Reviewerly/exHarmony huggingface dataset:
Linking university-wide key competencies and discipline specific major competencies for designing competency-based major curricula in Korean higher education

Objectives Competency-based education in Korean higher education is twofold - university-wide key competencies which shape the educational direction of the institution and discipline specific major competencies on which major education is based. As such, linking key competencies and major competencies in a systematic way can contribute to achieving totality and unity of university's major curricula. This study aims at laying the foundation for the systematization of competency-based higher education by developing systematic ways of linking key competencies and major competencies via understanding of the current status of competency-based education in Korean universities.&#x0D; Methods This study proposes four models of linking key competencies and major competencies that are developed from the comparative analysis of five universities in Korea. To validate the proposed model, FGI is conducted with curriculum experts, and the applicability of the model is explored by a survey among professors at major universities in Korea.&#x0D; Results First, this study finds the current competency-based education in Korean universities exhibit both a limitation and an advantage. Competency-based education was introduced among Korean universities as a means to evaluate education quality by the Ministry of Education. Oftentimes, competencies were selected without the interest and consensus of school members and competency-based curricula were not properly implemented. As a result, competency-based education in Korean universities was not fully functional in developing students' actual competencies. Nevertheless, the introduction of competency-based education contributed to the advancement of education quality management by systematizing a curriculum feedback system that reflects the needs of learners and society and emphasizing educators' responsibilities. Second, this study proposes four models of linking key competencies and major competencies, which are labelled as core major competencies model, equivalent key-major competencies model, overlapping key-major competencies model, inclusive major competencies model. They differ by the relative scope and specificity of key and major competencies.&#x0D; Conclusions Competency-based education in Korean higher education is in its initial stage. The four models that this study proposes can be utilized to further develop current competency-based major curricula.
Adaptive Registration for Optical and SAR Images With a Scale-Constrained Matching Method

Images registration for optical and synthetic aperture radar (SAR) is a key of multi-sensor images analysis. And parameters selection affects the final result of the registration algorithms for optical and SAR images. For images obtained by different sensors, how to choose an appropriate parameter for accurate registration is a key problem. In this letter, a parameter adaptive registration algorithm between optical and SAR images based on SIFT is proposed. Because of the different imaging mechanisms of these two kinds of images, the algorithm uses the multiscale Sobel operator to calculate the gradient for the optical image, while for the SAR image, a new adaptive operator based on the neighborhood pixel value is proposed to calculate the gradient. In the feature extraction, the adaptive value estimated by constant false alarm rate (CFAR) detection is used instead of the fixed threshold. Finally, a matching method constrained by image size scaling (Scale-Constrained Fast Sample Consensus) is proposed. The evaluation was designed in two aspects: feature extraction and image registration. The algorithm we proposed shows excellent performances in the aspects of repeatability, correct matching rate and root mean square error. The experimental results show that our method maintains the performance of the original algorithm and has some optimization and breakthrough.
C-FAR: A Compositional Framework for Anomaly Resolution in Intelligent Transportation Systems

In this paper, we present C-FAR, a framework for reasoning about anomalies in road-based intelligent transportation systems (ITS) based on video monitoring by the roadside camera infrastructure. The anomalies could span broad temporal and spatial ranges, including fine-grain (e.g., unsafe interactions among moving vehicles in real-time), medium-grain (e.g., aggressive/unsafe driving styles of individual vehicles over extended periods/distances), and coarse-grain (e.g., ensemble properties of the traffic over even longer time horizons). Unlike traditional approaches that utilize deep learning to recognize individual activities, C-FAR does so only for primitive movements and activities and then builds a comprehensive event logic framework. It also provides an optimal resolution of the detected/predicted anomalies by identifying the minimal changes in the controllable parameters of the system. We implemented a prototype system and tested it on three distinct real-world traffic data sets. We demonstrate that the proposed scheme can predict anomalies with over 84% recall level at 95% confidence level approximately 4.05 seconds before the incident.
Towards Continuous Professional Monitoring of Health Status Based on Energetic Balancing

Health and performance monitoring technologies are commonly used by athletes. Obese people on the other hand benefit less from empowering technologies that address their specific needs. It would arguably have a substantial positive impact if such could promote a more active lifestyle. The consequential costs of obesity are a matter of great concern for health professionals and European policy makers alike. The EU-funded STop Obesity Project (STOP) addresses these shortcomings. Its main work results are a platform and a gamified app supporting people with obesity under professional supervision. STOP employs smart sensors and artificial intelligence in the form of a chatbot that teaches healthy nutrition and physical activity. Users interact with it through digital avatars on their smartphones while supervising health care professionals are presented with the results through an analytics pipeline. The platform is based on the Knowledge Management Ecosystem Portal (KM-EP) for data management, and visualisation. It fuses data from the app with streams from various vendors' wearable health trackers. All the while ethical concerns feature prominently in the design of STOP. A feasibility study with obese volunteers showed that STOP in fact can be used as intended. Moreover, participants in a separate usability study gave generally positive ratings for its user experience.
A Virtual Reality Application for Stress Reduction: Design and First Implementation of ERMES Project

Recent studies have shown how Virtual Reality is one of the most effective innovative means for acting on the psychological well-being of patients with different health problems, in situations of isolation, burnout, and under high-stress levels. This paper presents the current state of work on the development of the ERMES project, which involves the creation of immersive, interactive virtual scenarios with the possibility of performing several individuals and participatory therapeutic activities through the use of Virtual Reality, promoting the psychological and physical well-being of patients in hospital wards. In particular, it aimed at providing patients with a tool that allows them to reduce stress levels, by practicing Mindfulness sessions, virtual museum exploration, and Art Therapy activities, with the possibility of safely sharing the experience with other patients.
EnterCY: A Virtual and Augmented Reality Tourism Platform for Cyprus

This demo paper presents EnterCY, an integrated Virtual and Augmented Reality Tourism platform for Cyprus. The platform's web-based, spatio-temporal virtual exploration component allows potential visitors to explore the rich cultural heritage, variety of activities, and wealth of sightseeing locations in Cyprus before their visit. EnterCY also enhances tourists' experiences during their visit through its mobile component, which offers on-site visual and audio guidance, personalized recommendations, as well as entertaining and learning features (e.g., story-telling), based on mobile-friendly Augmented Reality, location-awareness and Machine Learning technologies. Through Immersive Reality technologies, the platform provides for an after visit experience by creating personalized 360 video mementos of tourists' tours and supports integrated features that allow for experience sharing in popular social media platforms.
Mobile-Based Customers Management System in Ayunadi Supermarket

Ayunadi Swalayan is one of the retail companies that still relies on offline stores as the product marketing medium which makes Ayunadi Swalayan has difficulty in promoting their product to the customers. Ayunadi Swalayan needs the member data as well as the transaction data done by the members in order to convey the promotion. Furthermore, Ayunadi also needs an online product marketing medium which can make them able to compete with other more advanced retail companies. From those problems, therefore, it is needed to build a system that can manage the member data and the member transaction data as well as a medium to promote the product and convey the promotions to the customers. The system built is a mobile-based items ordering application by utilizing the push notification feature to convey the promotions, meanwhile the data such as the member data and the member transaction data are managed through the website. The testing method of the system are: (1) black-box testing method, (2) compatibility method, and (3) portability method. According to the result of the carried-out testing, it shown that the system built are able to manage the member data, the transaction member data, including sending the promotional messages and creating reports through the website. Meanwhile, the mobile application is able to run the items ordering process and receive the promotional messages which are sent through the website
Selective Kernel and Spatial Grouping Attention Network for Occluded Pedestrian Detection

Pedestrian detection has achieved significant progress on computer vision tasks in recent years. Most pedestrian detection methods employ deep convolutional neural networks to extract abstract features. However, convolution is a local operation that relies on down-sampling to obtain high-level semantic features, which cannot extract global image information or selectively focus on the input features. Furthermore, since the majority of the pedestrian's body is invisible under severe occlusion, the performance of existing pedestrian detectors remains further improvement. To this end, we propose a novel network with selective kernel and spatial grouping attention, i.e., SKGNet, for the occluded pedestrian detection task. Specifically, we first introduce a lightweight attention module, selective kernel and spatial grouping attention (SKG), which is embedded in the SKGNet's feature extraction backbone. The SKG module combines the properties of the selective kernel (SK) and spatial grouping enhancement (SGE) mechanisms to extract more critical features and improve the expressive ability of feature maps, ultimately improving the detection performance of the network. Moreover, we propose a mask-guided (MG) module to modulate full-body features, which can highlight the visible part of pedestrians while suppressing the occlusion part, thereby significantly improving occlusion detection performance. Extensive experiments show that SKGNet outperforms the existing advanced methods on the CityPersons dataset without excessive extra parameters and computations.
GesMoSketch: A System for 3D Sketching in AR with One Mobile Device

We present a novel approach for 3D sketching in Augmented Reality (AR). Unlike most existing methods that rely on positioning devices or algorithms, our approach only needs a mobile device to realize 3D sketching in AR scenes. The drawing process is transformed into the process of building blocks, which is intuitive and fun. The user draws directly on the device screen, but the drawing content can be placed anywhere in real space with the help of the planar surface proxy. The advantages of this method are not only low dependence on hardware but also a more natural drawing process and less fatigue. Gesture-based proxy plane manipulations are also very intuitive. A pilot study with 12 participants demonstrates the effectiveness and expressiveness of the approach.
Eavesdropping attack on a continuous-variable source-independent quantum random number generator with fluctuating local oscillator

Abstract In the practical continuous-variable source-independent quantum random number generator, it is usually necessary to introduce a local oscillator to amplify the received quantum signal due to its weak intensity. However, the intensity fluctuation of the local oscillator itself will lead to a deviation in evaluating the randomness without monitoring the local oscillator in real time and calibrating each output measurement, which will inevitably open a loophole for the eavesdropper to intercept the information of generated random numbers. Here, we present an eavesdropping attack utilizing this loophole. The results of numerical simulation indicate that when the proposed eavesdropping attack is performed, the evaluated extractable randomness value will be compromised severely, which will impact the communication security under the practical conditions. Meanwhile, we analyze the effects of practical imperfections in the presence of local oscillator fluctuation. Taking this as a reference, we may consider how to perfect the experimental system in practice and how to deal with this kind of eavesdropping attack.
A New Method of Non-Deep Network for Fast Vehicle Detection

To achieve object detection on low-computing devices such as embedded and mobile devices, we propose a new method of the non-deep network for fast vehicle detection. In our method, we use color transformation to address the problem of insufficient training data. To achieve effective object detection of different sizes, we introduce a non-deep network with a parallel double-stream design. The upper stream adopts the downsampling block which contains a 3*3 kernel size convolution layer to extract the feature of small objects. The lower stream uses the downsampling block which contains a 5*5 kernel size convolution layer to realize the detection of large targets. Finally, these extracted features of different receptive fields are fused in the fusion block. We use the one-level output feature from backbone for detection to improve model efficiency. The experimental results show that our network runs real-time on Jetson TX2, and achieves 30.46% mAP on COCO and 77.2% mAP on UA-DETRAC. Our detector is more accurate than YOLO-Fastest and faster than YOLOv4-tiny.
Bayesian Sensor Fusion of GNSS and Camera With Outlier Adaptation for Vehicle Positioning

In this paper we develop a method for vehicle positioning based on global navigation satellite system (GNSS) and camera information. Both GNSS and camera measurements have noise characteristics that vary in time. As a result, the measurements can abruptly change from reliable to unreliable from one time step to another. To adapt to the changing noise levels and hence improve positioning performance, we combine GNSS information with measurements from a forward looking camera, a steering-wheel angle sensor, wheel-speed sensors, and optionally an inertial sensor. We pose the estimation problem in an interacting multiple-model (IMM) setting and use Bayes recursion to choose the best combination of the estimators. In a simulation study, we compare vehicle models with varying complexity, and on a real road segment we show that the proposed method can accurately adjust to changing noise conditions.
Comparative Study of Cryptographic Algorithms in cloud storage data security

Cloud computing plays a vital role in this technical world. Users can access cloud computing services through an internet connection. We store any amount of data in the cloud in various of formats like image, visual, audio, text etc. The main reason for using the cloud is that the user can store data and access it from anywhere and at any time. Storing the data in cloud is very simple but it is very important to notice that they and secure. To keep data, secure in the cloud, several cryptographic algorithms have been introduced. This paper compares and contrasts different cryptographic algorithms that are used to preserve data in the cloud and focus on three major algorithms. To make the analysis and comparison, a simulation program was designed, and the results show that AES is a best method in terms of computation time, memory consumption, and level of security.
Towards a Cybersecurity Culture Framework for Mobile Banking in South Africa

Mobile banking has become the most preferred banking method globally due to the prevalence and popularity of mobile technology. The widespread adoption and usage of mobile devices for banking has attracted a lot of cybercrime. Despite the national government gazetting NCPF to standardise cybersecurity efforts in South Africa, it is not adequately regulated as cybercrime continues to rise. This paper surveyed the literature to develop a cybersecurity culture framework to mitigate cybersecurity challenges in the South African mobile banking sector. The socio-technical systems theory was adopted as the underlying research theory of this paper. A literature survey was conducted, and the outcomes were thematically interpreted. Findings revealed that critical components of a successful cybersecurity culture framework should incorporate support, cybersecurity collaboration, policy, and monitoring and evaluation. The proposed framework may help to secure mobile banking communities from ever-evolving cyberattacks.
A Recommender System based on Intuitionistic Fuzzy Sets for Software Requirements Prioritization

Requirements Prioritization (RP) is an important activity in requirements engineering aiming to give priority and order to requirements for implementation in the next version of a software project. RP is applied iteratively, according to various prioritization criteria, by multiple project stakeholders who may have different roles, needs and knowledge. In large-scale software projects, where the set of candidate requirements is large, stakeholders may not be interested in evaluating all requirements and they may not have the expertise, the time or the willingness to consider all candidate requirements. Recommender Systems (RS) can be a useful solution to information overload when stakeholders have to evaluate a large number of alternatives. During evaluation of requirements, it is often practically impossible to ensure that all stakeholders have complete knowledge on all requirements. Thus, stakeholders may show some degree of uncertainty and hesitation, as it is difficult to precisely evaluate each requirement according to each prioritization criterion. The intuitionistic Fuzzy Sets (IFSs) are an extension of fuzzy sets which can deal with stakeholders’ uncertainty and hesitation regarding the prioritization criteria importance and requirements ratings. The aim of this paper is to present an RS approach based on the collaborative filtering technique to effectively provide suggestions to stakeholders while prioritizing requirements. The proposed RS approach is tested using a publicly available large dataset of software requirements and the results show an improved performance.
A Natural Language Instruction Disambiguation Method for Robot Grasping

Robot grasping under the instruction of natural language has attracted increasing attention in various applications for its advantages in enabling natural and smooth human-robot interaction. At present, mainstream algorithms mainly solve problems of utilizing simple natural language instructions to guide the robot arm to perform some specific grasping. However, for two natural language instructions with different temporal logic and the same semantics, it is usually difficult for the robot to achieve semantic disambiguation, which further leads to the failure of the grasping task. In order to address this problem, we propose a new natural language instruction disambiguation method for robot grasping by combining sentence vector similarity calculation model and sentence temporal logic model. Firstly, the word vector is obtained through the Skip-gram model in Word2vec and a sentence vector is constructed. The semantic similarity of the sentence is then calculated by using the proposed cost function. Based on the semantic similarity of the sentence, the correct temporal logic form of the sentence is then extracted according to the temporal adverbial priority to further guide the grabbing process of the robot arm. The experimental results show that our method can successfully realize the semantic disambiguation for natural language instructions with different temporal logics and the same semantics, and further guide the robot arm to complete more complicated tasks than previous tasks.
ASAP: Adversarial Learning Based Secure Autoprecoder Design for MIMO Wiretap Channels

To avoid the non-convex and complicated secrecy rate optimization in the multiple-input and multiple-output (MIMO) system, deep learning (DL) is introduced in the secure precoder design recently. However, existing works neglect the eavesdropper with learning capacity, which is constructed by neural networks (NNs) and capable of enhancing its receiver NN according to the optimal secure transmitter. In this letter, we propose an adversarial learning-based secure autoprecoder (ASAP) for the MIMO wiretap channels, where the modulation and the precoder are jointly optimized and adversarially trained with the eavesdropping receiver. Numerical results verify the convergence of the ASAP method, which enables an optimal trade-off between the information secrecy and reliability. Moreover, joint optimization is especially important against the evolved eavesdropper.
Emersive VR: An expanded immersive VR practice

Creating an immersive isolated virtual environment is the canon in the current mainstream virtual reality (VR) practice and design. Audiences are usually situated in a singular and complete form of space in VR to receive the fully immersive experience. Nevertheless, in this article, I propose a different way of thinking about the practice and design of VR. Rather than seeing VR as a singular and isolated space in relation to other spaces, I am considering a potentiality to have a more expansive and reflexive VR practice and design. I call this emersive VR in contrast to immersive VR. Emersion is a concept borrowing from body ecology, which is a field based on the philosophy of awakening and consciousness. Emersion in body ecology is different from immersion in that people can still sustain self-consciousness without being subsumed by the other. In the notion of emersive VR, audiences are not located in a singular and complete virtual space but in a differential space between the physical and the virtual. The spatial difference is folded through the VR experience perceived by the audiences. In this article, I highlight two different approaches to construct an emersive VR experience: expanded sensory VR and reflexive storytelling. In both, audiences are not isolated in a singular virtual world but in a differential space with multiple embodiments. Emersive VR is a reconfiguration of immersive VR. It is to expand the idea of immersive VR and sketch a more dynamic future of VR as a critical medium.