Reviewerly/exHarmony huggingface dataset:
Vehicle Trajectory Planning: Minimum Violation Planning and Model Predictive Control Comparison

State trajectory planning is one of the primary self-driving cars technology enablers. However, state trajectory planning is a more complex and computationally demanding task compared to path planning. The vehicle’s east and north position, yaw, yaw rate, velocity, and battery state of charge variables trajectory planning with a particular focus on the safety and economy of the vehicle operation is concerned in this paper. Comparison of Model Predictive Control (MPC) and Minimum Violation Planning (MVP) used for trajectory planning is brought in this paper. The latter is a sampling-based algorithm based on the RRT* algorithm compared to the other optimization-based algorithm. A heuristic is introduced to convert the complex non-convex optimization planning task to a convex optimization problem. Next, MVP algorithm enhancement is proposed to reduce the calculation time. Both algorithms are tested on a selected testing scenario using a high fidelity nonlinear single-track model implemented in Matlab & Simulink environment.
SAN: Scene Anchor Networks for Joint Action-Space Prediction

In this work, we present a novel multi-modal trajectory prediction architecture. We decompose the uncertainty of future trajectories along higher-level scene characteristics and lower-level motion characteristics, and model multi-modality along both dimensions separately. The scene uncertainty is captured in a joint manner, where diversity of scene modes is ensured by training multiple separate anchor networks which specialize to different scene realizations. At the same time, each network outputs multiple trajectories that cover smaller deviations given a scene mode, thus capturing motion modes. In addition, we train our architectures with an outlier-robust regression loss function, which offers a trade-off between the outlier-sensitive L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and outlier-insensitive L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> losses. Our scene anchor model achieves improvements over the state of the art on the INTERACTION dataset, outperforming the StarNet architecture from our previous work.
Unsupervised Anomaly Detection Approach for Shift Quality Assessment Using Deep Neural Networks

It is necessary to calibrate the hydraulic pressure of the shift control to develop an automatic transmission (AT), and this calibration process entails a subjective shift quality assessment by experienced engineers. An objective shift quality assessment methodology has been explored for a long time to replace the engineer. The most recent data-based assessment model has attained a nearly human-like performance. However, preparing the large number of data labels required for supervised learning of the model has limitations. This study proposes an unsupervised anomaly detection model for objective shift quality assessment to address data label shortages and high data labeling costs. The proposed anomaly detection model is trained to classify a normal shift and an abnormal shift using just normal shift data. It is possible to easily obtain many train datasets from ordinary vehicles, and data labeling is not required. On the basis of real vehicle shift data, multiple anomaly detection models composed of various deep neural networks are developed and assessed. The evaluation results show that training exclusively on normal shift data can detect abnormal shifts; the best area under receiver operating characteristic curve is 0.902.
DST3D: DLA-Swin Transformer for Single-Stage Monocular 3D Object Detection

Monocular 3D object detection is an essential task for infrastructure-less autonomous navigation and driving due to its low cost. Most previous state-of-the-art monocular 3D object detection methods depended on Convolutional Neural Networks (CNNs). We show that this reliance on CNNs is not necessary and a Transformer-based method can also perform very well. In this paper, we present the development of a new and general framework DST3D to predict a 3D bounding box for each object based on DLA-Swin Transformer (DST) in an end-to-end fashion without any pre-trained network for depth estimation. We propose an object-scale adaptive Gaussian Kernel for generating ground truth keypoint heatmap, which associate the keypoint of the object with its size and help to enhance the network’s performance. In addition, while regressing 3D variables, we introduce a double predictions dropout loss, which significantly improves both training consistence and detection accuracy. All of these make our framework simple yet efficient. Compared with all state-of-the-art CNNs-based methods, our proposed DST3D network achieves comparative performance on challenging KITTI benchmark with a faster speed, giving the top results on both 3D object detection and Bird’s Eye View evaluation.
Robust Video Transmission System Using 5G/4G Networks for Remote Driving

Remote driving of vehicles using in-vehicle camera videos is essential for the uncrewed operation of automated vehicles. A remote operator views the in-vehicle camera videos to understand the situation around the vehicle and sometimes performs remote driving. Therefore, it is essential to satisfy three in-vehicle camera video transmission requirements: high image quality, low latency, and high reliability. Sub-6 and mm-Wave 5G networks are capable of high throughput and low latency video transmission and attract attention as a means of in-vehicle camera video transmission. However, 5G networks with high frequencies have narrower coverage than the existing 4G networks and are easily affected by object blockage. Therefore, cases of a sudden deterioration in network quality are likely to occur. Therefore, using 5G networks to transmit in-vehicle camera videos requires coping with sudden network quality deterioration. This paper proposes an in-vehicle camera image transmission system using 5G and 4G networks. In the proposed system, when the system detects a disturbance in the video transmitted via the 5G networks, it switches the display to the video transmitted via the 4G networks. When the 5G networks are available, the system displays the video with high quality and low latency. When unavailable, the system switches to the video transmitted via the 4G networks. We have confirmed on the network simulator that the proposed system has a shorter latency than the video transmission system using a jitter buffer to ensure reliability. In a video transmission experiment using commercial Sub-6 5G networks, we confirmed that the proposed system could detect all video disturbances and avoid the displayed video's disturbance by switching the video.
A Method of Color Images Compression

This paper presents a method for compressing a color image. The method works directly with color images without converting the images to another color scale. The method is applied to a set of specific images, but is also effective for different types of images. The obtained results and the efficiency of the method are compared with already known programs and algorithms for image compression, including those in which the image is converted into a new color scale, and then returned to the original color scale. Tables describe the results obtained from comparisons of different image compression methods.
Energy cost and accuracy impact of k-anonymity

European Union has aggregated the current societal concerns into two seemingly orthogonal directions: the Green Deal and the GDPR. In this paper, we begin to analyse trade-offs in preserving privacy, learning from the data, and saving energy. Considerable research studied the energy efficiency of software and the accuracy of machine learning models trained on anonymised datasets. However, to the best of our knowledge, no research has been conducted on the impact of anonymisation techniques on energy consumption. We measure the impact of anonymisation on the energy consumption and on the accuracy of machine learning models.We find that the k-value has a statistically significant impact on the energy consumption of the chosen anonymization algorithms. In terms of the accuracy of machine learning models, the generalization and suppression performs better in almost all cases, provided that proper anonymization hierarchies are used in the anonymization process. However, we find that for the larger and more complex dataset, the reduction in accuracy is limited while there is a significant difference in energy consumption. Thus when considering energy consumption we conclude that for larger datasets it might be worthwhile to consider using microaggregation over generalization and suppression.
Sustainability in Agile Software Development: A Survey Study among Practitioners

Sustainability is one of the most discussed topics across different disciplines. In software engineering, various sustainability definitions and approaches have been proposed and discussed in recent years. However, little is known about how agile processes support the development of sustainable software systems.In this study, we investigate how sustainability can be incorporated into agile software development processes, especially within the Scrum framework, and how this framework might be extended to support the consideration of sustainability.We present the results of a survey, in which we asked the participating practitioners how the elements of the Scrum framework can be used and adapted to address sustainability issues. In our research, we are especially interested in the different roles, events, and artefacts defined in the Scrum Guide and how they can support sustainable development.The results show that the practitioners see high potential for considering the sustainability impacts of software systems within Scrum, although not all the elements of the framework are equally suitable for all aspects of sustainability. We also make recommendations on how to address sustainability in Scrum.
Privacy-Preserving Online Proctoring using Image-Hashing Anomaly Detection

Online proctoring has become a necessity in online teaching. Video-based crowd-sourced online proctoring solutions are being used, where an exam-taking student's video is moni-tored by third-parties, leading to privacy concerns. In this paper, we propose a privacy-preserving online proctoring system. The proposed image-hashing-based system can detect the student's excessive face and body movement (i.e., anomalies) that is resulted when the student tries to cheat in the exam. The detection can be done even if the student's face is blurred or masked in video frames. Experiment with an in-house dataset shows the usability of the proposed system.
Qilin, a Robot-Assisted Chinese Language Learning Bilingual Chatbot

Robot-assisted language learning (RALL) offers a potentially fruitful approach to technology-enhanced instruction especially during the pandemic, as robot-learner interaction enhances a more immersive and personalized learning environment. However, the effectiveness of RALL within the context of an English speaker learning a tonal language such as Mandarin Chinese has been minimally researched. This manuscript presents a quasi-experimental study on the comparison of a multimodal virtual agent and embodied robot in influencing an adult English speaker's ability and interest in learning spoken Mandarin Chinese by carrying out a dialogue-based language learning activity. Half of the study participants were placed in a “control” group, tested with a completely virtual agent. The remainder were placed within an “experimental” group, tested with a physical robot. Participants interacted with three modes: translation, chat, and quiz of the bilingual chatbot Qilin. This was followed by the completion of a survey with 10-point rating scales and a qualitative question detailing their experience. Qilin utilizes speech services offered by the Google Cloud platform along with visual, auditory, and kinetic indicators according to respective operative agents. Favorability towards the physical embodiment of the robot resulted in higher levels of engagement and lower levels of perceived discouragement when interacting with the physical agent even though the speech and audio functions were identical for the virtual agent. The nonnative tones of the speakers and the polyphonic characters of Mandarin Chinese primarily contributed to levels of discouragement. An analysis of initial results and suggestions of future works is provided to foster research in Chinese RALL for English speakers.
Use of ICT in CLIL-classes for the Future Teachers Training

The article deals with the problem of improving the concept of Content and Language Integrated Learning (CLIL). At present, with the growing requirements for graduates of pedagogical universities and the search for ways of high-quality preparation for multilingualism, it becomes relevant to review the existing practices of teacher training. One of the solutions may be the formation of students' linguo-informational competence in CLIL-classes, as an interconnected study of the content of the subject and a foreign language based on the use of Information and Communication Technologies (ICT). The purpose of the study is to test the effectiveness of using ICT in CLIL-classes. Based on the results of the study conducted at Abai Kazakh National Pedagogical University (Abai University), there is proposed an educational technology based on the active use of ICT tools, blended learning and methodologically based on the principles of content and language integrated learning CLIL, communicative-activity and competence-based approaches. The paper describes Internet tools, the use of which in CLIL-classes will contribute not only to familiarization or review of the information being studied using audio or video materials, but to expand and deepen the content through the use of foreign language web tools. Therefore, fundamental computer skills developed on the basis of ICT tools can help students to master new material.
Goodbye Bitcoin: A general framework for migrating to quantum-secure cryptocurrencies

Quantum computing is the primary concern against security and privacy issues; And it is known that no digital assets have provable ownership in the post-quantum age. As a digital asset or investing method, cryptocurrencies are not far from this fact. This study presents a practical and comprehensive framework to secure current cryptocurrencies against quantum computers by offering a hard fork. The currently-using cryptocurrencies can apply the mentioned hard fork for providing security in the post-quantum age. To keep the currencies’ previous values in their post-quantum forks, the mentioned hard fork is designed based on a 1-to-1 proof of burn (PoB) consensus: a hash function and hash-based signature as two quantum-secure tools, are applied to prepare the migration to the quantum-secure fork. For example, and to show that the hard fork works, the quantum-secure fork of Bitcoin (QBTC) will be presented and analyzed in the heuristic form.
Multinomial Emoji Prediction Using Deep Bidirectional Transformers and Topic Modeling

As social networks grow more prominent, emojis become highly popular with a huge number of users. The primary function of emojis is to fill in emotional cues that might otherwise be missing from textual communication. These days, emojis are considered to be a large part of popular culture around the world. As a consequence, proper use of emojis in text messages will make you more friendly. Recently, emoji prediction becomes a challenging task, due to the large number of classes and the lack of suitable datasets. In this paper, we propose a multimodal emoji prediction model using the contextual information and the visual information. We used the EfficientNetB7 network to extract information from the images. EfficientNetB7 gives us the 10 most likely classes for each image. Also, we used Latent Dirichlet Allocation (LDA) as topic modeling to find hidden topics in the text. The topics extracted by LDA are then combined with the BERT network to improve the performance of this network. On our collected dataset, we achieved 46% accuracy for five emojis and 36% accuracy for 10 emojis.
Autonomous Power Supply Development for Hydrometeorological Monitoring Station

This paper presents the results of the investigation of the power supply system of the autonomous monitoring system. In particular, calculations of the system’s daily balance of energy consumption were performed. Also, the question of the influence of the angle of inclination of solar converters on energy generation is considered and the necessity of collecting solar radiation during the whole daylight, not focusing on the maximum amount of energy, is substantiated. This research aimed to develop an algorithm for calculating the power supply system for autonomous monitoring systems. The proposed algorithm takes into account the coordination of diagrams of generation and consumption of energy within the autonomous monitoring station and statistical analysis of information from the NASA website. Experiments have shown the effectiveness of this method. It can be used in future design of power supply systems for autonomous monitoring stations.
Human voice identification based on the detection of fundamental harmonics

The article is devoted to the development of a system for biometric identification of a person by voice. The article discusses algorithms for the analysis of audio recordings for biometric identification of a person by voice. The technique of experimental research is considered, the process of processing the results of identification is described. Used algorithms MFCC and PLP for digital processing and analysis of audio recordings. An algorithm based on multi-criteria optimization has been developed for acoustic speech analysis. Various algorithms are used, such as hidden Markov models (CMM or HMM), as well as a model of a mixture of Gaussian distributions (GMM or GMM); in recent years, Wave Net neural networks have been actively used. The result of determining the tone of speech and the content of speech for the purposes of identification by voice is obtained.In the work, to compare the recorded voice with the saved voice for the purpose of personal identification, an unlimited text independent recognition system was applied using the Gaussian mixture model. The recorded voices were processed and stored during the registration phase, and the probing voices were used for comparison during the verification/ recognition phase of the system. For biometric identification of a person by voice, the MFCC and PLP algorithms were used for digital processing and analysis of audio recordings. The result obtained makes it possible to determine the fundamental harmonics of speech for the purposes of identification by voice. The “Multiparameter automated system of biometric identification of a person” was developed on the Visual FoxPro DBMS. Today, speech identification and authentication is used in a wide range of applications ranging from smartphone applications to access control systems. Additional confirmation of the relevance of this area is the many research centers.
Towards to sustainability of education: the mutual cooperation with partners in Smart city project

The article presents the European evaluation within the scope of the project SMARTCITY:Innovative Approach Towards a Master Program on Smart Cities Technologies. The evaluation covers assessment of teachers from PC universities (SSTU, NSTU, NUM, MUST.KAZNU, ENU), who have been selected to participate in the project-related mobilities to increase knowledge in the field of SCT. In the project is planned developing of 18 teaching materials published with e-ISBN by PC teachers involved in the project.The article also covers quality assessment of teaching materials developed within the project by PC universities and assessment of students’ knowledge which is done at the beginning of their training in the EU Universities and during the Master theses defense.
A study of the human-robot synchronous control based on IMU and EMG sensing of an upper limb

In this paper, we develop an interactive interface for the remote control of a collaborative robot based on the operator’s posture. A self-developed circuit board is used to capture the signals of IMUs and EMG to make the collaborative robot follow the operator’s motion and imitate his/her gestures. The IMU sensor is adapted to detect the motion of the user’s upper limb to transfer to necessary commands of the collaborative robot based on the sensor fusion method. The EMG sensors are used to detect the user’s gesture based on the feature extraction of the EMG signals. The cascade support vector machine (SVM) is proposed to identify four different postures of the user.
Unmanned Aerial Vehicle Control by Eye-Tracking using Computer Vision and Machine Learning

In this work, we propose a system accessible to anyone, especially people with physically impaired and with spinal cord injury (SCI), by employing an unmanned aerial vehicle used in every aspect of today’s life and in pursuit of improving the quality of life. Our proposed system gives them accessible and affordable control over drones, improving their quality of life and allowing them to see beyond their room’s walls. In the following research, we designed and experimented with the control of a system based on eye-tracking using the web camera and indoor drone.
A Brief Review on Behavior Recognition Based on Key Points of Human Skeleton and Eye Gaze To Prevent Human Error

The review on human behavior recognition and human error behavior is discussed in this paper. Human error has advanced understanding of human behavior and provided a set of methods that are still used today. Human characteristics used to predict human behavior such as eye gaze and key point of human skeleton are covered. Finally, we discuss future research directions in the field of human behavior recognition. This systematical review will be beneficial to research and teaching in human behavior recognition.
Scanflow-K8s: Agent-based Framework for Autonomic Management and Supervision of ML Workflows in Kubernetes Clusters

Machine Learning (ML) projects are currently heavily based on workflows composed of some reproducible steps and executed as containerized pipelines to build or deploy ML models efficiently because of the flexibility, portability, and fast delivery they provide to the ML life-cycle. However, deployed models need to be watched and constantly managed, supervised, and debugged to guarantee their availability, validity, and robustness in unexpected situations. Therefore, containerized ML workflows would benefit from leveraging flexible and diverse autonomic capabilities. This work presents an architecture for autonomic ML workflows with abilities for multi-layered control, based on an agent-based approach that enables autonomic management and supervision of ML workflows at the application layer and the infrastructure layer (by collaborating with the orchestrator). We redesign the Scanflow ML framework to support such multi-agent approach by using triggers, primitives, and strategies. We also implement a practical platform, so-called Scanflow-K8s, that enables autonomic ML workflows on Kubernetes clusters based on the Scanflow agents. MNIST image classification and MLPerf ImageNet classification benchmarks are used as case studies to show the capabilities of Scanflow-K8s under different scenarios. The experimental results demonstrate the feasibility and effectiveness of our proposed agent approach and the Scanflow-K8s platform for the autonomic management of ML workflows in Kubernetes clusters at multiple layers.