Reviewerly/exHarmony huggingface dataset:
Spatio-temporal Motion Planning for Autonomous Vehicles with Trapezoidal Prism Corridors and Bézier Curves

Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free trajectories. A layered motion planning approach with decoupled path and speed planning is widely used for this purpose. This approach is prone to be suboptimal in the presence of dynamic obstacles. Spatial-temporal approaches deal with path planning and speed planning simultaneously; however, the existing methods only support simple-shaped corridors like cuboids, which restrict the search space for optimization in complex scenarios. We propose to use trapezoidal prism-shaped corridors for optimization, which significantly enlarges the solution space compared to the existing cuboidal corridors-based method. Finally, a piecewise Bézier curve optimization is conducted in our proposed corridors. This formulation theoretically guarantees the safety of the continuous-time trajectory. We validate the efficiency and effectiveness of the proposed approach in numerical and CommonRoad simulations.
Archiving website‐based references in academic papers: Problems caused by reference rot, potential solutions and limitations

Key points Web‐based references form an integral part of academic culture and literature, as well as scientific communication. The preservation of web‐based references that are cited in‐text or in reference lists of academic papers is essential to ensure the integrity of knowledge preservation. When access or links to web‐based resources and references disappear, or if they are dysfunctional, these phenomena are collectively referred to as reference rot. Even though reference rot degrades the integrity of information in academic papers, no industry‐wide solutions currently exist. Given its prominence, we propose the Internet Archive as a possible solution to address reference rot, but also note its weaknesses and limitations.
Hybrid Chaotic-Based PRNG for Secure Cryptography Applications

This paper suggests a novel one-dimensional (1D) map to address the limitations of traditional chaotic 1D maps. In contrast to traditional 1D maps, the proposed map has three control parameters a, μ, and c, allowing it to exhibit chaotic behavior over a wide range of values. The dynamic behavior of the new 1D map was analyzed using well-known numerical methods, including the bifurcation diagram and Lyapunov exponent. Both tests showed their complex and diverse behavior. In addition, a novel image encryption scheme was devised using the new function as its pseudorandom number generator. Rigorous statistical testing was applied to the proposed encryption algorithm. The mean square error (MSE) and peak signal-to-noise ratio (PSNR) results, in addition to subjecting 28 images to number of pixels change rate (NPCR) and unified average changing intensity (UACI) tests demonstrated the robustness of the system. The results of this study demonstrate the effectiveness of the new 1D map for use in secure image cryptography applications, providing a more robust and secure alternative to traditional chaotic 1D maps.
Surrogate-Assisted Automatic Parameter Adaptation Design for Differential Evolution

In this study, parameter adaptation methods for differential evolution are automatically designed using a surrogate approach. In particular, Taylor series are applied to model the searched dependence between the algorithm’s parameters and values, describing the current algorithm state. To find the best-performing adaptation technique, efficient global optimization, a surrogate-assisted optimization technique, is applied. Three parameters are considered: scaling factor, crossover rate and population decrease rate. The learning phase is performed on a set of benchmark problems from the CEC 2017 competition, and the resulting parameter adaptation heuristics are additionally tested on CEC 2022 and SOCO benchmark suites. The results show that the proposed approach is capable of finding efficient adaptation techniques given relatively small computational resources.
Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language

In this manuscript, we present our efforts to develop an accurate sentiment analysis model for Bosnian-language tweets which incorporated three elements: negation cues, AnA-words (referring to maximizers, boosters, approximators, relative intensifiers, diminishers, and minimizers), and sentiment-labeled words from a lexicon. We used several machine-learning techniques, including SVM, Naive Bayes, RF, and CNN, with different input parameters, such as batch size, number of convolution layers, and type of convolution layers. In addition to these techniques, BOSentiment is used to provide an initial sentiment value for each tweet, which is then used as input for CNN. Our best-performing model, which combined BOSentiment and CNN with 256 filters and a size of 4×4, with a batch size of 10, achieved an accuracy of over 92%. Our results demonstrate the effectiveness of our approach in accurately classifying the sentiment of Bosnian tweets using machine-learning techniques, lexicons, and pre-trained models. This study makes a significant contribution to the field of sentiment analysis for under-researched languages such as Bosnian, and our approach could be extended to other languages and social media platforms to gain insight into public opinion.
Keystroke Dynamics as a Language Profiling Tool: Identifying Mother Tongue of Unknown Internet Users

Understanding the distinct characteristics of unidentified Internet users is helpful in various contexts, including digital forensics, targeted advertising, and user interaction with services and systems. Keystroke dynamics (KD) enables the analysis of data derived from a user’s typing behaviour on a keyboard as one approach to obtain such information. This study conducted experiments on a developed dataset that recorded samples of typing in five different mother tongues to determine Internet users’ mother tongue. Based on only a few KD features and machine learning techniques, 82% accuracy was achieved in recognising an unknown user’s mother tongue. This research highlights the potential for KD as a reliable method for identifying the mother tongue of Internet users, with implications for various applications such as improving digital forensic investigations, targeted advertising strategies, and optimising user experiences with online services.
A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information. However, current research focused on these advanced vision tasks in maritime supervision is not sufficiently comprehensive. Taking into account the application of ship detection and tracking technology, this study proposes a deep learning-based ship speed extraction framework under the haze environment. First, a lightweight convolutional neural network (CNN) is used to remove haze from images. Second, the YOLOv5 algorithm is used to detect ships in dehazed marine images, and a simple online and real-time tracking method with a Deep association metric (Deep SORT) is used to track ships. Then, the ship’s displacement in the images is calculated based on the ship’s trajectory. Finally, the speed of the ships is estimated by calculating the mapping relationship between the image space and real space. Experiments demonstrate that the method proposed in this study effectively reduces haze interference in maritime videos, thereby enhancing the image quality while extracting the ship’s speed. The mean squared error (MSE) for multiple scenes is 0.3 Kn on average. The stable extraction of ship speed from the video achieved in this study holds significant value in further ensuring the safety of ship navigation.
A Decentralized Blockchain-Based Energy Market for Citizen Energy Communities

Despite the fact that power grids have been planned and utilized using centralized networks for many years, there are now significant changes occurring as a result of the growing number of distributed energy resources, the development of energy storage systems and devices, and the increased use of electric vehicles. In light of this development, it is pertinent to ask what an efficient approach would be to the operation and management of future distribution grids consisting of millions of distributed and even mobile energy elements. Parallel to this evolution in power grids, there has been rapid growth in decentralized management technology due to the development of relevant technologies such as blockchain networks. Blockchain is an advanced technology that enables us to answer the question raised above. This paper introduces a decentralized blockchain network based on the Hyperledger Fabric framework. The proposed framework enables the formation of local energy markets of future citizen energy communities (CECs) through peer-to-peer transactions. In addition, it is designed to ensure adequate load supply and observe the network’s constraints while running an optimal operation point by consensus among all of the players in a CEC. An open-source tool in Python is used to verify the performance of the proposed framework and compare the results. Through its distributed and layered management structure, the proposed blockchain-based framework proves its superior flexibility and proper functioning. Moreover, the results show that the proposed model increases system performance, reduces costs, and reaches an operating point based on consensus among the microgrid elements.
Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree

This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big data environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing a complete quantum decision tree classification algorithm that is accurate and efficient while also considering classification costs. The proposed method integrates the Bayesian algorithm and the quantum decision tree classification algorithm to handle incremental data. The proposed approach generates a suitable decision tree dynamically based on data objects and cost constraints. To handle incremental data, the Bayesian algorithm and quantum decision tree classification algorithm are integrated, and kernel functions obtained from quantum kernel estimation are added to a linear quantum support vector machine to construct a decision tree classifier using decision directed acyclic networks of quantum support vector machine nodes (QKE). The experimental findings demonstrate the effectiveness and adaptability of the suggested quantum classification technique. In terms of classification accuracy, speed, and practical application impact, the proposed classification approach outperforms the competition, with an accuracy difference from conventional classification algorithms being less than 1%. With improved accuracy and reduced expense as the incremental data increases, the efficiency of the suggested algorithm for incremental data classification is comparable to previous quantum classification algorithms. The proposed global decision tree paradigm addresses the critical issues that need to be resolved by quantum classification methods, such as the inability to process incremental data and the failure to take the cost of categorization into account. By integrating the Bayesian algorithm and the quantum decision tree classification algorithm and using QKE, the proposed method achieves high accuracy and efficiency while maintaining high performance when processing incremental sequences and considering classification costs. Overall, the theoretical and experimental findings demonstrate the effectiveness of the suggested quantum classification technique, which offers a promising solution for handling big data classification tasks that require high accuracy and efficiency.
The single- and two-photon scattering in the waveguide QED coupling to a giant atom

Abstract The giant atom, which means the nonlocal atom-waveguide coupling, gives lots of newfangled physics. In this paper, we study the single- and two-photon scattering in the waveguide quantum electrodynamics on a two-level giant atom. For single-photon scattering, we find that the transmission rate is dependent on the atomic size. For the two-photon scattering, using a reasonable wave function hypothesis, we can get the two-photon scattering state, which consists of the symmetric and anti-symmetric plane wave functions. The other eigenstate is the two-photon bound state, which is orthogonal to the two-photon scattering state. We find that the spatial extent of the two-photon bound state is related to the detuning between waveguide and atom, which is distinguished from the character of the general atom. Our study is beneficial to photon control and the fundamental research of the two-photon scattering.
How Accurate are GPT-3’s Hypotheses About Social Science Phenomena?

Abstract We test whether GPT-3 can accurately predict simple study outcomes in the social sciences. Ground truth outcomes were obtained by surveying 600 adult US citizens about their political attitudes. GPT-3 was prompted to predict the direction of the empirical inter-attitude correlations. Machine-generated hypotheses were accurate in 78% (zero-shot), 94% (five-shot and chained prompting), and 97% (extensive finetuning) of cases. Positive and negative correlations were balanced in the ground truth data. These results encourage the development of hypothesis engines for more challenging contexts. Moreover, they highlight the importance of addressing the numerous ethical and philosophical challenges that arise with hypothesis automation. While future hypothesis engines could potentially compete with human researchers in terms of empirical accuracy, they have inherent drawbacks that preclude full automations for the foreseeable future.
A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications

Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research. The proposed paradigm is built on the basis of economic consideration and is easy to implement. Yet, the used YOLOv4-tiny is one of the fastest object detection models to exist. A mask detection camera (MaskCam) that leverages the computing power of NVIDIA’s Jetson Nano edge nanodevices was built side by side with a smart camera application to detect a mask on the face of an individual. MaskCam distinguishes between mask wearers, those who are not wearing masks, and those who are not wearing masks properly according to MQTT protocol. Furthermore, a self-developed web browsing application comes with the MaskCam system to collect and visualize statistics for qualitative and quantitative analysis. The practical results demonstrate the superiority and effectiveness of the proposed smart mask detection system. On the one hand, YOLOv4-full obtained the best results even at smaller resolutions, although the frame rate is too small for real-time use. On the other hand, it is twice as fast as the other detection models, regardless of the quality of detection. Consequently, inferences may be run more frequently over the entire video sequence, resulting in more accurate output.
Reliability of domain authority scores calculated by Moz, Semrush, and Ahrefs

Search engine optimization (SEO), the practice of improving website visibility on search engines, faces the considerable challenges posed by the opacity of Google’s relevance ranking algorithm. Attempts at understanding how this algorithm operates have generated a sizeable number of studies in the worlds of both business and academia. Indeed, this research tradition has managed to present strong evidence regarding the participation of certain factors and their relative importance. For instance, there is a widespread consensus that domain authority is one of the key factors in optimizing positioning. This study seeks to determine the reliability of the domain authority scores provided by three leading platforms for SEO professionals: Moz’s Domain Authority, Semrush’s Authority Score, and Ahrefs’ Domain Rating, values obtained using different indices and applying different procedures. We hypothesize that the degree of coincidence is high, allowing us to deduce that the three tools are, therefore, highly reliable. The method of data triangulation is used to compare the values from these three sources. The degree of coincidence is determined using a statistical analysis based on Spearman’s correlation coefficient (rho). The sample of domains analyzed was selected from 61 neutral queries, which provided 16,937 results and a total of 3,151 domains. When examining the tools in pairs, the correlation coefficients obtained were above 0.9 in all cases. The rho coefficient of the global analysis was also 0.9. This confirms our hypothesis and demonstrates that the three platforms can be considered as providing reliable data. These results are clearly relevant given that SEO professionals depend heavily on domain authority values in their work, and the degree of reliability detected ensures that decision-making based on this indicator can be undertaken with confidence.
Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio

Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio (SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. In recent years, joint recognition of multiple networks has become increasingly popular. In complex wireless environments, there are multiple signal types and diversity of characteristics between different signals. Also, the existence of multiple interference in wireless environment makes the signal characteristics more complex. It is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time-frequency domain joint recognition model that combines two deep learning networks (DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN (multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component (IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3 (three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT (Fast Fourier Transform) is used to obtain frequency domain amplitude and phase (FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.
The Role of Blockchain Technology in the Metaverse Ecosystem

Metaverse is a virtual world based on AR and VR. The word Metaverse comprises “meta,” which means beyond, and “verse,” which means universe, which combines world beyond imagination. Today's Z generation wants everything over the tip of their fingers, even if they don't want to step out for purchasing groceries or daily household items and hang out with buddies. The Metaverse can provide a solution to this problem. Without stepping out of their home, they can virtually cheat and chat with friends and have a party with food and drink by sitting at their own homes by purchasing through virtual currency, own virtual assets including house, jewelry, etc. by creating their avatar using AR and VR technology. The use of blockchain technology solves the decentralization issue, maintenance of cryptocurrency, and other applications. Hence, the use of blockchain in the Metaverse ecosystem can benefit financial, non-financial, and privacy issues that may occur due to the described framework of the Metaverse ecosystem.
From regression models to machine learning approaches for long term Bitcoin price forecast

We carry on a long term analysis for Bitcoin price, which is currently among the most renowned crypto assets available on markets other than Forex. In the last decade Bitcoin has been under spotlights among traders all world wide, both because of its nature of pseudo–currency and for the high volatility its price has frequently experienced. Considering that Bitcoin price has earned over five orders of magnitude since 2009, the interest of investors has been increasingly motivated by the necessity of accurately predicting its value, not to mention that a comparative analysis with other assets as silver and gold has been under investigation, too. This paper reports two approaches for a long term Bitcoin price prediction. The first one follows more standard paradigms from regression and least squares frameworks. Our main contribution in this regard fosters conclusions which are able to justify the cyclic performance of Bitcoin price, in terms of its Stock–to–Flow. Our second approach is definitely novel in the literature, and indicates guidelines for long term forecasts of Bitcoin price based on Machine Learning (ML) methods, with a specific reference to Support Vector Machines (SVMs). Both these approaches are inherently data–driven, and the second one does not require any of the assumptions typically needed by solvers for classic regression problems.
Generating Complex Animated Characters of Various Art Styles With Optimal Beauty Scores Using Deep Generative Adversarial Networks

A generative adversarial network (GAN) is a generative model that is able to generate fresh content by using several deep learning techniques together. Due to its fascinating applications, including the production of synthetic training data, the creation of art, style-transfer, image-to-image translation, etc., the topic has gained a lot of attraction in the machine learning community. GAN consists of two networks: the generator and the discriminator. The generator will make an effort to create phony samples in an effort to trick the discriminator into thinking they are real samples. In order to distinguish generated samples from both actual and fraudulent samples, the discriminator will strive to do so. The main motive of this chapter is to make use of several types of GANs like StyleGANs, cycle GANs, SRGANs, and conditional GANs to generate various animated characters of different art styles with optimal attractive scores, which can make a huge contribution in the entertainment and media sector.