Reviewerly/exHarmony huggingface dataset:
Bonsai Algorithm: Grow Your Own Fermion-to-Qubit Mappings

Fermion-to-qubit mappings are used to represent fermionic modes on quantum computers, an essential first step in many quantum algorithms for electronic structure calculations. In this work, we present a formalism to design flexible fermion-to-qubit mappings from ternary trees. We intuitively discuss the connection between the structure of the generating trees and certain properties of the resulting mapping, such as the Pauli weight and the delocalization of mode occupation. Moreover, we introduce a recipe that guarantees that Fock basis states are mapped to computational basis states in qubit space, a desirable property for many applications in quantum computing. Based on this formalism, we introduce the Bonsai algorithm, which takes as input the potentially limited topology of the qubit connectivity of a quantum device and returns a tailored fermion-to-qubit mapping that reduces the swap overhead compared to other paradigmatic mappings. We illustrate the algorithm by producing mappings for the heavy-hexagon topology widely used in IBM quantum computers. The resulting mappings have a favorable Pauli-weight scaling O(N) on this connectivity while ensuring that no swap gates are necessary for single-excitation operations.4 MoreReceived 21 February 2023Accepted 27 June 2023DOI:https://doi.org/10.1103/PRXQuantum.4.030314Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithms for chemical calculationsQuantum information architectures & platformsQuantum simulationQuantum softwareQuantum Information
Towards Efficient Resource Allocation for Federated Learning in Virtualized Managed Environments

Federated learning (FL) is a transformative approach to Machine Learning that enables the training of a shared model without transferring private data to a central location. This decentralized training paradigm has found particular applicability in edge computing, where IoT devices and edge nodes often possess limited computational power, network bandwidth, and energy resources. While various techniques have been developed to optimize the FL training process, an important question remains unanswered: how should resources be allocated in the training workflow? To address this question, it is crucial to understand the nature of these resources. In physical environments, the allocation is typically performed at the node level, with the entire node dedicated to executing a single workload. In contrast, virtualized environments allow for the dynamic partitioning of a node into containerized units that can adapt to changing workloads. Consequently, the new question that arises is: how can a physical node be partitioned into virtual resources to maximize the efficiency of the FL process? To answer this, we investigate various resource allocation methods that consider factors such as computational and network capabilities, the complexity of datasets, as well as the specific characteristics of the FL workflow and ML backend. We explore two scenarios: (i) running FL over a finite number of testbed nodes and (ii) hosting multiple parallel FL workflows on the same set of testbed nodes. Our findings reveal that the default configurations of state-of-the-art cloud orchestrators are sub-optimal when orchestrating FL workflows. Additionally, we demonstrate that different libraries and ML models exhibit diverse computational footprints. Building upon these insights, we discuss methods to mitigate computational interferences and enhance the overall performance of the FL pipeline execution.
Positionality in Postdigital Research: The Power to Effect Change

Conducting postdigital research opens up an endless range of diverse topics and possible methodological approaches. This is a strength and a barrier to furthering powerful, disruptive opportunities for concrete forms of political change that the idea of the postdigital offers. As a messy, universal concept, the postdigital remains open to diverse, cultural interpretations of what to research, and how to research it, an inclusive space where all may contribute to postdigital dialogue. Yet to effect meaningful change our collaborative, edgeless, community also requires concrete examples of studies, that are in dialogue with the diverse, postdigital positionalities of each researcher. By postdigital positionality, I refer to a personal stance and critically reflexive conviction by each individual to meaningfully engage with their own identity, values and potential bias, as they conduct postdigital research within a neoliberal, data-driven, digitally unequal society, where culture wars rage. Following an exploration of the individual nature of postdigital research through positionality, the political economic structure that surrounds postdigital research is considered alongside the potential role of postdigital decoloniality research. A model of personal and community postdigital positionality is presented, with attention in particular to where the language of postdigital positionality engages with the language of postdigital community.
FEEL: FEderated LEarning Framework for ELderly Healthcare Using Edge-IoMT

Recent advancements in artificial intelligence (AI) and IoT technology have revolutionized the healthcare industry by providing effective remote healthcare. Furthermore, with the aging of the world’s population, remote health monitoring and recommendations are becoming imperative to provide cost-effective healthcare solutions for improving the quality of life of our senior citizens. The explosive growth of wearable sensors (IoT sensors) and health bands has facilitated the interconnection among patients and caregivers to enable assisted living by leveraging AI techniques. This work proposes an end-to-end connected smart home healthcare system (FEEL) for elderly people. Our proposed framework addresses the main challenges of the Internet of Medical Things (IoMT) system namely, the scarcity of labeled data and user’s diverse needs. The major contributions of the work are: 1) few-shot learning-enabled novel federated learning (FL) framework for health data and context information analysis and recommendation; 2) user and context-based knowledge graph (UKG) to represent and model health parameters and environmental impacts on recommendations; 3) deep learning architecture for activity monitoring and location estimation of the users; and 4) edge-fog-IoMT collaborative framework to collect, store, and share medical recommendations while protecting the privacy of the users. FEEL is specifically beneficial for elderly homes where several aged people stay together and require constant care. We aim to develop a novel AI module where along with the health parameters, the social context of the home can be augmented to provide an accurate and improved healthcare service. FEEL has been evaluated for three tasks, namely: 1) activity monitoring and location estimation; 2) fall detection; and 3) medical recommendations for unusual health conditions. A customized wearable device has been used to collect, store, and send health-related parameters. The experimental evaluation demonstrates promising accuracy (F1 score 0.86–0.94 range) for the tasks and outperforms the baselines by a significant margin ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 10$ </tex-math></inline-formula> %–16%).
Learning Meta-Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction

Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly been studied. Compared to KGs, TKGs contain rich temporal information, thus requiring temporal reasoning techniques for modeling. This poses a greater challenge in learning few-shot relations in the temporal context. In this paper, we follow the previous work that focuses on few-shot relational learning on static KGs and extend two fundamental TKG reasoning tasks, i.e., interpolated and extrapolated link prediction, to the one-shot setting. We propose four new large-scale benchmark datasets and develop a TKG reasoning model for learning one-shot relations in TKGs. Experimental results show that our model can achieve superior performance on all datasets in both TKG link prediction tasks.
Global Rectification and Decoupled Registration for Few-Shot Segmentation in Remote Sensing Imagery

Few-shot segmentation (FSS), which aims to determine specific objects in the query image given only a handful of densely labeled samples, has received extensive academic attention in recent years. However, most existing FSS methods are designed for natural images, and few works have been done to investigate more realistic and challenging applications, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., remote sensing image understanding. In such a setup, the complex nature of the raw images would undoubtedly further increase the difficulty of the segmentation task. To couple with potential inference failures, we propose a novel and powerful remote sensing FSS framework with global Rectification and decoupled Registration, termed R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net. Specifically, a series of dynamically updated global prototypes are utilized to provide auxiliary non-target segmentation cues and to prevent inaccurate prototype activation resulting from the variability between query-support image pairs. The foreground and background information flows are then decoupled for more targeted and tailored object localization, avoiding unnecessary confusion from information redundancy. Furthermore, we impose additional constraints to promote the interclass separability and intraclass compactness. Extensive experiments on the standard benchmark iSAID-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>i</i></sup> demonstrate the superiority of the proposed R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net over state-of-the-art FSS models. The code will be made available.
Image Encryption via Base-<i>n</i> PRNGs and Parallel Base-<i>n</i> S-Boxes

The fast-paced advancement in multimedia production and exchanges over unsecured networks have led to a dire need to develop security applications.In this regard, chaos theory has much to offer, especially high-dimensional (HD) chaotic functions of fractional order.The authors propose a new symmetric, secure and robust image encryption method in this research work.In this method, the authors hybridize the Chen and Chua chaotic functions with a Memristor circuit to benefit from the strengths of each.Such a hybridization of systems allows for the generation of pseudo-random numbers which are used to develop encryption keys and substitution boxes (S-boxes).For the application of the generated encryption keys towards carrying out data diffusion, instead of the most commonly used approach of bitstream level XOR, this work utilizes different logical and arithmetic operations, which is made possible by performing this process over variable numerical bases.Moreover, multiple S-boxes of varying basen are generated and utilized in a parallel fashion, carrying out data confusion.The computed numerical results reflect the superior capabilities of the proposed image encryption technique, signifying resilience and robustness against various attacks.
Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications

The landscape of healthcare data collaboration heralds an era of profound transformation, underscoring an exceptional potential to elevate the quality of patient care and expedite the advancement of medical research. The formidable challenge, however, lies in the safeguarding of sensitive information’s privacy and security - a monumental task that creates significant obstacles. This paper presents an innovative approach designed to address these challenges through the implementation of privacy-preserving federated learning models, effectively pioneering a novel path in this intricate field of research. Our proposed solution enables healthcare institutions to collectively train machine learning models on decentralized data, concurrently preserving the confidentiality of individual patient data. During the model aggregation phase, the proposed mechanism enforces the protection of sensitive data by integrating cutting-edge privacy-preserving methodologies, including secure multi-party computation and differential privacy. To substantiate the efficacy of the proposed solution, we conduct an array of comprehensive simulations and evaluations with a concentrated focus on accuracy, computational efficiency, and privacy preservation. The results obtained corroborate that our methodology surpasses competing approaches in providing superior utility and ensuring robust privacy guarantees. The proposed approach encapsulates the feasibility of secure and privacy-preserving collaboration on healthcare data, serving as a compelling testament to its practicality and effectiveness. Through our work, we underscore the potential of harnessing collective intelligence in healthcare while maintaining paramount privacy protection, thereby affirming the promise of a new horizon in collaborative healthcare informatics.
A survey on multi-label feature selection from perspectives of label fusion

With the rapid advancement of big data technology, high-dimensional datasets comprising multi-label data have become prevalent in various fields. However, these datasets often contain more relevant and redundant features, which can adversely affect the performance of machine learning algorithms. Multi-label feature selection (MLFS) has emerged as a crucial pre-processing step in multi-label learning to address this issue. This survey provides an overview of multi-label learning and its algorithms, including problem transformation and algorithm adaptation. We also introduced three traditional strategies for MLFS: filter, wrapper, and embedded-based methods. Furthermore, we categorize existing research on multi-label feature selection into six aspects based on label fusion: label transformation-based (Binary Relevance-based and Label Powerset-based), label correlation-based (second and high-order, high and hybrid order), label specific-based, semi-supervised-learning-based, missing and noisy labels-based, and label enhancement-based approaches. We provide a detailed introduction to each method’s common approaches and theories. Additionally, we conduct experimental comparisons on practical multi-label learning datasets to evaluate the advantages and disadvantages of different algorithms. We discuss the application of multi-label feature selection in various domains, such as data mining, computer vision, natural language processing, and bio-informatics. Finally, we outline potential future research directions in multi-label feature selection, including MLFS with online learning, active learning, label distribution learning, partial label learning, granular computing, and class-imbalanced learning.
EasyChain: an IoT-friendly blockchain for robust and energy-efficient authentication

The Internet of Everything (IoE) is a bigger picture that tries to fit the Internet of Things (IoT) that is widely deployed in smart applications. IoE brings people, data, processes, and things to form a network that is more connected and increases overall system intelligence. A further investigation of the IoE can really mean creating a distributed network focusing on edge computing instead of relying on the cloud. Blockchain is one of the recently distributed network technologies which by structure and operations provide data integrity and security in trust-less P2P networks such as IoE. Blockchain can also remove the need for central entities which is the main hurdle for the wide adoption of IoT in large networks. IoT “things” are resource-constrained both in power and computation to adopt the conventional blockchain consensus algorithms that are power and compute-hungry. To solve that problem, this paper proposes EasyChain, a blockchain that is robust along with running on a lightweight authentication-based consensus protocol that is known as Proof-of-Authentication (PoAh). This blockchain based on the lightweight consensus protocol replaces the power-hungry transaction, blocks validation steps, and provides ease of usage in resource-constrained environments such as IoE. The proposed blockchain is designed using the Python language for an easy understanding of the functions and increased ease of integration into IoE applications. The designed blockchain system is also deployed on a single-board computer to analyze its feasibility and scalability. The latency observed in the simulated and experimental evaluations is 148.89 ms which is very fast compared to the existing algorithms.
Assessing geochemical and natural radioactivity impacts of Hamadat phosphatic mine through radiological indices

The utilization of phosphorite deposits as an industrial resource is of paramount importance, and its sustainability largely depends on ensuring safe and responsible practices. This study aims to evaluate the suitability of phosphorite deposits for industrial applications such as the production of phosphoric acid and phosphatic fertilizers. To achieve this goal, the study meticulously examines the geochemical characteristics of the deposits, investigates the distribution of natural Radioactivity within them, and assesses the potential radiological risk associated with their use. The phosphorites are massive and collected from different beds within the Duwi Formation at the Hamadat mining area. They are grain-supported and composed of phosphatic pellets, bioclasts (bones), non-phosphatic minerals, and cement. Geochemically, phosphorites contain high concentrations of P2O5 (23.59-28.36 wt.%) and CaO (40.85-44.35 wt.%), with low amounts of Al2O3 (0.23-0.51 wt.%), TiO2 (0.01-0.03 wt.%), Fe2O3 (1.14-2.28 wt.%), Na2O (0.37-1.19 wt.%), K2O (0.03-0.12 wt.%), and MnO (0.08-0.18 wt.%), suggesting the low contribution of the detrital material during their deposition. Moreover, they belong to contain enhanced U concentration (55-128 ppm). They are also enriched with Sr, Ba, Cr, V, and Zn and depleted in Th, Zr, and Rb, which strongly supports the low detrital input during the formation of the Hamadat phosphorites. The high Radioactivity of the studied phosphorites is probably due to the widespread occurrence of phosphatic components (e.g., apatite) that accommodate U in high concentrations. Gamma spectrometry based on NaI (Tl) crystal 3×3 has been used to measure occurring radionuclides in the phosphorite samples. The results indicate that the radioactive concentrations' average values of 226Ra, 232Th, and 40K are 184.18±9.19, 125.82±6.29, and 63.82±3.19 Bq Kg-1, respectively. Additionally, evaluations have been made of the radiological hazards. The calculated risk indicators exceeded the recommended national and world averages. The data obtained will serve as a reference for follow-up studies to evaluate the effectiveness of the Radioactivity of phosphatic materials collected from the Hamdat mine area.
StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video

Face reenactment methods attempt to restore and re-animate portrait videos as realistically as possible. Existing methods face a dilemma in quality versus controllability: 2D GAN-based methods achieve higher image quality but suffer in fine-grained control of facial attributes compared with 3D counterparts. In this work, we propose StyleAvatar, a real-time photo-realistic portrait avatar reconstruction method using StyleGAN-based networks, which can generate high-fidelity portrait avatars with faithful expression control. We expand the capabilities of StyleGAN by introducing a compositional representation and a sliding window augmentation method, which enable faster convergence and improve translation generalization. Specifically, we divide the portrait scenes into three parts for adaptive adjustments: facial region, non-facial foreground region, and the background. Besides, our network leverages the best of UNet, StyleGAN and time coding for video learning, which enables high-quality video generation. Furthermore, a sliding window augmentation method together with a pre-training strategy are proposed to improve translation generalization and training performance, respectively. The proposed network can converge within two hours while ensuring high image quality and a forward rendering time of only 20 milliseconds. Furthermore, we propose a real-time live system, which further pushes research into applications. Results and experiments demonstrate the superiority of our method in terms of image quality, full portrait video generation, and real-time re-animation compared to existing facial reenactment methods. Training and inference code for this paper are at https://github.com/LizhenWangT/StyleAvatar.
How Good is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem

Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing deep learning, especially deep reinforcement learning, to automatically learn effective solvers for CO. The resultant new paradigm is termed neural combinatorial optimization (NCO). However, the advantages and disadvantages of NCO relative to other approaches have not been empirically or theoretically well studied. This work presents a comprehensive comparative study of NCO solvers and alternative solvers. Specifically, taking the traveling salesman problem as the testbed problem, the performance of the solvers is assessed in five aspects, i.e., effectiveness, efficiency, stability, scalability, and generalization ability. Our results show that the solvers learned by NCO approaches, in general, still fall short of traditional solvers in nearly all these aspects. A potential benefit of NCO solvers would be their superior time and energy efficiency for small-size problem instances when sufficient training instances are available. Hopefully, this work would help with a better understanding of the strengths and weaknesses of NCO and provide a comprehensive evaluation protocol for further benchmarking NCO approaches in comparison to other approaches.
AR-QR code for improving crew access to design and construction information

Traditional methods of delivering construction information, relying on paper-based shop drawings, specifications, and verbal instructions, often lead to increased mental workload, error rates, and difficulties in accessing information. This raises the question of how the traditional approach to accessing design and construction information on construction sites can be improved to address these issues. This paper describes a hybrid approach known as AR-QR code, which aims to enhance the conventional paper-based method of accessing such information using the power of Augmented Reality (AR). The research methodology involved: process mapping, prototype development, and evaluation. The evaluation results revealed that professionals expressed confidence in the potential of the AR-QR code approach to alleviate the limitations associated with traditional methods at construction sites. The outcomes of this study provide valuable insights into the need for a hybrid approach that preserves the advantages of traditional methods while avoiding their drawbacks.