International audience

In this paper, we present a bias and sustainability focused investigation of Automatic Speech Recognition (ASR) systems, namely Whisper and Massively Multilingual Speech (MMS), which have achieved state-of-the-art (SOTA) performances. Despite their improved performance in controlled settings, there remains a critical gap in understanding their efficacy and equity in real-world scenarios. We analyze ASR biases w.r.t. gender, accent, and age group, as well as their effect on downstream tasks. In addition, we examine the environmental impact of ASR systems, scrutinizing the use of large acoustic models on carbon emission and energy consumption. We also provide insights into our empirical analyses, offering a valuable contribution to the claims surrounding bias and sustainability in ASR systems.

Proceedings of Interspeech 2023

International audience

The Global Hydrology Resource Center (GHRC) Distributed Active Archive Center (DAAC), developed the Field Campaign eXplorer (FCX) to address a limitation in available visualization resources. FCX is a cloud-native, open-source system capable of visualizing multiple datasets in three dimensions. This includes data from ground-, airborne-, and satellite-based observations. The open-source nature will further allow users of FCX to develop their own extensions for both visualizations and analyses. This paper will discuss the architecture of FCX, its uses, and future development work.

International audience

International audience

Diverse airborne and ground-based environmental observations are important technologies for disaster assessment and response, as well as for the validation of environmental satellite observations and atmospheric models which can improve forecasts. The VISAGE (Visualization for Integrated Satellite, Airborne and Ground-based data Exploration) project is working to provide three-dimensional visualization and basic analytics capabilities for such datasets in an interactive user interface. The use of cloud-native, serverless technologies and analysis optimized data storage will position VISAGE for integration with other technologies into a Data Analytic Center Framework.

Accurate provenance information facilitates improved understanding of Earth science data and scientific reproducibility and can serve as an indicator of data quality. Provenance capture is an integral part of many modern workflow systems but may not have been considered in the design of legacy data production systems. Furthermore, in addition to data lineage, it is also important to capture contextual information needed for understanding how a data set was produced. This paper describes our experience in retrofitting a legacy data system to support capture, storage, and dissemination of provenance. Data inputs and transformations are logged automatically, while broader context information describing science algorithms and ancillary files is manually compiled. Provenance and context information are integrated for interactive user access and embedded into data files as XML documents compliant with the “Lineage” specification for geographic metadata defined by the International Organization for Standardization in the ISO 19115-2 standard. Lessons learned from this approach can inform others who need to incorporate provenance into a data system after the fact.

Pervasive computing environments consist of thousands of heterogeneous devices and network services. Service discovery protocols provide essential functionalities for users and client devices to discover and access services. Most existing protocols, however, only support discovery via static service attributes. Dynamic information such as service conditions, quality, and reliability is not available to users and clients. To disseminate dynamic service information, we need to properly control the communication and computational overhead. Thus, the solution will be viable for resource-constrained devices. We propose a novel service discovery approach, called DynamicSD. We use mathematical models-Markov Chains-to represent dynamic and uncertain service states. The Markov Chains are disseminated among clients, services, and directories. By deriving the properties of the Markov Chains, we attain dynamic service information. To the best of our knowledge, this is the first formal model to represent dynamic and uncertain service conditions in service discovery protocols. We implement a prototype protocol on wireless sensors. The performance measurements show that the communication and computational overhead that we introduce is low.

In mobile and pervasive computing environments, users may easily exchange information via ubiquitously available computers ranging from sensors, embedded processors, wearable and handheld devices, to servers. The unprecedented level of interaction between users and intelligent environments poses unparalleled privacy challenges. We identify a new attack that can be used to acquire users' private information---using reciprocity norms. By mutually exchanging information with users, an attacker may use a psychological method, the norm of reciprocity, to acquire users' private information. We implemented software to provide a rich shopping experience in a mobile and pervasive computing environment and embedded the reciprocity attack. Our experiments showed that participants were more willing to provide some types of private information under reciprocity attacks. To the best of our knowledge, this is the first attempt to understand the impact of the norm of reciprocity as an attack in mobile and pervasive computing environments. These human factors should be taken into consideration when designing security measures to protect people's privacy.

Various miniaturized computing devices that store our identities are emerging rapidly. They allow our identity information to be easily exposed and accessed via wireless networks. When identity information is associated with our personal and context information that is gathered by ubiquitous computi