Reviewerly/exHarmony huggingface dataset:
A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network

As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic signal to recognize is an effective method. Because traditional radiation source recognition technologies are difficult to obtain satisfactory expert features, automatic feature extraction methods based on deep learning have become popular. Although many deep learning schemes have been proposed, most of them are only used to solve the inter-class separable problem and ignore the intra-class compactness. In addition, the openness of the real space may invalidate the existing closed-set recognition methods. In order to solve the above problems, inspired by the application of prototype learning in image recognition, we propose a novel method for recognizing space radiation sources based on a multi-scale residual prototype learning network (MSRPLNet). The method can be used for both the closed- and open-set recognition of space radiation sources. Furthermore, we also design a joint decision algorithm for an open-set recognition task to identify unknown radiation sources. To verify the effectiveness and reliability of the proposed method, we built a set of satellite signal observation and receiving systems in a real external environment and collected eight Iridium signals. The experimental results show that the accuracy of our proposed method can reach 98.34% and 91.04% for the closed- and open-set recognition of eight Iridium targets, respectively. Compared to similar research works, our method has obvious advantages.
A Novel Technique to Assess Agile Systems for Stability

Abstract Agile systems, like the Kanban and Scrum frameworks, are built on assumptions of sustainability and stability, however, there is little empirical evidence on whether such systems are stable in practice or not. Therefore, in this study we aim to inspect the stability of Agile systems by leveraging the concept of stability described in Queueing Theory. We define a novel metric, the Stability Metric, as a way of assessing queueing systems, especially Agile systems. We inspect 926 Jira projects in 14 organizations with over 1.6 million product backlog items using this metric. The analysis showed that 72.89% of these Jira projects were not stable and stable systems, on average, had product backlog sizes 10 times shorter than unstable ones. These results suggest that while the goal of Agile is to create a sustainable, stable way of working, this is not guaranteed, and a better understanding of systems and queues may be required to help design, create, coach, and maintain optimal Agile systems.
Logical big data integration and near real-time data analytics

In the context of decision-making, there is a growing demand for near real-time data that traditional solutions, like data warehousing based on long-running ETL processes, cannot fully meet. On the other hand, existing logical data integration solutions are challenging because users must focus on data location and distribution details rather than on data analytics and decision-making. EasyBDI is an open-source system that provides logical integration of data and high-level business-oriented abstractions. It uses schema matching, integration, and mapping techniques, to automatically identify partitioned data and propose a global schema. Users can then specify star schemas based on global entities and submit analytical queries to retrieve data from distributed data sources without knowing the organization and other technical details of the underlying systems. This work presents the algorithms and methods for global schema creation and query execution. Experimental results show that the overhead imposed by logical integration layers is relatively small compared to the execution times of distributed queries.
Attribute-driven streaming edge partitioning with reconciliations for distributed graph neural network training

Current distributed graph training frameworks evenly partition a large graph into small chunks to suit distributed storage, leverage a uniform interface to access neighbors, and train graph neural networks in a cluster of machines to update weights. Nevertheless, they consider a separate design of storage and training, resulting in huge communication costs for retrieving neighborhoods. During the storage phase, traditional heuristic graph partitioning not only suffers from memory overhead because of loading the full graph into the memory but also damages semantically related structures because of its neglecting meaningful node attributes. What is more, in the weight-update phase, directly averaging synchronization is difficult to tackle with heterogeneous local models where each machine’s data are loaded from different subgraphs, resulting in slow convergence. To solve these problems, we propose a novel distributed graph training approach, attribute-driven streaming edge partitioning with reconciliations (ASEPR), where the local model loads only the subgraph stored on its own machine to make fewer communications. ASEPR firstly clusters nodes with similar attributes in the same partition to maintain semantic structure and keep multihop neighbor locality. Then streaming partitioning combined with attribute clustering is applied to subgraph assignment to alleviate memory overhead. After local graph neural network training on distributed machines, we deploy cross-layer reconciliation strategies for heterogeneous local models to improve the averaged global model by knowledge distillation and contrastive learning. Extensive experiments conducted on four large graph datasets on node classification and link prediction tasks show that our model outperforms DistDGL, with fewer resource requirements and up to quadruple the convergence speed.
Internet Based Text Material: Its Impact to Students’ Reading Comprehension on Skimming and Scanning for Information to the Seventh Grade of SMP Wahidiyah

&#x0D; &#x0D; &#x0D; &#x0D; ABSTRACT&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; The objective of this study intended to find out whether there is significance influence of internet based text material to the students’ reading comprehension on scanning and skimming for information to the Seventh Grade of SMP Wahidiyah. In this research text was specified on descriptive text. Descriptive quantitative research was implemented as the research design. The sample of this research was students from 7A at SMP Wahidiyah which consist of 41 students. They were given a pre-test and post-test. The result showed that the mean score before students taught using internet based text material was 68,41 and the score after being taught using internet based text was 93,4. The result of t-score was 17,59 and the t-table was 1,68 at the level of significance was 0,05. Furthermore, from the data it can be seen that the students’ score and their mean score was increased. Related to the hypothesis and the data analization, it can be concluded that the alternative hypothesis (Ha) was accepted and the null hypothesis (H0)was rejected. It means that there was significanct influence of internet based text material to the students’ reading comprehension on scanning and skimming for information to the Seventh Grade of SMP Wahidiyah. &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Keywords: internet based text, reading comprehension, scanning, skimming&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Solving Higher Order Binary Optimization Problems on NISQ Devices: Experiments and Limitations

With the recent availability of Noisy Intermediate-Scale Quantum devices, the potential of quantum computers to impact the field of combinatorial optimization lies in quantum variational and annealing-based methods. This paper further compares Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) in solving Higher Order Binary Optimization (HOBO) problems. This case study considers the hypergraph partitioning problem, which is used to generate custom HOBO problems. Our experiments show that D-Wave systems quickly reach limits solving dense HOBO problems. Although the QAOA demonstrates better performance on exact simulations, noisy simulations reveal that the gate error rate should remain under $$10^{-5}$$ to match D-Wave systems’ performance, considering equal compilation overheads for both device.
Calibrated Teacher for Sparsely Annotated Object Detection

Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidable missing annotations. As a result, the incomplete annotation in each image could provide misleading supervision and harm the training. Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations. Such a mechanism is sensitive to the threshold of the pseudo label score. However, the effective threshold is different in different training stages and among different object detectors. Therefore, the current methods with fixed thresholds have sub-optimal performance, and are difficult to be applied to other detectors. In order to resolve this obstacle, we propose a Calibrated Teacher, of which the confidence estimation of the prediction is well calibrated to match its real precision. In this way, different detectors in different training stages would share a similar distribution of the output confidence, so that multiple detectors could share the same fixed threshold and achieve better performance. Furthermore, we present a simple but effective Focal IoU Weight (FIoU) for the classification loss. FIoU aims at reducing the loss weight of false negative samples caused by the missing annotation, and thus works as the complement of the teacher-student paradigm. Extensive experiments show that our methods set new state-of-the-art under all different sparse settings in COCO. Code will be available at https://github.com/Whileherham/CalibratedTeacher.
DarkFeat: Noise-Robust Feature Detector and Descriptor for Extremely Low-Light RAW Images

Low-light visual perception, such as SLAM or SfM at night, has received increasing attention, in which keypoint detection and local feature description play an important role. Both handcraft designs and machine learning methods have been widely studied for local feature detection and description, however, the performance of existing methods degrades in the extreme low-light scenarios in a certain degree, due to the low signal-to-noise ratio in images. To address this challenge, images in RAW format that retain more raw sensing information have been considered in recent works with a denoise-then-detect scheme. However, existing denoising methods are still insufficient for RAW images and heavily time-consuming, which limits the practical applications of such scheme. In this paper, we propose DarkFeat, a deep learning model which directly detects and describes local features from extreme low-light RAW images in an end-to-end manner. A novel noise robustness map and selective suppression constraints are proposed to effectively mitigate the influence of noise and extract more reliable keypoints. Furthermore, a customized pipeline of synthesizing dataset containing low-light RAW image matching pairs is proposed to extend end-to-end training. Experimental results show that DarkFeat achieves state-of-the-art performance on both indoor and outdoor parts of the challenging MID benchmark, outperforms the denoise-then-detect methods and significantly reduces computational costs up to 70%. Code is available at https://github.com/THU-LYJ-Lab/DarkFeat.
Provable Pathways: Learning Multiple Tasks over Multiple Paths

Constructing useful representations across a large number of tasks is a key requirement for sample-efficient intelligent systems. A traditional idea in multitask learning (MTL) is building a shared representation across tasks which can then be adapted to new tasks by tuning last layers. A desirable refinement of using a shared one-fits-all representation is to construct task-specific representations. To this end, recent PathNet/muNet architectures represent individual tasks as pathways within a larger supernet. The subnetworks induced by pathways can be viewed as task-specific representations that are composition of modules within supernet's computation graph. This work explores the pathways proposal from the lens of statistical learning: We first develop novel generalization bounds for empirical risk minimization problems learning multiple tasks over multiple paths (Multipath MTL). In conjunction, we formalize the benefits of resulting multipath representation when adapting to new downstream tasks. Our bounds are expressed in terms of Gaussian complexity, lead to tangible guarantees for the class of linear representations, and provide novel insights into the quality and benefits of a multipath representation. When computation graph is a tree, Multipath MTL hierarchically clusters the tasks and builds cluster-specific representations. We provide further discussion and experiments for hierarchical MTL and rigorously identify the conditions under which Multipath MTL is provably superior to traditional MTL approaches with shallow supernets.
Coarse2Fine: Local Consistency Aware Re-prediction for Weakly Supervised Object Localization

Weakly supervised object localization aims to localize objects of interest by using only image-level labels. Existing methods generally segment activation map by threshold to obtain mask and generate bounding box. However, the activation map is locally inconsistent, i.e., similar neighboring pixels of the same object are not equally activated, which leads to the blurred boundary issue: the localization result is sensitive to the threshold, and the mask obtained directly from the activation map loses the fine contours of the object, making it difficult to obtain a tight bounding box. In this paper, we introduce the Local Consistency Aware Re-prediction (LCAR) framework, which aims to recover the complete fine object mask from locally inconsistent activation map and hence obtain a tight bounding box. To this end, we propose the self-guided re-prediction module (SGRM), which employs a novel superpixel aggregation network to replace the post-processing of threshold segmentation. In order to derive more reliable pseudo label from the activation map to supervise the SGRM, we further design an affinity refinement module (ARM) that utilizes the original image feature to better align the activation map with the image appearance, and design a self-distillation CAM (SD-CAM) to alleviate the locator dependence on saliency. Experiments demonstrate that our LCAR outperforms the state-of-the-art on both the CUB-200-2011 and ILSVRC datasets, achieving 95.89% and 70.72% of GT-Know localization accuracy, respectively.
Analysis of Determinants of Satisfaction Using E-Learning in Elementary School Students

The COVID-19 pandemic, a global health crisis, has made many sectors carry out their activities in new ways, including the education sector. Teaching and learning activities should not be conducted face-to-face during the COVID-19 pandemic. As a result, all learning activities are diverted through e-learning. Therefore, an evaluation of the use of e-learning needs to be carried out to ensure its practical use and a positive impact. This study evaluates the factors that affect the satisfaction of e-learning users. Data were collected by surveying 205 elementary school students in the 4th, 5th, and 6th grades, then analyzed using the structural equation model technique. The results showed that Technical System Quality, Information Quality, Learner Quality, and Perceived Usefulness affected the satisfaction of e-learning users. These results can be suggestions for schools to fix and improve e-learning performance so that students and teachers no longer have difficulties when an event requires online teaching and learning activities.
Panoramic Video Salient Object Detection with Ambisonic Audio Guidance

Video salient object detection (VSOD), as a fundamental computer vision problem, has been extensively discussed in the last decade. However, all existing works focus on addressing the VSOD problem in 2D scenarios. With the rapid development of VR devices, panoramic videos have been a promising alternative to 2D videos to provide immersive feelings of the real world. In this paper, we aim to tackle the video salient object detection problem for panoramic videos, with their corresponding ambisonic audios. A multimodal fusion module equipped with two pseudo-siamese audio-visual context fusion (ACF) blocks is proposed to effectively conduct audio-visual interaction. The ACF block equipped with spherical positional encoding enables the fusion in the 3D context to capture the spatial correspondence between pixels and sound sources from the equirectangular frames and ambisonic audios. Experimental results verify the effectiveness of our proposed components and demonstrate that our method achieves state-of-the-art performance on the ASOD60K dataset.
HRDoc: Dataset and Baseline Method toward Hierarchical Reconstruction of Document Structures

The problem of document structure reconstruction refers to converting digital or scanned documents into corresponding semantic structures. Most existing works mainly focus on splitting the boundary of each element in a single document page, neglecting the reconstruction of semantic structure in multi-page documents. This paper introduces hierarchical reconstruction of document structures as a novel task suitable for NLP and CV fields. To better evaluate the system performance on the new task, we built a large-scale dataset named HRDoc, which consists of 2,500 multi-page documents with nearly 2 million semantic units. Every document in HRDoc has line-level annotations including categories and relations obtained from rule-based extractors and human annotators. Moreover, we proposed an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem. By adopting a multi-modal bidirectional encoder and a structure-aware GRU decoder with soft-mask operation, the DSPS model surpass the baseline method by a large margin. All scripts and datasets will be made publicly available at https://github.com/jfma-USTC/HRDoc.
Research on rainy day traffic sign recognition algorithm based on PMRNet

&lt;abstract&gt;&lt;p&gt;The recognition of traffic signs is of great significance to intelligent driving and traffic systems. Most current traffic sign recognition algorithms do not consider the impact of rainy weather. The rain marks will obscure the recognition target in the image, which will lead to the performance degradation of the algorithm, a problem that has yet to be solved. In order to improve the accuracy of traffic sign recognition in rainy weather, we propose a rainy traffic sign recognition algorithm. The algorithm in this paper includes two modules. First, we propose an image deraining algorithm based on the Progressive multi-scale residual network (PMRNet), which uses a multi-scale residual structure to extract features of different scales, so as to improve the utilization rate of the algorithm for information, combined with the Convolutional long-short term memory (ConvLSTM) network to enhance the algorithm's ability to extract rain mark features. Second, we use the CoT-YOLOv5 algorithm to recognize traffic signs on the recovered images. In this paper, in order to improve the performance of YOLOv5 (You-Only-Look-Once, YOLO), the 3 × 3 convolution in the feature extraction module is replaced by the Contextual Transformer (CoT) module to make up for the lack of global modeling capability of Convolutional Neural Network (CNN), thus improving the recognition accuracy. The experimental results show that the deraining algorithm based on PMRNet can effectively remove rain marks, and the evaluation indicators Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are better than the other representative algorithms. The mean Average Precision (mAP) of the CoT-YOLOv5 algorithm on the TT100k datasets reaches 92.1%, which is 5% higher than the original YOLOv5.&lt;/p&gt;&lt;/abstract&gt;
Online Language Learning Strategies in Institutes of Higher Learning (IPT) Malaysia Post Covid-19

The Covid-19 pandemic that spreaded around the world in March 2020 has changed the nation’s education landscape. Standard Operating Procedures that demand presence at respective residences have made online learning one of the best platforms that replace face-to-face learning in educational institutions across the country. Thus, this study aims to propose an effective online language learning strategy to be a reference for educators and students to master language skills using post-Covid19 educational technology. This research employs quantitative methods cross sectional studies supported by qualitative data. The research instruments is questionnaires. A total of 280 respondents consisting of university students in the Arabic field at Malaysian universities answered the questionnaire. The results showed that the respondents know how to learn through online learning with a mean rate of 4.24 and the respondents agreed that the instructions given during online learning should be clear with a highest mean rate of 4.46. Among the online learning strategies used by students are always sharing opinions during online learning, responding using chat rooms to engage in discussions and making notes to improve understanding. Respondents are also satisfied with online learning platforms such as google meet, Webex and zoom.&#x0D;
Predicting Multiple Domain Queue Waiting Time via Machine Learning

This paper describes an implementation of the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology for a demonstrative case of human queue waiting time prediction. We collaborated with a multiple domain (e.g., bank, pharmacies) ticket management service software development company, aiming to study a Machine Learning (ML) approach to estimate queue waiting time. A large multiple domain database was analyzed, which included millions of records related with two time periods (one year, for the modeling experiments; and two year, for a deployment simulation). The data was first preprocessed (including data cleaning and feature engineering tasks) and then modeled by exploring five state-of-the-art ML regression algorithms and four input attribute selections (including newly engineered features). Furthermore, the ML approaches were compared with the estimation method currently adopted by the analyzed company. The computational experiments assumed two main validation procedures, a standard cross-validation and a Rolling Window scheme. Overall, competitive and quality results were obtained by an Automated ML (AutoML) algorithm fed with newly engineered features. Indeed, the proposed AutoML model produces a small error (from 5 to 7 min), while requiring a reasonable computational effort. Finally, an eXplainable Artificial Intelligence (XAI) approach was applied to a trained AutoML model, demonstrating the extraction of useful explanatory knowledge for this domain.