Reviewerly/exHarmony huggingface dataset:
High performance integrated photonic circuit based on inverse design method

The basic indexes of all-optical integrated photonic circuits include high-density integration, ultrafast response and ultralow energy consumption.Traditional methods mainly adopt conventional micro/nano-structures.The overall size of the circuit is large, usually reaches hundreds of microns.Besides, it is difficult to balance the ultrafast response and ultra-low energy consumption problem, and the crosstalk between two traditional devices is difficult to overcome.Here, we propose and experimentally demonstrate an approach based on inverse design method to realize a high-density, ultrafast and ultra-low energy consumption integrated photonic circuit with two all-optical switches controlling the input states of an all-optical XOR logic gate.The feature size of the whole circuit is only 2.5 μm × 7 μm, and that of a single device is 2 μm × 2 μm.The distance between two adjacent devices is as small as 1.5 μm, within wavelength magnitude scale.Theoretical response time of the circuit is 150 fs, and the threshold energy is within 10 fJ/bit.We have also considered the crosstalk problem.The circuit also realizes a function of identifying two-digit logic signal results.Our work provides a new idea for the design of ultrafast, ultra-low energy consumption all-optical devices and the implementation of high-density photonic integrated circuits.
Effect of Feature Selection on the Prediction of Direct Normal Irradiance

Solar radiation is capable of producing heat, causing chemical reactions, or generating electricity. Thus, the amount of solar radiation at different times of the day must be determined to design and equip all solar systems. Moreover, it is necessary to have a thorough understanding of different solar radiation components, such as Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), and Global Horizontal Irradiance (GHI). Unfortunately, measurements of solar radiation are not easily accessible for the majority of regions on the globe. This paper aims to develop a set of deep learning models through feature importance algorithms to predict the DNI data. The proposed models are based on historical data of meteorological parameters and solar radiation properties in a specific location of the region of Errachidia, Morocco, from January 1, 2017, to December 31, 2019, with an interval of 60 minutes. The findings demonstrated that feature selection approaches play a crucial role in forecasting of solar radiation accurately when compared with the available data.
Globalizing BERT-based Transformer Architectures for Long Document Summarization

Fine-tuning a large language model on downstream tasks has become a commonly adopted process in the Natural Language Processing (NLP) (Wang et al., 2019).However, such a process, when associated with the current transformer-based (Vaswani et al., 2017) architectures, shows several limitations when the target task requires to reason with long documents.In this work, we introduce a novel hierarchical propagation layer that spreads information between multiple transformer windows.We adopt a hierarchical approach where the input is divided in multiple blocks independently processed by the scaled dot-attentions and combined between the successive layers.We validate the effectiveness of our approach on three extractive summarization corpora of long scientific papers and news articles.We compare our approach to standard and pre-trained language-model-based summarizers and report state-of-the-art results for long document summarization and comparable results for smaller document summarization.
CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning

Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. Although significant progress has been achieved by using pre-trained models, substantial amounts of hallucinated content are found during the human evaluation. Pre-trained models are most commonly fine-tuned with cross-entropy loss for text summarization, which may not be an optimal strategy. In this work, we provide a typology of factual errors with annotation data to highlight the types of errors and move away from a binary understanding of factuality. We further propose a training strategy that improves the factual consistency and overall quality of summaries via a novel contrastive fine-tuning, called ConFiT. Based on our linguistically-informed typology of errors, we design different modular objectives that each target a specific type. Specifically, we utilize hard negative samples with errors to reduce the generation of factual inconsistency. In order to capture the key information between speakers, we also design a dialogue-specific loss. Using human evaluation and automatic faithfulness metrics, we show that our model significantly reduces all kinds of factual errors on the dialogue summarization, SAMSum corpus. Moreover, our model could be generalized to the meeting summarization, AMI corpus, and it produces significantly higher scores than most of the baselines on both datasets regarding word-overlap metrics.
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions.During a conversation involving various people, a person's emotions are influenced by the other speaker's utterances and their own emotional state over the utterances.In this paper, we propose COntextualized Graph Neural Network based Multimodal Emotion recognitioN (COGMEN) system that leverages local information (i.e., inter/intra dependency between speakers) and global information (context).The proposed model uses Graph Neural Network (GNN) based architecture to model the complex dependencies (local and global information) in a conversation.Our model gives state-of-theart (SOTA) results on IEMOCAP and MOSEI datasets, and detailed ablation experiments show the importance of modeling information at both levels.
The teaching of Natural Sciences in kindergarten based on the principles of STEM and STEAM approach

Across all levels of education, Natural Sciences is a scientific field that is gaining considerable research interest. However, in recent years a growing interest in the introduction of Science in Preschool Education can be seen among scientists, as they realize that the children’s first experiences will be the cornerstone of the relationship they are going to develop with this scientific field in the future. It is accepted that children approach and understand the new knowledge taught in the classroom in the context of what they already know. Their pre-existing beliefs and experiential knowledge are the foundations upon which the new concepts will be merged. Based on these perceptions, children approach, "decode," and understand new knowledge through the interaction of the two cognitive systems, the pre-existing and the taught new enriched cognitive patterns. The kindergarten environment cannot remain isolated and uninfluenced by the developments in a constantly changing world. The goal of Kindergarten is to properly prepare its students to become active citizens of their country by helping them get all the necessary skills. In this way, the STEM and STEAM approach seems to be progressively gaining ground in Preschool Education. Various researchers emphasize the importance and positive outcomes of implementing a STEM education program in Kindergarten, as children can acquire at an early age all the necessary resources that will play a decisive role in their later life. According to the above, this study is a literature and article review with its primary purpose to verify the above assumptions. More specifically, this article showcases the importance of teaching Science in Preschool Education and its practicability at this age group. There is also a presentation of the holistic educational STEAM approach. The main goal of this presentation is to emphasize the contribution of this educational approach towards a more effective teaching of Science in Kindergarten and in-depth learning and understanding of natural concepts by preschoolers.
BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation

Osteosarcoma is one of the most common bone tumors that occurs in adolescents. Doctors often use magnetic resonance imaging (MRI) through biosensors to diagnose and predict osteosarcoma. However, a number of osteosarcoma MRI images have the problem of the tumor shape boundary being vague, complex, or irregular, which causes doctors to encounter difficulties in diagnosis and also makes some deep learning methods lose segmentation details as well as fail to locate the region of the osteosarcoma. In this article, we propose a novel boundary-aware grid contextual attention net (BA-GCA Net) to solve the problem of insufficient accuracy in osteosarcoma MRI image segmentation. First, a novel grid contextual attention (GCA) is designed to better capture the texture details of the tumor area. Then the statistical texture learning block (STLB) and the spatial transformer block (STB) are integrated into the network to improve its ability to extract statistical texture features and locate tumor areas. Over 80,000 MRI images of osteosarcoma from the Second Xiangya Hospital are adopted as a dataset for training, testing, and ablation studies. Results show that our proposed method achieves higher segmentation accuracy than existing methods with only a slight increase in the number of parameters and computational complexity.
VCD

This paper provides an overview of the open Video Complexity Dataset (VCD) which comprises 500 Ultra High Definition (UHD) resolution test video sequences. These sequences are provided at 24 frames per second (fps) and stored online in losslessly encoded 8-bit 4:2:0 format. In this paper, all sequences are characterized by spatial and temporal complexities, rate-distortion complexity, and encoding complexity with the x264 AVC/H.264 and x265 HEVC/H.265 video encoders. The dataset is tailor-made for cutting-edge multimedia applications such as video streaming, two-pass encoding, per-title encoding, scene-cut detection, etc. Evaluations show that the dataset includes diversity in video complexities. Hence, using this dataset is recommended for training and testing video coding applications. All data have been made publicly available as part of the dataset, which can be used for various applications.
Swin transformer-based GAN for multi-modal medical image translation

Medical image-to-image translation is considered a new direction with many potential applications in the medical field. The medical image-to-image translation is dominated by two models, including supervised Pix2Pix and unsupervised cyclic-consistency generative adversarial network (GAN). However, existing methods still have two shortcomings: 1) the Pix2Pix requires paired and pixel-aligned images, which are difficult to acquire. Nevertheless, the optimum output of the cycle-consistency model may not be unique. 2) They are still deficient in capturing the global features and modeling long-distance interactions, which are critical for regions with complex anatomical structures. We propose a Swin Transformer-based GAN for Multi-Modal Medical Image Translation, named MMTrans. Specifically, MMTrans consists of a generator, a registration network, and a discriminator. The Swin Transformer-based generator enables to generate images with the same content as source modality images and similar style information of target modality images. The encoder part of the registration network, based on Swin Transformer, is utilized to predict deformable vector fields. The convolution-based discriminator determines whether the target modality images are similar to the generator or from the real images. Extensive experiments conducted using the public dataset and clinical datasets showed that our network outperformed other advanced medical image translation methods in both aligned and unpaired datasets and has great potential to be applied in clinical applications.
Biomedical Multimedia Encryption by Fractional-Order Meixner Polynomials Map and Quaternion Fractional-Order Meixner Moments

Chaotic systems are widely used in signal and image encryption schemes.Therefore, the design of new chaotic systems is always useful for improving the performance of encryption schemes in terms of security.In this work, we first demonstrate the chaotic behavior of fractional order Meixner polynomials (FrMPs) for introducing a new two-dimensional (2D) chaotic system called FrMPs map.This system is very sensitive to any variation by 15 10of its control parameters ( and µ β ).Next, we use FrMPs to introduce a new type of orthogonal transforms called quaternion fractional order Meixner moments (QFrMMs).The latter generalize the existing fractional order Meixner moments.To demonstrate the relevance of the proposed FrMPs map and QFrMMs in the field of signal and image processing, they are applied in the development of a new encryption scheme.The main advantage of this scheme is its applicability to the encryption of different types of biomedical data such as multi-biomedical signals, multiple grayscale medical images, color medical image, and grayscale medical image.Several simulation analysis (visual, histogram, runtime, correlation, robustness, etc.) are conducted to verify the efficiency of the proposed scheme.Simulation and comparison results confirm that our encryption method is effective in terms of high security level, high quality of the decrypted information, strong resistance to different types of attacks, etc.These findings support the suitability of the proposed scheme for the secure exchange of biomedical multimedia via a public communication channel.
Moodle course in teaching English language for specific purposes for masters in mechanical engineering

The central thesis of this paper is that e-learning courses can have a significant impact on English language for specific purposes (ESP) proficiency of mining mechanical engineering students. The purpose of this study is to assess the effectiveness of ESP Moodle-based course “English for Mining Mechanical Engineers” and to reveal the results of its experimental approbation. In order to identify the lectures’ and learners’ needs we have applied the survey research. The survey confirmed the greatest demand for Moodle courses that include all the elements of a coherent training manual to provide self-development of engineering students. The interview results contributed to design of author’s ESP course syllabus. The importance and originality of this study are that to approbate the course materials’ effectiveness two approaches have been adopted simultaneously. The first is blended learning method based on e-learning platform applied in the experimental group and the second one is classic in-class instructor-led studying used in a control group. Students’ progress in ESP proficiency has been assessed using the cross assessment method. The experiment has validated the initial hypothesis that the special online courses focused on honing foreign language skills and integrated in the domain of specific professional knowledge have a beneficial effect on students’ communicative competencies in general. There were identified the advantages of self-tuition based on Moodle platform. The Moodle course lets the teachers save considerable in-class time to focus more on communicative assignments. The findings of this study have a number of practical implications in ESP online courses development.
Security of Zero Trust Networks in Cloud Computing: A Comparative Review

Recently, networks have shifted from traditional in-house servers to third-party-managed cloud platforms due to its cost-effectiveness and increased accessibility toward its management. However, the network remains reactive, with less accountability and oversight of its overall security. Several emerging technologies have restructured our approach to the security of cloud networks; one such approach is the zero-trust network architecture (ZTNA), where no entity is implicitly trusted in the network, regardless of its origin or scope of access. The network rewards trusted behaviour and proactively predicts threats based on its users’ behaviour. The zero-trust network architecture is still at a nascent stage, and there are many frameworks and models to follow. The primary focus of this survey is to compare the novel requirement-specific features used by state-of-the-art research models for zero-trust cloud networks. In this manner, the features are categorized across nine parameters into three main types: zero-trust-based cloud network models, frameworks and proofs-of-concept. ZTNA, when wholly realized, enables network administrators to tackle critical issues such as how to inhibit internal and external cyber threats, enhance the visibility of the network, automate the calculation of trust for network entities and orchestrate security for users. The paper further focuses on domain-specific issues plaguing modern cloud computing networks, which leverage choosing and implementing features necessary for future networks and incorporate intelligent security orchestration, automation and response. The paper also discusses challenges associated with cloud platforms and requirements for migrating to zero-trust architecture. Finally, possible future research directions are discussed, wherein new technologies can be incorporated into the ZTA to build robust trust-based enterprise networks deployed in the cloud.
LET'S CONNECT IN METAVERSE. BRAND'S NEW DESTINATION TO INCREASE CONSUMERS' AFFECTIVE BRAND ENGAGEMENT &amp; THEIR SATISFACTION AND ADVOCACY

The bands are experiencing a change in the consumers' brand preferences pattern, where consumers are more prone to experience the product virtually using Metaverse. This descriptive study examined the antecedents (novelty, Interactivity, Vividness) of a brand's gamification marketing activities in the Metaverse and the impact on consumers' affective brand engagement. Also, this study explored the consumers' anticipated satisfaction and brand advocacy in the Metaverse. The conceptual model was analysed using SMART-PLS. The collected from more 9 countries were used to do the descriptive analysis. The finding suggests that brands can have more consumers' effective brand engagement and increase their virtual brand experience if they use gamification-based marketing activities in Metaverse.
Advances in Chip-Based Quantum Key Distribution

Quantum key distribution (QKD), guaranteed by the principles of quantum mechanics, is one of the most promising solutions for the future of secure communication. Integrated quantum photonics provides a stable, compact, and robust platform for the implementation of complex photonic circuits amenable to mass manufacture, and also allows for the generation, detection, and processing of quantum states of light at a growing system's scale, functionality, and complexity. Integrated quantum photonics provides a compelling technology for the integration of QKD systems. In this review, we summarize the advances in integrated QKD systems, including integrated photon sources, detectors, and encoding and decoding components for QKD implements. Complete demonstrations of various QKD schemes based on integrated photonic chips are also discussed.
CAIBC: Capturing All-round Information Beyond Color for Text-based Person Retrieval

Given a natural language description, text-based person retrieval aims to identify images of a target person from a large-scale person image database. Existing methods generally face a color over-reliance problem, which means that the models rely heavily on color information when matching cross-modal data. Indeed, color information is an important decision-making accordance for retrieval, but the over-reliance on color would distract the model from other key clues (e.g. texture information, structural information, etc.), and thereby lead to a sub-optimal retrieval performance. To solve this problem, in this paper, we propose to Capture All-round Information Beyond Color (CAIBC) via a jointly optimized multi-branch architecture for text-based person retrieval. CAIBC contains three branches including an RGB branch, a grayscale (GRS) branch and a color (CLR) branch. Besides, with the aim of making full use of all-round information in a balanced and effective way, a mutual learning mechanism is employed to enable the three branches which attend to varied aspects of information to communicate with and learn from each other. Extensive experimental analysis is carried out to evaluate our proposed CAIBC method on the CUHK-PEDES and RSTPReid datasets in both supervised and weakly supervised text-based person retrieval settings, which demonstrates that CAIBC significantly outperforms existing methods and achieves the state-of-the-art performance on all the three tasks.
Chaos Particle Swarm Optimization Enhancement Algorithm for UAV Safe Path Planning

For safe path planning of unmanned aerial vehicles (UAVs) in a three-dimensional (3D) environment with multiple threats, first, a cost function is introduced according to the terrain constraints and UAV overall performance constraints of the path planning problem. Then, improved nonlinear dynamic inertia weights (INDIW) are introduced into the particle swarm optimization (PSO) algorithm, and when the particles fall into the local optimum, the velocity is perturbed, and the velocity and improved nonlinear dynamic inertia weight PSO (VAINDIWPSO) algorithm are obtained. The algorithm improves the speed of convergence and fitness function value of the PSO algorithm. However, the impact of flyable path optimization is now not obvious. Therefore, to further enhance the overall performance of the VAINDIWPSO algorithm, the adaptive adjustment of the velocity is introduced, the chaotic initialization is carried out, and the improved logistic chaotic map is introduced into the algorithm, and an improved chaotic-VAINDIWPSO (IC-VAINDIWPSO) algorithm is obtained. Then, the corresponding relationship between the algorithm and constraints is used to efficiently search complicated environments and find paths with excessive security and small cost function. The simulation outcomes exhibit that in a complicated environment the IC-VAINDIWPSO algorithm substantially improves the speed of convergence of the algorithm, reduces the fitness function value of the algorithm and the initialization time of the algorithm, and the acquired path is additionally smoother. A near-optimal solution is obtained.
Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection

Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble feature selection method to choose not only the most related input parameters but also their past observations values. The machine learning algorithms used are: Support Vector Regression (SVR), Extreme Gradient Boosting (XGBT), Categorical Boosting (CatBoost) and Voting-Average (VOA), which integrates SVR, XGBT and CatBoost. The proposed ensemble feature selection is based on Pearson coefficient, random forest, mutual information and relief. Prediction accuracy is evaluated based on several metrics using a real database from Salvador, Brazil. Different prediction time-horizons are considered: 1 h, 2 h and 3 h ahead. Numerical results demonstrate that the proposed ensemble feature selection approach improves forecasting accuracy and that VOA performs better than the other algorithms in all prediction time horizons.