Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms

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Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

The conceptual design phase is a fascinating moment to observe how a design task is interpreted, as the (often implicit) relative importance students accord to the various requirements and constraints offers a window into the thinking underpinning their de ...

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved mod ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning-a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and ...

Fiber reinforced polymer confined-recycled aggregate concrete filled steel tube (FRP-RACFST) not only proposes excellent load-bearing capacity and corrosion resistance, but is also an environmentally friendly structure due to the incorporation of recycled ...

The transition towards a human-centered indoor climate is beneficial from occupants’ thermal comfort and from an energy reduction perspective. However, achieving this goal requires the knowledge of the thermal state of individuals at the level of body part ...

This dataset contains images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F5 ...

The integration of smart thermostats in home automation systems has created an opportunity to optimize space heating and cooling through the use of machine learning, for example for thermal model identification. Nonetheless, its full potential remains unta ...

Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

In recent years, there has been widespread concern about misinformation and hateful content on social media that are damaging societies. Being one of the most influential social media that practically serves as a newsearch engine, YouTube has accepted crit ...

The field of text-to-video retrieval has advanced significantly with the evolution of language models and large-scale pre-training on generated caption-video pairs. Current methods predominantly focus on visual and event-based details, making retrieval lar ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

Utilization of small molecules as passivation materials for perovskite solar cells (PSCs) has gained significant attention recently, with hundreds of small molecules demonstrating passivation effects. In this study, a high-accuracy machine learning model i ...

We propose an incentive mechanism for the sponsored content provider (CP) market in which the communication of users can be represented by a graph, and the private information of the users is assumed to have a continuous distribution function. The CP stipu ...

Technology supporting meditation is a multimillion-dollar market that continues to grow. There is also strong academic interest to understand and improve the impact technology can have for the user experience of practitioners. However, little work investig ...

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at th ...

We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users’ quality of experience (QoE). Most content today is accessed through edge caches, ...

Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms

Chattez avec Graph Search

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Sustainability and Ethicality are Peripheral to Students’ Software Design

Data-driven models in fusion exhaust: AI methods and perspectives

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Prediction rigidities for data-driven chemistry

On the (In)security of Peer-to-Peer Decentralized Machine Learning

Intelligent prediction framework for axial compressive capacity of FRP-RACFST columns

Toward contactless human thermal monitoring: A framework for Machine Learning-based human thermo-physiology modeling augmented with computer vision

Nissl_3, Raw images for Machine learning for histological annotation and quantification of cortical layers

Efficient and Accurate Peer-to-Peer Training of Machine Learning Based Home Thermal Models

OASIS: An integrated optimisation framework for activity scheduling

Referencing in YouTube Knowledge Communication Videos

Beyond Essentials: Nuanced and Diverse Text-to-video Retrieval

Privacy-preserving federated neural network training and inference

Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells

Incentive Mechanism in the Sponsored Content Market With Network Effects

Adapting Haptic Feedback for Guided Meditation

Surface segregation in high-entropy alloys from alchemical machine learning

Not Yet Another Digital ID: Privacy-Preserving Humanitarian Aid Distribution

Caching and Neutrality