Reviewerly/exHarmony huggingface dataset:
VRDriving: A Virtual-to-Real Autonomous Driving Framework Based on Adversarial Learning

Due to the high cost of driving testing and the data collection for autonomous cars in the real world, transferring driving models from the simulator to the real world is significant and urgent. This article proposes a novel virtual-to-real driving (VRDriving) framework based on adversarial learning, which is able to transfer the ability of end-to-end learning models trained by simulation virtual data sets to the real world. The framework consists of three parts: 1) an end-to-end learning-based estimator with the cost-sensitive loss function for driving commands estimation; 2) an encoder for extracting the driving domain features from unlabeled real driving images; and 3) a discriminator for distinguishing whether the virtual driving domain features are real or fake. The above three modules can be trained simultaneously. With such a novel learning framework, the driving domain features encoded from the real images are closer to the driving domain features inside the end-to-end estimator via adversarial learning mechanism. Three experiments show the proposed VRDriving framework performs the state-of-the-art estimation accuracy of the steering command than several baselines and other typical frameworks. Therefore, the VRDriving framework is promising to improve the development efficiency of future autonomous driving.
Full Frontal Intimacy - on HCI, Design &amp; Intimacy

The HCI community increasingly expands the borders of what topics are acceptable to explore, discuss and research. Recent workshops and papers on sexuality, intimate facets of the body and other sensitive topics show that intimacy in its widest sense and its impact on people's lives and well-being is relevant to the HCI community. In this paper, we draw on the results of an ongoing literature review on intimacy which shows that the concept is rarely defined, even though it has a variety of meanings. Nonetheless, as the parameters in which intimacy is explored are comparable in many instances, a predominant view prevails on what intimacy means. We share an imaginary abstract that breaks with these conventions and shows potential benefits of expanding the view on intimacy. This provocation therefore urges HCI researchers to rethink their idea of what intimacy is and offers directions of how to do so.
Movement interaction design for immersive media using interactive machine learning

Interactive Machine Learning is a promising approach for designing movement interaction because it allows developers to capture complex movements by simply performing them. We introduce a new tool being developed to make embodied interaction design faster, adaptable and accessible to developers of varying experience and background. Using the tool, we conduct workshops with creative practitioners and developers to explore techniques that equip users with embodied ideation design strategies encouraging full body interaction for immersive media.
Application of the Decision Tree Method to Predict Student Achievement Viewed from Final Semester Values

Abstract Along with the development of the human mindset progress, humans began to develop a system that can help humans in dealing with problems that arise so that they can solve them easily. The decision tree or better known as the decision tree is an implementation of a system that humans develop in finding and making decisions for these problems by taking into account the various factors that are related within the scope of the problem and among about education. Education is a conscious and planned effort to create an atmosphere of learning and learning process so that students can increase their potential. The function of education is very important as one of the factors driving development as human resources to increase the ability of the community to develop science. Thus, the teaching and learning process is one of the determining factors for the success of education in schools/madrasas, the low quality of education is a result of the low quality of the learning process carried out in school
Optimization Metrics Model: The Mobile Wallet for Merchant Ecosystem

Finance Technology (FinTech) is a combination of financial services and technology that ultimately changes the business model. Appears as changes in people's lifestyles are currently dominated by users of fast-paced information technology. Financial technology provider which includes payment systems, market support, investment management, and risk management, loans, financing, and capital providers, and other financial services. North Sumatra has 33 municipal districts with an area of 72,981.23 km2 and a population of 13,937,797 people, with a density of 191 people / km2. Each city district has various. Percentage of user demographics ranging from 0.07% to 66.9% spread in North Sumatera Province. This can be seen from each region having different interests and abilities. Each district has various merchants that are built on every corner or roadside sample of 1,300 user data. LRM method to transaction types is divided into two with a merchant transaction frequency value of 263 and transaction biller 448.
Improving Faster R-CNN Framework for Multiscale Chinese Character Detection and Localization

Faster R-CNN uses a region proposal network which consists of a single scale convolution filter and fully connected networks to localize detected regions. However, using a single scale filter is not enough to detect full regions of characters. In this letter, we propose a simple but effective way, i.e., utilizing variously sized convolution filters, to accurately detect Chinese characters of multiple scales in documents. We experimentally verified that our method improved IoU by 4% and detection rate by 3% than the previous single scale Faster R-CNN method.
An Investigation on Observer Minimum Circumnavigation Velocity for Bearings-Only Target Tracking

Circumnavigating and tracking a moving target with bearing measurements by only a single observer is an attractive technology in passive target tracking. Reducing the energy consumption of the observer in circumnavigation tracking is an urgent problem. Therefore, observer minimum circumnavigation velocity is investigated in this work for such circumnavigation tracking systems. First, based on the passive tracking method of the virtual intersecting localization algorithm (VILA) proposed by the authors, the geometric characteristics of the intersection line of sight are analyzed by considering the target-state prediction error and bearing measurement error. Second, based on the normal distribution assumption of states prediction error and bearings measurement error, the observer minimum circumnavigation velocity is deduced for no ill conditions in VILA. Finally, a target moving in a straight line and constant speed turning is selected to simulate the proposed theoretical results with observer velocity below and above the minimum circumnavigation velocity level. The probabilities of ill conditions of VILA are counted, and the simulation results verify the correctness of the proposed theory.
Bad Character

We frequently assume that adaptive hypertexts ought to adopt the customs, habits and inclinations of the reader, that our computational assistants ought to act as reliable servants, and that users - even new users - ought to like the hypertextual artifacts we create. This might be a mistake.
Use of Augmented Reality by Older Adults

Augmented Reality (AR) is an emerging technology that is gaining in popularity as it becomes more affordable. AR applications superimpose computerized objects on top of the users’ physical world. Through the use of mobile devices or AR glasses, users can see their physical environment around them as well as the digital artifacts. AR has been touted as the next technology to replace our current smartphone and desktop experiences for everyday activities. For example, online shopping, examination of health information, interior decorating, and family communication are activities that have the potential to be enhanced by AR. Older adults, in particular, stand to benefit from this technology. This paper discusses the usability and user experience (UX) of AR applications among older adults. In addition, it explains the heuristics that have been suggested for AR applications and how they can be applied to older adult populations. Such heuristics allow providers of AR solutions to more quickly and reliably assess usability and UX, which in turn facilitates informed iterative design/development, resulting in more satisfying, efficient, and effective solutions.
Spam Detection by Machine Learning-Based Content Analysis

The paper aims to present a Spam Detection system by a Content Analysis based on Machine Leaning. The system is composed of six units: Tokenization and Cleaning words, Lemmatization, Stopping Word Removal and Synonym Replacement, Term Selection, Bag-of-Words Representer, and Classifier. Experiments performed on two different datasets, i.e., SpamAssassin and Trec2007 show satisfactory results, comparable with the state of the art.
A Content Based Image Retrieval Approach based on Multiple Multimedia Features Descriptors in E-health Environment

The extensive digitalization of biomedical images and the implementation of ad hoc digital infrastructures as Picture Archiving and Communication Systems (PACS) need of novel techniques to improve their effectiveness in different e-health tasks. The improvement of processing capabilities of the computer systems allows the use of more accurate techniques to analyze biomedical images. In this context, the image retrieval process represents a key activity. For this purpose, the use of Content Based Image Retrieval (CBIR) techniques on biomedical images repository could improve the effectiveness in specific biomedical image retrieval and support the human decision making process. The aim of our paper is to implement a CBIR system based on local, global and novel deep descriptors extracted from images and compared to prove their efficiency in a real e-health scenario. Several experiments have been carried out using a real dataset and standard measures to show the effectiveness of our approach.
“Fail, Clickbait, Cringe, Cancel, Woke”: Vernacular Criticisms of Digital Advertising in Social Media Platforms

Within the commercial landscape of “social networking sites” turned into “social media platforms”, the aim of this paper is to gain insight on the ways in which ordinary users express criticisms towards digital advertising. In order to highlight the existence of a specific digital advertising literacy, we focus on the study of a new generation of vernacular expressions which seem to characterize a critical experience of ads and promotional contents on social media platforms: “fail”, “clickbait”, “cringe”, “cancel”, “woke”. A corpus of French-speaking Twitter contents documents the use of such terms as criticisms aimed at different digital promotional genres (ads, hashtags, sponsored posts, articles, banners, embedded videos). By conducting a micrological analysis of platformized user reactions, we show that an entire set of digital literacy skills can be conceptualized by observing the vernacular development of a vocabulary for the criticism of promotional contents and everyday ads.
Enhanced Action Recognition Using Multiple Stream Deep Learning with Optical Flow and Weighted Sum

Various action recognition approaches have recently been proposed with the aid of three-dimensional (3D) convolution and a multiple stream structure. However, existing methods are sensitive to background and optical flow noise, which prevents from learning the main object in a video frame. Furthermore, they cannot reflect the accuracy of each stream in the process of combining multiple streams. In this paper, we present a novel action recognition method that improves the existing method using optical flow and a multi-stream structure. The proposed method consists of two parts: (i) optical flow enhancement process using image segmentation and (ii) score fusion process by applying weighted sum of the accuracy. The enhancement process can help the network to efficiently analyze the flow information of the main object in the optical flow frame, thereby improving accuracy. A different accuracy of each stream can be reflected to the fused score while using the proposed score fusion method. We achieved an accuracy of 98.2% on UCF-101 and 82.4% on HMDB-51. The proposed method outperformed many state-of-the-art methods without changing the network structure and it is expected to be easily applied to other networks.
On minimum spanning tree streaming for hierarchical segmentation

The minimum spanning tree (MST) is one the most popular data structure used to extract hierarchical information from images. This work addresses MST construction in streaming for images. First, we focus on the problem of computing a MST of the union of two graphs with a non-empty intersection. Then we show how our solution can be applied to streaming images. The proposed solution relies on the decomposition of the data in two parts. One stable that does not change in the future. This can be stocked or used for further treatments. The other unstable needs further information before becoming stable. The correctness of proposed algorithm has been proven and confirmed in the case of morphological segmentation of remote sensing images.
2D Path Planning by Lion Swarm Optimization

In this paper we propose a novel path planning algorithm which combines the Lion Swarm Optimization (LSO) algorithm and Dijkstra algorithm, in order to achieve intelligent obstacle avoidance and find the optimal path between two points in a discrete domain. Generally, the optimal path planning is a NP-hard problem, it is difficult to solve with traditional optimization methods. So, we adopt Swarm Intelligence (SI) optimization algorithm which has good performance on efficiency and accuracy. Our algorithm is decoupled into two parts. First, Maklink graph theory and Dijkstra algorithm are used to generate initial planning paths. Then, the adjusted initial clusters of paths are substituted into the improved LSO algorithm to get the optimal path. Experiment results demonstrate that LSO is more effective and feasible in two-dimensional path planning than other algorithms.
Efficient Object Detection Method Based on Improved YOLOv3 Network for Remote Sensing Images

In the future, Drones or general Unmanned Aerial Vehicles(UAVs) need to implement online real-time object detection, but their memory and computing power are limited. An improved YOLOv3 is adopted in this paper, making full use of the lightweight network instead of the feature extraction network of YOLOv3 to achieve high-efficiency object detection for remote sensing images. In the case of similar detection accuracy, parameters and FLOPs (floating point operations) of the improved model are 2.5 times and 3.3 times smaller than YOLOv3, respectively. In addition, an IoU K-medians algorithm is proposed, which improves mAP by 7.0% on YOLOv3 and by 2.3% on the improved YOLOv3.Experiments show that the detection speed of the improved YOLOv3 is 101 frame/s at the fastest, and it is still 1.6 times faster than that of YOLOv3 when its mAP is 6% higher than that of YOLOv3. The efficient remote sensing object detection method proposed in this paper lays the foundation for future UAVs.
Augmented Reality and Microbit for Project-Based Learning

The research proposes an augmented reality system integrating with the BBC Microbit microcontroller for student’s project-based learning. It is a learning tool that helps the students to understand about the augmented reality (AR) and the Microbit technologies. To increase the student’s motivation, the final project was conducted as an AR projectile-based shooting game application. The Microbit was also used as an external remote controller. Each player has an AR marker to define the position and the orientation of the player’s avatar and the Microbit for shooting the bullets. The learning goal of this proposed system is to help the student to understand about physics simulation related to the projectile, augmented reality, C# programming, the block-based programming with Microbit, basic electronics, hardware communication, computer graphics, painting, and the coordinate system. Furthermore, the proposed system was designed to support for various teaching and learning approaches including the interdisciplinary learning, project-based learning, and STEAM education. This proposed hands-on learning tool assists the learner to understand about the integration of core knowledges in science, engineering, arts, and math. The proposed system was used by 294 participants including Thai high school students, vocational students, and teachers from 3 provinces of Thailand. The experimental results showed that the learning achievement of students received improvement up to 19–36.28% by comparing the average T-scores of pretest and posttest. In addition, the teachers and students showed a good level of satisfaction on using the proposed system in teaching and learning.
Rethinking User Interaction with Smart Environments—A Comparative Study of Four Interaction Modalities

Smart environments, comprised of networked embedded devices, improve the lives of their users by providing them with a variety of assistive services that traditional built environments are incapable of supporting. However, as the number of connected devices in smart environments continue to increase, so does the level of complexity involved in interacting with these environments. Traditional human-computer interaction techniques are not always well-suited for smart environments and this poses some unique usability challenges. To facilitate interactions within such technology-rich smart environments, new models and interaction interfaces need to be developed. In this paper we propose a multi-modal approach to smart environment interaction and explore two novel interaction interfaces: gesture-based interface and mixed-reality-based interface. We also conducted a user study to compare the learnability, efficiency and memorability of these new interfaces to two more commonly used interfaces: voice-based interface and a smartphone GUI-based interface. Our user study experiment involved four light control tasks that subjects were asked to complete using the four interaction interfaces. Study subjects found different interaction techniques to be more suitable for different tasks based on the type, complexity and context of the task. Our analysis of the study results and subject feedback suggest that a multi-modal approach is preferable to a uni-modal approach for interacting with smart environments. We suggest that novel interaction techniques be further explored in order to develop efficient multi-modal approaches along with the widely used techniques.
Massive Semantic Video Annotation in High-End Customer Service

In high-end hospitality industries such as airline lounges, high star hotels, and high-class restaurants, employee service skills play an important role as an element of the brand identity. However, it is very difficult to train an intermediate employee into an expert employee who can provide higher value services which exceed customers' expectations. To hire and develop employees who embody the value of the brand, it is necessary to clearly communicate the value of the brand to their employees. In the video analysis domain, especially analyzing human behaviors, an important task is the understanding and representation of human activities such as conversation, physical actions and their connections on the time. This paper addresses the problem of massively annotating video contents such as multimedia training materials, which then can be processed by human-interaction training support systems (such as VR training systems) as resources for content generation. In this paper, we propose a POC (proof of concept) system of a service skill assessing platform, which is a knowledge graph (KG) of high-end service provision videos massively annotated with human interaction semantics.
Anomaly Detection of Aerospace Facilities Using Ganomaly

In the field of aerospace, the abnormal detection of data is of great significance. The rapid and effective detection of abnormal parameters is key to find potential failures of spacecraft. Traditional methods of anomaly detection need much manual labour and material resources but cannot satisfy the requirements of real-time accuracy. At the same time, there are far more normal samples than abnormal samples, and the original classification methods cannot be applied. In this paper, we propose a GANomaly-based framework for anomaly detection of aerospace data. GANomaly is a framework that analyzes the underlying relationships of data using potential space, which is more in line with the characteristics of the payload data and the actual scenarios for anomaly detection. This article compares GANomaly with other anomaly detection methods on the public aerospace dataset and payload dataset respectively. The results show that the GANomaly-based anomaly detection framework has good capabilities for detecting abnormality of aerospace datasets.
A new class of polynomials from the spectrum of a graph, and its application to bound the k-independence number

The k-independence number of a graph is the maximum size of a set of vertices at pairwise distance greater than k. A graph is called k-partially walk-regular if the number of closed walks of a given length l≤k, rooted at a vertex v, only depends on l. In particular, a distance-regular graph is also k-partially walk-regular for any k. In this paper, we introduce a new family of polynomials obtained from the spectrum of a graph, called minor polynomials. These polynomials, together with the interlacing technique, allow us to give tight spectral bounds on the k-independence number of a k-partially walk-regular graph. With some examples and infinite families of graphs whose bounds are tight, we also show that the odd graph Oℓ with ℓ odd has no 1-perfect code. Moreover, we show that our bound coincides, in general, with the Delsarte's linear programming bound and the Lovász theta number θ, the best ones to our knowledge. In fact, as a byproduct, it is shown that the given bounds also apply for θ and Θ, the Shannon capacity of a graph. Moreover, apart from the possible interest that the minor polynomials can have, our approach has the advantage of yielding closed formulas and, so, allowing asymptotic analysis.
Ambient UX Research: User Experience Investigation Through Multimodal Quadrangulation

Cyber-physical systems refer to environments that are sensitive and responsive to people, where users’ activities in a physical environment are enhanced by digitized services, thus calling for an Ambient UX design approach. Designing for experiences in such complex systems implies facing UX investigation in a holistic manner. The present work encompasses an initial overview of the methods employed to investigate systematically different facets of UX by actively involving users and based on experiments involving biometrics monitoring and other solutions to collect data. Investigation approaches are conceived as belonging to four layers of analysis, namely (i) physiological, (ii) behavioural, (iii) self-reported, and (iv) expert evaluation. The major contribution of the current research lies in the methodological integration firstly adopting a theoretical stance for UX investigation through multimodal quadrangulation, and secondly in a discussion on the applications of the approach as performed in a multidisciplinary research laboratory.