Reviewerly/exHarmony huggingface dataset:
The Mystery of Pandora

Domestic violence against women has been a serious social and public health problem faced by contemporary society. This type of violence has grown in recent years, placing the woman not only as a victim of the aggressor but also of a sexist culture established over time. One of the ways to combat this problem is through prevention based on learning and awareness of its causes and damages. The use of 360-degree videos and immersive virtual reality (IVR) combined with serious games (SG) is a promising field of research in the educational field. They have the potential to provide more realistic and engaging environments, enhancing the engagement of their users, thus favoring more learning opportunities. This study presents The Mystery of Pandora, a SG that uses IVR and 360-degree videos to promote knowledge about domestic violence against women (DVAW). The game assessment results suggest a pedagogical potential for promoting knowledge DVAW. In addition, this work brings discussions about the benefits and techniques for the use of IVR and 360-degree videos in serious games.
HiRN: Hierarchical recurrent neural network for video super-resolution (VSR) using two-stage feature evolution

The aim of video super-resolution (VSR) is generate the high-resolution (HR) frames from their low-resolution (LR) counterparts. As one of the fundamental module of VSR, propagation process provides the path of feature map and specifies how the feature map is leveraged. In the recurrent propagation, the latent features can be propagated and aggregated. Therefore, adopting the recurrent strategy can resolve the limitation of sliding-window-based local propagation. Recently, bi-directional recurrent propagation-based latest methods have achieved powerful performance in VSR. However, existing bi-directional frameworks have structured by combining forward and backward branches. These structures cannot propagate and aggregate previous and future latent features of current branch. In this study, we suggest the hierarchical recurrent neural network (HiRN) based on feature evolution. The proposed HiRN is designed based on the hierarchical recurrent propagation and residual block-based backbone with temporal wavelet attention (TWA) module. The hierarchical recurrent propagation consists of two stages to combine advantages of low frame rate-based forward and backward schemes, and multi-frame rate-based bi-directional access structure. The proposed methods are compared with state-of-the-art (SOTA) methods on the benchmark datasets. Experiments show that the proposed scheme achieves superior performance compared with SOTA methods. In particular, the proposed HiRN achieves better performance than all compared methods in terms of SSIM on Vid4 benchmark. In addition, the proposed HiRN surpasses the existing GBR-WNN by a significant 3.03 dB in PSNR on REDS4 benchmark with fewer parameters.
Bluetooth Low Energy Device Identification Based on Link Layer Broadcast Packet Fingerprinting

With the rapid development of the Internet of Things (IoT), wireless technology has become an indispensable part of modern computing platforms and embedded systems. Wireless device fingerprint identification is deemed as a promising solution towards enhancing the security of device access authentication and communication process in the IoT scenario. However, the extraction of features from the network layer and its upper layers often confront restrictions from specific devices: the association with a certain wireless network and the access to the plaintext of the payload. Meanwhile, Bluetooth Low Energy (BLE) packets have been encrypted above the link layer, which makes those features difficult to extract. To tackle these problems, we introduce a novel method to identify BLE devices based on the fingerprint features in the data link layer. Initially, the BLE packets are collected through a receiver based on software-defined radio technology. Then, fields that reflect device differences in BLE broadcast packets are extracted through traffic analysis. Finally, a MultiLayer Perceptron (MLP) model is employed to recognize the category of BLE devices. An experimental result on a dataset with 15 types of BLE devices shows that the identification accuracy of the proposed method can reach 99.8%, which accomplishes better performance over previous work.
Do smaller businesses pay more bribes? Firm size, informal payments and mitigating strategies in Africa

In this paper, we sought to understand the effect of firm size on demand for and payment of bribes by African firms and the strategies needed to overcome them. We argued that size represents a selectionist pressure on firms to cede to corrupt demands while strategies (time to formalization, relational intensity and foreign participation) from the adaptive perspective of the organization are tools to deal with these pressures. We test our framework using data from the World Bank Enterprise Surveys in Africa. We find that the liability of smallness effect exists as the larger a firm gets the fewer bribes in percentage terms it pays out to corrupt public officials. We find also that time to formalization, relational intensity and foreign participation are significant moderators of this effect. The study makes a contribution by testing a contingent framework that integrates the selectionist and adaptive views of organizing.
Integrated development of industrial and regional economy using big data technology

Big data technology is incorporated to establish a coupled development model of the regional and industrial economies in this paper. The main objective is to increase the coupling development effect of the regional and industrial economies. We investigate the data processing algorithm of the industrial and regional economies using the echo state network. In this paper, we propose an enhanced Laplace deep echo state network. The proposed echo state model properties are theoretically examined. The enhanced network is integrated with data processing and big data technology to analyze the coupling process of the industrial and regional economies. The clustering analysis shows that the proposed coupled development model of the industrial and regional economy based on big data technology is successfully implemented.
Zero-Shot Learning with Joint Generative Adversarial Networks

Zero-shot learning (ZSL) is implemented by transferring knowledge from seen classes to unseen classes through embedding space or feature generation. However, the embedding-based method has a hubness problem, and the generation-based method may contain considerable bias. To solve these problems, a joint model with multiple generative adversarial networks (JG-ZSL) is proposed in this paper. Firstly, we combined the generation-based model and the embedding-based model to build a hybrid ZSL framework by mapping the real samples and the synthetic samples into the embedding space for classification, which alleviates the problem of data imbalance effectively. Secondly, based on the original generation-method model, a coupled GAN is introduced to generate semantic embeddings, which can generate semantic vectors for unseen classes in embedded space to alleviate the bias of mapping results. Finally, semantic-relevant self-adaptive margin center loss was used, which can explicitly encourage intra-class compactness and inter-class separability, and it can also guide coupled GAN to generate discriminative and representative semantic features. All the experiments on the four standard datasets (CUB, AWA1, AWA2, SUN) show that the proposed method is effective.
Teachers’ Professional Training through Augmented Reality: A Literature Review

Practicum is regarded as a fundamental aspect of the training of prospective teachers. In addition, digital tools are increasingly used to enrich a traditional face-to-face experience. However, the technological exploitation of Augmented Reality (AR) by undergraduate students studying early childhood and primary education is low. A Systematic Literature Review (SLR) on the use of Augmented Reality (AR) in teacher training was conducted. Based on the overarching objectives of the ERASMUS+ project, entitled Digital Practicum 3.0 Exploring Augmented Reality, Remote Classrooms, and Virtual Learning to Enrich and Expand Pre-service Teacher Education Preparation (2020-1-ES01-KA226-HE-096120), the ultimate purpose of this study was to assess whether the use of this resource favors learning and expertise. Two main results are prominent. First, it is noteworthy how the use of this digital technology is limited, given the scarcity of studies. Second, the research studies available focus largely on the benefits of the use of AR in teacher education at a theoretical level. Thus, future research needs to further explore the use of AR in teacher training specially focused on student teachers’ learning processes.
Self-paced multi-view positive and unlabeled graph learning with auxiliary information

Graph widely exists in the research study, with complex structure and rich information, and it has always been a powerful mode to analyze difficult problems. Meanwhile, graph classification is an important application problem in many fields, it means that we use the structure of known graph datasets to classify the unknown graph. Most of the current work is to classify through positive and unlabeled learning (PU learning) and multi-view learning respectively. For training and testing data, it is believed that they all have the same feature representation, but in fact, we can gain auxiliary features called privileged information for the training data. In the past, privileged information is always ignored in PU graph learning. To enhance the efficiency of the classifier, a new method called SMPUP is presented, which first extracts the features of graph from different perspectives, and introduces privileged information into the PU learning method to construct the classifier model. Further, we have introduced a self-paced learning (SPL) framework to ensure the model can learn efficiently. A large number of experiments with graph and biological datasets show that the SMPUP method performs better than previous methods.
Understanding Cultural Similarities of Archaeological Sites from Excavation Reports Using Natural Language Processing Technique

In this study, we applied natural language processing (NLP) techniques to texts of excavation reports on buried cultural properties to calculate the degree of similarity between the reports for determining archaeological sites that have a high degree of similarity. Specifically, we validated whether the similarity of sentence embeddings in the excavation reports of these sites is consistent with the existing classification. Four archaeological sites classified in existing archaeological research papers were used. For validation, 128 excavation reports from the four sites were used; sentence embeddings were obtained using Doc2Vec. We obtained the following results: 1) In applying NLP to excavation reports for determining the similarities of archaeological sites, merging the texts for each site into a single document and then processing it was more preferable than processing it in separate volumes of the excavation report. 2) The similarity based on sentence embedding of excavation reports using Doc2Vec was more consistent with the classification of the characteristics of archaeological sites than term frequency–inverse document frequency (TF-IDF). 3) When targeting a specific period, the sentence embedding exclusively for the text of the relevant period is consistent with the classification of the characteristics of the archaeological site from the artifacts and structural remains of that specific period. 4) When a specific period is targeted, the exclusive sentence embeddings of that period, obtained through the additive compositionality of sentence embeddings, can be used to classify the characteristics of archaeological sites based on the artifacts and structural remains on that period. Consequently, the similarities of texts based on NLP can reflect the similarities of archaeological sites. This holds true even for excavation reports that include spelling inconsistencies, optical character reader misrecognition, and garbled words.
An Effective Method for Sensing Power Safety Distance Based on Monocular Vision Depth Estimation

As an important index of risk protection, the safety distance is crucial to ensure the safe and stable operation of the power system and the safety of personnel’s life. Traditional monitoring methods are difficult to balance recognition accuracy and convenience. Therefore, this paper presents a power safety distance sensing method based on monocular visual images to achieve the recognition of the safety distance of external damage in complex scenes of transmission corridors, and proposed a power density depth distance model. In this model, a codec network with skip-connection to extract features and aggregate shallow and deep features for input power system images. Then, the regularization method, migration learning strategy, cosine annealing learning strategy, and data enhancement strategy are used to further optimize the model, so as to obtain a model with good accuracy and generalization in complex conditions. The effectiveness and superiority of the proposed method are verified in comparison to other external damage monitoring methods. The experimental results showed that the proposed method has high accuracy for the distance of external damage in the actual scenario. Moreover, the method has good generalizability, which can be easily deployed in video monitoring systems on different transmission corridors.
Enhanced Binary Black Hole algorithm for text feature selection on resources classification

Exploring an optimal combination of features from high-dimensional text vectors is an essential step for classification on Scientific and Technical Service (STS) resources. However, existing metaheuristic feature selection methods exhibit deteriorated performance and increased computational costs when dealing with high-dimensional datasets. Based on these issues, a novel Enhanced Binary Black Hole (EBBH) algorithm is investigated to improve the performance of mining the optimal subset of high-dimensional attributes in STS resources text. In the proposed EBBH, all operators employ a novel binary encoding framework to reduce the number of features, which provides a more efficient way to search the solution space. As a pre-processing step, prime feature attention assigns different selection weights based on the variable importance ranking from random forests, ensuring a high-quality initial population is generated. Moreover, we incorporate an adaptive enhancement strategy to maintain the balance between exploitation and exploration in the search process. The proposed EBBH is assessed on eight high-dimensional benchmark datasets and subsequently demonstrated successful application to five STS resource datasets. Comparative experiments with 12 state-of-the-art algorithms for high-dimensional problems revealed the superiority of EBBH in terms of fitness, classification accuracy, and selected features.
A multi-objective memetic algorithm for automatic adversarial attack optimization design

The phenomenon of adversarial examples has been revealed in variant scenarios. Recent studies show that well-designed adversarial defense strategies can improve the robustness of deep learning models against adversarial examples. However, with the rapid development of defense technologies, it also tends to be more difficult to evaluate the robustness of the defensed model due to the weak performance of existing manually designed adversarial attacks. To address the challenge, given the defensed model, the efficient adversarial attack with less computational burden and lower robust accuracy is needed to be further exploited. Therefore, we propose a multi-objective memetic algorithm for auto adversarial attack optimization design, which realizes the automatic search for the near-optimal adversarial attack towards defensed models. Firstly, the more general mathematical model of auto adversarial attack optimization design is constructed, where the search space includes not only the attacker operations, magnitude, iteration number, and loss functions but also the connection ways of multiple adversarial attacks. In addition, we develop a multi-objective memetic algorithm combining NSGA-II and local search to solve the optimization problem. Finally, to decrease the evaluation cost during the search, we propose a representative data selection strategy based on sorting cross entropy loss values of images output by models. Experiments on CIFAR10, CIFAR100, and ImageNet datasets show that our method can efficiently find near-optimal adversarial attacks with lower robust accuracy and less time cost, which can provide more reliable and efficient robustness evaluation for defensed models.
BoAu: Malicious traffic detection with noise labels based on boundary augmentation

The effectiveness of deep-learning-based malicious traffic detection systems relies on high-quality labeled traffic datasets. However, malicious traffic labeling approaches can easily lead to incorrect labeling, which can have a harmful impact on models. To this end, various methods for learning with noise labels have been proposed. They exclude suspected wrong samples from model updates to ensure accuracy. However, this also removes hard samples, resulting in poor model decision boundaries and the loss of ability to classify hard samples. In this paper, we propose a boundary-augmentation-based approach for malicious traffic identification named BoAu. Unlike other approaches, BoAu treats all samples, including hard samples, equally during training to construct more accurate decision boundaries and thus improve accuracy. Meanwhile, a decision boundary augmentation module is designed to mitigate the impact of mislabeled hard samples on decision boundary generation. The decision boundary augmentation module adaptively adjusts the losses of hard samples based on their distance from the cluster to which their labels belong and other clusters, thus driving the shared feature representation network to fit the true label distribution. We validated BoAu in identifying malicious traffic with noise labels on a dataset covering 22 classes of realistic encrypted malicious traffic. Experimental results showed that even under scenarios with up to 90% noise labels, the classification accuracy was still over 80%, which was better than the state-of-the-art approaches. In addition, we validated the applicability of BoAu on several public datasets, including CIC-IDS-2017 and IoT-23.
Set algebra — based algebraic evolutionary algorithm for binary optimization problems

In order to design algebraic evolutionary algorithms by set algebra, the intersection, union, complement, difference and symmetric difference operations of 0-1 vectors on {0,1}n are firstly defined based on set operations. Then, the isomorphism between algebraic system defined on {0,1}n and set algebra defined on power set P(Ω) of set Ω is proved. Therefrom a simple and fast implement method of set algebra is proposed. Third, symmetric difference operator and asymmetric mutation operator are successively proposed based on set algebra, they have global exploration and local exploitation capabilities respectively. On this basis, a novel algebraic evolutionary algorithm, named set algebra-based heuristic algorithm (SAHA), is proposed based on the operations of 0-1 vectors on {0,1}n for solving binary optimization problems. For verifying the performance of SAHA, it is used to solve 0-1 knapsack problem (0-1KP) and knapsack problem with single continuous variable (KPC), respectively. The comparison with the state-of-the-art algorithms of solving 0-1KP and KPC shows that SAHA can not only obtain excellent calculation results, but also is faster speed, it is most competitive for solving binary optimization problems.
Longest common substring in Longest Common Subsequence’s solution service: A novel hyper-heuristic

The Longest Common Subsequence (LCS) is the problem of finding a subsequence among a set of strings that has two properties of being common to all and the longest. The LCS has applications in computational biology and text editing, among many others. Due to the NP-hardness of the general longest common subsequence, numerous heuristic algorithms and solvers have been proposed to give the best possible solution for different sets of strings. None of them has the best performance for all types of sets. In addition, there is no method to specify the type of a given set of strings. Besides that, the available hyper-heuristic is not efficient and fast enough to solve this problem in real-world applications. This paper proposes a novel hyper-heuristic to solve the longest common subsequence problem using a new criterion to classify a set of strings based on their similarity. To do this, we offer a general stochastic framework to identify the type of a given set of strings. Following that, we introduce the set similarity dichotomizer (S2D) algorithm based on the framework that divides the type of sets into two. This algorithm is introduced for the first time in this paper and opens a new way to go beyond the current LCS solvers. Then, we present our proposed hyper-heuristic that exploits the S2D and one of the internal properties of the given strings to choose the best matching heuristic among a set of heuristics. We compare the results on benchmark datasets with the best heuristics and hyper-heuristics. The results show that our proposed dichotomizer (i.e., S2D) can classify datasets with 98% of accuracy. Also, our proposed hyper-heuristic obtains competitive performance in comparison with the best methods and outperforms best hyper-heuristics for uncorrelated datasets in terms of both quality of solutions and run time factors. All supplementary files, including the source codes and datasets, are publicly available on GitHub.1.