and inconsistencies, as well as underutilization of the potential of hybrid caches. This paper introduces the Cache Hybridization-aware Operating System (CHaOS), that is based on a co-design of the OS and the underlying hardware to systematically persist volatile states and data in case of power outages. Our evaluation demonstrates that CHaOS not only proves robust against power outages, but also can significantly reduce the backup latency. In our test of using CHaOS to execute an image processing application on a hybrid-cache system, already with 75% of caches being non-volatile, the backup latency drops to only 8.2% compared to a completely volatile cache. At the same time, the energy demand could be reduced due to lower static power consumption of NVM technology. Our investigations validate the feasibility of CHaOS and establish a strong foundation for future research.
• Handbook on Neurosymbolic AI and Knowledge Graphs Hitzler, Pascal; Dalal, Abhilekha; Mahdavinejad, Mohammad Saeid; Norouzi, Sanaz Saki (2025). IOS Press.

  Neural approaches have traditionally excelled at perceptual tasks like pattern recognition, whereas symbolic frameworks have offered powerful methods for knowledge representation, logical inference, and interpretability, but the current AI landscape is increasingly defined by hybrid systems that blend these complementary paradigms. This is particularly relevant in the context of knowledge graphs (KGs), which serve as a bridge between symbolic logic and the subsymbolic world of deep learning. The Handbook on Neurosymbolic AI and Knowledge Graphs deals with state-of-the-art neurosymbolic and KG-based AI, reflecting an ecosystem in which large language models, deep neural networks, and symbolic representations converge. It illustrates the progress that has been made, while also revealing emerging challenges in trustworthiness, interpretability, and scalability. The first four chapters are on the foundations of neural and symbolic AI. In the following chapters the authors explore the nuances of KG representation and embeddings, moving on to KG construction, integration, and quality, and covering challenges such as entity alignment, canonicalization, fusion, and the critical aspect of uncertainty management. Offering solutions that seamlessly combine symbolic logic with deep learning pipelines, the handbook deals with question answering, program synthesis, and dynamic KG methods, before moving on to the need to ensure transparency, accountability, and trust in systems operating on increasingly complex data. The final chapters demonstrate problem solving across news analytics, literary studies, life sciences, food computing, social media, and more. This work offers a comprehensive overview of these intersecting fields and will be of interest to researchers and developers looking for a practical guide to building AI systems that are robust, transparent, and ethically grounded.
• What it takes to solve the Hubble tension through scale-dependent modifications of the primordial power spectrum. Lee, Nanoom; Braglia, Matteo; Ali-Haïmoud, Yacine (2025).
• Network Traffic Intrusion Detection Strategy Based on E-GraphSAGE and LSTM. Bao, Haizhou; Chen, Minhao; Huo, Yiming; Yu, Guorong; Nie, Lei; Li, Peng; Wang, Yuxuan in Lecture Notes in Computer Science, D.-S. Huang, W. Chen, J. Guo (eds.) (2024). (Vol. 14870) 25–37.
• MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning. Wang, Ke; Ren, Houxing; Zhou, Aojun; Lu, Zimu; Luo, Sichun; Shi, Weikang; Zhang, Renrui; Song, Linqi; Zhan, Mingjie; Li, Hongsheng (2024).
• Are Large Language Models Possible to Conduct Cognitive Behavioral Therapy?. Shen, Hao; Li, Zihan; Yang, Minqiang; Ni, Minghui; Tao, Yongfeng; Yu, Zhengyang; Zheng, Weihao; Xu, Chen; Hu, Bin M. Cannataro, H. J. Zheng, L. Gao, J. Cheng, J. L. de Miranda, E. Zumpano, X. Hu, Y.-R. Cho, T. Park (eds.) (2024). 3695–3700.
• Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification. Zhou, Aojun; Wang, Ke; Lu, Zimu; Shi, Weikang; Luo, Sichun; Qin, Zipeng; Lu, Shaoqing; Jia, Anya; Song, Linqi; Zhan, Mingjie; Li, Hongsheng (2024).
• ULTRA-FAB5G: Unleashing the Potential of 5G for Industrial Digitalization. Perdigão, André; Santos, David; Fonseca, João Pedro; Silva, Rui; Correia, Marco; Marzouk, Fatma; Soeiro, Paulo; Corujo, Daniel; Quevedo, José; Aguiar, Rui L. (2024). 955–960.
• Research on Human-Computer Re-Interaction in AR Books. Wang, Kaixiang; Shi, Juanjuan; Wang, Chunpeng in Communications in Computer and Information Science, C. Stephanidis, M. Antona, S. Ntoa, G. Salvendy (eds.) (2024). (Vol. 2116) 128–136.
• CARGO: Crypto-Assisted Differentially Private Triangle Counting Without Trusted Servers. Liu, Shang; Cao, Yang; Murakami, Takao; Liu, Jinfei; Yoshikawa, Masatoshi (2024). 1671–1684.
• Speed Prediction of Multiple Traffic Scenarios with Local Fluctuation. Zhang, Tianyu; Li, Lin; Zhang, Rui; Tao, Xiaohui in Lecture Notes in Computer Science, W. Zhang, A. K. H. Tung, Z. Zheng, Z. Yang, X. Wang, H. Guo (eds.) (2024). (Vol. 14962) 421–436.
• FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. Zhang, Zeling; Cai, Dongqi; Zhang, Yiran; Xu, Mengwei; Wang, Shangguang; Zhou, Ao (2024). 126–133.
• GCPN: A Group Connected based Method for Continual Vertical Federated Recommender Systems in Data Ecosystems. Yuan, Haochen; He, Xiang; Hu, Ruihan; Wang, Zhongjie; Zhou, Jun; Feng, Yunqing; Gong, Lecheng (2024). 35–44.
• End-Users vs Software Practitioners: Recruitment Challenges and Strategies in Software Engineering Research. Wang, Wei; Hidellaarachchi, Dulaji; Grundy, John; Khalajzadeh, Hourieh; Obie, Humphrey O.; Madugalla, Anuradha (2024). 400–411.
• Motif-Based Linearizing Graph Transformer for Web API Recommendation. Zheng, Xin; Wang, Guiling; Zhang, Yuqi; Han, Boyang; Yu, Jian in Lecture Notes in Computer Science, W. Gaaloul, M. Sheng, Q. Yu, S. Yangui (eds.) (2024). (Vol. 15405) 138–145.
• Machine Learning-Driven Daily Demand Forecasting for Fresh Produce: A Case Study with Bananas. Seyam, Asmaa; Barachi, May El; Mathew, Sujith Samuel; Du, Bo; Shen, Jun (2024). 363–368.
• Differential Privacy with Data Removal for Online Happiness Assessment. Luo, Yanjie; Li, Lin; Wu, Xiaohua; Tao, Xiaohui in Lecture Notes in Computer Science, W. Zhang, A. K. H. Tung, Z. Zheng, Z. Yang, X. Wang (eds.) (2024). (Vol. 14964) 167–182.
• Adversarial AutoMixup. Qin, Huafeng; Jin, Xin; Jiang, Yun; El-Yacoubi, Mounîm A.; Gao, Xinbo (2024).
• An Efficient Online Learning Approach for Super-Resolution in Dynamic Environments. Liu, Rongqing; Li, Zhiqiang; Tian, Feng; Zheng, Jie; Cao, Rui; Zhao, Chuanyuan; Ren, Jie (2024). 84–89.
• Development of an Adaptive User Support System Based on Multimodal Large Language Models. Wang, Wei; Li, Lin; Wickramathilaka, Shavindra; Grundy, John C.; Khalajzadeh, Hourieh; Obie, Humphrey O.; Madugalla, Anuradha (2024). 344–347.
• Using Microposture Features and Optical Flows for Deepfake Detection. Chen, Kai; Ma, Duohe; Zhang, Zhenchao; Tang, Zhimin; Wang, Liming; Jiang, Junye in IFIP Advances in Information and Communication Technology, E. Kurkowski, S. Shenoi (eds.) (2024). (Vol. 724) 81–97.
• Research on Interactive Design of AR Books. Shi, Juanjuan; Wang, Kaixiang; Hao, Jie in Communications in Computer and Information Science, C. Stephanidis, M. Antona, S. Ntoa, G. Salvendy (eds.) (2024). (Vol. 2116) 119–127.
• Untenrum frei Stokowski, Margarete (2024). (Sonderausgabe ) Rowohlt Taschenbuch Verlag, Hamburg.

  «Untenrum frei» ist über die Jahre zu DEM feministischen Klassiker avanciert und Margarete Stokowski zur Inspirationsfigur vieler junger Menschen. Die Autorin schreibt in diesem Buch über die kleinen schmutzigen Dinge und die großen Machtfragen. Es geht darum, wie die Freiheit im Kleinen mit der Freiheit im Großen zusammenhängt, und am Ende wird deutlich: Es ist dieselbe. Stokowski zeigt, wie sich Rollenbilder und Schamgefühle manifestieren, wie sie uns einschränken – und dass wir sie loswerden können. Mit scharfsinnigem Blick auf die Details gelingt ihr ein persönliches, provokantes und befreiendes Buch.
• Inference over Unseen Entities, Relations and Literals on Knowledge Graphs. Demir, Caglar; Kouagou, N; Sharma, Arnab; Ngonga Ngomo, Axel-Cyrille (2024). arXiv–2410.
• From Planar via Outerplanar to Outerpath -- Engineering NP-Hardness Constructions (Poster Abstract). Geis, Joshua; Zink, Johannes (2024). 42:1–42:3.
• Mathematische Modellierung in MINT - Schülerperspektiven auf die Rolle der Mathematik im MINT-Kontext. Just, Janina; Fock, Alissa (2024).
• Neural Model-Applying Network (Neuman): A New Basis for Computational Cognition. Roth, Frederick (2024).
• Co‐Occurrence Patterns Do Not Predict Mutualistic Interactions Between Plant and Butterfly Species. Menares, Esteban; Saíz, Hugo; Schenk, Noëlle; de la Riva, Enrique G.; Krauss, Jochen; Birkhofer, Klaus (2024). 14(11)
• The Line-Based Dial-a-Ride Problem. Reiter, Kendra; Schmidt, Marie; Stiglmayr, Michael in OASIcs, P. C. Bouman, S. C. Kontogiannis (eds.) (2024). 14:1–14:20.
• Leading Manufacturer of High-Quality Coir Sheets | WeCoir in Coimbatore. WeCoir (WeCoir, ed.) (2024).
• Unlocking the Potential of Unlabeled Data: Semi-Supervised Learning for Stratification of Hyperspectral Images. Ranjan, Pallavi; Kumar, Rajeev; Girdhar, Ashish (2023). 938–943.
• Learning Human-Compatible Representations for Case-Based Decision Support. Liu, Han; Tian, Yizhou; Chen, Chacha; Feng, Shi; Chen, Yuxin; Tan, Chenhao (2023).
• De-anonymization Attacks on Metaverse. Meng, Yan; Zhan, Yuxia; Li, Jiachun; Du, Suguo; Zhu, Haojin; Shen, Xuemin Sherman (2023). 1–10.
• Adaptive User Interfaces for Software Supporting Chronic Diseases. Wang, Wei; Khalajzadeh, Hourieh; Grundy, John; Madugalla, Anuradha (2023). 215–218.
• Towards Adaptive User Interfaces: A Model-Driven Approach for mHealth Applications Targeting Chronic Disease. Wang, Wei (2023). 296–298.
• MagFingerprint: A Magnetic Based Device Fingerprinting in Wireless Charging. Li, Jiachun; Meng, Yan; Zhang, Le; Chen, Guoxing; Tian, Yuan; Zhu, Haojin; Shen, Xuemin Sherman (2023). 1–10.
• Improving IoT Services Through Business-Process-Aligned Modeling Method. Hu, Jianhang; Wang, Guiling; Wang, Hai; Bai, Wentao; Li, Junhua; Yu, Jian in Communications in Computer and Information Science, Z. Wang, S. Wang, H. Xu (eds.) (2023). (Vol. 1844) 57–71.
• 6G BRAINS Topology-aware Industry-grade Network Slice Management and Orchestration. Fonseca, João Pedro; Khadmaoui-Bichouna, Mohamed; Mendes, Bruno; Duarte, Paulo; Araújo, Marco; Corujo, Daniel; Sanchez-Navarro, Ignacio; Matencio-Escolar, Antonio; Salva-Garcia, Pablo; Alcaraz-Calero, José M.; Wang, Qi (2023). 418–423.
• Stochastic Bifurcation of the North Atlantic Circulation under a Midrange Future Climate Scenario with the NASA-GISS ModelE. Romanou, Anastasia; Rind, David; Jonas, Jeff; Miller, Ron; Kelley, Maxwell; Russell, Gary; Orbe, Clara; Nazarenko, Larissa; Latto, Rebecca; Schmidt, Gavin A. (2023). 36(18) 6141–6161.
• Plug-and-Play Feature Generation for Few-Shot Medical Image Classification. Guo, Qianyu; Du, Huifang; Jia, Xing; Gao, Shuyong; Teng, Yan; Wang, Haofen; Zhang, Wenqiang X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, H. Song (eds.) (2023). 1096–1103.
• GB-IDS: An Intrusion Detection System for CAN Bus Based on Graph Analysis. Meng, Yan; Li, Jiachun; Liu, Fazhong; Li, Shaofeng; Hu, Haotian; Zhu, Haojin (2023). 1–6.
• EM-Whisperer: A Voice Injection Attack via Powerline for Virtual Meeting Scenarios. Li, Jiachun; Meng, Yan; Zhang, Le; Liu, Fazhong; Zhu, Haojin (2023). 1–6.
• GenDT: mobile network drive testing made efficient with generative modeling. Sun, Chuanhao; Xu, Kai; Marina, Mahesh K.; Benn, Howard G. Bianchi, A. Mei (eds.) (2022). 43–58.
• SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models. Hou, Changyu; Wang, Jun; Qiao, Yixuan; Jiang, Peng; Gao, Peng; Xie, Guotong; Lin, Qizhi; Wang, Xiaopeng; Jiang, Xiandi; Wang, Benqi; Xiao, Qifeng G. Emerson, N. Schluter, G. Stanovsky, R. Kumar, A. Palmer, N. Schneider, S. Singh, S. Ratan (eds.) (2022). 1593–1596.
• Study on Spray Characteristics of Simulant Gel in Pressure Swirl Injector. Cho, Janghee; Lee, Donghee; Kang, Tae Gon; Moon, Heejang (2022). 23(4) 794–803.

  In this study, the effect of gellant concentration on spray characteristics was investigated in a pressure swirl injector. The cold-flow tests of simulant gel were carried out to identify the air core formation of non-Newtonian fluid within the injector. Morphological analysis of the spray shapes was performed with regards to the air core location. Results show that the spray patterns can be classified into four distinct shapes as reported earlier in the literature. However, the transiting border between spray patterns was somewhat unlike due to the different gellant type used in this study. By categorizing the breakup regimes with dimensionless numbers, critical curves dividing each breakup regions were proposed. Regarding the quantitative spray characteristics, whether the gellant concentration increased or the pressure drop decreased, the spray angle was reduced, while the breakup length was elongated. These facts were partly due to the increased momentum loss triggered by relatively high viscosity.
• CartaGenie: Context-Driven Synthesis of City-Scale Mobile Network Traffic Snapshots. Xu, Kai; Singh, Rajkarn; Bilen, Hakan; Fiore, Marco; Marina, Mahesh K.; Wang, Yue (2022). 119–129.
• LSM-Subgraph: Log-Structured Merge-Subgraph for Temporal Graph Processing. Ma, Jingyuan; Shi, Zhan; Liu, Shang; Zhang, Wang; Wu, Yutong; Wang, Fang; Feng, Dan in Lecture Notes in Computer Science, B. Li, L. Yue, C. Tao, X. Han, D. Calvanese, T. Amagasa (eds.) (2022). (Vol. 13421) 477–494.
• Investigation of Deep Learning Method for Fire Detection from Videos. Vu, Hoai Nam; Tran, Anh Dat; Dung, Nguyen Manh; Choi, Bokgil; Ro, Soonghwan (2021). 593–595.
• SpectraGAN: spectrum based generation of city scale spatiotemporal mobile network traffic data. Xu, Kai; Singh, Rajkarn; Fiore, Marco; Marina, Mahesh K.; Bilen, Hakan; Usama, Muhammad; Benn, Howard; Ziemlicki, Cezary G. Carle, J. Ott (eds.) (2021). 243–258.
• Human-Machine Collaboration Based Named Entity Recognition. Ren, Zhuoli; Yu, Zhiwen; Wang, Hui; Wang, Liang; Liu, Jiaqi in Communications in Computer and Information Science, Y. Sun, T. Lu, B. Cao, H. Fan, D. Liu, B. Du, L. Gao (eds.) (2021). (Vol. 1491) 342–355.
• Toward a Formalization of BPEL 2.0 : An Algebra Approach. Boumlik, L.; Mejri, M.; Boucheneb, H. (2020). 11(1) 01–20.

  The WS-BPEL 2.0 (Web Service Business Process Execution Language) has been the dominant standard to describe Web Services (WS) orchestration approach. It is a rich and expressive language that provides interesting features, among them we find four mechanisms to deal with abnormal situations (Event, Fault, Compensation and Termination) handlers, EFCT- handlers. However, WS-BPEL is not rigorously defined as a formal language making EFCT- handlers complicated and ambiguous. This paper aims to remove ambiguities from EFCT-handlers by formalizing their semantics.
• Developing Aids to Assist Acute Stroke Diagnosis. Ramesh, Vishwajith; Kim, Stephanie; Nguyen, Hong-An; Agrawal, Kunal; Meyer, Brett C.; Weibel, Nadir R. Bernhaupt, F. ’Floyd’ Mueller, D. Verweij, J. Andres, J. McGrenere, A. Cockburn, I. Avellino, A. Goguey, P. Bjøn, S. Zhao, B. P. Samson, R. Kocielnik (eds.) (2020). 1–7.
• A Covert Ultrasonic Phone-to-Phone Communication Scheme. Shi, Liming; Yu, Limin; Huang, Kaizhu; Zhu, Xu; Wang, Zhi; Li, Xiaofei; Wang, Wenwu; Wang, Xinheng in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, H. Gao, X. Wang, M. Iqbal, Y. Yin, J. Yin, N. Gu (eds.) (2020). (Vol. 349) 36–48.
• MEP-PSO Algorithm-Based Coverage Optimization in Directional Sensor Networks. Wang, Luqiao; Li, Changle; Wang, Hui; Zhang, Yao; Liu, Zhao (2020). 1–6.
• Hybrid Encryption Algorithms for Medical Data Storage Security in Cloud Database. Zhang, Fenghua (2019). 11(1) 57–73.

  Cloud database are derivatives of Cloud computing. At present, most medical institutions store data in cloud database. Although the cloud database improves the efficiency of use, it also poses a huge impact and challenge to the secure storage of data. The article proposes a hybrid algorithm to solve the data security problem in the hospital cloud database. First, the AES algorithm is improved. The improved algorithm is called P-AES algorithm. The P-AES algorithm is then combined with the RSA algorithm, called a hybrid algorithm. The experimental results show that the hybrid encryption algorithm has the advantages of fast encryption and decryption speed, high security, good processing ability for longer data, and can solve the data security problem in cloud database to a certain extent. The algorithm has been successfully applied to hospital information management system.
• CoLight: Learning Network-level Cooperation for Traffic Signal Control. Wei, Hua; Xu, Nan; Zhang, Huichu; Zheng, Guanjie; Zang, Xinshi; Chen, Chacha; Zhang, Weinan; Zhu, Yanmin; Xu, Kai; Li, Zhenhui W. Zhu, D. Tao, X. Cheng, P. Cui, E. A. Rundensteiner, D. Carmel, Q. He, J. X. Yu (eds.) (2019). 1913–1922.
• GraphScSh: Efficient I/O Scheduling and Graph Sharing for Concurrent Graph Processing. Liu, Shang; Shi, Zhan; Feng, Dan; Chen, Shuo; Wang, Fang; Peng, Yamei in Lecture Notes in Computer Science, X. Tang, Q. Chen, P. Bose, W. Zheng, J.-L. Gaudiot (eds.) (2019). (Vol. 11783) 3–15.
• A Large Scale Analysis of DNS Water Torture Attack. Luo, Xi; Wang, Liming; Xu, Zhen; Chen, Kai; Yang, Jing; Tian, Tian (2018). 168–173.
• A Safety Net: How Older Adults Build and Maintain Interpersonal Relationships. Wang, Xiying; Knearem, Tiffany; Gui, Fanlu; Gupta, Srishti; Zhu, Haining; Williams, Michael; Carroll, John M. R. L. Mandryk, M. Hancock, M. Perry, A. L. Cox (eds.) (2018).
• Discharge Coefficients of Flat-fan Nozzles. Post, S. L.; Roten, R. L.; Connell, R. J. (2017). 60(2) 347–351.
• Semantic motion segmentation for urban dynamic scene understanding. Qiu, Fan; Yang, Yi; Li, Hao; Fu, Mengyin; Wang, Shunting (2016). 497–502.
• Characterizing and Modeling Package Dynamics in Express Shipping Service Network. Tan, Xu; Shu, Yuanchao; Lu, Xie; Cheng, Peng; Chen, Jiming (2014). 144–151.
• The Rise of Mobile Social Video: An In-depth Analysis of Vine. Vandersmissen, Baptist; Godin, Fréderic; Tomar, Abhineshwar; Neve, Wesley De; de Walle, Rik Van in CEUR Workshop Proceedings, S. Papadopoulos, P. César, D. A. Shamma, A. Kelliher, R. C. Jain (eds.) (2014). (Vol. 1198)
• A Large Scale Audio Fingerprinting System. Jiang, Tao; Xiang, Kang; Lu, Jing; Wu, Rihui; Li, Xin; Dai, Feng in Lecture Notes in Computer Science, B. Huet, C.-W. Ngo, J. Tang, Z.-H. Zhou, A. G. Hauptmann, S. Yan (eds.) (2013). (Vol. 8294) 866–875.
• Towards fusion of collective knowledge and audio-visual content features for annotating broadcast video. Godin, Fréderic; Neve, Wesley De; de Walle, Rik Van R. C. Jain, B. Prabhakaran, M. Worring, J. R. Smith, T.-S. Chua (eds.) (2013). 329–332.
• Leveraging Existing Tools for Named Entity Recognition in Microposts. Godin, Fréderic; Debevere, Pedro; Mannens, Erik; Neve, Wesley De; de Walle, Rik Van in CEUR Workshop Proceedings, A. E. Cano, M. Rowe, M. Stankovic, A.-S. Dadzie (eds.) (2013). (Vol. 1019) 36–39.
• Drop size spectra in sprays from pressure-swirl atomizers. Tratnig, Andreas; Brenn, Günter (2010). 36(5) 349–363.
• An Adaptive and Self-Supervised Structured P2P Overlay for Autonomic Communication. Li, Deng; Chen, Zhigang; Liu, Hui; Vasilakos, Athanasios V. H. R. Arabnia, V. A. Clincy, L. T. Yang (eds.) (2007). 412–417.
• Adaptive non-linear clustering in data streams. Jain, Ankur; Zhang, Zhihua; Chang, Edward Y. P. S. Yu, V. J. Tsotras, E. A. Fox, B. Liu (eds.) (2006). 122–131.
• OntoNav: A Semantic Indoor Navigation System. Anagnostopoulos, Christos; Tsetsos, Vassileios; Kikiras, Panayotis; Hadjiefthymiades, Stathes in CEUR Workshop Proceedings, W. Mansoor, M. Khedr, D. Benslimane, Z. Maamar, M. Hauswirth, K. Aberer (eds.) (2005). (Vol. 165)
• ROUGE: A Package for Automatic Evaluation of Summaries. Lin, Chin-Yew (2004). 74–81.
• BLEU: a method for automatic evaluation of machine translation. Papineni, Kishore; Roukos, Salim; Ward, Todd; Zhu, Wei-Jing in ACL ’02 (2002). 311–318.

  Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.
• Spray characteristics of aerated-liquid pressure atomizers. Lefebvre, A. H.; Wang, X. F.; Martin, C. A. (1988). 4(4) 293–298.
• Exploratory Data Analysis Tukey, John W. in Addison-Wesley series in behavioral sciences (1977). Addison-Wesley, Reading, Massachusetts.