Abstract

Fairness across different demographic groups is an essential criterion for face-related tasks, Face Attribute Classification (FAC) being a prominent example. Apart from this trend, Federated Learning (FL) is increasingly gaining traction as a scalable paradigm for distributed training. Existing FL approaches require data homogeneity to ensure fairness. However, this assumption is too restrictive in real-world settings. We propose F3, a novel FL framework for fair FAC under data heterogeneity. F3 adopts multiple heuristics to improve fairness across different demographic groups without requiring data homogeneity assumption. We demonstrate the efficacy of F3 by reporting empirically observed fairness measures and accuracy guarantees on popular face datasets. Our results suggest that F3 strikes a practical balance between accuracy and fairness for FAC.

Abstract

Radiance Fields (RFs) have shown great potential to represent scenes from casually captured discrete views. Compositing parts or whole of multiple captured scenes could greatly interest several XR applications. Prior works can generate new views of such scenes by tracing each scene in parallel. This increases the render times and memory requirements with the number of components. In this work, we provide a method to create a single, compact, fused RF representation for a scene composited using multiple RFs. The fused RF has the same render times and memory utilizations as a single RF. Our method distills information from multiple teacher RFs into a single student RF while also facilitating further manipulations like addition and deletion into the fused representation.

Abstract

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are fre- quently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer lan- guage model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and appli- cations using LLMs, we publicly release our models and code under the Responsible AI License.1 Keywords: Language models, collaborative research

Abstract

Drought complexity may not be accurately characterized by univariate meteorological or hydrological drought indices under the intensification of hydrological cycle due to climate change and human activities. In particular, such drought indices require long series of hydro-meteorological data, which are unavailable over ungauged and data-scarce catchments. In this study, a multivariate drought index, Standardized Precipitation Actual Evapotranspiration Index-Agro (SPAEI-Agro), is proposed, which com- bines meteorological and hydrological variables as precipitation (P), actual evapotranspiration (AET), runoff and groundwater (GW) of ungauged catchments and sub-catchment scales with diverse climatol- ogy. SPAEI-Agro was able to characterize severe drought events more accurately in humid and dry sub- humid sub-catchments compared to Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Drought Index (SSDI) for Tunga-Bhadra River, India. SPEI (based upon potential evapotranspiration) and SSDI (based on streamflow) showed intensified drought characteristics com- pared to the new P, AET and GW-based drought indicator SPAEI-Agro in semi-arid and dry sub-humid climates.

Abstract

We present three related schemes to generate novel molecules based on seed molecule; all the methods use as input the voxelized representation of the seed molecule to generate the SMILES representation of the novel molecule. At heart of these methods are two networks (a) a variational autoencoder that uses a Riemannian metric to encode the latent space of (hence named RHVAE) and can use pharmacophoric requirements as additional input for decoder and (b) attentive captioning network (a type of recurring neural network) that can efficiently focus, capture and use the ‘content’ of input to generate the SMILES output of novel molecules. We analyze the performance of the three proposed methods. We demonstrate the generation of meaningful new molecules, by generating shapes through an autoencoder network which can then be passed to our attentive captioning network, while requiring smaller data sets for training and retaining similar performance to existing state-of-art methods. Keywords. De Novo Drug Design; Chemical Modeling; Variational autoencoders, Attentive RNNs, Remannian

Abstract

Attribute-based encryption (ABE) is widely used for a secure and efficient data sharing. The predetermined access policy of ABE shares the data with intended data users. However, ABE is not preferable in many applications that require collaboration among data users. In such applications, an authorized data user may be interested to collaborate with another data user who does not adhere to the access policy. Fixed access policy of ABE does not allow an authorized data user (who satisfies the access policy) to collaborate or share the data with any unauthorized data user (who fails to satisfy the access policy). Thus, due to the static and predefined access policy, data collaboration in ABE is significantly challenging. In this work,

Abstract

Due to high effectiveness and robust security proto- cols, lattice-based cryptography becomes a very broadly appli- cable optimistic post-quantum technique that is recently used in public key cryptosystem. An aggregate signature scheme enables a party to bundle a set of signatures together into a single short cryptographic signature, which can be verified by any verifier using the public information. In this paper, we provide a lattice- based aggregate signature scheme where the security depends on the difficulty of the Ring Learning-with-Error (Ring-LWE) problem. Next, we use the basic scheme in Internet of Drones (IoD) applications using the blockchain technology for secure and transparent data storage. The detailed security analysis and comparative study show that the proposed scheme provides superior security including resistance to quantum attacks and is efficient as compared to the existing state of art approaches. The testbed experimental results and the blockchain simulation demonstrate that the proposed scheme can be applied in real-life drones applications. Index Terms—Internet of Drones (IoD), unmanned aerial vehi- cles, lattice-based cryptography, aggregate signature, blockchain, security.

Abstract

Cyber threat hunting proactively searches for cyber threats, which are undetected by the traditional defense mechanisms. It scans deep to identify malicious programs (ie, malware) that escape from detection. It is important because sophisticated cyber threats can bypass the cyber security mechanisms. The performance of the cyber threat hunting can be improved through artificial intelligence (AI), especially, explainable AI (XAI), which adds trust component to the cyber threat hunting process. Due to the inclusion of XAI, the security experts get the full explanations of the detected threats as the working of the detection model in XAI is known. Information, like, which one is a threat, how it has been detected, and why it has been detected, can be obtained very easily due to the inclusion of XAI in the cyber threat hunting. Therefore, an XAI-envisioned mechanism for cyber threat hunting has been proposed (in short, XAISM-CTH). The network and threat models of XAISM-CTH are designed and discussed. The conducted security analysis proves the security of XAISM-CTH against various potential attacks. XAISM-CTH also performs better than the other existing schemes. At the end, a practical implementation of XAISM-CTH has been provided to observe its impact on the performance of the system. KEYWORDS cyber threat hunting, explainable artificial intelligence (XAI), intrusion detection, privacy, security

Abstract

Increasing use of electronic healthcare (eHealth) services demands efficient and secure solutions. Such solutions need to ensure the prevention of unauthorised access to the patient data and provide faster response. Fog computing is a viable solution to provide faster responses in eHealth systems. Key distribution and authentication play a major role in providing security to patient data. Existing centralized architectures are susceptible to single-point-of-compromise, that is, the entire system is vulnerable when the centralized authority keys are unexpectedly revealed to an adversary. In this paper, we present a fog-based semi-centralised architecture for key distribution and authentication, in which the key distribution service is delegated to individual fog-servers. Thus, the fog-servers become responsible for key distribution to the users without the involvement of the centralised authority, which forms a paradigm of multiple client-server architecture. Thus, achieving centralized trust by designing a single sign-on authentication in such environments is a challenging problem. We design a single sign-on authentication protocol for semi-centralized architectures to achieve centralized trust by ensuring that the user keys are independent of the centralized authority’s keys. A rigorous security analysis under the random oracle model is performed to prove that the proposed protocol is secure against single-point-of-compromise. We also conduct extensive experiments to show the practical perspectives of the proposed scheme. The results show that the protocol is suitable for eHealth applications, including emergency services.

Abstract

Unmanned Aerial Vehicle (UAV) can be employed in various applications, including traffic control, and delivery application. However, these services are susceptible to various security attacks because sensitive data are exchanged through an open channel. Thus, a secure authentication scheme is essential for UAV. UAV has limited storage resources and computing capabilities. To overcome these problems, cloud computing is considered as a promising solution. Cloud computing provides various properties such as storage availability and scalability. Unfortunately, the cloud server’s database can be a major target for an adversary because it is a centralized system. If an adversary intrudes on the cloud server’s database, he/she may attempt to intercept or learn the stored data. To mitigate these issues, we design a secure and lightweight authentication and key agreement scheme for cloud-assisted UAV using blockchain in flying ad-hoc networks, called LAKA-UAV. LAKA-UAV utilizes blockchain technology to ensure access control and data integrity using log transactions and the cloud server securely manages collected data from UAV. We prove the security of LAKA-UAV based on informal security analysis and formal security verification implementation. Based on testbed experiments using MIRACL, LAKA-UAV provides about 2.13 times more efficient performance on average compared with related schemes in terms of computation. We present the blockchain implementation using Hyperledger Sawtooth platform