Reviewerly/exHarmony huggingface dataset:
Factors Influencing the Adoption of Contact Tracing Applications: Protocol for a Systematic Review

Background Following the onset of the COVID-19 pandemic, digital contact tracing apps have become prevalent worldwide in a coordinated effort to curb the spread of COVID-19. However, their uptake has been low and slow due to privacy concerns, the lack of trust and motivational affordances, and their minimalist design. Objective The objective of this article is to present a protocol for a systematic review of the main factors, including facilitators and barriers, that influence the adoption of contact tracing apps. Methods We searched seven databases, namely, Scopus, CINAHL, PubMed (MEDLINE), IEEE Xplore Digital Library, Association for Computing Machinery (ACM) Digital Library, Web of Science, and Google Scholar, for relevant publications between October 30, 2020, and January 31, 2021. Three authors were involved in removing duplicates, screening, and selection of relevant articles according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-analysis Protocols) guidelines. Results Altogether, we retrieved 777 articles from the seven databases. As of May 14, 2021, we have completed the screening process and arrived at 13 eligible articles to be included in the systematic review. We hope to elicit, summarize, and report the main findings in the systematic review article by the end of August 2021. We expect to uncover facilitators and barriers related to app utility, data security, ease of use, and persuasive design that are deemed important to adoption of contact tracing apps. Conclusions The findings of the systematic review will help researchers to uncover the gaps in the adoption of contact tracing apps, and decision makers and designers to focus on the principal adoption factors necessary to create better and more effective contact tracing apps. International Registered Report Identifier (IRRID) DERR1-10.2196/28961
Quantum Encryption with Certified Deletion, Revisited: Public Key, Attribute-Based, and Classical Communication

Broadbent and Islam (TCC '20) proposed a quantum cryptographic primitive called quantum encryption with certified deletion. In this primitive, a receiver in possession of a quantum ciphertext can generate a classical certificate that the encrypted message has been deleted. Although their construction is information-theoretically secure, it is limited to the setting of one-time symmetric key encryption (SKE), where a sender and receiver have to share a common key in advance and the key can be used only once. Moreover, the sender has to generate a quantum state and send it to the receiver over a quantum channel in their construction. Deletion certificates are privately verifiable, which means a verification key for a certificate must be kept secret, in the definition by Broadbent and Islam. However, we can also consider public verifiability. In this work, we present various constructions of encryption with certified deletion.
Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples.In this paper, we focus on high-dimensional multi-class imbalanced problems.The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes.Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems.Nonetheless, few methods have been proposed to address the intersection of multiclass imbalanced and high-dimensional problems concurrently due to their intricate interactions.This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA).The Simulated Annealing (SA) algorithm is incorporated into GOA.SA is used to enhance the best solution found by the GOA algorithm.The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA.The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.
Quantum imaginary time evolution steered by reinforcement learning

Abstract The quantum imaginary time evolution is a powerful algorithm for preparing the ground and thermal states on near-term quantum devices. However, algorithmic errors induced by Trotterization and local approximation severely hinder its performance. Here we propose a deep reinforcement learning-based method to steer the evolution and mitigate these errors. In our scheme, the well-trained agent can find the subtle evolution path where most algorithmic errors cancel out, enhancing the fidelity significantly. We verified the method’s validity with the transverse-field Ising model and the Sherrington-Kirkpatrick model. Numerical calculations and experiments on a nuclear magnetic resonance quantum computer illustrate the efficacy. The philosophy of our method, eliminating errors with errors, sheds light on error reduction on near-term quantum devices.
Hand gesture recognition using 802.11ad mmWave sensor in the mobile device

We explore the feasibility of AI assisted hand-gesture recognition using 802.11ad 60GHz (mmWave) technology in smartphones. Range-Doppler information (RDI) is obtained by using pulse Doppler radar for gesture recognition. We built a prototype system, where radar sensing and WLAN communication waveform can coexist by time-division duplex (TDD), to demonstrate the real-time hand-gesture inference. It can gather sensing data and predict gestures within 100 milliseconds. First, we build the pipeline for the real-time feature processing, which is robust to occasional frame drops in the data stream. RDI sequence restoration is implemented to handle the frame dropping in the continuous data stream, and also applied to data augmentation. Second, different gestures RDI are analyzed, where finger and hand motions can clearly show distinctive features. Third, five typical gestures (swipe, palm-holding, pull-push, finger-sliding and noise) are experimented with, and a classification framework is explored to segment the different gestures in the continuous gesture sequence with arbitrary inputs. We evaluate our architecture on a large multi-person dataset and report > 95% accuracy with one CNN + LSTM model. Further, a pure CNN model is developed to fit to on-device implementation, which minimizes the inference latency, power consumption and computation cost. And the accuracy of this CNN model is more than 93% with only 2.29K parameters.
Spatial Labeling: Leveraging Spatial Layout for Improving Label Quality in Non-Expert Image Annotation

Non-expert annotators (who lack sufficient domain knowledge) are often recruited for manual image labeling tasks owing to the lack of expert annotators. In such a case, label quality may be relatively low. We propose leveraging the spatial layout for improving label quality in non-expert image annotation. In the proposed system, an annotator first spatially lays out the incoming images and labels them on an open space, placing related items together. This serves as a working space (spatial organization) for tentative labeling. During the process, the annotator observes and organizes the similarities and differences between the items. Finally, the annotator provides definitive labels to the images based on the results of the spatial layout. We ran a user study comparing the proposed method and a traditional non-spatial layout in an image labeling task. The results demonstrated that annotators can complete the labeling tasks more accurately using the spatial layout interface than the non-spatial layout interface.
Restoration method of sootiness mural images based on dark channel prior and Retinex by bilateral filter

Abstract Environmental changes and human activities can cause serious degradation of murals, where sootiness is one of the most common problems of ancient Chinese indoor murals. In order to improve the visual quality of the murals, a restoration method is proposed for sootiness murals based on dark channel prior and Retinex by bilateral filter using hyperspectral imaging technology. First, radiometric correction and denoising through band clipping and minimum noise fraction rotation forward and inverse transform were applied to the hyperspectral data of the sootiness mural to produce its denoised reflectance image. Second, a near-infrared band was selected from the reflectance image and combined with the green and blue visible bands to produce a pseudo color image for the subsequent sootiness removal processing. The near-infrared band is selected because it is better penetrating the sootiness layer to a certain extent comparing to other bands. Third, the sootiness covered on the pseudo color image was preliminarily removed by using the method of dark channel prior and by adjusting the brightness of the image. Finally, the Retinex by bilateral filter was performed on the image to get the final restored image, where the sootiness was removed. The results show that the images restored by the proposed method are superior in variance, average gradient, information entropy and gray scale contrast comparing to the results from the traditional methods of homomorphic filtering and Gaussian stretching. The results also show the highest score in comprehensive evaluation of edges, hue and structure; thus, the method proposed can support more potential studies or sootiness removal in real mural paintings with more detailed information. The method proposed shows strong evidence that it can effectively reduce the influence of sootiness on the moral images with more details that can reveal the original appearance of the mural and improve its visual quality.
Estimating the heavy metal concentrations in topsoil in the Daxigou mining area, China, using multispectral satellite imagery

Abstract A precise estimation of the heavy metal concentrations in soils using multispectral remote sensing technology is challenging. Herein, Landsat8 imagery, a digital elevation model, and geochemical data derived from soil samples are integrated to improve the accuracy of estimating the Cu, Pb, and As concentrations in topsoil, using the Daxigou mining area in Shaanxi Province, China, as a case study. The relationships between the three heavy metals and soil environmental factors were investigated. The optimal combination of factors associated with the elevated concentrations of each heavy metal was determined combining correlation analysis with collinearity tests. A back propagation network optimised using a genetic algorithm was trained with 80% of the data for samples and subsequently employed to estimate the heavy metal concentrations in the area. The validation results show that the RMSE of the proposed model is lower than those of the existing linear model and rule-based M5 model tree. From the spatial distribution map of the three metals concentrations using the proposed method, there are findings that high concentrations of the heavy metals studied occur in the mining area, across the slag storage area, on the sides of the road used for transporting ore materials, and along the base of slopes in the area. These findings are consistent with the survey results in the field. The validation and findings validate the effectiveness of the proposed method.
Adiabatic theorem revisited: The unexpectedly good performance of adiabatic passage

Adiabatic passage employs a slowly varying time-dependent Hamiltonian to control the evolution of a quantum system along the Hamiltonian eigenstates. For processes of finite duration, the exact time-evolving state may deviate from the adiabatic eigenstate at intermediate times, but in numerous applications it is observed that this deviation reaches a maximum and then decreases significantly towards the end of the process. We provide a straightforward theoretical explanation for this welcome but often unappreciated fact. Our analysis emphasizes a separate adiabaticity criterion for high-fidelity state-to-state transfer and it points to new effective shortcut strategies for near-adiabatic dynamics.
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering

State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples.This training scheme possibly explains empirical observations that these models achieve a high recall amongst their top few predictions, but a low overall accuracy, motivating the need for answer re-ranking.We develop a successful re-ranking approach (RECONSIDER) for span-extraction tasks that improves upon the performance of MRC models, even beyond large-scale pre-training.RE-CONSIDER is trained on positive and negative examples extracted from high confidence MRC model predictions, and uses in-passage span annotations to perform span-focused reranking over a smaller candidate set.As a result, RECONSIDER learns to eliminate close false positives, achieving a new extractive state of the art on four QA tasks, with 45.5% Exact Match accuracy on Natural Questions with real user questions, and 61.7% on TriviaQA.We will release all related data, models, and code 1 .
Target-Aware Data Augmentation for Stance Detection

The goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target.Despite substantial progress on this task, one of the remaining challenges is the scarcity of annotations.Data augmentation is commonly used to address annotation scarcity by generating more training samples.However, the augmented sentences that are generated by existing methods are either less diversified or inconsistent with the given target and stance label.In this paper, we formulate the data augmentation of stance detection as a conditional masked language modeling task and augment the dataset by predicting the masked word conditioned on both its context and the auxiliary sentence that contains target and label information.Moreover, we propose another simple yet effective method that generates target-aware sentence by replacing a target mention with the other.Experimental results show that our proposed methods significantly outperforms previous augmentation methods on 11 targets.
Corpus-Based Paraphrase Detection Experiments and Review

Paraphrase detection is important for a number of applications, including plagiarism detection, authorship attribution, question answering, text summarization, text mining in general, etc. In this paper, we give a performance overview of various types of corpus-based models, especially deep learning (DL) models, with the task of paraphrase detection. We report the results of eight models (LSI, TF-IDF, Word2Vec, Doc2Vec, GloVe, FastText, ELMO, and USE) evaluated on three different public available corpora: Microsoft Research Paraphrase Corpus, Clough and Stevenson and Webis Crowd Paraphrase Corpus 2011. Through a great number of experiments, we decided on the most appropriate approaches for text pre-processing: hyper-parameters, sub-model selection—where they exist (e.g., Skipgram vs. CBOW), distance measures, and semantic similarity/paraphrase detection threshold. Our findings and those of other researchers who have used deep learning models show that DL models are very competitive with traditional state-of-the-art approaches and have potential that should be further developed.
A Safari through FPGA-based Neural Network Compilation and Design Automation Flows

Thanks to the enormous computing power of GPUs, Machine Learning (ML) based on artificial neural networks has found its way into many important application fields. Sophisticated compiler infrastructures facilitate the task of mapping neural networks onto these accelerators. Recently, new developments have also led to compilation and design automation flows that target FPGA-based accelerators. Although not being as mature as their GPU counterparts, there exists a multitude of published and actively developed approaches with differing support levels for network classes, file formats, and target platforms. Neural network exchange file formats advance jointly with the modeling frameworks. In this paper, we take a quick safari through the jungle of neural network compilation flows for FPGA-based targets by reporting qualitative and quantitative metrics. For comparison, we study the classes of supported neural network architectures of each approach, and the corresponding compatibility of exchange formats, emphasizing ONNX, by examining available conversion tools. Besides, we look at several non-functional properties, including FPGA resource utilization and performance numbers for selected neural networks, but also soft criteria such as licensing, community support, and development activity. Finally, we also assess and discuss some deficiencies currently still affecting some approaches. We hope that our study supports interested readers to orient themselves in the jungle of available flows concerning both functionality and usability, as well as to guide further development and research activities in the endeavor of automated ML acceleration on FPGAs.
Personalized Response Generation via Generative Split Memory Network

Despite the impressive successes of generation and dialogue systems, how to endow a text generation system with particular personality traits to deliver more personalized responses remains under-investigated.In this work, we look at how to generate personalized responses for questions on Reddit by utilizing personalized user profiles and posting histories.Specifically, we release an open-domain single-turn dialog dataset made up of 1.5M conversation pairs together with 300k profiles of users and related comments.We then propose a memory network to generate personalized responses in dialogue that utilizes a novel mechanism of splitting memories: one for user profile meta attributes and the other for user-generated information like comment histories.Experimental results show the quantitative and qualitative improvements of our simple split memory network model over the state-of-the-art response generation baselines.The dataset and code are available here.
A Study of Class Meetings in VR: Student Experiences of Attending Lectures and of Giving a Project Presentation

We study student experiences of social VR for remote instruction, with students attending class from home. The study evaluates student experiences when: (1) viewing remote lectures with VR headsets, (2) viewing with desktop displays, (3) presenting with VR headsets, and (4) reflecting on several weeks of VR-based class attendance. Students rated factors such as presence, social presence, simulator sickness, communication methods, avatar and application features, and tradeoffs with other remote approaches. Headset-based viewing and presenting produced higher presence than desktop viewing, but had less-clear impact on overall experience and on most social presence measures. We observed higher attentional allocation scores for headset-based presenting than for both viewing methods. For headset VR, there were strong negative correlations between simulator sickness (primarily reported as general discomfort) and ratings of co-presence, overall experience, and some other factors. This suggests that comfortable users experienced substantial benefits of headset viewing and presenting, but others did not. Based on the type of virtual environment, student ratings, and comments, reported discomfort appears related to physical ergonomic factors or technical problems. Desktop VR appears to be a good alternative for uncomfortable students, and students report that they prefer a mix of headset and desktop viewing. We additionally provide insight from students and a teacher about possible improvements for VR class technology, and we summarize student opinions comparing viewing and presenting in VR to other remote class technologies.
Variational quantum solver employing the PDS energy functional

Recently a new class of quantum algorithms that are based on the quantum computation of the connected moment expansion has been reported to find the ground and excited state energies. In particular, the Peeters-Devreese-Soldatov (PDS) formulation is found variational and bearing the potential for further combining with the existing variational quantum infrastructure. Here we find that the PDS formulation can be considered as a new energy functional of which the PDS energy gradient can be employed in a conventional variational quantum solver. In comparison with the usual variational quantum eigensolver (VQE) and the original static PDS approach, this new variational quantum solver offers an effective approach to navigate the dynamics to be free from getting trapped in the local minima that refer to different states, and achieve high accuracy at finding the ground state and its energy through the rotation of the trial wave function of modest quality, thus improves the accuracy and efficiency of the quantum simulation. We demonstrate the performance of the proposed variational quantum solver for toy models, H$_2$ molecule, and strongly correlated planar H$_4$ system in some challenging situations. In all the case studies, the proposed variational quantum approach outperforms the usual VQE and static PDS calculations even at the lowest order. We also discuss the limitations of the proposed approach and its preliminary execution for model Hamiltonian on the NISQ device.
Inclusive Online Teaching and Digital Learning: Lessons Learned in the Time of Pandemic and Beyond

When the COVID-19 pandemic swept across the world in 2020 and into 2021, the entire system of education faced the most challenging task to provide education to students using virtual instruction. Within the United States specifically, the pandemic transformed teaching. Teachers were and have continued to be compelled to learn digital technology and integrate varied digital tools into their instruction. As guest editors, Eric and I had the opportunity to reflect on the many instructional challenges and valuable lessons learned about virtual teaching and learning in k-12 and higher education. One of the biggest lessons observed was exposure of huge equity gaps between the tech haves and have nots, regarding access to digital devices and reliable Wi-Fi. It was from this observation that the call for proposals of this special issue was developed. What does inclusion look like in the era of digital and virtual teaching? With this in mind, we were tasked to coedit this special issue of JCSR focusing on the theme “Inclusive Curriculum in the Era of Digital &amp; Virtual Learning.” We had the opportunity to review five exemplary articles responding to the theme.
Quantum Fourier transform is the building block for creating entanglement

Abstract This study demonstrates entanglement can be exclusively constituted by quantum Fourier transform (QFT) blocks. A bridge between entanglement and QFT will allow incorporating a spectral analysis to the already traditional temporal approach of entanglement, which will result in the development of new more performant, and fault-tolerant protocols to be used in quantum computing as well as quantum communication, with particular emphasis in the future quantum Internet.