Reviewerly/exHarmony huggingface dataset:
Imam: Word Embedding Model for Islamic Arabic NLP

This paper can be considered one of the first works to introduce an efficient distributed word representation model for different NLP tasks in the islamic domain. The Word Embedding Model and the algorithm on top of it is implemented in Imam application where user can ask the application to search for any data related to Isalmic domain and get an answer. The data is gathered from different resources (Maliks muwataa, Musnad Ahmad Ibn-hanbal, Sahih Muslim ahadith, Sahih Al-bukhari, Sunan Al-darimi, and more). The amount of records gathered was more than ninety thousand documents (Text Blocks) from 10 different books.After several sequential pipeline processes of Data cleaning, preprocessing and Normalization, Skip-gram technique was used to built the word2vec model and then At last tested with different methods, first by using the K-means clustering and then nonlinear dimensionality reduction technique to represent the data in 2D dimension, secondly by using word similarity to test model ability to understand the Quranic language. The tests clearly show that the model can be used effectively in different NLP Arabic Islamic tasks.
Synthetic vs. Real-World Continuous Landscapes: A Local Optima Networks View

Local optima networks (LONs) are a useful tool to analyse and visualise the global structure of fitness landscapes. The main goal of our study is to use LONs to contrast the global structure of synthetic benchmark functions against those of real-world continuous optimisation problems of similar dimensions. We selected two real-world problems, namely, an engineering design problem and a machine learning problem. Our results indicate striking differences in the global structure of synthetic vs real-world problems. The real-world problems studied were easier to solve than the synthetic ones, and our analysis reveals why; they have easier to traverse global structures with fewer nodes and edges, no sub-optimal funnels, higher neutrality and multiple global optima with shorter trajectories towards them.
A Novel Method based on Character Segmentation for Slant Chinese Screen-render Text Detection and Recognition

Screen rendering text has broad application prospects in the fields of medical records, dictionary screen capture, and screen-assisted reading. However, Chinese screen rendering text always has the challenges of small font size and low resolution. Obtaining a screen-rendered text image in a natural scene will have a certain tilt angle. These all pose great challenges for screen text recognition. This paper proposes a method based on character segmentation. In order to correct the tilt of the text image, a lightweight image text correction method is proposed. In order to obtain better character segmentation accuracy in extreme environments, a Gaussian extremum removal module is designed. Drawing on HCCR-GoogLeNet, a simple and efficient network architecture is designed for character recognition. Finally, this paper will slant text correction, horizontal text line detection, character segmentation, and character recognition to construct a complete network architecture to be applied to the screen rendering text recognition process scheme in natural scenes. The character recognition model was evaluated on ICDAR2013 and the homemade screen rendering dataset, and both achieved good results.
Criminal Investigation Image Retrieval Based on Deep Learning

This paper proposed a criminal investigation image retrieval algorithm based on deep learning. First, this paper improves Faster R-CNN object detection network making it can extract multi-scale features of images, so that it can better annotation semantic information for the image. To obtain a preliminary image list through image retrieval of the semantic information. Then use ResNet50 to extract image features, perform content-based retrieval in the image list returned by the first step of semantics to obtain the final criminal investigation image retrieval result, both the semantic and the content are combined for image retrieval. The experimental results show that the criminal investigation image retrieval algorithm proposed in this paper is superior to the simple content criminal investigation image retrieval algorithm in Precision and Retrieval rate.
Characterizing Generalized Rate-Distortion Performance of Video Coding: An Eigen Analysis Approach

Rate-distortion (RD) theory is at the heart of lossy data compression. Here we aim to model the generalized RD (GRD) trade-off between the visual quality of a compressed video and its encoding profiles (e.g., bitrate and spatial resolution). We first define the theoretical functional space W of the GRD function by analyzing its mathematical properties. We show that W is a convex set in a Hilbert space, inspiring a computational model of the GRD function, and a method of estimating model parameters from sparse measurements. To demonstrate the feasibility of our idea, we collect a large-scale database of real-world GRD functions, which turn out to live in a low-dimensional subspace of W. Combining the GRD reconstruction framework and the learned low-dimensional space, we create a low-parameter eigen GRD method to accurately estimate the GRD function of a source video content from only a few queries. Experimental results on the database show that the learned GRD method significantly outperforms state-of-the-art empirical RD estimation methods both in accuracy and efficiency. Last, we demonstrate the promise of the proposed model in video codec comparison.
User Portrait Technology Based on Stacking Mode

Because of the differences in code styles and programming levels among developers, it is prone to code irregularities, poor readability, and security vulnerabilities. Although developers can see their problems in the test report, it is difficult to guarantee that they will not make mistakes on the same problem. In this article, we propose a way to build a programming level for developers. The proposed method explores the Stacking model for building a programming level for developers. First, cluster the problems that occurred during the development process, give weight to the category information, score the developer's programming level, and divide the user groups into different categories. Then use the word vector to extract the features of the code defect, generate a feature matrix, and finally pass the feature matrix to the Stacking classifier to classify the defect information and update the developer's programming level portrait. Experimental results show that it is effective in predicting defect code information. In addition, a comparative study with the state-of-the-art method shows that the method is superior to existing methods in terms of classification rate, recall, precision and F-measure.
Reversible Information Hiding Algorithm Based on Multikey Encryption

This paper proposes a scheme of reversible data hiding in encrypted images based on multikey encryption. There are only two parties that are involved in this framework, including the content owner and the recipient. The content owner encrypts the original image with a key set which is composed by a selection method according to the additional message. Thus, the image can be encrypted and embedded at the same time. Additional message can be extracted given that the recipient side could perform decryption strategy by exploiting spatial correlation; then, original image can be recovered without any loss. Compare with other current information hiding mechanism, the proposed approach provides higher embedding capacity and is also able to perfectly reconstruct the original image as well as the embedded message. Rate distortion of the proposed method outperforms the previously published ones.
Smart Grid Digital Forensics Investigation Framework

The advancement in the development of smart grids may be carrying the risk of increased security vulnerabilities of the grid and allowing attackers to easily access the power system to either manipulate internal operation or disrupt the entire grid operations. In other words, a smart grid environment will be an important source of evidence in the event of a threat. Therefore, a systematic forensics investigation must be carried out to ensure that important steps are taken accordingly. Most digital forensics frameworks focused on the computing environment, which resulted in the absence of a knowledge base and resources to facilitate such an investigation in a smart grid environment. This study sheds light on the development of the proposed Smart Grid Digital Forensics Investigation Framework to support digital forensics investigations by taking the example of Stuxnet attacks. Existing digital forensics frameworks and models are reviewed to identify the suitability of phases to be included in the proposed framework.
Trusted Digital Identities for Mobile Devices

The pace of usage of personal devices has increased within industry, driving demand for trusted digital identities. As the world becomes increasingly mobile with the number of smartphone users growing yearly and the mobile web thriving, it is critical to implement strong security on mobile devices. As passwords are not a secure method of authentication, mobile devices and other forms of IoT require a means of two-factor authentication that meets strong security standards. The research presented in this paper provides a framework that updates the existing Public Key Infrastructure (PKI) in use by industry and governments today to accommodate the use of digital identities on smartphones, tablets, or even a smartwatch.
Quantum Software Engineering Supremacy in Intelligent Robotics

A new approach to a circuit implementation design of quantum algorithmic gates for the realization of quantum massive parallel computing presented. The main attention focused on the development of design method of fast quantum algorithm operators as superposition, entanglement and interference which are in general time-consuming operations due to the number of products that have to be performed. SW&HW support toolkit of supercomputing accelerator of quantum algorithm simulation on classical computer described. The method for performing Grover's interference without product operations introduced. New quantum genetic and quantum fuzzy inference algorithm gate design for intelligent robotic control considered. Quantum software engineering supremacy on Benchmarks demonstrated.
Classification of Multi-class Imbalanced Data Streams Using a Dynamic Data-Balancing Technique

The performance of classification algorithms with imbalanced streaming data depends upon efficient re-balancing strategy for learning tasks. The difficulty becomes more elevated with multi-class highly imbalanced streaming data. In this paper, we investigate the multi-class imbalance problem in data streams and develop an adaptive framework to cope with imbalanced data scenarios. The proposed One-Vs-All Adaptive Window re-Balancing with Retain Knowledge (OVA-AWBReK) classification framework will combine OVA binarization with Automated Re-balancing Strategy (ARS) using Racing Algorithm (RA). We conducted experiments on highly imbalanced datasets to demonstrate the use of the proposed OVA-AWBReK framework. The results show that OVA-AWBReK framework can enhance the classification performance of the multi-class highly imbalanced data.
Study of lightweight ABE for cloud based IoT

Cloud computing has made possible that resources are available when needed and on-demand. Cloud computing is used to access and store data on remote servers using internet. Storing data in remote cloud servers leads to privacy and security issues. Attribute based encryption is used extensively for access control in cloud. There has been an upsurge in the IoT devices and IoT devices have been using cloud for data storage. When IoT and cloud converge there is need for newer, lightweight and efficient access control techniques for cloud based IoT, as the IoT devices are resource constrained, and may not be able to support the access control techniques currently used. It is observed that, there is need for lightweight Attribute based encryption (ABE) technique for cloud based IoT.
Differential Fault Attack on Kreyvium &amp; FLIP

In this article, we propose key recovery attack on two stream ciphers: Kreyvium and FLIP <inline-formula><tex-math notation="LaTeX">$_{530}(42,128,360)$</tex-math></inline-formula> using Differential Fault Attack (DFA) technique. These two ciphers are being used in Fully Homomorphic Encryption (FHE) due to their low error growth during keystream generation. Kreyvium is an NFSR-based stream cipher and FLIP is a permutation-based stream cipher. We first show that the complete state of the Kreyvium can be recovered by injecting 3 faults and considering 450 many keystream bits. In case of FLIP, we show that if there is a 1-bit fault in the state of the cipher then from 9000 normal and faulty keystream bits the state (i.e., the secret key) of the cipher can be recovered. For single bit fault, one will require to solve a system of equations for each 530 possible fault locations to recover the correct key of FLIP. To the best of our knowledge, this is the first article which analyzes the security of these two FHE supported stream ciphers under DFA and it has been observed that DFA completely reveals the secret keys of these two ciphers with very minimal faults.
Content-based Recommender System Improvement using Hybrid Technique

The research in recommender systems has evolved considerably over the past years; however, to date the investigation on how the emotive state of the user could be used to complement such technologies is sparse. Many systems used the emotions of the user as implicit feedbacks in the recommender systems, but limited works analyzed the emotions and using them for item profile modelling. This system extracts the affective features of the user's face using the convolution neural network with three-dimension to build the item profile. Also, the support vector machine classifier is used for user profile building and introduces recommendations. The experiments conducted on an LDOS-PerAff-l dataset indicates that the hybrid technique contributes to improving the content-based recommender system performance using affective-automated features for item modelling instead of handcrafted features that extract using traditional techniques.
Overview of deep convolutional neural network pruning

In recent years, due to the rapid development of deep convolutional neural networks, deep learning model inference needs to consume a lot of computing resources. Most current edge devices cannot support deep learning applications with low latency, low power consumption, and high accuracy due to limited resources. Deep learning applications. Therefore, model compression and acceleration of deep networks are an effective solution, and network pruning that simplifies the model by removing redundant parameters in the inference stage is a hot research in this field in recent years. This paper divides the work into six aspects for a detailed analysis, combs the latest progress of deep neural network pruning technology from the perspective of different granular pruning and weight measurement standards, and finally points out the problems in the current research and analyzes Future research directions in the field of pruning.
An Adaptive Gradient Method with Differentiation Element in Deep Neural Networks

Current adaptive gradient algorithm (such as Adam) used in deep neural network has the advantages of fast training speed, simple tuning task and high computational efficiency. However, these methods are usually based on the gradient update using the root mean square of the past gradient, which often causes the learning rate shock. Thus the model overshoot may be large and even cannot converge. The PID optimization algorithm for deep neural network provides a new way to solve this problem. It introduces the idea of automatic control to solve the problem of overshooting in the stochastic gradient algorithm. The Adam algorithm is similar to an adaptive PI controller. Inspired by this, the differentiation element is introduced into Adam algorithm to accelerate model convergence. The algorithm was tested on MNIST, Cifar-10, Cifar-100 and Tiny-ImageNet data sets in the section of experiment. It is shown that the training speed by 10% on the premise of guaranteeing the accuracy of the model.
Image enhancement comparison to improve underwater cultural heritage survey

Abstract This work aims at presenting an underwater image application to obtain an improved 3D model of cultural assets. In 2016, more than 500 images were acquired by a GoPro Camera with a low resolution of 72 dpi and focal length of 3 mm, without flash and are now used to reconstruct the 3D model of some amphoras of a Roman shipwreck found in Albenga (Italy). We have applied state-of-art image enhancement techniques, such as ACE, CLAHE, LAB and SP algorithms, to improve the quality of underwater images affected by low contrast, poor visibility conditions, not uniform lighting, colour variations, noise and blur effect. The visual quality has been evaluated through quantitative metrics, like average luminance, information entropy, average gradient of image, UCIQE and UIQM. Then, our efforts have been devoted to the dense 3D point cloud generation using a SfM-MVS software. In particular, the 3D reconstruction results are in line with the metric evaluation: in fact, the more accurate 3D objects are obtained from that enhanced dataset with the highest measured image quality.
Method for Predicting Mobile Service Evolution from User Reviews and Update Logs

Because of rapid growth in mobile application markets, competition between companies that provide similar applications has become fierce. To improve user satisfaction for keeping existing users and attracting new users, application developers need to quickly respond to customer feedback regarding functionality and performance defects. In software engineering, specifying an accurate evolution plan according to user feedback is useful but quite difficult. Hence, we propose an approach for predicting and recommending evolution plans to application developers that includes: (1) when a new version of an App should be released; (2) which features should be updated in the next version and (3) if a new version is released, to what degree users would like or dislike it. This approach is based on an elaborate text analysis of massive numbers of user reviews and App update histories. A collocation-based mRAKE method is presented to extract requested and updated features from user reviews and update logs, and the intensity and sentiment scores of each feature are calculated to quantitatively represent time-series histories of App updates and user requests. Machine learning algorithms including linear support vector, Gaussian naïve Bayes and logistic regression are employed to discover the underlying correlation between user opinions embedded in their reviews and the App update behaviors of developers, and rich experiments were conducted on real data to validate the effectiveness of the proposed approach. Overall, our approach can achieve an average accuracy of 72.8% and 93.7% in release time recommendation and content updates of successive versions, respectively, and it can predict user reactions to a planned version with an average accuracy of above 89.0%.
A Scalable Architecture for Monitoring IoT Devices Using Ethereum and Fog Computing

With recent considerable developments in the Internet of Things (IoT), billions of resource-constrained devices are interconnected through the Internet. Monitoring this huge number of IoT devices which are heterogeneous in terms of underlying communication protocols and data format is challenging. The majority of existing IoT device monitoring solutions heavily rely on centralized architectures. Since using centralized architectures comes at the expense of trusting an authority, it has several inherent drawbacks, including vulnerability to security attacks, lack of data privacy, and unauthorized data manipulation. Hence, a new decentralized approach is crucial to remedy these drawbacks. One of the most promising technologies which is widely used to provide decentralization is blockchain. Additionally, to ease the burden of communication overhead and computational power on resource-constrained IoT devices, fog computing can be exploited to decrease communication latency and provide better network scalability. In this paper, we propose a scalable blockchain-based architecture for monitoring IoT devices using fog computing. To demonstrate the feasibility and usability of the proposed solution, we have implemented a proof-of-concept prototype, leveraging Ethereum smart contracts. Finally, a comprehensive evaluation is conducted. The evaluation results indicate that the proposed solution is significantly scalable and compatible with resource-constrained IoT devices.
A Learnable Gauss-Seidel Detector for MIMO Detection

Multiple-Input Multiple-Output (MIMO) is a key technology due to its high spectral efficiency and data rate in communication systems. Due to the high complexity of linear Minimum Mean Square Error (MMSE) detection, Gauss-Seidel iterative method is applied to MIMO detection as an approximate method of MMSE and achieves the effect of MMSE detection. In this paper, we propose a learnable Gauss-Seidel detector based on model-driven Deep Learning (DL) for MIMO systems. The proposed detector is designed by unfolding the Gauss-Seidel detection method. In the proposed detector, we add some parameters α, ß and δ that can be learned to improve the detection performance. Simulation results show that the proposed detector has better detection performance than traditional Gauss-Seidel detector.
Can a Transformer Assist in Scientific Writing? Generating Semantic Web Paper Snippets with GPT-2

The Semantic Web community has produced a large body of literature that is becoming increasingly difficult to manage, browse, and use. Recent work on attention-based, sequence-to-sequence Transformer neural architecture has produced language models that generate surprisingly convincing synthetic conditional text samples. In this demonstration, we re-train the GPT-2 architecture using the complete corpus of proceedings of the International Semantic Web Conference since 2002 until 2019. We use user-provided sentences to conditionally sample paper snippets, therefore illustrating cases where this model can help at addressing challenges in scientific paper writing, such as navigating extensive literature, explaining the Semantic Web core concepts, providing definitions, and even inspiring new research ideas.
Efficient architecture for deep neural networks with heterogeneous sensitivity

In this study, we present a neural network that consists of nodes with heterogeneous sensitivity. Each node in a network is assigned a variable that determines the sensitivity with which it learns to perform a given task. The network is trained via a constrained optimization that maximizes the sparsity of the sensitivity variables while ensuring optimal network performance. As a result, the network learns to perform a given task using only a few sensitive nodes. Insensitive nodes, which are nodes with zero sensitivity, can be removed from a trained network to obtain a computationally efficient network. Removing zero-sensitivity nodes has no effect on the performance of the network because the network has already been trained to perform the task without them. The regularization parameter used to solve the optimization problem was simultaneously found during the training of the networks. To validate our approach, we designed networks with computationally efficient architectures for various tasks such as autoregression, object recognition, facial expression recognition, and object detection using various datasets. In our experiments, the networks designed by our proposed method provided the same or higher performances but with far less computational complexity.