Reviewerly/exHarmony huggingface dataset:
Novel Public-Key Encryption with Continuous Leakage Amplification

Abstract Leakage of private information, such as the secret keys, has become a threat to the security of computing systems. It has become a common requirement that cryptographic schemes should withstand various leakage attacks, including the continuous leakage attacks. Although some research progresses have been made toward this area, there are still some unsolved issues. In the literature, the public-key encryption (PKE) constructions with (continuous) leakage resilience normally require the upper bound of leakage to be fixed. However, in many real-world applications, this requirement cannot provide sufficient protection against leakage attacks. In order to mitigate these problems, this paper demonstrates how to design a leakage amplified PKE scheme with continuous leakage resilience and chosen-plaintext attacks security. In our proposed PKE scheme, the leakage parameter can have an arbitrary length. Moreover, the length of permitted leakage in our scheme can be flexibly adjusted according to the leakage requirements of application environment. Its security is formally proved under the classic static assumption.
Graph Convolution Neural Network-Based Data Association for Online Multi-Object Tracking

In this paper, a graph convolutional network (GCN)-based multi-object tracking (MOT) algorithm, consisting of a module for extracting the initial features and a module for updating the features, that estimates the affinity between nodes is proposed. The feature extraction module utilizes the pose feature of the object such that the tracking is correct even when the object is partially occluded. Unlike previous graph neural network (GNN)-based MOT methods, this study is based on a GCN and includes a new feature update mechanism, which is updated by combining the output of the neural network and the node similarity between the tracker and detection nodes for each layer. The node feature is updated by aggregating the updated edge feature and the connection strength between the tracker and detection. In each GCN layer, the three networks for the node, edge update, and edge classification were designed to minimize the network parameters to enable faster MOT compared to other GCN-based MOTs. The entire GCN network was designed to learn end-to-end through an affinity loss. The experimental results for the MOT16 and 17 challenge datasets show that the proposed method achieves a superior or similar performance in terms of tracking accuracy and speed compared to state-of-the-art methods, including GCN-based MOT.
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack

The video-based action recognition task has been extensively studied in recent years. In this paper, we study the structural vulnerability of deep learning-based action recognition models against the adversarial attack using the one frame attack that adds an inconspicuous perturbation to only a single frame of a given video clip. Our analysis shows that the models are highly vulnerable against the one frame attack due to their structural properties. Experiments demonstrate high fooling rates and inconspicuous characteristics of the attack. Furthermore, we show that strong universal one frame perturbations can be obtained under various scenarios. Our work raises the serious issue of adversarial vulnerability of the state-of-the-art action recognition models in various perspectives.
Developing a Framework for Mobile Learning Adoption and Sustainable Development

Abstract Learning innovation for future education often includes digital approaches to enhance learning and to contribute to the development of twenty-first-century skills. There is evidence that mobile learning provides positive outcomes. However, there is a recognized lack of research in the field of frameworks and models that contributes to highlighting mobile learning rewards. This study aims to investigate the main characteristics of a strategic framework for the adaption and sustainable use of mobile learning. This study is based on a systematic review of 15 investigations published between 2009 and 2018. An adaptation of the strategic management framework by Jauch and Glueck (Business policy and strategic management, McGraw-Hill, London, 1988) was developed to show the results. The framework has a pedagogical foundation. Leaders, teachers, learners, families, and community members are identified as the key pillars upholding and maximizing mobile learning. The proposed framework is envisaged to serve as a guide for the educational community in implementing sustainable mobile learning.
Auto-adaptive Grammar-Guided Genetic Programming algorithm to build Ensembles of Multi-Label Classifiers

Multi-label classification has been used to solve a wide range of problems where each example in the dataset may be related either to one class (as in traditional classification problems) or to several class labels at the same time. Many ensemble-based approaches have been proposed in the literature, aiming to improve the performance of traditional multi-label classification algorithms. However, most of them do not consider the data characteristics to build the ensemble, and those that consider them need to tune many parameters to maximize their performance. In this paper, we propose an Auto-adaptive algorithm based on Grammar-Guided Genetic Programming to generate Ensembles of Multi-Label Classifiers based on projections of k labels (AG3P-kEMLC). It creates a tree-shaped ensemble, where each leaf is a multi-label classifier focused on a subset of k labels. Unlike other methods in the literature, our proposal can deal with different values of k in the same ensemble, instead of fixing one specific value. It also includes an auto-adaptive process to reduce the number of hyper-parameters to tune, prevent overfitting and reduce the runtime required to execute it. Three versions of the algorithm are proposed. The first, fixed, uses the same value of k for all multi-label classifiers in the ensemble. The remaining two deal with different k values in the ensemble: uniform gives the same probability to choose each available value of k, and gaussian favors the selection of smaller values of k. The experimental study carried out considering twenty reference datasets and five evaluation metrics, compared with eleven ensemble methods demonstrates that our proposal performs significantly better than the state-of-the-art methods.
An Improved Path Planning Algorithm for UAV Based on RRT

For RRT and RRT * algorithm, the search time is long, low sampling efficiency and planning path twists and turns, someone put forward a kind of narrow two-way rapid extension of optimal sampling space random tree algorithm. In each iteration of the algorithm, two random trees are extended, and two new nodes can be generated in one iteration to accelerate the expansion speed. Then an ellipse interval sampling is constructed with the starting point and the target point as the focus, and the length of the trajectory as the long axis. The idea of continuously reducing the sampling space in the iteration makes the two random trees expand towards their respective target points under a certain probability. In the simulation experiment, the proposed algorithm is compared with RRT*, and the results show that the improved algorithm improves the convergence speed and reduces the track distance under the premise of optimizing the number of track nodes.
PSAT-GAN: Efficient Adversarial Attacks Against Holistic Scene Understanding

Recent advances in deep neural networks (DNNs) have facilitated high-end applications, including holistic scene understanding (HSU), in which many tasks run in parallel with the same visual input. Following this trend, various methods have been proposed to use DNNs to perform multiple vision tasks. However, these methods are task-specific and less effective when considering multiple HSU tasks. End-to-end demonstrations of adversarial examples, which generate one-to-many heterogeneous adversarial examples in parallel from the same input, are scarce. Additionally, one-to-many mapping of adversarial examples for HSU usually requires joint representation learning and flexible constraints on magnitude, which can render the prevalent attack methods ineffective. In this paper, we propose PSAT-GAN, an end-to-end framework that follows the pipeline of HSU. It is based on a mixture of generative models and an adversarial classifier that employs partial weight sharing to learn a one-to-many mapping of adversarial examples in parallel, each of which is effective for its corresponding task in HSU attacks. PSAT-GAN is further enhanced by applying novel adversarial and soft-constraint losses to generate effective perturbations and avoid studying transferability. Experimental results indicate that our method is efficient in generating both universal and image-dependent adversarial examples to fool HSU tasks under either targeted or non-targeted settings.
Deep Neural Networks For Full-Reference And No-Reference Audio-Visual Quality Assessment

In the field of audio and visual quality assessment, most of previous works only focused on the single-mode visual or audio signal. However, for multi-mode signals, such as video and the accompanying audio, the overall perceptual quality depends on both video and audio. In this paper, we proposed an objective audio-visual quality assessment (AVQA) architecture for multi-mode signals based on deep neural networks. We first use a pretrained convolutional neural network to extract features of the single video frames and the concurrent short audio segments. Then, the extracted features are fed into Gated Recurrent Unit networks for time sequence modeling. Finally, we utilize the fully connected layers to fuse the qualities of audio and visual signals into the final quality score. The proposed architecture can be applied to both full-reference and no-reference AVQA. Experimental results on the LIVE-SJTU Database prove that our model outperforms the state-of-the-art AVQA methods.
How to do things with notes: The embodied socio-material performativity of sticky notes

This paper presents a detailed analysis of the uses of sticky notes in a range of design-oriented activities, from three different projects. The paper makes two principal contributions. First, it introduces a socio-material analytic for the study of design materials, and exemplifies this analysis with respect to video data of designers' uses of sticky notes. Second, it illustrates how the material properties of sticky notes come to have interactional consequences in the performance of particular social actions. Four general thematic considerations emerge from this socio-material analysis of sticky notes: (1) a reappreciation of the embodiment of space, ownership and actions-with-notes; (2) the interactional history of notes; (3) the social and conceptual inertia of gestalt contextures; and (4) the unity of the social, material and conceptual in embodied design activity.
Enriched and discriminative convolutional neural network features for pedestrian re‐identification and trajectory modeling

Abstract Understanding pedestrian flow patterns in urban areas could support the decision‐making for infrastructure planning. By incorporating computer vision techniques into surveillance video processing, human walking trajectories in a wide area could be identified by pedestrian re‐identification (ReID) across multiple cameras. Recent ReID methods mostly use convolutional neural networks equipped with deep learning techniques to extract discriminative human features from images for identity matching. However, they still suffer from realistic challenges such as occlusion and appearance variation. This paper develops a ReID‐based framework for pedestrian trajectory recognition across multiple cameras. Specifically, a generic approach of explainable model design is presented, which intuitively analyzes existing baseline models based on feature visualization. Hence, a new model named OSNet + BDB is developed that extracts discriminative‐and‐distributed features. Additionally, an incremental feature aggregation strategy is designed for more robust identity matching. Our ReID method notably outperforms its baselines by 4% identification F1 accuracy in public benchmarks. Practically, pedestrian flow statistics in a real building are extracted for behavioral modeling. Simulations of several what‐if layouts are then conducted for facility performance evaluation.
A Sustainable Solution for IoT Semantic Interoperability: Dataspaces Model via Distributed Approaches

In the past few decades, the prevalence of Internet of Things (IoT) applications has brought both opportunities and challenges of different categories. One of the main concerns nowadays is semantic interoperability. Although plenty of augmented ontologies and resource description frameworks have been designed to achieve semantic interoperation, there is still no sustainable solution to interconnect the increasing number of heterogeneous devices and corresponding data. Aiming at this problem, this article presents a dataspaces model utilizing distributed approaches to represent semantic information as a sustainable solution for IoT semantic interoperability. In this model, an attention-based entity embedding approach is designed to convert IoT entities into low-dimensional dense vectors, then calculations, including entities, relations, etc., can be further conducted. Consequently, the entity embeddings could reflect the semantic information of Class equivalences among entities. To recognize entity relations, the generated entity vectors and the arithmetic results of two entities are combined as input features. By feeding the input features to a well-trained Tensor-train modified DNN, the relation between two entities could be recognized. Finally, experiments are conducted on two data sets to evaluate the proposed approaches. Results indicate that the generated entity vectors could effectively reflect the semantic similarity between entities. More importantly, while achieving parameter compression and better generalizing ability, the relation recognition approach improves the relation recognition accuracy on new entities compared with the state-of-art models.
Perception of the digital transformation as a strategic advantage through the Covid 19 crisis? case of Tunisian banks

Today, digital transformation as a worldwide phenomenon has taken a great deal in corporate strategies. The implementation of strict confinement has resulted in a quite cancelation of transactions and movements. Digital transformation, synonym to accessibility, rapidity and reliability has been widely triggered during the COVID-19 pandemic. In essence, this research explores the effect of digital transformation on the pandemic outcome through identifying how digitization embraces opportunity and innovative strategy. A research model was proposed and empirically tested with partial least squares path-modeling approach, based on the methodological survey completed with Tunisian banks' CEO and operational service managing. The results have demonstrated the necessity of digitization as strategic planning to be deployed in both the short and long terms. It is considered a vector of innovation and sustainable development. It helps identify the essential aspects of business processes and how they should be employed to survive and thrive during crises.
Ultrafast non-destructive measurement of the quantum state of light with free electrons

We demonstrate that free electrons can be used as ultrafast non-destructive photon detectors. Particularly, we show how one can measure photon statistics, temporal coherence, and implement full quantum state tomography using free electrons.
Investigations of non-linear induction motor model using the Gudermannian neural networks

This study aims to solve the non-linear fifth-order induction motor model (FO-IMM) using the Gudermannian neural networks (GNN) along with the optimization procedures of global search as a genetic algorithm together with the quick local search process as active-set technique (GNN-GA-AST). The GNN are executed to discretize the non-linear FO-IMM to prompt the fitness function in the procedure of mean square error. The exactness of the GNN-GA-AST is observed by comparing the obtained results with the reference results. The numerical performances of the stochastic GNN-GA-AST are provided to tackle three different variants based on the non-linear FO-IMM to authenticate the consistency, significance and efficacy of the designed stochastic GNN-GA-AST. Additionally, statistical illustrations are available to authenticate the precision, accuracy and convergence of the designed stochastic GNN-GA-AST.
The Legality of Smart Contracts in a Decentralized Autonomous Organization (DAO)

This chapter discuss the legality of smart contract in a decentralized autonomous organization (DAO). The regulation framework of blockchain is developing rapidly with many countries such as Malta and Estonia utilizing and allowing the use of blockchain. While many researchers have discussed the legality of smart contract, its relation to DAO as one of blockchain's applications is rarely discussed. There are three issues discussed in this chapter: the definition of DAO, the definition of smart contract, and the legality of smart contract in DAO. Each country has their own legal threshold of a contract's legality, but there are generally five elements in a legal contract, namely offer and acceptance, consideration, intention, certainty, and completeness. It is possible for the smart contract to fulfil the five legal elements of a contract. As there are different regulations in different countries, there may have been different elements to the law. In conclusion, the legality of smart contracts, like blockchain itself, depends on who uses it, where it is used, and how it is used.
Feature recognition of irregular pellet images by regularized Extreme Learning Machine in combination with fractal theory

As we know, the fractal theory is an effective method for irregular image processing and analysis, which is difficult to implement using other baseline methods. However, Extreme Learning Machine (ELM) algorithm in combination with fractal theory can provide a better solution than the single fractal theory in image processing, transformation, data prediction, and so on. The features including fractal dimension, area ratio, and average perimeter of hematite, magnetite, calcium silicate, calcium ferrite, and stomata in the microstructure of irregular pellet images are extracted by artificial intelligence (AI) technologies. An AL-based ELM model suitable for the features of pellet images was constructed, the ELM model was optimized by regularization, and the feature parameters of pellet images were predicted by improved ELM algorithm, the optimal number of neuron nodes in the hidden layer was obtained for every mineral. The results show that the efficiency of model training and prediction in the feature for irregular pellet images is highly comparable to baseline methods. The method can be used as a reference in the intelligent prediction, control, and optimization of pellet image quality.
Gaze-Controlled Digital Signage for Public Health Education during Covid-19 Pandemic

For more than a decade, digital signages have been used in health facilities and public environment to provide fun and interactive approach of education. Unfortunately, interacting with conventional digital signage during Covid-19 pandemic raises a concern on its hygiene. Thus, touchless interaction is preferable to avoid direct contact on the touch screen. Here we present a novel study on effectiveness of gaze-based interaction in a digital signage for public health education. Instead of touching the screen, the users engage with the content by gazing at a dynamic button that moves in horizontal or vertical direction. Experimental results show that gazing at faster dynamic buttons (angular speed of 60.28°/s) than its slower counterpart (angular speed of 30.14°/s) requires shorter time to complete a three-steps task (T = 84, Z = −1.977, p < 0.05). Our study provides a scientific proof of concept for further development of touchless digital signage that complies with health protocols of the World Health Organization during Covid-19 pandemic.
MLFont: Few-Shot Chinese Font Generation via Deep Meta-Learning

The automatic generation of Chinese fonts is challenging due to the large quantity and complex structure of Chinese characters. When there are insufficient reference samples for the target font, existing deep learning-based methods cannot avoid overfitting caused by too few samples, resulting in blurred glyphs and incomplete strokes. To address these problems, this paper proposes a novel deep meta-learning-based font generation method (MLFont) for few-shot Chinese font generation, which leverages existing fonts to improve the generalization capability of the model for new fonts. Existing deep meta-learning methods mainly focus on few-shot image classification. To apply meta-learning to font generation, we present a meta-training strategy based on Model-Agnostic Meta-Learning (MAML) and a task organization method for font generation. The meta-training makes the font generator easy to fine-tune for new font generation tasks. Through random font generation tasks and extraction of glyph content and style separately, the font generator learns the prior knowledge of character structure in the meta-training stage, and then quickly adapts to the generation of new fonts with a few samples by fine-tuning of adversarial training. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods with more complete strokes and less noise in the generated character images.
Efficient JPEG Batch Steganography Using Intrinsic Energy of Image Contents

Batch steganography aims at properly allocating a large payload to multiple covers, so as to keep the whole covert communication at a satisfactory level of security. JPEG is currently one of the most widely used formats for image storage and transmission. This paper presents an efficient JPEG batch steganographic scheme, which allocates the payload in a linear manner w.r.t. a new heuristic measure - the intrinsic energy of JPEG image contents, in which more concerns are with the high frequency components, and the proposed measure could also be easily generalized to cover selection in batch steganographic applications. And a calibration strategy is elaborately designed to balance the security level when JPEG covers of various QFs are involved in JPEG batch steganography. In this way, the proposed scheme can effectively resolve the problem that the statistical undetectability fluctuates dramatically w.r.t. the size and quality factor when the batch set is involved with various image parameters, and consequently maintains the overall security of the practical JPEG batch steganographic system. Experimental results show that the proposed method exhibits security performance superior or comparable to the state-of-the-art batch schemes while maintaining a low computational cost.