Reviewerly/exHarmony huggingface dataset:
FVV Live: A Real-Time Free-Viewpoint Video System With Consumer Electronics Hardware

FVV Live is a novel end-to-end free-viewpoint video system, designed for low cost and real-time operation, based on off-the-shelf components. The system has been designed to yield high-quality free-viewpoint video using consumer-grade cameras and hardware, which enables low deployment costs and easy installation for immersive event-broadcasting or videoconferencing. The paper describes the architecture of the system, including acquisition and encoding of multiview plus depth data in several capture servers and virtual view synthesis on an edge server. All the blocks of the system have been designed to overcome the limitations imposed by hardware and network, which impact directly on the accuracy of depth data and thus on the quality of virtual view synthesis. The design of FVV Live allows for an arbitrary number of cameras and capture servers, and the results presented in this paper correspond to an implementation with nine stereo-based depth cameras. FVV Live presents low motion-to-photon and end-to-end delays, which enables seamless free-viewpoint navigation and bilateral immersive communications. Moreover, the visual quality of FVV Live has been assessed through subjective assessment with satisfactory results, and additional comparative tests show that it is preferred over state-of-the-art DIBR alternatives.
Designing an Efficient and Secure Message Exchange Protocol for Internet of Vehicles

In the advancements in computation and communication technologies and increasing number of vehicles, the concept of Internet of Vehicles (IoV) has emerged as an integral part of daily life, and it can be used to acquire vehicle related information including road congestion, road description, vehicle location, and speed. Such information is very vital and can benefit in a variety of ways, including route selection. However, without proper security measures, the information transmission among entities of IoV can be exposed and used for wicked intentions. Recently, many authentication schemes were proposed, but most of those authentication schemes are prone to insecurities or suffer from heavy communication and computation costs. Therefore, a secure message authentication protocol is proposed in this study for information exchange among entities of IoV (SMEP-IoV). Based on secure symmetric lightweight hash functions and encryption operations, the proposed SMEP-IoV meets IoV security and performance requirements. For formal security analysis of the proposed SMEP-IoV, BAN logic is used. The performance comparisons show that the SMEP-IoV is lightweight and completes the authentication process in just <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>0.198</mn> <mtext> ms</mtext> </math> .
InfoColorizer: Interactive Recommendation of Color Palettes for Infographics

When designing infographics, general users usually struggle with getting desired color palettes using existing infographic authoring tools, which sometimes sacrifice customizability, require design expertise, or neglect the influence of elements’ spatial arrangement. We propose a data-driven method that provides flexibility by considering users’ preferences, lowers the expertise barrier via automation, and tailors suggested palettes to the spatial layout of elements. We build a recommendation engine by utilizing deep learning techniques to characterize good color design practices from data, and further develop InfoColorizer, a tool that allows users to obtain color palettes for their infographics in an interactive and dynamic manner. To validate our method, we conducted a comprehensive four-part evaluation, including case studies, a controlled user study, a survey study, and an interview study. The results indicate that InfoColorizer can provide compelling palette recommendations with adequate flexibility, allowing users to effectively obtain high-quality color design for input infographics with low effort.
Hybrid attention network based on progressive embedding scale-context for crowd counting

The existing crowd counting methods usually adopt attention mechanisms to tackle background noise, or apply multilevel features or multiscale context fusion to tackle scale variation. However, these approaches deal with these two problems separately. In this paper, we propose a hybrid attention network (HAN) by employing progressive embedding scale-context (PES) information, which enables the network to simultaneously suppress noise and adapt head scale variation. We build the hybrid attention mechanism through two parallel spatial attention and channel attention modules, which makes the network focus more on the human head area and reduce the interference of background objects. In addition, we embed certain scale-context to the hybrid attention along the spatial and channel dimensions to alleviate the counting errors caused by the variation of perspective and head scale. Finally, we propose a progressive learning strategy through cascading multiple hybrid attention modules with embedding different scale contexts, which can gradually integrate different scale-context information into the current feature map from global to local. Ablation experiments show that the network architecture can gradually learn multiscale features and suppress background noise. Extensive experiments demonstrate that HANet obtains state-of-the-art counting performance on five mainstream datasets.
Photon-number entanglement generated by sequential excitation of a two-level atom

Entanglement and spontaneous emission are fundamental quantum phenomena that drive many applications of quantum physics. During the spontaneous emission of light from an excited two-level atom, the atom briefly becomes entangled with the photonic field. Here, we show that this natural process can be used to produce photon-number entangled states of light distributed in time. By exciting a quantum dot -- an artificial two-level atom -- with two sequential $\pi$ pulses, we generate a photon-number Bell state. We characterise this state using time-resolved intensity and phase correlation measurements. Furthermore, we theoretically show that applying longer sequences of pulses to a two-level atom can produce a series of multi-temporal mode entangled states with properties intrinsically related to the Fibonacci sequence. Our results on photon-number entanglement can be further exploited to generate new states of quantum light with applications in quantum technologies.
UWSpeech: Speech to Speech Translation for Unwritten Languages

Existing speech to speech translation systems heavily rely on the text of target language: they usually translate source language either to target text and then synthesize target speech from text, or directly to target speech with target text for auxiliary training. However, those methods cannot be applied to unwritten target languages, which have no written text or phoneme available. In this paper, we develop a translation system for unwritten languages, named as UWSpeech, which converts target unwritten speech into discrete tokens with a converter, and then translates source-language speech into target discrete tokens with a translator, and finally synthesizes target speech from target discrete tokens with an inverter. We propose a method called XL-VAE, which enhances vector quantized variational autoencoder (VQ-VAE) with cross-lingual (XL) speech recognition, to train the converter and inverter of UWSpeech jointly. Experiments on Fisher Spanish-English conversation translation dataset show that UWSpeech outperforms direct translation and VQ-VAE baseline by about 16 and 10 BLEU points respectively, which demonstrate the advantages and potentials of UWSpeech.
Potential and Pitfalls of Digital Voice Assistants in Older Adults With and Without Intellectual Disabilities: Relevance of Participatory Design Elements and Ecologically Valid Field Studies

OPINION article Front. Psychol., 01 July 2021Sec. Human-Media Interaction https://doi.org/10.3389/fpsyg.2021.684012
Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics

Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers. Recently, methods have been developed to mine hidden information from histopathological slides using deep learning applied to scanned and digitized slides; deep learning comprises a collection of computational methods which learn patterns in data in order to make predictions. Such methods have been reported to be successful in a variety of cancers for predicting the presence of biomarkers such as driver mutations, tumour mutational burden, and microsatellite instability. This information could prove valuable to pathologists and oncologists in clinical decision making for cancer treatment and triage for in-depth sequencing. In addition to identifying molecular features, deep learning has been applied to predict prognosis and treatment response in certain cancers. Despite reported successes, many challenges remain before the clinical implementation of such diagnostic strategies in the clinical setting is possible. This review aims to outline recent developments in the field of deep learning for predicting molecular genetics from histopathological slides, as well as to highlight limitations and pitfalls of working with histopathology slides in deep learning.
Genuine multipartite entanglement measure

Quantifying genuine entanglement is a crucial task in quantum information theory.In this work, we give an approach of constituting genuine $m$-partite entanglement measures from any bipartite entanglement and any $k$-partite entanglement measure, $3\leq k<m$. In addition, as a complement to the three-qubit concurrence triangle proposed in [Phys. Rev. Lett., 127, 040403], we show that the triangle relation is also valid for any continuous entanglement measure and system with any dimension. We also discuss the tetrahedron structure for the four-partite system via the triangle relation associated with tripartite and bipartite entanglement respectively. For multipartite system that contains more than four parties, there is no symmetric geometric structure as that of tri- and four-partite cases.
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID, imbalanced (statistical heterogeneity) and distribution shifted training data of FL is distributed in the federated network, which will increase the divergences between the local models and the global model, further degrading performance. In this paper, we propose a flexible clustered federated learning (CFL) framework named FlexCFL, in which we 1) group the training of clients based on the similarities between the clients’ optimization directions for lower training divergence; 2) implement an efficient newcomer device cold start mechanism for framework scalability and practicality; 3) flexibly migrate clients to meet the challenge of client-level data distribution shift. FlexCFL can achieve improvements by dividing joint optimization into groups of sub-optimization and can strike a balance between accuracy and communication efficiency in the distribution shift environment. The convergence and complexity are analyzed to demonstrate the efficiency of FlexCFL. We also evaluate FlexCFL on several open datasets and made comparisons with related CFL frameworks. The results show that FlexCFL can significantly improve absolute test accuracy by <inline-formula><tex-math notation="LaTeX">$+10.6\%$</tex-math></inline-formula> on FEMNIST compared with <i>FedAvg</i> , <inline-formula><tex-math notation="LaTeX">$+3.5\%$</tex-math></inline-formula> on FashionMNIST compared with <i>FedProx</i> , <inline-formula><tex-math notation="LaTeX">$+8.4\%$</tex-math></inline-formula> on MNIST compared with <i>FeSEM</i> , <inline-formula><tex-math notation="LaTeX">$+4.7\%$</tex-math></inline-formula> on Sentiment140 compare with <i>IFCA</i> . The experiment results show that FlexCFL is also communication efficient in the distribution shift environment.
Impact of Aerosol and Cloud on the Solar Energy Potential over the Central Gangetic Himalayan Region

We examine the impact of atmospheric aerosols and clouds on the surface solar radiation and solar energy at Nainital, a high-altitude remote location in the central Gangetic Himalayan region (CGHR). For this purpose, we exploited the synergy of remote-sensed data in terms of ground-based AERONET Sun Photometer and satellite observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Meteosat Second Generation (MSG), with radiative transfer model (RTM) simulations and 1 day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Clouds and aerosols are one of the most common sources of solar irradiance attenuation and hence causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The outputs of RTM results presented with high accuracy under clear, cloudy sky and dust conditions for global horizontal (GHI) and beam horizontal irradiance (BHI). On an annual basis the total aerosol attenuation was found to be up to 105 kWh m−2 for the GHI and 266 kWh m−2 for BHI, respectively, while the cloud effect is much stronger with an attenuation of 245 and 271 kWh m−2 on GHI and BHI. The results of this study will support the Indian solar energy producers and electricity handling entities in order to quantify the energy and financial losses due to cloud and aerosol presence.
Explainable Identification of Dementia From Transcripts Using Transformer Networks

Alzheimer's disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples' everyday life if not diagnosed on time. Very few works have exploited transformer-based networks and despite the high accuracy achieved, little work has been done in terms of model interpretability. In addition, although Mini-Mental State Exam (MMSE) scores are inextricably linked with the identification of dementia, research works face the task of dementia identification and the task of the prediction of MMSE scores as two separate tasks. In order to address these limitations, we employ several transformer-based models, with BERT achieving the highest accuracy accounting for 87.50%. Concurrently, we propose an interpretable method to detect AD patients based on siamese networks reaching accuracy up to 83.75%. Next, we introduce two multi-task learning models, where the main task refers to the identification of dementia (binary classification), while the auxiliary one corresponds to the identification of the severity of dementia (multiclass classification). Our model obtains accuracy equal to 86.25% on the detection of AD patients in the multi-task learning setting. Finally, we present some new methods to identify the linguistic patterns used by AD patients and non-AD ones, including text statistics, vocabulary uniqueness, word usage, correlations via a detailed linguistic analysis, and explainability techniques (LIME). Findings indicate significant differences in language between AD and non-AD patients.
PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods

Abstract Motivation Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. Results This release of PMLB (Penn Machine Learning Benchmarks) provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. Availability and implementation PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.
Group Signatures and More from Isogenies and Lattices: Generic, Simple, and Efficient

We construct an efficient dynamic group signature (or more generally an accountable ring signature) from isogeny and lattice assumptions. Our group signature is based on a simple generic construction that can be instantiated by cryptographically hard group actions such as the CSIDH group action or an MLWE-based group action. The signature is of size $$O(\log N)$$ , where N is the number of users in the group. Our idea builds on the recent efficient OR-proof by Beullens, Katsumata, and Pintore (Asiacrypt’20), where we efficiently add a proof of valid ciphertext to their OR-proof and further show that the resulting non-interactive zero-knowledge proof system is online extractable. Our group signatures satisfy more ideal security properties compared to previously known constructions, while simultaneously having an attractive signature size. The signature size of our isogeny-based construction is an order of magnitude smaller than all previously known post-quantum group signatures (e.g., 6.6 KB for 64 members). In comparison, our lattice-based construction has a larger signature size (e.g., either 126 KB or 89 KB for 64 members depending on the satisfied security property). However, since the $$O(\cdot )$$ -notation hides a very small constant factor, it remains small even for very large group sizes, say $$2^{20}$$ .
Trapping Sets of Quantum LDPC Codes

Iterative decoders for finite length quantum low-density parity-check (QLDPC) codes are attractive because their hardware complexity scales only linearly with the number of physical qubits. However, they are impacted by short cycles, detrimental graphical configurations known as trapping sets (TSs) present in a code graph as well as symmetric degeneracy of errors. These factors significantly degrade the decoder decoding probability performance and cause so-called error floor. In this paper, we establish a systematic methodology by which one can identify and classify quantum trapping sets (QTSs) according to their topological structure and decoder used. The conventional definition of a TS from classical error correction is generalized to address the syndrome decoding scenario for QLDPC codes. We show that the knowledge of QTSs can be used to design better QLDPC codes and decoders. Frame error rate improvements of two orders of magnitude in the error floor regime are demonstrated for some practical finite-length QLDPC codes without requiring any post-processing.
Qubit readout error mitigation with bit-flip averaging

Quantum computers are becoming increasingly accessible, and may soon outperform classical computers for useful tasks. However, qubit readout errors remain a significant hurdle to running quantum algorithms on current devices. We present a scheme to more efficiently mitigate these errors on quantum hardware and numerically show that our method consistently gives advantage over previous mitigation schemes. Our scheme removes biases in the readout errors allowing a general error model to be built with far fewer calibration measurements. Specifically, for reading out $n$-qubits we show a factor of $2^n$ reduction in the number of calibration measurements without sacrificing the ability to compensate for correlated errors. Our approach can be combined with, and simplify, other mitigation methods allowing tractable mitigation even for large numbers of qubits.
A Blockchain-Enabled Explainable Federated Learning for Securing Internet-of-Things-Based Social Media 3.0 Networks

Social media (SM) 3.0 integrates SM platforms, such as Facebook and Twitter, with the Internet of Things (IoT), and has a great potential to change how we interact with mobile devices, online platforms, and the world around us. This integration with end users produces large-scale and heterogeneous data sources that demand machine learning (ML)-based data analytics for decision-making and to provide security against ML and data privacy attacks. The development of privacy-aware ML models within a federated learning (FL) ecosystem can empower an entire network to learn from data in a decentralized manner. In this article, we propose a differentially privacy blockchain-based explainable FL (DP-BFL) framework by harnessing the ever-evolving power of SM 3.0 networks. This framework permits any Internet empowered device to partake and contribute data to a global privacy preserved model. In this framework, participants will upload the differentially private local updates to the miners of blockchain, where the local updates will be evaluated and rewarded. The experimental results obtained from real-world datasets, namely, SM 3.0 and MNIST, demonstrated that the proposed framework could achieve high utility, enhanced privacy, and elevated efficiency. More Specifically, the experimental analysis of our proposed framework reveals the following two key properties. First, our proposed DP-BFL yields noticeable performance improvements in the applied learning models with high privacy and comparable utility levels, in terms of accuracy and f-measure metrics, to a standard FL and centralized learning approaches under the restriction of privacy preservation. Second, given a certain number of the malicious entities, DP-BFL allowed an enhanced recognition of users' preferences in the SM 3.0 dataset and precise prediction of images' class in the MNIST dataset while mitigating the impact of the malicious entities' poisoned updates. Moreover, as the proposed DP-BFL attains DP on the local model's update, it is considered the same as the standard FL-based setting, along with some kinds of privacy preservation on the uploaded model's updates.
Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations

Due to the different losses caused by various photovoltaic (PV) array faults, accurate diagnosis of fault types is becoming increasingly important. Compared with a single one, multiple PV stations collect sufficient fault samples, but their data is not allowed to be shared directly due to potential conflicts of interest. Therefore, federated learning can be exploited to train a collaborative fault diagnosis model. However, the modeling efficiency is seriously affected by the model update mechanism since each PV station has a different computing capability and amount of data. Moreover, for the safe and stable operation of the PV system, the robustness of collaborative modeling must be guaranteed rather than simply being processed on a central server. To address these challenges, a novel asynchronous decentralized federated learning (ADFL) framework is proposed. Each PV station not only trains its local model but also participates in collaborative fault diagnosis by exchanging model parameters to improve the generalization without losing accuracy. The global model is aggregated distributedly to avoid central node failure. By designing the asynchronous update scheme, the communication overhead and training time are greatly reduced. Both the experiments and numerical simulations are carried out to verify the effectiveness of the proposed method.