Reviewerly/exHarmony huggingface dataset:
Dynamic Multimedia Encryption Using a Parallel File System Based on Multi-Core Processors

Securing multimedia data on disk drives is a major concern because of their rapidly increasing volumes over time, as well as the prevalence of security and privacy problems. Existing cryptographic schemes have high computational costs and slow response speeds. They also suffer from limited flexibility and usability from the user side, owing to continuous routine interactions. Dynamic encryption file systems can mitigate the negative effects of conventional encryption applications by automatically handling all encryption operations with minimal user input and a higher security level. However, most state-of-the-art cryptographic file systems do not provide the desired performance because their architectural design does not consider the unique features of multimedia data or the vulnerabilities related to key management and multi-user file sharing. The recent move towards multi-core processor architecture has created an effective solution for reducing the computational cost and maximizing the performance. In this paper, we developed a parallel FUSE-based encryption file system called ParallelFS for storing multimedia files on a disk. The developed file system exploits the parallelism of multi-core processors and implements a hybrid encryption method for symmetric and asymmetric ciphers. Usability is significantly enhanced by performing encryption, decryption, and key management in a manner that is fully dynamic and transparent to users. Experiments show that the developed ParallelFS improves the reading and writing performances of multimedia files by approximately 35% and 22%, respectively, over the schemes using normal sequential encryption processing.
Digital ethics in higher education: Modernizing moral values for effective communication in cyberspace

The digitalization of education is critical to transforming the education system in the face of the challenges of the post-pandemic technologized world. This research aims to determine the content of digital ethics and its role as a tool of effective communication in the cyberspace of each college, suitable for the practical solution of the assigned educational tasks. The data come from a questionnaire of 150 students and 60 faculty members in bachelor's and master's degree programs, conducted in April-May 2021 as part of a distance learning program. In addition, 30 administrators and 20 graduates were interviewed. The research results show that traditional moral values are undergoing significant change and must be modernized and codified. Relationships in the digital environment should be based on moral norms: duty, the self-discipline of the actors involved, mutual responsibility, and respect. The moral qualities displayed by individuals in the digital environment using digital technologies are the leading indicators of their professional suitability. The article mentions the main problems faced by the actors: digital literacy, personal image, place of implementation of pedagogical interaction, and rules of remote dialog. The study will contribute to further analysis and evaluation of the adequacy of specific measures taken by higher education institutions to develop and implement programs for ethical modernization of the educational process within the tasks of state educational reforms.
Pattern-based hybrid book recommendation system using semantic relationships

In the fields of machine learning and artificial intelligence, recommendation systems (RS) or recommended engines are commonly used. In today's world, recommendation systems based on user preferences assist consumers in making the best decisions without depleting their cognitive resources. They can be applied to a variety of things, including search engines, travel, music, movies, literature, news, gadgets, and dining. A lot of people utilize RS on social media sites like Facebook, Twitter, and LinkedIn, and it has proven beneficial in corporate settings like those at Amazon, Netflix, Pandora, and Yahoo. There have been numerous proposals for recommender system variations. However, certain techniques result in unfairly recommended things due to biased data because there are no established connections between the items and consumers. In order to solve the challenges mentioned above for new users, we propose in this work to employ Content-based Filtering (CBF) and Collaborative Filtering (CF) with semantic relationships to capture the relationships as knowledge-based book recommendations to readers in a digital library. When proposing things, patterns are more discriminative than single phrases. To capture the similarity of the books that the new user had retrieved, the patterns were grouped in a semantically equivalent manner using the Clustering method. The effectiveness of the suggested model is examined through a series of extensive tests employing Information Retrieval (IR) evaluation criteria. Recall Precision and F-Measure, two of the three widely used performance measuring metrics, were employed. The findings demonstrate that the suggested model performs noticeably better than cutting-edge models.
ChatbotSQL: Conversational agent to support relational database query language learning

SQL is a key programming language for data scientists, but its learning is sometimes challenging, especially if the learner does not have a proper computer science background. This article introduces ChatbotSQL, a conversational agent that aims to support the autonomous learning of the SQL language for database querying. ChatbotSQL proposes exercises and guides about the steps to be taken by the learner in case of doubts, providing customised feedback. ChatbotSQL has been created on top of IBM Watson Assistant and is available in a web platform. Students in a higher education database subject used ChatbotSQL extensively, showing a positive perception of its usefulness in supporting the resolution of complex queries.
Adaptive-Attention Completing Network for Remote Sensing Image

The reconstruction of missing pixels is essential for remote sensing images, as they often suffer from problems such as covering, dead pixels, and scan line corrector (SLC)-off. Image inpainting techniques can solve these problems, as they can generate realistic content for the unknown regions of an image based on the known regions. Recently, convolutional neural network (CNN)-based inpainting methods have integrated the attention mechanism to improve inpainting performance, as they can capture long-range dependencies and adapt to inputs in a flexible manner. However, to obtain the attention map for each feature, they compute the similarities between the feature and the entire feature map, which may introduce noise from irrelevant features. To address this problem, we propose a novel adaptive attention (Ada-attention) that uses an offset position subnet to adaptively select the most relevant keys and values based on self-attention. This enables the attention to be focused on essential features and model more informative dependencies on the global range. Ada-attention first employs an offset subnet to predict offset position maps on the query feature map; then, it samples the most relevant features from the input feature map based on the offset position; next, it computes key and value maps for self-attention using the sampled features; finally, using the query, key and value maps, the self-attention outputs the reconstructed feature map. Based on Ada-attention, we customized a u-shaped adaptive-attention completing network (AACNet) to reconstruct missing regions. Experimental results on several digital remote sensing and natural image datasets, using two image inpainting models and two remote sensing image reconstruction approaches, demonstrate that the proposed AACNet achieves a good quantitative performance and good visual restoration results with regard to object integrity, texture/edge detail, and structural consistency. Ablation studies indicate that Ada-attention outperforms self-attention in terms of PSNR by 0.66%, SSIM by 0.74%, and MAE by 3.9%, and can focus on valuable global features using the adaptive offset subnet. Additionally, our approach has also been successfully applied to remove real clouds in remote sensing images, generating credible content for cloudy regions.
A Dendritic Neuron Model Optimized by Meta-Heuristics with a Power-Law-Distributed Population Interaction Network for Financial Time-Series Forecasting

The famous McCulloch–Pitts neuron model has been criticized for being overly simplistic in the long term. At the same time, the dendritic neuron model (DNM) has been shown to be effective in prediction problems, and it accounts for the nonlinear information-processing capacity of synapses and dendrites. Furthermore, since the classical error back-propagation (BP) algorithm typically experiences problems caused by the overabundance of saddle points and local minima traps, an efficient learning approach for DNMs remains desirable but difficult to implement. In addition to BP, the mainstream DNM-optimization methods include meta-heuristic algorithms (MHAs). However, over the decades, MHAs have developed a large number of different algorithms. How to screen suitable MHAs for optimizing DNMs has become a hot and challenging area of research. In this study, we classify MHAs into different clusters with different population interaction networks (PINs). The performance of DNMs optimized by different clusters of MHAs is tested in the financial time-series-forecasting task. According to the experimental results, the DNM optimized by MHAs with power-law-distributed PINs outperforms the DNM trained based on the BP algorithm.
Security Quantification of Container-Technology-Driven E-Government Systems

With the rapidly increasing demands of e-government systems in smart cities, a myriad of challenges and issues are required to be addressed. Among them, security is one of the prime concerns. To this end, we analyze different e-government systems and find that an e-government system built with container-based technology is endowed with many features. In addition, overhauling the architecture of container-technology-driven e-government systems, we observe that securing an e-government system demands quantifying security issues (vulnerabilities, threats, attacks, and risks) and the related countermeasures. Notably, we find that the Attack Tree and Attack–Defense Tree methods are state-of-the-art approaches in these aspects. Consequently, in this paper, we work on quantifying the security attributes, measures, and metrics of an e-government system using Attack Trees and Attack–Defense Trees—in this context, we build a working prototype of an e-government system aligned with the United Kingdom (UK) government portal, which is in line with our research scope. In particular, we propose a novel measure to quantify the probability of attack success using a risk matrix and normal distribution. The probabilistic analysis distinguishes the attack and defense levels more intuitively in e-government systems. Moreover, it infers the importance of enhancing security in e-government systems. In particular, the analysis shows that an e-government system is fairly unsafe with a 99% probability of being subject to attacks, and even with a defense mechanism, the probability of attack lies around 97%, which directs us to pay close attention to e-government security. In sum, our implications can serve as a benchmark for evaluation for governments to determine the next steps in consolidating e-government system security.
Hybrid Graph Neural Network Recommendation Based on Multi-Behavior Interaction and Time Sequence Awareness

In recent years, mining user multi-behavior information for prediction has become a hot topic in recommendation systems. Usually, researchers only use graph networks to capture the relationship between multiple types of user-interaction information and target items, while ignoring the order of interactions. This makes multi-behavior information underutilized. In response to the above problem, we propose a new hybrid graph network recommendation model called the User Multi-Behavior Graph Network (UMBGN). The model uses a joint learning mechanism to integrate user–item multi-behavior interaction sequences. We designed a user multi-behavior information-aware layer to focus on the long-term multi-behavior features of users and learn temporally ordered user–item interaction information through BiGRU units and AUGRU units. Furthermore, we also defined the propagation weights between the user–item interaction graph and the item–item relationship graph according to user behavior preferences to capture more valuable dependencies. Extensive experiments on three public datasets, namely MovieLens, Yelp2018, and Online Mall, show that our model outperforms the best baselines by 2.04%, 3.82%, and 3.23%.
Computing the Integrated Information of a Quantum Mechanism

Originally conceived as a theory of consciousness, integrated information theory (IIT) provides a theoretical framework intended to characterize the compositional causal information that a system, in its current state, specifies about itself. However, it remains to be determined whether IIT as a theory of consciousness is compatible with quantum mechanics as a theory of microphysics. Here, we present an extension of IIT's latest formalism to evaluate the mechanism integrated information (φ) of a system subset to discrete, finite-dimensional quantum systems (e.g., quantum logic gates). To that end, we translate a recently developed, unique measure of intrinsic information into a density matrix formulation and extend the notion of conditional independence to accommodate quantum entanglement. The compositional nature of the IIT analysis might shed some light on the internal structure of composite quantum states and operators that cannot be obtained using standard information-theoretical analysis. Finally, our results should inform theoretical arguments about the link between consciousness, causation, and physics from the classical to the quantum.
Multiscale Fusion Algorithm for Underwater Image Enhancement Based on Color Preservation

Underwater images regularly exhibit color deviation and low contrast attributed to attenuation and scattering associated with wavelength and distance. For solving these two degradation problems, we propose an efficient underwater image enhancement model, which takes a multiscale fusion strategy as a pivot to concurrently achieve color balance and contrast enhancement. Specifically, we target two core degradation problems through two preprocessing steps. On the one hand, the white balance strategy enhances the appearance of the image by suppressing unnecessary colors to solve the color deviation problem. On the other hand, we employ a color-preserving adaptive histogram equalization (CP-AHE) to obtain an image with higher contrast by jointly processing the three color channels. In the fusion phase, a fusion strategy based on the detail-preserving decomposition of the edge-protected structural blocks is used to converge the white balance and CP-AHE inputs. Extensive experiments on common underwater image datasets have shown the advantages of the proposed method over other state-of-the-art underwater image methods in terms of visual and quantitative evaluation.
Parallel Dual-Hand Detection by Using Hand and Body Features for Robot Teleoperation

Visual hand-based robot teleoperation provides a powerful guarantee for robots to complete complex tasks. However, detection and distinction of dual hands on images are difficult because of the small differences between left and right hands. To solve this problem, a parallel dual-hand detection and distinction method that combines the features of hands with the relationship features between the dual hands and body pose is proposed to achieve robust and accurate dual-hand detection. This parallel dual-hand detection method includes a hand detection module, a body pose estimation module, and a fusion module. In the hand detection module, a hand detector that realizes fast and accurate hand detection by detecting the center and corner points of hands is designed. In the body pose estimation module, a body pose estimator with dual-hand positions is proposed. The fusion module is designed to fuse hand detection and dual-hand estimation results to achieve distinction between left and right hands. Finally, the parallel dual-hand detection method is applied to a bimanual robot teleoperation system by using a designed dual-hand teleoperation framework. The proposed parallel dual-hand detection method can achieve 98.54% mAP of hand detection with 18 frames per second on a custom dual-hand detection dataset, and the bimanual robot teleoperation method can achieve 95.4% average accuracy for teleoperation tasks. Experimental results show the high accuracy and speed of our proposed parallel dual-hand detection method and its practicability in bimanual robot teleoperation.
StructToken: Rethinking Semantic Segmentation With Structural Prior

In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, i.e., classify each pixel representation to a specific category. However, these methods only focus on learning better pixel representations or classification kernels while ignoring the structural information of objects, which is critical to human decision-making mechanism. In this paper, we present a new paradigm for semantic segmentation, named structure-aware extraction. Specifically, it generates the segmentation results via the interactions between a set of learned structure tokens and the image feature, which aims to progressively extract the structural information of each category from the feature. Extensive experiments show that our StructToken outperforms the state-of-the-art on three widely-used benchmarks, including ADE20K, Cityscapes, and COCO-Stuff-10K.
BTAA: Blockchain and TEE-Assisted Authentication for IoT Systems

In recent years, Internet of Things (IoT) technology has gained a lot of attention. With the development of IoT technology, it comes the need for devices with different trust domains to interact and collaborate. In order to protect the security and reliability of the communication between devices in different trust domains, it raises the concerns about the technology of cross-domain authentication. Traditional cross-domain authentication methods may lead to heavy key management overhead or depend on trusted third parties, while existing blockchain-based cross-domain authentication schemes do not prevent the possibility of mischief by malicious domain managers. In this article, we design an efficient Blockchain and trusted execution environment (TEE)-assisted secure device authentication scheme for cross-domain IoT system, called blockchain and TEE-assisted authentication. Our solution solves the problem that managers are not fully trusted, which in turns protect the security and reliability in the blockchain-based cross-domain communication. Specifically, blockchain is introduced to build trust between different domains, the identity-based signatures are used to verify the identity information of devices and the TEE is introduced to prevent the possibility of mischief by domain managers. Finally, our experiments show that the introduction of TEE has greatly improved system security with a low-efficiency reduction, which proves that our scheme can achieve highly secure distributed IoT authentication.
CBANet: Toward Complexity and Bitrate Adaptive Deep Image Compression Using a Single Network

In this work, we propose a new deep image compression framework called Complexity and Bitrate Adaptive Network (CBANet) that aims to learn one single network to support variable bitrate coding under various computational complexity levels. In contrast to the existing state-of-the-art learning-based image compression frameworks that only consider the rate-distortion trade-off without introducing any constraint related to the computational complexity, our CBANet considers the complex rate-distortion-complexity trade-off when learning a single network to support multiple computational complexity levels and variable bitrates. Since it is a non-trivial task to solve such a rate-distortion-complexity related optimization problem, we propose a two-step approach to decouple this complex optimization task into a complexity-distortion optimization sub-task and a rate-distortion optimization sub-task, and additionally propose a new network design strategy by introducing a Complexity Adaptive Module (CAM) and a Bitrate Adaptive Module (BAM) to respectively achieve the complexity-distortion and rate-distortion trade-offs. As a general approach, our network design strategy can be readily incorporated into different deep image compression methods to achieve complexity and bitrate adaptive image compression by using a single network. Comprehensive experiments on two benchmark datasets demonstrate the effectiveness of our CBANet for deep image compression. Code is released at https://github.com/JinyangGuo/CBANet-release.
Real-Time Stereo 3D Car Detection With Shape-Aware Non-Uniform Sampling

Pseudo-LiDAR based stereo 3D detectors have gained popularity due to their high accuracy. However, these methods need dense depth supervision and suffer from inferior speed. To solve these two issues, a recently introduced RTS3D builds an efficient 4D feature-consistency embedding (FCE) space for the object intermediate representation without depth supervision, where FCE space performs uniform sampling to generate feature sampling points, which ignores the importance of different object regions. In this paper, we observe that, compared with the inner region, the outer region of the object plays a more important role for accurate 3D detection. To fully exploit the useful information from the outer region, we propose a novel shape-aware non-uniform sampling strategy. Instead of uniform sampling, our proposed non-uniform sampling strategy performs dense sampling in outer region and sparse sampling in inner region. Therefore, more points are sampled from the outer region and more useful features are extracted for 3D detection. In addition, we design a high-level semantic enhanced FCE module to exploit more contextual information and suppress noise better. As a result, it further improves feature discrimination of each sampling point. Experimental results on the KITTI dataset show the effectiveness of the proposed method. Compared with the baseline RTS3D, our proposed method has 2.57% improvement on car AP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{3d}$ </tex-math></inline-formula> almost without extra network parameters. Moreover, our proposed method outperforms the state-of-the-art methods without extra supervision at a real-time speed.