Reviewerly/exHarmony huggingface dataset:
Multiple facets of self-rated digital competencies of pre-service teachers: A pilot study on the nomological network, empirical structure, and gender differences

Introduction Self-efficacy is an important predictor of teaching behavior. Within several research traditions (TPACK framework, school achievement tests), different ICT self-efficacy scales have been developed. The empirical structure of existing questionnaires has frequently been researched and discussed within the TPACK framework. However, for teacher ICT self-efficacy, non-instructional aspects as well as alternative instruments have rarely been investigated and compared to standard TPACK self-report scales. Methods In this study, we administer two sets of non-subject-specific ICT scales to a group of pre-service teachers (N = 165). We investigate the empirical structure using structural equation modeling. Results For both scales, the results show that instructional ICT self-efficacy forms a separate factor. For the remaining items, item difficulty and content drive further divisions. Further, more specific item formulations resulted in a higher range of scale means. Additionally, we find gender differences only on some non-instructional scales. Discussion Results confirm that the distinction between instructional vs. non-instructional ICT self-efficacy is important when developing or using questionnaires for pre-service teachers. Results also indicate that the usage of more specific task-related items is a promising alternative to general TPACK items.
An edge thinning algorithm based on newly defined single‐pixel edge patterns

Abstract To improve the uniformity of one-pixel width and continuity of the thinned edges, this paper proposes an edge thinning algorithm acting on grey-scale edge images based on 24 self-defined single-pixel connection patterns. First, for binary or blurred grey-scale gradient edge images, a distance–greyscale coupling algorithm is proposed to achieve gradient enhancement in the edge width direction. Then the elimination rules of noise points and gradient calculation method are given. Secondly, the marking rules of the first three pixels of each edge are given. The next pixel to be marked must meet that the new last three pixels belong to the 24 connection modes. Whether the qualified pixels are retained depends on its grey value and the local edge gradient. The algorithm is tested on four types of images. The results show that the proposed method can guarantee uniform, smooth, and connected one-pixel-wide lines that lie at the centre of the initial edges. The algorithm and the existing algorithms are performed on portrait image and four scenarios of the indoor datasets. Five evaluation indicators are statistically analyzed to prove the feasibility and effectiveness of the proposed algorithm.
Blind color image watermarking incorporating a residual network for watermark denoising and super-resolution reconstruction

Watermarking is a technique for hiding secret information in various types of multimedia data to protect intellectual property rights. The integration of deep learning technology with image watermarking is currently reshaping the application and promotion of relevant techniques developed so far. This paper presents a novel type of blind color image watermarking method that embeds a downsized color image into a host color image. Watermarking implementation involves partitioning the host image into non-overlapping blocks of 8 × 8 pixels, performing discrete cosine transform (DCT) for each block of every channel, and then manipulating the magnitudes of three designated DCT coefficients subject to a minimization constraint. The experimental results confirmed that the proposed image watermarking method outperformed six other methods in terms of zero-normalized cross-correlation (ZNCC). Moreover, watermark imperceptibility, as reflected by the measured peak signal-to-noise ratio and mean structural similarity metrics, remained satisfactory. In addition to this new style of color image watermarking, we employed a deep residual network to reduce noise and increase the resolution of the retrieved watermarks. Overall, the residual network achieved a satisfactory ZNCC level (> 0.88) when the watermark images were super-resolved by a factor of sixteen.
Chunk-based incremental feature learning for credit-card fraud data stream

Detecting fraud accurately in credit cards is critical as this financial sector incurs significant losses for cardholders. Nonetheless, most studies adopted standard machine learning and few incremental learning, which are inadequate for addressing credit card challenges, such as rapid data arrival, unlimited data, data sensitivity, and performance decline over time. For this purpose, we propose a chunk-based incremental feature learning approach that optimises the fraud model topology for each new chunk and keeps track of one chunk each time. The model consists of several connected sub-models, where a new sub-model is optimally created for each new chunk. To avoid the network growing indefinitely, we limit the number of sub-models. To this end, we retain the most relevant sub-models to the current chunk’s data distribution and re-combine them to create the optimal model. We evaluate our approach using two credit card datasets: the first of medium scale contains 2-day payments in 2013, and the second of considerable scale possesses 6-month payments in 2019. We split these datasets into multiple chunks to learn and test incrementally. We compare our approach with static learning methods trained with different scenarios. Moreover, we vary the number of historical sub-models to check their impact on the predictive performance.
Integrating multi-predictions encryption with histogram shifting secret-sharing for high-capacity two-layer image data hiding

With the aim to increase the embedding capacity as well as the quality of reversible data hiding (RDH), we present a high-capacity two-layer RDH based on multi-predictions interpolation and histogram shifting taking advantage of encryption and secret sharing. The highlight of this paper is to improve IRDH (Interpolation-based RDH) using secret sharing and multi-predictions. Secret sharing is designed for full ownership and partial ownership. Multi-predictions including Cross-Shape Average Prediction Interpolation (CSAPI), Weighted Prediction Interpolation based on Euclidean Distance (WPIED) and Different Direction Gradient Prediction Interpolation (DDGPI) are firstly proposed to hide data in different blocks in layer-1. Different blocks are divided by five texture features of EECVM (Energy-Entropy-Contrast-Variance-Mean). In layer-2, the difference histogram shifting method based on eight modes is adopted. Furthermore, ‘Matrix Traversal and Snake Scrambling’ and ‘Cyclic-shifting and S-box encryption’ are proposed for improving the security in watermarking images and carrier images. The experimental results provide superior outcomes with high capacity, imperceptibility, along with a good security.
COMPLETE LOGICS FOR ELEMENTARY TEAM PROPERTIES

Abstract In this paper, we introduce a logic based on team semantics, called $\mathbf {FOT} $ , whose expressive power is elementary, i.e., coincides with first-order logic both on the level of sentences and (possibly open) formulas, and we also show that a sublogic of $\mathbf {FOT} $ , called $\mathbf {FOT}^{\downarrow } $ , captures exactly downward closed elementary (or first-order) team properties. We axiomatize completely the logic $\mathbf {FOT} $ , and also extend the known partial axiomatization of dependence logic to dependence logic enriched with the logical constants in $\mathbf {FOT}^{\downarrow } $ .
SEGMENTATION OF MRI IMAGES USING A COMBINATION OF ACTIVE CONTOUR MODELING AND MORPHOLOGICAL PROCESSING

Image segmentation in brain magnetic resonance imaging (MRI) largely relates to dividing brain tissue into components like white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Using the segmentation outputs, medical images can be 3D reconstructed and visualized efficiently. It is common for MRI pictures to have issues such as partial volume effects, asymmetrical grayscale, and noise. As a result, high accuracy in brain MRI picture segmentation is challenging to achieve in practical applications. In this paper, we developed an effective algorithm for brain MRI image segmentation utilizing a combination of statistical and partial differential equation-based approaches, based on a neuro-mechanical model. The findings of this work demonstrate that by combining various segmentation approaches, it is possible to quickly segment brain MRI data at a degree of precision necessary for different applications. Here, we show that when we use nonlinear filtering, [Formula: see text]-means clustering, and active contour modeling, we can get very good results when we segment brain MRI images. It is clear that the proposed approach has higher segmentation performance and can properly separate brain tissue from a large number of MRI images.
Workflow task scheduling for homogeneous environments on multiprocessing based on IoT variant of beta artificial bee colony

Abstract In the present era of supercomputing, the efficient scheduling of tasks is the first priority of the IoT and cloud service provider. Their objective is to utilize the maximum resource in order to fulfill various clients' requests. The scheduling of task on multiple processors increases change of underutilization of resources. Hence, this demand‐efficient scheduling algorithm that fulfills computer service provider's demands. This paper proposes a modified artificial bee colony algorithm for scheduling of multiple tasks on multiple processors by introducing crossover operation in employee bee phase to reduce the slow convergence rate. This proposed approach is tested on various scenarios in which there exist condition dependency among the tasks exists. The experimental results reduced the proposed approach to reduce the overall makes pan on an average by 15.37%.
Multi-Source Knowledge Reasoning Graph Network for Multi-Modal Commonsense Inference

As a crucial part of natural language processing, event-centered commonsense inference task has attracted increasing attention. With a given observed event, the intention and reaction of the people involved in the event are required to be inferred with artificial intelligent algorithms. To solve this problem, sequence-to-sequence methods are widely studied, where the event is first encoded into a specific representation and then decoded to generate the results. However, all the existing methods learn the event representation only with the textual information, while the visual information is ignored, which is actually helpful for the commonsense reference. In this article, we first define a new task of multi-modal commonsense reference with both textual and visual information. A new event-centered multi-modal dataset is also provided. Then we propose a multi-source knowledge reasoning graph network to solve this task, where three kinds of relational knowledge are considered. Multi-modal correlations are learned to get the event’s multi-modal representation from a global perspective. Intra-event object relations are explored to capture the fine-grained event feature with an object graph. Inter-event semantic relations are also explored through the external knowledge to understand the semantic associations among events with an event graph. We conduct extensive experiments on the new dataset, and the results show the effectiveness of our method.
Dual Scene Graph Convolutional Network for Motivation Prediction

Humans can easily infer the motivations behind human actions from only visual data by comprehensively analyzing the complex context information and utilizing abundant life experiences. Inspired by humans’ reasoning ability, existing motivation prediction methods have improved image-based deep classification models using the commonsense knowledge learned by pre-trained language models. However, the knowledge learned from public text corpora is probably incompatible with the task-specific data of the motivation prediction, which may impact the model performance. To address this problem, this paper proposes a dual scene graph convolutional network (dual-SGCN) to comprehensively explore the complex visual information and semantic context prior from the image data for motivation prediction. The proposed dual-SGCN has a visual branch and a semantic branch. For the visual branch, we build a visual graph based on scene graph where object nodes and relation edges are represented by visual features. For the semantic branch, we build a semantic graph where nodes and edges are directly represented by the word embeddings of the object and relation labels. In each branch, node-oriented and edge-oriented message passing is adopted to propagate interaction information between different nodes and edges. Besides, a multi-modal interactive attention mechanism is adopted to cooperatively attend and fuse the visual and semantic information. The proposed dual-SGCN is learned in an end-to-end form by a multi-task co-training scheme. In the inference stage, Total Direct Effect is adopted to alleviate the bias caused by the semantic context prior. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance.
Quantum Optimal Control for Pauli Operators Based on Spin-1/2 System

Quantum control is an important field for quantum computing and quantum simulation. The key of quantum control is to realize quantum logic operators with high fidelity. In this work, based on spin-1/2 system, the optimal simulation of three Pauli logic operators are carried out by using quantum optimal control theory. Under Pauli z spin-presentation, the results show that under the given quantum initial state, the Pauli operators achieve the expected target quantum state with fidelity (0.9999). When the control pulse is applied on x-axis, the number of iterations required to optimize Pauli x operator to achieve the target state is the least, and the number of iterations required to optimize Pauli z operator is the most. In addition, the comparison shows that when the fidelity reach 0.9999, the population of the quantum final state can reach the ideal theoretical expectation. Besides, the optimization fidelity of Hadamard gate can also reach 0.9999 based on spin-1/2 system. Finally, the study on the phase evolution of quantum states shows that the phase difference between the initial and final quantum states of optimized Pauli x and Pauli z logic operator are π/2 respectively, and there is no phase difference between the final quantum state and the initial quantum state after the evolution of optimized Pauli y operator.
A new approach in developing an urban rail transit emergency knowledge graph based on operation fault logs

Urban rail transit emergencies in China’s large cities are frequent occurrences but currently, operation managers lack effective analysis tools that can help in reducing them. In this study we present a knowledge graph tool, developed using historical emergency text information from Beijing’s urban rail transit fault logs from which an information model is developed enabling key information to be mined and subsequently analyzed so that interrelationships within the text can be determined. The knowledge graph tool assists urban rail transit operation managers to analyze more effectively, through knowledge query and semantic search, the relations and attributes of emergencies enabling more insight into their root causes. Compared with traditional first and second order text parsing algorithms, the extended high order parsing algorithm proposed in this paper has better performance in the extraction of both phrases and inter-phrase relations, with an extraction accuracy of more than 85%. Furthermore, compared with traditional failure mode effect analysis methods, the extended method proposed in this paper can also calculate phrase attributes and therefore provide a reference for quantitative risk calculations.
Malicious URL Detection Using Machine Learning

In recent years cyberattacks have become destructive and targeted. With technological advancements, diverse threats are launching in a sophisticated way that targets people to defraud them. Many web applications have been struggling to improve the reliability and security of their platforms to protect users from fraud, revenue, or malware. These attacks use malicious uniform resource locators (URLs) to attack web users. These URLs host unwanted content in the form of junk emails, phishing, or unauthorized drive-by downloads. Unsuspecting people click these phishing URLs and become victims of unethical anonymous activities like identity theft (personal or financial details) and installation of viruses. Therefore, it is necessary to detect malicious URLs accurately for resolving security issues. Traditional protection method, such as blacklisting, remains a classical technique for the detection of malicious URLs due to its simplicity but cannot detect unknown malicious URLs; hence, machine learning approaches are being used for achieving better results. This chapter aims to provide a structural understanding of popular feature extraction techniques and machine learning algorithms.
Blockchain-Based Secure E-voting System Using Aadhaar Authentication

Election in any country is a very inflated and time-consuming process. Many countries have tried to implement numerous ways of conducting an election to make the process easy and time and cost-efficient. One such method is a blockchain-based secure voting system that has been in research for a long. However, it is still a challenge to implement it because of various reasons like security, availability of the internet to everyone, etc. Therefore, this paper presents a secure election system based on blockchain, tackling a few of these problems and making it more secure. The proposed system uses Aadhaar-based voter authentication and One-time password (OTP)-based verification. The proposed scheme uses a private blockchain that solves many security problems and even attacks like Sybil, Distributed denial of service (DDoS), etc. The proposed system can reduce the cost of elections to a large extent while increasing election security.
Game theory attack pricing for mining pools in blockchain-based IoT

The malicious mining pool can sacrifice part of its revenue to employ the computing power of blockchain network. The employed computing power carries out the pool mining attacks on the attacked mining pool. To realize the win-win game between the malicious mining pool and the employee, the paper proposes an Employment Attack Pricing Algorithm (EAPA) of mining pools in blockchain based on game theory. In the EAPA, the paper uses mathematical formulas to express the revenue of malicious mining pools under the employment attack, the revenue increment of malicious mining pools, and the revenue of the employee. It establishes a game model between the malicious mining pool and the employee under the employment attack. Then, the paper proposes an optimal computing power price selection strategy of employment attack based on model derivation. In the strategy, the malicious mining pool analyzes the conditions for the employment attack, and uses the derivative method to find the optimal utilization value of computing power, employees analyze the conditions for accepting employment, and use the derivative method to find the optimal reward value of computing power. Finally, the strategy finds the optimal employment computing power price to realize Nash equilibrium between the malicious mining pool and the employee under the current computing power allocation. The simulation results show that the EAPA could find the employment computing power price that realizes the win-win game between the malicious mining pool and the employee. The EAPA also maximizes the unit computing power revenue of employment and the unit computing power revenue of honest mining in malicious mining pool at the same time. The EAPA outperforms the state-of-the-art methods such as SPSUCP, DPSACP, and FPSUCP.
Research on Application of Artificial Intelligence Teaching Mode Based on Project-based Learning

AI learning emphasizes learning based on real projects. The projects that students need to do must be real, complete, meaningful, and can be completed in a short time. It helps students comprehensively apply various knowledge and skills in the process of exploration and practice, and improve the core quality of AI. This study made a preliminary exploration of the AI teaching mode based on project-based learning. Combined with teaching practice, it summarized the basic links of AI teaching based on project-based learning: based on the situation, ingenious project design, brainstorming, creativity generation, role assignment, each role, activity exploration, engineering design, iterative iteration, product optimization, achievement sharing, and ability improvement. Through these six main links to carry out teaching activities, the artificial intelligence teaching model based on project-based learning has achieved good teaching results.
Malcolm: Multi-agent Learning for Cooperative Load Management at Rack Scale

We consider the problem of balancing the load among servers in dense racks for microsecond-scale workloads. To balance the load in such settings tens of millions of scheduling decisions have to be made per second. Achieving this throughput while providing microsecond-scale latency and high availability is extremely challenging. To address this challenge, we design a fully decentralized load-balancing framework. In this framework, servers collectively balance the load in the system. We model the interactions among servers as a cooperative stochastic game. To find the game's parametric Nash equilibrium, we design and implement a decentralized algorithm based on multi-agent-learning theory. We empirically show that our proposed algorithm is adaptive and scalable while outperforming state-of-the art alternatives. In homogeneous settings, Malcolm performs as well as the best alternative among other baselines. In heterogeneous settings, compared to other baselines, for lower loads, Malcolm improves tail latency by up to a factor of four. And for the same tail latency, Malcolm achieves up to 60% more throughput compared to the best alternative among other baselines.
AADEC: Anonymous and Auditable Distributed Access Control for Edge Computing Services

Edge computing is an emerging distributed computing concept that allows edge servers to provide authorized consumers with various on-demand services. Due to highly dynamic and untrustworthy network environments, various potential security concerns (e.g., unauthorized access, data manipulation, and privacy leakage) have been the critical factors restricting the development of edge computing. A recent heterogeneous framework proposed by Dougherty et al. (CCS’21), named APECS, deploys token-based authorization and multiple attribute-based encryption (MABE) to guarantee access control and data confidentiality. While APECS achieves a secure asynchronous access control without the “always-on” cloud, it suffers from privacy leakage (caused by the public identity information) and fake data spreading issues (due to the data confidentiality). In this paper, we propose an Anonymous and Auditable Distributed Access Control Framework for Edge Computing (AADEC) to relieve these issues. AADEC is based on two building blocks that we designed, namely a conditional anonymous authentication and an auditable MABE with optimized performance. We also define the formal security models and present security proofs for our proposal. The final qualitative comparison and performance benchmark demonstrate that AADEC can achieve a trade-off among anonymity, confidentiality, auditability and efficiency.