Reviewerly/exHarmony huggingface dataset:
Supporting Deep Neural Network Safety Analysis and Retraining Through Heatmap-Based Unsupervised Learning

Deep neural networks (DNNs) are increasingly important in safety-critical systems, for example in their perception layer to analyze images. Unfortunately, there is a lack of methods to ensure the functional safety of DNN-based components. We observe three major challenges with existing practices regarding DNNs in safety-critical systems: (1) scenarios that are underrepresented in the test set may lead to serious safety violation risks, but may, however, remain unnoticed; (2) characterizing such high-risk scenarios is critical for safety analysis; (3) retraining DNNs to address these risks is poorly supported when causes of violations are difficult to determine. To address these problems in the context of DNNs analyzing images, we propose HUDD, an approach that automatically supports the identification of root causes for DNN errors. HUDD identifies root causes by applying a clustering algorithm to heatmaps capturing the relevance of every DNN neuron on the DNN outcome. Also, HUDD retrains DNNs with images that are automatically selected based on their relatedness to the identified image clusters. We evaluated HUDD with DNNs from the automotive domain. HUDD was able to identify all the distinct root causes of DNN errors, thus supporting safety analysis. Also, our retraining approach has shown to be more effective at improving DNN accuracy than existing approaches.
Information leakage resistant quantum dialogue with single photons in both polarization and spatial-mode degrees of freedom

In this paper, a novel quantum dialogue (QD) protocol is proposed based on single photons in both polarization and spatial-mode degrees of freedom. In the proposed QD protocol, the initial states of single photons in both polarization and spatial-mode degrees of freedom used for encoding are privately shared between two communicants through the direct transmissions of their auxiliary counterparts from one communicant to another. As a result, the information leakage problem is avoided. Moreover, the detailed security analysis also shows that the proposed QD protocol can resist Eve’s several famous active attacks, such as the Trojan horse attack, the intercept-resend attack, the measure-resend attack and the entangle-measure attack. The proposed QD protocol only needs single photons in both polarization and spatial-mode degrees of freedom as quantum resource and adopts single-photon measurements. As a result, it is feasible in practice as the preparation and the measurement of a single photon in both polarization and spatial-mode degrees of freedom can be accomplished with current experimental techniques.
Algorithmic Agency and Autonomy in Archaeological Practice

Abstract A key development in archaeology is the increasing agency of the digital tools brought to bear on archaeological practice. Roles and tasks that were previously thought to be uncomputable are beginning to be digitalized, and the presumption that computerization is best suited to well-defined and restricted tasks is starting to break down. Many of these digital devices seek to reduce routinized and repetitive work in the office environment and in the field. Others incorporate data-driven methods to represent, store, and manipulate information in order to undertake tasks previously thought to be incapable of being automated. Still others substitute the human component in environments which would be otherwise be inaccessible or dangerous. Whichever applies, separately or in combination, such technologies are typically seen as black-boxing practice with often little or no human intervention beyond the allocation of their inputs and subsequent incorporation of their outputs in analyses. This paper addresses the implications of this shift to algorithmic automated practices for archaeology and asks whether there are limits to algorithmic agency within archaeology. In doing so, it highlights several challenges related to the relationship between archaeologists and their digital devices.
On-Demand Storage and Retrieval of Microwave Photons Using a Superconducting Multiresonator Quantum Memory

We realize on-demand storage and retrieval of weak coherent microwave photon pulses at the single-photon level. A superconducting multiresonator system which is composed of a set of frequency-tunable coplanar waveguide resonators is implemented as the quantum memory. By dynamically tuning the resonant frequencies of the resonators, we achieve tunable memory bandwidth from 10 to 55 MHz, with well preserved phase coherence. We further demonstrate on-demand storage and retrieval of a time-bin flying qubit. This result opens up a prospect to integrate our chip-based quantum memory with the state-of-the-art superconducting quantum circuit technology for quantum information processing.
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model

Recently, there is an effort to extend finegrained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions.A key challenge for this ultra-fine entity typing task is that human annotated data are extremely scarce, and the annotation ability of existing distant or weak supervision approaches is very limited.To remedy this problem, in this paper, we propose to obtain training data for ultra-fine entity typing by using a BERT Masked Language Model (MLM).Given a mention in a sentence, our approach constructs an input for the BERT MLM so that it predicts context dependent hypernyms of the mention, which can be used as type labels.Experimental results demonstrate that, with the help of these automatically generated labels, the performance of an ultra-fine entity typing model can be improved substantially.We also show that our approach can be applied to improve traditional fine-grained entity typing after performing simple type mapping.
Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI

Abstract Purpose Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis of neurological diseases. Recently, deep learning methods for unsupervised anomaly detection (UAD) have been proposed for the analysis of brain MRI. These methods rely on healthy brain MRIs and eliminate the requirement of pixel-wise annotated data compared to supervised deep learning. While a wide range of methods for UAD have been proposed, these methods are mostly 2D and only learn from MRI slices, disregarding that brain lesions are inherently 3D and the spatial context of MRI volumes remains unexploited. Methods We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance compared to learning from slices. We evaluate and compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Results Using two publicly available segmentation data sets for evaluation, 3D VAEs outperform their 2D counterpart, highlighting the advantage of volumetric context. Also, our 3D erasing methods allow for further performance improvements. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE. Conclusions We propose 3D deep learning methods for UAD in brain MRI combined with 3D erasing and demonstrate that 3D methods clearly outperform their 2D counterpart for anomaly segmentation. Also, our spatial erasing method allows for further performance improvements and reduces the requirement for large data sets.
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors

In this paper, a marker-based, single-person optical motion capture method (DeepMoCap) is proposed using multiple spatio-temporally aligned infrared-depth sensors and retro-reflective straps and patches (reflectors). DeepMoCap explores motion capture by automatically localizing and labeling reflectors on depth images and, subsequently, on 3D space. Introducing a non-parametric representation to encode the temporal correlation among pairs of colorized depthmaps and 3D optical flow frames, a multi-stage Fully Convolutional Network (FCN) architecture is proposed to jointly learn reflector locations and their temporal dependency among sequential frames. The extracted reflector 2D locations are spatially mapped in 3D space, resulting in robust 3D optical data extraction. The subject’s motion is efficiently captured by applying a template-based fitting technique on the extracted optical data. Two datasets have been created and made publicly available for evaluation purposes; one comprising multi-view depth and 3D optical flow annotated images (DMC2.5D), and a second, consisting of spatio-temporally aligned multi-view depth images along with skeleton, inertial and ground truth MoCap data (DMC3D). The FCN model outperforms its competitors on the DMC2.5D dataset using 2D Percentage of Correct Keypoints (PCK) metric, while the motion capture outcome is evaluated against RGB-D and inertial data fusion approaches on DMC3D, outperforming the next best method by 4 . 5 % in total 3D PCK accuracy.
Examination of WAN traffic characteristics in a large-scale data center network

Large cloud service providers have built an increasing number of geo-distributed data centers (DCs) connected by WAN to host their diverse services. While we have seen a large body of work on traffic engineering of WAN, the WAN traffic characteristics of production DC networks remain not well understood. In this paper, we report on the network traffic observed in Baidu's DC network (DCN) that consists of tens of geo-distributed DCs. Baidu hosts both traditional services like Web and Computing, as well as emerging services, such as Analytics, AI, and Map. We analyze WAN traffic characteristics in Baidu's DCN from the perspectives of traffic demands, traffic communication among DCs, and traffic characteristics of diverse services. Specifically, we focus on the disparity that might exist among different types of services. We also discuss the implications of our findings for WAN traffic engineering, fabric design, and service deployment.
Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications.
Impacts of Augmented Reality Apps on First Graders’ Motivation and Performance in English Vocabulary Learning

The education community has gradually recognized the value of augmented reality (AR) technology and its positive impacts on student learning performance. Still, few studies have proposed specific solutions and results to confirm this phenomenon for first graders in English vocabulary learning. Hence, this research explores the effects of AR apps on first graders’ English learning motivation and performance in addition to analyzing the moderating effects of learning styles. For this purpose, the class was divided into the experimental group that used an AR app in learning and a control group that used traditional learning methods. A series of stochastics analyses were carried out. Based on the results, this study finds that incorporating AR apps into English vocabulary learning can significantly improve first graders’ motivation and English vocabulary learning performance. The study’s findings will be beneficial to educational practitioners and researchers in developing instructional theories, strategies, and materials and improving elementary students’ learning motivation and performance.
Artificial intelligence, big data, and blockchain in food safety

Abstract Food safety plays an essential role in our daily lives, and it becomes serious with the development of worldwide trade. To tackle the food safety issues, many advanced technologies have been developed to monitor the process of the food industry (FI) to ensure food safety, including the process of food production, processing, transporting, storage, and retailing. These technologies are often referred to as artificial intelligence (AI), big data, and blockchain, which have been widely applied in many research areas. In this review, we introduce these technologies and their applications in the food safety domain. Firstly, basic concepts of these technologies are presented. Then, applications for food safety from a data perspective based on these technologies are analyzed. Finally, future challenges of the applications of AI, big data, and blockchain are discussed.
A novel learning algorithm based on computing the rules’ desired outputs of a TSK fuzzy neural network with non-separable fuzzy rules

In this paper, a novel stepwise learning approach based on estimating desired premise parts' outputs by solving a constrained optimization problem is proposed. This learning approach does not require backpropagating the output error to learn the premise parts' parameters. Instead, the near best output values of the rules premise parts are estimated and their parameters are changed to reduce the error between current premise parts' outputs and the estimated desired ones. Therefore, the proposed learning method avoids error backpropagation, which lead to vanishing gradient and consequently getting stuck in a local optimum. The proposed method does not need any initialization method. This learning method is utilized to train a new Takagi-Sugeno-Kang (TSK) Fuzzy Neural Network with correlated fuzzy rules including many parameters in both premise and consequent parts, avoiding getting stuck in a local optimum due to vanishing gradient. To learn the proposed network parameters, first, a constrained optimization problem is introduced and solved to estimate the desired values of premise parts' output values. Next, the error between these values and the current ones is utilized to adapt the premise parts' parameters based on the gradient-descent (GD) approach. Afterward, the error between the desired and network's outputs is used to learn consequent parts' parameters by the GD method. The proposed paradigm is successfully applied to real-world time-series prediction and regression problems. According to experimental results, its performance outperforms other methods with a more parsimonious structure.
Optimal control of stimulated Raman adiabatic passage in a superconducting qudit

Abstract Stimulated Raman adiabatic passage (STIRAP) is a widely used protocol to realize high-fidelity and robust quantum control in various quantum systems. However, further application of this protocol in superconducting qubits is limited by population leakage caused by the only weak anharmonicity. Here, we introduce an optimally controlled shortcut-to-adiabatic (STA) technique to speed-up the STIRAP protocol in a superconducting qudit. By modifying the shapes of the STIRAP pulses, we experimentally realize a fast (32 ns) and high-fidelity (0.996 ± 0.005) quantum state transfer. In addition, we demonstrate that our protocol is robust against control parameter perturbations. Our stimulated Raman shortcut-to-adiabatic passage transition provides an efficient and practical approach for quantum information processing.
E-Learning Development Based on Internet of Things and Blockchain Technology during COVID-19 Pandemic

The suspension of institutions around the world in early 2020 due to the COVID-19 virus did not stop the learning process. E-learning concepts and digital technologies enable students to learn from a safe distance while continuing their educational pursuits. Currently, the Internet of Things (IoT) is one of the most rapidly increasing technologies in today’s digital world; and e-learning is one of the most powerful learning methods available. In today’s world, smart devices and new technologies assist teachers in concentrating on new models of student learning while avoiding time wastage. By examining the characteristics of the Internet of Things and the challenges that exist in the field of e-learning, the potential functions, benefits, and advancements of utilizing the Internet of Things in online education are identified and discussed. This article examines the existing and future condition of the Internet of Things world as it pertains to the topic of education and sophisticated capabilities available through the Internet of Things that enable the application of e-learning after an architecture has been designed. Students’ pulse rates, brain waves, and skin resistance are measured in real time by a collection of IoT sensors, including cameras, microphones, and wearable gadgets. By utilizing the proposed architecture, universities can change their distance learning tactics to maximize resources and boost efficiency without changing their overall academic activities. According to the study’s findings, e-learning has a favorable and statistically significant impact on students’ flexibility, learning experience, educational productivity, and overall quality of education.
EasyFL: A Low-Code Federated Learning Platform for Dummies

Academia and industry have developed several platforms to support the popular privacy-preserving distributed learning method -- Federated Learning (FL). However, these platforms are complex to use and require a deep understanding of FL, which imposes high barriers to entry for beginners, limits the productivity of researchers, and compromises deployment efficiency. In this paper, we propose the first low-code FL platform, EasyFL, to enable users with various levels of expertise to experiment and prototype FL applications with little coding. We achieve this goal while ensuring great flexibility and extensibility for customization by unifying simple API design, modular design, and granular training flow abstraction. With only a few lines of code, EasyFL empowers them with many out-of-the-box functionalities to accelerate experimentation and deployment. These practical functionalities are heterogeneity simulation, comprehensive tracking, distributed training optimization, and seamless deployment. They are proposed based on challenges identified in the proposed FL life cycle. Compared with other platforms, EasyFL not only requires just three lines of code (at least 10x lesser) to build a vanilla FL application but also incurs lower training overhead. Besides, our evaluations demonstrate that EasyFL expedites distributed training by 1.5x. It also improves the efficiency of deployment. We believe that EasyFL will increase the productivity of researchers and democratize FL to wider audiences.
Applying Federated Learning in Software-Defined Networks: A Survey

Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.
The possibility of sports industry business model innovation based on blockchain technology: Evaluation of the innovation efficiency of listed sports companies

The combination of blockchain and the sports industry is bound to be a trend in terms of innovation and the development of sports industry in the near future. Therefore, after analyzing the problems existing in the current business model of the sports industry, this paper takes 200 innovation indicators of 50 listed companies in the sports industry, among which 15 use blockchain and 35 do not use, as research samples and uses a three-stage DEA model to calculate their innovation efficiency. The results show that sports companies that use blockchain have better performance in terms of innovation efficiency than do those that do not, which illustrates the necessity of blockchain-based business model innovation. Then, by focusing on the internal structure and the case of the " vSport blockchain", this work shows that this business model innovation is feasible. The article first proposes a blockchain-based sports industry business model and then uses a combination of empirical and case studies to prove the necessity and feasibility of sports industry business model innovation.