Reviewerly/exHarmony huggingface dataset:
A Unified View on Graph Neural Networks as Graph Signal Denoising

Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a feature aggregation operation. The former normally uses feed-forward networks to transform features, while the latter aggregates the transformed features over the graph. Numerous recent works have proposed GNN models with different designs in the aggregation operation. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption. Such a unified view across GNNs not only provides a new perspective to understand a variety of aggregation operations but also enables us to develop a unified graph neural network framework UGNN. To demonstrate its promising potential, we instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes. Comprehensive experiments show the effectiveness of ADA-UGNN.
From luxury to necessity: Progress of touchless interaction technology

Touchless Technology is facilitating the move to Zero User Interface(UI) propelled by the COVID-19 pandemic which has accelerated the use of this technology due to hygiene requirements. Zero UI can be defined as a controlled interface that enables user interaction with technology through voice, gestures, hand interaction, eye tracking, and biometrics such as facial recognition and contactless fingerprints. Smart devices, IoT sensors, smart appliances, smart TVs, smart assistants and consumer robotics are predominant examples of devices in which Zero UI is becoming increasingly adopted. These control interfaces include natural interaction modes such as voice or gestures. Touchscreens and shared devices such as kiosks, self-service counters and interactive displays are present in our everyday lives. Each of these interactions however is a concern for consumers in a post-COVID-19 world where hygiene is of utmost importance. The one-stop solution to hygienic interactions includes touchless technology such as voice control, remote mobile screen take over, biometric, and gesture control as Zero User interfaces. With the breakthroughs in image recognition and natural language processing, powered by advanced computer vision and machine learning, "Zero UI" is becoming a new normal. This paper is focusing on the progress of the touchless interaction technology during the COVID-19 pandemic, which actually accelerated development in this concept and moved it from being a luxury to a life necessity.
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection

This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting. The advantage of FP is weakened as deeper backbones with more layers are used. In addition, it cannot keep up accurate detection of both small and large objects at the same time. To address these issues, we propose a new parallel FP structure with bi-directional (top-down and bottom-up) fusion and associated improvements to retain high-quality features for accurate localization. We provide the following design improvements: 1) parallel bifusion FP structure with a bottom-up fusion module (BFM) to detect both small and large objects at once with high accuracy; 2) concatenation and re-organization (CORE) module provides a bottom-up pathway for feature fusion, which leads to the bi-directional fusion FP that can recover lost information from lower-layer feature maps; 3) CORE feature is further purified to retain richer contextual information. Such CORE purification in both top-down and bottom-up pathways can be finished in only a few iterations; 4) adding of a residual design to CORE leads to a new Re-CORE module that enables easy training and integration with a wide range of deeper or lighter backbones. The proposed network achieves state-of-the-art performance on the UAVDT17 and MS COCO datasets.
Learning Cross-Modal Contrastive Features for Video Domain Adaptation

Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition. Existing video domain adaptation methods mainly rely on adversarial feature alignment, which has been derived from the RGB image space. However, video data is usually associated with multi-modal information, e.g., RGB and optical flow, and thus it remains a challenge to design a better method that considers the cross-modal inputs under the cross-domain adaptation setting. To this end, we propose a unified framework for video domain adaptation, which simultaneously regularizes cross-modal and cross-domain feature representations. Specifically, we treat each modality in a domain as a view and leverage the contrastive learning technique with properly designed sampling strategies. As a result, our objectives regularize feature spaces, which originally lack the connection across modalities or have less alignment across domains. We conduct experiments on domain adaptive action recognition benchmark datasets, i.e., UCF, HMDB, and EPIC-Kitchens, and demonstrate the effectiveness of our components against state-of-the-art algorithms.
Megnn: Meta-path extracted graph neural network for heterogeneous graph representation learning

Heterogeneous graphs with multiple types of nodes and edges are ubiquitous in the real world and possess immense value in many graph-based downstream applications. However, the heterogeneity within nodes and edges in heterogeneous graphs has brought pressing challenges for practical node representation learning. Existing works manually define multiple meta-paths to model the semantic relationships in heterogeneous graphs. Such strategies heavily rely on the quality of domain knowledge and require extensive hand-crafted works. In this paper, we propose a novel Meta-path Extracted heterogeneous Graph Neural Network (Megnn) that is capable of extracting meaningful meta-paths in heterogeneous graphs, providing insights about data and explainable conclusions to the model’s effectiveness. Concretely, Megnn leverages heterogeneous convolution to combine different bipartite sub-graphs corresponding to edge types into a new trainable graph structure. By adopting the message passing paradigm of GNNs through trainable convolved graphs, Megnn can optimize and extract effective meta-paths for heterogeneous graph representation learning. To enhance the robustness of Megnn, we leverage multiple channels to yield various graph structures and devise a channel consistency regularizer to enforce the node embeddings learned from different channels to be similar. Extensive experimental results on three datasets not only show the effectiveness of Megnn compared with the state-of-the-art methods, but also demonstrate the favorable interpretability of the extracted meta-paths.
Dynamic Scheduling for Over-the-Air Federated Edge Learning With Energy Constraints

Machine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is emerging as a promising training framework. As wireless devices involved in FEEL are resource limited in terms of communication bandwidth, computing power and battery capacity, it is important to carefully schedule them to optimize the training performance. In this work, we consider an over-the-air FEEL system with analog gradient aggregation, and propose an energy-aware dynamic device scheduling algorithm to optimize the training performance within the energy constraints of devices, where both communication energy for gradient aggregation and computation energy for local training are considered. The consideration of computation energy makes dynamic scheduling challenging, as devices are scheduled before local training, but the communication energy for over-the-air aggregation depends on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> -norm of local gradient, which is known only after local training. We thus incorporate estimation methods into scheduling to predict the gradient norm. Taking the estimation error into account, we characterize the performance gap between the proposed algorithm and its offline counterpart. Experimental results show that, under a highly unbalanced local data distribution, the proposed algorithm can increase the accuracy by 4.9% on CIFAR-10 dataset compared with the myopic benchmark, while satisfying the energy constraints.
Near-term quantum algorithms for linear systems of equations with regression loss functions

Abstract Solving linear systems of equations is essential for many problems in science and technology, including problems in machine learning. Existing quantum algorithms have demonstrated the potential for large speedups, but the required quantum resources are not immediately available on near-term quantum devices. In this work, we study near-term quantum algorithms for linear systems of equations, with a focus on the two-norm and Tikhonov regression settings. We investigate the use of variational algorithms and analyze their optimization landscapes. There exist types of linear systems for which variational algorithms designed to avoid barren plateaus, such as properly-initialized imaginary time evolution and adiabatic-inspired optimization, suffer from a different plateau problem. To circumvent this issue, we design near-term algorithms based on a core idea: the classical combination of variational quantum states (CQS). We exhibit several provable guarantees for these algorithms, supported by the representation of the linear system on a so-called ansatz tree. The CQS approach and the ansatz tree also admit the systematic application of heuristic approaches, including a gradient-based search. We have conducted numerical experiments solving linear systems as large as 2 300 × 2 300 by considering cases where we can simulate the quantum algorithm efficiently on a classical computer. Our methods may provide benefits for solving linear systems within the reach of near-term quantum devices.
A Robust One-Stage Detector for Multiscale Ship Detection With Complex Background in Massive SAR Images

With the development of synthetic aperture radar (SAR) imaging and deep learning, SAR ship detection based on convolutional neural networks (CNNs) has been extensively applied in the last few years. Nevertheless, there are two main obstacles in SAR ship detection: 1) the SAR images have too much noise, such as the interference from land area, making it difficult to distinguish ship objects from the surrounding background, and 2) due to the multiscale characteristics of ship objects, there are numerous false negatives in the detection results, especially for small objects. To alleviate the above problems, we propose a one-stage ship detector with strong robustness against scale changes and various interferences. First, to mitigate the disturbance from complex background, a coordinate attention module (CoAM) is introduced for obtaining more representative semantic features to accurately locate and distinguish ship objects. Second, a receptive field increased module (RFIM) is devised to capture multiscale contextual information to improve the detection performance for ships with diverse scales. Finally, we verify the robustness of our method on several public SAR datasets, i.e., SAR-Ship-Dataset, high-resolution SAR images dataset (HRSID), and SAR ship detection dataset (SSDD). The experimental results demonstrate that the proposed method has a competitive performance, exceeding other state-of-the-art methods by at least 2.6% AP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> on HRSID.
Geochemometric approach to groundwater quality and health risk assessment of heavy metals of Yankari Game Reserve and its environs, Northeast Nigeria

A popular eco-tourist destination happy haven, renowned for its distinctive groundwater springs, wildlife population and endowed with notable archeologic monuments and other appealing characteristics, thus the need for a baseline survey of the physiochemical measurements to ascertain the concentrations of major cations and heavy metals (Cd, Co, Cr, Cu, Fe, Li, Mn, Ni, and Pb)), their distribution across the sampling locations and to investigate the origin of Potentially Toxic Elements (PTEs) due to threats from anthropogenic and geogenic processes to its quality to evaluating its suitability for drinking, and the risk imposed on human health through the use of Water Quality Index (WQI), Hazard Quotient (HQ) and Hazard Index (HI). Concentrations of forty-one water samples from twenty natural springs, fifteen boreholes, five hand-dug wells, and a stream a were detected. Nine heavy metals (Fe, Mn, Li, Cd, Co, Cr, Cu, Pb, and Ni) were analysed with Flame Atomic Absorption Spectrometry (FAAS) and the data using different multivariate statistical approaches. Five component types; 24.40%, 14.50%, 12.10%, 9.64% and 9.24% extracted accounted for 69.79% total variance. The dominant variables derived from PCA's five components indicated the influence of the weathering of anhydrous carbonate, which released major cations and the PTEs. Calculated HQs and HI for adults and children were generally less than one (HQ < 1), indicating less or no potential carcinogenic, except for a heavy metal Cd. It exhibits an HQ and HI > 1, for children ingesting water from all the water sources. Hand-dug wells and surface water from the stream had HQ and HI > 1 for Cd for adults, which may not be harmful but with overtime exposure and may require monitoring; otherwise, it may pose more threats to adults and children with time.
The Framework DiKoLAN (Digital Competencies for Teaching in Science Education) as Basis for the Self-Assessment Tool DiKoLAN-Grid

For the planning and implementation of lessons with digital technologies, a subject-specific technology-related professional competence of teachers is of central importance. However, the competency frameworks developed so far remain in a general perspective and do not explicitly address subject-specific issues. Furthermore, digital competencies are predominantly measured with subject-unspecific self-assessment instruments, as subject-specific operationalizations for this area are not yet available in a differentiated form. In this article, the framework for Digital Competencies for Teaching in Science Education (DiKoLAN), a subject-specific framework for pre-service science teachers, is introduced, on the one hand, and, on the other hand, first results of a self-assessment tool based on the framework are described. DiKoLAN defines competency areas highly specific to science, as well as more general competency areas that include aspects common to all subjects. Each competency area is described by competency expectations, which, in turn, are structured with reference to the four technology-related dimensions of the TPACK framework (i.e., Technological and Pedagogical Content Knowledge) and three levels of performance (Name, Describe, Use/Apply). Derived from DiKoLAN, a corresponding self-assessment instrument (DiKoLAN-Grid) was developed and empirically tested for the two competency areas, (n = 118) and Information Search and Evaluation (n = 90), in biology student teachers. By means of path models, tendencies regarding structural correlations of the four components Special Tools (TK), Content-specific Context (TCK), Methods and Digitality (TPK), and Teaching (TPACK) are presented for both competency areas and discussed, as well as in comparison to previously conducted, subject-unspecific surveys.
MetaHuman Creator The starting point of the metaverse

February 11, 2021, Epic Games revealed a first look at MetaHuman Creator, a new browser-based app that will empower anyone to create a bespoke photorealistic digital human, fully rigged and complete with hair and clothing, in a matter of minutes. Metahuman Creator makes the digital avatars through an intuitive workflow, which means the Cyborg era is on the horizon.
Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman filter to enhance the perception performance. In addition, UAV path planning for a surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.
High-Speed NTT-based Polynomial Multiplication Accelerator for Post-Quantum Cryptography

This paper demonstrates an architecture for accelerating the polynomial multiplication using number theoretic transform (NTT). Kyber is one of the finalists in the third round of the NIST post-quantum cryptography standardization process. Simultaneously, the performance of NTT execution is its main challenge, requiring large memory and complex memory access pattern. In this paper, an efficient NTT architecture is presented to improve the respective computation time. We propose several optimization strategies for efficiency improvement targeting different performance requirements for various applications. Our NTT architecture, including four butterfly cores, occupies only 798 LUTs and 715 FFs on a small Artix-7 FPGA, showing more than 44% improvement compared to the best previous work. We also implement a coprocessor architecture for Kyber KEM benefiting from our high-speed NTT core to accomplish three phases of the key exchange in 9, 12, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{19}\mu \mathbf{s}$</tex> , respectively, operating at 200 MHz.
Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19

A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes. Despite this, clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (data drift), and so can the relationship between episode characteristics and associated clinical outcomes (concept drift). Practically this means deployed algorithms must be monitored to ensure their safety. We demonstrate how explainable machine learning can be used to monitor data drift, using the COVID-19 pandemic as a severe example. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission during an emergency department attendance. We then evaluate our model's performance on attendances occurring pre-pandemic (AUROC of 0.856 with 95%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC of 0.826 with 95%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature's SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.
OW-DETR: Open-world Detection Transformer

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model out-performs the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental object detection, OW-DETR outperforms the state-of-the-art for all settings on PASCAL VOC. Our code is available at https://github.com/akshitac8/OW-DEtr.
Explaining citizens’ resistance to use digital contact tracing apps: A mixed-methods study

Governments worldwide are using digital contact tracing (DCT) apps as a critical element in their COVID-19 pandemic lockdown exit strategy. Despite substantial investment in research and development, the public's acceptance of DCT apps has been phenomenally low, signaling resistance among potential users. Little is known about why people would resist using the DCT app, a useful innovation that can potentially save millions of human lives. This study explores the determinants and consequences of citizens' resistance to use DCT apps using a sequential two-stage mixed-methods approach. The preliminary qualitative study analyzed interviews of 24 Indian smartphone users who chose not to use or discontinued the DCT app after an initial trial. In the quantitative stage, an integrated model based on innovation resistance theory and distrust theory was tested using the survey data collected from 194 non-adopters of the DCT app from India. The findings revealed that the factors, distrust, value barrier, information privacy concerns, and usage barrier predicted the resistance to the DCT app, and resistance, in turn, predicted intention to use. Additionally, distrust was found to be a key mediator between innovation barriers and resistance. The insights from this study could help the developers and policymakers formulate strategies for implementing DCT interventions during future disease outbreaks.