Reviewerly/exHarmony huggingface dataset:
Improving nonnegative matrix factorization with advanced graph regularization

Nonnegative Matrix Factorization (NMF) produces interpretable solutions for many applications including collaborative filtering. Typically, regularization is needed to address issues such as overfitting and interpretability, especially for collaborative filtering where the rating matrices are sparse. However, the existing regularizers are typically constructed from the factorization results instead of the rating matrices. Intuitively, we regard these existing regularizers as representing either user factors or item factors and anticipate that a more holistic regularizer could improve the effectiveness of NMF. To this end, we propose a graph regularizer based on a linear projection of the rating matrix, and call the resulting method: Linear Projection and Graph Regularized Nonnegative Matrix Factorization (LPGNMF). We develop two iterative methods to minimize the cost function and derive two update rules named LPGNMF and F-LPGNMF. Additionally, we prove the value of the objective function decreases with LPGNMF and converges to a fixed point with F-LPGNMF. Finally, we test these methods against a number of NMF algorithms on different data sets and show both LPGNMF and F-LPGNMF always achieve smaller errors based on two different error measures.
The diversification benefits of cryptocurrency asset categories and estimation risk: pre and post Covid-19

We examine the diversification benefits of cryptocurrency asset categories.To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints.We estimate the inputs using lasso regression and elastic net regression, employing the shrunk Wishart stochastic volatility model and Gaussian random projection.We consider nine cryptocurrency asset categories, and find that all but two provide significant out-of-sample diversification benefits.The lower is investor risk aversion, the more beneficial are cryptocurrencies as portfolio diversifiers.During uncertain economic environments, such as the post-Covid-19 period, cryptocurrencies provide the same diversification benefits as in more stable environments.Our results are robust to different portfolio benchmarks, regression technique, transaction cost, portfolio constraints, higher moments and Black-Litterman models.
A chaotic video steganography technique for carrying different types of secret messages

Increasing the security of data in insecure communication networks is the motivation of researchers to propose different types of methods. Steganography is one of the security techniques that is used in videos, images, sounds, and other digital data. This technique embeds important data or secret messages into cover media to prevent unauthorized access. This paper proposes a new method for video steganography to develop our security purposes. The proposed method uses integer wavelet transform to increase the security and quality of video frames. This paper also presents an improved three-dimensional Sine chaotic map and uses it in the processes of the proposed video steganography. Hiding different types of messages with various sizes is the main aim of the suggested algorithm. The simulation results show that the proposed method has good imperceptibility and performance after the embedding process. Furthermore, the results of extracted messages after applying some noises on the stego videos, and the regular/singular (RS) steganalysis results demonstrate the acceptable robustness against some noises. Also, comparison results show that the proposed method outperforms some similar approaches.
Research on the Automation Integration Terminal of the Education Management Platform Based on Big Data Analysis

Education is a dynamic system by which students perceive the factors necessary to fit them into the society. Education is mainly intentional learning that grooms individuals to achieve success in their adult lives. Evaluation of teaching techniques, course management (CM), communication, and student monitoring are the main characteristics of today’s education system. The aim to plan the curriculum of education management in both schools and colleges leads to the implementation of an MS-BDA. The development process for evaluation of teaching techniques and CM includes the use of the sentiment analysis method, which assesses the emotional feelings of students studying the course by managing curriculum quality. The big data analysis with MNN is developed by considering the communication and student monitoring system. This system evaluates the monitoring model provided in MS-BDA for assessing student communication on merging the voice-over with the communication language processing system. The simulation analysis is performed based on accessibility, adaptability, and efficiency, proving the proposed framework’s reliability. Therefore, the system outputs an accuracy of 99.1% when compared to the existing methods.
Underwater Image Enhancement Based on Histogram-Equalization Approximation Using Physics-Based Dichromatic Modeling

This work proposes to develop an underwater image enhancement method based on histogram-equalization (HE) approximation using physics-based dichromatic modeling (PDM). Images captured underwater usually suffer from low contrast and color distortions due to light scattering and attenuation. The PDM describes the image formation process, which can be used to restore nature-degraded images, such as underwater images. However, it does not assure that the restored images have good contrast. Thus, we propose approximating the conventional HE based on the PDM to recover the color distortions of underwater images and enhance their contrast through convex optimization. Experimental results demonstrate the proposed method performs favorably against state-of-the-art underwater image restoration approaches.
Boosting Students’ Motivation in Learning Descriptive Writing through Instagram

Throughout years of learning English as a second language in Malaysia, rural area students were still found to be incompetent in English, especially in writing.The major problem faced by the rural area primary students is the inability to generate ideas with the limited vocabulary they acquire.In this 21 st century education, many studies have been carried out to improve students' writing skills on Instagram, however, less research could be found focused on primary schools.Therefore, this research aims to investigate primary students' perceptions on using Instagram as a motivation learning tool in descriptive writing.In this study, quantitative and qualitative research methods involved selecting 13 Year 4 students in Kahang, Johor using convenience sampling.Questionnaires and semi-structured interviews were used as the data collection method.The findings showed that students' perceptions on Instagram were relatively high and they were interested and motivated to use it for writing outside the classroom.It is suggested that more interactive activities can be conducted on Instagram in the future to promote language learning among rural primary students.
Lightweight Deep Learning for Road Environment Recognition

With recent developments in the field of autonomous driving, recognition algorithms for road environments are being developed very rapidly. Currently, most of the network models have good recognition rates, but as the accuracy rate increases, the models become more complex and thus lack real-time performance. Therefore, there is an urgent need to propose a lightweight recognition system for road environments to assist autonomous driving. We propose a lightweight road environment recognition system with two different detection routes based on the same backbone network for objects and lane lines. The proposed approach uses MobileNet as the backbone network to acquire the feature layer, and our improved YOLOv4 and U-Net allows the number of parameters of the model to be greatly reduced and combined with the improved attention mechanism. The lightweight residual convolutional attention network (LRCA-Net) proposed in this work allows the network to adaptively pay attention to the feature details that need attention, which improves the detection accuracy. Finally, the object detection model and the lane line detection model of this lightweight road environment detection system evaluated on the PASCAL VOC dataset and the Highway Driving dataset show that their mAP and mIoU reach 93.2% and 93.3%, respectively, achieving excellent performance compared to other methods.
Research on reversible image steganography of encrypted image based on image interpolation and difference histogram shift

In view of the mutual restriction between the image encryption and information hiding process in the ciphertext domain reversible algorithm, the existing ciphertext domain image reversible image steganography algorithm can hardly take into account the performance of security and hiding capacity. This paper proposes a reversible image steganography algorithm for encrypted images based on a new interpolation image and difference histogram shift. The pixel position of the carrier image is changed through the double scrambling operation of random image block recombination and Arnold transform. The chaotic sequence generated by logistic mapping is used as the diffusion sequence of the encryption algorithm, and DNA encoding and calculation are performed respectively to obtain the final encrypted image. According to the newly proposed interpolation algorithm, the encrypted image is enlarged, determine the pixel value of the target interpolated image according to the pixel value of the encrypted image, calculate the difference between the three adjacent pixel values of the target interpolated image, and use the difference histogram translation algorithm to convert the secret Information is embedded in an interpolated image, and only one bit of the pixel value can be modified in a single embedding process, and the embedding method is simple and effective. Experimental results show that under the premise of ensuring information security and image quality, the embedding capacity has also been greatly improved, reaching 1,572,864 bits. When the embedding rate is 0.67 bpp, the PSNR can also reach 58 dB and the image quality is maintained better.
Gradient-based refined class activation map for weakly supervised object localization

Weakly supervised object localization locates objects based on the localization map generated from the classification network. However, most existing methods utilize the information of the target class to locate objects based on the feature map of a single image, which ignores both the relationships of inter-class and intra-class. In this work, we propose a Gradient-based Refined Class Activation Map (GRCAM) approach to achieve more accurate localization. Two kinds of gradients are applied to reveal the relationships of inter-class and intra-class during the testing stage. First, we exploit the gradients of the classification loss function concerning the feature map to enhance class-specific information. The gradients of classification loss reveal the connection among the predicted probabilities of all classes. Second, we design a regression function that refers to the loss between the pseudo-bounding box coordinates containing category consistency and the predicted coordinates generated from the localization map. The predicted coordinates are revised by the gradients of the regression function. The gradients of the regression function reveal the consistency within a class. Despite the apparent simplicity, we demonstrate the advantages of GRCAM on ILSVRC and CUB-200-2011 in extensive experiments. Especially, on ILSVRC dataset, the proposed GRCAM achieves a new state-of-the-art Top-1 localization error of 42.94%.
Attributes learning network for generalized zero-shot learning

In the absence of unseen training data, zero-shot learning algorithms utilize the semantic knowledge shared by the seen and unseen classes to establish the connection between the visual space and the semantic space, so as to realize the recognition of the unseen classes. However, in real applications, the original semantic representation cannot well characterize both the class-specificity structure and discriminative information in dimension space, which leads to unseen classes being easily misclassified into seen classes. To tackle this problem, we propose a Salient Attributes Learning Network (SALN) to generate discriminative and expressive semantic representation under the supervision of the visual features. Meanwhile, ℓ1,2-norm constraint is employed to make the learned semantic representation well characterize the class-specificity structure and discriminative information in dimension space. Then feature alignment network projects the learned semantic representation into visual space and a relation network is adopted for classification. The performance of the proposed approach has made progress on the five benchmark datasets in generalized zero-shot learning task, and in-depth experiments indicate the effectiveness and excellence of our method.
Integrated Security Framework for Healthcare Using Blockchain and Fog Computing

Edge computing is a promising paradigm that enhances the capabilities of cloud computing in healthcare. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Edge computing is a promising model for improving cloud computing&#x2019;s capabilities. It is important to maintain a positive climate free of all forms of protection and privacy violations in order to continue using computing services. The edge computing environment&#x2019;s security and privacy concerns have hampered the technology&#x2019;s overall acceptance as a reliable paradigm.The blockchain technology emerges as a viable solution because it provides influential features such as continuous secrecy, authentication, and robustness. This proposed work propose a blockchain-enabled distributed security architecture based on edge cloud. Security attacks are detected at the cloud layer, and as a result, security attacks at the IoT network&#x2019;s edge layer are decreased. The gateway provides dynamic network traffic flow management, which aids in the detection of security attacks by identifying suspicious network traffic flows and reducing security attacks by blocking suspicious flows.For the next-generation IoT, the proposed security architecture has specified state-of-the-art contributions. The proposed algorithm is based on ECC based proxy encryption technique. Proxy re-encryption is a modern encryption scheme designed for protection in a distributed environment, including such smart applications that use the Internet of Things.
Cloud computing platform: Performance analysis of prominent cryptographic algorithms

Abstract With advancements in science and technology, cloud computing is the next big thing in the industry. Cloud cryptography is a technique that uses encryption algorithms to secure data. The significant advantage of cloud storage is no difficulty to get to, diminished equipment, low protection, and fixing cost so every association is working with the cloud. Encryption is the process of encoding information to prevent unauthorized access. Nowadays, we desire to secure the information that is to be stored in our computer or transmitted utilizing the internet against attacks. The cryptographic method depends on their response time, confidentiality, bandwidth, and integrity. Furthermore, security is a significant factor in cloud computing for ensuring client data is placed on the safe mode in the cloud. Our research paper compares the efficiency, usage, and utility of available cryptography algorithms. Evaluation results suggest which algorithm is better for which type of data and environment.
Predicting chaotic time series and replicating chaotic attractors based on two novel echo state network models

Neural network is the inevitable outcome of the rapid development of artificial intelligence. Based on the idea of homotopy and combined activation function, two novel echo state network (ESN) models are proposed. Compared with several activation functions commonly used in the neural network, the proposed models provide intuitive but effective approaches to chaotic forecasting, and the prediction accuracy is higher. Secondly, for the Mackey-Glass (MG) time series and Rössler attractor, the prediction errors and prediction step sizes of the two novel models are superior to many pre-existing ESN models, which demonstrates the merit of the proposed models. Moreover, several parameters play key roles in network training, such as spectral radius, sparse degree etc., and their effects on network performance are analyzed. Notably, it is also investigated that the trained network can replicate Rössler chaotic attractor well. At the end of the results, the parameters of the proposed models are optimized, and the relatively optimized parameters are obtained after a large number of data experiments.
Hyperspectral image denoising using constraint smooth rank approximation and weighted enhance 3DTV

The removal of mixed noise in hyperspectral images (HSI) plays a vital role in subsequent applications. Recently, the smooth rank approximation (SRA) method based on low-rank regularization shows strong property. However, it is difficult for a single regularization to describe prior information of HSI accurately and fully utilized spatial-spectral information, which makes SRA method cannot recover image details completely and also not good enough for the removal of strong mixed noise. This paper proposes a constrained SRA combined with weighted enhanced 3D total variation regularization (SAWTV) HSI restoration frameworks. In addition, considering the intrinsic group relations of different band to spatial and spectral difference images, a constrained SRA combined with weighted group sparsity regularization (SAWGS) restoration frameworks also be proposed. Among them, the introduction of rank constraint into the SRA model can better describe the low-rank characteristics of HSI and improve the denoising effect. Combined with weighted E3DTV regularization which are based on l1-norm and l2,1-norm respectively, it enhances the model's ability to restore local details and ensures the spatial-spectral smoothness. The ADMM method is used to solve the model. A large number of experiments on simulated and real HSIs show that this method has better performance in removing mixed noise than the latest method based on low rank and TV.
Clustered and deep echo state networks for signal noise reduction

Echo State Networks (ESNs) are Recurrent Neural Networks with fixed input and internal (hidden) weights, and adaptable output weights. The hidden part of an ESN can be considered as a discrete-time dynamical system, called reservoir. In classical ESNs, the internal connections are obtained from an Erdős-Rényi graph. A recent study proposed ESNs with clustered adjacency matrices (CESNs), where the clusters are either Erdős-Rényi graphs or Barabási-Albert-like graphs. In this work, we investigate the effectiveness of CESNs and apply them for signal denoising. In addition, we introduce and study deep CESNs with multiple clustered layers. We found that CESNs and deep CESNs can compete with deep ESNs for all tasks that we considered.
Designing effective masking strategies for cyberdefense through human experimentation and cognitive models

Masking strategies for cyberdefense (i.e., disguising network attributes to hide the real state of the network) are predicted to be effective in simulated experiments. However, it is unclear how effective they are against human attackers. We address three factors that challenge the effectiveness of the masking strategies in practice: (1) we relax the assumption of rationality of the attackers made by Game Theory/Machine Learning defense algorithms; (2) we provide a cognitive model of human attackers that can inform these defense algorithms; and (3) we provide a way to generate data on attacker’s decisions through simulation with a cognitive model. Two masking strategies of defense were generated using Game Theory and Machine Learning (ML) algorithms. The effectiveness of these two masking strategies of defense, risk averse and rational, are compared in an experiment with human attackers. We collected attacker’s decisions against the two masking strategies. With the limited human participant’s data, the results indicate that the risk averse strategy can reduce the defense losses compared to the rational masking strategy. We also propose a cognitive model based on Instance-Based Learning Theory that accurately represents and predicts the attacker’s decisions in this task. We demonstrate the model’s process by generating simulated data and comparing it to the attacker’s actual actions in the experiment. The model is able to capture the data at the aggregate and at the individual levels of attackers making decisions in both rational and risk averse defense algorithms. We propose that this model can be used to inform game theoretic defense algorithms and to produce synthetic data that can be used by ML algorithms to generate new defense strategies.
“Mhm...” – Conversational Strategies For Product Search Assistants

Online retail has become a popular alternative to in-store shopping. However, unlike in traditional stores, users of online shops need to find the right product on their own without support from expert salespersons. Conversational search could provide a means to compensate for the shortcomings of traditional product search engines. To establish design guidelines for such virtual product search assistants, we studied conversations in a user study (N = 24) where experts supported users in finding the right product for their needs. We annotated the conversations concerning their content and conversational structure and identified recurring conversational strategies. Our findings show that experts actively elicit the users' information needs using funneling techniques. They also use dialogue-structuring elements and frequently confirm having understood what the client was saying by using discourse markers, e.g., "mhm". With this work, we contribute insights and design implications for conversational product search assistants.
A novel image watermarking scheme using ULV decomposition

Matrix decompositions play an important role in most of watermarking techniques. Especially Singular Value Decomposition (SVD) is one of the most preferred techniques. In addition, matrix decompositions such as Non-negative Matrix Factorization (NMF), QR, LU are some of the methods used in previous studies. In this study, a new scheme using ULV Decomposition (ULVD) technique is proposed as an alternative matrix decomposition. In this scheme, which is a frequency-based technique using R-level Discrete Wavelet Transform (DWT), the scaling factor is determined adaptively according to the cover image and watermark used. Another issue to be solved in watermarking studies is the False Positive Problem (FFP). For this purpose, a control mechanism is used against the FFP in watermark embedding and watermark extracting processes. In addition, the watermarking process is carried out in any size, depending on the size of the cover image. As a result, the performed experiments show that the proposed scheme provides high imperceptibility and robustness.