Reviewerly/exHarmony huggingface dataset:
An Efficient and Secure Scheme of Verifiable Computation for Intel SGX

Cloud computing offers resource-constrained users big-volume data storage and energy-consuming complicated computation. However, owing to the lack of full trust in the cloud, the cloud users prefer privacy-preserving data computation with correctness verification. However, cryptography-based schemes introduce high computational costs to both the cloud and its users for verifiable computation with privacy preservation, which makes it difficult to support complicated computations in practice. Intel Software Guard Extensions (SGX) as a trusted execution environment is widely researched in various fields, and is regarded as a promising way to achieve efficient outsourced data computation with privacy preservation over the cloud. But we find two types of threats towards the computation with SGX: Disarranging Data-Related Code threat and Output Tampering and Misrouting threat. In this paper, we depict these threats using formal methods and propose an efficient and secure scheme to resist the threats and realize verifiable computation for Intel SGX. We prove the security and show the efficiency and correctness of our proposed scheme through theoretic analysis and extensive experiments. Furthermore, we compare our scheme with some cryptography-based schemes to show its high efficiency.
Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning

Introduction To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly, the field of explainable artificial intelligence (XAI) has resurfaced as a topic of active research, offering approaches to address the “how” and “why” of automated decision-making. Within this domain, counterfactual explanations (CFEs) have gained considerable traction as a psychologically grounded approach to generate post-hoc explanations. To do so, CFEs highlight what changes to a model's input would have changed its prediction in a particular way. However, despite the introduction of numerous CFE approaches, their usability has yet to be thoroughly validated at the human level. Methods To advance the field of XAI, we introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework. The Alien Zoo provides the means to evaluate usability of CFEs for gaining new knowledge from an automated system, targeting novice users in a domain-general context. As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study. Results Our results suggest the efficacy of the Alien Zoo framework for empirically investigating aspects of counterfactual explanations in a game-type scenario and a low-knowledge domain. The proof of concept study reveals that users benefit from receiving CFEs compared to no explanation, both in terms of objective performance in the proposed iterative learning task, and subjective usability. Discussion With this work, we aim to equip research groups and practitioners with the means to easily run controlled and well-powered user studies to complement their otherwise often more technology-oriented work. Thus, in the interest of reproducible research, we provide the entire code, together with the underlying models and user data: https://github.com/ukuhl/IntroAlienZoo .
Isolation and Quarantine for Coronavirus Disease 2019 in the United States, 2020–2022

Abstract Background Public health programs varied in ability to reach people with coronavirus disease 2019 (COVID-19) and their contacts to encourage separation from others. For both adult case patients with COVID-19 and their contacts, we estimated the impact of contact tracing activities on separation behaviors from January 2020 until March 2022. Methods We used a probability-based panel survey of a nationally representative sample to gather data for estimates and comparisons. Results An estimated 64 255 351 adults reported a positive severe acute respiratory syndrome coronavirus 2 test result; 79.6% isolated for ≥5 days, 60.2% isolated for ≥10 days, and 79.2% self-notified contacts. A total of, 24 057 139 (37.7%) completed a case investigation, and 46.2% of them reported contacts to health officials. More adults who completed a case investigation isolated than those who did not complete a case investigation (≥5 days, 82.6% vs 78.2%, respectively; ≥10 days, 69.8% vs 54.8%; both P &amp;lt; .05). A total of 84 946 636 adults were contacts of a COVID-19 case patient. Of these, 73.1% learned of their exposure directly from a case patient; 49.4% quarantined for ≥5 days, 18.7% quarantined for ≥14 days, and 13.5% completed a contact tracing call. More quarantined among those who completed a contact tracing call than among those who did not complete a tracing call (≥5 days, 61.2% vs 48.5%, respectively; ≥14 days, 25.2% vs 18.0%; both P &amp;lt; .05). Conclusions Engagement in contact tracing was positively correlated with isolation and quarantine. However, most adults with COVID-19 isolated and self-notified contacts regardless of whether the public health workforce was able to reach them. Identifying and reaching contacts was challenging and limited the ability to promote quarantining, and testing.
In-Network Video Quality Adaption using Packet Trimming at the Edge

This paper describes the effects of running innetwork quality adaption by trimming the packets of layered video streams at the edge. The video stream is transmitted using the BPP transport protocol, which is like UDP, but has been designed to be both amenable to trimming and to provide low-latency and high reliability. The traffic adaption uses the Packet Wash process of BPP on the transmitted Scalable Video Coding (SVC) video streams as they pass through a network function which is BPP-aware and embedded at the edge. Our previous work has either demonstrated the use of SDN controllers to directly implement Packet Wash, or the use of a network function in the core of the network to do the same task. This paper presents the first attempt to deploy and evaluate such a process in the edge. We compare the performance of transmitting video using BPP and the Packet Wash trimming, against alternative transmission schemes, namely TCP, UDP, and HTTP Adaptive Streaming (HAS). The results demonstrate that providing traffic engineering using in-network quality adaption using packet trimming, provides high quality at the receiver.
Where to look: Multi-granularity occlusion aware for video person re-identification

Video person re-identification(re-ID) plays an important role in intelligent video surveillance, which can automatically match the same person across video clips under non-overlapping cameras. Despite great progress in re-ID, the performance of most existing methods still is corrupted severely under partial occlusion. Although some multi-granularity methods have alleviated this dilemma, these methods still suffer from weak diversity of features and conflict between rigid horizontal partition and vertical occlusion. In this paper, we propose a novel video person re-ID framework, called Multi-Granularity Occlusion Aware (MGOA), which extracts multi-granularity features by precisely erasing the occlusion. Different from previous works based on multiple granularities, the proposed MGOA predicts the partial occlusion in a coarse-to-fine manner instead of erasing the occlusion in video clips by one step. Specifically, we first propose the multi-granularity feature extraction to obtain diverse features at different levels of feature maps, which is beneficial for the fine erasure of the occlusion. Moreover, to avoid the limitation of horizontal stripes that cannot handle vertical occlusion, we design Attention-Aware Occlusion Erasure (AA-OE) that can obtain the attention maps with coarse occlusion erasure in the coarse-grained branch and the attention maps with fine occlusion erasure in the fine-grained branch. It is worth noting that each granularity in our network is not independent but relevant through the top-down information transmission between granularities, which transfers the erased occlusion feature maps of the current branch to the next finer-grained branch for guiding AA-OE to obtain more discriminative features. Extensive experiments on three challenging public benchmarks show that our MGOA can deal well with occlusion and achieves state-of-the-art performance.
Fast and Robust Geometric Two-Qubit Gates for Superconducting Qubits and beyond

Quantum protocols based on adiabatic evolution are remarkably robust against imperfections of control pulses and system uncertainties. While adiabatic protocols have been successfully implemented for quantum operations such as quantum state transfer and single-qubit gates, their use for geometric two-qubit gates remains a challenge. In this paper, we propose a general scheme to realize robust geometric two-qubit gates in multilevel qubit systems where the interaction between the qubits is mediated by an auxiliary system (such as a bus or coupler). While our scheme utilizes stimulated Raman adiabatic passage (STIRAP), it is substantially simpler than STIRAP-based gates that have been proposed for atomic platforms, requiring fewer control tones and ancillary states, as well as utilizing only a generic dispersive interaction. We also show how our gate can be accelerated using a shortcuts-to-adiabaticity approach, allowing one to achieve a gate that is both fast and relatively robust. We present a comprehensive theoretical analysis of the performance of our two-qubit gate in parametrically modulated superconducting circuits comprising two fluxonium qubits coupled to an auxiliary system.
Whole slide images in artificial intelligence applications in digital pathology: challenges and pitfalls

The use of digitized data in pathology research is rapidly increasing. The whole slide image (WSI) is an indispensable part of the visual examination of slides in digital pathology and artificial intelligence applications; therefore, the acquisition of WSI with the highest quality is essential. Unlike the conventional routine of pathology, the digital conversion of tissue slides and the differences in its use pose difficulties for pathologists. We categorized these challenges into three groups: before, during, and after the WSI acquisition. The problems before WSI acquisition are usually related to the quality of the glass slide and reflect all existing problems in the analytical process in pathology laboratories. WSI acquisition problems are dependent on the device used to produce the final image file. They may be related to the parts of the device that create an optical image or the hardware and software that enable digitization. Post-WSI acquisition issues are related to the final image file itself, which is the final form of this data, or the software and hardware that will use this file. Because of the digital nature of the data, most of the difficulties are related to the capabilities of the hardware or software. Being aware of the challenges and pitfalls of using digital pathology and AI will make pathologists' integration to the new technologies easier in their daily practice or research.
The mediating role of the perceived value on the relationships between customer satisfaction, customer loyalty and e-marketing

The online markets have been growing during the last near years, and many businesses have generated customer satisfaction and loyalty by developing their marketing activities. The main aim of this study is to review the related body of the literature to develop a thorough conceptual research framework including e-marketing in order to determine the respective antecedent factors of customer satisfaction and loyalty. Another study aim is examining the mediating role of the perceived value on the interrelationships between e-marketing, customer satisfaction and loyalty. Through a questionnaire survey, the study collected the data from various online shoppers in Jordan with about 482 participants. The study results indicated that e-marketing had a significant effect on customer satisfaction but did not generate customer loyalty, and the customer satisfaction had a significant effect on customer loyalty. Another key result is the perceived value mediated the relations between e-marketing and both customer satisfaction and customer loyalty. The study's respective implications offer new insights in marketing literature and conclude that the successful marketing activities should focus on the perceived value that is felt by the customers.
Do you love them now? Use and non-use of academic ebooks a decade later

This survey of graduate students' and faculty's use and non-use of ebooks is a follow-up to the data captured in 2011 from the same mid-sized public university in southeast Texas. In addition to providing a change comparison of faculty and graduate students' ebooks usage over the past decade, the study also aims to contribute to the growing ebook literature examining faculty and graduate students' perception and use of ebooks for different academic activities. Survey results showed that while the proportion of respondents who are library ebook users had increased significantly over the past decade, both ebook users and non-users' attitude towards e- and print books remained consistent. Most ebook users appreciated features such as keyword searches and copy and pasting, but the majority also valued the option to print out pages and chapters. Respondents' format preferences also shifted based on their academic needs, although some of the feature limitations that prompted some users to switch from or avoid library ebooks were caused by DRM restrictions that could be removed by publishers. The findings have implications beyond the institution for academic library ebook acquisition and communication with local user populations. Other marketing and future research implications of the findings are also discussed.
FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection

Due to the flexibility and ease of deployment of Field Programmable Gate Arrays (FPGA), more and more studies have been conducted on developing and optimizing target detection algorithms based on Convolutional Neural Networks (CNN) models using FPGAs. Still, these studies focus on improving the performance of the core algorithm and optimizing hardware structure, with few studies focusing on the unified architecture design and corresponding optimization techniques for the algorithm model, resulting in inefficient overall model performance. The essential reason is that these studies do not address arithmetic power, speed, and resource consistency. In order to solve this problem, we propose a deep learning acceleration core architecture based on FPGAs, which is designed for target detection algorithms with CNN models, using multi-channel parallelization of CNN network models to improve the arithmetic power, using scheduling tasks and intensive computation pipelining to meet the algorithm’s data bandwidth requirements and unifying the speed and area of the orchestrated computation matrix to save hardware resources. The proposed framework achieves 14 Frames Per Second (FPS) inference performance of the TinyYolo model at 5 Giga Operations Per Second (GOPS) with 30% higher running clock frequency, 2–4 times higher arithmetic power, and 28% higher Digital Signal Processing (DSP) resource utilization efficiency using less than 25% of FPGA resource usage.
Sparse regression for plasma physics

Many scientific problems can be formulated as sparse regression, i.e., regression onto a set of parameters when there is a desire or expectation that some of the parameters are exactly zero or do not substantially contribute. This includes many problems in signal and image processing, system identification, optimization, and parameter estimation methods such as Gaussian process regression. Sparsity facilitates exploring high-dimensional spaces while finding parsimonious and interpretable solutions. In the present work, we illustrate some of the important ways in which sparse regression appears in plasma physics and point out recent contributions and remaining challenges to solving these problems in this field. A brief review is provided for the optimization problem and the state-of-the-art solvers, especially for constrained and high-dimensional sparse regression.
Off-Resonant Dicke Quantum Battery: Charging by Virtual Photons

We investigate a Dicke quantum battery in the dispersive regime, where the photons trapped into a resonant cavity are way more energetic with respect to the two-level systems embedded into it. Under such off-resonant conditions, even an empty cavity can lead to the charging of the quantum battery through a proper modulation of the matter-radiation coupling. This counterintuitive behaviour has its roots in the effective interaction between two-level systems mediated by virtual photons emerging from the fluctuations of the quantum electromagnetic field. In order to properly characterize it, we address relevant figures of merit such as the stored energy, the time required to reach the maximum charging, and the averaged charging power. Moreover, the possibility of efficiently extracting energy in various ranges of parameters is discussed. The scaling of stored energy and power as a function of the number $N$ of two-level systems and for different values of the matter-radiation coupling is also discussed showing, in the strong coupling regime, performances in line with what reported for the Dicke quantum battery in the resonant regime.
Image Feature Fusion Method Based on Edge Detection

Deep learning-based image processing algorithms have developed rapidly in the past decade and have shown significant improvements to extract image features when both sufficient computing power and big data are accessible. Thus, rapid advances in applications such as facial recognition and autonomous driving have been one of the implementation areas. On the other hand, edges as a low-level prevalence feature in images with independent semantics are practically adapted to attain better outcomes. However, neural network-based image feature extraction focusing on texture rather than shape leads to insufficient accuracy. To address this issue, an edge feature extraction method utilizing both conventional operators such as HDE and Sobel and a deep learning-based method is proposed to classify and retrieve images with better accuracy outcomes. By doing so, a large amount of data needed to conduct deep learning-based methods is decreased, the transferability of the model is achieved, classification and retrieval accuracies are enhanced, and the data is compressed. All these better results are attained with benchmark data sets. As a result, all these are achieved by proposing a novel method.
Young adults’ experience of housing and real estate chatbots in India: effort expectancy moderated model

Purpose This study aims to recognize the role of information system (IS) model on young adults’ experience of housing and real estate chatbots. This model of IS takes into account the quality of information, the quality of system and the quality of service. Design/methodology/approach This study uses a sample frame for analysis which comprises young adult population in India, i.e. between the ages of 18 and 35. A questionnaire consisting of five components was used to collect information in a structured manner. The 386 responses thus collected were analysed using the structural equation model. Findings It was found that there is a significant influence of the quality of information, quality of system and quality of service on young adults’ experience of housing and real estate chatbots. The findings also showed that there is moderation role of effort expectancy between the quality parameters and young adults’ user experience of housing and real estate chatbots. Research limitations/implications This study focusses exclusively on the young adults from various parts of India. Future research can consider larger population categories across age groups and across sectors employing chatbots. Practical implications This study will enable in-depth understanding of IS model – quality dimensions’ relation with the user experience. In particular, housing and real estate organisations will profit from the expanded usage of artificial intelligence through chatbots for user correspondence and communication. Originality/value To the best of the authors’ knowledge, this study is first of its kind, as it investigates how IS model – quality dimensions affect the young adults’ experience of housing and real estate chatbots in India. This study also ventures into identifying the moderation role of effort expectancy between the quality dimensions as per IS model and young adults’ experience of housing and real estate chatbots. This study will be useful for the stakeholders of housing and real estate industry.
Hog Features Based Handwritten Bengali Numerals Recognition Using SVM Classifier: A Comparison with Hopfield Implementation

Handwritten digit recognition (reading by computer) is a process that gives the technological capabilities to a system to recognize handwritten numerals using Machine Learning. It is very tough to recognize handwritten characters because of the non-similarity in characters by different people. Also, there exists no standard technique available in the industry which can be applicable to provide innovative solutions to all kinds of handwritten characters. Hence, there is a requirement to understand the nature of the script. In order to complete the task, there is a need to use unique extraction methods and a classifier. This work implements a Histogram of Oriented Gradient (HOG) features with Support Vector Machine (SVM) classifier and a Hopfield model to recognize handwritten Bengali numerals into the class of ten segments. SVM works in the way of mapping the input elements to a set of the high-dimensional feature vector. This works both in the case of linearly and nonlinearly and separable data. The experiments have been done using 1200 handwritten digits for 10 different classes (0 to 9). The overall accuracy (productivity) obtained from the implementation is 89.5%. The experimental outcomes from the Hopfield model give an accuracy of 95.5%. Also, it is found the Hopfield model is more capable of handling noisy inputs.
Dark Energy and Electromagnetism

The study addresses the following question: what is the source of the Dark Energy? The study provides sound argumentations that Electromagnetic Waves, from separate sources, can consolidate, even though such consolidations seem to embed paradoxes. The study also proposes a physical experiment which might implement consolidations of Electromagnetic Waves, from separate sources. In addition to the above, the study also addresses paradoxes related to the Mutual Annihilation and the Pair Production processes. The study proposes a resolution for all these paradoxes which also concludes that most of the Dark Energy might be related to Electromagnetism and that the Electric Charge is also a form of Energy.