Reviewerly/exHarmony huggingface dataset:
SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning

Privacy regulation laws, such as GDPR, impose transparency and security as design pillars for data processing algorithms. In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks. Several federated learning frameworks employ differential privacy to prevent private data leakage to unauthorized parties and malicious attackers. Many studies, however, highlight the vulnerabilities of standard federated learning to poisoning and inference, thus raising concerns about potential risks for sensitive data. To address this issue, we present SGDE, a generative data exchange protocol that improves user security and machine learning performance in a cross-silo federation. The core of SGDE is to share data generators with strong differential privacy guarantees trained on private data instead of communicating explicit gradient information. These generators synthesize an arbitrarily large amount of data that retain the distinctive features of private samples but differ substantially. In this work, SGDE is tested in a cross-silo federated network on images and tabular datasets, exploiting beta-variational autoencoders as data generators. From the results, the inclusion of SGDE turns out to improve task accuracy and fairness, as well as resilience to the most influential attacks on federated learning.
Nonlinear All‐Optical Coherent Generation and Read‐Out of Valleys in Atomically Thin Semiconductors

Abstract With conventional electronics reaching performance and size boundaries, all‐optical processes have emerged as ideal building blocks for high speed and low power consumption devices. A promising approach in this direction is provided by valleytronics in atomically thin semiconductors, where light‐matter interaction allows to write, store, and read binary information into the two energetically degenerate but non‐equivalent valleys. Here, nonlinear valleytronics in monolayer WSe 2 is investigated and show that an individual ultrashort pulse with a photon energy tuned to half of the optical band‐gap can be used to simultaneously excite (by coherent optical Stark shift) and detect (by a rotation in the polarization of the emitted second harmonic) the valley population.
Deep Learning and Machine Learning for Malaria Detection: Overview, Challenges and Future Directions

Public health initiatives must be made using evidence-based decision-making to have the greatest impact. Machine learning algorithms are created to gather, store, process, and analyze data to provide knowledge and guide decisions. A crucial part of any surveillance system is image analysis. The communities of computer vision and machine learning have become curious about it as of late. This study uses a variety of machine learning, and image processing approaches to detect and forecast malarial illness. In our research, we discovered the potential of deep learning techniques as innovative tools with a broader applicability for malaria detection, which benefits physicians by assisting in the diagnosis of the condition. We investigate the common confinements of deep learning for computer frameworks and organizing, including the requirement for data preparation, preparation overhead, real-time execution, and explaining ability, and uncover future inquiries about bearings focusing on these constraints.
An Automata-Based Framework for Verification and Bug Hunting in Quantum Circuits

We introduce a new paradigm for analysing and finding bugs in quantum circuits. In our approach, the problem is given by a ‍triple { P } C { Q } and the question is whether, given a set P of quantum states on the input of a circuit C , the set of quantum states on the output is equal to (or included in) a set Q . While this is not suitable to specify, e.g., functional correctness of a quantum circuit, it is sufficient to detect many bugs in quantum circuits. We propose a technique based on tree automata to compactly represent sets of quantum states and develop transformers to implement the semantics of quantum gates over this representation. Our technique computes with an algebraic representation of quantum states, avoiding the inaccuracy of working with floating-point numbers. We implemented the proposed approach in a prototype tool and evaluated its performance against various benchmarks from the literature. The evaluation shows that our approach is quite scalable, e.g., we managed to verify a large circuit with 40 qubits and 141,527 gates, or catch bugs injected into a circuit with 320 qubits and 1,758 gates, where all tools we compared with failed. In addition, our work establishes a connection between quantum program verification and automata, opening new possibilities to exploit the richness of automata theory and automata-based verification in the world of quantum computing.
Bitcoin network-based anonymity and privacy model for metaverse implementation in Industry 5.0 using linear Diophantine fuzzy sets

Abstract Metaverse is a new technology expected to generate economic growth in Industry 5.0. Numerous studies have shown that current bitcoin networks offer remarkable prospects for future developments involving metaverse with anonymity and privacy. Hence, modelling effective Industry 5.0 platforms for the bitcoin network is crucial for the future metaverse environment. This modelling process can be classified as multiple-attribute decision-making given three issues: the existence of multiple anonymity and privacy attributes, the uncertainty related to the relative importance of these attributes and the variability of data. The present study endeavours to combine the fuzzy weighted with zero inconsistency method and Diophantine linear fuzzy sets with multiobjective optimisation based on ratio analysis plus the multiplicative form (MULTIMOORA) to determine the ideal approach for metaverse implementation in Industry 5.0. The decision matrix for the study is built by intersecting 22 bitcoin networks to support Industry 5.0's metaverse environment with 24 anonymity and privacy evaluation attributes. The proposed method is further developed to ascertain the importance level of the anonymity and privacy evaluation attributes. These data are used in MULTIMOORA. A sensitivity analysis, correlation coefficient test and comparative analysis are performed to assess the robustness of the proposed method.
Online Semi-supervised Learning with Mix-Typed Streaming Features

Online learning with feature spaces that are not fixed but can vary over time renders a seemingly flexible learning paradigm thus has drawn much attention. Unfortunately, two restrictions prohibit a ubiquitous application of this learning paradigm in practice. First, whereas prior studies mainly assume a homogenous feature type, data streams generated from real applications can be heterogeneous in which Boolean, ordinal, and continuous co-exist. Existing methods that prescribe parametric distributions such as Gaussians would not suffice to model the correlation among such mixtyped features. Second, while full supervision seems to be a default setup, providing labels to all arriving data instances over a long time span is tangibly onerous, laborious, and economically unsustainable. Alas, a semi-supervised online learner that can deal with mix-typed, varying feature spaces is still missing. To fill the gap, this paper explores a novel problem, named Online Semi-supervised Learning with Mixtyped streaming Features (OSLMF), which strives to relax the restrictions on the feature type and supervision information. Our key idea to solve the new problem is to leverage copula model to align the data instances with different feature spaces so as to make their distance measurable. A geometric structure underlying data instances is then established in an online fashion based on their distances, through which the limited labeling information is propagated, from the scarce labeled instances to their close neighbors. Experimental results are documented to evidence the viability and effectiveness of our proposed approach. Code is released in https://github.com/wudi1989/OSLMF.
HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design

Adversarial attacks on thermal infrared imaging expose the risk of related applications. Estimating the security of these systems is essential for safely deploying them in the real world. In many cases, realizing the attacks in the physical space requires elaborate special perturbations. These solutions are often impractical and attention-grabbing. To address the need for a physically practical and stealthy adversarial attack, we introduce HotCold Block, a novel physical attack for infrared detectors that hide persons utilizing the wearable Warming Paste and Cooling Paste. By attaching these readily available temperature-controlled materials to the body, HotCold Block evades human eyes efficiently. Moreover, unlike existing methods that build adversarial patches with complex texture and structure features, HotCold Block utilizes an SSP-oriented adversarial optimization algorithm that enables attacks with pure color blocks and explores the influence of size, shape, and position on attack performance. Extensive experimental results in both digital and physical environments demonstrate the performance of our proposed HotCold Block. Code is available: https://github.com/weihui1308/HOTCOLDBlock.
Exposing the Self-Supervised Space-Time Correspondence Learning via Graph Kernels

Self-supervised space-time correspondence learning is emerging as a promising way of leveraging unlabeled video. Currently, most methods adapt contrastive learning with mining negative samples or reconstruction adapted from the image domain, which requires dense affinity across multiple frames or optical flow constraints. Moreover, video correspondence predictive models require mining more inherent properties in videos, such as structural information. In this work, we propose the VideoHiGraph, a space-time correspondence framework based on a learnable graph kernel. Concerning the video as the spatial-temporal graph, the learning objectives of VideoHiGraph are emanated in a self-supervised manner for predicting unobserved hidden graphs via graph kernel manner. We learn a representation of the temporal coherence across frames in which pairwise similarity defines the structured hidden graph, such that a biased random walk graph kernel along the sub-graph can predict long-range correspondence. Then, we learn a refined representation across frames on the node-level via a dense graph kernel. The self-supervision of the model training is formed by the structural and temporal consistency of the graph. VideoHiGraph achieves superior performance and demonstrates its robustness across the benchmark of label propagation tasks involving objects, semantic parts, keypoints, and instances. Our algorithm implementations have been made publicly available at https://github.com/zyqin19/VideoHiGraph.
How age-friendly is the use of augmented reality in the learning process? A Systematic Survey

Augmented reality has a direct connection to the visual perception of information. Scientists lamented that individuals perceive 70 to 80 percent of the information they receive with their eyes. Based on the claim, it can be deduced that AR has a major role to play in the learning process. This study aims to determine whether AR is age-friendly and to enable its use for learning across all age groups at all educational levels. The study adopts a qualitative survey design. The population of the study includes all students currently in Sokoto State, Nigeria (across all levels). The purposive sampling technique is used to select 192 students who have experience using AR for learning. For the study, an instrument called AR Key Information Interview was developed. It was validated, a pilot study was conducted, and a reliability index of 0.83 was found using Chrombact Alpha. The study finds that AR is age-friendly and can be used at all educational levels. The study concluded that AR is one of the best technologies that can promote lifelong learning in the 21st century. The study therefore recommended that more AR be developed to facilitate the implementation of our national curriculum for sustainable national development.
High-Level Features for Human Activity Recognition and Modeling

High-Level Features (HLF) are a novel way of describing and processing human activities. Each feature captures an interpretable aspect of activities, and a unique combination of HLFs defines an activity. In this article, we propose and evaluate a concise set of six HLFs on and across the CSL-SHARE and UniMiB SHAR datasets, showing that HLFs can be successfully extracted with machine learning methods and that in this HLF-space activities can be classified across datasets as well as in imbalanced and few-shot learning settings. Furthermore, we illustrate how classification errors can be attributed to specific HLF extractors. In person-independent 5-fold cross-validations, the proposed HLFs are extracted from 68% up to 99% balanced accuracy, and activity classification achieves 89.7% (CSL-SHARE) and 67.3% (UniMiB SHAR) accuracy. Imbalanced and few-shot learning results are promising, with the latter converging quickly. In a person-dependent evaluation across both datasets, 78% accuracy is achieved. These results demonstrate the possibilities and advantages of the proposed high-level, extensible, and interpretable feature space.
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information

With the increase of content pages and interactive buttons in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challenges in multi-domain and multi-task recommendations. The core of multi-task and multi-domain recommendation is to accurately capture user interests in multiple scenarios given multiple user behaviors. In this paper, we propose a plug-and-play Parameter and Embedding Personalized Network (PEPNet) for multi-domain and multi-task recommendation. PEPNet takes personalized prior information as input and dynamically scales the bottom-level Embedding and top-level DNN hidden units through gate mechanisms. Embedding Personalized Network (EPNet) performs personalized selection on Embedding to fuse features with different importance for different users in multiple domains. Parameter Personalized Network (PPNet) executes personalized modification on DNN parameters to balance targets with different sparsity for different users in multiple tasks. We have made a series of special engineering optimizations combining the Kuaishou training framework and the online deployment environment. By infusing personalized selection of Embedding and personalized modification of DNN parameters, PEPNet tailored to the interests of each individual obtains significant performance gains, with online improvements exceeding 1% in multiple task metrics across multiple domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million users every day.
COCO-DR: Combating Distribution Shift in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning

We present a new zero-shot dense retrieval (Ze-roDR) method, COCO-DR, to improve the generalization ability of dense retrieval by combating the distribution shifts between source training tasks and target scenarios.To mitigate the impact of document differences, COCO-DR continues pretraining the language model on the target corpora to adapt the model to target distributions via COtinuous COtrastive learning.To prepare for unseen target queries, COCO-DR leverages implicit Distributionally Robust Optimization (iDRO) to reweight samples from different source query clusters for improving model robustness over rare queries during fine-tuning.COCO-DR achieves superior average performance on BEIR, the zero-shot retrieval benchmark.At BERT Base scale, COCO-DR Base outperforms other ZeroDR models with 60× larger size.At BERT Large scale, COCO-DR Large outperforms the giant GPT-3 embedding model which has 500× more parameters.Our analysis show the correlation of COCO-DR's effectiveness in combating distribution shifts and improving zero-shot accuracy.Our code and model can be found at https://github.com/OpenMatch/COCO-DR.
Chromatic Plasmonic Polarizer-Based Synapse for All-Optical Convolutional Neural Network

Emerging memory devices have been demonstrated as artificial synapses for neural networks. However, the process of rewriting these synapses is often inefficient, in terms of hardware and energy usage. Herein, we present a novel surface plasmon resonance polarizer-based all-optical synapse for realizing convolutional filters and optical convolutional neural networks. The synaptic device comprises nanoscale crossed gold arrays with varying vertical and horizontal arms that respond strongly to the incident light's polarization angle. The presented synapse in an optical convolutional neural network achieved excellent performance in four different convolutional results for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digit data set. After training on 1,000 images, the network achieved a classification accuracy of over 98% when tested on a separate set of 10,000 images. This presents a promising approach for designing artificial neural networks with efficient hardware and energy consumption, low cost, and scalable fabrication.
Efficient Layer Compression Without Pruning

Network pruning is one of the chief means for improving the computational efficiency of Deep Neural Networks (DNNs). Pruning-based methods generally discard network kernels, channels, or layers, which however inevitably will disrupt original well-learned network correlation and thus lead to performance degeneration. In this work, we propose an Efficient Layer Compression (ELC) approach to efficiently compress serial layers by decoupling and merging rather than pruning. Specifically, we first propose a novel decoupling module to decouple the layers, enabling us readily merge serial layers that include both nonlinear and convolutional layers. Then, the decoupled network is losslessly merged based on the equivalent conversion of the parameters. In this way, our ELC can effectively reduce the depth of the network without destroying the correlation of the convolutional layers. To our best knowledge, we are the first to exploit the mergeability of serial convolutional layers for lossless network layer compression. Experimental results conducted on two datasets demonstrate that our method retains superior performance with a FLOPs reduction of 74.1% for VGG-16 and 54.6% for ResNet-56, respectively. In addition, our ELC improves the inference speed by 2× on Jetson AGX Xavier edge device.
Removing Objects From Neural Radiance Fields

Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGBD sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a userprovided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner, outperforming competing methods. We validate our approach by proposing a new and still-challenging dataset for the task of NeRF inpainting.
Tools and Ontologies for the Aggregation and Management of Cypriot Archaeological Datasets

This article focuses on the aggregation of Cypriot archaeological datasets, digitally archived in local repositories, into the ARIADNE portal. It considers, in particular, the development of an application profile for inscriptions and presents the integration of two collections, consisting of ancient coins and inscriptions carved on stones. It highlights the tools and ontologies developed for the aggregation and management of these digital resources, as well as the related pipeline and activities. The issues encountered are also presented, plus the solutions adopted and the successful results in the data aggregation of these collections into the infrastructure. Currently, thanks to the pipeline, and the semantic tools developed and used in ARIADNE, a collection of Cypriot medieval coins and a corpus of Ancient Greek inscriptions are now more widely accessible to the archaeological community.
Remodeling a Mobile Educational Metaverse Using a Co-Design Approach: Challenges, Issues, and Expected Features

The emergence of the metaverse has expanded the vital role of virtual worlds in education. It extends the traditional configuration of virtual spaces by offering a more immersive, interactive, and social experience. Unfortunately, there is insufficient literature on how to develop a metaverse application aligned with academic standards and best practices. In this study, we adopted a co-design approach in remodeling our mobile educational metaverse to draw a blueprint for existing and future metaverse technology adopters. We sent a follow-up survey to stakeholders inviting free-text comments. This survey focused on the challenges, issues, and expected features from the perspectives of developers, teachers, and students, respectively. Our results revealed several challenges that developers face when building a metaverse, such as technical infrastructure, student engagement, avatar customization, and performance optimization. Meanwhile, teachers emphasized potential issues that may arise from the use of metaverse technology in educational settings, such as learning outcomes, health and safety, digital citizenship, and ethics and morality. Finally, students expected several key features of an educational metaverse application, such as socialization and collaboration, virtual world interactions, metaverse optimization, realistic graphics, and minigames and activities. The implications of these findings are significant and can help other educational institutions seeking to integrate metaverse technology into their academic services. Overall, our study presents an initial foundation for further exploration and advancement within the rapidly evolving field of metaverse technology.