Reviewerly/exHarmony huggingface dataset:
Design of UAV target tracking controller based on visual servo

Abstract The task of tracking a moving target by the UAV has two major challenges: one is that the multi-angle and multi-scale changes caused by the target’s movement make the target detection more difficult, the other is that the stability and accurate of target tracking are difficult to guarantee. In order to solve these problems, using monocular vision to get the image information of the target and YOLOv4 is used to detect multi-angle and multi-scale moving targets. Then based on the image visual servo control method, the PID controller is designed, and the deviation between the target pixel and the ideal pixel is used as the input, the output of the controller is the desired pixel speed of the UAV in image coordinate system. the tracking speed of the UAV under the machine system is solved according to the image Jacobian matrix, and a tracking strategy is designed to improve the tracking accurate and stability. In the process of target tracking, the difference between the x-axis and Y-axis coordinates of the target center point and the image center point is less than 30 pixels. The results show that the proposed algorithm can keep the target in the center of the image.
A store-and-forward neural network to solve multicriteria optimal path problem in time-dependent networks

Abstract This paper introduces the constrained multi-objective optimal path problem in time-dependent networks. In the existing literatures, the constraints are all imposed on the objective function while the problem constraints are related to the non-objective function. It is the difference that makes the traditional algorithm unable to get a better solution quality. In this light, we propose a store-and-forward neural network (SFNN) that finds the better result. In the design of SFNN, the topology of neural network is the same as that of time-varying network, and each node is designed as store-and-forward neuron. Each neuron transmits information to other neurons by sending signals. The experimental results show that compared with the traditional methods, the accuracy is significantly improved when the calculation time is acceptable.
Passive decoy state phase matching quantum key distribution

In this paper, a passive decoy state phase matching quantum key distribution scheme is proposed based on the heralded pair coherent state light source to make the protocol more practical and safe. Without changing the intensity of the light source, we can derive four different data sets by dividing the raw key data into four groups according to the combined results of detectors in source end. The scheme can make use of the different data sets to estimate the parameters and extract the secret key. The key generation rate formula is derived after the parameter estimation. The performance of the proposed scheme is analyzed with and without taking statistical fluctuation into consideration. The simulation results show that compared with the existing active decoy-state method, the proposed passive method has slightly lower key rate when the transmission distance is short, and has obviously higher key rate when the transmission distance exceeds 406[Formula: see text]km. The maximum safety transmission distance of the proposed scheme is 452[Formula: see text]km which is relatively further. According to the influence of statistical fluctuation, the performance of the proposed scheme declines as the length of the data falls. Even though the data size is [Formula: see text], the max safety transmission distance is also more than 400[Formula: see text]km.
Teaching HCI Engineering: Four Case Studies

The paper presents the work carried out at the HCI Engineering Education workshop, organised by IFIP working groups 2.7/13.4 and 13.1. It describes four case studies of projects and exercises used in Human-Computer Interaction Engineering courses. We propose a common framework for presenting the case studies and describe the four case studies in detail. We then draw conclusions on the differences between the presented case studies that highlight the diversity and multidisciplinary aspects to be taught in a Human-Computer Interaction Engineering course. As future work, we plan to create a repository of case studies as a resource for teachers.
Developmental Research for Industrial Image Processing in the Engineering Education to Learn SDGs

Industrial image processing technology is widely introduced and applied in various fields such as industrial plants, bio-medical industry, agro technology, and also environmental fields. In addition, image acquisition devices are getting more compact and installed onto mobile phones and handy terminals. Which means that we always carry the devices, and can easily take images (still image and moving pictures) of good quality with high resolution, anytime and anywhere. In the research field, 2D code, known as QR code or Data Matrix, has a great potential for industrial applications. As we already know, QR code has been spread and used with various usage likewise ID recognition, URL display on the sticker, and/or the financial transaction's confirmation processing. Not only QR code, but Data Matrix is also frequently used as the printed tag on various material such as metal, wood, or plastic parts. These are used as a product ID or serial number to be recognized by using a mobile terminal. The 2D code technology can be a powerful tool to check and trace the marketing channel of each part of the product. On the other hand, Sustainable Development Goals (SDGs) have been proposed since 2015, researched and introduced to improve our environment and life. This paper describes the proposals of new application technology related to the concept of SDGs. The main contents are based on science and technology, but these have been developed and implemented by the young students of the college. The process and the details are described in the paper.
Action Recognition System for Senior Citizens Using Depth Image Colorization

This paper describes about the system which can be used at the care center for the purpose of elderly action recognition using depth camera. The depth image colorization is used for compression, visualization, and person detection process. The YOLOv5 (You Only Look Once) algorithm is used as object detector. The space-time features are extracted from depth sequences and they are recognized by linear SVM (Support Vector Machine) classifier. The random image sequences are generated for testing to recognize six actions. The results show that this system can detect the various actions with the average of 92% accuracy for different durations.
Training Entire-Space Models for Target-oriented Opinion Words Extraction

Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the sentence, TOWE extracts the corresponding opinion words for the aspect term. TOWE has two types of instance. In the first type, aspect terms are associated with at least one opinion word, while in the second type, aspect terms do not have corresponding opinion words. However, previous researches trained and evaluated their models with only the first type of instance, resulting in a sample selection bias problem. Specifically, TOWE models were trained with only the first type of instance, while these models would be utilized to make inference on the entire space with both the first type of instance and the second type of instance. Thus, the generalization performance will be hurt. Moreover, the performance of these models on the first type of instance cannot reflect their performance on entire space. To validate the sample selection bias problem, four popular TOWE datasets containing only aspect terms associated with at least one opinion word are extended and additionally include aspect terms without corresponding opinion words. Experimental results on these datasets show that training TOWE models on entire space will significantly improve model performance and evaluating TOWE models only on the first type of instance will overestimate model performance.
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding

In the age of large transformer language models, linguistic evaluation play an important role in diagnosing models' abilities and limitations on natural language understanding.However, current evaluation methods show some significant shortcomings.In particular, they do not provide insight into how well a language model captures distinct linguistic skills essential for language understanding and reasoning.Thus they fail to effectively map out the aspects of language understanding that remain challenging to existing models, which makes it hard to discover potential limitations in models and datasets.In this paper, we introduce CUR-RICULUM as a new format of NLI benchmark for evaluation of broad-coverage linguistic phenomena.CURRICULUM contains a collection of datasets that covers 36 types of major linguistic phenomena and an evaluation procedure for diagnosing how well a language model captures reasoning skills for distinct types of linguistic phenomena.We show that this linguisticphenomena-driven benchmark can serve as an effective tool for diagnosing model behavior and verifying model learning quality.In addition, our experiments provide insight into the limitation of existing benchmark datasets and state-of-the-art models that may encourage future research on re-designing datasets, model architectures, and learning objectives. 1 .
Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models

With many real-world applications of Natural Language Processing (NLP) comprising of long texts, there has been a rise in NLP benchmarks that measure the accuracy of models that can handle longer input sequences.However, these benchmarks do not consider the trade-offs between accuracy, speed, and power consumption as input sizes or model sizes are varied.In this work, we perform a systematic study of this accuracy vs. efficiency trade-off on two widely used long-sequence models -Longformer-Encoder-Decoder (LED) and Big Bird -during fine-tuning and inference on four datasets from the SCROLLS benchmark.To study how this trade-off differs across hyperparameter settings, we compare the models across four sequence lengths (1024, 2048, 3072, 4096) and two model sizes (base and large) under a fixed resource budget.We find that LED consistently achieves better accuracy at lower energy costs than Big Bird.For summarization, we find that increasing model size is more energy efficient than increasing sequence length for higher accuracy.However, this comes at the cost of a large drop in inference speed.For question answering, we find that smaller models are both more efficient and more accurate due to the larger training batch sizes possible under a fixed resource budget.
Review On Hand Gesture Recognition for Bengali Sign Language

Communication becomes difficult when interaction between the disabled and the general public are required. People with disabilities of various races communicate using various sign languages. For persons who are deaf or hard of hearing sign language is their primary mode of communication. However, the majority of our community does not understand sign language, taking them out in public is incredibly challenging. In order to make sign language understandable to the general public, computer vision-based methods are now widely used. Recognition of hand gesture is one of the computer vision based technologies for recognizing sign language, and it is attracting a lot of attention from analysis. For a long time, it has been a popular research area. In the area of hand gesture recognition in computer vision, some recent research has achieved outstanding improvements by employing deep learning techniques. In this paper we have discussed the previous research methods, technologies, datasets and models used in Bengal sign language gestures that are interconnected in terms of achieving a successful result. Therefore, this review article tried to reveal the independent techniques which are used to overcome the challenges in research.
Improving Image Quality Using Noise Removal Based on Learning Method for Surveillance Camera Images

Noise removable method, which performs denoising process from images with noise, has been widely used. In this paper, we propose a novel noise removable method based on a learning-based method for surveillance camera images. The experimental results show that our proposed method delivers the best performance compared with several currently available methods in subjective and objective evaluations.
Automatic Mining Method of Group Behavior Patterns Based on Incremental Spatiotemporal Trajectory Big Data

There is a problem of unclear group clustering in group behavior pattern mining, which leads to a long mining time. An automatic group behavior pattern mining method based on incremental spatiotemporal trajectory big data is proposed. The grid sequence of each road segment and the road segment information included in each grid are obtained using the group behavior pattern trajectory network. Using incremental trajectory data, the properties of incremental spatiotemporal trajectory big data are retrieved, and the group behavior pattern is grouped. In the obtained class, all data element records are categorized according to their data elements. Multiple attribute dimensions, such as data definition, limitations, and feature words, are used to standardize the spatiotemporal trajectory data pieces. To complete the autonomous mining of group behavior patterns, all subsequences are visited, computed, and compared. The test results show that when the group size threshold is 20, the running time of the group behavior pattern automatic mining method based on incremental spatiotemporal trajectory big data is 311.66, which is 141.29 s and 148.66 s shorter than that based on DBSCAN and K-means, respectively. Therefore, this method has higher execution efficiency.
Enhancement of Unmanned Aerial Vehicle Image with Shadow Removal Based on Optimized Retinex Algorithm

Images taken by UAVs have shadows due to terrain factors. The image pixel brightness of the shadow areas is compressed, and the information is deficient, which impacts the recognition of image information and thus limits the subsequent image application. Therefore, the shadow removal of the image is crucial. Image enhancement algorithm is capable of improving the whole and partial contrasts of images, highlighting detail information, and removing shadows. Three classical optical image enhancement algorithms are analyzed. The analysis results show that image would be enhanced excessively after the histogram equalization algorithm to the shadow image enhancement. The pixel brightness are compressed by the Mask homogenization algorithm enhancement and uneven brightness in some areas after the enhancement of the traditional Retinex algorithm. Using the Retinex enhancement algorithm, this study proposes a combination algorithm to remove the shadow of the UAV remote sensing image. The proposed algorithm integrates the Retinex algorithm with the two-dimensional (2D) gamma function to remove the brightness colour of the UAV image, so it is capable of removing the shadow area of the UAV image and correcting the uneven darkness attributed to the image enhancement. The acquired UAV image is used to perform the experiment, and it is integrated with the LOG algorithm to extract the enhanced image features. As indicated by the experimental results, the integrated algorithm is proved with better performance to remove the UAV image shadow. The shadow areas of the features cannot be extracted in the original image, but after using the new algorithm to remove the shadow, the ground edge features can be clearly extracted.
Diagnostic support of the formation of the communicative competency of future pedagogues in the process of solving professional tasks

The article presents the results of experimental work on monitoring the communicative competency of future pedagogues based on diagnostic support. The scientific research is based on the provisions that during the diagnostic support during the period of study at the university, closely interrelated and interdependent processes of students' own personal and professional development take place; their psychodiagnostic culture is formed as a substructure of the general professional culture; students' interest in self-development and professional self-improvement is stimulated. One of the directions of diagnostic support is presented in detail, namely the formation of students' communicative competency as the most important factor in the formation of future professionals. The system of technological effects carried out in the process of studying psychological and pedagogic principles is characterised, the central link of which is teaching students to solve practical psychodiagnostic pedagogic tasks. The results of experimental work with students on the formation of communicative competency, presented in the article, convincingly proved the effectiveness of this system. The support of the personal and professional development of future pedagogues on the basis of a diagnostic basis allows them to provide targeted assistance in the development of communicative competency and a key role in this process is assigned to the solution of professional tasks.
The Study on the Efficiency of Smart Learning in the COVID-19

This paper raised the need to examine how the online education environment triggered by COVID-19 and the smart learning environment can be established in consideration of the improvement of education and learning through learning analysis.Many studies are being conducted in Korea, and the Ministry of Education is continuously striving to build a smart school by promoting strategies for promoting smart education on the way to a talent powerhouse.Nevertheless, there is no unified definition of smart learning, and it can be seen as customized (individualized) learning using smart devices.However, most of the discussions on the construction of smart schools so far have limitations in that they are limited to physical spaces.Accordingly, the opinions of teachers and learners were not sufficiently reflected in the establishment of the facility.This study intends to study smart learning in various departments.In addition, the subjects students in charge of the co-researcher of this study were analyzed.The total number of subjects was 951, and 434 responded to this study survey.In addition, students were well accepting the online environment, and in the future, regardless of COVID-19, research will be presented to improve mutual communication between professors and students in smart learning.
Human Factors Related to Cybersickness Tolerance in Virtual Environment

Sleep quality, spatial ability, and pain sensitivity had been suggested to affect cybersickness discomfort. This study evaluated various human factors on the cybersickness symptoms and tolerance duration in a nausegenic virtual environment. Results showed that participants with better sleep quality had better cybersickness tolerance. Further, participants with lower pain threshold had slightly lower cybersickness tolerance and reported more cybersickness symptoms.
IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm

In this work we present the intelligent orchestrator of random generators (IORand), a hybrid procedural content generation (PCG) algorithm, driven by game experience, based on reinforcement learning and semi-random content generation methods. Our study includes a presentation of current PCG techniques and why a hybridization of approaches has become a new trend with promising results in the area. Moreover, the design of a new method for evaluating video game levels is presented, aimed at evaluating game experiences, based on graphs, which allows identifying the type of interaction that the player will have with the level. Then, the design of our hybrid PCG algorithm, IORand, whose reward function is based on the proposed level evaluation method, is presented. Finally, a study was conducted on the performance of our algorithm to generate levels of three different game experiences, from which we demonstrate the ability of IORand to satisfactorily and consistently solve the generation of levels that provide specific game experiences.
Continual Semantic Segmentation Leveraging Image-level Labels and Rehearsal

Despite the remarkable progress of deep learning models for semantic segmentation, the success of these models is strongly limited by the following aspects: 1) large datasets with pixel-level annotations must be available and 2) training must be performed with all classes simultaneously. Indeed, in incremental learning scenarios, where new classes are added to an existing framework, these models are prone to catastrophic forgetting of previous classes. To address these two limitations, we propose a weakly-supervised mechanism for continual semantic segmentation that can leverage cheap image-level annotations and a novel rehearsal strategy that intertwines the learning of past and new classes. Specifically, we explore two rehearsal technique variants: 1) imprinting past objects on new images and 2) transferring past representations in intermediate features maps. We conduct extensive experiments on Pascal-VOC by varying the proportion of fully- and weakly-supervised data in various setups and show that our contributions consistently improve the mIoU on both past and novel classes. Interestingly, we also observe that models trained with less data in incremental steps sometimes outperform the same architectures trained with more data. We discuss the significance of these results and propose some hypotheses regarding the dynamics between forgetting and learning.
Studying the User Adaptability to Hyperbolic Spaces and Delay Time Scenarios

To create immersive virtual experiences, it is crucial to understand how users perceive Virtual Environments (VEs) and which interaction techniques are most appropriate for their tasks. We created a tangible VE - the VR Lab - where it is possible to study space and time conditions to analyse the user&#x0027;s adaptability to different forms of interaction. As a case study, we restricted the scope of the investigation to two morphing scenarios. The space morphing scenario compares the adaptability of users to Euclidean versus hyperbolic spaces. The time morphing scenario aims to establish from which values the visual delay affects performance.
Sentiment Analysis and Predictions of COVID 19 Tweets using Natural Language Processing

Abstract: Sentiment analysis is one of the major tasks of NLP. In this paper, we analyzed sentiment of covid-19 tweets using popular data science tools and divided this data into three categories- positive, negative, and neutral. we prepared this model to predict the category of the tweet using different supervised classifiers and examined their accuracy. Keywords: NLP, Covid-19, Sentiment Analysis, TextBlob, KNN (K-Nearest Neighbor), RF (Random Forest), NB (Naïve Bayes) classifiers
Research on Visual Servo Control of UAV Pod Based on Improved PID Algorithm

Abstract In view of the traditional PID controller used in the UAV visual servo control, there are problems that the parameter setting is extremely troublesome, and the speed and stability of the system cannot be considered at the same time. An improved PID-based UAV pod visual servo control method is designed. By introducing the concept of integral separation, according to the size of the deviation between the target’s pixel coordinates in the image and the set expected pixel coordinates, it is decided whether the integral control is used or not, which can avoid large overshoot and improve control accuracy. The simulation experiment shows that the visual servo control algorithm based on improved PID has smaller tracking error and higher critical tracking speed than the visual servo control algorithm based on traditional PID, and it has better performance.