Reviewerly/exHarmony huggingface dataset:
Biological Eagle-eye Inspired Target Detection for Unmanned Aerial Vehicles Equipped with a Manipulator

Inspired by eagle eye mechanisms, the structure and information processing characteristics of the eagle's visual system are used for the target capture task of an unmanned aerial vehicle (UAV) with a mechanical arm. In this paper, a novel eagle-eye inspired multi-camera sensor and a saliency detection method are proposed. A combined camera system is built by simulating the double fovea structure on the eagle retina. A saliency target detection method based on the eagle midbrain inhibition mechanism is proposed by measuring the static saliency information and dynamic features. Thus, salient targets can be accurately detected through the collaborative work between different cameras of the proposed multi-camera sensor. Experimental results show that the eagle-eye inspired visual system is able to continuously detect targets in outdoor scenes and that the proposed algorithm has a strong inhibitory effect on moving background interference.
A Novel Source Code Clone Detection Method Based on Dual-GCN and IVHFS

Source code clone detection, which can identify code fragments with similar functions, plays a significant role in software development and quality assurance. Existing methods either extract single syntactic or semantic information, or ignore the associated information between code statements in different structures. It is difficult for these methods to effectively detect clone pairs with similar functions. In this paper, we propose a new model based on a dual graph convolutional network (GCN) and interval-valued hesitant fuzzy set (IVHFS), which we named DG-IVHFS. Specifically, we simplified and grouped the abstract syntax tree (AST) of source code to obtain the group representations. The group representations of the AST, as well as the control flow graph (CFG) representations, were transformed into graph structures, and then we applied GCNs on them to learn dependencies between nodes. In addition, we introduced IVHFS into the model for a more comprehensive evaluation of similarity. Our experimental results demonstrated that the precision, recall, and F1-scores of DG-IVHFS on the BigCloneBench and GoogleCodeJam datasets reached 98, 97 and 97% and 98, 93 and 95%, respectively, exceeding current state-of-the-art models. Moreover, our model performed well in terms of time consumption.
Vocational Education Information Technology Based on Cross-Attention Fusion Knowledge Map Recommendation Algorithm

With the rapid development of China’s economic development, the demand for technical talents in all walks of life is becoming more and more urgent. Therefore, the research on the intelligent method of vocational education information is becoming more and more important. In this research, the cross-attention fusion module and attention mechanism are introduced into the knowledge map recommendation algorithm to build an algorithm model. The attention mechanism is used to give corresponding attention to each neighbour node of the head node in the knowledge map, and a weight matrix is established to represent different importances of the additional information contained by each neighbour node, which further improves the interpretability of the recommendation. Finally, the model is analysed experimentally. The results show that CAF is superior to other algorithms in Recall and NDCG, which further verifies that attention mechanism plays a significant role in communication. It can be seen that CAF optimisation model is superior to other algorithms in many tests, and is superior to a similar algorithm MKR, which further verifies the effectiveness and superiority of cross-attention fusion module. The CAF model can still maintain its stability in the case of sparse data.
Do Machines Really Understand Meaning? (Again)

The adventure of artificial intelligence (AI) is based on a revolutionary idea, namely, that machines are able to understand and produce linguistic acts endowed with meaning. Over the past decades, this idea has gained acceptance in the scientific community, but its consequences for human nature were not really appreciated. Recent developments in AI, due especially to Deep Learning (DL), have changed things dramatically by creating computer architectures capable of performing previously impossible tasks in areas such as image recognition and language understanding. Criticisms that were raised decades ago against this possibility have thus been revived. These criticisms are no different in argument from those made in the first place. The reason they are being raised again is because of the social impact that the new machine performances have been able to achieve, and is not driven by truly scientific reasons, which indeed prove to be inadequate when compared to the more recent artificial semantics based on deep neural networks.
Blockchain‐based certificateless public auditing with privacy‐preserving for cloud‐based cyber‐physical systems

Summary Cloud‐server is an effective and flexible way to manage the massive amounts of data produced by cyber‐physical systems. It is a better option to outsource these data to the cloud. When data is outsourced to the cloud, users lose control over it, compromising the data's integrity. Many public auditing schemes were proposed to address this issue, where trusted third‐party auditors (TPAs) verify the integrity of data on behalf of the users. However, trusted TPAs are vulnerable and may not provide correct auditing results on time. Moreover, there is no trust nowadays. Blockchain‐based auditing schemes were introduced to prevent trusted third parties from overcoming this issue. However, most blockchain‐based public auditing schemes suffer from the key‐escrow issue. To overcome the key‐escrow problem and malicious auditors, in this article, a blockchain‐based certificateless public auditing with privacy‐preserving for cloud‐based cyber‐physical systems is constructed. The proposed scheme is secure against type I, II, III, and IV adversaries in the random oracle model. Further, the original proof of the data is masked using a random function to achieve data privacy when data is transferred between the cloud‐server and verifier. The performance analysis shows that the proposed scheme has higher efficiency and is suitable for cyber‐physical systems.
Design of High Precision Interval Observer for Robot System

In order to solve the problem of parameter uncertainty and unknown external interference of wheeled mobile robots (WMR) in a complex environment, the design of a high-precision interval observer for the robot system is proposed. In this paper, the kinematics and dynamics model of a wheeled mobile robot is derived first, and then the control strategy of high-precision interval observer is introduced to estimate and compensate for the unknown state and uncertainty of the system in real-time, which realizes the robustness of the system to disturbance and high adaptability to the environment. The stability of the system is proved by Lyapunov’s theory. The experimental results show that other methods based on coordinate transformation, though the design conditions are relaxed to a certain extent, bring some conservatism. The method proposed in this paper can obtain more accurate interval estimation, so the performance of the method proposed in this paper is better. In conclusion, the control method proposed in this paper can make the mobile robot system have good tracking control performance and strong robustness.
On the dynamic relationship between transaction volume and returns: evidence from the cryptocurrency market

Purpose Using vector autoregressive modelling (VAR) and Granger causality tests, this paper attempts to empirically investigate the dynamic relationship between return and volume of transactions of two main cryptocurrencies: Bitcoin and Ethereum. Design/methodology/approach Based on a generalized autoregressive conditional heteroskedasticity (GARCH) model with a transaction volume parameter in the conditional volatility equation. Findings The results provide empirical evidence of a positive contemporaneous relationship between the variation in transaction volume and the daily return of Bitcoin and Ethereum. The results also show that the conditional volatility of the returns is affected by the past volatility, which implies weak-form inefficiency for both Bitcoin and Ethereum markets. The results of the VAR model, testing Granger causality, indicate that the volume of transactions Granger-Causes Bitcoin and Ethereum returns. Furthermore, the findings show a Granger causal relation from returns to volume. Originality/value This result suggests that cryptocurrency returns can predict transaction volumes and vice versa.
A Token Classification-Based Attention Model for Extracting Multiple Emotion–Cause Pairs in Conversations

People exchange emotions through conversations with others and provide different answers depending on the reasons for their emotions. During a conversation, it is important to find not only such emotions but also their cause. Emotion–cause pair extraction (ECPE) is a task used to determine emotions and their causes in a single pair within a text, and various studies have been conducted to accomplish ECPE tasks. However, existing studies have limitations in that some models conduct the task in two or more steps, whereas others extract only one emotion–cause pair for a given text. We propose a novel methodology for extracting multiple emotion–cause pairs simultaneously from a given conversation with a single model. Our proposed model is a token-classification-based emotion–cause pair extraction model, which applies the BIO (beginning–inside–outside) tagging scheme to efficiently extract multiple emotion–cause pairs in conversations. The proposed model showed the best performance on the RECCON benchmark dataset in comparative experiments with existing studies and was experimentally verified to efficiently extract multiple emotion–cause pairs in conversations.
Use of Machine Learning in Interactive Cybersecurity and Network Education

Cybersecurity is a complex subject for students to pursue. Hands-on online learning through labs and simulations can help students become more familiar with the subject at security classes to pursue cybersecurity education. There are several online tools and simulation platforms for cybersecurity education. However, those platforms need more constructive feedback mechanisms, and customizable hands-on exercises for users, or they oversimplify or misrepresent the content. In this paper, we aim to develop a platform for cybersecurity education that can be used either with a user interface or command line and provide auto constructive feedback for command line practices. Moreover, the platform currently has nine levels to practice for different subjects of networking and cybersecurity and a customizable level to create a customized network structure to test. The difficulty of objectives increases at each level. Moreover, an automatic feedback mechanism is developed by using a machine learning model to warn users about their typographical errors while using the command line to practice. A trial was performed with students completing a survey before and after using the application to test the effects of auto-feedback on users' understanding of the subjects and engagement with the application. The machine learning-based version of the application has a net increase in the user ratings of almost every survey field, such as user-friendliness and overall experience.
High-Performance Multi-RNS-Assisted Concurrent RSA Cryptosystem Architectures

In public-key cryptography, the RSA algorithm is an inevitable part of hardware security because of the ease of implementation and security. RSA Cryptographic algorithm uses many modular arithmetic operations that decide the overall performance of the architecture. This paper proposes VLSI architecture to implement an RSA public-key cryptosystem driven by the Residue Number System (RNS). Modular exponentiation in the RSA algorithm is executed by dividing the entire process into modular squaring and multiplication operations. Based on the RNS employment in modulo-exponential operation, two RSA architectures are proposed. A Verilog HDL code is used to model the entire RSA architecture and ported in Zynq FPGA (XC7Z020CLG484-1) for Proof of Concept (PoC). The Cadence Genus Synthesizer tool characterizes a system’s performance for TSMCs standard Cell library. Partial RNS (Proposed-I)- and Fully RNS (Proposed-II)-based RSA architectures increase the operation speed by 13% and 35%, respectively, compared with the existing RSA. Even though there is an increase in parameters like area, power and PDP for a smaller key size, the improvement in area utilization and encryption/ decryption speed of RSA for a larger key size is evident from the analysis.
Optimizing FPGA-Based Convolutional Neural Network Performance

In deep learning, convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs), most commonly applied to analyze visual imagery. They are also known as Shift-Invariant or Space-Invariant Artificial Neural Networks (SIANNs), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Recently, various architectures for CNN based on FPGA platform have been proposed because it has the advantages of high performance and fast development cycle. However, some key issues including how to optimize the performance of CNN layers with different structures, high-performance heterogeneous accelerator design, and how to reduce the neural network framework integration overhead need to be improved. To overcome and improve these problems, we propose dynamic cycle pipeline tiling, data layout optimization, and a pipelined software and hardware (SW–HW)-integrated architecture with flexibility and integration. Some benchmarks have been tested and implemented on the FPGA board for the proposed architecture. The proposed dynamic tiling and data layout transformation improved by 2.3 times in the performance. Moreover, with two-level pipelining, we achieve up to five times speedup and the proposed system is 3.8 times more energy-efficient than the GPU.
A Novel Genetic-Inspired Binary Firefly Algorithm for Feature Selection in the Prediction of Cervical Cancer

More than 85% of women die from cervical cancer (CC) in developing countries, which is one of the major causes of premature mortality worldwide. Early diagnosis and treatment are essential to reducing cancer mortality as they lead to greater improvements and longer patient survival. CC is associated with several risk factors. The data set may contain redundant, irrelevant, and unreliable features, due to which we may get unreliable results in the process of classification. Feature selection techniques could be observed as a probable solution for this type of problem. In this study, a Novel Genetic-inspired Binary Firefly Algorithm with Random Forest (NGBFA-RF) is proposed as a solution for dimensionality reduction and to find a good set of features to be used in the process of classification. This study was based on the CC Risk Factors, which contain 32 risk factors and four dependent variables. The imbalance of data was alleviated by using the SMOTE data sampling technique. The proposed method’s main goal is to improve predictive accuracy with a small number of features, thereby reducing classification errors. The proposed novel algorithm based on the Firefly Algorithm with genetic operations has shown better results than the other existing models. The efficacy of the proposed novel algorithm has been assessed based on accuracy, recall, precision, [Formula: see text]-score, and AUC–ROC curve values. Results have exposed that a reduced feature set is helpful in getting a better accuracy of 98% with only five features in the classification with NGBFA-RF along with the hybrid ensemble classifier.
Assessment and Evaluation of Learning in a Constructivist Paradigm

The paper focuses on the characteristics of assessment in a constructivist learning environment, the purpose of assessment and evaluation in constructivist approach. The paper also focuses on the various assessment tools and process that are to be used in a constructivist teaching learning environment to continuously assess the students to further their learning. The paper discuses about Formative assessment, Alternative assessment ,Authentic assessment, the tools of assessment as concept map, portfolio ,rubric ,process of assessment as self assessment ,peer assessment and various other strategies that can be used in a constructivist paradigm.
It's Not Just What You Say, It's How You Say It: Mitigating the Impacts of Greenhouse Gas Emissions with Effective Content

Purpose: The goal of this article is to provide a tutorial for measuring the sustainability impacts of our content. Sustainability considerations should be just as important as the accessibility and usability of content. Fortunately, following our known best practices for effective content creation and governance also results in a smaller carbon footprint. This not only benefits the planet but also positions content creators as valuable business assets. Method: An extensive literature review led to the development of processes and guidelines that connect our existing best practices for content with a new paradigm for sustainable content. Results: This tutorial will examine five content scenarios and the relative impacts of each: modifications to individual web pages, content governance (removal of redundant, outdated, and trivial content), email blasts, videos, and podcasts. Metrics will be provided for justifying content decisions to management, and recommendations will be given for both optimizing usability and sustainability. Conclusion: Merging established best practices for content creation and curation with measurable energy-to-emissions data gives us powerful tools for justifying content choices and demonstrating measurable value to both the organization and the planet.
A novel twin-support vector machine for binary classification to imbalanced data

Purpose Binary classification on imbalanced data is a challenge; due to the imbalance of the classes, the minority class is easily masked by the majority class. However, most existing classifiers are better at identifying the majority class, thereby ignoring the minority class, which leads to classifier degradation. To address this, this paper proposes a twin-support vector machines for binary classification on imbalanced data. Design/methodology/approach In the proposed method, the authors construct two support vector machines to focus on majority classes and minority classes, respectively. In order to promote the learning ability of the two support vector machines, a new kernel is derived for them. Findings (1) A novel twin-support vector machine is proposed for binary classification on imbalanced data, and new kernels are derived. (2) For imbalanced data, the complexity of data distribution has negative effects on classification results; however, advanced classification results can be gained and desired boundaries are learned by using optimizing kernels. (3) Classifiers based on twin architectures have more advantages than those based on single architecture for binary classification on imbalanced data. Originality/value For imbalanced data, the complexity of data distribution has negative effects on classification results; however, advanced classification results can be gained and desired boundaries are learned through using optimizing kernels.
A Study on the Life of Mid-sized Naval Noncommissioned Officers Using Q Methodology

The purpose of this study is to classify the perception of life as a navy based on the subjective perception of a non-commissioned officer, a mid-level officer, and to examine its characteristics.In this study, Q sample statements were selected from a total of 40 Q sample statements through interviews and literature surveys of mid -sized naval officers.As a result of the analysis using the QUANL program, it was found that type 1 was the internal conflict type according to the role subtitle, type 2 was the mission consciousness self-confidence type for the special job, type 3 was the change-seeking type of the organizational system, type 4 was the stability-seeking type for the living environment, and type 5 was the anxiety type for the compensation deficiency.Based on these contents, this study is
Multimodal Information Perception and Understanding: Application of Smart Glove in Virtual‐Reality Fusion Chemistry Experiment Platform

Abstract Virtual reality (VR) experimental platforms are frequently utilized in the teaching of chemical experiments because of its high level of interest, lifelike effect, and safe and dependable advantages. However, users are unable to operate genuine experimental equipment on the present VR experimental platform, and these systems are unable to precisely assess and manage users’ operation intentions. In light of the aforementioned concerns, this paper develops a smart glove with cognitive capabilities, which is composed of a variety of simple commercial sensors and binocular cameras. The smart glove can be applied to the virtual‐reality fusion chemical experiment platform established in this paper. Additionally, a navigation interaction algorithm based on multimodal intention understanding is also proposed in this study (hereinafter referred to as NIAMIU algorithm). The smart glove can accurately and efficiently obtain the user's operation intention through the algorithm. The experiment showcased that the smart glove detailed in this paper is capable of precisely determining the position of users’ hands in space and guiding and rectifying the user's interactive behavior. Meanwhile, users may converse with the smart glove in an unsupervised setting and conclude the experiment by following the directions supplied by the smart glove. In comparison to a conventional data glove, the smart glove designed in this paper considerably augments the accuracy and efficiency of human‐computer interaction.
A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation

As sensory and computing technology advances, multi-modal features have been playing a central role in ubiquitously representing patterns and phenomena for effective information analysis and recognition. As a result, multi-modal feature representation is becoming a progressively significant direction of academic research and real applications. Nevertheless, numerous challenges remain ahead, especially in the joint utilization of discriminatory representations and complementary representations from multi-modal features. In this article, a discriminant information theoretic learning (DITL) framework is proposed to address these challenges. By employing this proposed framework, the discrimination and complementation within the given multi-modal features are exploited jointly, resulting in a high-quality feature representation. According to characteristics of the DITL framework, the newly generated feature representation is further optimized, leading to lower computational complexity and improved system performance. To demonstrate the effectiveness and generality of DITL, we conducted experiments on several recognition examples, including both static cases, such as handwritten digit recognition, face recognition, and object recognition, and dynamic cases, such as video-based human emotion recognition and action recognition. The results show that the proposed framework outperforms state-of-the-art algorithms.