Reviewerly/exHarmony huggingface dataset:
DeCS: A Deep Neural Network Framework for Cold Start Problem in Recommender Systems

With the exponential growth of e-commerce platforms, recommendation systems are widely used in predicting user interests, improving user experience, and increasing the number of sales. However, recommendation performance degrades for users who have very little interaction or new users who have never opted for the service. Consequently, the recommender systems cannot suggest items and services to these users due to the cold start issue. Naturally, a compelling demand for an efficient recommender system is essentially needed to guide users toward items of their interests. This paper proposes a deep neural network (DNN) framework that addresses the cold start problem in recommendation systems. The proposed framework named “DeCS” works primarily in stages that involve creating embeddings and vectors followed by training and prediction of three fundamental metrics-mean square error (MSE), mean absolute error (MAE), and root MSE (RMSE) by the framework. Several experiments evaluate the DeCS framework for different recommender metrics at various datasets. Predictions show that the proposed DeCS model achieves the MSE, RMSE, and MAE metrics in the range of 0.4338-1.2911, 0.6883-1.1362, and 0.4691-0.8745, respectively. Further, the result shows that the proposed approach improves these metrics by 15.81% compared to many state-of-the-art methods.
Automatic Image Colorization using Ensemble of Deep Convolutional Neural Networks

Image colorization is a technique to add color values to the grayscale image by learning the mapping between input intensity and probable chrominance values. This paper proposes an end-to-end image colorization architecture based on an ensemble of deep convolutional neural networks (DCNN). Ensemble DCNN architecture for image colorization is our novel contribution. The architecture takes inspiration from the encoder-decoder design. The encoder comprises various pre-trained DCNN models, and the decoder consists of a series of convolution and up-sampling layers. The decoder enables the merging and propagation of multi-level features from DCNN models. Further, we explore different fusion strategies to combine multi-level features from DCNNs as part of an ensemble encoder. We have experimented with DIV2k and CIFAR10 datasets. The performance of our proposed approach is evaluated in terms of subjective and reference-based image quality assessment metrics, which shows that our results are pretty competitive compared to the existing approaches.
SAU-NET: Scale Aware Polyp Segmentation using Encoder-Decoder Network

Colorectal Cancer has become a major cause of death in recent times. To improve the chances of survival, detecting early signs and identifying polyps in a routine examination is necessary. In this pursuit, an automatic computer-aided diagnosis (CAD) system to detect early signs of the disease onset is crucial. Deep Learning is at the base of recent advances in CAD systems, and its successes in CAD encourage it to be used in colorectal cancer analysis. Efficient segmentation of polyps from the colonoscopy images can aid radiologists immensely in the task of polyps identification and analysis. Therefore, this paper proposes an encoder-decoder-based architecture to segment polyps. Our proposed model takes into account multi-scale features that are present in the images. For efficient feature extraction, residual encoder blocks and Squeeze and Excitation modules are used to enhance channel inter-dependencies. Again, a residual dense decoder is used to improve the reconstruction in the decoder module. During the encoding stage, spatial information loss leads to a semantic gap between encoder and decoder. To handle this, a modified skip connection is proposed to bridge the semantic gap between the encoder and the corresponding decoder. The proposed model achieved an 85.15% dice score, 74.26% IoU, and 83.88% mean IoU on the Kvasir-SEG dataset. The results show that the proposed model can segment polyps more efficiently than many other popular models in the literature. Comparatively, fewer parameters in the proposed model prevail it efficient in real-time use.
Rotation invariant Gabor convolutional neural network for image classification

Gabor filters have been recently integrated with deep convolutional neural networks to learn better features with fewer model parameters. However, during feature learning the rotation invariance is not well addressed. In this paper, we propose a rotation invariant Gabor convolutional neural network (RIGCN) for image classification. First, we transform each input image to generate multiple rotated image instances and feed them into a weight-sharing Siamese network architecture to learn Gabor-guided deep convolutional features. Then, we compute the maximum and average feature responses from all the rotated instances of the same input image and send them into a convolutional fusion module to obtain a rotation invariant image representation. Finally, we use the cross-entropy loss for image classification. In our method, the use of Siamese architecture enables us to obtain rotation invariant features from rotated image instances. The use of convolutional fusion operator enables us to obtain richer statistical features with high efficiency. Experimental results on several benchmark datasets demonstrate the effectiveness of RIGCN for rotation invariant image classification.
Concept of Blockchain Technology and Its Emergence

Blockchain refers to a distributed ledger technology that helps people to regulate and manage their information without any intermediaries. This technology emerges as a promising panacea for authentication and authorization with potential for use in every possible domain including financial, manufacturing, educational institutions, etc. Blockchain has its birth through the concept of Bitcoin, a digital cryptocurrency by Satoshi Nakamoto, called as Blockchain 1.0. Blockchain 2.0 came into existence in 2014 with Ethereum and smart contracts. The challenges such as scalability, interoperability, sustainability, and governance led to the next generation of Blockchain also called as IOTA, a blockchainless cryptocurrency for the internet of things runs on the top of their own ledger called Tangle, which is immune towards quantum computers. This disruptive technology evolved to provide cross chain support and more security through Blockchain 4.0. Finally, the chapter concludes by discussing the various applications of this technology and its advantages and security issues.
Characterization of diffractive relief structures over large areas by stitching interference microscopic topography

The development of diffractive optical elements (DOEs)enables fascinatingly compact design and chromatic aberration correction. Fabrication errors of DOEs include periodic pattern error, relief profile error and roughness which affect imaging quality and diffraction efficiency. Characterization of the DOE requires thus multi-scale measurement of the surface topography over the full aperture of 10 mm scale. This paper presents a method based on the white light interferometry in combination of subaperture stitching. Firstly, the principle of measurement and subaperture layout design is introduced. The measuring system is easily set up by integrating a scanning platform with the interference microscope. Subaperture stitching algorithm is developed based on the surface height change related to the scanning motion error. The slope of the relief profile is used to model the deviations of overlapping point pairs induced by lateral shift and rotation. Finally, the topography and fabrication error of a Fresnel lens surface are experimentally obtained and characterized over the full aperture through stitching of totally 67 subapertures.
Big Data Analysis and Modeling of Higher Education Reform Based on Cloud Computing Technology

In view of the current educational environment, the development of higher education cannot be separated from the support of information technology. The era of big data has had various impacts on higher education and accelerated the development of higher education. Cloud computing technology has good application prospects for the data analysis and mathematical modeling content involved in the reform of higher education. This study analyzes and calculates the concerns of the four main contents involved in the educational reform of a higher education institution as the research object. In order to quantify the application effects of different cloud computing technologies, the paper introduces three typical cloud computing technologies to analyze the research data and establish related mathematical models. The results show that the prediction accuracy based on the immune cloning algorithm is the best. Based on the calculation results, it can be found that the corresponding data of the two main contents of teaching scene setting and teaching quality detection show an obvious polynomial function relationship.
A hybrid lexicon-based and neural approach for explainable polarity detection

In this work, we propose BERT-WMAL, a hybrid model that brings together information coming from data through the recent transformer deep learning model and those obtained from a polarized lexicon. The result is a model for sentence polarity that manages to have performances comparable with those at the state-of-the-art, but with the advantage of being able to provide the end-user with an explanation regarding the most important terms involved with the provided prediction. The model has been evaluated on three polarity detection Italian dataset, i.e., SENTIPOLC, AGRITREND and ABSITA. While the first contains 7,410 tweets released for training and 2,000 for testing, the second and the third respectively include 1,000 tweets without splitting , and 2,365 reviews for training, 1,171 for testing. The use of lexicon-based information proves to be effective in terms of the F1 measure since it shows an improvement of F1 score on all the observed dataset: from 0.664 to 0.669 (i.e, 0.772%) on AGRITREND, from 0.728 to 0.734 (i.e., 0.854%) on SENTIPOLC and from 0.904 to 0.921 (i.e, 1.873%) on ABSITA. The usefulness of this model not only depends on its effectiveness in terms of the F1 measure, but also on its ability to generate predictions that are more explainable and especially convincing for the end-users. We evaluated this aspect through a user study involving four native Italian speakers, each evaluating 64 sentences with associated explanations. The results demonstrate the validity of this approach based on a combination of weights of attention extracted from the deep learning model and the linguistic knowledge stored in the WMAL lexicon. These considerations allow us to regard the approach provided in this paper as a promising starting point for further works in this research area.
Cybersecurity in Travel and Tourism: A Risk-Based Approach

As the travel and tourism sector is embracing emerging technologies to redefine products, services, and consumer experiences, their cyber ecosystems become increasingly vulnerable to security risks related with these technologies, the huge amount of financial transactions they carry out, and the valuable customer data they store. Over the last few years, several high-profile organizations in the sector made negative headlines because they did not pay appropriate attention to these risks and took an approach to cybersecurity that was fragmented, technology-focused, and compliance-oriented. It is evident that a step change is needed, and this chapter presents a more comprehensive, business-driven, and risk-based approach to building cybersecurity capability in an organization. The chapter starts with the business case for a cybersecurity strategy and then unfolds the components of a risk-based approach to cybersecurity.
Entropy-Based Breast Cancer Detection in Digital Mammograms Using World Cup Optimization Algorithm

Breast cancer is one of the deadliest cancers for women. Early detection of skin cancer gives a high chance for the women to escape from the malady and obtain a cure at the initial stages. In other words, early detection of breast cancer has a direct relation by the women's quality of life. In this case, mammography images are important. Indeed, the main test used for screening and early diagnosis of breast cancer is mammography. In recent years, computer-aided cancer detection has been turned into an active field of research and showed a promising future. In this study, a new optimization algorithm based on thresholding is introduced. A WCO algorithm is employed as the optimization algorithm. WCO is a new meta-heuristic approach which is inspired by the FIFA world cup challenge. The presented method utilizes random samples as candidate solutions from the search space inside the image histogram with considering to the objective function that is utilized by the Kapur's method.
Front and back surface measurement of the transparent planar element based on multi-frequency fringe deflectometry

As a highly accurate metrology, phase measuring deflectometry (PMD) can be used for in-situ surface shape measurement. However, due to the reflection off the back surface, PMD cannot measure both the front and back surfaces of the transparent planar element simultaneously. Therefore, this paper proposes a method for measuring the front and back surfaces of the transparent planar element. The phase distribution corresponding to the front and back surfaces can be firstly acquired by multi-frequency fringe deflectometry. Then, the front and back surface shapes can be obtained by inverse ray-tracing and nonlinear optimization. Numerical simulation and experiment verify the proposed method. The surface shape of window glass with a thickness of 10 mm is measured in the experiment. The surface shape error is around 50 nm in the root mean square with a diameter of 51 mm.
A miniaturized computational spectrometer with optimum number of nanophotonic filters: Deep-learning autoencoding and inverse design-based implementation

In the last decade, computational spectrometers emerged as a new paradigm in the miniaturization of optical spectrometers. These systems are composed of distinct filters or detectors, and the input spectrum is reconstructed from the spectral response of these encoding elements. For improving the performance of these systems, much work has been done in designing efficient and advanced algorithms for spectrum recovery. Still, the remaining challenge is finding the optimal basis of filters for the encoding operation. In this article, we propose a systematic approach based on deep learning for the intelligent design of spectral encoding elements. This approach consists of two steps: first, we utilize an autoencoder to find the optimal spectral responses with minimal correlation and then use a conditional generative adversarial network to inverse design the corresponding filter structures of those extracted spectral responses. With this approach, we design 15 optimal filters that can be used as meta-pixel in hyperspectral image sensors and computational spectrometers. The realized computational spectrometer with compact size can also be embedded in smartphones as an external gadget and utilize the available processing power of these devices. Deep-learning assisted step-by-step design of the proposed miniaturized computational spectrometer • Presenting a miniaturized computational spectrometer with optimum number of filters • Finding the optimum filters’ spectral responses by an autoencoder neural network • Inverse design of desired filters by a conditional generative adversarial network • Attaining the filters with minimal correlation and diverse spectral response
Secure and Fine-Grained Flow Control for Subscription-Based Data Services in Cloud-Edge Computing

With the popularity of cloud computing services, an increasing number of users begin to use subscription-based services. Due to the semi-trusted cloud servers that may access the outsourced data, and malicious senders who may publish unauthorized data or junk data, access control encryption (ACE) schemes have been studied recently to enforce secure data write control as well as read control. However, their access control policies are specified by the authority or publishers, which do not apply to the subscriptions. In this paper, we propose DSFlow, a secure and fine-grained flow control system for subscription-based data services. DSFlow is designed in the cloud-edge computing architecture, which employs edge nodes to control the communications between publishers and cloud servers by sanitizing the original ciphertexts to resist malicious publishers, and allows any valid subscriber to decrypt the sanitized ciphertexts in cloud. We introduce a receiver-policy attribute-based ACE (RA-ACE) scheme for DSFlow, which embeds the fine-grained access control policy within the receiver's decryption key. We give a concrete construction of RA-ACE from key-policy attribute-based encryption, structure-preserving signature and non-interactive zero-knowledge proof, and formally prove the no-read rule and no-write rule of RA-ACE. The experiments demonstrate the efficiency of DSFlow compared with existing schemes.
Parallel LiDARs Meet the Foggy Weather

LiDARs play an important role in autonomous driving. They can provide accurate information about the surroundings, which is essential for perception tasks. Unfortunately, LiDARs’ performance will be seriously influenced by adverse weather. Adverse weather is a big challenge to the development of perception systems in autonomous driving. Because of the high cost and time intensity, the lack of LiDAR data under adverse weather is the main problem. Enhanced data is a proper method to solve the data shortage problem. Parallel LiDARs based on parallel theories can provide high-fidelity data through the precise modeling of weather in artificial systems. This paper focuses on Parallel LiDARs in fog. A more precise physical model is used to improve the existing work. The influence of different fog conditions on models is explained with quantitative results in detail. This work is of great help to the targeted improvements of trained models in adverse weather.
Federated Learning-Based Cross-Enterprise Recommendation With Graph Neural Networks

Recommender systems are technology-driven marketing solutions for businesses that analyze user behavior data. However, collaborative data sharing between enterprises is often prohibited by privacy protection regulations, leading to insufficient data for graph neural networks (GNNs) training. Fortunately, federated learning (FL), a collaborative training framework without exposing source data, can be applied congruently. Nevertheless, most of FL-based GNN model training methods adopt federated averaging, which performs poorly on highly heterogeneous graph data. To solve this problem, a FL-based GNN Model Training framework for cross-enterprise recommendation, named FL-GMT, is proposed. Specifically, a GNN-based recommendation model is deployed as the local training model. Then, considering the performance inequity caused by uneven sample quality, a loss-based federated aggregation algorithm is designed, effectively improving the performance of disadvantaged participants. To improve the system stability at the end of the aggregation, a dynamic update method of loss attention is designed. Extensive experiments on benchmark datasets demonstrate that FL-GMT outperforms baselines in terms of system fairness, stability, and accuracy.
Accelerated Federated Learning via Greedy Aggregation

Federated learning is a distributed computing framework aiming at finding a shared model parameter while protecting the privacy of local agents by sharing only locally updated model parameters without sharing local data with a central server. Through an iterative procedure between agent-side local updates and central server-side aggregation, federated learning reaches its maximum performance after sufficient iterations which is the the possible best performance via central learning. In practice, the statistical heterogeneity of the client’s local dataset (non-iid) degrades the performance and convergence speed of federated learning compared to the iid case. In this letter, we propose two schemes: (i) server-driven approach; and (ii) client-driven approach, both based on greedy algorithm to tackle the statistical heterogeneity of federated learning. Numerical results validate the advantage of the proposed schemes by improving the performance of state-of-the-art federated learning techniques, e.g., FedAvg, FedProx, FedAdam, and FedDyn with reduced wireless communication cost. Further investigation compares the two proposed schemes to show that the server-driven approach possesses better performance while the client-driven approach has better wireless communication efficiency.
Sentiment Analysis using BERT, LSTM, and Cognitive Dictionary

Due to the epidemic situation, in order to greatly reduce the infection risk of face-to-face interviews, this paper implements the BERT combined with RCNN to judge the positive and negative directions of the text, and then uses BERT's next sentence prediction (NSP) to find out the topic-related sentences in the text. Finally, a cognitive dictionary is used to calculate the degree of agreement or disagreement, so as to obtain the degree of support of the reviewer. This paper is also useful for letting visitors or authors know what the respondents' views are.
One shot ancient character recognition with siamese similarity network

Ancient character recognition is not only important for the study and understanding of ancient history but also has a profound impact on the inheritance and development of national culture. In order to reduce the study of difficult professional knowledge of ancient characters, and meanwhile overcome the lack of data, class imbalance, diversification of glyphs, and open set recognition problems in ancient characters, we propose a Siamese similarity network based on a similarity learning method to directly learn input similarity and then apply the trained model to establish one shot classification task for recognition. Multi-scale fusion backbone structure and embedded structure are proposed in the network to improve the model's ability to extract features. We also propose the soft similarity contrast loss function for the first time, which ensures the optimization of similar images with higher similarity and different classes of images with greater differences while reducing the over-optimization of back-propagation leading to model overfitting. Specially, we propose a cumulative class prototype based on our network to solve the deviation problem of the mean class prototype and obtain a good class representation. Since new ancient characters can still be found in reality, our model has the ability to reject unknown categories while identifying new ones. A large number of experiments show that our proposed method has achieved high-efficiency discriminative performance and obtained the best performance over the methods of traditional deep learning and other classic one-shot learning.