Reviewerly/exHarmony huggingface dataset:
Immersive Learning Aid for Children with Autism(ASD) using Object Recognition

Autism Spectrum Disorder is a chronic developmental disorder afflicting patients in their early formative years. Without a definitive cure, children can experience a spectrum of symptoms from difficulties with communication, social interaction, and obsessive/repetitive behaviour. These symptoms affect the children's opportunities to experience and learn ideas and concepts, unlike their peers. With assisted learning and therapy, children with Autism can become independent members of society. The proposed system contributes to the child's learning curriculum by offering education attractively and entertainingly. The premise of a smartphone or a tablet as the only hardware requirement breeds familiarity with the user and promotes comfort when utilizing the application. The system consists of an application that renders augmented reality objects onto the device screen when the camera module recognizes target objects in the frame. The application processes target information using the Vuforia Engine by scanning the images of the video stream for targets. The target is either an image or three-dimensional object stored locally in a database. The application can identify and process multiple targets to render a combination of text, image, audio, video, and three-dimensional objects. The application's ease of usage combined with immersion provides a conducive environment for learning concepts and subjects of various fields.
Embedding Virtual Reality Technology in Teaching 3D Design for Secondary Education

As a new medium in modern education, virtual reality technology has stimulated the changes of pedagogical practice and added further opportunities for experiential learning. The immersive and interactive experience of VR fits seemingly well with practical subjects such as Creative Design. Design-related courses in secondary education usually appeal to the students with their practical elements, which also help in developing a student's creative and cognitive ability. The dual coding learning theory states that the learning process can be improved by using the symmetrical visual and language systems of the left and right hemispheres. This paper presents a novel teaching framework that combines classroom learning with VR technology. We devise the course structure based on Bloom’s Taxonomy and fill in knowledge and skills related to 3D Design. In collaboration with a local school, we implemented and delivered the proposed course to a group of students. After the course, we use questionnaires and interviews to collect and analyze the attendees’ feedback. The results show that the interactive experience in VR coincides better with the students’ perception of 3D conceptual design. The teaching methods are also well-received by them. Based on the findings, we suggest that immersive VR technology is a promising tool for developing practical courses such as product design and development.
Immersion Analysis Through Eye-Tracking and Audio in Virtual Reality

In this study, using Head Mounted Display (HMD), which is one of the biggest advantage of Virtual Reality (VR) environment, tracks the user's gaze in 360 • video content, and examines how the gaze pattern is distributed according to the user's immersion.As a result of analyzing the gaze pattern distribution of contents with high user immersion and contents with low user immersion through a questionnaire, it was confirmed that the higher the immersion, the more the gaze distribution tends to be concentrated in the center of the screen.Through this experiment, we were able to understand the factors that make users immerse themselves in the VR environment, and among them, the importance of the audio of the content was shown.Furthermore, it was found that the shape of the gaze distribution for grasping the degree of immersion by the subject of the content was different.While reviewing the experimental results, we also confirmed the necessity of research to recognize specific objects in a VR environment.
Trajectory Generation for Drones in Confined Spaces Using an Ellipsoid Model of the Body

We address the problem of motion planning for aerial drones to allow them to fly through confined spaces and narrow gaps between obstacles. Finding safe and feasible trajectories for steering multiple drones towards some desired positions in tight spaces or guiding them through gaps smaller in width than their diameters is impossible without taking the drones' orientations into account. To incorporate the orientation into the motion planning problem we employ an ellipsoid model of the drone body, and utilize the separating hyperplane theorem for convex sets to derive constraints that guarantee collision avoidance between the ellipsoid-shaped drone and obstacles modeled as ellipsoids, spheres, or polygons. The resulting set of constraints is seamlessly integrated into the motion planning method based on Bézier curve whose properties are exploited for efficient evaluation of the constraints. The efficacy of the proposed method in generating feasible and collision-free trajectories is demonstrated via different simulations involving single and multiple drones.
Edge/Fog-based Architecture Design for Intelligent Surveillance Systems in Smart Cities: A Software Perspective

Surveillance systems are critical for the growth of smart cities. These systems can be thought of as the cities' vision organs. It is anticipated that smart cities will produce a massive amount of data. Thus, to ensure the safety of its people, it is essential to conduct an efficient and real-time analysis of these data in order to receive timely responses in the event of catastrophic incidents. As a result, the process of transferring this vast data to the cloud for processing is relatively slow. In this paper, we present the software perspective to design and implement EFISS, a multilayer computing-based architecture for an intelligent, resource-efficient, and real-time surveillance system for smart cities. The framework, which is comprised of edge-fog computational layers, would help in the prevention of crime and the prediction of criminal incidents in smart cities. The EFISS can identify and validate crimes in real-time, using artificial intelligence (AI) and an event-driven method to transmit crime data to protective services and police units, allowing rapid intervention while conserving resources.
“Can You Clarify What You Said?”: Studying the Impact of Tutee Agents’ Follow-Up Questions on Tutors’ Learning

Students learn by teaching others as tutors. Advancement in the theory of learning by teaching has given rise to many pedagogical agents. In this paper, we exploit a known cognitive theory that states if a tutee asks deep questions in a peer tutoring environment, a tutor benefits from it. Little is known about a computational model of such deep questions. This paper aims to formalize the deep tutee questions and proposes a generalized model of inquiry-based dialogue, called the constructive tutee inquiry, to ask follow-up questions to have tutors reflect their current knowledge (aka knowledge-building activity). We conducted a Wizard of Oz study to evaluate the proposed constructive tutee inquiry. The results showed that the constructive tutee inquiry was particularly effective for the low prior knowledge students to learn conceptual knowledge.
Modelling a TurtleBot3 Based Delivery System for a Smart Hospital in Gazebo

The design of a “smart hospital” environment is described in this paper. Mobile ground robots perform transportation tasks between multiple stations located in different rooms while navigating in an environment with moving objects such as humans and other mobile robots. The robot is equipped with a distance sensor (LIDAR), based on the indicators of which objects are detected and the robot is localized. Robots can be assigned tasks to be executed through a centralized interface. Tasks are assigned to a specific robot, and, depending on the type of task, the robot is autonomously directed to the station associated with the task in the building. We are considering the concept of defining possible robot behaviors as a finite set of states with certain transitions. To test the system, a hospital map was constructed in a Gazebo simulation.
Doing linguistics with a corpus: Methodological considerations for the everyday user. EgbertJesse, LarssonTove and BiberDouglas, Cambridge: Cambridge University Press, 2020

International Journal of Applied LinguisticsVolume 31, Issue 2 p. 321-323 BOOK REVIEW Doing linguistics with a corpus: Methodological considerations for the everyday user. Egbert, Jesse, Larsson, Tove and Biber, Douglas, Cambridge: Cambridge University Press, 2020 Zahra Ghane, Corresponding Author Zahra Ghane zagha100@uni-duesseldorf.de orcid.org/0000-0002-7518-0216 Heinrich Heine University, Düsseldorf, Germany Correspondence Zahra Ghane, Heinrich Heine University, Düsseldorf, Germany. Email: zagha100@uni-duesseldorf.deSearch for more papers by this authorMehrdad Vasheghani Farahani, Mehrdad Vasheghani Farahani Leipzig University, Leipzig, GermanySearch for more papers by this author Zahra Ghane, Corresponding Author Zahra Ghane zagha100@uni-duesseldorf.de orcid.org/0000-0002-7518-0216 Heinrich Heine University, Düsseldorf, Germany Correspondence Zahra Ghane, Heinrich Heine University, Düsseldorf, Germany. Email: zagha100@uni-duesseldorf.deSearch for more papers by this authorMehrdad Vasheghani Farahani, Mehrdad Vasheghani Farahani Leipzig University, Leipzig, GermanySearch for more papers by this author First published: 11 June 2021 https://doi.org/10.1111/ijal.12373Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume31, Issue2Supplement: Corpus-based language teaching and learning: applications and implicationsJuly 2021Pages 321-323 RelatedInformation
Geometric Transformation-Based Network Ensemble for Open-Set Recognition

Open-set recognition focuses on the problem of determining whether a given query image belongs to one of the classes known to the network. Recent works in open-set recognition have attempted to solve this problem using an external deep network by either modeling known class samples or simulating open-set samples at the expense of more parameters. In this work, we propose a modified network structure and an inference rule that leads to better open-set recognition performance. First, we show that networks learn different representations when they are trained on datasets subjected to extreme geometric transformations. By exploiting this fact, the proposed mechanism learns multiple representations from the same set of image data using a set of parallel networks with identical structures. During inference, decisions of all independent networks are fused using majority voting to arrive at predictions. The proposed method obtains state-of-the-art open-set detection performance on multiple object recognition datasets.
Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis

The ability of deep neural networks to generate state-of-the-art results on many NLP problems has been apparent to everyone for some years now. However, when there is not enough labeled data or the test dataset has domain shift, these networks face many challenges and results are getting worse.In this article, we present a method for adapting the domain from formal to colloquial (in sentiment classification). Our method uses two approaches, adversarial training and weak supervision, and only needs a few shots of labeled data.In the first stage, we label a crawled dataset (containing colloquial and formal sentences) with weakly supervised sentiment tags using a sentiment vocabulary network. Then we fine-tune a pre-trained model with adversarial training on this weak dataset to generate domain-independent representations. In the last stage, we train the above fine-tuned neural network with just 50 samples of data (formal domain) and test it on colloquial.Experimental results show that our method outperforms the state-of-the-art model (Pars BERT) on the same data with 15% higher F1 measure.
Matting Enhanced Mask R-CNN

We propose a novel and effective method for high-quality instance segmentation. Top-performing "detect-then-segment" approaches (e.g., Mask R-CNN) rely on region-of-interest (ROI) cropping operations to obtain the final masks, but their performance is restricted by blurry boundary and average loss weight. Here, we develop a unique perspective of image segmentation as an image matting problem. Our method, termed MMask R-CNN, enjoys two advantages: 1) Present a novel matting enhanced mask head to generate trimap-based mat-ting features as auxiliary priors, which enhance accurate estimation of alpha values for boundary pixels. 2) Explicitly represents the classification uncertainty with confidence indicator alpha matte, and design a Uncertainty-Aware Binary Cross-Entropy Loss to assign larger weights to pixels with higher uncertainty. We evaluate the proposed method through extensive experiments on the COCO dataset. The experimental results show that our method outperforms well-tuned Mask R-CNN baseline by 2.3% AP.
Spatial-Temporal Human-Object Interaction Detection

In this paper, we propose a new instance-level human-object interaction detection task on videos called ST-HOID, which aims to distinguish fine-grained human-object interactions (HOIs) and the trajectories of subjects and objects. It is motivated by the fact that HOI is crucial for human-centric video content understanding. To solve ST-HOID, we propose a novel method consisting of an object trajectory detection module and an interaction reasoning module. Furthermore, we construct the first dataset named VidOR-HOID for ST-HOID evaluation, which contains 10,831 spatial-temporal HOI instances. We conduct extensive experiments to evaluate the effectiveness of our method. The experimental results demonstrate that our method outperforms the baselines generated by the state-of-the-art methods of image human-object interaction detection, video visual relation detection and video human-object interaction recognition.
Efficient Hardware Implementation of Forward Conversion WNS-RNS on FPGA

The Residue number system is a modern powerful tool for solving a number of specialized tasks: digital signal processing, cryptography, increasing reliability, accelerating computations, etc. The effectiveness of its use largely depends on the solution of the problem of reducing the delay and area costs for the forward conversion of numerical data from the weighted number system to the residue number system and reverse conversion. The paper proposes a method of forward conversion to the residue number system based on distributed data processing. It's hardware implementation on FPGA and comparison with the standard algorithm of the IEEE Numeric_std library are presented.
Design of Instrumented Glove for Hand Motion Evaluation

Measurement of fingers and hand kinematics during the grasping activities and manipulation with various objects play an essential role in ergonomics, sport science, occupational medicine, and rehabilitation, as it is an indicator of hand function. That is why the design and realization of a device for finger flexion and extension and forearm motion became the main objective of this article. The work deals with designing and implementing the system prototype, its calibration, and testing under laboratory conditions. The designed device is promising to be used in occupational medicine and rehabilitation applications such as the device to assess the hand function impairment and track patient hand function disability recovery progress.
FM-VSR: Feature Multiplexing Video Super-Resolution for Compressed Video

Due to the limitation of shooting conditions in the real world, there exist many insufficient-resolution videos. To be transmitted under limited bandwidth conditions, low-resolution videos often have to be compressed further, which introduces more compression artifacts and results in severer damage to the video quality. Accordingly, video super-resolution in practical applications must repair compressed damage and down-sampling damage simultaneously, which can be intuitively solved by two subtasks, including compressed video quality enhancement (VQE) and video super-resolution (VSR). Therefore, we proposed a novel model, the Feature Multiplexing Video Super-Resolution for Compressed Video (FM-VSR) to effectively handle such multi-tasks. Firstly, we use a complicated Deformable Regression Pyramid (DRP) module to align the reference frame and each supporting frame. Then feature multiplexing structure is utilized in VSR to make better use of the VQE information. Finally, a skip-attention structure is introduced in the reconstruction module to predict the high-quality video frame. Many experiments on benchmark datasets show that our method can achieve better performance both in quantitative and qualitative than the state-of-the-art (SOTA) two-stage approaches.
Multi-Agent Path Planning Approach Using Assignment Strategy Variations in Pursuit of Moving Targets

This study aims to investigate the problem of assignment strategies for multiple agents. In multi-agent scenarios, agents compute a path towards the goal, while these goal destinations in some cases are predefined in advance. The topic of assignment strategies, where agents need to identify and assign goal destination at the initial position, before making any move, has not been studied sufficiently. This problem becomes even more challenging if the goal destinations change over the period of time. This study investigates for new approaches to the assignment strategy in order to improve the efficiencies introducing three novel approaches for multiple agents in multiple moving targets environments: twin-cost, cover-cost and weighted-cost criteria. These new methods have been tested against existing overall the best approach in the literature. Empirical analysis is performed on grid-based gaming benchmarks. The performance is measured for the successful completeness of the test runs and achieving the shortest distance travelled. The experimental results suggest that the new assignment strategy methods exhibit better results in comparison with the existing approaches, where even some individual cases improve approximately by 23% especially when the means are the same.
Multi-USV Adaptive Exploration Using Kernel Information and Residual Variance

Using a team of robots for estimating scalar environmental fields is an emerging approach. The aim of such an approach is to reduce the mission time for collecting informative data as compared to a single robot. However, increasing the number of robots requires coordination and efficient use of the mission time to provide a good approximation of the scalar field. We suggest an online multi-robot framework m-AdaPP to handle this coordination. We test our framework for estimating a scalar environmental field with no prior information and benchmark the performance via field experiments against conventional approaches such as lawn mower patterns. We demonstrated that our framework is capable of handling a team of robots for estimating a scalar field and outperforms conventional approaches used for approximating water quality parameters. The suggested framework can be used for estimating other scalar functions such as air temperature or vegetative index using land or aerial robots as well. Finally, we show an example use case of our adaptive algorithm in a scientific study for understanding micro-level interactions.
HLFNet: High-low Frequency Network for Person Re-Identification

Person re-identification (re-ID) technology has attracted many scholars in the past few years. With the recent developments of deep learning technology, person re-ID has been greatly improved. However, the main chalenge of re-ID is to distinguish the detailed information in different images. Consequently, it is of significant importance to extract fine-grained features in the re-ID tasks. In the present study, a novel method, called the high-low frequency network (HLFNet), is proposed to effectively use the image information of different frequencies and focus on the detailed information between different individual images. In this regard, high frequency and low-frequency information are initially extracted from the original image, and then two backbones are applied to extract the features from the two information branches. Different frequencies of image information complement each other so that a better recognition effect can be achieved. Moreover, a local branch is utilized to extract the distinguishable local features for guiding the global feature branch in the training stage. Finally, only the extracted global feature from the trained network is required in the inference phase of re-ID. Performed experiments demonstrate that the proposed method can significantly enhance the feature representation accuracy and achieve the state-of-the-art performance on diverse benchmarks.
PrivGuard: Sensitivity Guided Anonymization based PPDM with Automatic Selection of Sensitive Attributes

The concept of Privacy Preserving Data Mining (PPDM) is the method of extraction of hidden patterns or knowledge from great volumes of data without revealing sensitive personal or sensitive business information. The data used for data mining operation may contain sensitive data such as Social Security Number, Salary, Name, Credit card Number etc. Disclosure of such information is threat to the privacy of individuals. The aim of PPDM is to provide privacy of sensitive information in the data used for data mining. Several methods have been developed based on Anonymization, Perturbation and Cryptography. All these methods take list of sensitive attributes as input from data owner. Not only that, another limitation is they perform transformations on the data without considering the level of sensitivity of the attributes in order to provide privacy. We proposed a framework for PPDM based on anonymization guided by the sensitivity rank of the attribute. This work also automatically identifies the sensitive attributes in the data.The proposed work, PrivGuard: Sensitivity Guided Anonymization based PPDM with Automatic Selection of Sensitive Attributes finds sensitive attributes in the database by finding the Sensitivity Rank for each attribute. In order to find Sensitivity Rank for attributes it finds the rank of attribute by calculating attribute evaluation measures such as InformationGain, Symmetric Uncertainty attribute evaluation, Gain Ratio, OneR attribute evaluation etc. Then, computes the sensitivity rank and uses this to decide how much anonymization is required to provide the privacy. This method can fix the balance between data privacy and data utility by applying appropriate level of anonymization using taxonomy tree of the attribute. The level of anonymization is calculated by finding the generalization score based on attribute sensitivity rank. Finally, C4.5 and Naive Bayes classifiers are built on anonymized data and compared with other anonymization methods. Our method outperforms than existing methods and observed that our results are very near to results of data mining using original data.