Forecasting of the Traffic Situation in the Hannover Region

The main requirement of road traffic participants is to know the current traffic situation. Such data is typically obtained from routing services where the time of many different individual trips is taken into account.

In the context of Data4UrbanMobility tools were developed that allow to predict the traffic situation based on such time series data. The following figure presents an interface to visualize typical time series patterns as well as outliers present in the data:

The prediction of the traffic situation is made available in the form of a map based interface for the end user:

Data4UrbanMobility Data Protection Regulation

The work on the Data4UrbanMobility data protection regulation is completed. The document is publicly available and can be found here.

First Version of MiC-App Available

A first version of the novel MiC-App (Move in the City) App is now available for D4UM-associates as well as a protected group of public users. The mobile MiC-App is a tool to gather data.

MiC was developed by the Institute for Sustainable Urbanism at the University of Braunschweig and the Projektionisten GmbH. MiC links the growing  awareness of  digital citizen rights with the potential of evaluation big datasets. Therefore MiC gives the opportunity to citizen to actively participate in a citizen science project to take part in the development of the mobility of the feature.

MiC gathers data of the users movement, where the user has the about which data should be recorded. All data is pseudonymised such that the privacy of the contributing citizen is ensured.

Current Status:

In the first version of the app, the user can easily start and end the tracking of his/her movement. It is worth to point out, that the user decides when he is tracked and when not. A summary of his/her activity is available for the user as well as the opportunity to issue feedback or even delete all of his contributed data.

Updated System with Dashboard V2

With the new version of our system,  the dashboard will provide even more insights into the impact of public events on the traffic situation.

The coloring and labels let us easily distinguish between the different type of events. By clicking on the label we show the typically affected subgraph for that event type. This allows the user to check what specific routes are typically affected by an event at that location.

Examples: Visualisation of a concert and a football game.

In addition, the graph at the top right gives additional information on how big the impact around the events start time tends to be.

{API}
We enriched the api endpoints with additional information from the data models that were developed as part of the research efforts.

D4UM App Version 1.0

We just released the first Version of the D4UM App. Every project member now has access to the application and can try out its features. Let’s quickly go over some of its main features.
The EFA integration (EFA is a routing engine covering Lower Saxony and Bremen ) allows for quick access to tip information using all available public transport options. Our focus, when designing the application, was on quick and easy navigation to provide a simple and easy to use trip planning tool.

Departures and Connections

On the departure screen we show the user the closes stops for public transportation in his immediate vicinity. On the connection screen the user can fill in his desired starting location( either an address or an existing stop ) and destination and query for what connections are available to him. The provided information contains real time data , meaning we are able to visualized delays for any given connection.

Map

On the map screen you can see and or find all available stops of public transportation. This allows for providing the user with a great way to find out what stops are available in their city. By clicking on any of the shown stops will open the departure screen and provide you with the information mentioned above. To better visualize a selected connection, we show the route you plan to travel on the map.

Menu / Settings

Additional features can be found in the settings menu of the application. Here you can find settings that allow you to customize your routing results for both the departures and connection screen. The best way to let us know what you think about the application is to use the feedback module. This can be found here as well. First click on the emoji that best describe how you feel about the app. And then put in any additional information or ideas or thoughts you may have. Now what is left is just to press send and you will send us an email.

We look forward to hearing from you.

Quantification  and Prediction of Impact of Public Events

Current Data4UrbanMobility research results allow for measuring and prediction of spatial impact on road traffic of public events. Connected, affected street segments nearby public events are identified to measure the spatial impact. The approach is depicted in the following figure:

(Karte von https://www.openstreetmap.org)

An event is marked as yellow dot, affected streets in red and the measured impact in dark blue. Moreover, an approach making use of machine learning algorithms was developed to predict the impact determined in this way, resulting an error-reduction of up to 40% when compared to existing state-of-the-art approaches.

D4UM – Platform V1 Released

The first version of the Data4UrbanMobiltiy platform has been released. The platform was designed and implemented following a 3-tier-architecture. The platform provides RESTfull Web services for mobility applications like dashboards or mobile apps. As a demonstration, an interactive map application has been developed that visualizes the spatial impact of public events. The following figure shows a screenshot of the application.

The figure shows 4 public events in the city of Hannover. The colors represent different types of public events (e.g. concerts, fairs, sport events). The circles visualize the spatial impact on road traffic caused by the public events.

Comprehensive Set of Requirements

The Data4UrbanMobility analysis of requirements includes requirements of the application partners Region Hannover (RH) and Wolfsburg AG (WAG) as well as non functional requirements. The requirements were collected by MOMA. The L3S derived research question for data analysis which are based  on the requirements of RH and WAG. The research question address especially the information needs of end-users.

The current research questions particularly include

1. Automated verification of traffic warnings and prediction of their impact
2. Identification of events and prediction of their impact
3. Investigation of correlation of road traffic data, public transportation query logs, traffic warnings and twitterfeeds
4. Determination of optimal traveling timepoints

Growing Data Collection

ISU create a comprehensive data matrix containing potential source of mobility related data. The Data4UrbanMobility data model describes all project relevant data sets and sets them into context. This makes the data available in a unified manor for both analysis and applications. The selected data sources were transformed according to the Data4UrbanMobility data model by L3S. The data quality of selected data sources (i.e. public transportation query logs and road traffic data) was examined.

Tools for extracting the relevant information from the datasets were developed to enable the integration of the datasets.

• Street and graph extraction from OpenStreetMap
• Bulkloader for public transportation queries
• Integration of “Zentrales Haltestellen Verzeichniss” (central registry of public transportation stops)

The current collection (December 12th 2017) contians

EFA-Logs: 17 million public transportation queries
Road traffic data: 174 thousand street sements with a frequency of 15 minutes
GTFS-data: 90 thousand. public transportation stops, 2.6 thousand routes
Weather: Radolan “Regenraster” (rain grid)
Twitter: 2,5 Mio. Tweets starting at June 2017

OSM: 440 thousand streets 
Events: 21 thousand public events (August 14th 2016-July 17th 2018)

Traffic warnings: 13 thousand warning (since June 2017)

Visualization of Public Transportation Information

In order to allow intuitive analytics of public transportation information, the PROJEKTIONISTEN (PROJ) developed a dashboard web application. First prototypes visualize queries addressed to the regional timetable information system EFA (www.efa.de). The prototypes serve as foundations for exploration analyses as well as the implementation of future versions of the dashboard. The following figure shows an integrated visualization of the most frequent origins and destinations of the queries.

Analysen der EFA-Logs

Analysis of EFA Public Transportation Query Logs

Analyses regarding the impact of public events on public transportation are currently conducted to address early research questions. To this extend, explorative data analyses of the impact of major public events such as football games and medium sized events such as concerts were conducted. Visual analytics were used as a first step towards comprehensive analyses, which show start-like patterns for city center which identify mobility hubs of central importance.

The figure shows the direct connection between origin and destination of public transportation queries. Darker colors correspond to more frequent queried trips. Star-like pattern identify the central train station and the central metro station.

Analyses of single stations reveal weekday dependent patterns.

The figure depicts the average number of queries with the destination “Hannover Stadionbrücke”. Differences emerge between Weekends and workdays.

The impact of public events on the queries can be visualized  as well.

The figure shows the number of queries with the Destination “Hannover Stadionbrücke” for Wednesday, April 26th 2017 (orange) as well as the average number of queries on a Wednesday for the same destination. On this day a concert took place in venue nearby. The concert start at 8 pm. The significant deviations between 5 pm and 7 pm is highly likely to be caused by visitors of the concert. This shows that public transportation queries are a valuable information source to investigate the impact of public events on mobility infrastructure.

• Evaluating the Efficiency and Effectiveness of Learned Sparse Retrieval with the lsr_benchmark. Fröbe, Maik; Schlatt, Ferdinand; Rulli, Cosimo; Hagen, Tim; Merker, Jan Heinrich; Hendriksen, Gijs; Lassance, Carlos; Nardini, Franco Maria; Venturini, Rossano; Potthast, Martin in Lecture Notes in Computer Science (2026).
• Investigating Counterclaims in Causality Extraction from Text. Hagen, Tim; Deckers, Niklas; Wolter, Felix; Scells, Harrisen; Potthast, Martin (2026). abs/2510.08224
• Overview of Touché 2026: Argumentation Systems. Kiesel, Johannes; Feger, Marc; Hagen, Tim; Heineking, Sebastian; Heinrich, Maximilian; Fröbe, Maik; Boland, Katarina; Pertsch, Wilhelm; Romberg, Julia; Zelch, Ines; Dietze, Stefan; Hagen, Matthias; Potthast, Martin; Stein, Benno in Lecture Notes in Computer Science, R. Campos, A. Jatowt, Y. Lan, M. Aliannejadi, C. Bauer, S. MacAvaney, Z. Ren, S. Verberne, N. Bai, M. Mansoury (eds.) (2026). (Vol. 16485) 277–286.
• GIS ANALYSIS OF LINEAR AND SPIRAL GEOMETRIES IN THE BOSNIAN VALLEY OF THE PYRAMIDS. Osmanagich, Sam (2026).
• Retrieval-Augmented Generation -- The Future of Search? (Dagstuhl Seminar 25391). Hagen, Matthias; Mothe, Josiane; Muresan, Smaranda; Potthast, Martin; Zhang, Min; Stein, Benno; Heineking, Sebastian (M. Hagen; J. Mothe; S. Muresan; M. Potthast; M. Zhang; B. Stein; S. Heineking, eds.) (2026). 15(9) 71–159.
• Trigger Warnings are Grounded in a Shared Vocabulary: A Corpus Analysis with User-Generated Labels. Heineking, Sebastian; Wiegmann, Matti; Wolska, Magdalena; Stein, Benno; Potthast, Martin S. Piperidis, N. Bel, H. van den Heuvel, N. Ide, S. Krek, A. Toral (eds.) (2026). 7106–7125.
• Care: Sorge als Gegenstand, Theorie, Methode und Ethik der Wissenschafts- und Technikforschung. Ochs, Casten; Wenten, Klara-Aylin A. Krüger, J.-F. Schrape, C. Schubert, J. Schubert (eds.) (2026). 101–118.
• A Large-Scale, Cross-Disciplinary Corpus of Systematic Reviews. Achkar, Pierre; Gollub, Tim; Simons, Arno; Scells, Harrisen; Potthast, Martin (2026). abs/2604.22864
• A.I., Journalism and the Uncertain Future of the Public Square. Sulzberger, Arthur Gregg (2026).

  Publisher A.G. Sulzberger warns A.I. companies are violating settled law and urges news organizations to stand up for their rights to ensure a sustainable future for reporting.
• Affirmative Kritik: Wie Tech-CEOs Ethik als Verkaufsmasche nutzen. Geuter, Jürgen (2026).

  Seit der „KI“-Boom im vollen Gange ist, veröffentlichen alle paar Monate leitende Angestellte und die Geschäftsführer der großen „KI“-Labs wie OpenAI oder Anthropic Manifeste über die existenziellen Gefahren von „KI“, oft flankiert von einigen Wissenschaftler:innen und befreundeten NGO-Vertreter:innen. Es geht um die Notwendigkeit einer Pause in der Entwicklung solcher Systeme oder darum, dass neue Regulierung und soziale Praktiken zum Umgang mit solchen Systemen notwendig seien.
• Topic-Specific Classifiers are Better Relevance Judges than Prompted LLMs. Gienapp, Lukas; Potthast, Martin; Yates, Andrew; Scells, Harrisen; Yang, Eugene (2026).
• Empathy in leadership communication: Experimental evidence from two vignette studies on AI’s role in message improvement and observers’ perceptions. Sedefoglu-Ulucak, D.; Ohly, S.; Schmelz, J. (2026). 185 109096.
• Analyzing the role of family socio-economic status, education and work characteristics in times of generative artificial intelligence and digital divide. Neufeld, K.; Ohly, S.; Sedefoglu-Ulucak, D.; Steinhardt, I.; Mauermeister, S. (2026). 31(3) 354–372.
• Relationships between human energy and work‐related constructs: A meta‐analysis. Schmitt, Antje; Sandra Ohly, Sandra; Kleine, Anne‐Kathrin; Schnafel, Ann‐Kathrin (2026). 75(1) e70073.
• Humans, LLMs, and Measures Do Not Align in Attributed Information Retrieval. Gienapp, Lukas; Lang, Jenny; Potthast, Martin; Scells, Harrisen (2026).
• A Pipeline to Bootstrap the Evaluation of Retrieval-Augmented Generation for the Automation of Systematic Reviews in Computer Science. Achkar, Pierre; Gollub, Tim; Simons, Arno; Scells, Harrisen; Fröbe, Maik; Potthast, Martin E. Yang, D. Lawrie, S. MacAvaney, J. Mayfield, L. Soldaini, A. Yates (eds.) (2026). 65–70.
• Hierarchical Background Knowledge in FCA via Must-Link-Before Constraints. Gutekunst, Klara M.; Dürrschnabel, Dominik; Schneider, Stefan; Potthast, Martin (2026).
• Does unauthorized immigration drive community crime rates? Evidence from 11,500 U.S. neighborhoods. Kubrin, Charis E.; Christopher, Derek; Hodgen, Cheyenne; Luo, Xiaoshuang Iris; Hipp, John R. (2026). 1–18.

  Research on the immigration-crime nexus has grown substantially, yet few studies differentiate among immigrants of different legal statuses, including undocumented immigrants. Of those that do, analyses are conducted at state and metropolitan area levels, leaving unanswered the question of how the presence of undocumented immigrants is associated with neighborhood crime rates—our focus. To construct neighborhood-level estimates of the undocumented population, we access restricted-use individual-level Census data, where tract of residence can be identified, and apply an imputation procedure. We combine these estimates with a dataset containing crime information for roughly 11,500 neighborhoods across U.S. cities over a decade. Controlling for change in foreign-born citizens and lawful permanent residents as well as other correlates of community crime rates, we estimate neighborhood-level fixed effects models to determine how change in undocumented residents is associated with change in violent and property crime levels between 2010 and 2018. We find that neighborhoods that experienced greater increases in the share of undocumented residents saw greater reductions in property crime and no detectable change in violent crime. We also document distinct patterns in which increasing undocumented residents is associated with decreasing aggravated assaults but increasing robberies, consistent with a possible victimization explanation.
• Unlawful by design: Exposing the human rights costs of generative AI. Technical Report (POL 40/0996/2026), International, Amnesty (2026).

  This briefing examines how standalone generative AI systems, based on unlawful web scraping, are in conflict with international human rights law (IHRL) and standards through their design, development and deployment. While these technologies promise sophisticated automation and efficiency, they rely on data collection and model training practices that abuse privacy rights, enable discrimination, and threaten freedom of expression and thought. Amnesty International finds that standalone generative AI systems, based on unlawful web scraping, depend on mass invasions of privacy by design, and are fundamentally incompatible with IHRL. As such, Amnesty International is calling for a prohibition of such systems.
• Auto-Judge: A Cross-Task Benchmark for Comparing LLM Judges for Citation-Grounded RAG Systems. Farzi, Naghmeh; Hagen, Tim; Yang, Eugene; Fröbe, Maik; Pradeep, Ronak; Rahmani, Hossein A.; Wang, Xi; Zendel, Oleg; Potthast, Martin; Dietz, Laura A. Moffat, F. Scholer, H. Bast, M. Najork, M. Zhang (eds.) (2026). 3159–3166.
• Creating Specialized RAG-Based Search Engines Using the Open Web Index. Nussbaumer, Alexander; Dinzinger, Michael; Heineking, Sebastian; Hendriksen, Gijs; Holz, Felix; Zerhoudi, Saber; Potthast, Martin; Granitzer, Michael in Lecture Notes in Computer Science (2026).
• Drosophila melanogaster Fly Odors Alone are not Sufficient to Attract other Flies. Unni, Anjana Pisharody; Rihani, Karen; Sachse, Silke; Hansson, Bill S; Knaden, Markus (2026). 52(4)
• An Open SERP Mining Infrastructure for the Archive Query Log. Merker, Jan Heinrich; Ruth, Simon; Scells, Harrisen; Potthast, Martin in Lecture Notes in Computer Science, R. Campos, A. Jatowt, Y. Lan, M. Aliannejadi, C. Bauer, S. MacAvaney, A. Anand, Z. Ren, S. Verberne, N. Bai, M. Mansoury (eds.) (2026). (Vol. 16486) 561–577.
• Detecting RAG Advertisements Across Advertising Styles. Heineking, Sebastian; Pertsch, Wilhelm; Zelch, Ines; Bevendorff, Janek; Stein, Benno; Hagen, Matthias; Potthast, Martin (2026). abs/2603.04925
• Representation Fidelity:Auditing Algorithmic Decisions About Humans Using Self-Descriptions. Elstner, Theresa; Potthast, Martin (2026). 2603.05136
• AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference. Ludwig, Lew (2026). 48(2) 200–201.
• Data Annotations as Pedagogical Hints:From Subjective Labels to Critical Thinking. Raumanns, Ralf; Elstner, Theresa; Ferger-Andrews, Louis; Carlsen, Louise M.; Potthast, Martin; Schouten, Josien P. W. Pluim Gerard; Cheplygina, Veronika (2026). 2607.20149
• Interactional foundations for critical AI literacies. Dingemanse, Mark (2026).

  The ubiquity and ease of use of large language models makes it easy to overlook the interactional and interpretive processes at play. To understand the attraction of this technology we need to trace its sociotechnical roots. From divination and horoscopes and from ELIZA to present-day large language models, I document how people have been thinking with things, outsourcing judgement, and making sense of interactively presented non-sense. Following the lead of Lucy Suchman to “slow down discourses of the ‘smart’ machines”, I consider the interactional foundations of our engagement with technologies of language. I make the case that the fluid output, fine-tuned overconfidence, and interactive design of these computational artefacts conspire to exploit our interpretive processes and interactional infrastructure, rendering them irresistible to lay people and researchers alike. This means that a deep understanding of processes of human interaction and sense-making will be a foundational resource for the growing arsenal of methods in critical AI literacy.
• Robust Instrument-Agnostic Music Transcription for Western Classical Music. Venohr, Yannik; Weiß, Christof (2026).
• Optimal control and the dynamics of ancestral lineages. LaMont, Colin (2026). 233(4) iyag122.

  In resource-constrained populations, the growth rate of an allele is coupled to the frequency of its competitors. As these frequencies are unobserved, exact inference of evolutionary parameters demands integrating over the full ensemble of possible frequency histories. This high-dimensional path integral is computationally prohibitive, and standard phylogenetic approaches avoid its evaluation by treating lineages as unconstrained birth–death processes. However, ignoring the carrying capacity comes at a cost: it necessitates an abundance of free parameters to describe simple allelic replicator dynamics and implicitly assumes an incorrect drift model. We propose a new approach by defining the fitness potential (Ψ)—a dynamical quantity formally equivalent to the logarithm of reproductive value. By exploiting a duality between evolutionary dynamics and stochastic optimal control, we show that Ψ allows for the analytic marginalization of the unknown population history. This transformation yields a tractable likelihood function that strictly enforces competitive constraints while accounting for the information loss due to genetic drift in finite populations. We demonstrate that this framework enables parsimonious, site-specific inference of selection coefficients from phylogenetic trees. This dual-control framework shows ancestral lineages behave as optimal agents: they do not simply climb local fitness gradients, but “anticipate” future selection by navigating a global landscape of reproductive value.
• Revisiting Two-Stage Audio Chord Recognition in the Deep-Learning Era: Leveraging Multi-Pitch Estimation and Symbolic Pretraining. Ding, Yiwei; Venohr, Yannik; Strahl, Sebastian; Müller, Meinard; Weiß, Christof (2026).
• A Collection of Systematic Reviews in Computer Science. Achkar, Pierre; Gollub, Tim; Potthast, Martin in CEUR Workshop Proceedings, I. Frommholz, C. K. Kreutz, P. Mayr, G. Cabanac (eds.) (2026). (Vol. 4187) 71–77.
• Resurrecting the Golden Lyre of Ur: A Physics-Based Virtual Instrument Using Visual Haptic Surrogates for Hand-Tracked Interaction. Zhang, Yaozhong; Weiß, Christof; von Mammen, Sebastian in Lecture Notes in Computer Science (2026).
• ASRA-GNN: ADAPTIVE SIGNED RELATION-AWARE GRAPH NEURAL NETWORK FOR FRIEND RECOMMENDATION. M1, Pharsana Parveen; A2, Stanis Arul Mary W. Broll, C. Greenhalgh, E. F. Churchill (eds.) (2026).
• UP2DATE: Shared Task on Systematic Review Updates. Achkar, Pierre; Gollub, Tim; Potthast, Martin; Eickhoff, Carsten; Scells, Harrisen in CEUR Workshop Proceedings, I. Frommholz, C. K. Kreutz, P. Mayr, G. Cabanac (eds.) (2026). (Vol. 4187) 78–83.
• Insomnia as an Independent Behavioral Correlate of Estimated Type 2 Diabetes Risk: Evidence from Three Validated Risk Assessment Scales. Valles, José Luis Ribes; López, Pedro Juan Tárraga; González, Ángel Arturo López; Campayo, Irene Coll; Busquets-Cortés, Carla; Ramírez-Manent, José Ignacio (2026). 17(3) e0019.

  BACKGROUND: Sleep disturbances, particularly insomnia, are increasingly recognized as behavioral determinants of type 2 diabetes (T2DM). However, their contribution to validated diabetes risk scores beyond traditional sociodemographic and lifestyle factors remains insufficiently explored. OBJECTIVE: To examine the association between insomnia severity, sociodemographic variables, lifestyle habits, and estimated T2DM risk using three validated non-invasive risk assessment scales. METHODS: A cross-sectional study was conducted among 84,898 Spanish workers aged 18-69 years undergoing occupational health evaluations (2021-2024). Sociodemographic and behavioral data were collected through standardized questionnaires, including adherence to the Mediterranean diet (MEDAS-14), physical activity (IPAQ-SF), and smoking status. Insomnia was assessed using the Insomnia Severity Index (ISI). Diabetes risk was estimated using three validated non-invasive risk-assessment tools: the Finnish Diabetes Risk Score, the QDScore/QDiabetes, and the Trinidad Risk Assessment Questionnaire for Type 2 Diabetes Mellitus. As these instruments were developed and validated in different populations, comparisons of absolute risk categories across scales should be interpreted with caution. Multivariable logistic regression models adjusted for age, sex, social class, smoking, diet, and physical activity were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Severe insomnia was associated with approximately 2.1-2.6-fold higher odds of high estimated diabetes risk across all three scales in the pooled analyses, with significant trends across categories of insomnia severity (all P for trend \textless0.001). This association persisted across sex and activity strata. Adding ISI modestly improved model discrimination (ΔAUC ≈ +0.015; P≤0.003) and reclassification (continuous net reclassification improvement [NRI], 6.5\%-8.1\%). CONCLUSIONS: Insomnia severity was independently associated with higher estimated risk of T2DM beyond measured sociodemographic and lifestyle determinants. Incorporating sleep health-via brief tools such as the ISI-into diabetes risk assessment could enhance early prevention strategies in clinical and occupational settings.
• Pseudo-Goldstone mode in altermagnetic \($\mathbf\alpha$\)-MnTe: High-field electron spin resonance studies. Povarov, K. Yu.; Wosnitza, J.; Rößler, S.; Schmidt, M.; Tsirlin, A. A.; Zvyagin, S. A. (2026). 114(2) L020407.
• Relaxed Term Matching for Neural Axiomatic Diagnostics. Parry, Andrew; Fröbe, Maik; Potthast, Martin; MacAvaney, Sean; Stein, Benno; Hagen, Matthias; Ganguly, Debasis A. Moffat, F. Scholer, K. Bi, C. L. A. Clarke (eds.) (2026). 274–284.
• The Geometric Structure of Topic Models. Hirth, Johannes; Hanika, Tom in Lecture Notes in Computer Science, P. Cellier, B. Ganter, R. Missaoui (eds.) (2025). (Vol. 15941) 19–34.
• When Do Challenge Stressors Promote Work Motivation? Exploring the Role of Day-Level Self-Efficacy. Prem, Roman; Ohly, Sandra; Weigelt, Oliver; Siestrup, Katja; Kubicek, Bettina (2025). 2025(1) 21972.
• The Mechanisms of Agile Management Wenten, Klara-Aylin (2025). Routledge, London.
• The Future of Consumption. Agenda for a new perspective on consumer Research. Lamla, Jörn; Heidbrink, Ludger; Hohnsträter, Dirk; Loer, Kathrin; Roschka, Jakob (2025).
• Konsum. Lamla, Jörn; Roschka, Jakob M. Endreß, B. Rampp (eds.) (2025). 523–527.
• Pricing, Design and Cultivation – Conceptual Tools for the Normative Assessment of Fairness in the Age of Media Platformization. Kropf, Jonathan; Lamla, Jörn; Uhlmann, Markus (2025).
• Digitale Resonanz – oder: Wie KI Dinge zum Klingen bringt. Anmerkungen zur Politischen Ontologie bei Rosa und Latour. Lamla, Jörn B. Hollstein, J. Oberthür (eds.) (2025). 514–522.
• Artificial Intelligence as a Hybrid Life Form. On the Critique of Cybernetic Expansion. Lamla, Jörn I. Spiecker gen. Döhmann, C. Burchard (eds.) (2025). 331–353.
• Machbarkeitsstudie für ein Bundesinstitut für Verbraucherforschung. Abschlussbericht einer vom Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz in Auftrag gegebenen „Studie zur Weiterentwicklung der Verbraucherforschung durch Einrichtung eines Bundesinstituts für Verbraucherforschung“ Kenning, Peter; Christian, Kastrop; Lamla, Jörn; Reisch, Lucia (2025).
• Creating the “Casual Listener”: Consecrating Institutions and Spotify’s “Curational Turn”. Kropf, Jonathan R. Diaz-Bone, A. Lehmann-Wermser (eds.) (2025). 293–312.
• Work design and performance. Ohly, Sandra; Venz, Laura (2025). 147–173.
• „Mit einer KI bleibe ich in dem Kosmos an Informationen gefangen, die ich schon habe.“ Im Gespräch mit dem Produktbewerter Holger Brackemann von der Stiftung Warentest. Krenn, Karoline; Kropf, Jonathan K. Krenn, J. Kropf, S. Laser, C. Ochs (eds.) (2025). 119–133.
• Dynamiken digitaler Bewertung. Über Gestaltungsspielräume in Infrastrukturen – von KI bis Queering Krenn, Karoline; Kropf, Jonathan; Laser, Stefan; Ochs, Carsten (2025). Springer VS, Wiesbaden.
• Dynamiken digitaler Bewertung. Ein Vorschlag zur Sortierung. Krenn, Karoline/; Kropf, Jonathan; Laser, Stefan; Ochs, Carsten K. Krenn, J. Kropf, S. Laser, C. Ochs (eds.) (2025). 1–27.
• Jenseits der Logik der Empfehlung. Formale Begriffsanalyse als Grundlage für eine neue Variante zur Vermittlung von Nutzenden und journalistischen Inhalten. Uhlmann, Markus; Hirth, Johannes; Horn, Viktoria (2025). 137–155.
• Humane work design: How does the digitization of work change core work characteristics in different occupations?. Ohly, Sandra; Granica, Amina; Herwig, Hannes; Kühlewind, Niklas; Paetzold, Tobias; Vinhoven, Lana; Wiemers, Luisa; Zhang, Xi (2025). 79(4) 569–580.
• Effects of task switching on depletion, motivation, and creativity: Examining the nature of tasks. Nuhn, Christina; Ohly, Sandra (2025).
• Perspektiven zur (Neu-)Bestimmung soziodigitaler Autonomie und Privatheit. Uhlmann, Markus (2025). 49(3)
• Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization. Achkar, Pierre; Gollub, Tim; Potthast, Martin in CEUR Workshop Proceedings, I. Frommholz, P. Mayr, G. Cabanac, S. Verberne, C. K. Kreutz (eds.) (2025).
• Simplified Longitudinal Retrieval Experiments: A Case Study on Query Rewriting and Document Boosting. Keller, Jüri; Fröbe, Maik; Hendriksen, Gijs; Alexander, Daria; Potthast, Martin; Schaer, Philipp in Lecture Notes in Computer Science, J. Carrillo-de-Albornoz, A. G. S. Herrera, J. Gonzalo, L. Plaza, J. Mothe, F. Piroi, P. Rosso, D. Spina, G. Faggioli, N. Ferro (eds.) (2025). (Vol. 16089) 117–127.
• Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders. Schlatt, Ferdinand; Fröbe, Maik; Scells, Harrisen; Zhuang, Shengyao; Koopman, Bevan; Zuccon, Guido; Stein, Benno; Potthast, Martin; Hagen, Matthias in Lecture Notes in Computer Science, C. Hauff, C. Macdonal, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, N. Tonellotto (eds.) (2025). (Vol. 15573) 1–19.
• Call for Research on the Impact of Information Retrieval on Social Norms. Gollub, Tim; Achkar, Pierre; Potthast, Martin; Stein, Benno in Lecture Notes in Computer Science, C. Hauff, C. Macdonal, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, N. Tonellotto (eds.) (2025). (Vol. 15575) 371–384.
• Ranking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions. Heineking, Sebastian; Probst, Jonas; Steinbach, Daniel; Potthast, Martin; Scells, Harrisen C. Hauff, C. Macdonal, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, N. Tonellotto (eds.) (2025). 128–137.
• ImageCLEF 2025: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications. Ionescu, Bogdan; Müller, Henning; Stanciu, Dan-Cristian; Idrissi-Yaghir, Ahmad; Radzhabov, Ahmedkhan; de Herrera, Alba García Seco; Andrei, Alexandra; Storr as, Andrea; Ben Abacha, Asma; Bracke, Benjamin; Lecouteux, Benjamin; Stein, Benno; Macaire, Cécile; Friedrich, Christoph M.; Schmidt, Cynthia Sabrina; Fabre, Diandra; Schwab, Didier; Dimitrov, Dimitar; Esperanc ca-Rodier, Emmanuelle; Constantin, Gabriel; Becker, Helmut; Damm, Hendrik; Schäfer, Henning; Rodkin, Ivan; Koychev, Ivan; Kiesel, Johannes; Rückert, Johannes; Malvehy, Josep; textcommabelowStefan, Liviu-Daniel; Bloch, Louise; Potthast, Martin; Heinrich, Maximilian; Riegler, Michael A.; Dogariu, Mihai; Codella, Noel; Halvorsen, Pr al; Nakov, Preslav; Brüngel, Raphael; Novoa, Roberto Andres; Das, Rocktim Jyoti; Hicks, Steven A.; Gautam, Sushant; Pakull, Tabea M. G.; Thambawita, Vajira; Kovalev, Vassili; Yim, Wen-Wai; Xie, Zhuohan in Lecture Notes in Computer Science, C. Hauff, C. Macdonal, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, N. Tonellotto (eds.) (2025). (Vol. 15576) 398–406.
• Counterfactual Query Rewriting to Use Historical Relevance Feedback. Keller, Jüri; Fröbe, Maik; Hendriksen, Gijs; Alexander, Daria; Potthast, Martin; Hagen, Matthias; Schaer, Philipp in Lecture Notes in Computer Science, C. Hauff, C. Macdonald, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, N. Tonellotto (eds.) (2025). (Vol. 15574) 138–147.
• A Test Collection for Dataset Retrieval. Kolyada, Nikolay; Stein, Benno; Potthast, Martin in Lecture Notes in Computer Science, C. Hauff, C. Macdonal, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, N. Tonellotto (eds.) (2025). (Vol. 15576) 372–380.
• Web-scale Retrieval Experimentation with chatnoir-pyterrier. Merker, Jan Heinrich; Bevendorff, Janek; Fröbe, Maik; Hagen, Tim; Scells, Harrisen; Wiegmann, Matti; Stein, Benno; Hagen, Matthias; Potthast, Martin in Lecture Notes in Computer Science, C. Hauff, C. Macdonal, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, N. Tonellotto (eds.) (2025). (Vol. 15576) 96–104.
• Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking. Schlatt, Ferdinand; Fröbe, Maik; Scells, Harrisen; Zhuang, Shengyao; Koopman, Bevan; Zuccon, Guido; Stein, Benno; Potthast, Martin; Hagen, Matthias in Lecture Notes in Computer Science, C. Hauff, C. Macdonal, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, N. Tonellotto (eds.) (2025). (Vol. 15574) 323–334.
• The Second International Workshop on Open Web Search (WOWS). Farzana, Sheikh; Fröbe, Maik; Granitzer, Michael; Hendriksen, Gijs; Hiemstra, Djoerd; Potthast, Martin; Vries, Arjen; Zerhoudi, Saber in Lecture Notes in Computer Science (2025).
• Kursorischer Vergleich der Übersetzungsdienste ChatGPT, Google Translate, DeepL und eTranslation Hagen, Tim; Potthast, Martin (2025).
• ARCHITECTURAL PATTERNS AND PERFORMANCE ANALYSIS OF INTEGER SURROGATE KEYS FOR TIME-SERIES DATA WAREHOUSING. Stumpf, Sviatoslav; Povyshev, Vladislav (2025). 8(12) e81638.

  The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.
• Time to Align! Modelling Musical Timelines for Music Information Retrieval and Digital Musicology. Hentschel, Johannes; Berndt, Axel; Cancino-Chacón, Carlos; Dixon, Simon; Foo, Anne; Gotham, Mark; Hu, Patricia; Köster, Maik; Martins, Felipe D.; Mauro, Davide A.; Müller, Meinard; Neuwirth, Markus; Pacha, Alexander; Page, Kevin R.; Peter, Silvan; Polyakov, Egor; Pugin, Laurent; Weigl, David M.; Weiß, Christof; Widmer, Gerhard (2025). 9(1) 384–404.
• Verfassungsschutz: wie der Geheimdienst Politik macht Steinke, Ronen (2025). Piper, München.

  Eine engagierte Reportage, die zeigt, warum der Verfassungsschutz zum Problem geworden ist. Schützt der Verfassungsschutz die Demokratie wirklich? Noch nie in der Geschichte der Bundesrepublik gab es so viele Agentinnen und Agenten, die im Inland die eigene Bevölkerung ausforschen. Das Personal des Verfassungsschutzes hat sich in den letzten 20 Jahren verdoppelt, sein Budget verdreifacht. Ein kritischer Blick hinter die Kulissen des Verfassungsschutzes Ronen Steinke recherchiert seit Jahren im Milieu der Inlandsspione. Er hat Spionagechefs interviewt und Agentinnen bei der Arbeit begleitet. Er zeigt, wie V-Leute vorgehen. Und er stellt eine fundamentale Frage: Schützt dieser Geheimdienst die Demokratie - oder schädigt er sie nicht eher? Wie tickt der Geheimdienst, der jahrelang von Hans-Georg Maaßen geführt wurde? Der deutsche Verfassungsschutz ist etwas sehr Besonderes. Einen solchen Geheimdienst haben andere westliche Demokratien nicht. Es ist ein Geheimdienst, der im Inland späht. Er richtet sich nicht gegen Kriminelle, sondern gegen Personen und Gruppen, die als politisch verwerflich erklärt werden. Er spioniert Bürgerinnen und Bürger aus, die keine Gesetze verletzen. Dabei hat der Verfassungsschutz enorm große Freiheiten, enorm große Macht. Er hat viel mehr Einfluss auf politische Bewegungen, als es der Öffentlichkeit bewusst ist.
• AI as Normal Technology: An alternative to the vision of AI as a potential superintelligence Narayanan, Arvind; Kapoor, Sayash (2025).
• Plagiate in der Wissenschaft: Katz-und-Maus-Spiel um die Redlichkeit. Schwägerl, Christian (2025).

  Täuschungen mittels KI sind kaum noch nachweisbar, und manche Plagiatsjäger treiben ein zweifelhaftes Spiel. Im Kampf gegen Forschungsbetrug braucht es dringend neue Ansätze.
• Jenseits der Logik der Empfehlung. Formale Begriffsanalyse als Grundlage für eine neue Variante zur Vermittlung von Nutzenden und journalistischen Inhalten. Uhlmann, Markus; Hirth, Johannes; Horn, Viktoria (2025). 137–155.
• Team OpenWebSearch at LongEval: Using Historical Data for Scientific Search. Alexander, Daria; Fröbe, Maik; Hendriksen, Gijs; Hagen, Matthias; Hiemstra, Djoerd; Potthast, Martin; Vries, Arjen in CEUR Workshop Proceedings, G. Faggioli, N. Ferro, P. Rosso, D. Spina (eds.) (2025). (Vol. 4038) 3316–3326.
• Corpus Subsampling: Estimating the Effectiveness of Neural Retrieval Models on Large Corpora. Fröbe, Maik; Parry, Andrew; Scells, Harrisen; Wang, Shuai; Zhuang, Shengyao; Zuccon, Guido; Potthast, Martin; Hagen, Matthias in Lecture Notes in Computer Science, C. Hauff, C. Macdonald, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, N. Tonellotto (eds.) (2025). (Vol. 15572) 453–471.
• The Two Paradigms of LLM Detection: Authorship Attribution vs. Authorship Verification. Bevendorff, Janek; Wiegmann, Matti; Richter, Emmelie; Potthast, Martin; Stein, Benno W. Che, J. Nabende, E. Shutova, M. T. Pilehvar (eds.) (2025). 3762–3787.
• Overview of the ``Voight-Kampff’’ Generative AI Authorship Verification Task at PAN and ELOQUENT 2025. Bevendorff, Janek; Wang, Yuxia; Karlgren, Jussi; Wiegmann, Matti; Fröbe, Maik; Tsivgun, Akim; Su, Jinyan; Xie, Zhuohan; Abassy, Mervat; Mansurov, Jonibek; Xing, Rui; Ta, Minh Ngoc; Elozeiri, Kareem Ashraf; Gu, Tianle; Tomar, Raj Vardhan; Geng, Jiahui; Artemova, Ekaterina; Shelmanov, Artem; Habash, Nizar; Stamatatos, Efstathios; Gurevych, Iryna; Nakov, Preslav; Potthast, Martin; Stein, Benno in CEUR Workshop Proceedings, G. Faggioli, N. Ferro, P. Rosso, D. Spina (eds.) (2025). (Vol. 4038) 3504–3534.
• Overview of the Plagiarism Detection Task at PAN 2025. Greiner-Petter, André; Fröbe, Maik; Wahle, Jan Philip; Ruas, Terry; Gipp, Bela; Aizawa, Akiko; Potthast, Martin in CEUR Workshop Proceedings, G. Faggioli, N. Ferro, P. Rosso, D. Spina (eds.) (2025). (Vol. 4038) 3575–3585.
• From Keyterms to Context: Exploring Topic Description Generation in Scientific Corpora. Achkar, Pierre; Murugaboopathy, Satiyabooshan; Kreuter, Anne; Gollub, Tim; Potthast, Martin; Campbell, Yuri Y. Dong, W. Xiao, H. Zhang, R. Zhang, O. Ernst, L. Wang, F. Liu (eds.) (2025).
• The German Commons -- 154 Billion Tokens of Openly Licensed Text for German Language Models. Gienapp, Lukas; Schröder, Christopher; Schweter, Stefan; Akiki, Christopher; Schlatt, Ferdinand; Zimmermann, Arden; Genêt, Phillipe; Potthast, Martin (2025). abs/2510.13996
• Conceptual Topic Aggregation. Gutekunst, Klara M.; Dürrschnabel, Dominik; Hirth, Johannes; Stumme, Gerd P. Cellier, B. Ganter, R. Missaoui (eds.) (2025). 3–18.

  The vast growth of data has rendered traditional manual inspection infeasible, necessitating the adoption of computational methods for efficient data exploration. Topic modeling has emerged as a powerful tool for analyzing large-scale textual datasets, enabling the extraction of latent semantic structures. However, existing methods for topic modeling often struggle to provide interpretable representations that facilitate deeper insights into data structure and content.
• Overview of Touché 2025: Argumentation Systems. Kiesel, Johannes; c Cau gri c Cöltekin; Gohsen, Marcel; Heineking, Sebastian; Heinrich, Maximilian; Fröbe, Maik; Hagen, Tim; Aliannejadi, Mohammad; Anand, Sharat; Erjavec, Tomavz; Hagen, Matthias; Kopp, Matyávs; Ljubevsi’c, Nikola; Meden, Katja; Mirzakhmedova, Nailia; Morkevivcius, Vaidas; Scells, Harrisen; Wolter, Moritz; Zelch, Ines; Potthast, Martin; Stein, Benno in Lecture Notes in Computer Science, J. C. de Albornoz, J. Gonzalo, L. Plaza, A. García Seco de Herrera, J. Mothe, F. Piroi, P. Rosso, D. Spina, G. Faggioli, N. Ferro (eds.) (2025).
• Mirror, Mirror on the Wall: Enabling Computational Research on Beauty Ideals. Gollub, Tim; Achkar, Pierre; Potthast, Martin; Stein, Benno (2025).
• Overview of PAN 2025: Voight-Kampff Generative AI Detection, Multilingual Text Detoxification, Multi-Author Writing Style Analysis, and Generative Plagiarism Detection. Bevendorff, Janek; Dementieva, Daryna; Fröbe, Maik; Gipp, Bela; Greiner-Petter, André; Karlgren, Jussi; Mayerl, Maximilian; Nakov, Preslav; Panchenko, Alexander; Potthast, Martin; Shelmanov, Artem; Stamatatos, Efstathios; Stein, Benno; Wang, Yuxia; Wiegmann, Matti; Zangerle, Eva in Lecture Notes in Computer Science, J. C. de Albornoz, J. Gonzalo, L. Plaza, A. G. S. Herrera, J. Mothe, F. Piroi, P. Rosso, D. Spina, G. Faggioli, N. Ferro (eds.) (2025). (Vol. 16089) 388–411.
• Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins. Gienapp, Lukas; Deckers, Niklas; Potthast, Martin; Scells, Harrisen H. Zamani, L. Dietz, B. Piwowarski, S. Bruch (eds.) (2025). 275–285.
• Axioms for Retrieval-Augmented Generation. Merker, Jan Heinrich; Fröbe, Maik; Stein, Benno; Potthast, Martin; Hagen, Matthias H. Zamani, L. Dietz, B. Piwowarski, S. Bruch (eds.) (2025). 67–77.
• ReNeuIR at SIGIR 2025: The Fourth Workshop on Reaching Efficiency in Neural Information Retrieval. Bruch, Sebastian; Fröbe, Maik; Hagen, Tim; Nardini, Franco Maria; Potthast, Martin N. Ferro, M. Maistro, G. Pasi, O. Alonso, A. Trotman, S. Verberne (eds.) (2025). 4153–4156.
• Large Language Model Relevance Assessors Agree With One Another More Than With Human Assessors. Fröbe, Maik; Parry, Andrew; Schlatt, Ferdinand; MacAvaney, Sean; Stein, Benno; Potthast, Martin; Hagen, Matthias N. Ferro, M. Maistro, G. Pasi, O. Alonso, A. Trotman, S. Verberne (eds.) (2025). 2858–2863.
• The Viability of Crowdsourcing for RAG Evaluation. Gienapp, Lukas; Hagen, Tim; Fröbe, Maik; Hagen, Matthias; Stein, Benno; Potthast, Martin; Scells, Harrisen N. Ferro, M. Maistro, G. Pasi, O. Alonso, A. Trotman, S. Verberne (eds.) (2025). 159–169.
• TIREx Tracker: The Information Retrieval Experiment Tracker. Hagen, Tim; Fröbe, Maik; Merker, Jan Heinrich; Scells, Harrisen; Hagen, Matthias; Potthast, Martin N. Ferro, M. Maistro, G. Pasi, O. Alonso, A. Trotman, S. Verberne (eds.) (2025). 3764–3771.
• TITE: Token-Independent Text Encoder for Information Retrieval. Schlatt, Ferdinand; Hagen, Tim; Potthast, Martin; Hagen, Matthias N. Ferro, M. Maistro, G. Pasi, O. Alonso, A. Trotman, S. Verberne (eds.) (2025). 2493–2503.
• Optimisation of Logistic Operations using AI. Levin, Semen (S. Levin, ed.) (2024). 13(2)

  In today's world, optimising logistical operations becomes a defining factor in the success of retailers. The complexity and dynamism of modern supply chains require the application of advanced technologies for accurate demand forecasting and inventory management. Artificial Intelligence (AI) demonstrates immense potential in addressing these challenges, offering a new level of efficiency and adaptability. This research focuses on applying AI to analyse a retailer's operational data to optimise its logistical operations. Utilising analysis models such as SARIMA for time series and random forests for machine learning enabled precise daily sales forecasts, identification of seasonal peaks, and optimisation of inventory levels and delivery route planning. Particular attention was paid to the analysis of variable importance, revealing key factors affecting the efficiency of logistical operations, including historical sales data, weather conditions, distance to delivery locations, and holiday periods. The investigation results show that integrating AI into logistics improves forecast accuracy and inventory management efficiency and contributes to reducing logistical costs and enhancing customer satisfaction by optimising delivery time and costs. The data-driven and AI-based analytical approach provides a comprehensive view of logistics management, demonstrating how technological innovations can transform traditional methods and create a competitive advantage in a dynamic market environment. The research underscores the significance of adopting data and analytics as the basis for strategic planning and decision-making, reinforcing the idea that AI serves as a valuable extension of companies' analytical capabilities in the field of logistics
• AI snake oil: what artificial intelligence can do, what it can’t, and how to tell the difference Narayanan, Arvind; Kapoor, Sayash (2024). Princeton University Press, Princeton; Oxford.

  Confused about AI and worried about what it means for your future and the future of the world? You’re not alone. AI is everywhere—and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works and why it often doesn’t, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil—products that don’t work, and probably never will. While acknowledging the potential of some AI, such as ChatGPT, AI Snake Oil uncovers rampant misleading claims about the capabilities of AI and describes the serious harms AI is already causing in how it’s being built, marketed, and used in areas such as education, medicine, hiring, banking, insurance, and criminal justice. The book explains the crucial differences between types of AI, why organizations are falling for AI snake oil, why AI can’t fix social media, why AI isn’t an existential risk, and why we should be far more worried about what people will do with AI than about anything AI will do on its own. The book also warns of the dangers of a world where AI continues to be controlled by largely unaccountable big tech companies. By revealing AI’s limits and real risks, AI Snake Oil will help you make better decisions about whether and how to use AI at work and home.
• Preventing Counterfeit Products using Cryptography, QR Code and Webservice. Shaik, Cheman (2021, February).

  We propose a model of mobile agents to construct social networks, based on a system of moving particles by keeping track of the collisions during their permanence in the system. We reproduce not only the degree distribution, clustering coefficient and shortest path length of a large data base of empirical friendship networks recently collected, but also some features related with their community structure. The model is completely characterized by the collision rate and above a critical collision rate we find the emergence of a giant cluster in the universality class of two-dimensional percolation. Moreover, we propose possible schemes to reproduce other networks of particular social contacts, namely sexual contacts.
• EXACT SOLUTIONS OF SCHRÖDINGER EQUATION WITH SOLVABLE POTENTIALS FOR NON PT/PT-SYMMETRIC. Antia, Akaninyene D. (2015). (Vol. 4) 47–54.

  We have obtained explicitly the exact solutions of the Schrodinger equation with Non PT/PT symmetric Rosen Morse II, Scarf II and Coulomb potentials. Energy eigenvalues and the corresponding unnormalized wave functions for these systems for both Non PT and PT symmetric are also obtained using the Nikiforov-Uvarov (NU) method.
• A Novel Dencos Model For High Dimensional Data Using Genetic Algorithms. Vijayakumar, T.; V.Nivedhitha; K.Deeba; Bama, M. Sathya (2012). 2(1) 35–43.

  We propose a model of mobile agents to construct social networks, based on a system of moving particles by keeping track of the collisions during their permanence in the system. We reproduce not only the degree distribution, clustering coefficient and shortest path length of a large data base of empirical friendship networks recently collected, but also some features related with their community structure. The model is completely characterized by the collision rate and above a critical collision rate we find the emergence of a giant cluster in the universality class of two-dimensional percolation. Moreover, we propose possible schemes to reproduce other networks of particular social contacts, namely sexual contacts.
• MORPES: A Model for Personalized Rendering of Web Content on Mobile Devices. K.S.Kuppusamy1; and G.Aghila2 W. Broll, C. Greenhalgh, E. F. Churchill (eds.) (2012). 47–54.
• General Methodology for developing UML models from UI. Reddy, Ch Ram Mohan; Geetha, D Evangelin; Srinivasa, KG; Kumar, T V Suresh; Kanth, K Rajani (2011). 2(4) 01–16.

  In recent past every discipline and every industry have their own methods of developing products. It may be software development, mechanics, construction, psychology and so on. These demarcations work fine as long as the requirements are within one discipline. However, if the project extends over several disciplines, interfaces have to be created and coordinated between the methods of these disciplines. Performance is an important quality aspect of Web Services because of their distributed nature. Predicting the performance of web services during early stages of software development is significant. In Industry, Prototype of these applications is developed during analysis phase of Software Development Life Cycle (SDLC). However, Performance models are generated from UML models. Methodologies for predicting the performance from UML models is available. Hence, In this paper, a methodology for developing Use Case model and Activity model from User Interface is presented. The methodology is illustrated with a case study on Amazon.com.
• Der politische Mensch. Negt, Oskar (2010).