Reviewerly/exHarmony huggingface dataset:
Reference-Based Super-Resolution Method for Remote Sensing Images with Feature Compression Module

High-quality remote sensing images play important roles in the development of ecological indicators’ mapping, urban-rural management, urban planning, and other fields. Compared with natural images, remote sensing images have more abundant land cover along with lower spatial resolutions. Given the embedded longitude and latitude information of remote sensing images, reference (Ref) images with similar scenes could be more accessible. However, existing traditional super-resolution (SR) approaches always depend on increases in network depth to improve performance, which limits the acquisition and application of high-quality remote sensing images. In this paper, we proposed a novel, reference-image-based, super-resolution method with feature compression module (FCSR) for remote sensing images to alleviate the above issue while effectively utilizing high-resolution (HR) information from Ref images. Specifically, we exploited a feature compression branch (FCB) to extract relevant features in feature detail matching with large measurements. This branch employed a feature compression module (FCM) to extract features from low-resolution (LR) and Ref images, which enabled texture transfer from different perspectives. To decrease the impact of environmental factors such as resolution, brightness and ambiguity disparities between the LR and Ref images, we designed a feature extraction encoder (FEE) to ensure accuracy in feature extraction in the feature acquisition branch. The experimental results demonstrate that the proposed FCSR achieves significant performance and visual quality compared to state-of-the-art SR methods. Explicitly, when compared with the best method, the average peak signal-to-noise ratio (PSNR) index on the three test sets is improved by 1.0877%, 0.8161%, 1.0296%, respectively, and the structural similarity (SSIM) index on four test sets is improved by 1.4764%, 1.4467%, 0.0882%, and 1.8371%, respectively. Simultaneously, FCSR obtains satisfactory visual details following qualitative evaluation.
TMTNet: A Transformer-Based Multimodality Information Transfer Network for Hyperspectral Object Tracking

Hyperspectral video with spatial and spectral information has great potential to improve object tracking performance. However, the limited hyperspectral training samples hinder the development of hyperspectral object tracking. Since hyperspectral data has multiple bands, from which any three bands can be extracted to form pseudocolor images, we propose a Transformer-based multimodality information transfer network (TMTNet), aiming to improve the tracking performance by efficiently transferring the information of multimodality data composed of RGB and hyperspectral in the hyperspectral tracking process. The multimodality information needed to be transferred mainly includes the RGB and hyperspectral multimodality fusion information and the RGB modality information. Specifically, we construct two subnetworks to transfer the multimodality fusion information and the robust RGB visual information, respectively. Among them, the multimodality fusion information transfer subnetwork is designed based on the dual Siamese branch structure. The subnetwork employs the pretrained RGB tracking model as the RGB branch to guide the training of the hyperspectral branch with little training samples. The RGB modality information transfer subnetwork is designed based on a pretrained RGB tracking model with good performance to improve the tracking network’s generalization and accuracy in unknown complex scenes. In addition, we design an information interaction module based on Transformer in the multimodality fusion information transfer subnetwork. The module can fuse multimodality information by capturing the potential interaction between different modalities. We also add a spatial optimization module to TMTNet, which further optimizes the object position predicted by the subject network by fully retaining and utilizing detailed spatial information. Experimental results on the only available hyperspectral tracking benchmark dataset show that the proposed TMTNet tracker outperforms the advanced trackers, demonstrating the effectiveness of this method.
A Novel Chinese Overlapping Entity Relation Extraction Model Using Word-Label Based on Cascade Binary Tagging

In recent years, overlapping entity relation extraction has received a great deal of attention and has made good progress in English. However, the research on overlapping entity relation extraction in Chinese still faces two key problems: one is the lack of datasets with overlapping entity instances, and the other is the lack of a neural network model that can effectively solve overlapping entity relation extraction. To address the above problems, this paper produces an interpersonal relationship dataset, NewsPer, for news texts and proposes a Chinese overlapping entity relation extraction model, DepCasRel. First, the model uses “Word-label” to incorporate the character features of Chinese text into the dependency analysis graph, and then uses the same binary labeling method to label the head and tail entities embedded in the text. Finally, the text’s triples are extracted. DepCasRel solves the problem that traditional methods make it difficult to extract triples with overlapping entities. Experiments on our manually annotated dataset NewsPer show that DepCasRel can effectively encode the semantic and structural information of text and improve the performance of an overlapping entity relation extraction model.
An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase

Over the past two decades, to overcome the limitations of certain algorithms, ensemble strategies or self-adaptive mechanisms for evolutionary computation algorithms have been proposed. Regardless of how these strategies or mechanisms were designed, their objective was to control the balance between the global and local search capabilities during the evolutionary process. Inspired by this, a novel ensemble strategy with three groups to improve the performance of the ensemble particle swarm optimizer (EPSO) is proposed. The first group uses a covariance matrix adapted retreat phase (CMAR), the second group induces the niching behavior for inertia weight particle swarm optimization (PSO), and the third group maintains the characteristics of a large subpopulation of EPSO. Furthermore, a sample pool and replacement mechanism are proposed to perturb the three groups. This strategy also recommends a group of empirical allocation rates for subpopulations based on various proportion combination tests. The performance of the proposed strategy is evaluated using CEC2005 benchmark functions with 10, 30, and 50-dimensional tests and compared with those of the state-of-the-art PSO variants: EPSO, a modified PSO using adaptive strategy (MPSO), PSO variant for single-objective numerical optimization (PSO-sono), terminal crossover and steering-based PSO (TCSPSO), self-adapting hybrid strategy PSO (SaHSPS), and pyramid PSO (PPSO). Experimental results demonstrate that the improved EPSO, using CMAR, niching behavior, and sample pool, performs best.
Structure-Aware Deep Networks and Pixel-Level Generative Adversarial Training for Single Image Super-Resolution

The resolution of current display devices is getting higher and higher, and 4K/8K display devices have become popular, which require image super-resolution technologies to enlarge and restore the input low-resolution images into high-resolution ones. In addition, image super-resolution technologies can facilitate various vision-based measurement applications. Recently, super-resolution methods based on generative adversarial networks (GANs) have become mainstream. However, some recent studies have shown that GAN-based image super-resolution methods will cause structural distortion. Existing methods alleviate the problem of structural distortion by enhancing structure generation. But this type of method cannot essentially solve the problem of structural distortion caused by adversarial training. In contrast, this article proposes pixel-level generative adversarial training to solve the structural distortion problem, which finely constrains the structure of images during the adversarial training process. In addition, to better generate the structure and details of images and make full use of similar texture details within images, we build a structure-aware image super-resolution network, which not only enhances the structure generation through gradient guidance but also effectively integrates non-local self-similarity modules in a multi-level manner. Experimental results show that the proposed method achieves better quantitative and qualitative results than the state-of-the-art methods. The ablation experiments show that the proposed structure-aware deep network and pixel-level adversarial training can improve the performance of image super-resolution.
FHAAPS: Efficient Anonymous Authentication with Privacy Preservation Scheme for Farm-to-Home Communication

With the advancement in information and communication technology (ICT), secure farm-to-home communication has become an emerging concept. Food is the most basic essential commodity for the survival of human beings which is produced by farmers. However, because of the presence of intermediaries, farmers/producers do not make a sufficient profit and also the consumers have to pay more money to buy food items from these mediators. As a result, in this work, we proposed an efficient farm-to-home anonymous authentication privacy-preserving scheme in which the storehouse will buy goods from farmers and sell them to the consumers at the base rate. Moreover, in our scheme, an unprofitable trusted delivery agent assists in the transfer of food commodities between end users and storehouses to provide maximum profit for the farmers and consumers. Further, essential security parameters are provided to the end users and it provides conditional privacy to the delivery agent; i.e., if any mishap occurs, then the malicious delivery agent’s privacy is revoked to avoid further damage to the system. The performance analysis section shows that our scheme supports the transfer of food commodities with minimum computational and communication costs.
The Effects of Mobile AR-based Biology Learning Experience on Students’ Motivation, Self‐Efficacy, and Attitudes in Online Learning

The purpose of this study is to enhance online biology learning with mobile augmented reality (AR) applications and to assess the impact of mobile AR applications on students' motivation, self-efficacy, and attitudes toward biology learning. Students were interviewed, and the usefulness of mobile AR applications was evaluated using a quasi-experimental pretest-posttest approach. The study group consists of 71 high school students, 26 in the control group and 45 in the experimental group, attending a public high school in the Western Black Sea Region of Turkey during the academic year 2020-2021. The self-efficacy ratings of the experimental group of students who participated in mobile AR-based biology learning were statistically higher than those of the control group after a 12-week trial. However, there were no statistically significant differences between experimental and control group students' motivation and attitudes toward biology learning. In addition, as a result of student interviews, mobile AR applications were deemed innovative, non-distracting, successful in knowledge acquisition, engaging, intriguing, and entertaining, boosting information retention, concretizing the subject, and facilitating learning.
Vision UFormer: Long-range monocular absolute depth estimation

We introduce Vision UFormer (ViUT), a novel deep neural long-range monocular depth estimator. The input is an RGB image, and the output is an image that stores the absolute distance of the object in the scene as its per-pixel values. ViUT consists of a Transformer encoder and a ResNet decoder combined with the UNet style of skip connections. It is trained on 1M images across ten datasets in a staged regime that starts with easier-to-predict data such as indoor photographs and continues to more complex long-range outdoor scenes. We show that ViUT provides comparable results for normalized relative distances and short-range classical datasets such as NYUv2 and KITTI. We further show that it successfully estimates absolute long-range depth in meters. We validate ViUT on a wide variety of long-range scenes showing its high estimation capabilities with a relative improvement of up to 23%. Absolute depth estimation finds application in many areas, and we show its usability in image composition, range annotation, defocus, and scene reconstruction. Our models are available at cphoto.fit.vutbr.cz/viut.
A fine-grained modal label-based multi-stage network for multimodal sentiment analysis

Sentiment analysis is a challenging but valuable research topic in affective computing. It can improve the quality of various real-world applications, including financial market prediction, disease analysis even politics. As sentiment may be expressed by text, image, audio, video, etc., multimodal sentiment analysis has emerged to capture information in multiple ways. Take video as an example, the analysis process may be difficult since the modalities in the video are heterogeneous and may express different sentiments. To deal with such issues, a Fine-grained modal label-based Multi-Stage Network (FmlMSN) is proposed. Utilizing seven sentiment labels in unimodal, bimodal and trimodal, the model focuses on information at different granularities from text, audio, image and the combinations of them. Meanwhile, inspired by the idea of stacking ensemble learning which is still limited in sentiment analysis, multi-stage training is performed for base learners of acoustic-visual, visual-textual and acoustic-textual. In each stage, the singleton modality and pair-wise modalities are interconnected by hard parameter sharing multi-task learning. Subsequently, the hidden bimodal features are used to train the meta-learner for the final sentiment prediction. Extensive experiments on three public datasets, including one in Chinese and two in English indicate that our model outperforms the existing state-of-the-art methods. Furthermore, empirical analysis suggests that the model is flexible and can reduce training time and calculation to some extent.
Review of Information Security Management Frameworks

Most organizations are looking to mitigate or avoid different kinds of risks which may affect the confidentiality and integrity of sensitive information. They spend a lot of money to protect their assets from such threats. Organizations highly require information security management to preserve the continuity of their businesses. Several security frameworks have been offered in the literature to protect the confidentiality, integrity, and availability of sensitive data. Thus, this book chapter reviews the importance of information security management frameworks and highlights the differences and similarities between information security management frameworks.
Efficient color code decoders in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi><mml:mo>&amp;#x2265;</mml:mo><mml:mn>2</mml:mn></mml:math> dimensions from toric code decoders

We introduce an efficient decoder of the color code in $d\geq 2$ dimensions, the Restriction Decoder, which uses any $d$-dimensional toric code decoder combined with a local lifting procedure to find a recovery operation. We prove that the Restriction Decoder successfully corrects errors in the color code if and only if the corresponding toric code decoding succeeds. We also numerically estimate the Restriction Decoder threshold for the color code in two and three dimensions against the bit-flip and phase-flip noise with perfect syndrome extraction. We report that the 2D color code threshold $p_{\textrm{2D}} \approx 10.2\%$ on the square-octagon lattice is on a par with the toric code threshold on the square lattice.
THE EFFECTS OF BLENDED LEARNING APPROACH ON STUDENT MOTIVATION FOR LEARNING PHYSICS

The topic of Direct current is one of the areas where the physics students' lack of deep understanding was identified. The aim of the research was to examine the effect of the blended learning approach on students' motivation for learning physics. The sample included 128 upper-secondary school students from Novi Sad (Serbia) with an average age of 16. Of these, 64 students made up the experimental group and 64 students made up the control group. In the control group, the students did not actively participate in simulated experiments, and in the experimental group learning was realized through the application of the blended learning approach (BLA), since the students had an active role in conducting simulations themselves. The obtained results showed that BLA contributes to the overall motivation for learning physics, as well as self-efficacy and physics learning value. Also, an increase in motivation was observed in two dimensions of motivation among female students in the experimental group. The implementation of BLA caused a significant increase in the motivation of students with lower achievement. Therefore, the main recommendation, based on the obtained research results, would be for teachers to apply BLA to a greater extent because it contributes to student motivation. Keywords: blended learning approach, experimental research, physics education, student motivation
Real-Time And Robust 3D Object Detection with Roadside LiDARs

This work aims to address the challenges in autonomous driving by focusing on the 3D perception of the environment using roadside LiDARs. We design a 3D object detection model that can detect traffic participants in roadside LiDARs in real-time. Our model uses an existing 3D detector as a baseline and improves its accuracy. To prove the effectiveness of our proposed modules, we train and evaluate the model on three different vehicle and infrastructure datasets. To show the domain adaptation ability of our detector, we train it on an infrastructure dataset from China and perform transfer learning on a different dataset recorded in Germany. We do several sets of experiments and ablation studies for each module in the detector that show that our model outperforms the baseline by a significant margin, while the inference speed is at 45 Hz (22 ms). We make a significant contribution with our LiDAR-based 3D detector that can be used for smart city applications to provide connected and automated vehicles with a far-reaching view. Vehicles that are connected to the roadside sensors can get information about other vehicles around the corner to improve their path and maneuver planning and to increase road traffic safety.
Knowledge Management and Total Quality Management Impact on Employee Effectiveness in Emerging Industries: Case of Tunisian Small and Medium Enterprises

Employee effectiveness is highly important for all economic activities. Several factors can affect its degree, either positively or negatively. In this vein, this work aims to examine the impact of knowledge management and total quality management on employee effectiveness in the industry of emerging countries. For that, Tunisian small and medium enterprises were taken as an example. The proposed methodology consists first of providing a research model linking the correlation between knowledge management elements, total quality management practices, and employee effectiveness. Then, a survey was designed and broadcast to more than 3000 Tunisian small and medium enterprises. Hence, 206 responses were collected from several industrial fields, and collected data analysis was achieved by SPSS software. For testing research hypotheses, multiple regression analysis, factor analysis, and structural equation modeling were employed. The finding points out that total quality management and knowledge management have a positive impact on staff effectiveness. This impact is highlighted through the roles of knowledge management elements and total quality management practices on human resources behavior and their ability enhancement. Consequently, a significant increase in productivity can be seen in the operational processes of the company. This work is one of the first studies to research total quality management and knowledge management impact on staff effectiveness in Tunisian small and medium enterprises. Besides, it reflects the maturity and the awareness of respondent companies’ managers to the practice of these concepts in emerging economies.
Generalized Toffoli Gate Decomposition Using Ququints: Towards Realizing Grover’s Algorithm with Qudits

Qubits, which are the quantum counterparts of classical bits, are used as basic information units for quantum information processing, whereas underlying physical information carriers, e.g., (artificial) atoms or ions, admit encoding of more complex multilevel states—qudits. Recently, significant attention has been paid to the idea of using qudit encoding as a way for further scaling quantum processors. In this work, we present an efficient decomposition of the generalized Toffoli gate on five-level quantum systems—so-called ququints—that use ququints’ space as the space of two qubits with a joint ancillary state. The basic two-qubit operation we use is a version of the controlled-phase gate. The proposed N-qubit Toffoli gate decomposition has O(N) asymptotic depth and does not use ancillary qubits. We then apply our results for Grover’s algorithm, where we indicate on the sizable advantage of using the qudit-based approach with the proposed decomposition in comparison to the standard qubit case. We expect that our results are applicable for quantum processors based on various physical platforms, such as trapped ions, neutral atoms, protonic systems, superconducting circuits, and others.
AI: Can It Make a Difference to the Predictive Value of Ultrasound Breast Biopsy?

(1) Background: This study aims to compare the ground truth (pathology results) against the BI-RADS classification of images acquired while performing breast ultrasound diagnostic examinations that led to a biopsy and against the result of processing the same images through the AI algorithm KOIOS DS TM (KOIOS). (2) Methods: All results of biopsies performed with ultrasound guidance during 2019 were recovered from the pathology department. Readers selected the image which better represented the BI-RADS classification, confirmed correlation to the biopsied image, and submitted it to the KOIOS AI software. The results of the BI-RADS classification of the diagnostic study performed at our institution were set against the KOIOS classification and both were compared to the pathology reports. (3) Results: 403 cases were included in this study. Pathology rendered 197 malignant and 206 benign reports. Four biopsies on BI-RADS 0 and two images are included. Of fifty BI-RADS 3 cases biopsied, only seven rendered cancers. All but one had a positive or suspicious cytology; all were classified as suspicious by KOIOS. Using KOIOS, 17 B3 biopsies could have been avoided. Of 347 BI-RADS 4, 5, and 6 cases, 190 were malignant (54.7%). Because only KOIOS suspicious and probably malignant categories should be biopsied, 312 biopsies would have resulted in 187 malignant lesions (60%), but 10 cancers would have been missed. (4) Conclusions: KOIOS had a higher ratio of positive biopsies in this selected case study vis-à-vis the BI-RADS 4, 5 and 6 categories. A large number of biopsies in the BI-RADS 3 category could have been avoided.