Reviewerly/exHarmony huggingface dataset:
Micro Maneuvers: Obstacle Detection for Standing Vehicles using Monocular Camera

Structure-from-motion (SfM) is still the preferred solution for 3D perception of rigid geometry in vehicles using monocular cameras. SfM provides the motion estimation of the ego-vehicle and the localization of obstacles. For the computation of 3D structure from monocular video, camera motion is required. The motion is provided by the movement of the vehicle. To avoid accidents at the beginning of the drive, the obstacle detection is needed before the vehicle starts to move, especially for autonomous vehicles. The idea followed in this paper is the execution of a small vehicle maneuver (micro maneuver) to compute the surrounding scene using SfM. The maneuver leads to the desired camera motion and is executed before starting to drive without any driver interaction. Then, obstacles are detected and considered for any driving action. Two micro maneuver types executed on a standing vehicle are under consideration: (a) steering the front wheels and (b) using the vehicle's handbrake and engine torque. We analyze the generated camera motion and the reconstructed scene. Since the resulting camera motion is small, state of the art keyframe selection techniques are compared. The application of obstacle detection using the 3D point cloud showcases the accuracy of the reconstructed scene. Based on the experiments, the most promising methodology is identified.
Gamification In E-Learning For Playgroup

E-learning is an electronic learning system that includes various types of media that can provide and display text, audio, images, animation, and video streaming, and includes technology applications and processes such as audio or video tape, satellite TV, CD-ROM, and computer-based learning, whether using an intranet, extranet or website-based. Early childhood education, especially pre-school playgroup in the age range up to the age of 6 years is a very important education in providing the basis for knowledge, attitudes and skills so that E-Learning is one of the efforts to improve the abilities of pre-school playgroup children. Therefore, gamification in E-Learning as a game application is needed to teach knowledge, attitudes and skills towards Pre-school playgroup children by playing which can train the cognitive and motor skills of the child. Game application development from ELearning will be developed using the Game Development Life Cycle (GDLC) method with Construct 3 development software. Testing will be carried out using Alpha Test and Beta Test. The results of the Alpha Test and Beta Test tests carried out by end-users stated that the system was appropriate and the features were running well. This application is expected to help Preschool playgroup students in improving their learning abilities. The results of the Alpha Test and Beta Test tests carried out by end-users stated that the system was appropriate and the features were running well. This application is expected to help Pre-school playgroup students in improving their learning abilities. The results of the Alpha Test and Beta Test tests carried out by end-users stated that the system was appropriate and the features were running well. This application is expected to help Pre-school playgroup students in improving their learning abilities.
A Force Estimation Sensor System TestBed for Geological Phenomena Analysis based on Biomimetic

Our work focuses, on designing and proposing a force estimation sensor system for Geological phenomena data collection and analysis, based on Biomimetic. In this sense, biomimetic term, is used, as proposed by Schmitt in its classical sense, with regatd to the study of nature’s methods, processes and mechanisms. In particular, strain gauge sensors with force sensing resistor (FSR), formate a clustering to estimate ground stress and tension levels, as stated by natures’ mechanisms in earthworms to detect electrical signals. Our sensor system is proposed as a testbed to estimate geological landslide phenomena. Our experimental results from the data collection system is conducted in LabVIEW.
The Task of Synthesizing the Kazakh Language Based on the seq2seq Approach for a Question-Answer System

Currently, many texts are synthesized using a computer. Sentence synthesis is becoming more common in various fields. For example, it is used in intelligent systems that can explain to the user the progress of solving a specific problem, decision support systems that help the user make decisions based on the developed alternative, smart homes, voice assistants, chat bots, and so on. Machine learning is one of the most effective approaches to solving the problem of text synthesis. Machine learning algorithms can determine how to perform important tasks by analyzing examples. In the task of synthesizing sentences using machine learning, it is possible to replace a number of components of the entire system with neural networks, which allows not only to approach existing algorithms in quality, but even to significantly surpass them. The article investigates analogous sentence synthesis systems, considers the problem of sentence synthesis in the Kazakh language for the task of a question-answer system. This work consists of two subtasks. The first describes the collection and processing of text resources necessary to solve the problem. For this subtask, an electronic question-answer corpus for the Kazakh language was collected and processed. This corpus consists of 60,000 questions and answers on various topics. The second sentence synthesis problem was solved on the basis of machine learning using the seq2seq method. Based on the chosen method and the assembled corpus, a number of experiments were carried out and the results were obtained. Based on the results obtained, the quality was assessed using the BLEU metric and an expert.
Applying A New Requirement Template for Business, User, and Functional Requirements: A Real Transformation Journey For Business Analysis

Accurate and appropriate requirements are fundamental prerequisites for successful software project implementation. There are numerous methods and techniques to elicit and document the software requirements. However, none of them propose an end-to-end solution from business-level requirements to system-level requirements. Therefore, a requirement writing template -requirement cube- was developed in a prominent Islamic Bank in Turkey based on 5W1H questions along with the software. This paper explains the original idea, how the idea was transformed during the application within the organization, and the architecture of the software that was developed. During implementation, the hierarchy of the requirements changed. One more level - acceptance criteria- was added to the business requirement and the business requirement was moved to the software development request module. Besides, another transition level-sub-function-was introduced to integrate enterprise architecture with the requirements. Consequently, the initial theoretical requirement cube structure evolved into an applicable, practical solution for the software requirements.
Examining the influence of national digital identity and smart pass platform on accelerating the processes of digital transformation

This work is set to examine the influence of National Digital Identity (NDI) and UAE smart pass (SP) platforms on accelerating digitalization processes nationwide. The work identifies related sup-variables grouped into a proposed construct to evaluate and measure their mediating effects on strategic partners’ adoptions of SP as the primary user authentication system for their digitalization journey. The views of 44 public and private entities were collected and validated using regression and several structural modeling techniques, the results of which demonstrate the validity of the proposed structural model of the work as indicated by the findings assuring that implementations with SP as the primary authentication platform lower operational expenditures, fulfill the demand of safe identity verification, and satisfy regulatory compliance. Our work is the first in the region to demonstrate the strategic value of the national authentication application such as UAE Smart Pass (UAE Pass) which in support of NDI significantly accelerate and align adaptation initiatives closer to attaining their goals.
Local Geometrical Deep Matrix Factorization for Multi-View Clustering

Multi-view clustering aims to exploit information from data described by various views to optimize clustering performance. Multi-view clustering methods based on deep matrix factorization are capable of extracting intrinsic category information from data that contains a variety of knowledge. And the optimization process of deep matrix factorization suffers from several constraints. To address these issues, this paper proposes a multi-view clustering model via deep matrix factorization smoothed by local geometrical loss. Based on the assumption that samples close in original space are more likely to be in the same category, the local geometrical information is preserved during the training process of deep matrix factorization by maximizing the conditional distribution on the graphs built by different views. Then, inspired by the connection between deep auto-encoder and deep matrix factorization, we leverage a deep auto-encoder based solution to handle the optimization of the objective function. By adding an activation function to each layer of deep auto-encoders, the nonnegativity of features output from layers in deep matrix factorization is strictly guaranteed during the training process. Finally, we conduct extensive experiments on four datasets to validate that the proposed method is superior to state-of-the-arts.
An Object Detection Method Enhanced by Sparse Point Cloud for Low Illumination in Autonomous Driving

Object Detection is an essential task in autonomous driving. The existing object detection methods are susceptible to the variable lighting, which leads to the decrease of detection accuracy. To address the above issue, we introduce LRPN, a robust detection network with multi-modal feature fusion based on prior knowledge of lidar points. LRPN aims to improve the accuracy of object detection in light changing scenes by using fusion method of sparse point cloud and image. We propose two fusion modules: 1) Sparse lidar feature are fused with image in the feature extractor network. 2) Guided anchors are created based on visual projection using lidar points. Our method is applied to Faster RCNN and achieved higher precision under different illumination conditions. Compared with the original Faster RCNN network, the mAP of the method proposed in this paper is raised by 2.2 % under good illumination and 3.7% under low illumination. The results show that the introduction of sparse point cloud can significantly improve the detection effect, especially in low illumination scenarios.
A Unified Trajectory Planning and Tracking Control Framework for Autonomous Overtaking Based on Hierarchical MPC

In this paper, we develop a unified trajectory planning and tracking control framework for autonomous overtaking in structured roads. The framework consists of two individual MPC to optimize the lateral and longitudinal motion in two successive steps. The lateral MPC use the vehicle kinematic model in Frenét coordinate frame as prediction model, which is expert in describing motion in curved roads over kinematic model in Cartesian. By reformulating the MPC as a quadratic program (QP) problem, the lateral planning controller calculates the optimal steering angle control command to manage lane keeping and lane changing behaviors. Then the longitudinal planning controller will choose the mode of speed keeping or target following in accordance with the lateral result and generate the safety corridor based on ST-Graph for collision avoidance. Eventually the optimal acceleration will be given by longitudinal MPC. The hierarchical structure reduces the complexity of optimization problems and improves the efficiency for overtaking planning and control. In simulation, we use two scenarios to validate the proposed autonomous overtaking framework. The result shows the framework can deal with different overtaking maneuvers well.
Multiphase Compartment Models of Distribution of New Coronavirus Infections

A fundamentally new multiphase compartmental mathematical model for predicting the spread of several waves of coronavirus infection has been developed. Quality indicators in comparison with existing single-phase models are analyzed. The developed model will allow to model several waves of the process of spreading new coronavirus infections, to predict the process of loading the medical system, as well as the needs for staff, equipment and hospital beds during pandemics.
High-quality 3D Clothing Reconstruction and Virtual-Try-On: Pants case

Virtual try-on (VTON) is filling the gap between online and offline shopping. This paper extends Cloth3D, which uses top clothing only, and proposes a pipeline for high-resolution virtual try-on for pants based on 3D clothing reconstruction. In-shop pants image is first reconstructed into 3D by finding the SMPL body model fitted to the pants and building the clothing model. Then, the clothing model is reposed to the human reference image and projected to a 2D image to get 3D warped pants. These warped pants and the identities from the reference person image are going through the blending network to get the try-on. Moreover, a target segmentation is also estimated for control input for the blending (in-painting) network. Our experiments and evaluation on a new fashion dataset show natural VTON results for service.
GaDocNet: Rethinking the Anchoring Scheme and Loss Function in Vietnamese Document Images

In recent years, page object detection has received much attention from document image understanding. However, its application has many limitations in Vietnamese document images. In this paper, we address the page object detection problem in the Vietnamese document image. Specially, we experiment with four state-of-the-art object detection methods: Dynamic Faster R-CNN, Guided Anchoring Faster R-CNN, PointRend, and CascadeTabNet on the Vietnamese image document dataset named UIT-DODV. UIT-DODV dataset is the first Vietnamese document image dataset with four objects: Table, Figure, Caption, and Formula. In addition, we further evaluate the bounding box regression loss functions of the IoU family. Then we propose the EIoU loss function for efficiently page object detection in Vietnamese document images. Based on the preliminary experimental results, we present GaDocNet along with the EIoU loss function. The proposal achieves 76.1%, which is 1.6% higher than the baseline on the UIT-DODV dataset. Moreover, we evaluate with Deformable DETR, PAA, Reppoints, Foveabox, FSAF, and ATSS on UIT-DODV. The empirical evaluation points out the advantages of our approach, which is the foundation for further works.
Towards Natural Virtual Mouse with mm-Wave Radar

This paper introduces the design and the implementation of a natural virtual mouse based on mm-wave radar. Key challenges and algorithm solutions have been addressed. We imitated the interactions of a physical mouse by moving hand or fingers on desk surface. Three virtual mouse functions have been implemented, including cursor moving, clicking and wheel scrolling. This is the first radar-based natural virtual mouse with detection range of 60cm. We provide larger area and less restrictions for the interactions.
Modeling and Analyzing of Photovoltaic Solar Power Systems using PVsyst

Malaysia’s demand for renewable energy-focused solar has skyrocketed. The requirement to estimate the resource’s potential will undoubtedly be beneficial. Understanding the possibilities of these energy sources can assist the common users, driving technical advancement, and improving analysis evaluation. Essential tools are required to develop a better system’s design for harnessing solar energy precisely and efficiently by considering numerous criteria such as system design, sizing, shading, and outcome comparison. In this paper, we present details on the system’s design and development using a web-based tool, PVsyst that assists in estimating and quantifying the components of a solar power system. The latitude and longitude of 2.73 0N and 101.94 0E at Seremban, Negeri Sembilan Malaysia obtains an average solar radiation of about lS.05kWh/year annually. The photovoltaic solar power system that incorporates new technology to provide simplicity yet efficiency in modeling and analyzing the solar plant has also been described. It addresses the issue of nuisance in designing resulting in significant time consumption in the process of developing the solar power system
A peer, “wallet-only” consensus schema for ownership transfer over finite populations

Consensus achievement is a scalable process, which relies on the mutual “Proof of something” among the consenting parties. Not every application has the same consensus needs: a classification of escalating complexity proofing cases is presented in [1], where it is proved that high complexity consensus policies can be effectively delivered on a pure P2P manner even over finite, even trivial capacity systems. The Proof of Existence (PoE) is utilized there as a minimal universal proofing case, the increased complexity of which is too high to be handled by a single atom, yet it can be delivered effectively on a collective basis. Irrespective of the undoubtable universality and soundness of PoE, it constitutes but the first and least-demanding proofing class identified. Universal consensus achievement on ownership transfer and monetary transactions requires elevated complexity proofing policies, capable of safeguarding the uniqueness of the transferred tokens as well as the finality of the transactional blockchain. In this work we present a pure P2P consensus schema for ownership transfer over finite populations. It relies on the IoT micro-Blockchain architecture which delivers a robust verifiable atomic consistency framework and facilitates the delivery of Proof of Originality-PoO and Proof of Consistency-PoC based consensus. We assert that PoO and PoC are necessary and capable to deliver robust consensus on ownership transfer transactions, and we study their traits over finite populations.
Employing a Seq2Seq Model for Spelling Correction in Albanian Language

In this paper we present a model which detects and corrects spelling mistakes in Albanian language. Most of the available literature and published papers discuss the implementation and optimization of spell checkers for the English language. Until now, unfortunately, there is a lack of published works for the processing of Albanian language. We are going to explain the process of implementing a spelling corrector for Albanian, from the dataset creation until the provision of the results. The proposed model is based in the Sequence to Sequence (Seq2Seq) model with Bahdanau Attention. Since there is a lack of public datasets in Albanian, we created a dataset with 958,116 sentences collected from electronic books, Wikipedia articles and various legal documents in Albanian language. We experimented with the hyperparameters values in our neural network to find the optimal parameters which provided the best results. We propose that by enriching the initial dataset, not only in dimension but also by linking it with other tools such as POS (Part of Speech) tagging, a higher level of accuracy can be achieved.
Determination of Video Service Quality in an IP Environment with the Use of Different Software Tools: A Comparison Study

The paper deals with the determination of video service quality in an IP environment with the use of different software tools. The typical impairment parameters of the IP transport platform, i.e. packet loss and burst factor, are taken into account. The encoding rates for video content also play an important role here. In the study, the advantages and disadvantages of the different software tools for determining video service quality are analysed. Two groups of measurement techniques are considered here: a) signal-based methods, and b) parameterized models. The output from these results are discussed and graphically displayed in several diagrams.
An Impact of the Encoding Bitrate on the Quality of Streamed Video Presented on Screens of Different Resolutions

The paper discusses the problem of video quality deterioration caused by watching low resolution videos on high resolution displays. The authors take into account three video codecs, i.e. H.264, H.265 and VP9. The tested video samples are encoded using different bitrates from 300 kbps to 6000 kbps and three spatial resolutions −360p, 480p and 720p. The authors show how the coding bitrates and resizing of the spatial resolution may influence the video quality perceived by the end user. The objective quality values are compared with the results of the previous subjective evaluations. The results showed high correlations between selected objective quality metrics like PSNR, SSIM and VMAF and the quality perceived by the users.
Verifying Security Properties of the Source Code of Access Control Tools Using Frama-C

The paper discusses the features of the approach most commonly used to verify access control tools, where a high-level specification (security model) is generated. Serious problems are revealed in the applied approach, since there is a discrepancy between the object being verified and the real object requiring verification. As an alternative approach to verifying access control tools, it is proposed to verify security properties at the source code level, for example, for ANCI C code using Frama-C and special theorem provers. The paper considers the features of the Frama-C core, plug-ins and theorem provers. Using a synthetic example of a system, where subject-object interaction is arranged within the Bell-LaPadula Model, a possibility is demonstrated to generate the properties of a security model in the form of low-level contracts for several functions. A possibility of forming new function conditions by analyzing covert channels for the Bell-LaPadula mandate policy is demonstrated, taking into account the hierarchical structure of the system objects, as well as the corresponding change in the source code to meet the new conditions. In conclusion, the advantages of verifying access control tools at the source code level in comparison with the previously used approach are given. The features are listed that arise in case of a change in the source code and in case of verifying access control tools using standard library functions. A possible direction is proposed for further research on dynamic verification of the source code of access control tools.