Reviewerly/exHarmony huggingface dataset:
Effectiveness Analysis of Multiple Initial States Simulated Annealing Algorithm, a Case Study on the Molecular Docking Tool Autodock Vina

Simulated Annealing (SA) algorithm is not effective with large optimization problems for its slow convergence. Hence, several parallel Simulated Annealing (pSA) methods have been proposed, where the increase of searching threads can boost the speed of convergence. Although these methods can obtain satisfactory solutions to these optimization problems, there is no rigorous mathematical analyses on their effectiveness. Thus, this paper introduces a probabilistic model, on which a theorem about the effectiveness of multiple initial states parallel SA (MISPSA) has been proven. The theorem also demonstrates that the increasing parallelism in pSA algorithm with the reducing of search depth in each thread could obtain the same or even higher probability of finding the global optimal solution.
A Novel Blind High-Order Modulation Classifier Using Accumulated Constellation Temporal Convolution for OSTBC-OFDM Systems

Deep learning (DL)-based blind modulation classification (BMC) has flourished in the single carrier system for low-order modulation signals. However, the research of DL-based BMC has not been well explored in the OSTBC-OFDM system (Orthogonal Space-Time Block Coded-Orthogonal Frequency Division Multiplexing), especially high-order modulation types. This brief proposes a novel BMC algorithm for high-order modulation signals (e.g., 4096QAM), which is based on reconstruction processing and accumulation constellation temporal convolution BMC network (ACTC-BMCNet). First, we employ the blind zero-forcing (ZF) equalization algorithm to reconstruct the damaged signal and enhance the signal representation power. Then, the constellation accumulation strategy is executed on the raw constellation diagram (CD) to generate the accumulated CD (ACD) image for reducing constellation dispersion. After that, instead of traditional 2D convolution, the constellation temporal convolution enabled in ACTC-BMCNet is leveraged to reduce computational overhead and extract the more discriminative feature. Monte-Carlo experiments are performed on fourteen modulation signals, and results indicate that the proposed ACTC-BMCNet possesses a superior classification accuracy and a faster recognition speed than the DL-based benchmark classifiers.
Hybrid Gray Wolf Optimization and Cuckoo Search Algorithm based on the Taguchi Theory

A novel method was proposed which combines gray wolf optimizer (GWO) and cuckoo search algorithm (CS) based on the Taguchi theory in this paper. Generally speaking, the traditional GWO has a powerful ability to exploit locally and the traditional CS has a powerful ability to explore globally in the solution space. With the advantages of these two algorithms, a hybrid algorithm (GWO-CS) combined by the orthogonal array in Taguchi theory was developed. The Taguchi theory and its key method, orthogonal arrays, are widely used in the industrial field to improve the robustness of product design. The test results of the proposed algorithm (GWO-CS) with CEC2017 indicated its effectiveness compared with traditional and other homogeneous algorithms.
Trajectory Estimation of a Flying Robot With a Single Ranging Beacon and Derived Velocity Constraints

The performance of the trajectory estimation with a single beacon highly depends on the observability of the system, which is further determined by the excitation patterns of the robot. Because the desired excitation patterns regarding specific observability conditions cannot always be guaranteed in real-time, the trajectory estimation may casually fail with divergence. To tackle this issue, indirectly derived velocity constraints are first introduced in this work, and their effects on improving the performance of trajectory estimation are comparatively investigated. Considering the geometric evolution equation and using the state augmentation technique, seven different velocity constraints are evaluated. The comparative results indicate that the trajectory estimation divergence can be most effectively avoided with a pure velocity magnitude constraint. On this basis, a velocity-constrained moving horizon estimation algorithm is implemented and verified in an indoor experimental testbed to accurately estimate the trajectory of the flying robot with a single ultrawideband beacon in a long time interval. The results also quantitatively illustrate that the overall performance of the proposed approach outperforms that of the latest benchmark approach in both estimation precision and computational efficiency. In particular, with approximately the same estimation precision, the computational time cost of the proposed approach is less than 1/10 of that of existing approaches.
Estimation of Extrinsic Parameters With Trifocal Tensor for Intelligent Vehicle-Mounted Cameras

In this article, the extrinsic parameter estimation of a camera mounted on an intelligent vehicle is addressed. The trifocal tensor is utilized to construct vision dynamics which relates image coordinates, velocity signals, and extrinsic parameters. Artificial visual patterns such as chessboards and planar reference objects used in homography-based methods are no longer required. An auxiliary tensor decouples the rotational extrinsic parameters from the translational ones. A key frame strategy is adopted to deal with the field of view constraint and an unknown distance is eliminated from the vision dynamics to counter the scale change caused by key frame switching. The Lyapunov method is used to design nonlinear observers, which estimate the extrinsic parameters at each time step based all collected valid historical data. Performance of the proposed method is verified by both simulation and experimental results.
Monitoring Social Distancing With Single Image Depth Estimation

The recent pandemic emergency raised many challenges regarding the countermeasures aimed at containing the virus spread, and constraining the minimum distance between people resulted in one of the most effective strategies. Thus, the implementation of autonomous systems capable of monitoring the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">social distance</i> gained much interest. In this paper, we aim to address this task leveraging a single RGB frame without additional depth sensors. In contrast to existing single-image alternatives failing when ground localization is not available, we rely on single image depth estimation to perceive the 3D structure of the observed scene and estimate the distance between people. During the setup phase, a straightforward calibration procedure, leveraging a scale-aware SLAM algorithm available even on consumer smartphones, allows us to address the scale ambiguity affecting single image depth estimation. We validate our approach through indoor and outdoor images employing a calibrated LiDAR + RGB camera asset. Experimental results highlight that our proposal enables sufficiently reliable estimation of the inter-personal distance to monitor social distancing effectively. This fact confirms that despite its intrinsic ambiguity, if appropriately driven single image depth estimation can be a viable alternative to other depth perception techniques, more expensive and not always feasible in practical applications. Our evaluation also highlights that our framework can run reasonably fast and comparably to competitors, even on pure CPU systems. Moreover, its practical deployment on low-power systems is around the corner.
A Coherent Approach to Analyze Sentiment of Cryptocurrency

In this paper, we have tried to analyze the real-time Twitter data of some popular cryptocurrencies, like Bitcoin. Bitcoin has been by far the largest cryptocurrency in terms of market size. Its market capitalization currently sits at over 1 Trillion US dollars. Even though it has gained popularity during this decade, still the cryptocurrency has seen many significant price swings on both daily as well as long-term valuations. In recent times, the influence of social media platforms like Twitter can be seen on cryptocurrency as well. Twitter is being used as a news sourceAyush Hans Kunal Ravindra Mohadikar Ekansh for many users who need to buy or sell Bitcoin. Therefore, understanding the sentiment behind the tweets which have a direct impact on price direction can help the user to trade in cryptocurrency better. The real-time data extracted using the APIs can help the user get the tweet sentiment at the time of purchase which can provide him/her an advantage in buying/selling the cryptocurrency. By combining the commonly used sentiment analysis tools like VADER and TextBlob into a single model, we can get a more accurate sentiment analysis of the tweets.
Merging Spaces: Augmented Reality, Temporary Public Art, and the Reinvention of Site

The chapter explores how augmented reality (AR) has redefined temporary, site-specific public art, expanding the field by introducing new practices, and offering possibilities for public engagement that did not exist before. This chapter investigates temporary and site-specific public art before AR technology, then looks at Broadway Augmented—an innovative early augmented reality public art project located in Sacramento, California—and finishes with recent projects that exemplify how the field has expanded. Developments in AR technologies, mobile devices, and ubiquitous networks have meant that augmented reality art can have an immediate public presence, responding to social, environmental, and cultural issues as they are unfolding, making it a pivotal form of public art for the twenty-first century.
PNRNet: Physically-Inspired Neural Rendering for Any-to-Any Relighting

Existing any-to-any relighting methods suffer from the task-aliasing effects and the loss of local details in the image generation process, such as shading and attached-shadow. In this paper, we present PNRNet, a novel neural architecture that decomposes the any-to-any relighting task into three simpler sub-tasks, i.e. lighting estimation, color temperature transfer, and lighting direction transfer, to avoid the task-aliasing effects. These sub-tasks are easy to learn and can be trained with direct supervisions independently. To better preserve local shading and attached-shadow details, we propose a parallel multi-scale network that incorporates multiple physical attributes to model local illuminations for lighting direction transfer. We also introduce a simple yet effective color temperature transfer network to learn a pixel-level non-linear function which allows color temperature adjustment beyond the predefined color temperatures and generalizes well to real images. Extensive experiments demonstrate that our proposed approach achieves better results quantitatively and qualitatively than prior works.
Digital Literacy Among Students of Pedagogical Faculties in Poland—A Systematic Literature Analysis

The aim of this article is to explore the level of digital literacy (DL) among students of the pedagogical faculties in Poland based on previous research. DL is one of the key competences of future teaching staff. The discussion on DL and how it is measured is part of the reflection on the shape of the future school and educational programmes at universities of pedagogy. Understanding the state of preparation of new pedagogical cohorts in the intensively developing information society requires the organization of those conclusions reached so far by the relevant research. For this purpose, a systematic analysis of the literature (peer-reviewed articles) indexed in repositories and scientific databases such as Google Scholar, EBSCO, Scopus and Web of Science was used. Based on data from 2001 to 2021 on Polish students, these being future educators, it was noted that: (1) Measurement of DL is rare, with preference given to theoretical analyses; (2) The research is dominated by quantitative techniques and tools mostly deprived of theoretical framework and psychometric properties based on a number of inconsistent indicators; (3) Occasionally DigCompEdu, TPACK, Eurostat typologies are used for measurement; (4) DL measurement is mostly based on students’ self-declarations; (5) Studies are conducted in different centres without representative samples, and the samples used are usually small; (6) The level of DL is most often described as average or good; (7) Students of pedagogical faculties prefer to rely on common software and services of the information society; (8) Attitude towards new media in education is diversified within this group; and (9) There is a need to develop new, standardized research tools measuring DL, which might nullify the Dunning–Kruger effect.
Image Recognition Methods Based on Deep Learning

With the development of science and technology worldwide, computer technology has gradually been widely used in various fields like the military and biomedical domains. In this situation, people’s requirements for image recognition are increasing. However, the efficiency of manual image recognition is very low, and image recognition has always been a weak field in computer technology. This paper will introduce various deep learning models such as feedforward neural networks, convolutional neural networks, and recurrent neural networks. We will talk about how these models work and then compare and analyze these different networks. In this paper, we will try to train a convolutional neural network from a data set. The “dogs-vs-cats” data set from Kaggle will be used. In the “dogs-vs-cats” experiment part, some basic tasks in image processing will be discussed, such as feature extraction method, which is one of the most important steps in each link of image processing. Finally, the machine should be able to distinguish between pictures of dogs and cats. Moreover, we will also discuss some preliminary applications of deep learning in image recognition.
Hypercomplex Generative Adversarial Networks for Lightweight Semantic Labeling

Following recent advances on parameterized hypercomplex multiplication [21], we explore the usefulness of hypercomplex convolutions and deconvolutions in a document labeling task. We show that the proposed Hypercomplex Generative Adversarial Networks achieve excellent results while requiring significantly less independent parameters than real-valued models.
SOFTWARE QUALITY MANAGEMENT SYSTEM

The Software quality management system has been developed. The system allows you to determine the state of software quality at each hierarchical level of the management system, taking into account the cost of ensuring the required quality. The system also allows the IT company manager to set the potential costs for the transition of software quality indicators from one state to another. It was found out that software quality is a multifaceted concept that can be adequately expressed by some hierarchical structure of characteristics and attributes, which is called the model of software product quality. To assess the quality of the software, a set of criteria and aggregate indicators were used, which maximally characterise it in terms of functionality and usage options. It is established that the software quality model is the main attribute of the complex system of its assessment. The quality model determines which characteristics of the software product must be taken into account when transiting the control system from one state to another. The level of software quality that meets the stated and indirect needs of various stakeholders and is relevant to them is directly reflected in the quality model of the software product in the form of its properties. The software quality model classifies product properties into characteristics and sub-characteristics. A method for selecting stressful variants of software quality state according to input criteria or aggregate indicators of its current quality has been developed. The method allows to determine the current state of software quality at each hierarchical level according to the corresponding aggregate indicator, taking into account its development cost. A method for selecting the optimal version of the software quality management system from the set of permissible has been developed. The method takes into account the structure of criteria and aggregated indicators of current software quality at each hierarchical level of the management system. It is established that the task of choosing the optimal version of the software quality management system belongs to the tasks of multicriteria optimization. The task takes into account the following criteria: portability of the product and its ease of maintenance, product safety and compatibility, ease of use of the product and reliability of its work, as well as the functional suitability of the product and its efficiency of use. An example of implementation of the software quality management system in both quantitative and cost ratio is given. The example allows us to understand the essence of the method of choosing the optimal variant for the management system, as well as the features of the method of selection of stressful variants of the system state according to two criteria or aggregate indicators.
A Survey-Based Study to Identify User Annoyances of German Voice Assistant Users

Voice user interfaces (VUIs) offer an intuitive, fast and convenient way for humans to interact with machines and computers. Yet, whether they'll be truly successful and find widespread uptake in the near future depends on the user experience (UX) they offer. With this survey-based study (n = 108), we aim to identify the major annoyances German voice assistant users are facing in voice-driven human-computer interactions. The results of our questionnaire show that irritations appear in six categories: privacy issues, unwanted activation, comprehensibility, response quality, conversational design and voice characteristics. Our findings can help identify key areas of work to optimize voice user experience in order to achieve greater adaptation of the technology. In addition, they can provide valuable information for the further development and standardization of voice user experience (VUX) research.
Evaluating Binary Classifiers with Word Embedding Techniques for Public Grievances

Public grievance happens when the administration fails to meet the expectations of people. For any government, it is preeminent need to settle people’s complaints. The government offers great deal of services in almost all areas of life to its citizens. Indian Government provides various e-platforms such as websites and mobile applications for Indians to register their complaints for any of the services but due to lack of awareness of these platforms and the lengthy registration process, people are turning to easily accessible user-friendly sources like social media to convey their grumble through microblogging. In this experiment we have focused on Grievances posted on Twitter for Indian Railways. Identifying genuine complaints among lacs of social media messages is quite a big challenge. This paper aims to use Machine Learning binary classifiers along with word embedding techniques to perform this classification job that can predict that the given text is a grievance or not. To find out the best classifier with the combination of suitable word embedding techniques, we associated six binary classifiers: Naïve Bays, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbor, with three-word embedding techniques: TFIDF, Word2Vec, BERT. Experiments have shown that, in many cases, text classification results are very close to each other, which can make the situation puzzled to pick the right model. Hence, we have used AUC ROC curves and K-Fold cross-validation method to validate the experimental results.
Detecting One-Pixel Attacks Using Variational Autoencoders

In the field of medical imaging, artificial intelligence solutions are used for diagnosis, prediction and treatment processes. Such solutions are vulnerable to cyberattacks, especially adversarial attacks targeted at machine learning algorithms. One-pixel attack is an adversarial method against image classification algorithms based on neural networks. In this study, we show that a variational autoencoder can be used to detect such attacks in the context of medical imaging. We use adversarial one-pixel images generated from the TUPAC16 dataset and apply the variational autoencoder as a filter before letting the images pass to the classifier. The results indicate that the variational autoencoder model efficiently detects one-pixel attacks.
Learning from Anywhere, Anytime: Utilitarian Motivations and Facilitating Conditions to Use Mobile Learning Applications

This contribution investigates higher education students’ perceptions about mobile learning (m-learning) applications, as well as the effects of social influences and of appropriate facilitating conditions, on their intentions to continue using them. A structured survey questionnaire integrated valid measures from the Technology Acceptance Model (TAM) and from the Unified Theory of Acceptance and Use of Technology (UTAUT) to better explain their acceptance and use of m-learning software. The findings reported that facilitating conditions including the provision of resources, ongoing training opportunities and technical support, were affecting the respondents’ engagement with m-learning programs. The respondents indicated that they were not influenced by others, to use mobile technologies for educational purposes. The results also suggest that they were well acquainted (and habituated) with the use of mobile devices and their applications. Evidently, they helped them improve their learning journeys.
My Tutor is an AI: The Effects of Involvement and Tutor Type on Perceived Quality, Perceived Credibility, and Use Intention

With the advancement of AI technology, AI tutors are already being utilized in classrooms throughout the world as teaching aides, tutors, and peer learning specialists. AI tutors are good at facilitating various teaching-learning practices within and outside the classroom, and support students 24/7. However, little is known whether AI tutors can be as effective in learning languages as human tutors, and what factors would cause student learning outcome differences between human tutors and AI tutors, especially for those students who are not engaged in learning. The current study sought to address these questions by discovering the combination of two factors: involvement (high vs. low) and tutor type (human tutor vs. AI tutor) under two conditions: weak and high writing quality. The findings indicate that there is an interaction effect between user involvement and tutor type. When user involvement is low, the AI tutor is perceived to have a higher writing quality than the human tutor; when user involvement was high, tutor type did not affect the perceived writing quality of the tutor, no matter the tutor was a human or an AI. The reason that the human tutor is preferred is that the human tutor is perceived to have a higher level of controllability than the AI tutor. The writing quality of tutors affects the credibility of tutors as well.