Reviewerly/exHarmony huggingface dataset:
Equivalent noise properties of scalable continuous-variable cluster states

Optical continuous-variable cluster states (CVCSs) in combination with Gottesman-Kitaev-Preskill (GKP) qubits enable fault-tolerant quantum computation so long as these resources are of high enough quality. Previous studies concluded that a particular CVCS, the quad rail lattice (QRL), exhibits a lower GKP gate-error rate than others do. We show in this work that many other experimentally accessible CVCSs also achieve this level of performance by identifying operational equivalences to the QRL. Under this equivalence, the GKP Clifford gate set for each CVCS maps straightforwardly from that of the QRL, inheriting its noise properties. Furthermore, each cluster state has at its heart a balanced four-splitter---the four-mode extension to a balanced beam splitter. We classify all four-splitters, show they form a single equivalence class under SWAP and parity operators, and give a construction of any four-splitter with linear optics, thus extending the toolbox for theoretical and experimental cluster-state design and analysis.
Within-Document Arabic Event Coreference: Challenges, Datasets, Approaches and Future Direction

Event coreference resolution is a crucial component in Natural Language Processing (NLP) applications as it directly affects text summarization, machine translation, classification, and textual entailment. However, the research on this task for Arabic language is limited, compared to other languages such as English, Chinese and Spanish. This paper aims to review the state-of-the-art approaches in event coreference (EC) within the context of coreference resolution tasks, emphasizing the significance of EC in NLP. The focus is placed on the latest developments in Arabic language processing related to event coreference. To fill this gap, a comprehensive study of existing work is conducted, and new approaches are suggested. The paper highlights the challenges specific to Arabic event coreference resolution, such as the variability of verb forms, pronoun ambiguity, ellipsis and null arguments, lexical and morphological variation, lack of annotated resources, discourse and pragmatic context, and cultural and contextual sensitivity. Addressing these challenges requires a deep understanding of Arabic linguistics, advanced NLP techniques, and the availability of annotated resources. Furthermore, this paper examines the existing datasets and methods for Arabic event coreference and proposes an annotation scheme. By leveraging existing NLP algorithms and developing event coreference resolution systems tailored for Arabic, the accuracy and performance of NLP tasks can be significantly improved.
Detecting multipartite entanglement via complete orthogonal basis

We study genuine tripartite entanglement and multipartite entanglement in arbitrary n-partite quantum systems based on complete orthogonal basis (COB). While the usual Bloch representation of a density matrix uses three types of generators, the density matrix with COB operators has one uniformed type of generators which may simplify related computations. We take the advantage of this simplicity to derive useful and operational criteria to detect genuine tripartite entanglement and multipartite entanglement. We first convert the general states to simpler forms by using the relationship between general symmetric informationally complete measurements and COB. Then we derive an operational criteria to detect genuine tripartite entanglement. We study multipartite entanglement in arbitrary dimensional multipartite systems. By providing detailed examples, we demonstrate that our criteria can detect more genuine entangled and multipartite entangled states than the previously existing criteria.
Enhancing Photon Throughput of Miniaturized Passive Depth-Detection Cameras via Broadband Dispersion-Engineered Metalenses

With the continuous advancement of technology, there is a growing demand for depth-detection cameras with a small footprint and high photon throughput. Metalenses offer an excellent platform for implementing single-lens depth-detection cameras. However, the narrow operational bandwidth of the metalenses used in these miniaturized cameras significantly limits the photon throughput. Here, we propose a broadband dispersionless double-helix metalens (DL-DH metalens) to enhance the photon throughput and mitigate motion blur in miniaturized passive depth-detection cameras. Through engineering the dispersion, the DL-DH metalens was designed to achieve an irrotational double-helix point spread function (DH-PSF) over the broadband wavelength, thus eliminating the depth-detection error caused by chromatic aberration. The operational bandwidth of the fabricated metalens was expanded to 200 nm for obtaining higher-photon throughput, resulting in a 10-fold reduction in the exposure time compared with that observed for a narrow operational bandwidth of 30 nm. The sharp images could be captured by the DL-DH metalens to acquire accurate depth information, even in the presence of moving objects. Our proposed metalens provides a solution for enhancing the photon throughput in miniaturized passive depth-detection cameras operated in low-light environments.
A unified method to revoke the private data of patients in intelligent healthcare with audit to forget

Revoking personal private data is one of the basic human rights. However, such right is often overlooked or infringed upon due to the increasing collection and use of patient data for model training. In order to secure patients' right to be forgotten, we proposed a solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing an approach called knowledge purification. To implement our solution, we developed an audit to forget software (AFS), which is able to evaluate and revoke patients' private data from pre-trained deep learning models. Here, we show the usability of AFS and its application potential in real-world intelligent healthcare to enhance privacy protection and data revocation rights.
Robust Machine Learning for Low-Power Wearable Devices: Challenges and Opportunities

Wearable devices are becoming increasingly popular for a number of applications, including health monitoring, rehabilitation, and fitness. More broadly, low-power Internet of Things (IoT) devices are being used for environmental monitoring, wide area sensing, and smart cities. The low-power devices have been enabled through advances in sensor technology, processing, and communication technology. Similarly, potentially transformative applications using wearable devices are enabled through the use of edge machine learning (ML) algorithms. The ML algorithms process the sensor data at runtime to identify and monitor the parameters of interest to the users. To be effective and provide a high quality of service to the users, the ML algorithms must be robust to changing environmental conditions and potential user errors. For instance, the user may change the orientation or position of the sensors due to an error or change in preferences. The ML models must adapt to these changes and provide accurate monitoring. To this end, this chapter reviews the current state of the art and challenges in robust machine learning algorithms for wearable devices. We also perform a case study with a human activity recognition application to analyze the feasibility of using generative networks for robust activity recognition. Overall, this chapter aims to serve as a resource for researchers working at the intersection of wearable devices and edge ML.
Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction

The intermittence and randomicity of renewable energy can seriously disrupt its productivity and reliability, particularly in unforeseen circumstances. Yet, most existing renewable energy prediction (REP) methods fail to model the inter-temporal correlations among input variables and suffer from impractical decomposition process, resulting in less good prediction accuracy. Aiming to improve these issues and power REP technique to better serve modern society, this work proposes a novel decomposition spectral graph convolutional network, named DASGCN, as a general solution for REP. Compared with existing REP models, DASGCN has two main characteristics: (1) It incorporates a carefully designed decomposition block which can decompose input multivariate time series efficiently, (2) A multi-channel adaptive adjacent matrix is proposed to model the complex correlation among entangled input variables automatically and generate the interpretable graph structure without prior domain knowledge. The comprehensive experiments on four real-world datasets demonstrate that the proposed DASGCN can significantly outperform benchmark models and achieve high performance on wind speed, wind power, solar radiation and photovoltaic power prediction tasks.
Deep Neural Network-Based Electron Microscopy Image Recognition for Source Distinguishing of Anthropogenic and Natural Magnetic Particles

Deep learning models excel at image recognition of macroscopic objects, but their applications to nanoscale particles are limited. Here, we explored their potential for source-distinguishing environmental particles. Transmission electron microscopy (TEM) images can reveal distinguishable features in particle morphology from various sources, but cluttered foreground objects and scale variations pose challenges to visual recognition models. In this proof-of-concept work, we proposed a novel instance segmentation model named CoMask to tackle these issues with atmospheric magnetic particles, a key species of PM2.5. CoMask features a densely connected feature extraction module to excavate multiscale spatial cues at the single-particle level and enlarges the receptive field size for improved representation capability. We also employed a collaborative learning strategy to further improve performance. Compared with other state-of-the-art models, CoMask was competitive on benchmark and TEM data sets. The application of CoMask not only enables the source-distinguishing of magnetic particles but also opens up a new vista for machine learning applications.
Methods to balance the exploration and exploitation in Differential Evolution from different scales: A survey

Inspired by the evolutionary process in nature, Differential Evolution (DE) has been widely concerned and used as a numerical global optimizer for decades of years, since its emerging in 1997. However, the performance of DE essentially depends on the balance of its exploration ability and exploitation ability. To better summarize the recent works on DE, especially from 2019 to 2023, this paper analysed the balancing strategies from different scales, including from the algorithm level, the operator level and the parameter level. And then, all of the recent works are categorized and discussed according to different scales. From the algorithm level, the hybridizing methods of DE are mainly reviewed. For the evolution operators, both the enhanced operators and operator selection strategies are introduced. And for the parameters of DE, mainly different adaptation controlling strategies are summarized. The main purpose of this paper is to give an update summary of DE research and review these works on exploration–exploitation balancing from different scales.
A 22-nm 1,287-MOPS/W Structured Data-Path Array for Binary Ring-LWE PQC

A $0.0131-\mathrm{mm}^{2}$ structured data-path array for binary Ring-LWE (BRLWE) post-quantum cryptography (PQC) is fabricated in 22-nm CMOS with an energy efficiency of 1,287 MOPS/W at $0.45 \mathrm{~V}$ and throughput of 6.92 MOPS at $0.8 \mathrm{~V}$. The proposed digital compute-in-memory (DCIM) architecture maximizes the data locality, introduces a novel ternary multiplication method that halves the arithmetic complexity, and optimizes the negative wrapped convolution scheme resulting in a structured dataflow of $23 \%$ smaller area, 2.6X higher throughput and 2X better energy efficiency than SotA. Furthermore, the proposed DCIM architecture significantly reduces the information leakage of cache and timing channels by statically storing the key words in memory.
Can Architecture Knowledge Guide Software Development With Generative AI?

Incorporating architecture knowledge into data that large language models are trained on and to tools that software engineers use to solve large scoped development problems ranging from technology upgrades, language translation, to software evolution is an unexplored area with challenging problems, but exciting applications if successful.
Layerwise Interoperability in Metaverse: Key to Next-Generation Electronic Commerce

Metaverse creates a fully immersive, self-sustaining, and hyper spatiotemporal virtual world where people from the physical world interact virtually irrespective of distances, communication protocols, regulations, and policies. The digital version of the real world should facilitate seamless communication, borderless transactions, and scalable infrastructure, encouraging participation and inclusivity. However, interoperability has become a significant issue that restricts the growth of the metaverse in reality due to independent standards, lone platforms, and unresolved communications challenges across the metaverse. This work discusses the necessity of metaverse interoperability from a socio-economic angle, emphasizing the need for connecting metaverses for distributed exchange, business interoperability, universal mobility, multi-application integration, and platform-agnostic experiences. We identified the components of interoperability that facilitate cross-metaverse communication to enable unbounded transactions and inter-metaverse user experiences. In addition, we proposed a four-layer architecture to achieve our goal of interoperable metaverse communication. Furthermore, we discuss how blockchain technology aids the expansion of interoperable metaverse through cross-platform transactions. Finally, we discussed key interoperability issues including dissimilar data standardization, different governance model, dissimilar consensus mechanisms, incompatible APIs, and heterogeneous technologies which make inter-metaverse communications challenging.
Toward Blockchain-based Fashion Wearables in the Metaverse: the Case of Decentraland

Among the earliest projects to combine the Meta-verse and non-fungible tokens (NFTs) we find Decentraland, a blockchain-based virtual world that touts itself as the first to be owned by its users. In particular, the platform’s virtual wearables (which allow avatar appearance customization) have attracted much attention from users, content creators, and the fashion industry. In this work, we present the first study to quantitatively characterize Decentraland’s wearables, their publication, minting, and sales on the platform’s marketplace. Our results indicate that wearables are mostly given away to promote and increase engagement on other cryptoasset or Metaverse projects, and only a small fraction is sold on the platform’s marketplace, where the price is mainly driven by the preset wearable’s rarity. Hence, platforms that offer virtual wearable NFTs should pay particular attention to the economics around this kind of assets beyond their mere sale.
Text-to-Metaverse: Towards a Digital Twin-Enabled Multimodal Conditional Generative Metaverse

Developing realistic and interactive virtual environments is a major hurdle in the progress of Metaverse. At present, majority of Metaverse applications necessitate the manual construction of 3D models which is both time-consuming and costly. Additionally, it is challenging to design environments that can promptly react to users’ actions. To address this challenge, this PhD Forum paper proposes a novel approach to generate virtual worlds using digital twin (DT) technology and AI through a Text-to-Metaverse pipeline. This pipeline converts natural language input into a scene JSON, which is used to generate a 3D virtual world using two engines: Generative Script Engine (GSE) and Generative Metaverse Engine (GME). GME generates a design script from the JSON file, and then uses it to generate 3D objects in an environment. It aims to use multimodal AI and DT technology to produce realistic and highly detailed virtual environments. The proposed pipeline has potential applications including education, training, architecture, healthcare and entertainment, and could change the way designers and developers create virtual worlds. While this paper covers an initial work as per the PhD Forum’s guidelines, it contributes to the research on generative models for multimodal data and provides a new direction for creating immersive virtual experiences.
The Metaverse for Intelligent Healthcare using XAI, Blockchain, and Immersive Technology

The metaverse combines the real world and the virtual world, allowing people to interact with their avatars in a setting supported by cutting-edge technologies like 5G or 6G internet, virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality, blockchain, digital twins, internet of things, and artificial intelligence (AI), all of which are enhanced by practically limitless data. The metaverse, a term coined to describe the virtual world instead of physical worlds, has the potential to revolutionize general healthcare. The Metaverse already provides opportunities for remote patient care, medical education, surgeries training, anti-aging healthcare, and medical research opportunities with implementation. According to the studies, the metaverse in medical domain revenue is expected to reach $640B by 2027. Different fields such as the financial industry, manufacturing industry, media, entertainment, etc. are taking advantage of healthcare tools to bring state-of-the-art technology into their domain. In this study, we are going to discuss the current state of metaverse technology in digital healthcare and its uses in the metaverse. We will also discuss the challenges that the metaverse presents for the healthcare industry. Also, we will conclude with a discussion of the future of metaverse in healthcare and the potential impact it could have on the delivery of healthcare services. Finally, this paper aims to provide an XAI, BC, and Immersive technology-based framework for metaverse healthcare and its potential to improve patient outcomes and transform the healthcare industry.
MpVit-Unet: Multi-path Vision Transformer Unet for Sellar Region Lesions Segmentation

The sellar area is located in the central part of the middle cranial fossa and the surrounding area with a complex structure, which is a common site of intracranial diseases. At present, the differential diagnosis of parasellar lesions mainly relies on magnetic resonance imaging, but the imaging features are very similar, and the huge differences in tumor size, shape, and appearance make the task of segmentation of sellar lesions still challenging. Due to the inherent limitations of convolution operations, Methods based on convolutional neural networks cannot meet the requirements of segmentation accuracy in the saddle region lesion segmentation task. The proposal of Vision Transformer (Vit) effectively solves these limitations and can efficiently learn global and long-range information interaction. Inspired by the Multi-Path Vision Transformer (MpVit), We propose a U-shaped encoder-decoder structure using MpVit as the backbone network and propose a skip connection with ResBlock, which we call MpVit-Unet. Experiments show that our proposed model achieves 1.45% and 0.8 % higher than the state-of-the-art methods on the T2W1 dataset mIou score and Dice score. On the CE-T1WI dataset, the mIou score and Dice score achieve 1.64% and 0.54% higher than the state-of-the-art methods.
Semantic Segmentation for Autonomous Driving

The task of semantic segmentation involves labeling each pixel in an image with its corresponding object class, which is achieved by clustering regions belonging to the same category using artificial intelligence. This is an important step from image processing to image analysis and has numerous applications in areas such as automatic driving, image enhancement, and 3D map reconstruction. With the emergence of deep learning, several sophisticated and efficient algorithms have been developed for this task. This chapter aims to review these methods, starting with a discussion of state-of-the-art semantic segmentation methods for both single modality and data fusion, emphasizing their contributions and significance in the field. Additionally, an overview of commonly used datasets is provided to assist researchers in selecting the appropriate dataset for their needs and goals. A comprehensive summary of evaluation metrics used to assess semantic segmentation results, along with corresponding benchmarks for a number of classic datasets, is also presented. Finally, practical applications of semantic segmentation in autonomous driving are explored, and conclusions are drawn on the current state of the art.
Automated Cyberbullying Activity Detection using Machine Learning Algorithm

Cyberbullying is the use of technology to harass, intimidate, or harm another person by making hurtful comments, sending threatening messages to humiliate someone in social media. It is important to recognize the signs of cyberbullying activities and takes steps to prevent it. Automated Machine Learning algorithms and Text mining concepts for detecting and classifying bullying messages in social media environment. The abusive texts are clustered using Multinomial Naïve Bayes, LinearSVC, Logistic Regression, K-Nearest neighbour to build a classifier from training datasets. Implementation uses Suspicious-communications-on-social-platforms dataset.
Evaluating Clustering Algorithms: An Analysis using the EDAS Method

Data clustering is frequently utilized in the early stages of analyzing big data. It enables the examination of massive datasets encompassing diverse types of data, with the aim of revealing undiscovered correlations, concealed patterns, and other valuable information that can be leveraged. The assessment of algorithms designed for handling large-scale data poses a significant research challenge across various fields. Evaluating the performance of different algorithms in processing massive data can yield diverse or even contradictory results, a phenomenon that remains insufficiently explored. This paper seeks to address this issue by proposing a solution framework for evaluating clustering algorithms, with the objective of reconciling divergent or conflicting evaluation outcomes. “The multicriteria decision making (MCDM) method” is used to assess the clustering algorithms. Using the EDAS rating system, the report examines six alternative clustering algorithms “the KM algorithm, EM algorithm, filtered clustering (FC), farthest-first (FF) algorithm, make density-based clustering (MD), and hierarchical clustering (HC)”—against, six clustering external measures. The Expectation Maximization (EM) algorithm has an ASi value of 0.048021 and is ranked 5th among the clustering algorithms. The Farthest-First (FF) Algorithm has an ASi value of 0.753745 and is ranked 2nd. The Filtered Clustering (FC) algorithm has an ASi value of 0.055173 and is ranked 4th. The Hierarchical Clustering (HC) algorithm has the highest ASi value of 0.929506 and is ranked 1st. The Make Density-Based Clustering (MD) algorithm has an ASi value of 0.011219 and is ranked 6th. Lastly, the K-Means Algorithm has an ASi value of 0.055376 and is ranked 3rd. These ASi values provide an assessment of each algorithm’s overall performance, and the rankings offer a comparative analysis of their performance. Based on the result, we observe that the Hierarchical Clustering algorithm achieves the highest ASi value and is ranked first, indicating its superior performance compared to the other algorithms.