Reviewerly/exHarmony huggingface dataset:
Depth‐Aware Shadow Removal

Abstract Shadow removal from a single image is an ill‐posed problem because shadow generation is affected by the complex interactions of geometry, albedo, and illumination. Most recent deep learning‐based methods try to directly estimate the mapping between the non‐shadow and shadow image pairs to predict the shadow‐free image. However, they are not very effective for shadow images with complex shadows or messy backgrounds. In this paper, we propose a novel end‐to‐end depth‐aware shadow removal method without using depth images, which estimates depth information from RGB images and leverages the depth feature as guidance to enhance shadow removal and refinement. The proposed framework consists of three components, including depth prediction, shadow removal, and boundary refinement. First, the depth prediction module is used to predict the corresponding depth map of the input shadow image. Then, we propose a new generative adversarial network (GAN) method integrated with depth information to remove shadows in the RGB image. Finally, we propose an effective boundary refinement framework to alleviate the artifact around boundaries after shadow removal by depth cues. We conduct experiments on several public datasets and real‐world shadow images. The experimental results demonstrate the efficiency of the proposed method and superior performance against state‐of‐the‐art methods.
ShadowPatch: Shadow Based Segmentation for Reliable Depth Discontinuities in Photometric Stereo

Abstract Photometric stereo is a well‐established method with outstanding traits to recover surface details and material properties, like surface albedo or even specularity. However, while the surface is locally well‐defined, computing absolute depth by integrating surface normals is notoriously difficult. Integration errors can be introduced and propagated by numerical inaccuracies from inter‐reflection of light or non‐Lambertian surfaces. But especially ignoring depth discontinuities for overlapping or disconnected objects, will introduce strong distortion artefacts. During the acquisition process the object is lit from different positions and self‐shadowing is in general considered as an unavoidable drawback, complicating the numerical estimation of normals. However, we observe that shadow boundaries correlate strongly with depth discontinuities and exploit the visual structure introduced by self‐shadowing to create a consistent image segmentation of continuous surfaces. In order to make depth estimation more robust, we deeply integrate photometric stereo with depth‐from‐stereo. Having obtained a shadow based segmentation of continuous surfaces, allows us to reduce the computational cost for correspondence search in depth‐from‐stereo. To speed‐up computation further, we merge segments into larger meta‐segments during an iterative depth optimization. The reconstruction error of our method is equal or smaller than previous work, and reconstruction results are characterized by robust handling of depth‐discontinuities, without any smearing artifacts.
Measuring Indirect Coupling Complexity of Software Systems

Software evolution is a time consuming, costly, and complex activity. Once developers are assigned a programming task or change request, they need to complete it as fast as possible without increasing the existing code's overall complexity. Therefore, they need to know the dependencies of software components before applying any code changes. As the code matures, it becomes more difficult to detect indirect coupling relationships among the components, which is a serious problem for project managers. Such hidden relationships may cause further complexity in the system, poor estimation of the effort, and degradation of the code quality. The purpose of this research is to propose a suite of metrics that are grounded on measurement theory and that enhance the scope, strength, and usefulness of accepted software metrics by taking advantage of the hidden relationships among software components. The following research questions guided our work: (RQ1) How to measure software complexity using indirect coupling to take advantage of weighted differences between methods?, and (RQ2) How could indirect coupling metrics help to assist programmers during maintenance tasks? This rigorously introduced suite exhibit well-known desirable metrics properties. Furthermore, it also can be used as an aid in project management and maintenance tasks. The theoretically rigorous enhancement of software metrics by fine-graining them and gathering the hidden relationships among components proved to provide additional significant insight that can benefit both project managers and developers in their job.
A proposal for assessing and evolving an agile software development method

To meet the time requested for software industry needs, small and medium enterprises use agile methods, such as Scrum, to produce software in a shorter development cycle. However, the lack of knowledge on how to implement and use agile methods correctly results in an empirical adoption of them, most of the time without getting the expected results like inefficient software development. This paper presents a proposal to evaluate agile methods, specifically Scrum. The method is addressed to very small organizations (VSEs) and small and medium-sized enterprises (SMEs). In addition, the method includes a tool for its implementation that allows evaluating organizations and offering the required information to implement Scrum and increase the organization's maturity using Scrum.
INTERNATIONAL DEBATER’S POLITENESS STRATEGY IN INTERLANGUAGE PRAGMATICS OF REQUEST

The study aims to investigate the speech acts of international debaters in the 2022 Asian English Olympics in giving requests. This study focuses on the request speech acts and the politeness strategy produced by international debaters with different cultural backgrounds. This study used a qualitative approach because it observed the request speech acts of international debaters in applying the politeness strategy. There were four participants, consisting of Uzbekistan, Philippines, Hong Kong, and Malaysian debaters. The data contained the requests delivered by the international debaters conducted through WhatsApp messenger, both group and private messages. In collecting the data, the researcher was a part of the participants in the debate competition. Thus, the researcher could collect the data in the private group made special for the participants who joined the debate competition. Additionally, the researcher's relationship with the participants was the opponent of the debate team. Finally, it is found that the request speech acts by showing a demand, command, and force expression. Thus, politeness strategy, social distance in which, status and relationship, and background impact the way of giving requests.
An Open-Source High-Level Fusion Algorithm in ROS for Automated Driving Applications

Accurate environment perception can be achieved with multiple sensors with different measuring principles – such as camera, lidar and radar sensors. In automated driving applications, this diversity aims, for example, to reduce the susceptibility of sensors to inclement weather. Fusion combines data from sensors and creates a generic representation of the vehicle's surroundings – which is known as environment model. Based on this, a safe trajectory can be planned and driving functions can actuate accordingly. In this paper, a high-level fusion algorithm is implemented. Its aim is to combine different object lists generated at the sensor-level into a unique global object list. This software is implemented in Python in a ROS workspace and is published as open-source code on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . It was verified that the algorithm can successfully fuse data acquired from simulation-based test drives with sensor models, and create a global object list based on the detections at the sensor-level.
Easing the Learning of the Robustness Diagram by Using an Assisted Modeler Tool: An Empirical Study with Ungraduated Students

One of the critical parts of software capstone projects is modeling since students need to learn it to be able to produce largescale and reliable software. UML is considered a de facto standard for software modeling and is widely accepted in education and industry. However, UML is sometimes misused due to discrepancies between different models. Further, most popular modeling tools used in academic environments have numerous weaknesses, including being overly complex, which could result in a cognitive overload. We proposed a hybrid method named Scrumconix as a base to teach software engineering. This method includes the robustness diagram that closes the gap between use case specification and detailed design and code, which is helpful for developers and particularly for students. We also developed a modeler (called Robix) to support and ease the learning of this diagram by applying the diagram rules. We evaluated the Robix modeler with ungraduated students, and we obtained that those who used the Robix modeler during a learning phase had significantly fewer errors in an academic evaluation at applying the diagram rules and using its stereotypes than those who did not use our modeler. Concerning usability and usefulness perception, the Robix modeler had high rates of both factors by the participants. These results encourage us to conduct another study with a larger sample to corroborate the obtained results and corroborate whether the modeler also eases the representation of use case flow in the robustness diagram.
A Collaborative Web System for Mexican Sign Language: Content Validation Algorithm

The Mexican Sign Language (LSM) has allowed communication between deaf and non-deaf people; however, studying this language can bring some complications given its visual format. Although books represent signs on paper, there is no better learning than when the sign is recorded on video, and the movement and joints can be visualized in three dimensions. There are video repositories on the internet to search for signs or phrases; however, this information is disjointed, or the sites specialized in these topics have very few videos; in both cases, the search for signs and learning LSM becomes complicated. For this situation, a Collaborative Web System is proposed that allows searching for signs with text inputs and finding representative videos of the search. In this way, the research supports people in establishing communication with deaf people. Furthermore, the site is collaborative, as deaf and non-deaf people can collaborate by adding their knowledge about LSM. The testing stage simulated a user ranking and content validation algorithm. This simulation allowed adjustments to the algorithms before releasing the application to real users. With this proposal, we intend to generate a repository of audiovisual content that allows users to exploit other aspects of communication, such as the conversion of videos with signs to text or sound, which can be in recorded videos or real-time through mobile devices.
The impact of the COVID-19 Pandemic on Higher Education - A Case of Success of ICT Educational Programs

The COVID-19 pandemic has had a big impact in all human being activities; education context was especially affected. A disrupted shift from face-to-face teaching-learning practices to learning activities and/or pedagogical activities supported by digital platforms or social networks was lived. Limitations in Internet access and mastering of technological media impacted the pedagogical, psychological, and affective side of the students. This paper presents a study on the changes caused by the COVID-19 pandemic in terms of school dropout; an analysis was carried out on the percentages of the indicator of non-enrollment of each one of the educational programs at a Mexican university. The results showed that educational programs with an ICT orientation beat this contingency in a better way. This encourages us to continue this research to investigate how new professionals are getting ready for the fashion of working from home.
Toward Developing an Ontology for Assessing Quality of User Stories in Scrum Framework

User stories are an increasingly popular textual notation for capturing requirements in agile software development. They describe small system parts oriented to the user and guide developers' daily work. Most users use them in combination with Scrum, but despite their popularity, user stories are often too crude, so misunderstandings or dependencies remain unpredictable. Moreover, research efforts on them are limited since there is no scientific evidence on the effectiveness and validation of user stories. In turn, The International IT Research Firm Standish Group mentions incomplete agile requirements as their main impediment to success. This ongoing research proposes the semantic relationship assessment of an ontology for evaluating the user story's quality. In addition, the authors conducted a qualitative validation of the relationships of the ontology with Software Engineering practitioners. This work's result will help propose an ontology to assess the quality of a user story, and it will guide development teams during the creation, analysis, and development of user stories.
UAV Target Tracking Technology Based on Improved GM-PHD Filter

The integration of 6G technology and blockchain has promoted the booming development of UAV industry, but the accompanying national social security issues have also attracted public attention. In view of UAV's characteristics of “low, slow and small” and flexible turning, there is an urgent need for a technology that can eliminate background clutter and complete target tracking of multiple UAVs. At present, most of the existing researches tend to track the technology processing which depends on the detection results, and the detection performance is poor and the tracking instability is easy to occur. Based on GM-PHD filter and combined with machine learning, this paper proposes a DGM-PHD filter with high tracking performance, and evaluates it through MATLAB simulation software, using OSPA distance as evaluation index. The results show that the DGM-PHD filter performs better than the GM-PHD filter in the multi-UAV tracking problem under the uniformly accelerated linear motion, and achieves good tracking effect.
A Study of Remote Sensing Image Ground Segmentation on Deep Learning

Remote sensing image segmentation plays an important role in the field of satellite image research. However, when dealing with the non-linear relationship with high spatial complexity, the accuracy of ground object segmentation is often low. Therefore, this paper proposes two methods of deep learning to improve the accuracy of ground object segmentation. This paper develops a method of integrated segmentation based on UNET integrated network. Several trained UNET network models are integrated with the weighted average method to get the UNET integrated network model. Then the CBAM (convolutional block attention module) is combined with UNET to get UNET attention model. The experimental results show that the segmentation accuracy of the improved UNET is higher.
Multi-cell MEC Space Partition and Dynamic Adjustment Scheme for VR Video Transmission

Aiming at the problem of delay requirements in VR panoramic video transmission, based on the MEC server resources on the edge side to reduce the calculation and transmission delay, an optimal partitioning method of MEC collaborative space based on maximum and minimum distance clustering is proposed. It is difficult to determine the initial value of the actual cluster classification of MEC in the distance method. In the “user-MEC/base station-cloud” task offloading architecture of VR video transmission on the edge side, based on the above collaborative space division scheme, the boundary of the MEC collaborative subspace is dynamically adjusted according to the resource usage in different regions and different time periods. The purpose is to maximize the It ensures that the edge terminal can complete the user's task request to the maximum extent, improves the transmission efficiency and quality of VR panoramic video, and reduces energy consumption, providing users with a smooth and immersive VR video experience.
Research on key technologies of UAV real-time recognition and tracking based on YOLOv4

With the development of the Internet of Things (IoT) technology and the fifth generation of mobile communication technology (5G), unmanned aerial vehicle (UAV) has been widely used in urban life, such as UAV photography and monitoring, and UAV delivery. However, UAV targets are generally small in size, and characteristics between multiple UAVs are not obvious. Interference from other flying objects and complex electromagnetic environment bring great challenges to accurate detection and stable tracking of UAV targets. Therefore, according to the characteristics of urban environment, it is very necessary to establish a high-efficiency UAV target real-time detection and tracking system. In our study, a UAV detection and tracking method with YOLOv4 as the target detector and Deep-SORT as the target tracker is proposed, which can realize the high-efficiency and high-precision identification and tracking of multiple UAV targets.
Regularizing Label-Augmented Generative Adversarial Networks Under Limited Data

Training generative adversarial networks (GANs) using limited training data is challenging since the original discriminator is prone to overfitting. The recently proposed label augmentation technique complements categorical data augmentation approaches for discriminator, showing improved data efficiency in training GANs but lacks a theoretical basis. In this paper, we propose a novel regularization approach for the label-augmented discriminator to further improve the data efficiency of training GANs with a theoretical basis. Specifically, the proposed regularization adaptively constrains the predictions of the label-augmented discriminator on generated data to be close to the moving averages of its historical predictions on real data, and vice versa. We theoretically establish a connection between the objective function with the proposed regularization and a f-divergence that is more robust than the previous reversed Kullback-Leibler divergence. Experimental results on various datasets and diverse architectures show the significantly improved data efficiency of our proposed method compared to state-of-the-art data-efficient GAN training approaches for training GANs under limited training data regimes.
Simultaneous Video Retrieval and Alignment

With the growth of the video streaming industry, video retrieval and video alignment are facing high levels of demand. Several studies have demonstrated the feasibility of these methods for various problems related to video retrieval and alignment independently, but test in a unified framework has never been done. However, in real-world applications, it is also concurrently necessary not only to find which video pairs are similar (video retrieval) but also to align the positions of the pair that are related (video alignment). In this paper, we present a new task that simultaneously retrieves and aligns videos. As a solution to this task, a simultaneous retrieval and alignment framework abbreviated as SRA is proposed, which is a two-stage approach consisting of a foreground proposal stage and a downstream stage to efficiently process untrimmed videos. Furthermore, two criteria are suggested to support the new task: a metric mAP@J assessing how highly the related videos are ranked and how well relevant positions are assigned in that video, and a dataset FIVR+A that includes video-level relationships and hierarchical segment-level annotations. Finally, we conduct multi-pronged analyses to assess how our approach handles the new task in various experiments.
ConnectomeNet: A Unified Deep Neural Network Modeling Framework for Multi-Task Learning

Despite recent advances in deep neural networks (DNNs), multi-task learning has not been able to utilize DNNs thoroughly. The current method of DNN design for a single task requires considerable skill in deciding many architecture parameters <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> before training begins. However, extending it to multi-task learning makes it more challenging. Inspired by findings from neuroscience, we propose a unified DNN modeling framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ConnectomeNet</i> that encompasses the best principles of contemporary DNN designs and unifies them with transfer, curriculum, and adaptive structural learning, all in the context of multi-task learning. Specifically, ConnectomeNet iteratively resembles connectome neuron units with a high-level topology represented as a general-directed acyclic graph. As a result, ConnectomeNet enables non-trivial automatic sharing of neurons across multiple tasks and learns to adapt its topology economically for a new task. Extensive experiments, including an ablation study, show that ConnectomeNet outperforms the state-of-the-art methods in multi-task learning such as the degree of catastrophic forgetting from sequential learning. For the degree of catastrophic forgetting, with normalized accuracy, our proposed method (which becomes 100%) overcomes mean-IMM (89.0%) and DEN (99.97%).
A hybrid security system for drones based on ICMetric technology

Recently, the number of drones has increased, and drones' illegal and malicious use has become prevalent. The dangerous and wasteful effects are substantial, and the probability of attacks is very high. Therefore, an anomaly detection and protection system are needed. This paper aims to design and implement an intelligent anomaly detection system for the security of unmanned aerial vehicles (UAVs)/drones. The proposed system is heavily based on utilizing ICMetric technology to exploit low-level device features for detection. This technology extracts the accelerometer and gyroscope sensors' bias to create a unique number known as the ICMetric number. Hence, ICMetric numbers represent additional features integrated into the dataset used to detect drones. This study performs the classification using a deep neural network (DNN). The experimental results prove that the proposed system achieves high levels of detection and performance metrics.
CPPE-5: Medical Personal Protective Equipment Dataset

We present a new challenging dataset, CPPE-5 (Medical Personal Protective Equipment), with the goal to allow the study of subordinate categorization of medical personal protective equipments, which is not possible with other popular data sets that focus on broad-level categories (such as PASCAL VOC, ImageNet, Microsoft COCO, OpenImages, etc). To make it easy for models trained on this dataset to be used in practical scenarios in complex scenes, our dataset mainly contains images that show complex scenes with several objects in each scene in their natural context. The image collection for this dataset focuses on: obtaining as many non-iconic images as possible and making sure all the images are real-life images, unlike other existing datasets in this area. Our dataset includes five object categories (coveralls, face shields, gloves, masks, and goggles), and each image is annotated with a set of bounding boxes and positive labels. We present a detailed analysis of the dataset in comparison to other popular broad-category datasets as well as datasets focusing on personal protective equipments, we also find that at present, there exist no such publicly available datasets. Finally, we also analyze performance and compare model complexities on baseline and state-of-the-art models for bounding box results. Our code, data, and trained models are available at https://git.io/cppe5-dataset.