Reviewerly/exHarmony huggingface dataset:
Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers

Abstract Objective To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices. Materials and Methods Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app. Results We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84–89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age. Discussion We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection. Conclusion Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.
Implementing a Hand Gesture Recognition System Based on Range-Doppler Map

There have been several studies of hand gesture recognition for human-machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range, velocity, and angle. With these signatures, we can customize our system to different scenarios. We proposed an end-to-end training deep learning model (neural network and long short-term memory), that extracts the transformed radar signals into features and classifies the extracted features into hand gesture labels. In our training data collecting effort, a camera is used only to support labeling hand gesture data. The accuracy of our model can reach 98%.
Condensing Graphs via One-Step Gradient Matching

As training deep learning models on large dataset takes a lot of time and resources, it is desired to construct a small synthetic dataset with which we can train deep learning models sufficiently. There are recent works that have explored solutions on condensing image datasets through complex bi-level optimization. For instance, dataset condensation (DC) matches network gradients w.r.t. large-real data and small-synthetic data, where the network weights are optimized for multiple steps at each outer iteration. However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved nested optimization. To bridge the gap, we investigate efficient dataset condensation tailored for graph datasets where we model the discrete graph structure as a probabilistic model. We further propose a one-step gradient matching scheme, which performs gradient matching for only one single step without training the network weights. Our theoretical analysis shows this strategy can generate synthetic graphs that lead to lower classification loss on real graphs. Extensive experiments on various graph datasets demonstrate the effectiveness and efficiency of the proposed method. In particular, we are able to reduce the dataset size by 90% while approximating up to 98% of the original performance and our method is significantly faster than multi-step gradient matching (e.g. 15x in CIFAR10 for synthesizing 500 graphs). Code is available at \url{https://github.com/amazon-research/DosCond}.
Short-Term Memory Sampling for Spread Measurement in High-Speed Networks

Per-flow spread measurement in high-speed networks can provide indispensable information to many practical applications. However, it is challenging to measure millions of flows at line speed because on-chip memory modules cannot simultaneously provide large capacity and large bandwidth. The prior studies address this mismatch by entirely using on-chip compact data structures or utilizing off-chip space to assist limited on-chip memory. Nevertheless, their on-chip data structures record massive transient elements, each of which only appears in a short time interval in a long-period measurement task, and thus waste significant on-chip space. This paper presents short-term memory sampling, a novel spread estimator that samples new elements while only holding elements for short periods. Our estimator can work with tiny on-chip space and provide accurate estimations for online queries. The key of our design is a short-term memory duplicate filter that reports new elements and filters duplicates effectively while allowing incoming elements to override the stale elements to reduce on-chip memory usage. We implement our approach on a NetFPGA-equipped prototype. Experimental results based on real Internet traces show that, compared to the state-of-the-art, short-term memory sampling reduces up to 99% of on-chip memory usage when providing the same probabilistic assurance on spread-estimation error.
Fast and Constrained Absent Keyphrase Generation by Prompt-Based Learning

Generating absent keyphrases, which do not appear in the input document, is challenging in the keyphrase prediction task. Most previous works treat the problem as an autoregressive sequence-to-sequence generation task, which demonstrates promising results for generating grammatically correct and fluent absent keyphrases. However, such an end-to-end process with a complete data-driven manner is unconstrained, which is prone to generate keyphrases inconsistent with the input document. In addition, the existing autoregressive decoding method makes the generation of keyphrases must be done from left to right, leading to slow speed during inference. In this paper, we propose a constrained absent keyphrase generation method in a prompt-based learning fashion. Specifically, the prompt will be created firstly based on the keywords, which are defined as the overlapping words between absent keyphrase and document. Then, a mask-predict decoder is used to complete the absent keyphrase on the constraint of prompt. Experiments on keyphrase generation benchmarks have demonstrated the effectiveness of our approach. In addition, we evaluate the performance of constrained absent keyphrases generation from an information retrieval perspective. The result shows that our approach can generate more consistent keyphrases, which can improve document retrieval performance. What’s more, with a non-autoregressive decoding manner, our model can speed up the absent keyphrase generation by 8.67× compared with the autoregressive method.
Laneformer: Object-Aware Row-Column Transformers for Lane Detection

We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in autonomous driving. The dominant paradigms rely on purely CNN-based architectures which often fail in incorporating relations of long-range lane points and global contexts induced by surrounding objects (e.g., pedestrians, vehicles). Inspired by recent advances of the transformer encoder-decoder architecture in various vision tasks, we move forwards to design a new end-to-end Laneformer architecture that revolutionizes the conventional transformers into better capturing the shape and semantic characteristics of lanes, with minimal overhead in latency. First, coupling with deformable pixel-wise self-attention in the encoder, Laneformer presents two new row and column self-attention operations to efficiently mine point context along with the lane shapes. Second, motivated by the appearing objects would affect the decision of predicting lane segments, Laneformer further includes the detected object instances as extra inputs of multi-head attention blocks in the encoder and decoder to facilitate the lane point detection by sensing semantic contexts. Specifically, the bounding box locations of objects are added into Key module to provide interaction with each pixel and query while the ROI-aligned features are inserted into Value module. Extensive experiments demonstrate our Laneformer achieves state-of-the-art performances on CULane benchmark, in terms of 77.1% F1 score. We hope our simple and effective Laneformer will serve as a strong baseline for future research in self-attention models for lane detection.
Explore Inter-contrast between Videos via Composition for Weakly Supervised Temporal Sentence Grounding

Weakly supervised temporal sentence grounding aims to temporally localize the target segment corresponding to a given natural language query, where it provides video-query pairs without temporal annotations during training. Most existing methods use the fused visual-linguistic feature to reconstruct the query, where the least reconstruction error determines the target segment. This work introduces a novel approach that explores the inter-contrast between videos in a composed video by selecting components from two different videos and fusing them into a single video. Such a straightforward yet effective composition strategy provides the temporal annotations at multiple composed positions, resulting in numerous videos with temporal ground-truths for training the temporal sentence grounding task. A transformer framework is introduced with multi-tasks training to learn a compact but efficient visual-linguistic space. The experimental results on the public Charades-STA and ActivityNet-Caption dataset demonstrate the effectiveness of the proposed method, where our approach achieves comparable performance over the state-of-the-art weakly-supervised baselines. The code is available at https://github.com/PPjmchen/Composition_WSTG.
Conditional Hyper-Network for Blind Super-Resolution With Multiple Degradations

Although single-image super-resolution (SISR) methods have achieved great success on single degradation, they still suffer performance drop with multiple degrading effects in real scenarios. Recently, some blind and non-blind models for multiple degradations have been explored. However, those methods usually degrade significantly for distribution shifts between the training and test data. Towards this end, we propose a conditional meta-network framework (named CMDSR) for the first time, which helps SR framework learn how to adapt to changes in input distribution. We extract degradation prior at task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet). Specifically, the ConditionNet of our framework first learns the degradation prior from a support set, which is composed of a series of degraded image patches from the same task. Then the adaptive BaseNet rapidly shifts its parameters according to the conditional features. Moreover, in order to better extract degradation prior, we propose a task contrastive loss to decrease the inner-task distance and increase the cross-task distance between task-level features. Without predefining degradation maps, our blind framework can conduct one single parameter update to yield considerable SR results. Extensive experiments demonstrate the effectiveness of CMDSR over various blind, even non-blind methods. The flexible BaseNet structure also reveals that CMDSR can be a general framework for large series of SISR models. Our code is available at \url{https://github.com/guanghaoyin/CMDSR}.
Artificial Intelligence in Education: Fears and Faiths

Artificial Intelligence (AI) aims to develop models with human-like cognitive functions. Since its conception in the mid-50s, it has achieved big success in almost all areas and domains, starting from games to autonomous robotic surgery, and it is evolving rapidly. Indeed, AI-based machines pervade all domains and it is used for many purposes. In developing countries, it is widely used in almost all everyday life tasks. Yet, the idea that machines can act as humans and make decisions on behalf of individuals scares many people and raises many concerns and controversies. This tends to be the case in Morocco as well. Over the past few years, AI has made its way in the field of education and it is revolutionizing it. In this survey study, we probed the opinions of Moroccans about AI and their fears and hopes towards AI and its use in education. The majority of the respondents to the survey expressed serious worries about the future of using AI, especially in the early childhood education. Nevertheless, they seem to be rather cautiously optimistic about using AI in providing technical assistance in teaching-related tasks.
Workaround prediction of cloud alarms using machine learning

Cloud-based systems implies to applications, resources or services provided to users as per their requirement through the Internet using a cloud computing provider's server.These clouds triggers alarm events to indicate the health of system.Monitoring these alarms is essential for maintaining the health and continuous functioning of cloud.Because of humungous alarms triggered on daily basis, notifying critical alarms in time and taking required action is quite challenging task.In this paper machine learning model is implemented using decision tree classifier to analyse each alarm and predict if any action required for that alarm or not and also notify the concerned team via creating JIRA tickets.
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction

Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations, and consequently rich interactions across the constituent entities and relations.While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly represent it with entities, and novelly align them to identify valid triples.We perform comprehensive experiments 1 on document-level relation extraction and joint entity and relation extraction along with ablations to demonstrate the advantage of the proposed method.
CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation

Building appropriate scenarios to meet the personalized demands of different user groups is a common practice. Despite various scenario brings personalized service, it also leads to challenges for the recommendation on multiple scenarios, especially the scenarios with limited traffic. To give desirable recommendation service for all scenarios and reduce the cost of resource consumption, how to leverage the information from multiple scenarios to construct a unified model becomes critical. Unfortunately, the performance of existing multi-scenario recommendation approaches is poor since they introduce unnecessary information from other scenarios to target scenario. In this paper, we show it is possible to selectively utilize the information from different scenarios to construct the scenario-aware estimators in a unified model. Specifically, we first do analysis on multi-scenario modeling with causal graph from the perspective of users and modeling processes, and then propose the Causal Inspired Intervention (CausalInt) framework for multi-scenario recommendation. CausalInt consists of three modules: (1) Invariant Representation Modeling module to squeeze out the scenario-aware information through disentangled representation learning and obtain a scenario-invariant representation; (2) Negative Effects Mitigating module to resolve conflicts between different scenarios and conflicts between scenario-specific and scenario-invariant representations via gradient based orthogonal regularization and model-agnostic meta learning, respectively; (3) Inter-Scenario Transferring module designs a novel TransNet to simulate a counterfactual intervention and effectively fuse the information from other scenarios. Offline experiments over two real-world dataset and online A/B test are conducted to demonstrate the superiority of CausalInt.
Exploring Transformer and Multilabel Classification for Remote Sensing Image Captioning

High-resolution remote sensing images are now available with the progress of remote sensing technology. With respect to popular remote sensing tasks like scene classification, image captioning provides comprehensible information about such images by summarizing the image content in human-readable text. Most existing remote sensing image captioning methods are based on deep learning-based encoder-decoder frameworks, using Convolutional Neural Network or Recurrent Neural Network as the backbone of such frameworks. Such frameworks show a limited capability to analyze sequential data and cope with the lack of captioned remote sensing training images. Recently introduced Transformer architecture exploits self-attention to obtain superior performance for sequence-analysis tasks. Inspired by this, in this work, we employ a Transformer as an encoder-decoder for remote sensing image captioning. Moreover, to deal with the limited training data, an auxiliary decoder is used that further helps the encoder in the training process. The auxiliary decoder is trained for multi-label scene classification due to its conceptual similarity to image captioning and capability of highlighting semantic classes. To the best of our knowledge, this is the first work exploiting multi-label classification to improve remote sensing image captioning. Experimental results on the UC Merced caption data set show the efficacy of the proposed method. The implementation details can be found in https://gitlab.lrz.de/ai4eo/captioningMultilabel.
Open Science: Challenges, Possible Solutions and the Way Forward

Everyone agrees that scientific communication should be free for all. Unfortunately, accessing publications from many reputed journals comes at a high cost—a cost that many researchers and institutions cannot afford. Although, open-access publication model is considered by many as a possible route to ensure that science is free for all; however, it is fraught with its own challenges. This review attempts at exploring the possibilities of keeping science accessible. Firstly, we re-visit the meaning of “open science” as a comprehensive concept which includes open source, data, access, resources, peer review etc. and not merely open access publication model. Next, we have discussed the global initiatives towards open access—the Budapest Open Access Initiative, Bethesda Statement on Open Access Publishing, Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities, cOAlition S and its Plan S initiative, UNESCO Recommendation on Open Science and the San Francisco Declaration on Research Assessment (DORA). Following this we have included the various open access initiatives from India. In the next part, we have focused on problems with dissemination of scientific outcomes and the challenges associated with existing publication models. Finally, we explore the possible solutions to the existing challenges, which include promotion of pre-print servers and other ideas that we have detailed in the manuscript.
Exploring Deceptive Design Patterns in Voice Interfaces

Deceptive design patterns (sometimes called "dark patterns") are user interface design elements that may trick, deceive, or mislead users into behaviors that often benefit the party implementing the design over the end user. Prior work has taxonomized, investigated, and measured the prevalence of such patterns primarily in visual user interfaces (e.g., on websites). However, as the ubiquity of voice assistants and other voice-assisted technologies increases, we must anticipate how deceptive designs will be (and indeed, are already) deployed in voice interactions. This paper makes two contributions towards characterizing and surfacing deceptive design patterns in voice interfaces. First, we make a conceptual contribution, identifying key characteristics of voice interfaces that may enable deceptive design patterns, and surfacing existing and theoretical examples of such patterns. Second, we present the findings from a scenario-based user survey with 93 participants, in which we investigate participants' perceptions of voice interfaces that we consider to be both deceptive and non-deceptive.
Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms

Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm's effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here-parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.
The adoption of the metaverse concepts in Romania

Abstract The metaverse is a controversial concept, hyped by some but viewed as a marketing strategy by others. Although the first mentioning of the metaverse dates back to 1992, it has gained attention in the last years, especially after Facebook changed its name to “Meta” in 2021. Based on immersive technologies such as AR and VR, digital interactions and commerce within the virtual world, the metaverse is expected to grow along with its enabling technologies. This article aims to discover the status of the adoption of the metaverse concepts in Romania, among people with digital skills. The analysis was performed based on an online questionnaire, posted on public social media accounts. The results of the study have shown that few people have tried applications within the metaverse, and even fewer have invested in metaverse tokens. People who played RPG or virtual world games seem to have used metaverse applications more than those who have not, thus, this could be a good driver for metaverse adoption. Awareness of the metaverse concept was not a driver of metaverse adoption. However, results show that people who purchase cryptocurrencies and NFTs would also be interested in the metaverse, more than people who have not transacted these digital assets.