Reviewerly/exHarmony huggingface dataset:
Image Encryption and Decryption System through a Hybrid Approach Using the Jigsaw Transform and Langton’s Ant Applied to Retinal Fundus Images

In this work, a new medical image encryption/decryption algorithm was proposed. It is based on three main parts: the Jigsaw transform, Langton’s ant, and a novel way to add deterministic noise. The Jigsaw transform was used to hide visual information effectively, whereas Langton’s ant and the deterministic noise algorithm give a reliable and secure approach. As a case study, the proposal was applied to high-resolution retinal fundus images, where a zero mean square error was obtained between the original and decrypted image. The method performance has been proven through several testing methods, such as statistical analysis (histograms and correlation distributions), entropy computation, keyspace assessment, robustness to differential attack, and key sensitivity analysis, showing in each one a high security level. In addition, the method was compared against other works showing a competitive performance and highlighting with a large keyspace (&gt;1×101,134,190.38). Besides, the method has demonstrated adequate handling of high-resolution images, obtaining entropy values between 7.999988 and 7.999989, an average Number of Pixel Change Rate (NPCR) of 99.5796%±0.000674, and a mean Uniform Average Change Intensity (UACI) of 33.4469%±0.00229. In addition, when there is a small change in the key, the method does not give additional information to decrypt the image.
Local Fractal Fourier Transform and Applications

In this manuscript, we review fractal calculus and the analogues of both local Fourier transform with its related properties and Fourier convolution theorem are proposed with proofs in fractal calculus. The fractal Dirac delta with its derivative and the fractal Fourier transform of the Dirac delta are also defined. In addition, some important applications of the local fractal Fourier transform are presented in this paper such as the fractal electric current in a simple circuit, the fractal second order ordinary differential equation, and the fractal Bernoulli-Euler beam equation. All discussed applications are closely related to the fact that, in fractal calculus, a useful local fractal derivative is a generalized local derivative in the standard calculus sense. In addition, a comparative analysis is also carried out to explain the benefits of this fractal calculus parameter on the basis of the additional alpha parameter, which is the dimension of the fractal set, such that when $alpha=1$, we obtain the same results in the standard calculus.
A Novel Way to Generate Adversarial Network Traffic Samples against Network Traffic Classification

Network traffic classification technologies could be used by attackers to implement network monitoring and then launch traffic analysis attacks or website fingerprint attacks. In order to prevent such attacks, a novel way to generate adversarial samples of network traffic from the perspective of the defender is proposed. By adding perturbation to the normal network traffic, a kind of adversarial network traffic is formed, which will cause misclassification when the attackers are implementing network traffic classification with deep convolutional neural networks (CNN) as a classification model. The paper uses the concept of adversarial samples in image recognition for reference to the field of network traffic classification and chooses several different methods to generate adversarial samples of network traffic. The experiment, in which the LeNet-5 CNN is selected as a classification model used by attackers and Vgg16 CNN is selected as the model to test the transferability of the adversarial network traffic generated, shows the effect of the adversarial network traffic samples.
Computing a Possibility Theory Repair for Partially Preordered Inconsistent Ontologies

We address the problem of handling inconsistency in uncertain knowledge bases that are specified in the lightweight fragments of description logics DL-Lite. More specifically, we assume that the TBox component is coherent, stable, and fully reliable. However, the ABox component may be inconsistent with respect to the TBox, partially preordered and uncertain. Uncertainty is encoded in the framework of possibility theory. In this context, we propose an extension of standard possibilistic DL-Lite. We represent the ABox as a symbolic weighted base, where the weights attached to the assertions are ordered according to a strict partial order. We define a tractable method for computing a single possibilistic repair for a partially preordered weighted ABox. The idea is to consider the possibilistic compatible bases of such an ABox, which intuitively encode all the possible extensions of a partial order, and compute the possibilistic repair of each compatible base. We then compute the intersection of all these possibilistic repairs to obtain a single repair for the initial ABox. We also provide an equivalent characterization by introducing the notion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pi$</tex-math></inline-formula> -accepted assertions. This ensures that the computation of the partially preordered possibilistic repair can be achieved in polynomial time in DL-Lite.
Oriental Language Recognition (OLR) 2020: Summary and Analysis

The fifth Oriental Language Recognition (OLR) Challenge focuses on language recognition in a variety of complex environments to promote its development. The OLR 2020 Challenge includes three tasks: (1) cross-channel language identification, (2) dialect identification, and (3) noisy language identification. We choose Cavg as the principle evaluation metric, and the Equal Error Rate (EER) as the secondary metric. There were 58 teams participating in this challenge and one third of the teams submitted valid results. Compared with the best baseline, the Cavg values of Top 1 system for the three tasks were relatively reduced by 82%, 62% and 48%, respectively. This paper describes the three tasks, the database profile, and the final results. We also outline the novel approaches that improve the performance of language recognition systems most significantly, such as the utilization of auxiliary information.
A Fast Stereo Matching Network with Multi-Cross Attention

Stereo matching networks based on deep learning are widely developed and can obtain excellent disparity estimation. We present a new end-to-end fast deep learning stereo matching network in this work that aims to determine the corresponding disparity from two stereo image pairs. We extract the characteristics of the low-resolution feature images using the stacked hourglass structure feature extractor and build a multi-level detailed cost volume. We also use the edge of the left image to guide disparity optimization and sub-sample with the low-resolution data, ensuring excellent accuracy and speed at the same time. Furthermore, we design a multi-cross attention model for binocular stereo matching to improve the matching accuracy and achieve end-to-end disparity regression effectively. We evaluate our network on Scene Flow, KITTI2012, and KITTI2015 datasets, and the experimental results show that the speed and accuracy of our method are excellent.
Hierarchical Social Recommendation Model Based on a Graph Neural Network

With the continuous accumulation of social network data, social recommendation has become a widely used recommendation method. Based on the theory of social relationship propagation, mining user relationships in social networks can alleviate the problems of data sparsity and the cold start of recommendation systems. Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation. The model is based on the graph neural network. It unifies the attention mechanism and bidirectional LSTM into the same framework and uses a multilayer perceptron. In addition, an embedded propagation method is added to learn the neighbor influences of different depths and extract useful neighbor information for social relationship modeling. We use this method to solve the problem that the current research methods of social recommendation only extract the superficial level of social networks but ignore the importance of the relationship strength of the users at different levels in the recommendation. This model integrates social relationships into user and project interactions, not only capturing the weight of the relationship between different users but also considering the influence of neighbors at different levels on user preferences. Experiments on two public datasets demonstrate that the proposed model is superior to other benchmark methods with respect to mean absolute error and root mean square error and can effectively improve the quality of recommendations.
A Comparison of Different Source Code Representation Methods for Vulnerability Prediction in Python

In the age of big data and machine learning, at a time when the techniques and methods of software development are evolving rapidly, a problem has arisen: programmers can no longer detect all the security flaws and vulnerabilities in their code manually. To overcome this problem, developers can now rely on automatic techniques, like machine learning based prediction models, to detect such issues. An inherent property of such approaches is that they work with numeric vectors (i.e., feature vectors) as inputs. Therefore, one needs to transform the source code into such feature vectors, often referred to as code embedding. A popular approach for code embedding is to adapt natural language processing techniques, like text representation, to automatically derive the necessary features from the source code. However, the suitability and comparison of different text representation techniques for solving Software Engineering (SE) problems is rarely studied systematically. In this paper, we present a comparative study on three popular text representation methods, word2vec, fastText, and BERT applied to the SE task of detecting vulnerabilities in Python code. Using a data mining approach, we collected a large volume of Python source code in both vulnerable and fixed forms that we embedded with word2vec, fastText, and BERT to vectors and used a Long Short-Term Memory network to train on them. Using the same LSTM architecture, we could compare the efficiency of the different embeddings in deriving meaningful feature vectors. Our findings show that all the text representation methods are suitable for code representation in this particular task, but the BERT model is the most promising as it is the least time consuming and the LSTM model based on it achieved the best overall accuracy (93.8%) in predicting Python source code vulnerabilities.
FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization

Transducer-based models, such as RNN-Transducer and transformer-transducer, have achieved great success in speech recognition. A typical transducer model decodes the output sequence conditioned on the current acoustic state and previously predicted tokens step by step. Statistically, The number of blank tokens in the prediction results accounts for nearly 90\% of all tokens. It takes a lot of computation and time to predict the blank tokens, but only the non-blank tokens will appear in the final output sequence. Therefore, we propose a method named fast-skip regularization, which tries to align the blank position predicted by a transducer with that predicted by a CTC model. During the inference, the transducer model can predict the blank tokens in advance by a simple CTC project layer without many complicated forward calculations of the transducer decoder and then skip them, which will reduce the computation and improve the inference speed greatly. All experiments are conducted on a public Chinese mandarin dataset AISHELL-1. The results show that the fast-skip regularization can indeed help the transducer model learn the blank position alignments. Besides, the inference with fast-skip can be speeded up nearly 4 times with only a little performance degradation.
Monocular Pedestrian 3D Localization for Social Distance Monitoring

Social distancing protocols have been highly recommended by the World Health Organization (WHO) to curb the spread of COVID-19. However, one major challenge to enforcing social distancing in public areas is how to perceive people in three dimensions. This paper proposes an innovative pedestrian 3D localization method using monocular images combined with terrestrial point clouds. In the proposed approach, camera calibration is achieved based on the correspondences between 2D image points and 3D world points. The vertical coordinates of the ground plane where pedestrians stand are extracted from the point clouds. Then, using the assumption that the pedestrian is always perpendicular to the ground, the 3D coordinates of the pedestrian’s feet and head are calculated iteratively using collinear equations. This allows the three-dimensional localization and height determination of pedestrians using monocular cameras, which are widely distributed in many major cities. The performance of the proposed method was evaluated using two different datasets. Experimental results show that the pedestrian localization error of the proposed approach was less than one meter within tens of meters and performed better than other localization techniques. The proposed approach uses simple and efficient calculations, obtains accurate location, and can be used to implement social distancing rules. Moreover, since the proposed approach also generates accurate height values, exclusionary schemes to social distancing protocols, particularly the parent-child exemption, can be introduced in the framework.
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media

Adverse Events (AE) are harmful events resulting from the use of medical products.Although social media may be crucial for early AE detection, the sheer scale of this data makes it logistically intractable to analyze using human agents, with NLP representing the only low-cost and scalable alternative.In this paper, we frame AE Detection and Extraction as a sequence-to-sequence problem using the T5 model architecture and achieve strong performance improvements over competitive baselines on several English benchmarks (F1 = 0.71, 12.7% relative improvement for AE Detection; Strict F1 = 0.713, 12.4% relative improvement for AE Extraction).Motivated by the strong commonalities between AE-related tasks, the class imbalance in AE benchmarks and the linguistic and structural variety typical of social media posts, we propose a new strategy for multi-task training that accounts, at the same time, for task and dataset characteristics.Our multi-task approach increases model robustness, leading to further performance gains.Finally, our framework shows some language transfer capabilities, obtaining higher performance than Multilingual BERT in zero-shot learning on French data.
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals

When a model attribution technique highlights a particular part of the input, a user might understand this highlight as making a statement about counterfactuals (Miller, 2019): if that part of the input were to change, the model's prediction might change as well.This paper investigates how well different attribution techniques align with this assumption on realistic counterfactuals in the case of reading comprehension (RC).RC is a particularly challenging test case, as token-level attributions that have been extensively studied in other NLP tasks such as sentiment analysis are less suitable to represent the reasoning that RC models perform.We construct counterfactual sets for three different RC settings, and through heuristics that can connect attribution methods' outputs to high-level model behavior, we can evaluate how useful different attribution methods and even different formats are for understanding counterfactuals.We find that pairwise attributions are better suited to RC than tokenlevel attributions across these different RC settings, with our best performance coming from a modification that we propose to an existing pairwise attribution method. 1
Demonstration of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>1</mml:mn><mml:mo>→</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math> continuous-variable quantum telecloning

The distribution of the quantum states to remote receivers is an indispensable technology in quantum communication, quantum networks, and quantum information processing. Perfect distribution does not allow any quantum information loss of the distributed quantum state. Quantum telecloning, which is made up of quantum cloning and quantum teleportation, is a remote quantum information distribution protocol. Here, we experimentally demonstrate unconditional $1\ensuremath{\rightarrow}3$ quantum telecloning of coherent states, and the fidelities of the cloned states are $0.64\ifmmode\pm\else\textpm\fi{}0.01, 0.64\ifmmode\pm\else\textpm\fi{}0.01$, and $0.49\ifmmode\pm\else\textpm\fi{}0.01$, respectively. No information about the unknown state is lost during the telecloning process by exploiting the quadripartite entangled modes as an auxiliary resource. Deterministic $1\ensuremath{\rightarrow}3$ quantum telecloning has great potential for applications in advanced quantum technology.
Deep Pairwise Hashing for Cold-start Recommendation

Recommendation efficiency and data sparsity problems have been regarded as two main challenges of real-world recommendation systems. Most existing works focus on improving recommendation accuracy instead of efficiency. In this paper, we propose a Deep Pairwise Hashing (DPH) to map users and items to binary vectors in the Hamming space, where a user's preference for an item can be efficiently calculated by the Hamming distance, which significantly improves the efficiency of online recommendation. To alleviate data sparsity and cold-start problems, the item content information exploited and integrated to learn effective representations of items. Specifically, we first pre-train robust item representation from item content data by a robust Denoising Auto-encoder instead of other deterministic deep learning frameworks. Then we fine-tune the entire recommender framework by adding a pairwise loss function with discrete constraints, which is more consistent with the ultimate goal of producing a ranked list of items. Finally, we adopt the alternating optimization method to optimize the proposed model with discrete constraints. Extensive experiments conducted on three different datasets show that DPH can significantly advance the state-of-the-art frameworks regarding data sparsity and cold-start item recommendation.
Smart Toys in Early Childhood and Primary Education: A Systematic Review of Technological and Educational Affordances

The present paper presents a systematic review of the last 30 years that concerns records on Smart Toys and focuses on toys regarding early childhood and primary education children (3–12 years old). This paper aims to analyse and categorise smart toys (50 articles) in terms of their technological and educational affordances. The results show that the toys are designed based on four main technological affordances and their combinations. The educational affordances of smart toys are studied in terms of different use modes and their learning objectives aimed to identify specific objectives in different subjects and objectives based on transversal competencies such as problem solving, spatial thinking, computational thinking, collaboration and symbolic thinking. Finally, with the multiple correspondence analysis, the correlations between smart toys’ individual technological and educational affordances are grouped with the evolution of affordances related to their development date. In conclusion, in recent years, smart toys concern special sciences (programming) and some 21st-century skills (STEM and computational thinking). In contrast, in the first 20 years, the interest focused more on transverse skills, such as collaboration, emotional thinking, symbolic thinking, story-telling and problem solving.
ADA-CMM: A Capability Maturity Model for Advanced Data Analytics

Despite the rising importance of advanced data analytics, there is limited guidance on how organizations should leverage it.The benefits that an organization can gain through advanced data analytics depends on the organization's ability to gain and use relevant capabilities.This study introduces a capability maturity model (ADA-CMM) for advanced data analytics to help organizations assess their current state of capabilities for managing advanced data analytics.We used the Delphi method to develop the maturity model and performed a survey to evaluate its validity.The results confirm that the maturity level of the advanced data analytics capabilities of organizations is positively related to the business value that they can capture from their use, which in turn found positively related to organizational performance.ADA-CMM can be used by organizations as a self-assessment tool and to create a roadmap for improving their relevant capabilities.
Remote sensing image recommendation based on spatial–temporal embedding topic model

Through research and analysis of the existing remote sensing image sharing and distribution systems, remote sensing image recommendation mode can be divided into subscription recommendation and active recommendation. The first mode provides data query retrieval and subscription distribution services. However, retrieval and subscription services are based on query and subscription keywords, which are problematic or insufficiently active for users. Moreover, these processes cannot discover the latent requirements of a user. Therefore, how to recommend remote sensing images to users accurately and actively is a challenging problem. Research on the active remote sensing image recommendation is rare. The typical method is a space–time periodic task model (STPT), which realizes personalized remote sensing image recommendation based on simulation user log records. However, STPT is not accurate enough because it uses the minimum bounding rectangle as the filter condition of spatial feature and considers that the user’s acquisition of images is periodic, so the data that match the periodic rules is more likely to be returned, resulting in a low recall rate. Additionally, it is less efficient for large-scale image recommendation tasks because it takes a long time to calculate the time-period using Fourier transform method. In this study, we propose a spatial–temporal embedding topic (STET) model to solve the recommendation problem of remote sensing images. This model processes the spatial, temporal, and content information of remote sensing images and constructs a topic model, thereby fully applying the continuity characteristics of space and time and improving the training efficient of the recommendation model. Compared with state-of-the-art models, the results based on large scale real-world datasets show that our model not only significantly improves the recall by more than 10%, the normalized discounted cumulative gain by more than 10% when K is 100 with the precision remaining above 97%, but it also greatly reduces the training time.