IEEE Transactions on Multimedia
IEEE TMM Special Issue on Large Multi-modal Models for Dynamic Visual Scene Understanding
网址：http://www.signalprocessingsociety.org/tmm//
专刊详情请查阅官网 https://signalprocessingsociety.org
IEEE Internet of Things Magazine
Ambient Internet of Things and Near-Zero Energy Communications
网址：https://www.comsoc.org/publications/magazines/ieee-internet-things-magazine/
In
recent years, Internet of Things (IoT) has attracted much attention
aiming at interconnecting more ‘things’ for various applications to gain
productivity, efficiency, and sustainability. In today’s IoT networks,
IoT devices commonly rely on batteries with a finite lifespan that needs
to be replaced or recharged manually. Given the expanding number of IoT
devices, the necessity for battery replacement or recharge
significantly escalates maintenance costs, environmental concerns, and
even safety risks for some use cases. This will challenge the widespread
adoption and proliferation of IoT solutions. For the sustainable
development of IoT ecosystems, a new paradigm of Ambient IoT has emerged
to actualize near-zero energy communications. Ambient IoT are
battery-less devices (or with limited energy storage, yet no need for
replacement or recharge) with ultra-low complexity and ultra-low power
consumption to enable a variety of new applications.
Recognizing
the potential of low-cost, low-complexity, and battery-free IoT
devices, 3GPP, IEEE, and Bluetooth have recently explored the
feasibility of integrating such devices into their ecosystems. Key
enabling technologies driving Ambient IoT are energy harvesting, ambient
backscattering communications, and low-power computing. Specifically,
ambient backscattering communications, which rely on passive reflection
and modulation of an incident radio-frequency (RF), makes signal
modulation and transmission viable, even when operating from limited
harvested energy. Despite the potentials, the implementation of Ambient
IoT still faces significant challenges. These include ensuring a stable
power supply from intermittently harvested energy sources, meeting
communication needs with limited harvested energy, facilitating the
connection of a large number of Ambient IoT devices, and addressing
concerns related to user privacy and security.
This
Special Issue (SI) endeavors to explore pioneering advancements,
innovations, and challenges in creating ambient IoT devices that operate
with minimal to zero external power sources. By tapping into ambient
energy and leveraging energy-efficient technologies, such as backscatter
communications, ambient IoT holds the promise of revolutionizing the
landscape of connected and sustainable smart environments.
Topics of interest may include, but are not limited to:
-
Exploring New Air Interfaces, i.e., Frequency Bands, Waveforms,
Modulation Schemes, and Uplink/Downlink Multiple Access Protocols, for
Ambient IoT.
- Low-complexity Signal Detection and Channel Coding for Ambient IoT.
- AI/Machine Learning Applications for Ambient IoT.
- Lightweight Communications and Signalling Protocols for Ambient IoT.
- Optimization of Communication Protocols for Ambient IoT.
- Architecture Enhancement for Ambient IoT devices.
- Spectrum Sharing Techniques for Ambient IoT Networks.
- Scalability and Network Management for Massive Ambient IoT Deployment.
- Latency, Data Rate, and Link Reliability Studies for Ambient IoT.
- Energy Harvesting and Energy Storage Solutions for Ambient IoT.
- Device Complexity and Power Consumption Solutions for Ambient IoT.
- Coverage, Device Density, and Interference Analysis.
- Device mobility and Positioning Solutions for Ambient IoT.
- Interoperability and Integration to Existing Wireless Systems and Standardization Efforts.
- Comparison with Existing Low-Power IoT Solutions (NB-IoT, LTE-M, LoRa, SigFox, RFID, etc.).
- Authentication, Provisioning, and User Privacy Tailored for Ambient IoT.
- Security Mechanisms for Ambient IoT.
- Deployment Scenarios, Use cases, and Services for Ambient IoT.
- Analysis, Simulation, and Testbeds for Ambient IoT.
Call for Papers: Special Issue on Foundation Models for Multimedia
网址：https://www.computer.org/multimedia-magazine//
The
rapid advancement in AI technology continues to revolutionize
multimedia applications, integrating them seamlessly into our daily
activities. AI-powered multimedia applications have become
indispensable, offering enhanced user experiences across various
platforms. The development of foundation models holds great promise in
further enhancing the inferability and reliability of multimedia data,
driving innovation in this field.
Foundation
models, characterized by their ability to be adapted to a multitude of
downstream tasks, represent the next wave in AI evolution. These models
are trained on extensive datasets using self-supervised learning
techniques, enabling them to perform a wide range of functions across
different domains. Despite their transformative potential, there remain
significant challenges and opportunities in fully harnessing their
capabilities for multimedia applications.
Active
research topics in foundation models include AI agents, hallucination
mitigation, long-context modeling, and robust automatic evaluation. AI
agents leverage foundation models to perform complex tasks autonomously,
while efforts to address hallucinations aim to enhance the accuracy and
reliability of generated content. Long-context modeling focuses on
improving the models’ ability to understand and utilize extensive
contextual information, and robust automatic evaluation seeks to develop
more reliable metrics for assessing model performance.
This
special issue aims to bring together researchers and practitioners from
both industry and academia to present the latest high-quality research
and technical innovations in the field of foundation models for
multimedia. By addressing current challenges and exploring new
frontiers, we aim to advance the understanding and application of
foundation models, paving the way for the next generation of AI-powered
multimedia solutions.
Topics of interest include but are not limited to:
- Technical advances in foundation models (e.g., innovations in model architectures and training procedures)
- Applications of foundation models in industries (e.g., success stories and lessons learned from industrial deployments)
- Applications of foundation models in various areas (e.g., robotics and autonomous systems)
- The technical challenges and opportunities of foundation models
(e.g., addressing issues of bias, fairness, and ethical considerations)
- Advances in technical principles behind foundation models (e.g., enhancements in model fine-tuning and adaptation techniques)
- Advanced AI systems involving foundation models (e.g., multimodal learning applications and cross-domain adaptability)
- Foundation models for next-generation intelligent services (e.g.,
future prospects and directions for intelligent service development)
- Other research topics that are closely related to foundation models for multimedia
Call for Papers: Special Issue on Cybersecurity and Privacy in Global Conflict and Disaster Regions
网址：https://www.computer.org/web/computingnow/securityandprivacy/
Electronic surveillance and cyberattacks are used in occupied conflict regions by aggressors to monitor, cut off communications, intimidate, disrupt, or target [3]. Cyberatttacks are also levied across borders against countries, their populations, and institutions [4], as well as against the United Nations [5] and international, non-governmental organizations (NGOs) providing humanitarian aid [6,7,8,9]. And, attackers are known to take advantage of individuals in vulnerable situations following major natural disasters, such as hurricanes, floods, and wildfires [10].
This Special Issue of IEEE Security & Privacy on “Cybersecurity and Privacy in Global Conflict and Disaster Regions” solicits articles on topics related to the aforementioned situations, including threats, mitigations, policies, and future directions.
To better convey scope, a few examples of articles published in other venues that demonstrate desired scope of this Special Issue include (but are not limited to):
- Jessica McClearn and Rikke Bjerg Jensen, and Reem Talhouk. Othered, Silenced and Scapegoated: Understanding the Situated Security of Marginalised Populations in Lebanon. Proceedings of the 32nd USENIX Security Symposium, pp. 4625–4642, 2023.
- Dimitrios Serpanos and Theodoros Komninos. The Cyberwarfare in Ukraine. Computer, 55(7):88-91, IEEE, July 2022.
- Martin Husák, Martin Laštovička, and Tomáš Plesník. 2022. Handling Internet Activism During the Russian Invasion of Ukraine: A Campus Network Perspective. Digital Threats: Research and Practice, 3(3):17, ACM, September 2022.
- Kasra EdalatNejad, Wouter Lueks, Justinas Sukaitis, Vincent Graf Narbel, Massimo Marelli, Carmela Troncoso. Janus: Safe Biometric Deduplication for Humanitarian Aid Distribution. Proceedings of the IEEE Symposium on Security and Privacy, 2024.
- Rakesh Verma, Devin Crane, and Omprakash Gnawali. Phishing during and after disaster: Hurricane Harvey. Proceedings of Resilience Week, pp. 88-94. 2018.
Submitted articles should focus on facts and emphasize technical or scientific aspects (including human factors) of cybersecurity and privacy, or threats to cybersecurity and privacy, in global conflict and disaster regions. We encourage a broad and unbiased exploration and discussion of various threats, mitigations, and situations.
Pieces focusing on important topics such as social media, mis/disinformation and generative AI are only in scope insofar as those pieces tie strongly to cybersecurity and privacy.
Not in scope for this issue include articles that are opinion pieces or political in nature.
When in doubt about scope, prospective authors should please recall traditional themes of cybersecurity and privacy conference and journal venues.
For author information, including style guidance and formatting requirements, please visit the Author Information page. Full papers (5000-7000 words) and more technical pieces will be peer-reviewed, as is typical for IEEE Security & Privacy. Shorter or less technical pieces (2000-4000 words) will be reviewed and managed internally by the Editorial Board. Manuscripts should not be published or currently submitted for publication elsewhere.
All pieces should initially be sent to [email protected] for a determination by the Editorial Board on the article category and associated next steps. Manuscripts that are determined best addressed via peer review will be redirected to submission via ManuscriptCentral.
- Sean Peisert, Berkeley Lab and UC Davis, USA
- Trent Jaeger, The Pennsylvania State University, USA
- Fabio Massacci, University of Trento, Italy, and VU Amsterdam, The Netherlands
- Nele Mentens, KU Leuven, Belgium, and Leiden University, the Netherlands
- Laurie Williams, North Carolina State University, USA
- Jianying Zhou, Singapore University of Technology and Design, Singapore
- Mary Ellen Zurko, MIT Lincoln Laboratory, USA
Call for Papers: Special Issue on Intelligent and Autonomous Systems in Government
网址：https://www.computer.org/csdl/magazine/ex/
Artificial intelligence (AI)-based intelligent and autonomous systems are transforming human lives in unprecedented ways. As AI technology proliferates our society, national and local governments play a key role in enabling its widespread deployment towards improving the quality of human life, as well as in developing and shaping AI policy towards the betterment of human society. Government AI projects present some unique challenges including deploying to large population scales at national and/or regional levels, ensuring fair and responsible use of the technology, guaranteeing accessibility across different economic, demographic and socio-cultural levels, and providing methods to transparently measure and report to the public metrics related to the technology’s accountability, adoption and impact. The research methods and techniques developed in academia and industry, which form the underlying technology in many government projects, are usually not designed with many of these factors in mind. They then have to be rebuilt or redesigned before being integrated in government systems and applications. This technology ‘gap’ often culminates in significant challenges such as over-budgeted government expenses, extended project timelines, and poorly designed end-products that might not align with the project goals. Our special issue aims to bridge this technology gap through articles that describe challenges, experiences, success stories and lessons learned from the planning, design, implementation, evaluation and/or deployment of AI-based government projects. We envisage that these discussions will inform researchers with best practices for developing technologies that are relevant to, and well aligned with the needs of government projects.
Topics of interest include:
- Intelligent autonomous systems and software tools for different government-related applications including; transportation and civilian infrastructure; medicine, healthcare and emergency response; defense and international peacekeeping; cyber-security; and agriculture, environment and natural resources management;
