Reviewerly/exHarmony huggingface dataset:
Virtual Interface With Kinect 3D Sensor for Interaction With Bedridden People

The human-machine interaction has evolved significantly in the last years, allowing a new range of opportunities for developing solutions for people with physical limitations. Natural user interfaces (NUI) allow bedridden and/or physically disabled people to perform a set of actions trough gestures thus increasing their quality of life and autonomy. This paper presents a solution based on image processing and computer vision using the Kinect 3D sensor for development of applications that recognize gestures made by the human hand. The gestures are then identified by a software application that triggers a set of actions of upmost importance for the bedridden person, for example, trigger the emergency, switch on/off the TV or control the bed slope. It was used a shape matching technique for six gestures recognition, being the final actions activated by the Arduino platform. The results show a success rate of 96%. This system can improve the quality of life and autonomy of bedridden people, being able to be adapted for the specific necessities of an individual subject.
BRAIN TUMOR SEGMENTATION BASED ON U-NET WITH IMAGE DRIVEN LEVEL SET LOSS

This paper presents an approach for brain tumor segmentation based on deep neural networks. The paper proposes to utilize U-Net as an architecture of the approach to capture the fine and soars information from input images. Especially, to train the network, instead of using commonly used cross-entropy loss, dice loss or both, in this study, we propose to employ a new loss function including Level set loss and Dice loss function. The level set loss is inspired from Mumford-Shah functional for unsupervised task. Meanwhile, the Dice loss function measures the similarity between the predicted mask and desired mask. The proposed approach is then applied to segment brain tumor from MRI images as well as evaluated and compared with other approaches on a dataset of nearly 4000 brain MRI scans. Experiment results show that the proposed approach achieves high performance in terms of Dice coefficient and Intersection over Union (IoU) scores.
CCANet: Exploiting Pixel-wise Semantics for Irregular Scene Text Spotting

Despite the progress in regular scene text spotting, how to detect and recognize irregular text with efficiency and accuracy remains a challenging task. In this work, we propose a novel Corner and Character Assisted Network (CCANet) which exploits pixel-wise semantics to learn explicit text corner and character center positions with low computational cost. Concretely, in the detection stage, we develop a pixel-level Corner Rectification Branch to refine the inaccurately regressed text corners; in the recognition stage, we design another pixel-level Character Enhancement Branch which generates a Gaussian-like character center heatmap to provide attention guidance for the decoding process. To overcome the reliance of character-level annotations, we adopt an iterative approach to generate pseudo-GT label for the character heatmap, which regards the attention peak position of the attention-based recognizer as the true character center. The extensive experiments conducted on two irregular text benchmarks, Total-Text and CTW1500, demonstrate that the proposed CCANet achieves competitive and even new state-of-the-art performance.
The Design of a Study Concerning the Capture of Important Design Bits at the Whiteboard

It is anecdotally known that designers would like to capture for potential later reference certain information from their software design meetings that take place at the whiteboard, with decisions and the rationale behind them often quoted as most important. What gets talked about at the whiteboard, however, tends to be much richer than just decisions and rationale. To date, no study has examined what developers recognize in the moment as important information for potential future use. Our long-term research focuses on this problem and consists of two components: (1) understanding what information developers deem potentially important for future use while they are designing at a whiteboard, and (2) developing a novel tool to assist them in capturing and later accessing this information. This paper introduces the design of the study we plan to conduct toward the first component. We motivate the research, discuss what we learned in our preliminary work in a pilot study with three teams of students, and then detail the future study that we intend to conduct with professionals.
Influence of the professional-pedagogical community on the activities of a modern teacher

The paper shows that the dynamically occurring changes in education dictate all new requirements for teachers, for the fulfillment of which it is necessary to integrate their efforts within the professional and pedagogical community. The aim of the study was to study the role of the professional pedagogical community in the activities of the teacher. With the help of content analysis of scientific articles, numerous and disparate categories describing the concept of a professional pedagogical community were considered, their synthesis was made in the form of a mental map, the analysis of which made it possible to clarify the influence of the ethos of the professional pedagogical community on the content of the activity of an individual teacher. Based on a review of the functions carried out by the teacher, given in the normative documents regulating the activities of the educational sphere, the substantiation of the requirements for the teachers competencies is given. The comparison of professional tasks solved by teachers (methodological, informational and communicative-regulatory directions) made it possible to find their consistency with the tasks of the professional pedagogical community (self-development and community management, development of the teaching profession in general), which makes it a promising tool for solving modern pedagogical problems. The developed mental map, which clearly demonstrates the relationship between the characteristics used to describe the professional pedagogical community, can be used by researchers to further study the phenomenon of the professional pedagogical community.
Designing 3D-printer Models for Students with Vision Impairment or Low Vision

The ability to design educational models for 3D printing has an immersive impact on finding new ways to improve the learning process for students with vision impairment or low vision. In this paper, we present mockup designs of three models that allow interactive exploration. We provide details on the layout of the models, as well as examples of how to use them. The models are designed specifically for our target group of students but can be used by other students too. The models are about mathematical topics that rely on tactile interactivity. The Pythagorean theorem model presents a tactile version of a famous visual proof of the theorem. The second model explores a planar cross-section of various solids. We suggest using simple solids with unexpected cross-sections. The third model is a redesigned slide ruler for multiplication and division.
Neural Architecture Search for Resource-Constrained Internet of Things Devices

The traditional process of extracting knowledge from the Internet of Things (IoT) happens through Cloud Computing by offloading the data generated in the IoT device to processing in the cloud. However, this regime significantly increases data transmission and monetary costs and may have privacy issues. Therefore, it is paramount to find solutions that achieve good results and can be processed as close as possible to an IoT object. In this scenario, we developed a Neural Architecture Search (NAS) solution to generate models small enough to be deployed to IoT devices without significantly losing inference performance. We based our approach on Evolutionary Algorithms, such as Grammatical Evolution and NSGA-II. Using model size and accuracy as fitness, our proposal generated a Convolutional Neural Network model with less than 2 MB, achieving an accuracy of about 81 % in the CIFAR-10 and 99 % in MNIST, with only 150 thousand parameters approximately.
Research on signal modulation based on machine learning intelligent algorithm and computer automatic identification

Abstract In the process of communication, modulation signal recognition and classification are an important part of non-cooperative communication. Automatic modulation recognition technology of communication signals based on feature extraction and pattern recognition is a key research object in the radio field. The use of neural network can achieve automatic recognition of a variety of modulation signals and achieve good results. In this method, the received signal is preprocessed to obtain the complex baseband signal including in-phase component and orthogonal component. As the data set of the input convolution neural network model, the signal further optimizes the traditional method of manual extraction of expert features for communication signal recognition, which has great limitations and low accuracy under low signal-to-noise ratio, and the simulation results are verified. The results show that the proposed method has stronger feature representation ability and competitiveness in automatic modulation recognition, and is helpful to promote the application of deep learning in the field of automatic modulation recognition.
Research on Intelligent Processing Technology of Computer Image Recognition Based on Machine Vision

Abstract Although computer image recognition can distinguish very clear images, it is only limited to a certain number of years. Or it can only be clearly distinguishable from some range of graphs, and expanding the range or changing the range will not achieve the state that people want it to achieve. Nowadays, China’s equipment in this field can only conduct activity research in the two-dimensional space, and can only decompose and analyze the two-dimensional pictures. For high-dimensional and complex graphs, they still cannot be processed and the processing requirements are not up to standard. Moreover, the development of this technology is also affected by many human factors. The operation and implementation of this technology mainly rely on the high-energy and powerful processing power of the big data computing system. If we want to ensure the quality of image processing is very good, we must closely combine the recognition image and the related technology of the computer, and do not separate from each other. In this paper, the development of the technology from the status quo, the development of the principle and characteristics of the development of the technology for an in-depth study of the technology, but also the development prospect of the technology is described.
State Estimation for the Unknown Motion Model with Bearing-only Measurement

This paper presents the problem of state estimation for moving objects with bearing-only measurements in the case that the a priori motion model is unknown, i.e., its acceleration is absolutely unknown, which can not be dealt with by the mainstream Kalman-like schemes based on the known motion model. First, the essential reason disadvantage of the bearing-only measurement in the states estimation is described, and hence an alternative measurement is proposed. Then, an unknown input observer is proposed to estimate the position and velocity of the object. Finally, the numeric simulation demonstrates the effectiveness in terms of estimation accuracy and terminative time.
A Dynamic Hierarchical Genetic-Fuzzy Sugeno Network

A class of predictive algorithms that use genetic-fuzzy trees has risen to prominence in recent years. These algorithms are defined by three features: fuzzy logic, cascading decision trees, and genetic optimization. This paper presents an algorithm in this class that uses a cascading tree of Takagi-Sugeno systems, where the structure of the tree and the order of the elements within the tree are calculated procedurally by genetic algorithm. The resulting structure is unlike a tree and more akin to a honeycomb. This algorithm is shown herein to compare favorably with similar classes of predictive algorithms.
Improving Similarity Metric of Multi-modal MR Brain Image Registration Via a Deep Ensemble

Brain image registration fuses and aligns sets of structural or functional images within individual and population studies. The similarity metric is an image registration component used for detecting the same target region in different images. Multi-modal image registration constitutes one of the greatest challenges in medical imaging as it adds even more variability to the tissue and organ appearance, shape, and positioning. This paper contains two contributions to solve this complex problem: (1) we propose a solution to compute the similarity metric based on a deep ensemble method. It combines multiple traditional and deep similarity metrics into a single improved similarity map; (2) we propose novel evaluation metrics to validate the results. Experiment results in the context of T1- and T2-weighted MR images of the human brain show a major improvement to the state-of-the-art, especially in reducing the false-positive region occurrences.
Off-Policy: Soft Actor-Critic-based Adaptive Streaming for 360-degree Video in Heterogeneous Wireless Networks

Poor data-efficiency and delayed reward limit the implementation of deep reinforcement learning (DRL) approach for 360-degree video streaming in heterogeneous wireless networks. In this paper, we leverage DRL-based approach and priority-based multi-path communication for 360-degree video streaming. First, we adopt soft actor-critic for discrete action (SAC-D) to decide network utilization ratio of each link, which features a state-of-art off-policy learning on the far future decisions for multiple paths. Second, we propose a priority-aware frame scheduling to further maximize video quality. The scheduling can order SVC bit stream to flexibly utilize the spatial and quality characteristics of tile-based approach and then reduce computational complexity. Finally, we evaluate the proposed scheme on a semi-physical test platform. Experience results show that our algorithm significantly outperforms the comparison algorithm in terms of overall QoE quality and base layer (BL) freeze ratio.
A Study on Application of Curriculum Learning in Deep Reinforcement Learning : Action Acquisition in Shooting Game AI as Example

In recent years, deep reinforcement learning has garnered significant attention because it can be applied to higher-dimensional environments compared with traditional reinforcement learning. However, the number of trials increases in behavior acquisition, particularly in tasks with high dimensions and sparse rewards. To improve learning speed, we apply curriculum learning, which improves the learning performance by changing the difficulty of the task in a stepwise manner, to the behavior acquisition of a shooting game as well as conduct experiments. We compare the learning performance with and without the application of curriculum learning and confirm the faster behavior acquisition of the shooting game AI through experimental evaluation. Additionally, we analyze and discuss the development of other tasks and an algorithm for automatic curriculum generation.
Experimental Analysis on Path Planning Strategy using Dijkstra Algorithm and Follow the Carrot Method for Indoor Mobile Robot Navigation

This research paper presents experimental analysis of global-local path planning strategy using Dijkstra algorithm and Follow the Carrot method implemented in differential drive mobile robot for indoor delivery mechanism. Experiment results show that Dijkstra algorithm performed an accurate, precise, and high stability of path planning strategy in supporting our indoor robot navigation. Experimental analysis has been performed to measure maximum positioning error, maneuverability of mobile robot, and accuracy of path tracking algorithm. Our research findings show that the more complicated the map in between the initial position and final position, the measured error rate has increased linearly. With a medium level of mobile robot&#x0027;s speed, which is set to 0.12 m/s, the measured maximum positioning error during path tracking process is 7.5 cm and the average positioning error is 2.5 cm. In terms of maneuverability, our mobile robot has demonstrated capability of coping with a sharp U-turn and eventually can correct its relative position to the targeted path.
Challenge Base Learning 360 Filmmakers: perspectives and advance positive action toward the Sustainable Development Goals to empower learners as creators and changemakers

research-article Challenge Base Learning 360 Filmmakers: perspectives and advance positive action toward the Sustainable Development Goals to empower learners as creators and changemakers Share on Author: Arlette Audiffred Hinojosa PrepaTec Morelia, Mexico PrepaTec Morelia, MexicoView Profile Authors Info & Claims TEEM'21: Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21)October 2021 Pages 784–788https://doi.org/10.1145/3486011.3488060Online:20 December 2021Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
One Shot 3D Reconstruction by Observing Multiple Spherical Mirrors

Since 3D information is helpful to 3D scene observation and recognition, 3D information acquisition and display technologies are used in various fields. Several methods to acquire 3D information such as laser scanner (LiDAR), time of flight (ToF) sensor and 3D photogrammetry from multi-view images are under active development. However, the common disadvantage of these methods is that they require a special equipment or large-scale observation system. This paper proposes a method to estimate 3D information from a monocular image that pseudo captures multi-view images. It utilizes a catadioptric imaging system given by multiple (at least two) curved mirrors and stereo-vision technique. The catadioptric imaging system has a wider field of view than that of a typical camera based on perspective projection. It is suitable for reconstructing 3D information of open spaces with a monocular image. We confirm the quality of estimated 3D information by generating arbitrary viewpoint images.
An Intelligent Search Algorithm for Extremist Texts

This paper describes the developed software system for analyzing text resources from social networks in relation to their attitude to statements that violate the law. The developed system is a set of microservices that allows for a two-stage classification of texts using a naive Bayesian classifier and linguistic algorithms and models. The paper presents the results of experiments on the texts of the social network Vkontakte, confirming the effectiveness of the proposed models.
Compressing Images Using Contextual Interpolator Parameterization

The paper proposes a parameterized contextual image interpolator. The parameters of the interpolator are estimated at each point of the image using the resampled images. The estimated parameters are used to interpolate the image pixels of the required resolution. The proposed parameterization reduces the number of adjustable coefficients of the context interpolator, reduces the complexity, and increases the quality of the estimation of these coefficients. The proposed interpolator is built into the compression method based on the representation of the image in the form of a quadtree. The specified embedding allows to significantly increase the compression ratio of the compression method. Computational experiments in natural digital images confirm the gain of up to nine percent in compression ratio.
The Depth feature adaptive fusion vision tracking algorithm based on game theory

Multi-feature fusion in convolutional neural network can effectively improve the robustness of visual tracking algorithm, but how to carry out multi-feature fusion is a key problem. Aiming at this problem, this paper proposes a depth feature adaptive fusion visual tracking algorithm based on game theory. Firstly, VGG19 network is used to extract the shallow and deep features of the target, and then the game theory is used to continuously game the shallow and deep features to give full play to the advantages of each feature to achieve a balanced state, and finally get the tracking results of the target. In order to evaluate the performance of the proposed algorithm, the proposed algorithm is compared with other popular visual tracking algorithms on OTB100 and TC128 datasets respectively, the experimental results show that the proposed algorithm can effectively improve the tracking success rate and accuracy, and has obvious advantages in fast motion, motion blur and other attributes.
Mobile Device Usability Testing: A Contrast of Various Operating Systems

This paper describes a study that compares the results of mobile app usability testing in various environments. The comparison includes two of the most popular platforms among users, Apple's iOS and Google's with five of the eligible participants. Google Drive is the name of the app that was developed for the Google Android platform on the Samsung Galaxy S10 smartphone and the Apple iOS platform on the iPhone Pro 11. Participants were given a scenario to complete. The scenario is broken down into three tasks that have to be completed in a specific order which is: Increase the capacity, Move, and Search. Each task's performance was evaluated using specific criteria, i.e., time taken to complete the task, total mistakes in the task, and time taken to correct a mistake. To collect user feedback, suggestions, and notes, the System Usability Scale (SUS) questionnaire was used. According to the findings of the empirical study and the evaluation review, there were no significant usability issues in terms of efficiency on either platform. There was a discrepancy in the implementation of tasks in a task for the benefit of one of the systems and in another task for the benefit of the other system. Taking all of these disparities and results into account, as well as what was revealed by a more comprehensive analysis of the SUS survey responses, we come to the conclusion that the iOS model outperformed the Android platform.
Eye Gaze-Based Student Readability Analysis

Eye gaze detection and tracking have been an active research field in the past years as it adds convenience to a variety of applications. In the eye gaze tracking problem, the goal is to determine where on a monitor screen a computer user is looking—the gaze point. It has yet to become a popular tool. The main goal of this proposed work is to focus on the importance of similar tools to evaluate the readability of a student. The proposed work shall be focusing on the eye gaze of the student to determine the Areas of Interest of the student to help him understand and focus on the difficult parts. The proposed work aims at mapping the eye gaze of the student to the screen and triggers a click event to show the meaning of the desired word. This will further help the student understand the context of the passage and answer the questions given to them based on the passage accordingly. As a result, it helps in recording and saving the behavioral attributes, as well as the final score of each student, which helps in analyzing the student’s readability and draws meaningful conclusions.