Reviewerly/exHarmony huggingface dataset:
Entanglement of three quantum memories via interference of three single photons

Quantum network has significant applications both practically and fundamentally. A hybrid architecture with photons and stationary nodes is highly promising. So far, experimental realizations are limited to two nodes with two photons. Going beyond state of the art by entangling many photons with many quantum nodes is highly appreciated. Here, we report an experiment realizing hybrid entanglement between three photons and three atomic-ensemble quantum memories. We make use of three similar setups, in each of which one pair of photon-memory entanglement with high overall efficiency is created via cavity enhancement. Through three-photon interference, the three quantum memories get entangled with the three photons. Via measuring the photons and applying feedforward, we heraldedly entangle the three memories. Our work demonstrates the largest size of hybrid memory-photon entanglement, which may be employed as a build block to construct larger and complex quantum network.
Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry

Design research is important for understanding and interrogating how emerging technologies shape human experience. However, design research with Machine Learning (ML) is relatively underdeveloped. Crucially, designers have not found a grasp on ML uncertainty as a design opportunity rather than an obstacle. The technical literature points to data and model uncertainties as two main properties of ML. Through post-phenomenology, we position uncertainty as one defining material attribute of ML processes which mediate human experience. To understand ML uncertainty as a design material, we investigate four design research case studies involving ML. We derive three provocative concepts: thingly uncertainty: ML-driven artefacts have uncertain, variable relations to their environments; pattern leakage: ML uncertainty can lead to patterns shaping the world they are meant to represent; and futures creep: ML technologies texture human relations to time with uncertainty. Finally, we outline design research trajectories and sketch a post-phenomenological approach to human-ML relations.
A scope of mobile health solutions in COVID-19 pandemics

COVID-19 has become an international emergency. The use of digital solutions can be effective in managing, preventing, and overcoming the further spread of infectious disease outbreaks. Accordingly, the use of mobile-health (m-health) technologies has the potential to promote public health. This review aimed to study the application of m-health solutions for the management of the COVID-19 outbreak.The search strategy was done in Medline (PubMed), Embase, IEEE, and Google Scholar by using related keywords to m-health and COVID-19 on July 6, 2020. English papers that used m-health technologies for the COVID-19 outbreak were included.Of the 2046 papers identified, 16 were included in this study. M-health had been used for various aims such as early detection, fast screening, patient monitoring, information sharing, education, and treatment in response to the COVID-19 outbreak. M-health solutions were classified into four use case categories: prevention, diagnosis, treatment, and protection. The mobile phone-based app and short text massaging were the most frequently used modalities, followed by wearables, portable screening devices, mobile-telehealth, and continuous telemetry monitor during the pandemics.It appears that m-health technologies played a positive role during the COVID-19 outbreak. Given the extensive capabilities of m-health solutions, investigation and use of all potential applications of m-health should be considered for combating the current Epidemics and mitigating its negative impacts.
Multiclass Prediction Model for Student Grade Prediction Using Machine Learning

Today, predictive analytics applications became an urgent desire in higher educational institutions. Predictive analytics used advanced analytics that encompasses machine learning implementation to derive high-quality performance and meaningful information for all education levels. Mostly know that student grade is one of the key performance indicators that can help educators monitor their academic performance. During the past decade, researchers have proposed many variants of machine learning techniques in education domains. However, there are severe challenges in handling imbalanced datasets for enhancing the performance of predicting student grades. Therefore, this paper presents a comprehensive analysis of machine learning techniques to predict the final student grades in the first semester courses by improving the performance of predictive accuracy. Two modules will be highlighted in this paper. First, we compare the accuracy performance of six well-known machine learning techniques namely Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (kNN), Logistic Regression (LR) and Random Forest (RF) using 1282 real student's course grade dataset. Second, we proposed a multiclass prediction model to reduce the overfitting and misclassification results caused by imbalanced multi-classification based on oversampling Synthetic Minority Oversampling Technique (SMOTE) with two features selection methods. The obtained results show that the proposed model integrates with RF give significant improvement with the highest f-measure of 99.5%. This proposed model indicates the comparable and promising results that can enhance the prediction performance model for imbalanced multi-classification for student grade prediction.
Blockchain Technologies in Logistics and Supply Chain Management: A Bibliometric Review

The emergence of blockchain technology has sparked significant attention from the supply chain management (SCM) and logistics communities. In this paper, we present the results from a thorough bibliometric review that analytically and objectively identifies the intellectual structure of this field, the seminal papers, and the most influential scholars. We employ a knowledge domain visualization technique to generate insights that go beyond other review studies on blockchain research within logistics and SCM. The analysis starts with selecting a total of 628 papers from Scopus and the Web of Science that were published during 2016–2020. The bibliometric analysis output demonstrates that the number of blockchain papers has rapidly increased since 2017. The most productive researchers are from the USA, China, and India. The top academic institutions contributing to the literature are also identified. Based on network analyses, we found that the literature concentrates mainly on the conceptualization of blockchain; its potentials for supply chain sustainability; its adoption triggers and barriers; and its role in supporting supply chain agility, trust, protection of intellectual property, and food/perishable supply chains. Besides systematically mapping the literature, we identify several research gaps and propose numerous actionable research directions for the future. This study enriches the extant blockchain literature, provides a timely snapshot of the current state of research, and examines the knowledge structure of blockchain research in logistics and SCM with the help of evidence-based scientometric methods.
CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations

The rise of deep learning in today’s applications entailed an increasing need in explaining the model’s decisions beyond prediction performances in order to foster trust and accountability. Recently, the field of explainable AI (XAI) has developed methods that provide such explanations for already trained neural networks. In computer vision tasks such explanations, termed heatmaps, visualize the contributions of individual pixels to the prediction. So far XAI methods along with their heatmaps were mainly validated qualitatively via human-based assessment, or evaluated through auxiliary proxy tasks such as pixel perturbation, weak object localization or randomization tests. Due to the lack of an objective and commonly accepted quality measure for heatmaps, it was debatable which XAI method performs best and whether explanations can be trusted at all. In the present work, we tackle the problem by proposing a ground truth based evaluation framework for XAI methods based on the CLEVR visual question answering task. Our framework provides a (1) selective, (2) controlled and (3) realistic testbed for the evaluation of neural network explanations. We compare ten different explanation methods, resulting in new insights about the quality and properties of XAI methods, sometimes contradicting with conclusions from previous comparative studies. The CLEVR-XAI dataset and the benchmarking code can be found at https://github.com/ahmedmagdiosman/clevr-xai.
Transfer learning: a friendly introduction

Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.
Choosing Linguistics over Vision to Describe Images

In this paper, we address the problem of automatically generating human-like descriptions for unseen images, given a collection of images and their corresponding human-generated descriptions. Previous attempts for this task mostly rely on visual clues and corpus statistics, but do not take much advantage of the semantic information inherent in the available image descriptions. Here, we present a generic method which benefits from all these three sources (i.e. visual clues, corpus statistics and available descriptions) simultaneously, and is capable of constructing novel descriptions. Our approach works on syntactically and linguistically motivated phrases extracted from the human descriptions. Experimental evaluations demonstrate that our formulation mostly generates lucid and semantically correct descriptions, and significantly outperforms the previous methods on automatic evaluation metrics. One of the significant advantages of our approach is that we can generate multiple interesting descriptions for an image. Unlike any previous work, we also test the applicability of our method on a large dataset containing complex images with rich descriptions.
Recurrent Adaptation Networks for Online Signature Verification

Online signature verification remains a challenging task owing to large intra-individual variability. To tackle this problem, in this paper, we propose to use recurrent neural networks (RNN) for representation learning in the dynamic time warping framework. Metric-based loss functions are designed explicitly to minimize intra-individual variability and enhance inter-individual variability and to guide the RNN in learning discriminative representations for online signatures. An RNN variant-gated auto regressive units-is proposed and shows a better generalization performance in our framework. Furthermore, we interpret the online signature verification problem as a meta-learning problem: one client is considered as one task, therefore, different clients compose the task space. Based on this formulation, we design an end-to-end trainable meta-layer that learns to adapt to different clients, allowing fast adaptation to new clients in the test stage. In addition, a new descriptor-the length-normalized path signature-is proposed to describe online signatures. Our proposed system achieves a state-of-the-art performance on three benchmark datasets, namely, MCYT-100, Mobisig, and e-BioSign.
Synthetic Gauge Field for Two-Dimensional Time-Multiplexed Quantum Random Walks

Temporal multiplexing provides an efficient and scalable approach to realize a quantum random walk with photons that can exhibit topological properties. But two-dimensional time-multiplexed topological quantum walks studied so far have relied on generalizations of the Su-Shreiffer-Heeger model with no synthetic gauge field. In this work, we demonstrate a two-dimensional topological quantum random walk where the nontrivial topology is due to the presence of a synthetic gauge field. We show that the synthetic gauge field leads to the appearance of multiple band gaps and, consequently, a spatial confinement of the quantum walk distribution. Moreover, we demonstrate topological edge states at an interface between domains with opposite synthetic fields. Our results expand the range of Hamiltonians that can be simulated using photonic quantum walks.
Motion Sickness Prediction in Stereoscopic Videos using 3D Convolutional Neural Networks

In this paper, we propose a three-dimensional (3D) convolutional neural network (CNN)-based method for predicting the degree of motion sickness induced by a 360° stereoscopic video. We consider the user's eye movement as a new feature, in addition to the motion velocity and depth features of a video used in previous work. For this purpose, we use saliency, optical flow, and disparity maps of an input video, which represent eye movement, velocity, and depth, respectively, as the input of the 3D CNN. To train our machine-learning model, we extend the dataset established in the previous work using two data augmentation techniques: frame shifting and pixel shifting. Consequently, our model can predict the degree of motion sickness more precisely than the previous method, and the results have a more similar correlation to the distribution of ground-truth sickness.
The mosh pit – An area for excess or a place of learning?

Spaces and places of learning are becoming more and more differentiated. Nowadays classical concert-halls are also considered as educational places. But what about other spaces where concerts take place? The article traces this question and highlights that heavy metal concerts can also be considered as settings for education processes.
Error Feedback Fixes SignSGD and other Gradient Compression Schemes

Sign-based algorithms (e.g. signSGD) have been proposed as a biased gradient compression technique to alleviate the communication bottleneck in training large neural networks across multiple workers. We show simple convex counter-examples where signSGD does not converge to the optimum. Further, even when it does converge, signSGD may generalize poorly when compared with SGD. These issues arise because of the biased nature of the sign compression operator. We then show that using error-feedback, i.e. incorporating the error made by the compression operator into the next step, overcomes these issues. We prove that our algorithm EF-SGD with arbitrary compression operator achieves the same rate of convergence as SGD without any additional assumptions. Thus EF-SGD achieves gradient compression for free. Our experiments thoroughly substantiate the theory and show that error-feedback improves both convergence and generalization. Code can be found at \url{https://github.com/epfml/error-feedback-SGD}.
A New Benchmark and Approach for Fine-grained Cross-media Retrieval

Cross-media retrieval is to return the results of various media types corresponding to the query of any media type. Existing researches generally focus on coarse-grained cross-media retrieval. When users submit an image of "Slaty-backed Gull" as a query, coarse-grained cross-media retrieval treats it as "Bird", so that users can only get the results of "Bird", which may include other bird species with similar appearance (image and video), descriptions (text) or sounds (audio), such as "Herring Gull". Such coarse-grained cross-media retrieval is not consistent with human lifestyle, where we generally have the fine-grained requirement of returning the exactly relevant results of "Slaty-backed Gull" instead of "Herring Gull". However, few researches focus on fine-grained cross-media retrieval, which is a highly challenging and practical task. Therefore, in this paper, we first construct a new benchmark for fine-grained cross-media retrieval, which consists of 200 fine-grained subcategories of the "Bird", and contains 4 media types, including image, text, video and audio. To the best of our knowledge, it is the first benchmark with 4 media types for fine-grained cross-media retrieval. Then, we propose a uniform deep model, namely FGCrossNet, which simultaneously learns 4 types of media without discriminative treatments. We jointly consider three constraints for better common representation learning: classification constraint ensures the learning of discriminative features for fine-grained subcategories, center constraint ensures the compactness characteristic of the features of the same subcategory, and ranking constraint ensures the sparsity characteristic of the features of different subcategories. Extensive experiments verify the usefulness of the new benchmark and the effectiveness of our FGCrossNet. The new benchmark and the source code of FGCrossNet will be made available at https://github.com/PKU-ICST-MIPL/FGCrossNet_ACMMM2019.
Quantum algorithms for jet clustering

Identifying jets formed in high-energy particle collisions requires solving optimization problems over potentially large numbers of final-state particles. In this work, we consider the possibility of using quantum computers to speed up jet clustering algorithms. Focusing on the case of electron-positron collisions, we consider a well-known event shape called thrust whose optimum corresponds to the most jet-like separating plane among a set of particles, thereby defining two hemisphere jets. We show how to formulate thrust both as a quantum annealing problem and as a Grover search problem. A key component of our analysis is the consideration of realistic models for interfacing classical data with a quantum algorithm. With a sequential computing model, we show how to speed up the well-known O(N^3) classical algorithm to an O(N^2) quantum algorithm, including the O(N) overhead of loading classical data from N final-state particles. Along the way, we also identify a way to speed up the classical algorithm to O(N^2 log N) using a sorting strategy inspired by the SISCone jet algorithm, which has no natural quantum counterpart. With a parallel computing model, we achieve O(N log N) scaling in both the classical and quantum cases. Finally, we consider the generalization of these quantum methods to other jet algorithms more closely related to those used for proton-proton collisions at the Large Hadron Collider.
Fusion of Video and Inertial Sensing for Deep Learning–Based Human Action Recognition

This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D convolutional neural network, respectively, for recognizing actions. Two types of fusion are considered-Decision-level fusion and feature-level fusion. Experiments are conducted using the publicly available dataset UTD-MHAD in which simultaneous video images and inertial signals are captured for a total of 27 actions. The results obtained indicate that both the decision-level and feature-level fusion approaches generate higher recognition accuracies compared to the approaches when each sensing modality is used individually. The highest accuracy of 95.6% is obtained for the decision-level fusion approach.
Performance Modeling of Hyperledger Sawtooth Blockchain

With the rapid development of blockchain platforms, it is important that different implementations are tested and analyzed for comparative purposes. One such implementation is Hyperledger Sawtooth, a new member of the Hyperledger family. Sawtooth blockchain is a permissioned implementation developed in part by Intel. While research has been done on Hyperledger Fabric, research on Sawtooth is not well documented. Using the Hyperledger Caliper benchmarking tool, we aim to test the performance of the blockchain and identify potential issues.
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References

The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multireference evaluation.Existing metrics have been shown to correlate poorly with human judgement, particularly in open-domain dialog.One alternative is to collect human annotations for evaluation, which can be expensive and time consuming.To demonstrate the effectiveness of multi-reference evaluation, we augment the test set of DailyDialog with multiple references.A series of experiments show that the use of multiple references results in improved correlation between several automatic metrics and human judgement for both the quality and the diversity of system output.
On Empirical Comparisons of Optimizers for Deep Learning

Selecting an optimizer is a central step in the contemporary deep learning pipeline. In this paper, we demonstrate the sensitivity of optimizer comparisons to the hyperparameter tuning protocol. Our findings suggest that the hyperparameter search space may be the single most important factor explaining the rankings obtained by recent empirical comparisons in the literature. In fact, we show that these results can be contradicted when hyperparameter search spaces are changed. As tuning effort grows without bound, more general optimizers should never underperform the ones they can approximate (i.e., Adam should never perform worse than momentum), but recent attempts to compare optimizers either assume these inclusion relationships are not practically relevant or restrict the hyperparameters in ways that break the inclusions. In our experiments, we find that inclusion relationships between optimizers matter in practice and always predict optimizer comparisons. In particular, we find that the popular adaptive gradient methods never underperform momentum or gradient descent. We also report practical tips around tuning often ignored hyperparameters of adaptive gradient methods and raise concerns about fairly benchmarking optimizers for neural network training.