Reviewerly/exHarmony huggingface dataset:
Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning

This paper presents an integrated motion planning system for autonomous vehicle (AV) parking in the presence of other moving vehicles. The proposed system includes 1) a hybrid environment predictor that predicts the motions of the surrounding vehicles and 2) a strategic motion planner that reacts to the predictions. The hybrid environment predictor performs short-term predictions via an extended Kalman filter and an adaptive observer. It also combines short-term predictions with a driver behavior cost-map to make long-term predictions. The strategic motion planner comprises 1) a model predictive control-based safety controller for trajectory tracking; 2) a search-based retreating planner for finding an evasion path in an emergency; 3) an optimization-based repairing planner for planning a new path when the original path is invalidated. Simulation validation demonstrates the effectiveness of the proposed method in terms of initial planning, motion prediction, safe tracking, retreating in an emergency, and trajectory repairing.
Towards Efficient 3D Human Motion Prediction using Deformable Transformer-based Adversarial Network

Human motion prediction is a crucial step for achieving human-robot interactions. While recent transformer-based methods have shown great potentials in 3D human motion prediction, they still suffer from mode collapse to non-plausible poses and quadratically computational complexity with respect to the increasing length of input sequences. In this paper, we propose a novel spatio-temporal deformable transformer-based adversarial network (STDTA) for 3D human motion prediction. First, we design a spatio-temporal deformable transformer module to capture the correlations between human joints while reducing the computational costs. Second, we introduce the adversarial training mechanism and design fidelity and continuity discriminators to maintain smoothness and stability for the long-term prediction. Finally, extensive experiments on Human 3.6M and AMASS benchmarks demonstrate that the proposed STDTA achieves state-of-the-art performance.
Scientometric Analysis of Research on Information and Library Policy

The article presents tools for searching and analyzing keywords in the scientific electronic library eLIBRARY.ru. On the basis of identifying common keywords in research on information and library policy, as well as determining of the most used keywords in each of the scientific areas, the current state and prospects for the integration of library science, political science and sociological research are determined. The author comes to the following conclusions. The most important topics are related to such keywords as “information resources”, “information equality”, “information space”; library studies on the issues of state regulation of the development of the media space and the promotion of news information have not been recorded, there are very few studies on the actualization of libraries as subjects of the promotion of state civilizational values.
A Bilayer Scalable Nakamoto Consensus Protocol for Blockchain Systems

How to simultaneously achieve high security level, good scalability, and full decentralization is still a big issue in the design of a blockchain system. In this article, we propose Bicomp, a bilayer, scalable Nakamoto consensus protocol that is based purely on decentralized Nakamoto consensus. Bicomp works to generate two types of blocks - microblocks for concurrent in-network transaction serialization and macroblocks for chain formation. A leader is elected at the beginning of each round through Proof of Work-based computation; it puts microblocks mined by different nodes into one macroblock at the end of its tenure. Such bilayer design limits a leader's power and encourages as many nodes as possible to concurrently participate in the transaction serialization process, promoting the sharding nature of the system. Furthermore, several mechanisms are elaborately designed to reduce the transaction overlapping and further limit a leader's power, including a novel transaction diversity-based metric for deciding the branch legality. Security issues and potential attacks to Bicomp are extensively discussed. Experimental results based on 500 nodes all over the world show that Bicomp achieves high throughput performance.
Deep Learning based Animal Detection and Tracking in Drone Video Footage

In this paper, we propose a multiple animal tracking system in drone footage that is designed and implemented using a Deep Neural Network (DNN) based tracking-by-detection approach. The proposed system consists of two main components, namely the sub-system for animal detection, and the sub-system for animal tracking. In the animal detection component, we exploit the effective use of YOLO-V5 to detect individual animals and in the tracking component, we use a centroid tracking algorithm to associate the location of the detected animals in consecutive video frames. The performance of the proposed system is analyzed on drone video footage containing herds of Arabian Oryx with complex patterns of movement of individual animals. All videos were recorded by using a drone flying over known oryx feeding points in the desert areas of the UAE. The experimental results showed that our tracking system can detect and track individual oryxes within herds, accurately, even when the oryxes are very close to each other, partially occluded and their walking paths cross each other.
Sarcasm Over Time and Across Platforms: Does the Way We Express Sarcasm Change?

Sarcasm is a sophisticated form of speech used to convey a message other than the apparent one. To date, there are numerous papers that have discussed the idea of automatic sarcasm detection and how it could be used for sentiment analysis improvement. The objective of this paper is to provide non-experts with a comprehensive overview of the state of research in this field and the main findings regarding sarcasm detection. Therefore, in this paper, we survey the state-of-the-art work done in this field, we recapitulate the research effort done, with focus on the more recent works, and we present the expected performance out of the proposed works. Nevertheless, we study in detail how this form of speech is used in different platforms, and how the way we express it evolves over time. We also discuss the proposition that suggests that sarcasm is a polarity switcher for sentiment analysis. To achieve these goals, run some experiments on 3 different data sets, collected from 3 different platforms, and compare how sarcasm is employed in each. These platforms are Twitter, Reddit, and some news websites. Our experiments show that the way sarcasm is expressed is highly dependent on language mastery and the platform used. For instance, in the Twitter data set, whose users vary widely in age, language mastery, and understanding what sarcasm means, the overall precision of detection of sarcastic statement reaches 89.31%. In the reddit data set, the precision of detection of such statements is about 55.33%, and in the news data set, the precision reaches over 96.67%. Our experiments also show that, to a great extent, it is safe to affirm that sarcasm, when employed, switches the polarity of a given piece of text: for the 3 platforms presented above, sarcasm has been a polarity switcher for 89.3%, 89.1%, and 92.0% of their respective instances.
Multistep-Ahead Chaotic Time Series Prediction Based on Hierarchical Echo State Network With Augmented Random Features

Multistep-ahead chaotic time series prediction is a kind of highly nonlinear problem, which puts forward higher requirements both for the dynamical memory and nonlinearity of the model. Echo state network (ESN) is frequently employed in the realm of chaotic time series modeling and prediction, but the basic ESN has been proved to have an antagonistic trade-off between nonlinear transformation and memory capacity. To overcome this trade-off, a new architecture named hierarchical echo state network with augmented random features (HESN-ARF) is proposed. On the basis of traditional linear random projection, the proposed HESN-ARF further leverages nonlinear kernel transformation to construct augmented random features, which can enable the linear and nonlinear properties to be fully represented. Moreover, the HESN-ARF utilizes low-rank kernel approximation to further reduce the computational cost, preserving the advantage of efficient modeling as much as possible while ensuring the capacities of nonlinear transformation and dynamical memory simultaneously. The proposed HESN-ARF can mine and learn the latent evolution patterns hidden in the dynamic system layer by layer through the hierarchical strategy, and achieves excellent performance in multi-step-ahead chaotic time series prediction, as demonstrated by experimental findings on two synthetic chaotic systems and a real-world meteorological dataset.
PERSONAL READINESS TO CHANGE OF THE MANAGERS OF INSTITUTION OF GENERAL SECONDARY EDUCATION IN KYIV: RESULTS OF ANALYTICAL RESEARCH

The article presents the results of an analytical research of personal readiness for change of the administrators (principals and their assistants) of Institutions of general secondary education, conducted by the Chair of Educational Leadership of Borys Grinchenko Kyiv University in October-November 2021. In organizing the research we used the method of "Personal Change Readiness Survey", created by A. Rolnik, S. Heather, M. Gold, K. Hall in the adaptation of N. Bazhanova, G. Bardier. The phenomenon of psychological readiness for changes is considered. This phenomenon was studied through the research of personal characteristics of the administrators of the schools and the identification of dominant psychological factors that affect the process of their adaptation to change. Respondents demonstrated a medium-level of advantage in such indicators as "passion", "ingenuity", "confidence". The indicator of "optimism" was defined as insufficient to implement changes in educational institutions. Also, the slow pace of education reforms can be explained by the low level of indicators "courage", "adaptability", "tolerance for uncertainty". The results of the research revealed also contradictions between the high self-esteem of respondents regarding their attitude to change and towards the introduction of innovations in general secondary education, and low levels of such components of personal readiness of school managers for change as "courage", "adaptability", "tolerance for uncertainty". These factors are especially important for the effective transformation of schools and for the implementation of reforms in Ukraine. Low indicators may indicate a distorted and unconstructive system of motivation in the work of the administrators of the institutions of general education and, therefore, be a deterrent in making changes and implementing new ones.
Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies. Blockchain, data encryption, and deep learning (DL) models can be utilized to design efficient security solutions for IoT healthcare applications. In this aspect, this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning (BPPIE-ODL) technique for IoT healthcare applications. The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server. The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm (DFA) with signcryption technique to encrypt the medical images captured by the IoT devices. Besides, blockchain (BC) can be utilized as a distributed data saving approach for generating a ledger, which permits access to the users and prevents third party's access to encrypted data. In addition, the classification process includes SqueezeNet based feature extraction, softmax classifier (SMC), and Nadam based hyperparameter optimizer. The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture. For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique, a comprehensive experimental analysis is carried out. The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.
Blockchain Enable IoT Using Deep Reinforcement Learning: A Novel Architecture to Ensure Security of Data Sharing and Storage

With the continuous development of the Internet of Things, more and more social sectors and smart devices are connected to the Internet of Things. This has led to a spurt of data growth and posed challenges to data security. To solve this problem, the Internet of Things needs a more secure and efficient storage method. Nowadays, one of the most important creative technological advancements that plays a significant role in the professional world is blockchain technology. And Both academia and industry attach great importance to the research of blockchain application technology. Some scholars believe that the blockchain itself is a secure distributed database. So, Blockchain is also considered a safe way to store data. In this paper, we introduce blockchain into the Internet of Things to ensure the security of Internet of Things data. At the same time, we have solved the problem of quantifying the degree of blockchain decentralization, which provides conditions for system optimization. After that, we proposed a system optimization model based on deep reinforcement learning to dynamically adjust system parameters. The simulation results show that the decentralization of the blockchain and the security of the system are guaranteed, and the throughput of the system has been improved.
General Frame-Wise Steganalysis of Compressed Speech Based on Dual-Domain Representation and Intra-Frame Correlation Leaching

Frame-wise steganalysis is of significance for active steganography defense. By frame-wise detection, we can accurately find the embedding position of secret information and destroy the covert channel further. However, there is currently no research specifically aiming at frame-wise steganalysis of low-bit-rate compressed speech. Besides, most of the existing steganalysis methods are specifically designed for a specific category of steganography methods. They are difficult to apply to practical scenarios where the steganography algorithms are uncertain. In this paper, a general frame-wise steganalysis method for low-bit-rate compressed speech is proposed. To extract rich feature from a speech frame, we propose a dual-domain representation, which conducts feature extraction both in the compressed domain and the decoded time domain. In addition, we propose an efficient steganalysis network named Stegaformer to leach the intra-frame correlation from the obtained representation to enable steganalysis. In Stegaformer, an adaptive local correlation enhancement module is introduced to effectively models the local characteristics, which compensates for the drawback of traditional Transformer-based models. Experimental results show that our method performs better than the existing steganalysis methods in detecting multiple steganography methods for a speech frame.
Classifying Changes in Motion Behaviour Due to a Hospital Stay Using Floor Sensor Data – A Single Case Study

In this paper we discuss methods to classify behavioural differences of individuals before and after a hospital stay, which can be detected using only data recorded with a sensor floor. The used sensor floor offers the possibility of recording the movement behaviour of individuals as inconspicuously as possible because it is laid under the normal floor covering. The aspect of unobtrusive monitoring promises a versatile use, especially in nursing, which benefits both medical staff and patients. A Multi-Layer Perceptron (MLP), a Support Vector Machine (SVM), a Gaussian Naive Bayes (GNB) and a Random Decision Forest (RDF) were used to classify the data. The results of the methods are represented with the metrics Accuracy, F1-Score, Precision, Recall/Sensitivity and Specificity. For MLP, SVM and RDF the results are very good and show that behavioural changes can be detected using only data recorded by a sensor floor.
Stress Prediction Using Per-Activity Biometric Data to Improve QoL in the Elderly

Abstract To improve the QoL of the elderly, it is essential to predict their stress states. In general, the stress state varies from day to day or time to time depending on what activities are performed and how long/strong. However, most existing studies predict the stress state using biometric data and specific activities (e.g., sleep time, exercise time and amount) as explanatory variables, but do not consider all daily living activities. Therefore, it is necessary to predict the stress state by linking various daily living activities and biometric information. In this paper, we propose a method to improve the prediction accuracy of stress estimation by linking daily living activities data and biometric data. Specifically, we construct a machine learning model in which the objective variable is the result of a stress status questionnaire obtained every morning and evening, and the explanatory variables are the types of daily living activities performed in the 24 h prior to the questionnaire and the feature values calculated from the biometric data during each of the performed activities. The results of the evaluation experiments using the one month data collected from five elderly households, show that the proposed method (using per-activity biometric features) improves the prediction accuracy by more than 10% from the baseline methods (with biometric features without considering activities).
Communicative strategies for building public confidence in data governance: Analyzing Singapore's COVID-19 contact-tracing initiatives

Effective social data governance rests on a bedrock of social support. Without securing trust from the populace whose information is being collected, analyzed, and deployed, policies on which such data are based will be undermined by a lack of public confidence. The COVID-19 pandemic has accelerated digitalization and datafication by governments for the purposes of contact tracing and epidemiological investigation. However, concerns about surveillance and data privacy have stunted the adoption of such contact-tracing initiatives. This commentary analyzes Singapore's contact-tracing initiative to uncover the reasons for public resistance and efforts by the state to address them. The government's contact-tracing program encompassing its proprietary TraceTogether app and physical token initially triggered vociferous public criticisms of Big Brother style surveillance. Using a dialogic communication framework, we analyze the TraceTogether initiative to interrogate the communicative strategies that were used to overcome public resistance. We argue that these strategies reflect a top-down approach that prioritizes transactional dissemination of information, in line with Singapore's technocratic stance toward governance. We further assert that such communicative tactics represent missed opportunities to foster public confidence in social data governance through greater trust building. We propose solutions for more dialogic communicative forms that build trust, so that officials can develop a sound understanding of the public concerns, increase the level of public engagement, and incorporate public feedback into policies that govern data use.
Video Sparse Transformer With Attention-Guided Memory for Video Object Detection

Detecting objects in a video, known as Video Object Detection (VOD), is challenging since appearance changes of objects over time may bring detection errors.Recent research has focused on aggregating features from adjacent frames to compensate for the deteriorated appearances of a frame.Moreover, using distant frames is also proposed to deal with deteriorated appearances over several frames.Since an object's position may change significantly at a distant frame, they only use features of object candidate regions, which do not depend on their position.However, such methods rely on object candidate regions' detection performance and are not practical for deteriorated appearances.In this paper, we enhance features element-wisely before the object candidate region detection, proposing Video Sparse Transformer with Attention-guided Memory (VSTAM).Furthermore, we propose aggregating element-wise features sparsely to reduce processing time and memory cost.In addition, we introduce an external memory update strategy based on the utilization of the aggregation to hold long-term information effectively.Our method achieved 8.3% and 11.1% accuracy gain from the baseline on ImageNet VID and UA-DETRAC datasets.Our method demonstrates superior performance against state-of-the-art results on widely used VOD datasets.
Toward Reference Architectures: A Cloud-Agnostic Data Analytics Platform Empowering Autonomous Systems

This work introduces a scalable, cloud-agnostic and fault-tolerant data analytics platform for state-of-the-art autonomous systems that is built from open-source, reusable building blocks. As the baseline for further new reference architectures, it represents an architecture blueprint for processing, enriching and analyzing various feeds of structured and non-structured input data from advanced Internet-of-Things (IoT) based use cases. The platform builds on industry best practices, leverages on solid open-source components in a reusable fashion, and is based on our experience gathered from numerous IoT and Big Data research projects. The platform is currently used in the framework of the National Laboratory for Autonomous Systems in Hungary (abbreviated as ARNL). The platform is demonstrated through selected use cases from ARNL including the areas of smart/autonomous production systems (collaborative robotic assembly) and autonomous vehicles (mobile robots with smart vehicle control). Finally, we validate the platform through the evaluation of its streaming ingestion capabilities.
Image Hazing and Dehazing: From the Viewpoint of Two-Way Image Translation With a Weakly Supervised Framework

Image dehazing is an important task since it is the prerequisite for many downstream high-level computer vision tasks. Previous dehazing methods depend on either the hand-designed priors/assumptions or supervised learning with plenty of data, which are not easy to implement in practice. Meanwhile, synthesizing hazy images is also significant in many scenes like multi-weather image generation. In this paper, we change the viewpoint of this task to image translation and develop a weakly supervised framework to achieve it. Instead of simply considering the hazy image as the source domain and the haze-free image as the target domain for translation, we design a feature representation scheme that generates a domain indicator, and embed it into the decoder to achieve both hazing and dehazing within one network. This design significantly reduces the complexity of network and can be more easily extended to multi-domain translation tasks than the previous methods, which need one pair of generator-discriminator for each direction of the translation. Meanwhile, aiming at solving the haze-relevant task, we design a haze attention module, which takes the local entropy map as the input. Unlike the previous weakly supervised dehazing methods, our approach only requires unpaired hazy and haze-free images rather than any intermediate supervising data like the transmission map or atmospheric light defined in the atmospheric scattering model. Experimental results on synthetic datasets show our method can achieve competitive results when compared with the state-of-the-art methods and yield more appealing dehazing and hazing results on real-world images.