Reviewerly/exHarmony huggingface dataset:
EnsembleVehicleDet: Detection of Faraway Vehicles with Real-Time Consideration

While detecting surrounding vehicles in autonomous driving is possible with advances in object detection using deep learning, there are cases where small vehicles are not being detected accurately. Additionally, real-time processing requirements must be met for implementation in autonomous vehicles. However, detection accuracy and execution speed have an inversely proportional relationship. To improve the accuracy–speed tradeoff, this study proposes an ensemble method. An input image is downsampled first, and the vehicle detection result is acquired for the downsampled image through an object detector. Then, warping or upsampling is performed on the Region of Interest (RoI) where the small vehicles are located, and the small vehicle detection result is acquired for the transformed image through another object detector. If the input image is downsampled, the effect on the detection accuracy of large vehicles is minimal, but the effect on the detection accuracy of small vehicles is significant. Therefore, the detection accuracy of small vehicles can be improved by increasing the pixel sizes of small vehicles in the transformed image more than the given input image. To validate the proposed method’s efficiency, the experiment was conducted with Argoverse vehicle data used in an autonomous vehicle contest, and the accuracy–speed tradeoff improved by up to a factor of two using the proposed ensemble method.
Active basis choice for quantum key distribution with entangled states

Abstract Entangled quantum key distribution (QKD) is a promising way to generate pairs of unconditionally secret keys. In this paper we review possible realizations of entanglement-based QKD and assess their feasibility in terms of implementation complexity and provided security. We also propose a novel active basis choice approach that enables to use only one single-photon detector per user. The paper provides all necessary details including the required electro-optic crystal configurations to implement such a scheme experimentally.
A Robust Real-Time Ellipse Detection Method for Robot Applications

Over the years, many ellipse detection algorithms have been studied broadly, while the critical problem of accurately and effectively detecting ellipses in the real-world using robots remains a challenge. In this paper, we proposed a valuable real-time robot-oriented detector and simple tracking algorithm for ellipses. This method uses low-cost RGB cameras for conversion into HSV space to obtain reddish regions of interest (RROIs) contours, effective arc selection and grouping strategies, and the candidate ellipses selection procedures that eliminate invalid edges and clustering functions. Extensive experiments are conducted to adjust and verify the method’s parameters for achieving the best performance. The method combined with a simple tracking algorithm executes only approximately 30 ms on a video frame in most cases. The results show that the proposed method had high-quality performance (precision, recall, F-Measure scores) and the least execution time compared with the existing nine most advanced methods on three public actual application datasets. Our method could detect elliptical markers in real-time in practical applications, detect ellipses adaptively under natural light, well detect severely blocked and specular reflection ellipses when the elliptical object was far from or close to the robot. The average detection frequency can meet the real-time requirements (&gt;10 Hz).
Quantum Weighted Fractional-Order Transform

Quantum Fourier transform (QFT) transformation plays a very important role in the design of many quantum algorithms. Fractional Fourier transform (FRFT), as an extension of the Fourier transform, is particularly important due to the design of its quantum algorithm. In this paper, a new reformulation of the weighted fractional Fourier transform (WFRFT) is proposed in order to realize quantum FRFT; however, we found that this reformulation can be applied to other transformations, and therefore, this paper presents the weighted fractional Hartley transform (WFRHT). For the universality of application, we further propose a general weighted fractional-order transform (WFRT). When designing the quantum circuits, we realized the quantum WFRFT via QFT and quantum phase estimation (QPE). Moreover, after extending our design to the WFRHT, we were able to formulate the quantum WFRHT. Finally, in accordance with the research results, we designed the quantum circuit of the general WFRT, and subsequently proposed the quantum WFRT. The research in this paper has great value as a reference for the design and application of quantum algorithms.
Fusion Objective Function on Progressive Super-Resolution Network

Recent advancements in Single-Image Super-Resolution (SISR) have explored the network architecture of deep-learning models to achieve a better perceptual quality of super-resolved images. However, the effect of the objective function, which contributes to improving the performance and perceptual quality of super-resolved images, has not gained much attention. This paper proposes a novel super-resolution architecture called Progressive Multi-Residual Fusion Network (PMRF), which fuses the learning objective functions of L2 and Multi-Scale SSIM in a progressively upsampling framework structure. Specifically, we propose a Residual-in-Residual Dense Blocks (RRDB) architecture on a progressively upsampling platform that reconstructs the high-resolution image during intermediate steps in our super-resolution network. Additionally, the Depth-Wise Bottleneck Projection allows high-frequency information of early network layers to be bypassed through the upsampling modules of the network. Quantitative and qualitative evaluation of benchmark datasets demonstrate that the proposed PMRF super-resolution algorithm with novel fusion objective function (L2 and MS-SSIM) improves our model’s perceptual quality and accuracy compared to other state-of-the-art models. Moreover, this model demonstrates robustness against noise degradation and achieves an acceptable trade-off between network efficiency and accuracy.
FLoCIC: A Few Lines of Code for Raster Image Compression

A new approach is proposed for lossless raster image compression employing interpolative coding. A new multifunction prediction scheme is presented first. Then, interpolative coding, which has not been applied frequently for image compression, is explained briefly. Its simplification is introduced in regard to the original approach. It is determined that the JPEG LS predictor reduces the information entropy slightly better than the multi-functional approach. Furthermore, the interpolative coding was moderately more efficient than the most frequently used arithmetic coding. Finally, our compression pipeline is compared against JPEG LS, JPEG 2000 in the lossless mode, and PNG using 24 standard grayscale benchmark images. JPEG LS turned out to be the most efficient, followed by JPEG 2000, while our approach using simplified interpolative coding was moderately better than PNG. The implementation of the proposed encoder is extremely simple and can be performed in less than 60 lines of programming code for the coder and 60 lines for the decoder, which is demonstrated in the given pseudocodes.
On Construction and Estimation of Mixture of Log-Bilal Distributions

Recently, the use of mixed models for analyzing real data sets with infinite domains has gained favor. However, only a specific type of mixture model using mostly maximum likelihood estimation technique has been exercised in the literature, and fitting the mixture models for bounded data (between zero and one) has been neglected. In statistical mechanics, unit distributions are widely utilized to explain practical numeric values ranging between zero and one. We presented a classical examination for the trade share data set using a mixture of two log-Bilal distributions (MLBDs). We examine the features and statistical estimation of the MLBD in connection with three techniques. The sensitivity of the presented estimators with respect to model parameters, weighting proportions, sample size, and different evaluation methodologies has also been discussed. A simulation investigation is also used to endorse the estimation results. The findings on maximum likelihood estimation were more persuasive than those of existing mixture models. The flexibility and importance of the proposed distribution are illustrated by means of real datasets.
SocioPedia+: a visual analytics system for social knowledge graph-based event exploration

In the recent era of information explosion, exploring event from social networks has recently been a crucial task for many applications. To derive valuable comprehensive and thorough insights on social events, visual analytics (VA) system have been broadly used as a promising solution. However, due to the enormous social data volume with highly diversity and complexity, the number of event exploration tasks which can be enabled in a conventional real-time visual analytics systems has been limited. In this article, we introduce SocioPedia+, a real-time visual analytics system for social event exploration in time and space domains. By introducing the dimension of social knowledge graph analysis into the system multivariate analysis, the process of event explorations in SocioPedia+ can be significantly enhanced and thus enabling system capability on performing full required tasks of visual analytics and social event explorations. Furthermore, SocioPedia+ has been optimized for visualizing event analysis on different levels from macroscopic (events level) to microscopic (knowledge level). The system is then implemented and investigated with a detailed case study for evaluating its usefulness and visualization effectiveness for the application of event explorations.
Analysis of the Phenomenon of Chinese Educational Involution and Recommendations

At the end of 2020, the time of the COVID-19 outbreak, a word suddenly became particularly popular, called "involution". Originally, this is a sociological term that refers to a social phenomenon in which the competition for social resources increases when resources are limited and there are no incremental resources. But as soon as the word appeared, it was almost immediately associated with the field of education. More and more people are trying to improve themselves by taking extracurricular classes, studying abroad, and so on, in order to get more educational and social resources, which is undoubtedly a kind of "involution". Under the current situation of educational involution, this paper will study the phenomenon of educational involution in depth, which will help to understand the concept of educational involution, its causes, solutions, and future development trends. This paper examines the causes of educational involution from three perspectives, including historical, economic, and social causes. Then, it provides two suggestions. The first suggestion is that students need to have a clear understanding of their preferences and goals and make themselves as a reference for academic progress rather than other peers; the second suggestion is to expand educational resources to reduce educational involution. Finally, it is speculated that the trend of education after involution is that the criteria for evaluating students will become diversified, and scores will no longer be the only evaluation criterion.
Discriminatively Unsupervised Learning Person Re-Identification via Considering Complicated Images

State-of-the-art purely unsupervised learning person re-ID methods first cluster all the images into multiple clusters and assign each clustered image a pseudo label based on the cluster result. Then, they construct a memory dictionary that stores all the clustered images, and subsequently train the feature extraction network based on this dictionary. All these methods directly discard the unclustered outliers in the clustering process and train the network only based on the clustered images. The unclustered outliers are complicated images containing different clothes and poses, with low resolution, severe occlusion, and so on, which are common in real-world applications. Therefore, models trained only on clustered images will be less robust and unable to handle complicated images. We construct a memory dictionary that considers complicated images consisting of both clustered and unclustered images, and design a corresponding contrastive loss by considering both kinds of images. The experimental results show that our memory dictionary that considers complicated images and contrastive loss can improve the person re-ID performance, which demonstrates the effectiveness of considering unclustered complicated images in unsupervised person re-ID.
BPG-Based Lossy Compression of Three-Channel Noisy Images with Prediction of Optimal Operation Existence and Its Parameters

Nowadays, there is a clear trend toward increasing the number of remote-sensing images acquired and their average size. This leads to the need to compress the images for storage, dissemination, and transfer over communication lines where lossy compression techniques are more popular. The images to be compressed or some of their components are often noisy. They must therefore be compressed taking into account the properties of the noise. Due to the noise filtering effect obtained during lossy compression of noisy images, an optimal operating point (OOP) may exist. The OOP is a parameter that controls the compression for which the quality of the compressed image is closer (closest) to the corresponding noise-free image than the quality of the noisy (original, uncompressed) image according to some quantitative criterion (metric). In practice, it is important to know whether the OOP exists for a given image, because if the OOP exists, it is appropriate to perform the compression in the OOP or at least in its neighborhood. Since the real image is absent in practice, it is impossible to determine a priori whether the OOP exists or not. Here, we focus on three-channel-remote-sensing images and show that it is possible to easily predict the existence of the OOP. Furthermore, it is possible to predict the metric values or their improvements with appropriate accuracy for practical use. The BPG (better portable graphics) encoder is considered a special case of an efficient compression technique. As an initial design step, the case of additive white Gaussian noise with equal variance in the three components is considered. While previous research was mainly focused on predicting the improvement (reduction) of the PSNR and PSNR-HVS-M metrics, here we focus on the modern visual quality metrics, namely PSNR-HA and MDSI. We also discuss what to do if, according to the prediction, an OOP is absent. Examples of lossy compression of noisy three-channel remote sensing images are given. It is also shown that the use of three-dimensional compression provides a compression ratio increase by several times compared with component-wise compression in the OOP.
An interactive tool for visualization and prediction of solar radiation and photovoltaic generation in Colombia

This paper presents the building process of an interactive instrument called the Colombian Solar Atlas able to easily visualize meteorological data but also assess the current and future potentials of solar photovoltaic generation throughout the whole territory of Colombia, South America. This new tool is based on two different meteorological databases. The first one is done with historical data extracted from satellite imagery information, and the other one corresponds to data issues from regional-scale climate change projection models. The satellite database was validated with different in-situ measurements. The Colombian Solar Atlas uses basic and advanced photovoltaic generation models to estimate the generation of a custom solar installation. With this tool, a user selects a point on the map and can have directly pertinent information to search for an optimal location with a spatial resolution of 4 km2. This tool is the first open interactive online tool particularly adapted to study the photovoltaic power potential in Colombia, considering the country’s needs and native language.
A Comparative Review of Sentimental Analysis Using Machine Learning and Deep Learning Approaches

The sentiment data provides vital information about the feedback of the user’s opinion, attitude and emotions. The business of product development and digital marketing teams entirely depends upon the outcome of these sentiments and they apply various Data Mining techniques, Machine Learning and Deep Learning approaches to analyse the depth of the dataset. The Sentiment Analysis provides the automatic data mining of reviews, comments, opinions and suggestions, received from various input methods, including text, audio notes, images and emoticons, through Natural Language Processing. The analysis assists in the classification of reviewer feedback in terms of positive, negative and neutral categories. In this study, the opinions shared by individuals over various social networking sites in the case of any big event, the release of any new product or show and political events were analysed. Machine Learning and Deep Learning techniques are discussed and used dominantly to illustrate the outcome of opinions and events. The accurate analysis of vast information shared by individuals free of cost and without any influence can provide vital information for organisations and management authorities. This review analyses various techniques in the field of Aspect-Based Sentiment Analysis along with their features and research scopes and thus, it helps researchers to focus on more precise works in the future. Among the machine learning algorithms, Random Forest performed much better as compared to other methods, and among the Deep Learning approaches, Multichannel CNN outperformed with the highest accuracy of 96.23%. The paper includes the comparative study of multiple Machine Learning and Deep Learning techniques for the evaluation of sentiment data and concludes with the challenges and scope of Sentiment Analysis.
Moving indoors: a systematic literature review of locomotion in virtual indoor environments

Virtual indoor environments provide a unique combination of indoor cartography, virtual reality, and virtual locomotion. So far however, the indoor aspect of such an environment seems to be neglected even though it may affect how the locomotion of users is conducted as well as how this affects their spatial behaviour. To get an idea about the state of the research, systematic review of peer-reviewed articles mainly from the Scopus database was conducted in this paper. It aims to get a broad idea about the interest in research of virtual locomotion and to figure out what type of research in the indoor environment was conducted. Special interest was put on the types of locomotion techniques, their interaction aspects and research topics. These characteristics were analysed and compared to earlier systematic reviews in this topic. To accommodate the indoor-related themes, environmental aspects (size) and the motivation for using an indoor environment were also obtained from the relevant articles analysed. This way, the paper tries to provide a basis for further research in using indoor environments in virtual reality.
Interactive Audio-Visual Course Teaching of Music Education Based on VR and AI Support

People’s living standards have gradually improved, people have higher and higher requirements at the spiritual level, and music is increasingly loved by people. In the process of music development, music teaching reform is the focus of the current society. Based on the current educational background, the teaching mode of teachers has changed, breaking the ecological balance in the traditional classroom, which has brought advantages to music teaching. Aesthetic education, especially music education, plays a balanced and promoting role in intellectual education and even the whole quality education. Music can effectively reduce the pressure of intellectual education itself on students’ bodies and minds, so that intellectual education can achieve better results. Virtual reality (VR) and artificial intelligence (AI), as the latest technologies in the new century, have been rapidly integrated into the music education (ME) industry. By applying VR and AI technologies to the music teaching process, students’ learning ability can be effectively improved. Through the use of VR and AI technology, this text has created an interactive curriculum teaching mode and verified the feasibility of combining VR and AI technology with music teaching through experimental analysis. The experimental results show that the recognition of bass signal, midrange signal, and treble signal has reached 100, 90, and 100%, respectively. In general, the recognition accuracy of the algorithm in this text is very high, which can satisfy the requirements of music signal recognition and can be applied to daily music teaching.
Gender identity and influence in human-machine communication:A mixed-methods exploration

The advancement of conversational technologies stimulates new research agenda on the patterns, norms, and social impacts of human-machine communication (HMC) as a novel process. Conversational agents (CAs), a prevalent example of machines that communicate with users directly, are usually depicted as females in assisting roles. This study intends to explore empirical evidence of how “gendered” technologies might influence HMC and potentially reinforce gender stereotyping in human-human communication. We applied a mixed-methods approach to explore users' gender-related responses and evaluations in the interaction with CAs. First, we observed unrestricted interactions between 36 human participants and Amazon Alexa in a laboratory and qualitatively analyzed the transcripts to detect gendered communication cues. We then conducted a 2 × 3 (participant gender: female vs. male; CA gender: female vs. male vs. neutral) online experiment where 250 participants interacted with a customized chatbot created by the researcher. Results showed participants’ different emotions/tones, engagement, (non)accommodation, as well as credibility, attraction, and likeability evaluations between human-CA gender pairs.
Robust polarization state generation for long-range quantum key distribution

We present a new compact and robust polarization state transmitter designed to execute the BB84 quantum key distribution protocol. Our transmitter prepares polarization states using a single commercial-off-the-shelf phase modulator. Our scheme does not require global biasing to compensate thermal and mechanical drifts, as both of the system's two time-demultiplexed polarization modes share a single optical path. Furthermore, the transmitter's optical path entails a double-pass through the phase modulation device for each polarization mode, allowing multiple phase rotations to be impinged on each light pulse. We present a proof-of-concept prototype of this transmitter topology and demonstrate a mean intrinsic quantum bit error rate below 0.2% over a 5 hour measurement.