Data4UrbanMobility wird mit einem Stand auf der Dienstleistungstagung 2019 “Service Systems Innovation”  in Paderborn präsentiert werden. Die Tagung findet am 08. und 09. April 2019 statt. Data4UrbanMobiltiy wird auf der Themeninsel 4: “Individuell und Urban” zu finden sein.

• Climate tipping points and their potential impact on drinking water supply planning and management in Europe. van Thienen, Peter; ter Maat, Herbert; Stofberg, Sija (2025). 3 e3-.

  The current generation of climate models has proven very helpful in understanding and projecting anthropogenic climate change but has also shown to be insufficient for studying the interactions of tipping elements and their impact on overall climate stability. As a consequence, tipping elements are mostly absent from climate projections that are commonly used by the drinking water industry to test the resilience of their systems. There is, however, mounting evidence for the existence and potential (possibly even imminent) activation of some of these tipping elements. The drinking water sector is, by necessity, slow-moving as its infrastructure is meant to operate for many decades and in practice often does so even longer. The time scales of possible changes associated with tipping element activations may, however, be much shorter. We provide a review of the current understanding of climate tipping elements and present a simple model that investigates potential magnitudes and time scales of rapid climate change associated with tipping element activations. We study the potential consequences for drinking water supply systems, focusing on Europe, and argue that given the associated deep uncertainty and far-reaching consequences, it is essential to include tipping scenarios in the decision-making processes in the drinking water sector.
• Intrusion Detection Model using Machine Learning Algorithms on NSL-KDD Dataset. Alshamy, Reem (R. Alshamy, ed.) (2025). 16(6) 75–88.

  Big data, generated by various sources such as mobile devices, sensors, and the Internet of Things (IoT), has many characteristics such as volume, velocity, variety, variability, veracity, validity, vulnerability, volatility, visualization, and value. An Intrusion Detection System (IDS) is essential for cybersecurity to detect intrusions before or after attacks. Traditional software methods struggle to store, manage, and analyze big data, developing new techniques for effective and rapid intrusion detection in organizations and enterprises. This study introduces the IDS Random Forest (RF) model in binary and multiclass classification for intrusion detection. In this model, we used the Synthetic Minority Oversampling TEchnique (SMOTE) to address class imbalances, and the RF classifier to classify attacks using the Network Security Laboratory (NSL)-KDD dataset. In the experiment, we compared the IDS-RF model with the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Logistic Regression (LR) classifiers in terms of accuracy, precision, recall, f1-score, and times for training and testing. The experimental results showed that the IDS-RF model achieved high performance in binary and multiclass classification compared to others. In addition, the proposed model also achieved high accuracies for each class (Normal, DoS, Probe, U2R, or R2L) and obtained 98.69%, 99.72%, 98.93%, 95.13%, and 89%, respectively.
• Learning Energy-Based Models for 3D Human Pose Estimation. Zhu, Xianglu; Zhang, Zhang; Wang, Wei; Wang, Zilei; Wang, Liang (2024). 1–8.
• Fake Alignment: Are LLMs Really Aligned Well?. Wang, Yixu; Teng, Yan; Huang, Kexin; Lyu, Chengqi; Zhang, Songyang; Zhang, Wenwei; Ma, Xingjun; Jiang, Yu-Gang; Qiao, Yu; Wang, Yingchun K. Duh, H. Gómez-Adorno, S. Bethard (eds.) (2024). 4696–4712.
• VC-EKF for Graphical Nonlinear Systems. Guo, Simeng; Li, Wenling; Zhang, Bin; Liu, Yang (2024). 645–650.
• CONVERGENCE ANALYSIS OF NEWTON-COTES METHODS: OPTIMIZING SUB INTERVALS SELECTION FOR PRECISE INTEGRAL APPROXIMATION Catapang, Lea A. (2024). (Vol. 11)
• From Static to Dynamic: A Deeper, Faster, and Adaptive Language Modeling Approach. Li, Jiajia; Li, Qiwei; Wang, Ping (2024). 1–8.
• Learning Defendant-aware Label Representation for Multi-Defendant Charge Prediction. Chen, Yu; Zhang, You; Wang, Jin; Xu, Dan; Zhang, Xuejie (2024). 1–8.
• Hedonic Signals in Crowdfunding. Blohm, Ivo; Schulz, Moritz; Leimeister, Jan Marco (2024). 66 757–775.

  This study draws on signaling theory to investigate the effect of hedonic signals in crowdfunding projects on funding performance. It compares the effect of hedonic signals across reward-, equity-, and donation-based crowdfunding platforms by combining archival data from 18 platforms and a large-scale panel of 64 experts that rate the strength of hedonic signals in 108 crowdfunding projects. Through the application of mixed linear modeling, the findings indicate a positive influence of stronger hedonic signals on funding performance. However, there are substantial differences across platform types. Increasing the strength of hedonic signals by one standard deviation increases funding performance by 28.9% on reward platforms, while there are no systematic effects on equity and donation platforms. This study contributes to existing crowdfunding research by clarifying the role of hedonic signals in crowdfunding and shedding light on the increasing need to better consider the characteristics of different crowdfunding platforms in crowdfunding research.
• RobustTSN: A Framework for Protecting Time-Sensitive Networking against Unexpected Delays. Feng, Xingbo; Wang, Yi; Lin, Jiashuo; Li, Weichao; Zhan, Shuangping; Liu, Yan; Zhang, Jin; Wang, Jianping (2024). 1–6.
• Multi-modal fusion for business process prediction in call center scenarios. Cheng, Long; Du, Li; Liu, Cong; Hu, Yang; Fang, Fang; Ward, Tomás (2024). 108 102362.
• InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model. Liu, Haogeng; You, Quanzeng; Wang, Yiqi; Han, Xiaotian; Zhai, Bohan; Liu, Yongfei; Chen, Wentao; Jian, Yiren; Tao, Yunzhe; Yuan, Jianbo; He, Ran; Yang, Hongxia L.-W. Ku, A. Martins, V. Srikumar (eds.) (2024). 485–492.
• Domain-Driven LLM Development: Insights into RAG and Fine-Tuning Practices. dos Santos Junior, José Cassio; Hu, Rachel; Song, Richard; Bai, Yun-Fei R. Baeza-Yates, F. Bonchi (eds.) (2024). 6416–6417.
• AdapCC: Making Collective Communication in Distributed Machine Learning Adaptive. Zhao, Xiaoyang; Zhang, Zhe; Wu, Chuan (2024). 25–35.
• Adversarial Prompt Tuning for Vision-Language Models. Zhang, Jiaming; Ma, Xingjun; Wang, Xin; Qiu, Lingyu; Wang, Jiaqi; Jiang, Yu-Gang; Sang, Jitao in Lecture Notes in Computer Science, A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, G. Varol (eds.) (2024). (Vol. 15103) 56–72.
• Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning. Kreis, Benedikt; Dengler, Nils; de Heuvel, Jorge; Menon, Rohit U.; Perur, Hamsa Datta; Bennewitz, Maren (2024). 698–705.
• Sublinear Distributed Product Checks on Replicated Secret-Shared Data over ℤ2k without Ring Extensions. Li, Yun; Escudero, Daniel; Duan, Yufei; Huang, Zhicong; Hong, Cheng; Zhang, Chao; Song, Yifan (2024). 2024 700.
• EEA-Net: edge-enhanced assistance network for infrared small target detection. Wang, Chen; Hu, Xiaopeng; Gao, Xiang; Wei, Haoyu; Tao, Jiawei; Wang, Fan (2024). 35(4) 70.
• Lightweight image super-resolution via multi-branch aware CNN and efficient transformer. Gao, Xiang; Wu, Sining; Zhou, Ying; Wu, Xinrong; Wang, Fan; Hu, Xiaopeng (2024). 36(10) 5285–5303.
• Differentiable Neural Architecture Search Based on Efficient Architecture for Lightweight Image Super-Resolution. Sheng, Chunyin; Gao, Xiang; Hu, Xiaopeng; Wang, Fan in Lecture Notes in Computer Science, S. Rudinac, A. Hanjalic, C. C. S. Liem, M. Worring, B. Þór Jónsson, B. Liu, Y. Yamakata (eds.) (2024). (Vol. 14556) 169–183.
• LCFormer: linear complexity transformer for efficient image super-resolution. Gao, Xiang; Wu, Sining; Zhou, Ying; Wang, Fan; Hu, Xiaopeng (2024). 30(4) 232.
• Advancing TSN flow scheduling: An efficient framework without flow isolation constraint. Feng, Xingbo; Wang, Yi; Lin, Jiashuo; Li, Weichao; Zhan, Shuangping; Liu, Yan; Zhang, Jin; Wang, Jianping (2024). 252 110688.
• MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast. Hu, Shiyan; Zhao, Kai; Qiu, Xiangfei; Shu, Yang; Hu, Jilin; Yang, Bin; Guo, Chenjuan (2024). abs/2410.15997
• SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing. Guo, An; Zhou, Yuan; Tian, Haoxiang; Fang, Chunrong; Sun, Yunjian; Sun, Weisong; Gao, Xinyu; Luu, Anh Tuan; Liu, Yang; Chen, Zhenyu (2024). abs/2409.08081
• InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding. Liu, Haogeng; You, Quanzeng; Han, Xiaotian; Wang, Yiqi; Zhai, Bohan; Liu, Yongfei; Tao, Yunzhe; Huang, Huaibo; He, Ran; Yang, Hongxia (2024). abs/2403.01487
• UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation. Sun, Ye; Zhang, Hao; Zhang, Tiehua; Ma, Xingjun; Jiang, Yu-Gang (2024). abs/2410.09909
• Pre-trained Model-based Actionable Warning Identification: A Feasibility Study. Ge, Xiuting; Fang, Chunrong; Zhang, Quanjun; Wu, Daoyuan; Yu, Bowen; Zheng, Qirui; Guo, An; Lin, Shangwei; Zhao, Zhihong; Liu, Yang; Chen, Zhenyu (2024). abs/2403.02716
• ROSE: Register Assisted General Time Series Forecasting with Decomposed Frequency Learning. Wang, Yihang; Qiu, Yuying; Chen, Peng; Zhao, Kai; Shu, Yang; Rao, Zhongwen; Pan, Lujia; Yang, Bin; Guo, Chenjuan (2024). abs/2405.17478
• Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning. Wang, Yiqi; Chen, Wentao; Han, Xiaotian; Lin, Xudong; Zhao, Haiteng; Liu, Yongfei; Zhai, Bohan; Yuan, Jianbo; You, Quanzeng; Yang, Hongxia (2024). abs/2401.06805
• Movable Antenna Empowered Physical Layer Security Without Eve’s CSI: Joint Optimization of Beamforming and Antenna Positions. Feng, Zhiyong; Zhao, Yujia; Yu, Kan; Li, Dong (2024). abs/2405.16062
• CityLight: A Universal Model Towards Real-world City-scale Traffic Signal Control Coordination. Zeng, Jinwei; Yu, Chao; Yang, Xinyi; Ao, Wenxuan; Yuan, Jian; Li, Yong; Wang, Yu; Yang, Huazhong (2024). abs/2406.02126
• Non-Local Spatial-Wise and Global Channel-Wise Transformer for Efficient Image Super-Resolution. Gao, Xiang; Wu, Sining; Wang, Fan; Hu, Xiaopeng in Lecture Notes in Computer Science, S. Rudinac, A. Hanjalic, C. C. S. Liem, M. Worring, B. Þór Jónsson, B. Liu, Y. Yamakata (eds.) (2024). (Vol. 14556) 70–85.
• Multi-Object Tracking with Partial-Level Features and Adaptive Threshold Mechanism. Kong, Lingjie; Hu, Xiaopeng; Wang, Fan (2024). 1–8.
• Live Demonstration for Input-Sparsity-Aware RRAM Processing-in-Memory Chip. Wang, Junjie; Liu, Shuang; Pan, Ruicheng; Yan, Shiqin; Liu, Yihe; Liu, Yang (2024). 1–2.
• Management of Atrial Fibrillation. Alenghat, Francis J.; Alexander, Jason T.; Upadhyay, Gaurav A. (2024). 332(22) 1936.
• Decreased northern hemisphere precipitation from consecutive CO2 doublings is associated with significant AMOC weakening. Zhang, X; Waugh, D W; Mitevski, I; Orbe, C; Polvani, L M (2024). 3(4) 041005.

  Previous studies found many climate properties such as northern hemisphere (NH) surface temperature and precipitation respond non-monotonically when CO2 is increased from 1× to 8×CO2 relative to pre-industrial levels. Here, we explore the robustness of the non-monotonicity in the NH precipitation response in 11 coupled climate models. Eight models show a decrease in NH precipitation under repeated CO2 doubling, indicating that the non-monotonic response is a common but not universal result. Although common, the critical CO2 level where the NH precipitation decrease first occurs differs widely across models, ranging from 2×CO2 to 8×CO2. These models also show a prominent weakening in the Atlantic meridional overturning circulation (AMOC) at the same critical CO2 level, with the AMOC weakening leading the precipitation decrease. The sensitivities of NH precipitation and the AMOC to CO2 doublings are positively correlated, especially when the AMOC weakens beyond 10 Sv. This suggests that the differences in models’ AMOC response can explain their contrasting NH precipitation responses, where models with a large AMOC weakening have decreased NH precipitation. Regionally, this decrease in NH precipitation is the most prominent over the North Atlantic, Europe and the tropical Pacific. Our results suggest that special care must be taken with the use of pattern scaling to inform regional climate decision-making.
• South Atlantic meridional overturning circulation and its respective heat and freshwater transports from sustained observations near 34.5°S. Pita, Ivenis; Goes, Marlos; Volkov, Denis L.; Dong, Shenfu; Schmid, Claudia (2024). 11
• 129I and 236U distribution in the subpolar North Atlantic unravels water mass provenance in AR7W and A25 lines. Leist, Lisa G. T.; Castrillejo, Maxi; Smith, John Norton; Christl, Marcus; Vockenhuber, Christof; Velo, Antón; Lherminier, Pascale; Casacuberta, Núria (2024). 11

  The subpolar North Atlantic (SPNA) is crucial in the global ocean circulation system and one of the few regions where deep convection occurs. The intermediate and deep waters formed in the SPNA have long been investigated, yet their sources and pathways are not fully understood. In this study, we employ a combination of two radionuclide tracers, namely, ¹²⁹I and ²³⁶U, to understand water mass provenance and mixing in the SPNA. The concentrations measured between Portugal and Greenland and across the Labrador Sea in 2020/2021 agreed with previously observed tracer distributions. The highest tracer concentrations were measured in the East Greenland Current (EGC), Denmark Strait Overflow Water (DSOW), and, to a lesser extent, in the eastward-flowing Labrador Sea Water (LSW). In contrast, waters of southern origin such as the North East Antarctic Bottom Water and North East Atlantic Central Water (ENACW) carried comparably smaller amounts of ¹²⁹I. By using a binary mixing model, we estimated that the EGC contains about 29%–32% of the Polar Surface Water outflowing the Fram Strait. DSOW was mainly derived from 20% to 35% Return Atlantic Water and mixed with LSW. The Iceland Scotland Overflow Water (ISOW) evolved into North East Atlantic Deep Water in the Irminger and Labrador seas primarily by mixing with LSW and, to a lesser extent, with DSOW. The ¹²⁹I and ²³⁶U binary mixing approach was less conclusive for LSW, reaching the current limitation of the model. This study suggests potential benefits and limitations of using ¹²⁹I and ²³⁶U to investigate the mixing and provenance of water masses in the SPNA.

• Profile: Rob Roelfsema. Roelfsema, MRG (2024). 244(3) 767–768.
• The Emotion Dynamics of Literary Novels. Vishnubhotla, Krishnapriya; Hammond, Adam; Hirst, Graeme; Mohammad, Saif M. (2024).
• Guard cells count the number of unitary cytosolic Ca2+ signals to regulate stomatal dynamics. Huang, Shouguang; Roelfsema, M. Rob G.; Gilliham, Matthew; Hetherington, Alistair M.; Hedrich, Rainer (2024). 34(23) 5409–5416.e2.
• Gaining or cutting SLAC: the evolution of plant guard cell signalling pathways. Sussmilch, Frances C.; Maierhofer, Tobias; Herrmann, Johannes; Voss, Lena J.; Lind, Christof; Messerer, Maxim; Müller, Heike M.; Bünner, Maria S.; Ache, Peter; Mayer, Klaus F. X.; Becker, Dirk; Roelfsema, M. Rob G.; Geiger, Dietmar; Schultz, Jörg; Hedrich, Rainer (2024). 244(6) 2295–2310.
• Cognitive Biases, Social Influence, and the Blindness to Manipulation: An Analysis of Human Thinking and Decision-Making. Matteson, Harlen (2024).
• Potassium extrusion by plant cells: evolution from an emergency valve to a driver of long‐distance transport. Hmidi, Dorsaf; Muraya, Florence; Fizames, Cécile; Véry, Anne‐Aliénor; Roelfsema, M. Rob G. (2024). 245(1) 69–87.
• Pazifismus und Kriegsdienstverweigerung in der frühen Kirche: Eine Quellensammlung Gerhards, Thomas in edition pace (2024). BoD – Books on Demand, Norderstedt.

  Die hier ohne Änderungen erneut edierte Quellensammlung "Pazifismus und Kriegsdienstverweigerung in der frühen Kirche" kursierte 1984 als Geheimtipp unter friedensbewegten Christenmenschen und wurde dann aufgrund der starken Nachfrage bis 1991 vom deutschen Zweig des Internationalen Versöhnungsbundes in sechs Auflagen verbreitet, versehen mit einem Vorwort von Konrad Lübbert. Im einleitenden Teil erläuterte der Bearbeiter Thomas Gerhards vor vier Jahrzehnten seine Intention: "Eine der großen Fragen, mit denen ich mein Studium der Theologie begann, lautete: Wie kommt es, dass Christen, denen Jesus die völlige Gewaltlosigkeit vorlebte ..., nicht klarer gegen das immer erschreckendere Wettrüsten Stellung beziehen? Müsste seine Kirche die Haltung Jesu nicht deutlicher herausstellen? Ist, angesichts der heutigen Situation, die Kriegsdienstverweigerung für eine/n Christin/en nicht die notwendige Konsequenz? Ich entdeckte, dass die frühe Kirche viel entschlossener die gewaltlose Botschaft Jesu zu leben suchte. Aus zweijähriger Beschäftigung mit dem Thema erwuchs diese Quellensammlung, da ich immer wieder feststellte, wie ... unzureichend das Wissen um die Haltung der frühen Christen zu Krieg und Kriegsdienst war. - Die Dokumente aus den ersten drei Jahrhunderten des Christentums sind zu bedeutsam, als dass man sie - wie die herrschende Kirchenhistorie - mit wenigen Sätzen abtun und dann zum 'Gerechten Krieg' übergehen kann."
• Care Models to Improve Pain and Reduce Opioids Among Patients Prescribed Long-Term Opioid Therapy: The VOICE Randomized Clinical Trial. Krebs, Erin E.; Becker, William C.; Nelson, David B.; DeRonne, Beth M.; Jensen, Agnes C.; Kats, Allyson M.; Morasco, Benjamin J.; Frank, Joseph W.; Makris, Una E.; Allen, Kelli D.; Naylor, Jennifer C.; Mixon, Amanda S.; Bohnert, Amy; Reznik, Thomas E.; Painter, Jacob T.; Hudson, Teresa J.; Hagedorn, Hildi J.; Manuel, Jennifer K.; Borsari, Brian; Purcell, Natalie; Hammett, Patrick; Amundson, Erin C.; Kerns, Robert D.; Barbosa, Monica R.; Garvey, Caitlin; Jones, Elzie J.; Noh, Maureen Y.; Okere, Jennifer B.; Bhushan, Sujata; Pinsonnault, John; Williams, Beth E.; Herbst, Ellen; Lagisetty, Pooja; Librodo, Sara; Mapara, Payal S.; Son, Elizabeth; Tat, Christina; Marraffa, Rebecca A.; Seys, Randy L.; Baxley, Catherine; Seal, Karen H.; Abelleira, Audrey; Adams, Melissa; Anicich, Adam; Ankawi, Brett; Arnold, Stephanie; Aryal, Anuj; Atkins, David; Atkinson, Timothy; Backe, Kristen; Bair, Matthew; Bajpai, Smita; Baller, Kristeen; Bangerter, Ann; Barnett, Steven; Bartlett, Stephen; Bartnik, Mary K.; Baskin, Joel; Blehm, Ronald; Bloechl, Philip P.; Boening, Natassia; Boesch, Nicole; Bolander, Sondra; Bolf, Marianne; Bonchak, John; Booth, Karen; Bordanea, Margaret; Borowa, Dominika; Bowden, Marnie; Britnell, Sara; Broderick, Daniel; Bronars, Carrie; Brooks, Koren; Brown, Cheryl B.; Brudvik, Erin; Buchanan, Katrina; Buesgens, Carolyn; Buonora, Michele; Burch, Rebekka; Burton, Caitlin; Butler, Celia A.; Byerly, Susan; Byrd, Catherine; Byrd, Gina; Campbell, Emily; Cariveau, Desmond; Carmody, Suzanne; Carr, Medenna; Carr, Thomas; Cervone, Dana; Chandler, Michael; Clarke, Kendra; Clay, Michael; Coy, William B.; Craine, Michael; Culver, David; Cutting, Andrea; Dadabayev, Alisher; Daly, David; Degerstrom, Rose; Denietolis, Angela L.; DePesa, Natasha; Desmet, Paula; Devlin, Amber; Dike, Dozie; Diokpa, Chinedu; Dogra, Meenakshi; Drexler, Karen; Drum-Oden, Anna; Duvall, Tina; Eads, Frances K.; Edmond, Sara N.; Eichelsdoerfer, Edward; Ellifritz, Christina; Ellison, Jenna; Erwin, Kimberly; Escobedo, Christi; Feighner, Lydia; Feuchs, Ashley; Fike, Ashley; Finetti, Toni; Franchina, Livia; Freeman, John T.; Frontera, Rebecca A.; Galvan, Rachel; Gayle, Sherrhonda; Givens, Courtney; Glenn, Rosie; Godwin, Keely; Goldstein, Michael; Gosselin, Kelly; Graham, Stephen; Grimm, Kimberlea F.; Gumidyala, Amitha; Hagerman, Christine; Hale, Anne; Hall, Levi M.; Harden, Jacqueline; Hawkins, Jeanette; Hepton, Stephanie; Herynk, Aleks; Heuermann, Brandon; Hidalgo, Nina; Hoffmann, John; Holland deguire, Christopher; Hope, Kaydance; Horowitz, Dana; Huang, Andre; Huffman, Katie F.; Hunt, Neal E.; Hyde, Stephanie; Jacobson, Kristi; Johnson, Eric; Kaiser, Betty; Kauzlarich, Kimberly; Kavathekar, Rahul; Knutson, Rena; Kross, Tawny; LaHaie, Edward; Lahidji, Sam; Le, Grace; Lelovic, Mira; Lewis, Martein; Linden, Erin; Lindsay, David; Lively-Brindley, Lisa; Lorenzo, Janet; Luciani, Lisa; Lusk, Cassie; Mahal-Lichty, Jill; Maloy, Patricia; Marcolivio, Kimberly; Marcy, Bradley; Marek, Lindsay; Mason, Bindu; McCamish, Nicole; McKissack, Michael; Miller, Elaine; Moon, Janis; Morris, Heidi; Mundt, Jennifer; Nazario, Mitchell; Nelson, Angela; Nichols, Angela; Njau, Esther; Nolan, Charlotte; Nolan, Patricia; Nugent, Sean; Oakley, Joshua W.; Ofstad, Helen; Olney, Ronald L.; Olson, Jed; Ortolan, Veronica; Osborne, Jeffrey; Osler, Bruce; Packard-Milam, Michele; Palyo, Sarah; Patel, Anjali; Wiener, Chelsea H.; Peter, Doris; Philippe, Michel; Reddy, Saritha; Reed, Justin; Rettmann, Nancy; Rezaei, David; Roberti, Alyssa; Robinson, Verona; Roca, Henri; Romano, Eleni; Rosema, Tara; Rouse, Robert; Rozzero, Paul; Rutks, Indulis; Sabet, Raha F; Sacra, Elijah; Sadowski, Nicole; Salameh, Hope; Sandbrink, Friedhelm; Sauve, Clarke; Schopmeyer, Kathryn A.; Schroeder, Allison; Sculley, Megan; Sellinger, John; Sewell, Kenneth; Shah Barman, Mona; Sharpe, Pamela; Sides, Tracy; Silver, Kristin E.; Siver, Malloree; Sloan, Jessica; Smith, Christine; Stevens, Sean; Stoll, Jennifer; Stoll, Sandra; Stoner, Thomas; Strand, Malcolm; Sumner, Jean A.; Surginer, Bobby; Svabek, Kaitlin; Swanson, Gina; Tappainer, Carla; Taylor, Brent C.; Theis, Alec; Thomas, Laura C.; Thomas, Gay R; Tiene, Kathryn; Udouj, Randall; Ugbo, Ebonne; Varughese, Shaina R.; Venkatraman, Shanti; Viera, Katherine; Walker, Janet; Walker, Jennifer; Wang, Gloria; Watson, Kayleen; Watson, Erin; Westmoreland, William C.; Wilbur, Eileen; Wolfman, Alan; Woloszyn, Melinda; Zanga, Joseph; Zuniga, Samara (2024).
• Benchmarking Large Language Models with a Unified Performance Ranking Metric. Leon, Maikel (2024). 4(4) 15–27.
• Designing Social Robots for Learning: HRI Participatory Design Workshop. Baksh, Farnaz; Zorec, Matevz Borjan; Kruusamäe, Karl in Lecture Notes in Computer Science, Y.-P. Cheng, M. Pedaste, E. Bardone, Y.-M. Huang (eds.) (2024). (Vol. 14786) 215–223.
• LightTR: A Lightweight Framework for Federated Trajectory Recovery. Liu, Ziqiao; Miao, Hao; Zhao, Yan; Liu, Chenxi; Zheng, Kai; Li, Huan (2024). 4422–4434.
• Adversarial Prompt Tuning for Vision-Language Models. Zhang, Jiaming; Ma, Xingjun; Wang, Xin; Qiu, Lingyu; Wang, Jiaqi; Jiang, Yu-Gang; Sang, Jitao (2023). abs/2311.11261
• Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?. Sun, Weisong; Fang, Chunrong; Miao, Yun; You, Yudu; Yuan, Mengzhe; Chen, Yuchen; Zhang, Quanjun; Guo, An; Chen, Xiang; Liu, Yang; Chen, Zhenyu (2023). abs/2312.00413
• Viewpoint Push Planning for Mapping of Unknown Confined Spaces. Dengler, Nils; Pan, Sicong; Kalagaturu, Vamsi; Menon, Rohit U.; Dawood, Murad; Bennewitz, Maren (2023). abs/2303.03126
• Fake Alignment: Are LLMs Really Aligned Well?. Wang, Yixu; Teng, Yan; Huang, Kexin; Lyu, Chengqi; Zhang, Songyang; Zhang, Wenwei; Ma, Xingjun; Jiang, Yu-Gang; Qiao, Yu; Wang, Yingchun (2023). abs/2311.05915
• Multi-branch aware module with channel shuffle pixel-wise attention for lightweight image super-resolution. Gao, Xiang; Xu, Lijuan; Wang, Fan; Hu, Xiaopeng (2023). 29(1) 289–303.
• Graph-based View Motion Planning for Fruit Detection. Zaenker, Tobias; Rückin, Julius; Menon, Rohit U.; Popovic, Marija; Bennewitz, Maren (2023). abs/2303.03048
• Graph-Based View Motion Planning for Fruit Detection. Zaenker, Tobias; Rückin, Julius; Menon, Rohit U.; Popovic, Marija; Bennewitz, Maren (2023). 4219–4225.
• NBV-SC: Next Best View Planning Based on Shape Completion for Fruit Mapping and Reconstruction. Menon, Rohit U.; Zaenker, Tobias; Dengler, Nils; Bennewitz, Maren (2023). 4197–4203.
• DawnIK: Decentralized Collision-Aware Inverse Kinematics Solver for Heterogeneous Multi-Arm Systems. Marangoz, Salih; Menon, Rohit U.; Dengler, Nils; Bennewitz, Maren (2023). 1–8.
• Viewpoint Push Planning for Mapping of Unknown Confined Spaces. Dengler, Nils; Pan, Sicong; Kalagaturu, Vamsi; Menon, Rohit U.; Dawood, Murad; Bennewitz, Maren (2023). 1178–1184.
• Radikaler Universalismus: jenseits von Identität Boehm, Omri (2023). Ullstein, Berlin.

  Ist der Universalismus heute noch zu retten? Ja, aber wir müssen zurück zu seinem Ursprung: Erst wenn wir den humanistischen Appell der biblischen Propheten und Immanuel Kants wirklich verstehen, können wir Ungerechtigkeit kompromisslos bekämpfen – im Namen des radikalen Universalismus, nicht in dem der Identität. Mit „Radikaler Universalismus“ liefert Omri Boehm mehr als eine Neuinterpretation, er revolutioniert unser grundlegendes Verständnis von dem, was Universalismus eigentlich ist. Dabei beruft er sich auf Kant und seine oft missverstandene Wiederbelebung des ethischen Monotheismus der jüdischen Propheten. Ein kühner Entwurf, der in seiner Furchtlosigkeit einen Ausweg aus der festgefahrenen Identitätsdebatte eröffnet.
• Secure Ultra-reliable and Low Latency Communication in NOMA-UAV Networks. Zhao, Xiao; Yu, Kan; Li, Dong; Liu, Xiaowu; Luo, Chuanwen (2023). 127–134.
• An Evaluation of a Hybrid STEM-Robotics Programme for Students with Special Needs in Guyana. DeFreitas, Penelope; Baksh, Farnaz; Zorec, Matevz Borjan; Todd, Joyann; Vanlewin, Obena in Communications in Computer and Information Science, C. Stephanidis, M. Antona, S. Ntoa, G. Salvendy (eds.) (2023). (Vol. 1833) 262–269.
• Cross-Level Network Security Element Fusion Extraction Method Based on Deep Learning. Zhao, Jianping; Wang, Huan; Wang, Yichuan; Li, Hua; Wang, Yan (2023). 247–254.
• Deep learning-based Crop Row Following for Infield Navigation of Agri-Robots. de Silva, Rajitha; Cielniak, Grzegorz; Wang, Gang; Gao, Junfeng (2022). abs/2209.04278
• Fruit Mapping with Shape Completion for Autonomous Crop Monitoring. Marangoz, Salih; Zaenker, Tobias; Menon, Rohit U.; Bennewitz, Maren (2022). abs/2203.15489
• Proposal for a Maturity Continuum Model for Open Research Data. Guirlet, Marielle; Bongi, Gaia; Point, Elise; Urvoy, Grégoire; Schneider, René Martin (2022). 17(1) 6.
• Energy Alignment for Bias Rectification in Class Incremental Learning. Zhao, Bowen; Chen, Chen; Xiao, Xi; Ju, Qi; Xia, Shutao (2022). 3513–3517.
• Fruit Mapping with Shape Completion for Autonomous Crop Monitoring. Marangoz, Salih; Zaenker, Tobias; Menon, Rohit U.; Bennewitz, Maren (2022). 471–476.
• Energy Aligning for Biased Models. Zhao, Bowen; Chen, Chen; Ju, Qi; Xia, Shutao (2021). abs/2106.03343
• Perception, Awareness, Presentation: Using Meditation Techniques to Teach Data Visualisation Literacy - A Case Study. Schneider, René Martin in Communications in Computer and Information Science, S. Kurbanoglu, S. Spiranec, Y. Ünal, J. Boustany, D. Kos (eds.) (2021). (Vol. 1533) 99–108.
• An Efficiency Optimization Control Method With Fast Dynamic Response For Muti-Phase Interleaved BUCK Converter. Zhang, Peng; Liu, Yang; Zhao, Jin; Zhang, Hanshu; Lv, Qiaoqiang (2021). 1–5.
• On the Generation of Disassembly Ground Truth and the Evaluation of Disassemblers. Li, Kaiyuan; Woo, Maverick; Jia, Limin (2020). abs/2012.09155
• CAP: Context-aware App Usage Prediction with Heterogeneous Graph Embedding. Chen, Xinlei; Wang, Yu; He, Jiayou; Pan, Shijia; Li, Yong; Zhang, Pei (2019). 3(1) 4:1–4:25.
• Privacy-Preserving Text Similarity via Non-Prefix-Free Codes. Külekci, M. Oguzhan; Habib, Ismail; Aghabiglou, Amir in Lecture Notes in Computer Science, G. Amato, C. Gennaro, V. Oria, M. Radovanovic (eds.) (2019). (Vol. 11807) 94–102.
• Deep Semi-Supervised Anomaly Detection. Ruff, Lukas; Vandermeulen, Robert A.; Görnitz, Nico; Binder, Alexander; Müller, Emmanuel; Müller, Klaus-Robert; Kloft, Marius (2019). abs/1906.02694
• Improving Base Station Anonymity using Data Funneling and PID Controlled Fake Packets. Aliyu, Farouq; Umar, Sani; Al-Kharoubi, Talal M.; Baroudi, Uthman (2019). 1–6.
• Towards New Approach to Enhance Learning Based on Internet of Things and Virtual Reality. Fahim, Mohamed; Jakimi, Abdeslam; Lahcen, El Bermi S. Amzazi, A. E. Afia, M. Essaaidi (eds.) (2018). 54:1–54:5.
• Investigating the Relationship between Literary Genres and Emotional Plot Development. Kim, Evgeny; Padó, Sebastian; Klinger, Roman B. Alex, S. Degaetano-Ortlieb, A. Feldman, A. Kazantseva, N. Reiter, S. Szpakowicz (eds.) (2017). 17–26.

  Literary genres are commonly viewed as being defined in terms of content and stylistic features. In this paper, we focus on one particular class of lexical features, namely emotion information, and investigate the hypothesis that emotion-related information correlates with particular genres. Using genre classification as a testbed, we compare a model that computes lexicon-based emotion scores globally for complete stories with a model that tracks emotion arcs through stories on a subset of Project Gutenberg with five genres. Our main findings are: (a), the global emotion model is competitive with a large-vocabulary bag-of-words genre classifier (80\%F1); (b), the emotion arc model shows a lower performance (59 \% F1) but shows complementary behavior to the global model, as indicated by a very good performance of an oracle model (94 \% F1) and an improved performance of an ensemble model (84 \% F1); (c), genres differ in the extent to which stories follow the same emotional arcs, with particularly uniform behavior for anger (mystery) and fear (adventures, romance, humor, science fiction).
• A Unified Approach to Interpreting Model Predictions. Lundberg, Scott M; Lee, Su-In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (eds.) (2017). (Vol. 30)
• Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. Schlegl, Thomas; Seeböck, Philipp; Waldstein, Sebastian M.; Schmidt-Erfurth, Ursula; Langs, Georg (2017). abs/1703.05921
• Distributed spectrum trading via dynamic matching with evolving preferences. Wang, Jingyi; Long, Yan; Wang, Jie; Errapotu, Sai Mounika; Guo, Yuanxiong; Pan, Miao (2016). 1–6.
• A Framework for Estimating Long Term Driver Behavior. Gadepally, Vijay; Krishnamurthy, Ashok K. (2016). abs/1607.03189
• Graph Fourier Transform based on Directed Laplacian. Singh, Rahul; Chakraborty, Abhishek; Manoj, B. S. (2016). abs/1601.03204
• On spectral analysis of node centralities. Singh, Rahul; Chakraborty, Abhishek; Manoj, B. S. (2016). 1–5.
• A Framework for Estimating Driver Decisions Near Intersections. Gadepally, Vijay; Krishnamurthy, Ashok K.; Özgüner, Ümit (2014). 15(2) 637–646.
• Parallel MATLAB Techniques. Krishnamurthy, Ashok K.; Samsi, Siddharth; Gadepally, Vijay (2014). abs/1407.2636
• An efficient computational framework for the analysis of whole slide images: Application to follicular lymphoma immunohistochemistry. Samsi, Siddharth; Krishnamurthy, Ashok K.; Gurcan, Metin N. (2012). 3(5) 269–279.
• Sentiment Analysis and Opinion Mining Liu, Bing (2012). (Vol. 5) 1–167. Morgan & Claypool Publishers \($\lbrace$\)LLC\($\rbrace$\).
• Exploring the Selection and Training of Tour Leaders: An Evaluation Model Based on the Analytic Hierarchy Process. Sun, Yuang-Liang; Huang, Han-Chen (2011). 13(299--300) 62.
• 転移学習. 敏弘神嶌 (2010). 25(4) 572–580.
• Comparative Performance Study for Integrated 3G/WLAN Networks Using Mobile IP, SIP, and m-SCTP Protocols. Mahmoud, Ashraf S.; Al-Helali, Abdul-Aziz; Abu-Amara, Marwan H.; Al-Kharoubi, Talal M.; Sheltami, Tarek R. (2010). 1–5.
• Analysis of Handoff Delay Components for Mobile IP-Based 3GPP UMTS/WLAN Interworking Architecture. Al-Helali, Abdul-Aziz; Mahmoud, Ashraf S. Hasan; Al-Kharoubi, Talal M.; Sheltami, Tarek R. (2009). 798–803.
• Packet Scheduling Algorithm with QoS Provision in HSDPA. Wang, Ying-Hong; Huang, Kuo-Feng; Le, Chengyi I. Awan, M. Younas, T. Hara, A. Durresi (eds.) (2009). 616–623.
• Characterization of Vertical Handoff Delay for Mobile IP Based 3G/WLAN Integrated Networks. Al-Helali, Abdul-Aziz; Mahmoud, Ashraf S.; Al-Kharoubi, Talal M.; Sheltami, Tarek R. (2009).
• Experiences from Cyberinfrastructure Development for Multi-user Remote Instrumentation. Calyam, Prasad; Kalash, Abdul; Ludban, Neil; Gopalan, Sowmya; Samsi, Siddharth; Tomko, Karen A.; Hudak, David E.; Krishnamurthy, Ashok K. (2008). 293–300.
• A Novel Dual-Mode User Equipment Design and Enhanced Gateway Selection Algorithm for B3G Networks. Al-Helali, Abdul-Aziz; Mahmoud, Ashraf S. Hasan; Al-Kharoubi, Talal M.; Sheltami, Tarek R. W. chi Feng, Y. Yang, G. Agrawal (eds.) (2008). 162–166.
• Survey of Parallel MATLAB Techniques and Applications to Signal and Image Processing. Krishnamurthy, Ashok K.; Nehrbass, John; Chaves, Juan Carlos; Samsi, Siddharth (2007). 1181–1184.
• Der nonkonformistische Intellektuelle: die Entwicklung der kritischen Theorie zur Frankfurter Schule Demirović, Alex in Suhrkamp-Taschenbuch Wissenschaft (1999). Suhrkamp, Frankfurt am Main.

  Vernunft und Wahrheit sind kein allein der Philosophie vorbehaltenes Thema, sondern stellen im spezifischen Sinn eine politische Praxis dar: Dies nötigt zu einer gesellschaftstheoretischen Erweiterung des Gegenstandsbereichs der politischen Theorie und der Ideengeschichte. Eine solche Überlegung verdankt den Arbeiten Michel Foucaults ebensoviel wie den älteren Vertretern der Kritischen Theorie, die Vernunft als materielles Verhältnis verstanden wissen wollten. Die Kritische Theorie ist in der vorliegenden Untersuchung Gegenstand einer so verstandenen politischen Archäologie theoretischer Praxis.
• Sketching Martin, Judy (1991). HarperCollins, [London].