Miracle-dz/UniFAR-FTUs huggingface dataset:
Dense Adaptive Grouping Distillation Network for Multimodal Land Cover Classification with Privileged Modality

Multimodal land cover classification (MLCC) is a fundamental problem in remote sensing interpretation, which can obtain excellent performance on account of the complementary information between the optical and SAR modalities. However, it is usually impossible to obtain multimodal data at the same time, due to the restriction of imaging conditions. When one of the modalities data is completely missing during test phase, classical multimodal learning methods might not be able to handle the MLCC task with privileged modality. In this paper, we propose an efficient Dense Adaptive Grouping Distillation Network (DAGDNet), which learns privileged information from available modalities in the train sets, and improves the classification performance in the test sets when one modality data is scarce. More specifically, to relieve the heterogeneous gaps between different modalities and then transfer the privileged information, we propose an Interactive Gated-based Feature Grouping Module (IG-FGM), which decomposes multimodal features into modalities-shared and modality-specific components to realize the decoupling of multimodal features and grouping distillation. Furthermore, the IG-FGM is inserted into different layers of the “teacher" network to implement progressive blending of multi-modalities. Then, to adaptively highlight the importance of hierarchical features distillation and grouping distillation, we propose a Multi-stage Adaptive Distillation Learning (MS-ADL) strategy so that the weights of different distillation losses are required to change continuously along with the training process. Finally, we evaluate the superior performances of our model on representative co-registered optical and SAR datasets.
Modified Bilateral Filter for Feature Enhancement in Mesh Denoising

Mesh models resulting from scanners are inevitably noisy; hence, removing the noise in scanned meshes becomes an essential task in the services using three-dimensional mesh models. Filtering-based methods are simple but have some constraints in eliminating noise because they degrade the features in the mesh models while removing the noise. In this study, we design a feature enhancement filter that is combined with a conventional denoising filter to remove the noise while enhancing the features. The designed enhancement filter is applied only to the feature areas in the mesh model. Results from experiments on synthetic and natural scanned models validate that the proposed method can restore false features by integrating conventional filtering-based methods, and outperforms other state-of-the-art methods.
Study on the methods of train locating speed measuring and data transmission for maglev train

This paper analyzes the characteristics of Maglev signal and communication system, since the rail circuit can not be used for Maglev, emphases are put on the applications of microwave and rail cable for train locating speed measuring and data transmission, formulas of these applications are given, and these applications are compared with each other. Based on these analyses, and taking the current status of maglev in our country into account, solutions of train location, speed measuring and data transmission for current stage is provided and relevant test results are given.
Real-time heart rate sonification for athletes

In this paper, we describe a sonification system that delivers instantaneous heart rate information to an athlete. The sonification system uses a textitPolar H7 heart rate sensor to monitor the heart rate of the user. We describe the techniques used for the processing of the captured heart rate data, its sonification and the relevance of the feedback methods via both a subjective and objective analysis. Our tests prove the strength of having a method for the sonification of instantaneous heart rate. Our testing procedure also serves as a pointer for evaluation of the efficacy of sonification methods in general.
Evidence on the role of social media in the illegal trade of Iranian wildlife

The combination of increasing trade across an ever more globalized world and the ubiquity of social media access has led to unprecedented levels of wildlife exploitation. In this study, we opportunistically surveyed Instagram and Telegram from 2019 to 2020, two of Iran's most prominent social media platforms, for advertisements of illegally captured wildlife in Iran. In total we documented 305 advertisements for 63 species, including birds (29%), amphibians (27%), reptiles (26%), and mammals (17%). Trade was most active in June, which may be due to increased availability of young animals, following spring births. The majority of the species advertised for sale (65%) were classified by the IUCN Red List of Threatened Species as Least Concern, and 5% of the species we documented as being traded are Endangered. Some Endangered species advertised for sale were Caspian seal and Saker falcon. While the sale of all species is illegal in Iran, 25% of species were also listed on CITES as being prohibited for international trade. However, these domestic and international laws are not well‐enforced within Iran, in light of the scale of open trade we observed. We recommend that authorities devote more time to monitoring these online platforms, and that resources are provided to in‐country enforcement efforts during the spring and summer, the observed peak of capture and trade. We also suggest that further research be conducted into the sources of wildlife, motivations for selling wildlife, and motivations for purchasing wildlife.
Systematic study of physicochemical and electrochemical properties of carbon nanomaterials

Carbon nanomaterials exhibit exceptional properties and broad horizon applications, where graphene is one of the most popular allotropes of this family due to its astounding performance in every stratum vis-à-vis other classical materials. The large surface area of 2630 m2 g−1, high electrical conductivity, and electron mobility of non-toxic graphene nanomaterials serve as the building blocks for supercapacitor studies. In this article, comparative studies are carried out between electrochemically exfoliated graphene sheets (GSs), solvothermally synthesized graphene quantum dots (GQDs) and acid refluxed carbon nanotubes (CNTs) as an energy storage electrode nanomaterial through cyclic voltammetry (CV). The electrochemical properties of the materials are well correlated with the physicochemical characteristics obtained from Raman, Fourier-transform infrared, and absorption spectroscopy. Thin GSs (0.8–1 nm) and small size (6–10 nm) GQDs fabricated by using laboratory-grade 99% purity graphite rods resulted in promising low-cost materials at mass scale as compared to conducting CNTs. The 0D graphene quantum dots proved to be an excellent energy electrode material in an alkaline electrolyte solution compared to other carbon nanomaterials. The distinct characteristic features of GQDs, like superior electrical properties, large surface area, and abundant active sites make them an ideal candidate for utilization in supercapacitors. The GQDs exhibited an enhanced specific capacitance of 113 F g−1 in 6 mol L−1 KOH through cyclic voltammetry.
Research on internal positioning method of pipeline robot based on data fusion and point cloud registration

Limited by the shielding effect from the pipeline structure and materials, pipeline robots usually rely on sensors such as odometers and inertial measurement units (IMUs), resulting in significant error accumulation effect and difficulty in ameliorating positioning accuracy. In this paper, we present an internal positioning method in the pipeline based on data fusion and point cloud registration to improve this problem. First of all, the Extended Kalman Filter (EKF) fuses the odometer and IMU data, acquiring the estimated pose's initial value and calculating the derived position. Secondly, the conditional filtering of the known pipeline point cloud is carried out using the derived position to solve the inaccurate problem caused by the significant difference in the number of points between the two groups in point cloud matching. Eventually, the improved iterative closest point (ICP) algorithm is applied to the filtered point cloud and the data of the two line-laser sensors, obtaining accurate pose information of the robot. The proposed method enables the robot to locate only by its sensor data, which is especially suitable for use in pipelines where the environment is harsh and wireless signals are often not received. In our experiments, we first demonstrate the effectiveness and necessity of point cloud filtering, and then compare our algorithm with the traditional intralocalization algorithms under the Gazebo/ROS simulation platform. The results show that the positioning accuracy of our method is better than the traditional internal positioning method.
An Octagon Patch Antenna for OAM Mode Generation

This paper presents the generation of various OAM modes using single octagonal shape patch antenna. The proposed OAM antenna is compact in size and can generate multiple OAM modes using proper feeding techniques. The proposed OAM antenna is designed and simulated for resonating frequency of 5.2 GHz, WLAN band and it can generate OAM beam with topology charge l = 0, ±1, ±2 and ±3. Beam of topology charge ±3 is also generated but with little distortion.
All Fiber-Optic Multigas (NH₃, NO₂, and CO) Sensor Based on MoWS₂ Coated Fiber

In this paper, we fabricate and demonstrate Molybdenum Tungsten Disulfide (MoWS2) composite based all-optical gas sensor for environmental monitoring applications. This proposed sensor is fabricated by coating the synthesized MoWS2 nanosheets on a side polished fiber (SPF) and analyzed the sensing performance of three important hazardous gases such as ammonia (NH3), Nitrogen dioxide (NO2) and Carbon monoxide (CO). The MoWS2 nanosheets are synthesized using the hydrothermal exfoliation technique. This novel nanocomposite offers broadband absorption and desorption of gas molecules due to more edge sites, which in turn enhances the sensing properties for various gases. Thus, the MoWS2 coated SPF sensor utilized for sensing three hazardous gases such as NH3, NO2 and CO. Besides, the gas sensor is operated at normal humidity (60–75%) and room temperature for varying gas concentration from 0 to 500 ppm. The sensor shows high sensitivity of 86, 72, and 48% for various gases such as NH3, NO2 and CO respectively. Furthermore, the results exhibit rapid response and fast recovery time, which are measured as 21 s and 60 s for NH3 gas, whereas 42 s and 71 s for NO2 gas and 93 s and 56 s for CO gas. The selectivity test for the sensor is conducted with methane and formaldehyde along with NH3, CO, and NO2 gases in the same experimental condition. Further, the results confirm that the sensor possesses a high degree of selectivity for NH3 gas among the other gases. Hence, the proposed sensor is eminently suitable to detect hazardous gases in high traffic and industrial fields.
A longitudinal inter-vehicle distance controller application for autonomous vehicle platoons

Autonomous vehicle platoons are a promising solution to road safety, efficient road utilization, emission reduction, among other problems facing today’s transportation industry. However, consistently maintaining the desired inter-vehicle distance is one of the major problems facing autonomous vehicle platoons. In this study, we propose a proportional–integral–derivative (PID)-based cost-efficient algorithm to control the longitudinal inter-vehicle distance between successive members of an autonomous vehicle platoon. In our approach, calculations of the control algorithm are decentralized, and the data used in the control algorithm is obtained using one sensor per platoon member making the algorithm cost-efficient both computationally and financially. The proposed algorithm was implemented using the Robot Operating System (ROS) and applied to 3D vehicle models in simulations designed to mimic the natural environment in order to demonstrate and evaluate the suitability of the proposed algorithm for demanding and applicable scenarios. We performed meticulous simulations using the ROS framework in conjunction with the gazebo platform. In the proposed approach, the desired inter-vehicle distance between platoon members was successfully kept with a maximum absolute error of 5 m under any given scenario at any given time while maintaining platoon formation and ensuring that no collisions occur among platoon members.
DE-SEP: Distance and Energy Aware Stable Election Routing Protocol for Heterogeneous Wireless Sensor Network

Sensing technology has undergone a revolution as a result of technological advancements, where the wireless sensor network (WSN) is considered as a major research area in recent times. WSN is made up of battery-powered multiple sensor nodes with limited energy, which eventually encourage the researchers in designing an energy efficient routing protocol to prolong the network lifetime. Hierarchical routing protocols can play an important role to improve the network’s energy efficiency utilizing the threshold-based cluster head (CH) selection techniques. In this article, a cluster based proactive routing protocol named as Distance and Energy Aware Stable Election Routing Protocol (DE-SEP) is proposed to ensure optimum energy conservation. Here, both the energy and distance criteria are incorporated in CH selection to give priority for comparatively higher energy nodes and nearby nodes from base station (BS) to be selected as CH. Moreover, the proposed protocol imposes a limit on the number of CHs so that an optimum number of CHs can be utilized to avoid unnecessary cluster formation and reduce energy consumption. Simulation results show that DE-SEP outperforms the existing baseline protocols like P-SEP, M-SEP and SEP in terms of various performance matrices such as network lifetime, stability period, half lifetime, throughput, and normalized residual energy. In particular, the performance of DE-SEP protocol in terms of normalized residual energy is increased by a maximum of 5%, 41% and 41% in comparison to P-SEP, M-SEP and SEP respectively.
Text-to-Face Generation with StyleGAN2

Synthesizing images from text descriptions has become an active research area with the advent of Generative Adversarial Networks. The main goal here is to generate photo-realistic images that are aligned with the input descriptions. Text-to-Face generation(T2F) is a sub-domain of Text-to-Image generation(T2I) that is more challenging due to the complexity and variation of facial attributes. It has a number of applications mainly in the domain of public safety. Even though several models are available for T2F, there is still the need to improve the image quality and the semantic alignment. In this research, we propose a novel framework, to generate facial images that are well-aligned with the input descriptions. Our framework utilizes the highresolution face generator, StyleGAN2, and explores the possibility of using it in T2F. Here, we embed text in the input latent space of StyleGAN2 using BERT embeddings and oversee the generation of facial images using text descriptions. We trained our framework on attributebased descriptions to generate images of 1024x1024 in resolution. The images generated exhibit a 57% similarity to the ground truth images, with a face semantic distance of 0.92, outperforming state-of-the-artwork. The generated images have a FID score of 118.097 and the experimental results show that our model generates promising images.
Semiconductor Factory and Equipment Clock Synchronization for e-Manufacturing | NIST

This report from the Data Utilization project (MFGM027M) describes the potential methods, challenges, and recommendations for addressing factory and equipment clock synchronization and time stamping and discusses why these are critical to the industry as the upcoming generations of tools and fabrication facilities are being built. It will also recommend areas for standardization and best practices for clock synchronization and time stamping. Official contribution of the National Institute of Standards and Technology; not subject to copyright in the United States. Disclaimer: Any mention of commercial products within this document is for information only; it does not imply recommendation or endorsement.
Jobs as Gordian Knots: A New Perspective Linking Individuals, Tasks, Organizations, and Institutions

Abstract Jobs fundamentally influence and are influenced by individuals, organizations, and societies. However, jobs themselves are largely conceptualized in an atomized and disembodied way. They are understood as being designed, altered, and dissolved and bringing their consequences one at a time. I advance an alternative view of jobs as a system of ties that span jobs, organizations, and the environment beyond organizational boundaries. These ties create Gordian Knots that hold jobs in place and explain how they change. I illustrate the model with case study evidence and propose an agenda for research on jobs as organizational systems.
Tackling sparse cost in safe reinforcement learning for obstacle avoidance

Safe Deep Reinforcement Learning (SDRL) has been studied in many domains. In previous work, based on Augmented Lagrangian Method, we proposed DDPG-ALM algorithm in an industrial oil pump case study to learn a safe controller. In this paper, we apply our algorithm to a more general platform, Safety Gym, to test the performance of our algorithm. To solve the issue of sparse cost in Safety Gym, we propose the notion of State Shaping which aggregates safe states to pay more attention to the unsafe states. Furthermore, to pay more attention to safety-related experience, we combine the advantages of Prioritized Experience Replay and Double Replay Buffer and propose a method called Safe Prioritized Experience Replay (SPER). Our experiments show that, State Shaping and SPER can deal with the sparse cost well and provide safer policies. Ablation studies show that State Shaping reduces average episode cost of all evaluated trajectories from 2.67 to 0.45 and SPER reduces from 0.45 to 0.20 while PER only reduces to 0.30. Moreover, our DDPG-ALM based algorithm not only automatically finds a balance between reward and cost, but also converges faster than other existing benchmark algorithms.
Local-Global Graph Pooling via Mutual Information Maximization for Video-Paragraph Retrieval

As a task of cross-modal retrieval between long videos and paragraphs, video-paragraph retrieval is a non-trivial task. Unlike traditional video-text retrieval, the video in video-paragraph retrieval usually contains multiple clips. Each clip corresponds to a descriptive sentence; all the sentences constitute the corresponding paragraph of the video. Previous methods for video-paragraph retrieval usually encode videos and para-graphs from segment-level (clips and sentences) and overall-level (videos and paragraphs). However, there are also contents about actions and objects that exist in the segment. Hence, we propose a Local-Global Graph Pooling Network (LGGP) via Mutual Information Maximization for video-paragraph retrieval. Our model disentangles videos and paragraphs into four levels: overall-level, segment-level, motion-level, and object-level. We construct the Hierarchical Local Graph (segment-level, motion-level, and object-level) and the Hierarchical Global Graph (overall-level, segment-level, motion-level, and object-level), respectively, for semantic interaction among different levels. Meanwhile, to obtain hierarchical pooling features with fine-grained semantic information, we design hierarchical graph pooling methods to maximize the mutual information between pooling features and corresponding graph nodes. We evaluate our model on two video-paragraph retrieval datasets with three different video features. The experimental results show that our model establishes state-of-the-art results for video-paragraph retrieval. Our code will be released at https://github.com/PengchengZhang1997/LGGP.
The Mediating Role of Self-Alienation between Fear of Covid-19 and Smartphone Addiction

: The Covid-19 pandemic, which affected all areas of life, also continues to affect addiction situations. The present study was conducted to investigate the relationships among self-alienation, fear of Covid-19 and smartphone addiction. Within the scope of the research, 4 hypotheses were tested within the quantitative design. Using the “Structural Equation Model” (SEM), the mediating role of “Self-alienation” between “Fear of Covid-19” and “Smartphone Addiction” was examined. This study included 619 university students who were enrolled in the 2021-2022 academic year. These students were 429 (69.3%) female and 190 (30.7%) male. Participants are studying via distance education. Data were obtained with “Personal Information Form”, “Fear of Covid-19 Scale”, “Alienation Scale” and “Smartphone Addiction Scale”. As a result of the research, the mediating role of self-alienation was found. In addition, the participants got high level scores of “Fear of Covid-19”, “Self-alienation” and “Smartphone Addiction”, due to the epidemic.