Im Rahmen der MHP Lab – A Porsche Company First Tuesday Veranstaltung mit dem Themenschwerpunkt “Mobility as a Service – don’t hesitate to cooperate!” (www.mhp.com/de/first-tuesday) hat die PROJEKTIONISTEN GmbH mit ihrer Mobilitätsmarke 1KLANG (www.1klang.mobi) an einem Stand und in Pitch und Podiumsdiskussion u.a. auch das Projekt Data4UrbanMobility vorgestellt. Somit konnten Einblicke in die Forschung rund um Data4UrbanMobility ermöglicht und interessierten Besuchern der Veranstaltung Fragen rund um die Mobilitätsforschung beantwortet werden.
Es konnten diverse Aspekte aus dem Portfolio des Urban Mobility Assistance (UMA) (www.urbanmobilityassistance.de), einem App Portfolio auf Basis von Entwicklungen der Wolfsburg AG und 1KLANG gezeigt werden. Hierin werden u.a. die Erkenntnisse aus D4UM zu Incentivierung und Community einfliessen. Ebenso konnte auch die Mobilitäts-App D4UM gezeigt werden, über welche Reiseauskünfte, Veranstaltungen und weitere nützliche Features für den Endnutzer abgerufen werden.
Auch die MiC-App (Move in the City) wurde gezeigt, welche als ein Cititzen Science Projekt weitere Daten über Tracking dem D4UM Projekt Daten zur Verfügung stellen kann. Hier sind in interessanten Diskussionen Möglichkeiten der Kooperation mit dem finnischen Unternehmen MOPRIM (www.moprim.com) erörtert worden.

• 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.
• 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)
• „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
• VeriBin: Adaptive Verification of Patches at the Binary Level. Wu, Hongwei; Wu, Jianliang; Wu, Ruoyu; Sharma, Ayushi; Machiry, Aravind; Bianchi, Antonio (2025).
• Boundedness of polarized log Calabi-Yau fibrations with bounded bases. Jiang, Xiaowei; Jiao, Junpeng; Zhu, Minzhe (2025).
• 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.
• 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.
• 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.
• Enhancing Public Reputation Systems: Trust Scaling to Mitigate Voter Subjectivity. Nguyen, Manh Hung (2025). 142–146.
• A SURVEY ON A MODEL FOR PESTICIDE RECOMMENDATION USING MACHINE LEARNING. lakshmi S Abbigeri, Vijaya; Devanagavi, Geetha D (2025).
• CHaOS: A Persistent Lightweight Cache Hybridization-aware OS. Wilbert, Nils; Szymanski, Matthias; Wildermann, Stefan; Herzog, Henriette; Hönig, Timo; Teich, Jürgen (2025). (Vol. 38)
• 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.
• 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.
• Structures of ΔD421 Truncated Tau Fibrils. El Mammeri, Nadia; Duan, Pu; Hong, Mei (2025). 437(10)
• 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).
• 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.
• 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.
• 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.
• 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.
• 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.
• Runtime Anomaly Detection for MEC Services with Multi-Timescale and Dimensional Feature. Yang, Qian; Li, Hongjia; Chen, Kai; Wang, Jiankai; Wang, Liming; Xu, Zhen (2024). 1–6.
• 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.
• Leading Manufacturer of High-Quality Coir Sheets | WeCoir in Coimbatore. WeCoir (WeCoir, ed.) (2024).
• 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.
• 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.
• CARGO: Crypto-Assisted Differentially Private Triangle Counting Without Trusted Servers. Liu, Shang; Cao, Yang; Murakami, Takao; Liu, Jinfei; Yoshikawa, Masatoshi (2024). 1671–1684.
• 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.
• 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.
• 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.
• 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.
• 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).
• 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.
• Adversarial AutoMixup. Qin, Huafeng; Jin, Xin; Jiang, Yun; El-Yacoubi, Mounîm A.; Gao, Xinbo (2024).
• 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).
• Approximating the Maximum Weight Cycle/Path Partition in Graphs with Weights One and Two. Guo, Xinmeng; Yu, Wei; Liu, Zhaohui in Lecture Notes in Computer Science, Y. Chen, X. Gao, X. Sun, A. Zhang (eds.) (2024). (Vol. 15161) 116–127.
• Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation. Liu, Hao; Li, Xin; Gong, Mingming; Liu, Bing; Wu, Yunfei; Jiang, Deqiang; Liu, Yinsong; Sun, Xing M. J. Wooldridge, J. G. Dy, S. Natarajan (eds.) (2024). 3603–3611.
• 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)
• 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.
• A Survey on In-context Learning. Dong, Qingxiu; Li, Lei; Dai, Damai; Zheng, Ce; Ma, Jingyuan; Li, Rui; Xia, Heming; Xu, Jingjing; Wu, Zhiyong; Chang, Baobao; Sun, Xu; Li, Lei; Sui, Zhifang Y. Al-Onaizan, M. Bansal, Y.-N. Chen (eds.) (2024). 1107–1128.
• 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.
• A Statistical Approach to Monte Carlo Denoising. Sakai, Hiroyuki; Freude, Christian; Auzinger, Thomas; Hahn, David; Wimmer, Michael T. Igarashi, A. Shamir, H. (Richard) Zhang (eds.) (2024). 68:1–68:11.
• Temporal Gate-Attention Network for Meta-path based Explainable Recommendation. Gou, Lixi; Zhou, Renjie; Wan, Jian; Zhang, Jilin; Yao, Yue; Yang, Chang V. S. Sheng, C. Hicks, C. Ling, V. Raghavan, X. Wu (eds.) (2023). 219–226.
• 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.
• Learning Human-Compatible Representations for Case-Based Decision Support. Liu, Han; Tian, Yizhou; Chen, Chacha; Feng, Shi; Chen, Yuxin; Tan, Chenhao (2023).
• GazeVQA: A Video Question Answering Dataset for Multiview Eye-Gaze Task-Oriented Collaborations. Ilaslan, Muhammet Furkan; Song, Chenan; Chen, Joya; Gao, Difei; Lei, Weixian; Xu, Qianli; Lim, Joo; Shou, Mike Zheng H. Bouamor, J. Pino, K. Bali (eds.) (2023). 10462–10479.
• Towards Adaptive User Interfaces: A Model-Driven Approach for mHealth Applications Targeting Chronic Disease. Wang, Wei (2023). 296–298.
• 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.
• Unlocking the Potential of Unlabeled Data: Semi-Supervised Learning for Stratification of Hyperspectral Images. Ranjan, Pallavi; Kumar, Rajeev; Girdhar, Ashish (2023). 938–943.
• 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.
• 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.
• 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.
• 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.
• Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals. Raedt, Maarten De; Godin, Fréderic; Develder, Chris; Demeester, Thomas Y. Goldberg, Z. Kozareva, Y. Zhang (eds.) (2022). 11386–11400.
• 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.
• Active Example Selection for In-Context Learning. Zhang, Yiming; Feng, Shi; Tan, Chenhao Y. Goldberg, Z. Kozareva, Y. Zhang (eds.) (2022). 9134–9148.
• HCL: Improving Graph Representation with Hierarchical Contrastive Learning. Wang, Jun; Li, Weixun; Hou, Changyu; Tang, Xin; Qiao, Yixuan; Fang, Rui; Li, Pengyong; Gao, Peng; Xie, Guotong in Lecture Notes in Computer Science, U. Sattler, A. Hogan, C. M. Keet, V. Presutti, J. P. A. Almeida, H. Takeda, P. Monnin, G. Pirrò, C. d’Amato (eds.) (2022). (Vol. 13489) 108–124.
• 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.
• 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.
• Game Theoretic Approach to Extreme Fast Charging Location. Lu, Haibing; Chen, Xi; Dai, Jiangpeng; Wu, Bin; Fang, Yi; Samorani, Michele; Li, Zhen (2021). 1–5.
• Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations. Parambath, Shameem Ahamed Puthiya; Anagnostopoulos, Christos; Murray-Smith, Roderick; MacAvaney, Sean; Zervas, Evangelos in Proceedings of Machine Learning Research, V. N. Balasubramanian, I. W. Tsang (eds.) (2021). (Vol. 157) 564–579.
• SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization. Li, Deng; Wu, Yue; Zhou, Yicong in Lecture Notes in Computer Science, J. Lladós, D. Lopresti, S. Uchida (eds.) (2021). (Vol. 12824) 538–553.
• 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.
• 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.
• Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control. Chen, Chacha; Wei, Hua; Xu, Nan; Zheng, Guanjie; Yang, Ming; Xiong, Yuanhao; Xu, Kai; Li, Zhenhui (2020). 3414–3421.
• 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.
• Generating Well-Formed Answers by Machine Reading with Stochastic Selector Networks. Bi, Bin; Wu, Chen; Yan, Ming; Wang, Wei; Xia, Jiangnan; Li, Chenliang (2020). 7424–7431.
• MEP-PSO Algorithm-Based Coverage Optimization in Directional Sensor Networks. Wang, Luqiao; Li, Changle; Wang, Hui; Zhang, Yao; Liu, Zhao (2020). 1–6.
• 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.
• 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.
• A Deep Cascade Model for Multi-Document Reading Comprehension. Yan, Ming; Xia, Jiangnan; Wu, Chen; Bi, Bin; Zhao, Zhongzhou; Zhang, Ji; Si, Luo; Wang, Rui; Wang, Wei; Chen, Haiqing (2019). 7354–7361.
• Exploiting Coarse-to-Fine Task Transfer for Aspect-Level Sentiment Classification. Li, Zheng; Wei, Ying; Zhang, Yu; Zhang, Xiang; Li, Xin (2019). 4253–4260.
• 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 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).
• A Large Scale Analysis of DNS Water Torture Attack. Luo, Xi; Wang, Liming; Xu, Zhen; Chen, Kai; Yang, Jing; Tian, Tian (2018). 168–173.
• Pathologies of Neural Models Make Interpretation Difficult. Feng, Shi; Wallace, Eric; II, Alvin Grissom; Iyyer, Mohit; Rodriguez, Pedro; Boyd-Graber, Jordan L. E. Riloff, D. Chiang, J. Hockenmaier, J. Tsujii (eds.) (2018). 3719–3728.
• Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?. Godin, Fréderic; Demuynck, Kris; Dambre, Joni; Neve, Wesley De; Demeester, Thomas E. Riloff, D. Chiang, J. Hockenmaier, J. Tsujii (eds.) (2018). 3275–3284.
• Deep Correlation Structure Preserved Label Space Embedding for Multi-label Classification. Wang, Kaixiang; Yang, Ming; Yang, Wanqi; Yin, Yilong in Proceedings of Machine Learning Research, J. Zhu, I. Takeuchi (eds.) (2018). (Vol. 95) 1–16.
• Augmented Reality-Based Interactive Simulation Application in Double-Slit Experiment. Wang, Tao; Zhang, Han; Xue, Xiaoru; Cai, Su M. E. Auer, D. G. Zutin (eds.) (2017). (Vol. 22) 701–707.
• Rank Ordering Constraints Elimination with Application for Kernel Learning. Xie, Ying; Ding, Chris H. Q.; Gong, Yihong; Wu, Zongze S. Singh, S. Markovitch (eds.) (2017). 2775–2781.
• Semantic motion segmentation for urban dynamic scene understanding. Qiu, Fan; Yang, Yi; Li, Hao; Fu, Mengyin; Wang, Shunting (2016). 497–502.
• In the Quest of Defining Smart Digital City in Medini Iskandar Malaysia, Iskandar Puteri, Malaysia. Ghazali, Masitah; Okamura, Tetsuo; Abdullah, Taufiq; Sunar, Mohd Shahrizal; Mohamed, Farhan; Ismail, Nor Azman (2016). 19–23.
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• 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.
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  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.
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