Reviewerly/exHarmony huggingface dataset:
Multimodal Augmented Reality Applications for Training of Traffic Procedures in Aviation

Mid-air collision is one of the top safety risks in general aviation. This study addresses the description and experimental assessment of multimodal Augmented Reality (AR) applications for training of traffic procedures in accordance with Visual Flight Rules (VFR). AR has the potential to complement the conventional flight instruction by bridging the gap between theory and practice, and by releasing students’ time and performance pressure associated with a limited simulator time. However, it is critical to assess the impact of AR in the specific domain and to identify any potential negative learning transfer. Multimodal AR applications were developed to address various areas of training: guidance and feedback for the correct scanning pattern, estimation if an encountering traffic is on collision course and application of the relevant rules. The AR applications also provided performance feedback for collision detection, avoidance and priority decisions. The experimental assessment was conducted with 59 trainees (28 women, 31 men) assigned to an experimental group (AR training) and a control group (simulator training). The results of tests without AR in the flight simulator show that the group that trained with AR obtained similar levels of performance like the control group. There was no negative training effect of AR on trainees’ performance, workload, situational awareness, emotion nor motivation. After training the tasks were perceived as less challenging, the accuracy of collision detection has improved, and the trainees reported less intense negative emotions and fear of failure. Furthermore, a scanning pattern test in AR showed that the AR training group performed the scanning pattern significantly better than the control group. In addition, there was a significant gender effect on emotion, motivation and preferences for AR features, but not on performance. Women liked the voice interaction with AR and the compass hologram more than men. Men liked the traffic holograms and the AR projection field more than women. These results are important because they provide experimental evidence for the benefits of multimodal AR applications that could be used complementary to the flight simulator training.
Super-Resolution Reconstruction-Based Plant Image Classification Using Thermal and Visible-Light Images

Few studies have been conducted on thermal plant images. This is because of the difficulty in extracting and analyzing various color-related patterns and features from the plant image obtained using a thermal camera, which does not provide color information. In addition, the thermal camera is sensitive to the surrounding temperature and humidity. However, the thermal camera enables the extraction of invisible patterns in the plant by providing external and internal heat information. Therefore, this study proposed a novel plant classification method based on both the thermal and visible-light plant images to exploit the strengths of both types of cameras. To the best of our knowledge, this study is the first to perform super-resolution reconstruction using visible-light and thermal plant images. Furthermore, a method to improve the classification performance through generative adversarial network (GAN)-based super-resolution reconstruction was proposed. Through the experiments using a self-collected dataset of thermal and visible-light images, our method shows higher accuracies than the state-of-the-art methods.
Onboard Distributed Trajectory Planning through Intelligent Search for Multi-UAV Cooperative Flight

Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is proposed, which takes full advantage of intelligent discrete and continuous search algorithms. Firstly, the Monte Carlo tree search (MCTS) is used as the task allocation algorithm to solve the cooperative obstacle avoidance problem. Taking the task allocation decisions as the constraint, knowledge-based particle swarm optimization (Know-PSO) is used as the optimization algorithm to solve the onboard distributed cooperative trajectory planning problem. Simulation results demonstrate that the proposed intelligent MCTS-PSO search framework is effective and flexible for multiple UAVs to conduct the cooperative trajectory planning and obstacle avoidance. Further, it has been applied in practical experiments and achieved promising results.
Backward private dynamic searchable encryption with update pattern

Symmetric Searchable Encryption (SSE) realizes to outsource data to an untrusted server privately. Dynamic SSE (DSSE) is a practical application which supports data addition and deletion. However, these update operations incur leakage during search and update queries. Therefore, forward and backward privacy were introduced to address the update and search operation leakage in DSSE. Forward privacy was extensively studied in the literature. Backward privacy with three types of revealing level was studied little. The strongest backward privacy was achieved by the expensive tools, impractical! Wheras the practical DSSEs gained weak backward privacy only. In this paper, we present a practical DSSE scheme to achieve stronger backward security—backward privacy with update pattern. We take advantage of key-homomorphic pseudorandom functions and our scheme requires only 1 roundtrip to get search result.
Multidimensional grid-based clustering with local differential privacy

Privacy-preserving clustering has received increasing research attention in recent years. Local differential privacy (LDP) is a privacy model without relying on trusted third parties. It plays a crucial role in distributed privacy-preserving clustering. Most previous methods focus on partition-based clustering (e.g., k-means), which necessitates many iterative interactions. Massive iterations cause a division of the privacy budget and increase the amount of individual noise, affecting the clustering utility. To address this issue, we turn to grid-based clustering and design the GC-LDP algorithm to balance the privacy and clustering utility with only three rounds of interactions. In GC-LDP, our core contribution is to develop a non-uniform grid division method via the coefficient of variation (CV), which can generate a grid structure approximating the global data distribution within two rounds of interactions. Besides, we designed a new perturbation mechanism to reduce the amount of individual noise injection. Further, a cell aggregation method is developed by exploring the relative density difference among grids to achieve multi-density clustering. Theoretical analysis and experiments on real-world datasets show that GC-LDP can obtain high-quality clustering results while satisfying local differential privacy.
What is Missing in XAI So Far?

Abstract With the perspective on applications of AI-technology, especially data intensive deep learning approaches, the need for methods to control and understand such models has been recognized and gave rise to a new research domain labeled explainable artificial intelligence (XAI). In this overview paper we give an interim appraisal of what has been achieved so far and where there are still gaps in the research. We take an interdisciplinary perspective to identify challenges on XAI research and point to open questions with respect to the quality of the explanations regarding faithfulness and consistency of explanations. On the other hand we see a need regarding the interaction between XAI and user to allow for adaptability to specific information needs and explanatory dialog for informed decision making as well as the possibility to correct models and explanations by interaction. This endeavor requires an integrated interdisciplinary perspective and rigorous approaches to empirical evaluation based on psychological, linguistic and even sociological theories.
USE OF DIGITAL SERVICES AND TOOLS IN THE PROCESS OF PROFESSIONAL TRAINING OF FUTURE MUSIC TEACHERS

The scientific article is devoted to the issue softraining future music teachers, in particular, the use of modern digital services, tools, information technologies and resources in distance and blended learning in institutions of higher pedagogical education. The purpose of the article consists ingeneralization of the information on modern teaching tools, in particular digital services and tools, which are expedient to use indistance and blended learning format in the training of future music teachers. The legal framework is presented, which determines the need for the active implementation of computer educational technologies and increasing the digital competence of scientific and pedagogical workers, in particular: the Framework of Digital Competence for Citizens of Ukraine (2021), the Concept of the Development of Digital Competences (2021), the Conceptual and Reference Framework Digital Competence of Pedagogical and Scientific-Pedagogical Workers (project 2021), etc. The essence of the concept "digital competence" isrevealed. The classification of modern teaching aids, which are used when training future music teachers indistance and blended formofeducation, is presented: traditional (simpleandcomplex), multimedia (traditional, electronic, hardware, software), mobile (toolsforlearningmobilecontent, mobileapplications), digital (services, educationalplatforms). The article pays special attention to digital service sand tools used for teaching and learning music in Ukraine and China. The requirements for the effective use of distance and blended learning tools are defined. It was concluded that the use of distance and blended learning tools, in particular multimedia, mobile and digital tools, is aimedat forming both the professional and digital competence of future music teachers, the development of the irdigitalliteracy. Prospects for furtherscientific research are outlined, which consist in the study and analysis of the experience of using digital tools in the process of training music teachers in the People's Republic of China in the "Internet+" era.
Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population

In Chile, 18% of the population is over 60 years old and is projected to reach 31% in three decades. An aging population demands the development of strategies to improve quality of life (QoL). In this randomized trial, we present the implementation and evaluation of the Quida platform, which consists of a network of unintrusive sensors installed in the houses of elderly participants to monitor their activities and provide assistance. Sixty-nine elderly participants were included. A significant increase in overall QoL was observed amongst participants allocated to the interventional arm (p < 0.02). While some studies point out difficulties monitoring users at home, Quida demonstrates that it is possible to detect presence and movement to identify patterns of behavior in the sample studied, allowing us to visualize the behavior of older adults at different time intervals to support their medical evaluation.
A Deep Learning Based Data Recovery Approach for Missing and Erroneous Data of IoT Nodes

Internet of things (IoT) nodes are deployed in large-scale automated monitoring applications to capture the massive amount of data from various locations in a time-series manner. The captured data are affected due to several factors such as device malfunctioning, unstable communication, environmental factors, synchronization problem, and unreliable nodes, which results in data inconsistency. Data recovery approaches are one of the best solutions to reduce data inconsistency. This research provides a missing data recovery approach based on spatial-temporal (ST) correlation between the IoT nodes in the network. The proposed approach has a clustering phase (CL) and a data recovery (DR) phase. In the CL phase, the nodes can be clustered based on their spatial and temporal relationship, and common neighbors are extracted. In the DR phase, missing data can be recovered with the help of neighbor nodes using the ST-hierarchical long short-term memory (ST-HLSTM) algorithm. The proposed algorithm has been verified on real-world IoT-based hydraulic test rig data sets which are gathered from things speak real-time cloud platform. The algorithm shows approximately 98.5% reliability as compared with the other existing algorithms due to its spatial-temporal features based on deep neural network architecture.
Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval

Remote sensing technology has advanced rapidly in recent years. Because of the deployment of quantitative and qualitative sensors, as well as the evolution of powerful hardware and software platforms, it powers a wide range of civilian and military applications. This in turn leads to the availability of large data volumes suitable for a broad range of applications such as monitoring climate change. Yet, processing, retrieving, and mining large data are challenging. Usually, content-based remote sensing image (RS) retrieval approaches rely on a query image to retrieve relevant images from the dataset. To increase the flexibility of the retrieval experience, cross-modal representations based on text–image pairs are gaining popularity. Indeed, combining text and image domains is regarded as one of the next frontiers in RS image retrieval. Yet, aligning text to the content of RS images is particularly challenging due to the visual-sematic discrepancy between language and vision worlds. In this work, we propose different architectures based on vision and language transformers for text-to-image and image-to-text retrieval. Extensive experimental results on four different datasets, namely TextRS, Merced, Sydney, and RSICD datasets are reported and discussed.
CNN Based Image Classification of Malicious UAVs

Unmanned Aerial Vehicles (UAVs) or drones have found a wide range of useful applications in society over the past few years, but there has also been a growth in the use of UAVs for malicious purposes. One way to manage this issue is to allow reporting of malicious UAVs (e.g., through a smartphone application) with the report including a photo of the UAV. It would be useful to able to automatically identify the type of UAV within the image in terms of the manufacturer and specific product identification using a trained image classification model. In this paper, we discuss the collection of images for three popular UAVs at different elevations and different distances from the observer, and using different camera zoom levels. We then train 4 image classification models based upon Convolutional Neural Networks (CNNs) using this UAV image dataset and the concept of transfer learning from the well-known ImageNet database. The trained models can classify the type of UAV contained in unseen test images with up to approximately 81% accuracy (for the Resnet-18 model), even though 2 of the UAVs represented in the UAV image dataset are visually similar, and the fact that the UAV image dataset contains images of UAVs that are a significant distance from the observer. This provides a motivation to expand the study in the future to include more UAV types and other usage scenarios (e.g., UAVs carrying loads).
Hey Max, Can You Help Me? An Intuitive Virtual Assistant for Industrial Robots

Assisting employees in acquiring the knowledge and skills necessary to use new services and technologies on the shop floor is critical for manufacturers to adapt to Industry 4.0 successfully. In this paper, we employ a learning, training, assistance-formats, issues, tools (LTA-FIT) approach and propose a framework for a language-enabled virtual assistant (VA) to facilitate this adaptation. In our system, the human–robot interaction is achieved through spoken natural language and a dashboard implemented as a web-based application. This type of interaction enables operators of all levels to control a collaborative robot intuitively in several industrial scenarios and use it as a complementary tool for developing their competencies. Our proposed framework has been tested with 29 users who completed various tasks while interacting with the proposed VA and industrial robots. Through three different scenarios, we evaluated the usability of the system for LTA-FIT based on an established system usability scale (SUS) and the cognitive effort required by the users based on the standardised NASA-TLX questionnaire. The qualitative and quantitative results of the study show that users of all levels found the VA user friendly with low requirements for physical and mental effort during the interaction.
A Low Memory Requirement MobileNets Accelerator Based on FPGA for Auxiliary Medical Tasks

Convolutional neural networks (CNNs) have been widely applied in the fields of medical tasks because they can achieve high accuracy in many fields using a large number of parameters and operations. However, many applications designed for auxiliary checks or help need to be deployed into portable devices, where the huge number of operations and parameters of a standard CNN can become an obstruction. MobileNet adopts a depthwise separable convolution to replace the standard convolution, which can greatly reduce the number of operations and parameters while maintaining a relatively high accuracy. Such highly structured models are very suitable for FPGA implementation in order to further reduce resource requirements and improve efficiency. Many other implementations focus on performance more than on resource requirements because MobileNets has already reduced both parameters and operations and obtained significant results. However, because many small devices only have limited resources they cannot run MobileNet-like efficient networks in a normal way, and there are still many auxiliary medical applications that require a high-performance network running in real-time to meet the requirements. Hence, we need to figure out a specific accelerator structure to further reduce the memory and other resource requirements while running MobileNet-like efficient networks. In this paper, a MobileNet accelerator is proposed to minimize the on-chip memory capacity and the amount of data that is transferred between on-chip and off-chip memory. We propose two configurable computing modules: Pointwise Convolution Accelerator and Depthwise Convolution Accelerator, to parallelize the network and reduce the memory requirement with a specific dataflow model. At the same time, a new cache usage method is also proposed to further reduce the use of the on-chip memory. We implemented the accelerator on Xilinx XC7Z020, deployed MobileNetV2 on it, and achieved 70.94 FPS with 524.25 KB on-chip memory usage under 150 MHz.
The Multimodal Sentiment Analysis of Online Product Marketing Information Using Text Mining and Big Data

Currently, the internet is increasingly popular. More people are used to sharing their feelings about various things on the internet. Online product marketing information is also growing. How to mine the required information from the massive information with the support of big data technology has become a big problem. Thereby, based on the text mining of online product marketing information, this work discusses the text preprocessing methods and the temporal convolution network (TCN) based on a convolutional neural network (CNN). Moreover, on this basis, multimodal attention mechanism (AM) and cross-modal transformer structure are added to build a TCN based on AM (AM-TCN) model to analyze the multimodal emotion of online product marketing information. The results show that the accuracy of the AM-TCN model is 2.88% higher than that of the TCN model alone, and F1 is 3.47% higher. Moreover, the accuracy rate of the AM-TCN is 1.22% higher than that of the next highest recurrent multistage fusion network, and the F1 value is 0.95% higher.
Robot Path Planning of Improved Adaptive Ant Colony System Algorithm Based on Dijkstra

Path planning is one of the key technologies of robot. Aiming at the problems of slow convergence speed and inefficient search of traditional Ant Colony System Algorithm, an adaptive Ant Colony System Algorithm based on Dijkstra is proposed in the paper. Firstly, Dijkstra algorithm is applied to searching the initial path in the grid environment, constructing the initial path, optimizing the initial pheromone in the region, therefore, the Ant Colony System Algorithm avoid falling into blind search in the initial stage; In the transition probability, the disguised angle probability function and parameter adaptive pseudo-random proportion rule are introduced to improve the search efficiency and convergence speed of the algorithm, and eliminate the inferior ant path; Finally, B-spline interpolation curve is used to smooth the path. Compared with the traditional Ant Colony System Algorithm, the simulation results in the grid environment demonstrating its effectiveness to improve convergence speed and to enhance search efficiency are provided. The characteristics of the improved Ant Colony System Algorithm are faster convergence speed and better planning.
Smart Helmet using IoT for Alcohol Detection and Location Detection System

Every day, a large number of people die in road accidents around the world. The use of a smart helmet band is an effective solution to the problem. The smart helmet band is an innovative concept that makes motorcycle riding safer than ever before. By utilizing GPS technology, the smart helmet band can assist in the event of an accident. There are basically 3 sections into which the system is spilt, a helmet circuit, a mobile application and car circuits. The helmet circuit features an IR and alcohol detection sensor at first. Road accidents are fast increasing in developing countries like India, as transportation technology advances and the total number of cars increases. This technological innovation also exacerbated traffic hazards. Two-wheelers are responsible for 25% of all traffic fatalities. As a result, traffic accidents become more common, resulting in severe loss of life due to insufficient emergency assistance. If these two conditions are not met, the Smart Helmet part of the preventative prevents the relay from turning on the engine. The microprocessor controls the function of the relay, and as a result, the ignition.
A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing

Low latency video transmission is gaining importance in time-critical applications using real-time cloud-based systems. Cloud-based Virtual Reality (VR), remote control, and AI response systems are emerging use cases that demand low latency and good reliability. Although there are many video transmission schemes that claim low latency, they vary over different network conditions. Therefore, it is necessary to develop methods that can accurately measure end-to-end latency online, continuously, without any content modification. This research brings these applications one step closer to addressing these next generation use cases. This paper analyzes the cause of end-to-end latency within a video transmission system, and then proposes three methods to measure the latency: timecode, remote online, and lossless remote video online. The corresponding equipment was designed and implemented. The actual measurement of the three methods using related equipment proved that our proposed method can accurately and effectively measure the end-to-end latency of the video transmission system.