Reviewerly/exHarmony huggingface dataset:
Less is More: Removing Redundancy of Graph Convolutional Networks for Recommendation

While Graph Convolutional Networks (GCNs) have shown great potential in recommender systems and collaborative filtering (CF), they suffer from expensive computational complexity and poor scalability. On top of that, recent works mostly combine GCNs with other advanced algorithms which further sacrifice model efficiency and scalability. In this work, we unveil the redundancy of existing GCN-based methods in three aspects: (1) Feature redundancy . By reviewing GCNs from a spectral perspective, we show that most spectral graph features are noisy for recommendation, while stacking graph convolution layers can suppress but cannot completely remove the noisy features, which we mostly summarize from our previous work; (2) Structure redundancy . By providing a deep insight into how user/item representations are generated, we show that what makes them distinctive lies in the spectral graph features, while the core idea of GCNs ( i.e., neighborhood aggregation) is not the reason making GCNs effective; and (3) Distribution redundancy . Following observations from (1), we further show that the number of required spectral features is closely related to the spectral distribution, where important information tends to be concentrated in more (fewer) spectral features on a flatter (sharper) distribution. To make important information be concentrated in as few features as possible, we sharpen the spectral distribution by increasing the node similarity without changing the original data, thereby reducing the computational cost. To remove these three kinds of redundancies, we propose a Simplified Graph Denoising Encoder (SGDE) only exploiting the top- K singular vectors without explicitly aggregating neighborhood, which significantly reduces the complexity of GCN-based methods. We further propose a scalable contrastive learning framework to alleviate data sparsity and to boost model robustness and generalization, leading to significant improvement. Extensive experiments on three real-world datasets show that our proposed SGDE not only achieves state-of-the-art but also shows higher scalability and efficiency than our previously proposed GDE as well as traditional and GCN-based CF methods.
JPEG-Compatible Joint Image Compression and Encryption Algorithm with File Size Preservation

Joint image compression and encryption algorithms are intensively investigated due to their powerful capability of simultaneous image data compression and sensitive information protection. Unfortunately, most of the existing algorithms suffered from either poor compression efficiency or weak encryption strength, making them vulnerable to cryptanalysis. To address these limitations, we propose a chaos-based JPEG-compatible joint image compression and encryption algorithm. We separate the luminance and chrominance coefficients to preserve file size and encrypt the discrete cosine transform (DCT) coefficients in parallel. The proposed inter-block DC encryption strategy achieves high encryption intensity based on permutation-substitution structure. In addition, we apply both inter- and intra-block permutations to AC coefficients, and strengthen the encryption using an inter-block substitution for non-zero AC coefficients. The result of security and performance analyses demonstrate that the proposed algorithm offers robust encryption of image data while maintaining compression efficiency for real-time transmission.
A Fuzzy MARCOS-Based Analysis of Dragonfly Algorithm Variants in Industrial Optimization Problems

Metaheuristics are commonly employed as a means of solving many distinct kinds of optimization problems. Several natural-process-inspired metaheuristic optimizers have been introduced in the recent years. The convergence, computational burden and statistical relevance of metaheuristics should be studied and compared for their potential use in future algorithm design and implementation. In this paper, eight different variants of dragonfly algorithm, i.e. classical dragonfly algorithm (DA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA), and hybridization of dragonfly algorithm and artificial bee colony (HDA) are applied to solve four industrial chemical process optimization problems. A fuzzy multi-criteria decision making tool in the form of fuzzy-measurement alternatives and ranking according to compromise solution (MARCOS) is adopted to ascertain the relative rankings of the DA variants with respect to computational time, Friedman’s rank based on optimal solutions and convergence rate. Based on the comprehensive testing of the algorithms, it is revealed that DADE, QGDA and classical DA are the top three DA variants in solving the industrial chemical process optimization problems under consideration.
A light-weighted convolutional neural network for image classification in autopilot system

Abstract Autopilot has been a heated technology in recent days, and it is used in plenty of car brands. People are becoming dependent on autopilot technology. However, before this technology becomes mature, there are still a lot of insufficiencies that need to improve. The focus of this essay is on the object detection shortage that exists in the autopilot system. This essay is first going to introduce a lite CNN-based method to improve the object detection and identification module. In the result part of the essay, the essay is going to show the result of the training and some new prediction function will be implemented. Also, some accuracy improving methods including data augmentation and Dropout will be introduced, and the effect of the improving methods will be shown in the essay. After training, the autopilot system is going to distinguish pedestrians from vehicles. Although the road condition is far more complex than just vehicles and pedestrians, for convenience this essay is only going to implement the two datasets. Also, some further improvement intension will be introduced.
Comprehensive Data Life Cycle Security in Cloud Computing: Current Mastery and Major Challenges

One of the gravest concerns related to cloud computing pertains to data security. As businesses start on digital transformation, there is a clear requirement for privacy and data protection. Organizations today have more data, applications, and websites than they have ever had before. Data security has risen to the top of the priority list for cloud computing security. Although numerous solutions have been put forward, most of them exclusively target individual stages within the data life cycle, such as storage. This approach proves inadequate in effectively tackling the security challenges of cloud data, given that threats can arise at any phase of the data life cycle. Any security breach occurring during any stage of the data life cycle process has the potential to compromise overall data security. Hence, it becomes imperative to address data security across all phases of the data life cycle. This article’s primary contribution lies in introducing a novel viewpoint concerning data security solutions aligned with the data life cycle. This perspective holds significant importance and can serve as a road map for crafting a comprehensive security strategy. The article conducts an exhaustive review of the complete data life cycle and identifies a research gap comprising unresolved challenges that could potentially serve as research inquiries for our forthcoming endeavors.
Continuous-bag-of-words and Skip-gram for word vector training and text classification

Abstract Natural language processing is one of the most challenging parts in the study of artificial intelligence and is widely used in real-life applications. One of the basic questions is how to calculate the probability of a particular text sequence appearing in a certain context. Word2Vec is a powerful tool that provides a solution to the question for its ability to transform words into word vectors, and to train in high efficiency on large datasets and corpora. It has many models of which Continuous-Bag-Of-Words and Skip-gram are of great significance and also known to many people. Furthermore, some extended techniques related to the models are also proposed in order to simultaneously decrease required training time and increase the rate of accuracy for the training. Even though there are now a number of papers that describe these fundamental concepts, the quality vary greatly. To better understand the models and their extensions, and how well they behave when used for real tasks, different combinations of the models and techniques are made in this paper so as to compare their performance in processing large input data and the ability for correct prediction in the task of text classification. This is done as it could lead to more provision of details and understandings of the model for subsequent researches on this field of study.
Improved deep semantic dictionary learning for multi-label image classification

Abstract Multi-label image classification is a practical and challenging task in the field of machine learning. It is a fundamental but essential task used in auto driving, internet image classification, and other fields. Many researchers have done various researches on relevant aspects. Deep Semantic Dictionary Learning (DSDL) is a multi-label image classification method brought out by previous researchers which combines the semantic meaning information into the classification task. However, it only uses one kind of feature extraction network as the feature map generator without considering other alternatives. In this paper, six types of pretrained networks are applied as alternative choices to validate the performance of DSDL on different neural networks. Experiments are conducted on the VOC2007 dataset and the result shows the inner relation of different series of neural networks and demonstrates that different feature extraction networks perform variously on different tasks. The ResNet-152 achieves the mean average precision (mAP) of 94.5% and outperforms the original DSDL method of 0.3% mAP.
Research on application of model ensemble in sports image classification based on environmental information

Abstract In recent years, a deep convolutional neural network has been widely used in the field of image classification. However, training a satisfactory network is very arduous, not only to tune the hyper-parameters in the network but also to avoid the overfitting problem caused by a deep neural network. Another point is that it is difficult for a neural network to learn subtle details without human annotation. Therefore this paper proposed a preferable classification algorithm for sports classification tasks that combines the deep neural network with the object detection algorithm to obtain the prediction result. In this paper, the author compared the differences between classification directly using neural networks and a modified model ensemble approach to classification from a holistic perspective, as well as elaborating the advantages and disadvantages of the two approaches. The conclusion shows that the use of the improved model ensemble classification algorithm performs better than the direct use of neural networks and also achieves a high degree of accuracy in the test set.
Towards a Platform for Higher Education in Virtual Reality of Engineering Sciences

Innovative pedagogy is more oriented towards the use of new technology in future education. Virtual and augmented realities, as well as the Metaverse, are today the catalysts for the development of future teaching tools. They allow students to discover, via their smartphones or headsets, destinations around the world while remaining in their classrooms or even from their homes. These technologies are also used to put into practice, especially for the engineering sciences, lessons, and practical work by creating immersive experiences for users. This will significantly change the role of the teacher and the student in digital spaces. The realization of VR or AR learning platforms requires multidisciplinary skills ranging from 3D modeling to web and mobile platforms. In this paper, we present a method for creating a teaching platform for engineering sciences based on WebXR. We have created in our virtual environment a lesson about the Arduino board to train STEM students.
Can machine recognize a long-missed old friend? A test to the FaceNet face recognition algorithm

Abstract Facial recognition receives heated discussion in recent years. Given the increasing demand for portable and easy-to-use face recognition programs, several coding libraries have been developed that includes the algorithm for identifying and classifying faces. One example of such libraries, the “face_recognition” library developed by Adam Geitgey allows fast execution for face recognition tasks and requires only 1 photo as training input. It adopts the Convolutional Neural Network (CNN) from OpenFace, which is built based on the FaceNet algorithm. Although the study claims that the algorithm has a high base on tests from common face dataset, it is not clear whether the algorithm can identify people consistently when they are in their different ages. In this paper, a face dataset is constructed by collecting publicly available photos from celebrities that can be traced to a certain date of taken. These photos are grouped by 5 pieces per person with each photo taken roughly 4 years apart from the previous one. Then, experiments are conducted to test the effectiveness of face_recognition library on identifying the person from these groups of photos. The results report an over 75% success rate for the algorithm on image identification tasks, indicating a good ability for the face encoding to resist the gap of time.
“All-Versus-Nothing” Contextuality in Graph States

In this chapter I introduce a unified construction of Greenberger–Horne–Zeilinger (GHZ)-type paradoxes for graph states, providing insights into the contradictions between classical theory and quantum mechanics. While GHZ-type paradoxes have been extensively studied across various systems, their full range of applicability remains unknown, and a unified framework has yet to be discovered. By demonstrating that GHZ-type paradoxes extend beyond graph states, we broaden the understanding of their existence. Additionally, the presented results have significant implications for quantum state verification in graph states, entanglement detection, and the development of GHZ-type steering paradoxes for mixed states. Through a photonic experiment, we verify the GHZ-type paradoxes by measuring the success probability of their corresponding perfect Hardy-type paradoxes and showcase their practical applications. This research enriches our comprehension of quantum paradoxes within the field of quantum foundations and holds potential for a wide range of quantum information tasks.
A Parallel Processing Architecture for Querying Distributed and Heterogeneous Data Sources

Collecting information from distributed data sources in the web of data is a topic of great interest since the number of data sources is eloquently increasing. The general objective of our work is to set up an aggregated search engine able to integrate an answer for a SPARQL query, and this, by collecting data from web accessible data sources alongside local databases via a single query interface. Runtime optimization is an essential task in such search engines, for this purpose, we present a general view on different processing steps in aggregated search engines to highlight the costliest step in terms of runtime, then, we propose an efficient parallel processing architecture to optimize runtime. The proposed parallel processing architecture allows collecting intermediate results from each data source independently, thus, this architecture supports answers integration from heterogeneous data sources, in this context, we present the ontology-based data access paradigm and its importance to query heterogeneous data sources in our aggregated search engine.
Postlude

I conclude the thesis with seven open questions regarding the future study of contextuality, each supplemented with my rudimentary opinions and some brief discussion.
Comparison and analysis of various deep learning models for super-resolution reconstruction of turbulent flows

Abstract Single image super-resolution (SR) has become a promising research topic, with many deep learning-based models invented to reconstruct high-fidelity high-resolution (HR) images from low-resolution (LR) images. Motivated by a large amount of turbulent flow field data collected by experimental measurements and numerical simulation, researchers begin investigating the application of these data-driven deep learning models to conduct SR reconstruction of LR flow field data. Due to the limitations of experimental equipment and computing power, sometimes researchers can only obtain LR data. However, deep learning models can quickly reconstruct HR spatial-temporal turbulent data from LR data so that researchers can easily conduct further qualitative and quantitative analyses. This article reviews the development of flow field data SR reconstruction models and the problems encountered from the two aspects of network structure and loss function definition. Finally, we propose the research direction of applying the conditional generative adversarial network (cGAN) to turbulent flow SR reconstruction since seldom study has been conducted in this field.
Augmented Data Warehouses for Value Capture

Nobody can deny the monumental impact of Big Data on Data Warehouse ( $$\mathcal{D}\mathcal{W}$$ ) technology. This impact has been materialized by the spectacular adaptation of Big Data solutions, initially developed to deal with the different Big Data V’s (Volume, Variety, Velocity, Veracity), to the context of DW. This gives raise to the concept of augmented DW. The usage of MapReduce and Spark to manage volume, stream processing systems for deploying stream DW, and multimodel DW to handle data variety, are examples of these adaptations. By deeply analyzing these adaptations, we figure out that the V corresponding to Big Data Value did not get the same attention as the other Vs. DWs are built to capture value, otherwise, they will have a marginal utility. One of the major problems of the value capturing in DW concerns the lack of data when performing the warehouse exploration through OLAP queries. This situation is caused by the absence of concepts/hierarchies/instances satisfying these queries in the initial sources used to build the target DW. To increase the utility of a DW in terms of value capture, the selection of alternative resources is recommended. In this paper, we first illustrate the different scenarios requiring concept and data enrichment and focus on the usage of external resources such as Linked Open Data and ontologies to deal with the absence of data. Secondly, we propose a technique to value capturing by revisiting the $$\mathcal{D}\mathcal{W}$$ life cycle phases. Value metrics are given based on the value-capturing requirements. Finally, experiments are conducted to show the effectiveness of our proposal.
BERT for Arabic NLP Applications: Pretraining and Finetuning MSA and Arabic Dialects

In recent practices, the BERT model that utilizes contextual word Embedding with transfer learning has arisen as a popular state-of-the-art deep learning model. It improved the performance of several Natural Language Processing (NLP) Applications [1]. In this paper, following the effectiveness that these models demonstrated, we use the advantages of training Arabic Transformer-based representational language models to create three Arabic NLP applications for two Arabic varieties; MSA and Arabic Dialects. We build an Arabic representational language model using BERT as the Transformer-based training model [2]. Then we compare the resulting model to the pre-trained multi-lingual models. This step is accomplished by building multiple Arabic NLP applications and then evaluating they are evaluating their performances. Our system had an accuracy of 0.91 on the NER task, 0.89 on the document classification application, and 0.87 on the sentiment Analysis application. This work proved that using a language-specific model outperforms the trained multilingual models on several NLP applications.
Multimodal Learning for Road Safety Using Vision Transformer ViT

This paper proposes a novel approach for multimodal learning that combines visual information from images with structured data from a multi-column dataset. The approach leverages the power of vision transformer (ViT) models, which are based on self-attention layers, to extract high-quality visual features from images. The proposed approach was evaluated on a road safety problem, specifically the identification of high-risk areas for road accidents. The experiment was conducted on a dataset of road sections in Morocco and demonstrated the effectiveness of the proposed approach in improving the accuracy of the classification task. The results show that the multimodal approach outperformed unimodal approaches that relied solely on either visual data or structured data. The proposed approach can be applied to various domains beyond road safety, such as healthcare, finance, and social media analysis, where multimodal data is abundant and complex relationships between different data modalities need to be captured. The contribution of this work is the development of a multimodal classification model that combines visual and structured data leveraging the power of vision transformer (ViT) for image encoding, resulting in high-quality visual features. This approach is a departure from classical convolutional models, which lack self-attention layers, making it suitable for a wide range of applications.
Review of ansatz designing techniques for variational quantum algorithms

Abstract For a large number of tasks, quantum computing demonstrates the potential for exponential acceleration over classical computing. In the NISQ era, variable-component subcircuits enable applications of quantum computing. To reduce the inherent noise and qubit size limitations of quantum computers, existing research has improved the accuracy and efficiency of Variational Quantum Algorithm (VQA). In this paper, we explore the various ansatz improvement methods for VQAs at the gate level and pulse level, and classify, evaluate and summarize them.
Face image generation and feature visualization using deep convolutional generative adversarial networks

Abstract Generative Neural Networks (GAN) aims to generate realistic and recognizable images, including portraits, cartoons and other modalities. Image generation has broad application prospects and important research value in the fields of public security and digital entertainment, and has become one of the current research hotspots. This article will introduce and apply an important image generation model called GAN, which stands for Generative Adversarial Network. Unlike recent image processing models such as Variational Autoencoders (VAE), The discriminative network evaluates potential candidates while the GAN generates candidates. As a result, the discriminative network distinguishes created and real candidates, while the generative network learns to map from a latent space to an interest data distribution. In this article, the GAN model and some of its extensions will be thoroughly applied and implemented based on the dataset of CelebA, and details will be discussed through the images and graphs generated by the model. Specific training methods for various models and optimization algorithms can be produced by the GAN framework. The experiment’s findings in this article will show how the framework’s potential may be quantified and qualitatively assessed using the samples that were produced.