Reviewerly/exHarmony huggingface dataset:
Advanced orthogonal opposition‐based learning‐driven dynamic salp swarm algorithm: Framework and case studies

Salp swarm algorithm (SSA) is a relatively new bio-inspired meta-heuristic optimization algorithm that mimics the navigating and foraging behavior of salps in oceans. This paper presents an orthogonal quasi-opposition-based learning-driven dynamic SSA (OBDSSA) for solving global optimization problems. The proposed methodology integrates orthogonal quasi-opposition-based learning (OQOBL) tactic and dynamic learning (DL) strategy with SSA to improve its performance. The OQOBL technique is used to enrich the exploration and development capability of the canonical SSA to help the salp chain jump out of local optimum, while the DL mechanism is applied to the basic approach to expand the neighborhood searching capabilities of the search agents. To investigate the proposed operators and OBDSSA algorithm, 18 widely used benchmark functions and parameter extraction problem of photovoltaic (PV) model have been experimented upon. The comparisons reveal that OBDSSA outperforms all competitors, including the standard SSA, SSA variants, and other state-of-the-art algorithms. Finally, the developed approach is applied to path planning and obstacle avoidance (PPOA) tasks in autonomous mobile robots (AMR) and satisfactory results are obtained.
Variations of QKD Protocols Based on Conventional System Measurements: A Literature Review

Cryptography is an unexpected revolution in information security in the recent decades, where remarkable improvements have been created to provide confidentiality and integrity. Quantum cryptography is one such improvement that has grown rapidly since the first announced protocol. Quantum cryptography contains substantial elements that must be addressed to ensure secure communication between legitimate parties. Quantum key distribution (QKD), a technique for creating a secret key, is one of the most interesting areas in quantum cryptography. This paper reviews some well-known quantum key distribution techniques that have been demonstrated in the past three decades. Furthermore, this paper discusses the process of creating a secret key using quantum mechanics and cryptography methods. Moreover, it explains the relationships between many basic aspects of QKD protocols and suggests some improvements in the cryptosystem. An accurate quantitative comparison between the QKD protocols is presented, especially the runtime execution for each QKD protocol. In addition, the paper will demonstrate a general model of each considered QKD protocol based on security principles.
Communication-efficient federated recommendation model based on many-objective evolutionary algorithm

The federated recommendation system (FedRS), which is the application of the recommendation system (RS) in federated learning, has been creatively developed as increasing attention has been paid to user privacy protection. However, traditional federated learning consumes excessive communication time and resource, which seriously limits the development of the FedRS. To reduce the communication cost and improve the recommendation performance of FedRS, an improved many-objective federated recommendation model with a novel parameter reduction strategy is proposed in this paper. The model aims to optimize the number of parameters shared between the server and client to improve communication efficiency in the federated process. Optimal parameter selection solutions can be obtained using the many-objective evolutionary algorithm (MaOEA), which can optimize the recommendation accuracy, novelty, diversity, and communication efficiency of FedRS simultaneously. Furthermore, the reference vector guided evolutionary algorithm (RVEA) was adopted to evaluate the proposed model. Experiments were performed on two famous recommendation datasets to examine the superiority of RVEA for the evaluation. The performance results indicated that the proposed model can not only provide accurate, diverse, and novel recommendations for the client, but can also realize efficient communication.
Narrative inquiry for CALL teacher preparation programs amidst the COVID-19 pandemic: language teachers’ technological needs and suggestions

Abstract Drawing on qualitative research, this study explores the Iranian EFL teachers’ technological needs and their suggestions for using computer-assisted language learning (CALL) during the COVID-19 pandemic. We analyzed the narratives produced by 66 English as a Foreign Language (EFL) teachers for their themes through deductive and inductive thematic analysis phases using MAXQDA. The findings indicate that CALL teacher preparation programs should prioritize technological pedagogical knowledge (TPK), then technological content knowledge (TCK), and finally technological knowledge (TK). Moreover, teachers stated that CALL teacher preparation programs should develop their knowledge concerning the intersections of TCK/TPK and TPK/TK. Furthermore, Iranian EFL teachers suggested that CALL can be used during the pandemic if cooperation among teachers, students, and parents will be made, technological-related infrastructures will be developed, and teacher preparation programs will develop EFL teachers’ collaboration, digital literacy, teacher autonomy, and TPK with special attention to the educational needs made by the pandemic situation. The findings have implications for teacher educators, professional development course designers and providers, and decision-makers by highlighting promising directions to devote their precious time and resources.
Building vulnerability assessment in seismic areas using ensemble learning: A Nepal case study

The vulnerability assessment is a crucial method for earthquake-prone areas to manage seismic risk. This paper develops an Extreme gradient boosting (XGBoost)-based model to assess the seismic vulnerability of buildings in Kavrepalanchok, Nepal. The vulnerability assessment model is trained based on the building survey data of the 2015 Gorkha earthquake. To rectify the impacts of class imbalance in raw data, the Synthetic Minority Oversampling Technique (SMOTE) technique is adopted in the data preprocessing stage. Then, XGBoost models for the 3 selected target variables are developed respectively. Finally, the performances of the developed models are evaluated, and the significant features that influence the vulnerability are investigated using the SHapley Additive exPlanations (SHAP) technique. Important findings are: (1) The developed SMOTE-XGBoost model for the overall building damage achieves the highest macro f1 score of 0.729 compared with other machine learning models. (2) The SMOTE technique is favorable when the label requiring more attention is the minor group (e.g., severe damage). (3) The foundation type, the roof type, and the building age are identified as the most significant features for the overall building damage grade. The developed learning model can be used as a decision tool to automate building vulnerability assessment in seismic areas using pre-earthquake data, providing decision support for disaster risk reduction at an early stage.
Nonlinear dynamics analysis of cryptocurrency price fluctuations based on Bitcoin

The price fluctuation of cryptocurrencies represented by Bitcoin has nonlinear structure characteristics. We select the Bitcoin closing price data from 2013 to 2021, and use GARCH (1,1)-GED to fit the volatility series. We confirm that Bitcoin price Fluctuation has nonlinear dynamics through BDS test, Hurst exponent, correlation dimension test and Lyapunov exponent. We find that the price fluctuation of cryptocurrency does not obey the random walk, and its fluctuation is positively correlated with time. Bullish information and bearish information have basically the same impact on cryptocurrency fluctuations. Cryptocurrency price fluctuations have cyclical trends and inherent long-term unpredictability, as well as certain fractal and chaos characteristics. ARCH effect and long memory characteristics of cryptocurrency return series show that cryptocurrency price fluctuations are Clustering and persistence. These two characteristics constitute the nonlinear dynamic mechanism of Bitcoin price fluctuation. Overall, our study has important implications for investors and regulators within cryptocurrency markets.
G<sup>3</sup>SR: Global Graph Guided Session-Based Recommendation

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G3SR). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.
RVLSM: Robust variational level set method for image segmentation with intensity inhomogeneity and high noise

Intensity inhomogeneity and high noise are two common but challenging issues in image segmentation and is particularly pronounced when the two issues simultaneously appear in one image. As a result, most existing level set methods yield poor performance when applied to these images. To address this issue, this paper proposes a robust variational level set method (RVLSM) based on adaptive diffusion mechanism and local cluster criterion, which can not only correct the severe inhomogeneous intensity but also denoise in segmentation. Specifically, we first define an adaptive-scale representation term using the proposed adaptive diffusion mechanism to transform the image data towards diffusion-induced space, which successfully restrains different types/levels of noise while enhancing image details. Then, a new bias field correction term is constructed via estimating the bias in transformed domain to better correct the severe inhomogeneous intensity while segmentation. Finally, an enhanced fourth-order piecewise polynomial penalty term is designed to eradicate numerical calculation instability and tedious re-initialization during the evolution of the level set. The experimental results on synthetic and real images with severe intensity inhomogeneity and high noise demonstrate the superiority of the proposed method over most existing methods in both accuracy and robustness.
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning

This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers have recently been thought of as a promising alternative to convolutional neural networks for visual recognition. But when there is no sufficient data, it gets stuck in overfitting and shows inferior performance. To improve data efficiency, we propose hierarchically cascaded transformers that exploit intrinsic image structures through spectral tokens pooling and optimize the learnable parameters through latent attribute surrogates. The intrinsic image structure is utilized to reduce the ambiguity between foreground content and background noise by spectral tokens pooling. And the attribute surrogate learning scheme is designed to benefit from the rich visual information in image-label pairs instead of simple visual concepts assigned by their labels. Our Hierarchically Cascaded Transformers, called HCTransformers, is built upon a self-supervised learning framework DINO and is tested on several popular few-shot learning benchmarks. In the inductive setting, HCTransformers surpass the DINO baseline by a large margin of 9.7% 5-way 1-shot accuracy and 9.17% 5-way 5-shot accuracy on miniImageNet, which demonstrates HCTransformers are efficient to extract discriminative features. Also, HCTransformers show clear advantages over SOTA few-shot classification methods in both 5-way 1-shot and 5-way 5-shot settings on four popular benchmark datasets, including miniImageNet, tieredImageNet, FC100, and CIFAR-FS. The trained weights and codes are available at https://github.com/StomachCold/HCTransformers.
Contrastive Regularization for Semi-Supervised Learning

Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance. In this study, we analyze that the consistency regularization restricts the propagation of labeling information due to the exclusion of samples with unconfident pseudo-labels in the model updates. Then, we propose contrastive regularization to improve both efficiency and accuracy of the consistency regularization by well-clustered features of unlabeled data. In specific, after strongly augmented samples are assigned to clusters by their pseudolabels, our contrastive regularization updates the model so that the features with confident pseudo-labels aggregate the features in the same cluster, while pushing away features in different clusters. As a result, the information of confident pseudo-labels can be effectively propagated into more unlabeled samples during training by the well-clustered features. On benchmarks of semi-supervised learning tasks, our contrastive regularization improves the previous consistency-based methods and achieves state-ofthe-art results, especially with fewer training iterations. Our method also shows robust performance on open-set semi-supervised learning where unlabeled data includes out-of-distribution samples.
DualSC: Automatic Generation and Summarization of Shellcode via Transformer and Dual Learning

A shellcode is a small piece of code and it is executed to exploit a software vulnerability, which allows the target computer to execute arbitrary commands from the attacker through a code injection attack. Similar to the purpose of automated vulnerability generation techniques, the automated generation of shellcode can generate attack instructions, which can be used to detect vulnerabilities and implement defensive measures. While the automated summarization of shellcode can help users unfamiliar with shellcode and network information security understand the intent of shellcode attacks. In this study, we propose a novel approach DualSC to solve the automatic shellcode generation and summarization tasks. Specifically, we formalize automatic shellcode generation and summarization as dual tasks, use a shallow Transformer for model construction, and design a normalization method Adjust_QKNorm to adapt these low-resource tasks (i.e., insufficient training data). Finally, to alleviate the out-of-vocabulary problem, we propose a rule-based repair component to improve the performance of automatic shellcode generation. In our empirical study, we select a high-quality corpus Shellcode_IA32 as our empirical subject. This corpus was gathered from two real-world projects based on the line-by-line granularity. We first compare DualSC with six state-of-the-art baselines from the code generation and code summarization domains in terms of four performance measures. The comparison results show the competitiveness of DualSC. Then, we verify the effectiveness of the component setting in DualSC. Finally, we conduct a human study to further verify the effectiveness of DualSC.
Ontology-enhanced Prompt-tuning for Few-shot Learning

Few-shot Learning (FSL) is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to benefit the few-shot setting in various tasks. However, the priors adopted by the existing methods suffer from challenging knowledge missing, knowledge noise, and knowledge heterogeneity, which hinder the performance for few-shot learning. In this study, we explore knowledge injection for FSL with pre-trained language models and propose ontology-enhanced prompt-tuning (OntoPrompt). Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text. We further introduce span-sensitive knowledge injection via a visible matrix to select informative knowledge to handle the knowledge noise issue. To bridge the gap between knowledge and text, we propose a collective training algorithm to optimize representations jointly. We evaluate our proposed OntoPrompt in three tasks, including relation extraction, event extraction, and knowledge graph completion, with eight datasets. Experimental results demonstrate that our approach can obtain better few-shot performance than baselines.
Underwater Light Field Retention : Neural Rendering for Underwater Imaging

Underwater Image Rendering aims to generate a true-to-life underwater image from a given clean one, which could be applied to various practical applications such as underwater image enhancement, camera filter, and virtual gaming. We explore two less-touched but challenging problems in underwater image rendering, namely, i) how to render diverse underwater scenes by a single neural network? ii) how to adaptively learn the underwater light fields from natural exemplars, i,e., realistic underwater images? To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering). Specifically, UWNR is a data-driven neural network that implicitly learns the natural degenerated model from authentic underwater images, avoiding introducing erroneous biases by hand-craft imaging models.Compared with existing underwater image generation methods, UWNR utilizes the natural light field to simulate the main characteristics of the underwater scene. Thus, it is able to synthesize a wide variety of underwater images from one clean image with various realistic underwater images.Extensive experiments demonstrate that our approach achieves better visual effects and quantitative metrics over previous methods. Moreover, we adopt UWNR to build an open Large Neural Rendering Underwater Dataset containing various types of water quality, dubbed LNRUD. The source code and LNRUD are available at https://github.com/Ephemeral182/UWNR.
X -Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning

3D dense captioning aims to describe individual objects in 3D scenes by natural language, where 3D scenes are usually represented as RGB-D scans or point clouds. However, only exploiting single modal information, e.g., point cloud, previous approaches fail to produce faithful descriptions. Though aggregating 2D features into point clouds may be beneficial, it introduces an extra computational burden, especially in the inference phase. In this study, we investigate a cross-modal knowledge transfer using Transformer for 3D dense captioning, namely X-Trans2Cap. Our proposed X-Trans2Cap effectively boost the performance of single-modal 3D captioning through the knowledge distillation enabled by a teacher-student framework. In practice, during the training phase, the teacher network exploits auxiliary 2D modality and guides the student network that only takes point clouds as input through the feature consistency constraints. Owing to the well-designed cross-modal feature fusion module and the feature alignment in the training phase, X-Trans2Cap acquires rich appearance information embedded in 2D images with ease. Thus, a more faithful caption can be generated only using point clouds during the inference. Qualitative and quantitative results confirm that X-Trans2Cap outperforms previous state-of-the-art by a large margin, i.e., about +21 and +16 CIDEr points on ScanRefer and Nr3D datasets, respectively.
Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries

The prediction of global solar radiation in a region is of great importance as it provides investors and politicians with more detailed knowledge about the solar resource of that region, which can be very beneficial for largescale solar energy development.In this sense, the main objective of this study is to predict the daily global solar radiation data of 27 cities (Brussels, Paris, Lisbon, Madrid…), located in 27 countries, which have mostly different solar radiation distributions in Europe.In this research, six different machine-learning algorithms (Linear model (LM), Decision Tree (DT), Support Vector Machine (SVM), Deep Learning (DL), Random Forest (RF) and Gradient Boosted Trees (GBT)) are used.In the training of these algorithms, daily air temperature(Ta), wind speed(Va), relative humidity(RH) and solar radiation of these cities are used.The data is supplied from the Meteonorm tool and cover the last years grouped in two periods (1960-1990; 2000-2019).To decide on the success of these algorithms, four different statistical metrics (Average Relative Error (ARE), Average absolute Error (AAE), Root Mean Squared Error (RMSE), and R 2 (R-Squared)) are discussed in the study.In addition, the forecasting of air temperature and global solar radiation of these cities in 2050 and 2100 were made using three of the most recent Intergovernmental Panel on Climate Change (IPCC) scenarios (RCP2.6;RCP 4.5, and RCP 8.5).The results show that ARE, R, 2 and RMSE values of all algorithms are ranging from 0.114 to 6.321, from 0.382 to 0.985, from 0.145 to 2.126 MJ/m 2 , respectively.By analysing all the algorithms, it is noticed that the Decision tree exhibited the worst result in terms of R, 2 and RMSE metrics.Among the six prediction algorithms, the DL was recognized as the only algorithm that exceeded the t-critical value (The t-critical value is the cutoff between retaining or rejecting the null hypothesis).Globally, all the six machine learning algorithms used in this research can be applied to predict the daily global solar radiation data with good accuracy.Despite this, the SVM model is the best model among all the six models used.It is followed by the DL, LM, GB, RF and DT, respectively.