Reviewerly/exHarmony huggingface dataset:
Anonymous Authentication Scheme for Federated Learning

As a distributed machine learning model, federated learning ensures the legitimate use of data and user privacy while training the global model. Existing privacy protection mechanisms for federated learning either need to balance training accuracy and privacy protection requirements, or lack of design from the perspective of groups. In this paper, we design a privacy protection mechanism from the perspective of groups for federated learning. By resorting to cryptographic techniques, the proposed mechanism is free of the tradeoff between accuracy and privacy. In particular, we aim to develop novel approaches for the asymmetric group key agreement (AGKA) protocol with efficient operations and lower storage cost, as well as to further support anonymous group authentication. First, we propose a BLS-AGKA protocol by using the Boneh-Lynn-Shacham (BLS) signature, which is computationally efficient and requires a relatively small storage cost. Second, to further achieve the privacy-preserving demand in federated learning, we construct an anonymous authentication scheme based on the proposed BLS-AGKA protocol, which supports anonymous group authentication. Finally, it is shown that the proposed protocol and scheme guarantee the desired security properties, including session-key security, unforgeability, and anonymity. In addition, the performance of the proposed scheme is superior to relevant existing works as well.
Blockchain Topology Optimization Based on Node Clustering

Since blockchain was born more than ten years ago, its performance optimization has always been a hot topic in the academic community. Since blockchain is essentially a distributed system, it needs to set a consensus mechanism to enable nodes scattered around to reach consensus and tolerate a certain number of faulty nodes. In addition, for each block to be linked, it needs to be verified by all nodes and approved by more than half of the nodes before it can be truly linked. All these will add up to a large delay. As the number of nodes in the blockchain system increases, this delay will seriously reduce the efficiency of the system in processing transactions and reduce the scalability of the blockchain. Therefore, based on the goals of optimizing the blockchain system, increasing the transaction per second(TPS), and improving the scalability, this paper has improved the topology of the blockchain, and designed the corresponding consensus mechanism and message propagation mechanism.
Throughput Measurements and Capacity Estimates for Quantum Connections

The throughput of conventional and quantum network connections is an important performance metric, which is typically specified by bits per second (bps) and entangled quntum bits per second (ebps), respectively. It is measured over practical quantum network connections using specialized methods, and estimated using analytical bounds for which extensive theory has been developed. For practical connections, however, these two quantities have often been hard to correlate due to the lack of measurements and estimates derived under well-characterized common conditions. They both differ significantly from the conventional network throughput of TCP which employs buffers and loss recovery mechanisms. We describe a conventional-quantum testbed that enables the comparison of these two quantities both qualitatively and quantitatively. The bps and ebps throughput is measured over fiber connections of lengths over 75 kilometers, which show that the former decreases significantly slower with distance than the latter and in a qualitatively different way. The analytic capacity estimates of ebps are derived using approximations based on light intensity measurements, and they decrease more rapidly with distance than the measured ebps throughput. These results provide qualitative insights into the conventional transport mechanisms based on buffers, and the conditions used in deriving the analytical ebps capacity estimates.
Privacy Preservation in Kubernetes-based Federated Learning: A Networking Approach

Federated Learning (FL) is a distributed Machine Learning paradigm that allows multiple clients to collaboratively train a model under the control of a central server while keeping data locally in edge devices. To simplify workload management in FL ecosystems, cloud computing and container-based approaches such as Kubernetes (K8s) have been proposed for scalable deployment. Nonetheless, K8s can violate fundamental FL privacy principles, e.g., the inherent flat networking approach in K8s can potentially allow FL clients to access other client or domain resources. The latter poses an open research problem and gap in the literature because serious privacy risks can arise from attackers gaining access to any client in the FL setup. To address this problem, this paper presents a networking approach via network isolation at the link layer level, and authentication and data packet encryption at the network layer level. The former allows to create secure resource sharing, and the latter is used to protect in-transit data. For this purpose, we use a K8s networking operator and a secure network protocol suite. The above combination facilitates on-demand link-layer connectivity, per-link data source authentication, and confidentiality between FL actors. We tested our approach on a network testbed composed of different geo-located nodes where FL clients are deployed. Our promising results showcase the feasibility of the solution for privacy preservation at the network level in K8s-based FL.
Shallow Neural Networks for Channel Estimation in Multi-Antenna Systems

In this paper, we investigate neural network-based channel estimation strategies for point-to-point multi-input multi-output (MIMO) systems. In an attempt to keep computational complexity low, we restrict ourselves to shallow architectures with a single hidden layer. Specifically, we consider (i) fully-connected feedforward neural networks; and (ii) ID/2D convolutional neural networks. The analysis includes an assessment of the estimation error performance, along with the computational complexity as-sociated to the training and inference phases. Several benchmarks are considered, such as the conventional least squares or (linear) MMSE estimators, and other deep neural network architectures from the literature.
Intrusion Detection System for IoHT Devices using Federated Learning

With the growing number of sensitive data transmit-ted in IT infrastructures, healthcare organizations and compa-nies that generate users' wearable data have become a target for attackers. To protect electronic healthcare data, Internet of Healthcare Things (IoHT) devices must be protected by robust Intrusion Detection Systems (IDS) to provide a secure environment. Since it is undesirable to collect this data and perform machine learning tasks directly, recently, to preserve users' privacy, federated learning has obtained attention from the government and healthcare organizations. Unlike the cen-tralized paradigm, federated learning is a privacy-aware machine learning framework designed to analyze data without sharing it. This paper proposes a deep neural network in federated learning (DNN-FL) to detect anomalies in the IoHT traffic that may result in security threats. We evaluate the detection performance of our proposal using metrics such as accuracy and precision. The proposed DNN-FL is validated using the wustl-ehms-2020 and ECU- IoHT datasets. It reached 91.40% of accuracy in detecting attacks in the first dataset and 98.47% in the second. All the developed source code in this work is being made publicly available to ensure reproducibility.
A novel multiple-model estimation algorithm based on Shannon Entropy

The conventional multiple-model (MM) estimation algorithms combing with radar sensor achieve great success in radar sensing. To obtain a satisfactory performance on state estimation, the fixed model set always has a large number of models. Thus, those algorithms hold the burden computational complexities, and can not guarantee the accuracy. In this paper, a new algorithm is proposed to overcome this issue. First, a reliable selection criteria, Shannon Entropy, is presented for MM algorithm. Furthermore, using the new election criteria, a novel multiple-model estimation fusion based on Shannon Entropy (MM-SE) is proposed to parallel and correct estimation probabilities of models. Simulation results highlight the correctness and effectiveness of the proposed methods.
Making memorability of scenes better or worse by manipulating their contour properties

Why are some images more likely to be remembered than others? Past research has explored both low-level image properties, such as colour and spatial frequencies, and high-level properties, such as scene semantics. Recent work from our group suggests that memorability for line drawings and photographs of scenes is correlated with specific contour features, such as contour curvature and orientation, as well as mid-level perceptual grouping features, such as contour junctions. Here, we examine whether this relationship is merely correlational, or if manipulating these features causes images to be remembered better or worse. To this end, we manipulated contour properties as well as grouping properties that describe the spatial relationships between contours in the line drawings of real-world scenes and measured the effect of these manipulations on memorability. We trained a Random Forest model to predict scene memorability from contour and perceptual grouping features computed from the line drawings. Then, we used the trained model to predict the contribution of each contour to the memorability of the scene. Next, each line drawing was split into two half-images, one containing the contours with high predicted memorability scores and the other containing the contours with low predicted memorability scores. Since both versions were derived from the same original drawing, image identity was left intact by this manipulation. In a new memorability experiment, we find that the half-images predicted to be more memorable were indeed remembered better than the half-images predicted to be less memorable. Our findings suggest that specific contour and perceptual grouping cues are causally involved in committing real-world images to memory. We demonstrate that by measuring and manipulating these cues, we can isolate the contributions of image features at different visual processing stages to image memorability, thereby bridging the gap between low-level features and scene semantics in our understanding of memorability.
A decision support system in precision medicine: contrastive multimodal learning for patient stratification

Abstract Precision medicine aims to provide personalized healthcare for patients by stratifying them into subgroups based on their health conditions, enabling the development of tailored medical management. Various decision support systems (DSSs) are increasingly developed in this field, where the performance is limited to their capability of handling big amounts of heterogeneous and high-dimensional electronic health records (EHRs). In this paper, we focus on developing a deep learning model for patient stratification that can identify and explain patient subgroups from multimodal EHRs. The primary challenge is to effectively align and unify heterogeneous information from various modalities, which includes both unstructured and structured data. Here, we develop a Con trastive M ultimodal learning model for EHR (ConMEHR) based on topic modelling. In ConMEHR, modality-level and topic-level contrastive learning (CL) mechanisms are adopted to obtain a unified representation space and diversify patient subgroups, respectively. The performance of ConMEHR will be evaluated on two real-world EHR datasets and the results show that our model outperforms other baseline methods.
Immersion or Disruption?

In this paper, we aim to contribute to the understanding of how readers experience immersion in digital reading experiences, more specifically with digital reading supported by (3D-)audio tracks. We formulate user and content requirements for implementing (3D-)audio soundtracks for readers in a digital reading application. The main research question addressed in this paper is: (how) can audio aid the immersion of readers in digital fiction stories? To answer this question, three online focus group discussions were organised in Belgium and Germany. As part of the set-up of the Horizon Europe project Möbius, 18 participants tested different 3D-audio tracks while reading via the Thorium Reader application. The results first address how participants define immersion, and how the role of audio in immersion can become paradoxical. Then, the paper presents a detailed evaluation of the factors en- or disabling immersion for the specific 3D-audio tracks, and how these insights can be implemented in reading apps via user and content requirements.
A Preliminary Study of the Eye Tracker in the Meta Quest Pro

This paper presents the preliminary results of an accuracy testing of the Meta Quest Pro's eye tracker. We conducted user testing to evaluate the spatial accuracy, spatial precision and subjective performance under head-free and head-restrained conditions. Our measurements indicated an average accuracy of 1.652° with a precision of 0.699° (standard deviation) and 0.849° (root mean square) for a visual field spanning 15° during head-free. The signal quality of Quest Pro's eye-tracker is comparable to existing AR/VR eye-tracking headsets. Notably, careful considerations are required when designing the size of scene objects, mapping areas of interest, and determining the interaction flow. Researchers should also be cautious about interpreting the fixation results when multiple targets are within close proximity. Further investigation and better specification information transparency are needed to establish its capabilities and limitations.
More Immersed but Less Present: Unpacking Factors of Presence Across Devices

The production of immersive media often involves 360-degree viewing on mobile or immersive VR devices, particularly in the field of immersive journalism. However, it is unclear how the different technologies used to present such media affect the experience of presence. To investigate this, a laboratory experiment was conducted with 87 participants who were assigned to one of three conditions: HMD-360, Monitor-360, or Monitor-article, representing three distinct levels of technological immersion. All three conditions represented the same base content, with high and mid-immersion featuring a panoramic 360-video and low-immersion presenting an article composed of a transcript and video stills.
Deep Reinforcement Learning Agent for Dynamic Pruning of Convolutional Layers

Convolutional neural networks have become ubiquitous in image classification tasks. The state-of-the-art models for image classifications use convolutional layers in one way or another. There is a need for deploying deep learning models, especially the real-time vision models, in the edge devices to get better latency. But deploying such models in edge devices are becoming critical as the networks are becoming deeper and more dense. An overparameterized network is not necessarily required in many of the use cases of such deployment. This led researcher to develop technique for optimizing smaller and shallower networks, network architecture search techniques, and deep learning model compression techniques. In this research, we proposed a framework that utilizes deep determinisitic policy gradient, a class of deep reinforcement learning algorithm, to the learn the best set of filters considering the intrinsic dimensionality of the dataset, feature of each layer and the criteria based on which the filters of a convolutional layer will be ranked. By learning this relationship, we can prune off unnecessary filters which will reduce both computational and memory requirement for the model without losing too much accuracy. Our method showed that the model can prune off 66% filters overall.
Machine Learning driven Skill Prioritisation for Human Resource Planning

Performance Management1 is of paramount importance in Human Resources Planning. To address today's growing skill challenge, employers need to determine existing employee expertise deficit to achieve much needed business outcomes. Determining skill gap amongst employees helps the resource planners to decide between revising their hiring procedures to not overlook expert talent and expansion of their training programs for existing employees with the required skills to enhance their capacity to deliver future projects. This paper explores a well-known unsupervised learning algorithm: Collaborative filtering to predict and prioritise the employees across their domain expertise based on multiple factors such as existing domain expertise, location of the employees, availability of the employees, relevant learnings, prior experience and work performance to handle the skill specific projects in the future. Employee prioritisation for the future across domain specific expertise using this unsupervised machine learning algorithm will potentially help in optimizing the skilled resource pool and make the resource planning a bit seamless for operational resource managers in any industry vertical.
UAS Integration Safety and Security Technology Ontology

Unmanned Aerial Systems (UAS) are a versatile and essential tool for law enforcement, first responders, utility providers, and the public. Integrating the UAS into the National Airspace System (NAS) poses a significant challenge to policymakers and manufacturers. A UAS Integration Safety and Security Technology Ontology (ISSTO) has been developed in the Web Ontology Language (OWL) to aid this integration. ISSTO is a domain ontology covering aviation topics corresponding to flights, aircraft types, manufacturers, temporal/spatial, waivers and authorizations, track data, NAS facilities, air traffic control advisories, weather phenomena, surveillance and security equipment, and events, sensor types, radio frequency ranges, actions, and outcomes. As ISSTO is a domain ontology, it models the current state of UAS integration into the NAS and provides a comprehensive view of every aspect of UAS.
Using Knowledge Graphs to ensure Privacy Policies in decentralized data collection systems

As data collection systems become more complex and pervasive, ensuring transparency and accountability in the acquisition and use of personal data becomes increasingly critical. This work investigates the use of knowledge graphs as a solution to this problem, emphasizing their capacity to represent and enforce privacy laws in a decentralized setting. Knowledge graphs provide full privacy management and can be applied also to decentralized systems by providing a consistent representation of data and privacy policies. We specifically discuss how knowledge graphs can be used to track consent management and data retention policies; we also present a case study of our framework in action, demonstrating how it can be used to ensure transparency in an increasingly popular decentralized data collection system. The implementation of such a framework in a decentralized context shows that the use of knowledge graphs can provide a transparent and accountable view of the data collection process, improving trust and confidence in the system among both data subjects and regulators.
A Generic Data Synthesis Framework for Privacy-Preserving Point-of-Interest Recommender Systems

Personalization services offered by Point-of-Interest (POI) recommender systems are becoming increasingly popular, especially in the context of mobile devices. However, data privacy regulations and user concerns regarding privacy often prevent the transfer and storage of user data, which poses a challenge for these systems. To address this issue, privacy-preserving recommender systems have gained importance. This paper proposes a generic framework for generating synthetic user data for POI recommendations based on differential privacy, random response, and user grouping. The proposed framework can accommodate various data feedback without compromising privacy and is compatible with non-private recommender systems, allowing for future improvements and flexibility. Our experiments on real-world datasets demonstrate that the framework strikes a balance between privacy protection and accurate recommendations.
Weakly-supervised Semantic Segmentation on Historical Document Images

Weakly-Supervised Semantic Segmentation (WSSS) has been widely studied as a feasible option to reduce the expensive annotation costs of deep semantic segmentation models. While numerous studies have proposed novel approaches for generating pseudo-labels and demonstrated their effectiveness, their domain is limited to natural scene images. There is a growing need for a comprehensive exploration of WSSS in the domain of document images, given the increasing number of digitized historical document images and the growing importance of document image segmentation for successful information retrieval. Importantly, document images possess inherent characteristics that distinguish them from natural scene images, rendering conventional image-level labels unsuitable. Consequently, the application of recent WSSS frameworks designed for natural scene images is limited. In this work, we propose a simple yet effective pseudo-label generation technique using content-adaptive geometric feature analysis. This approach enables the training of a segmentation model in a weakly-supervised manner without relying on image-level labels. Our method utilizes a Gravity-map, which can highlight potential regions of interest without requiring a priori-knowledge, serving as an initial coarse pixel-level label. The Gravity-map is subsequently refined through simple binarization and noise removal to form a pseudo-label. Finally, a segmentation model is trained using the generated pseudo-labels. Experimental results on the publicly available historical document collection demonstrate that the proposed pseudo-label generation technique offers a viable option for training the semantic segmentation model in the document image domain.
Deep Learning Based End-to-End Specular Reflection Removal for Medical Endoscopic Images

Endoscopic images usually have many overexposed regions due to strong and focused light sources and, consequently, physicians need to change the camera angle for a clear view from time to time. This work targets removing the specular reflections from the endoscopy images but maintaining the structural and semantic texture in the images. Unlike the state-of-the-art image inpainting methods, the proposed method treats the specular reflection removal problem as an image-to-image translation task by skipping the specular detection step. The technique uses Dense Multi-scale Fusion Network (DMFN), which aims for small size and clear images, as the base and revises the network to support the large size and noisy endoscopy images. Moreover, the method can recover structure from large overexposed regions. Experimental results show that under different glare ratios and granularity settings, it is more effective than the Partial Convolution Model when recovering the corrupted pixels: the mean average error of pixel values decreases 13.2%, and the PSNR value increases 2.62dB. Qualitative analysis shows that even in the worst case when the proposed model generates output images with MAE up to 4.30, its recoveries still possess good visual qualities. The proposed model can process a 480 × 768 image in 16.2ms on a computer equipped with 36 CPU cores and an RTX-3090 GPU. It usually takes at least 20 minutes for a human to recover such images.