Reviewerly/exHarmony huggingface dataset:
Static Linear Algebra Problems Solving via Elegant Design Formula and Simplified Explicit Form of Zhang Neural Network with Illustrative Instances

Owing to the parallelism feature and convenient hardware implementation of recurrent neural network (RNN), many RNN models have been proposed to solve linear and nonlinear algebra problems. There into, Zhang neural network (ZNN) and gradient neural network (GNN) have received considerable attention and been exploited to investigate various dynamic models for solving various issues. However, the ZNN and GNN are depicted in quite rigorous formulations, which may limit the development of methodology of dynamic model design. This paper considers developing a new RNN design method named simplified Zhang neural network (SZNN) with more general formulation and versatile instances for statics. The comparisons between SZNN and two conventional methods are analyzed through theoretical results and simulation experiments of linear equation. Furthermore, to show the effectiveness and versatility of SZNN, Sylvester equation and matrix inversion are solved by the proposed SZNN models, and the simulation results substantiate the excellent convergence of the SZNN models.
Making use of quantitative content analysis: Insights from academia and business practice

This article features a moderated conversation between the author and five leading researchers and practitioners in the field of quantitative content analysis and, more specifically, computer-aided textual analysis (CATA). The interview subjects discuss the factors contributing to CATA’s rise as a research method as well as the benefits and pitfalls of using CATA. The conversation also covers best-practice advice that companies may employ to increase the accuracy of CATA results. These include using different programs or algorithms for the same problem, known as algorithm error.
Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology

Advances in data collection in radiation therapy have led to an abundance of opportunities for applying data mining and machine learning techniques to promote new data-driven insights. In light of these advances, supporting collaboration between machine learning experts and clinicians is important for facilitating better development and adoption of these models. Although many medical use-cases rely on spatial data, where understanding and visualizing the underlying structure of the data is important, little is known about the interpretability of spatial clustering results by clinical audiences. In this work, we reflect on the design of visualizations for explaining novel approaches to clustering complex anatomical data from head and neck cancer patients. These visualizations were developed, through participatory design, for clinical audiences during a multi-year collaboration with radiation oncologists and statisticians. We distill this collaboration into a set of lessons learned for creating visual and explainable spatial clustering for clinical users.
Low-resource Sinhala Speech Recognition using Deep Learning

Automatic speech recognition has progressed considerably in the past several decades for most of the European languages, but still, it is a prominent research area for most of the low-resourced languages. This project presents a study to build an automatic speech recognition (ASR) system using the Kaldi toolkit [1] for the Sinhala language which is one of the low-resourced languages with a large lexical variety. We experiment on different deep neural architectures such as pre-trained DNN, DNN, TDNN, TDNN+LSTM to enhance the acoustic modeling process and each of their performances is investigated in this document. A statistical model of GMM-HMM is also trained on the same dataset keeping it as the baseline model for comparing the effectiveness of a deep learning approach. For the language model, a corpus containing more than 20K sentences taken from UCSC LTRL [2] is used to generate the 220K extended lexicon. The experiments are conducted using a phonetically balanced training dataset consisting of twenty-five hours of speech data collected from fifty females and twenty males and tested on 1.6 hours of speech data. The results obtained show that deep neural network architectures exceed the performance of the statistical baseline model by giving a lower Word Error Rate (WER) of 7.48% on the test data set. The best observed lowest WERs were produced by the TDNN architectures.
Digital Modulation Classification based on Higher-order Moments and Characteristic Function

In this paper, a method based on moments and characteristic function is proposed for the classification of digital modulation signals. The method is robust against the random phase of the signals. The features used in this method are the modulus of moments and the number of peaks within modified characteristic function. The former is particularly effective for classifying signals into subclasses at low SNR, while the latter is used for classifying signals within subclasses. To remove the effect of magnitude of received signals on modulation classification, the received signals are normalized by power. A signal power estimation algorithm is proposed accordingly.
Joint Demosaicking and Denoising for CFA and MSFA Images Using a Mosaic-Adaptive Dense Residual Network

Color filter array (CFA) has been a basis for modern photography and recently multispectral filter array (MSFA) has gradually found its wide application. A deep learning network capable of joint demosaicking and denoising for both CFA and MSFA raw images is proposed in this paper. First, a novel dense residual network that includes multiple types of skip connections is introduced to learn features at different resolutions. Then, mosaic adaptive convolution and data augmentation based on mosaic shifting are put forward to fully make use of common characteristics of CFA and MSFA mosaic images. Moreover, an L1 loss function normalized by noise standard deviation is suggested to train the deep residual network so it does not rely on an explicit input of known or estimated noise standard deviation. Extensive experiments using simulated and real mosaic images from CFA cameras demonstrate that the proposed mosaic-adaptive dense residual network (MDRN) outperforms other state-of-the-art deep learning algorithms significantly. For simulated MSFA mosaics and real MSFA raw images, it also shows much improved results compared to other methods.
Attribute-Based Keyword Search Encryption Scheme with Verifiable Ciphertext via Blockchains

In order to realize the sharing of data by multiple users on the blockchain, this paper proposes an attribute-based searchable encryption with verifiable ciphertext scheme via blockchain. The scheme uses the public key algorithm to encrypt the keyword, the attribute-based encryption algorithm to encrypt the symmetric key, and the symmetric key to encrypt the file. The keyword index is stored on the blockchain, and the ciphertext of the symmetric key and file are stored on the cloud server. The scheme uses searchable encryption technology to achieve secure search on the blockchain, uses the immutability of the blockchain to ensure the security of the keyword ciphertext, uses verify algorithm guarantees the integrity of the data on the cloud. When the user's attributes need to be changed or the ciphertext access structure is changed, the scheme uses proxy re-encryption technology to implement the user's attribute revocation, and the authority center is responsible for the whole attribute revocation process. The security proof shows that the scheme can achieve ciphertext security, keyword security and anti-collusion. In addition, the numerical results show that the proposed scheme is effective.
Edge Detection Technique by Histogram Processing with Canny Edge Detector

Edge detection is one of the significant properties of many computer vision systems in image recognition, enhancement, compression, restoration, and so on. Though many research results were published, researchers still try to develop a more efficient edge detection algorithm that meets the current needs. We propose an image pre-processing based edge detection technique using canny edge detector. The main problem of canny edge detector is that it cannot identify the edges which are slightly vague due to the Gaussian smoothing. In this study, we solve the problem using the histogram processing of an image before using the canny edge detector. The result demonstrates that the proposed approach gives better outcomes compared to the state-of-the-art edge detectors.
Insulator Detection Dased on GIOU-YOLOv3

Insulator is an important power equipment component and plays an important role in power transmission.In view of the problem that the self-destructive damage of the insulator may cause the transmission line fault, the method that GIOU-YOLOv3 insulator detection and positioning is proposed. Compared with the IOU loss used in the traditional anchor-based detection method in YOLOv3, an insulator detection method which replaces the original loss function with the "GIoU loss function" in this paper. Without increasing the size of model, the detection accuracy of the insulator is improved. In the subsequent comparative experiment, the effectiveness of this method is verified.
Mine reclamation based on remote sensing information and error compensation extreme learning machine

In view of the many shortcomings of traditional copper ore detection methods, this paper proposes a new method for copper ore content detection, which is to detect copper ore content through spectral information. And we propose an error compensation extreme learning machine model. Using the error compensation extreme learning machine and the spectral information of the copper ore can quickly and accurately identify the copper content in the copper ore. Finally, the satellite remote sensing data were simulated, and the copper content distribution in the mining area was obtained. This provides guidance for future mining, reclamation and ore prospecting.
An Efficient Algorithm for Generating Harmonized Stereoscopic 360° VR Images

Providing realistic immersive experiences through virtual reality (VR) has become a major goal of video technology. Techniques to generate monocular VR images have been extended to the stereoscopic mode by adding the sense of depth. Research on the generation of stereoscopic VR images relies on either specialized equipment or general-purpose cameras. We propose an efficient algorithm to generate stereoscopic 360° VR images from pictures captured using affordable conventional cameras. To overcome the limited ability of conventional cameras to harmonize left and right VR images, we propose a model that represents the relation between left and right views using Euler angles. Experimental results show that the proposed algorithm provides better-harmonized images than other methods both qualitatively and quantitatively.
A Deep Semantic Segmentation Model for Image-based Table Structure Recognition

Table structure recognition is a crucial step for automatic table information extraction. It is conventional to utilize the features such as ruling lines or words for parsing the rows, columns and cells in a table. However, these conventional methods are ineffective for image-based tables when ruling lines are not visible or the words cannot be recognized through the OCR system. In order to overcome these problems, we propose a deep semantic segmentation model for image-based table structure recognition. Specifically, it is an end-to-end semantic segmentation neural network to determine a pixel-wise prediction map for an input table image where the labels are row separator, column separator, cell content and background. Moreover, by making the connected componnet analysis on the prediction map, we can obtain the bounding boxes of row separators, column separators and cell contents, more accurately. Then we number row/column separators in order by coordinate sorting. Thus, we can make full use of relative positions between row/column separators and cell contents, and further assign the row/column number to each cell. Due to the lack of training data, a large amount of synthetic data are automatically generated in our experiments. It is demonstrated by the experimental results that our proposed model is suitable for various table types, which can achieve 0.9769 and 0.9343 average F1 scores on a generative dataset when the IoU threshold is set to 0.6 and 0.8, respectively.
Style-Invariant Cardiac Image Segmentation with Test-Time Augmentation

Deep models often suffer from severe performance drop due to the appearance shift in the real clinical setting. Most of the existing learning-based methods rely on images from multiple sites/vendors or even corresponding labels. However, collecting enough unknown data to robustly model segmentation cannot always hold since the complex appearance shift caused by imaging factors in daily application. In this paper, we propose a novel style-invariant method for cardiac image segmentation. Based on the zero-shot style transfer to remove appearance shift and test-time augmentation to explore diverse underlying anatomy, our proposed method is effective in combating the appearance shift. Our contribution is three-fold. First, inspired by the spirit of universal style transfer, we develop a zero-shot stylization for content images to generate stylized images that appearance similarity to the style images. Second, we build up a robust cardiac segmentation model based on the U-Net structure. Our framework mainly consists of two networks during testing: the ST network for removing appearance shift and the segmentation network. Third, we investigate test-time augmentation to explore transformed versions of the stylized image for prediction and the results are merged. Notably, our proposed framework is fully test-time adaptation. Experiment results demonstrate that our methods are promising and generic for generalizing deep segmentation models.
Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer

In this paper, novel variants for the Ensemble Particle Swarm Optimizer (EPSO) are proposed where ten chaos maps are merged to enhance the EPSO’s performance by adaptively tuning its main parameters. The proposed Chaotic Ensemble Particle Swarm Optimizer variants (C.EPSO) are examined with complex nonlinear systems concerning equal order and variable-order fractional models of Permanent Magnet Synchronous Motor (PMSM). The proposed variants’ results are compared to that of its original version to recommend the most suitable variant for this non-linear optimization problem. A comparison between the introduced variants and the previously published algorithms proves the developed technique’s efficiency for further validation. The results emerge that the Chaotic Ensemble Particle Swarm variants with the Gauss/mouse map is the most proper variant for estimating the parameters of equal order and variable-order fractional PMSM models, as it achieves better accuracy, higher consistency, and faster convergence speed, it may lead to controlling the motor’s unwanted chaotic performance and protect it from ravage.
Phishing Attack Detection using Machine Learning Classification Techniques

Phishing attacks are the most common form of attacks that can happen over the internet. This method involves attackers attempting to collect data of a user without his/her consent through emails, URLs, and any other link that leads to a deceptive page where a user is persuaded to commit specific actions that can lead to the successful completion of an attack. These attacks can allow an attacker to collect vital information of the user that can often allow the attacker to impersonate the victim and get things done that only the victim should have been able to do, such as carry out transactions, or message someone else, or simply accessing the victim's data. Many studies have been carried out to discuss possible approaches to prevent such attacks. This research work includes three machine learning algorithms to predict any websites' phishing status. In the experimentation these models are trained using URL based features and attempted to prevent Zero-Day attacks by using proposed software proposal that differentiates the legitimate websites and phishing websites by analyzing the website's URL. From observations, the random forest classifier performed with a precision of 97%, a recall 99%, and F1 Score is 97%. Proposed model is fast and efficient as it only works based on the URL and it does not use other resources for analysis, as was the case for past studies.
AI-Enabled High-Level Layer for Posture Recognition Using The Azure Kinect in Unity3D

Posture recognition is one of the challenging tasks in computer vision. It lays on top of the pose estimation of the different body joints, and can be used in many applications. In the medical field, it can serve to assist patients in rehabilitation. In games it can be an elegant form of computer human interaction. Different Artificial Intelligence techniques were used over the years to precisely output the joint positions of the body from a single or stream of images. One of the great solutions that tacked well the pose estimation challenge is the Kinect camera, however further process is required to create and detect body postures. This article presents a customizable high-level layer that allows its users to easily create and manage body postures in unity3d projects allowing them to focus more on the other aspects of their project. The layer offers two detection methods, both scored more than 95% accuracy in each of the tested postures.
J-Recs: Principled and Scalable Recommendation Justification

Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users. Justifying recommendations, i.e., explaining why a user might like the recommended item, has been shown to improve user satisfaction and persuasiveness of the recommendation. In this paper, we develop a method for generating post-hoc justifications that can be applied to the output of any recommendation algorithm. Existing post-hoc methods are often limited in providing diverse justifications, as they either use only one of many available types of input data, or rely on the predefined templates. We address these limitations of earlier approaches by developing J-Recs, a method for producing concise and diverse justifications. J-Recs is a recommendation model-agnostic method that generates diverse justifications based on various types of product and user data (e.g., purchase history and product attributes). The challenge of jointly processing multiple types of data is addressed by designing a principled graph-based approach for justification generation. In addition to theoretical analysis, we present an extensive evaluation on synthetic and real-world data. Our results show that J-Recs satisfies desirable properties of justifications, and efficiently produces effective justifications, matching user preferences up to 20% more accurately than baselines.
Encryption scheme based on the automorphism group of the Ree function field

Internet of things (IoT) is a growing technology with a big market and impact to our lives. It can ease various different tasks for us. Meanwhile, IoT has many serious security threats, like data breaches, side-channel attacks, and virus and data authentication. Our present classical cryptography, like the Rivest-Shamir-Adleman (RSA) algorithm, work well under the classical computers. However, the technology is slowly shifting towards quantum computing, which has immense processing power and is more than enough to break the current cryptographic primitives in affordable time. So, it is required to design quantum cryptographic algorithms to prevent our systems from security breaches even before quantum computers will be available for commercial purposes on the market. In this paper, we describe a new implementation of MST3 cryptosystems based on the group of automorphisms of the field of the Pu function. The main difference of the presented implementation is the extension of the logarithmic signature and, as a consequence, the presence of multi-stage recovery of message parts from the ciphertext. The presented implementation of the cryptosystem is more reliable. The cryptanalysis complexity and message size for encryption are square times larger than the MST3 cryptosystem in the Suzuki group. This approach shows advantages and it is a quantum safe for the IoT use.
A survey on semi-structured web data manipulations by non-expert users

Since the emergence of web 2.0, data started floating all over the web, through online and offline applications, and across all application domains. Web data (semi-structured data loaded through web browsers and applications communicating via internet protocols such as HTTP), in particular XML-based data, is being used for simple commercial information display (i.e., XHTML/HTML in commercial websites), instant messaging (e.g., XMPP for messaging in Whatsapp, Skype, Gtalk etc.), financial transactions (i.e., CDF3 in ecommerce), medical record processing and storage (e.g., HL7 for electronic medical records), social media (e.g., XHTML/HTML in facebook, LinkedIn, Google Plus, etc.), and others. This phenomenon rendered web data manipulation (i.e., monitoring, modifying, controlling, etc.) by IT (information technology) experts, computer technicians and engineers utterly difficult seeing its exponential growth rate in volume and diversity. Not to mention the dynamicity of the data which is continuously changing on the clock and its heterogeneity (e.g., HTML/HTML5, XML, XHTML, RDF, OWL, etc.). Consequently, the manipulation of web data and in particular XML data (since XML has become one of the most essential data types used in computer communications) has shifted from the hands of computer scientists and programmers towards public computer users in all application domains. This has brought a new criterion into the web data manipulation research field, web data manipulation by non-experts. In this paper, we study and analyze existent techniques for manipulating semi-structured web data, particularly XML data, from a non-expert point of view while relating it to traditional manipulation techniques defined in the literature (i.e., filtering, adaptation, data extraction, transformation, access control, encryption, etc.). Web data manipulation techniques by non-experts were categorized under 3 major titles: (i) XML-oriented visual languages dealing with XML data extraction and transformations, (ii) Mashups tackling mainly XML restructuring with value manipulations, and (iii) Dataflow visual programming languages targeting non-expert manipulations and providing means to visually manipulate scientific data. A full analysis was conducted which allowed existent approaches/techniques to be compared and evaluated providing an overview of the current requirements on this subject.
Improving SSD for detecting small target in Remote Sensing Image

This paper proposes an improved SSD algorithm to identify small targets in remote sensing images. After introducing the performance of the traditional neural network in target recognition, this article focuses on explaining the SSD algorithm, a new improved algorithm then is proposed to solve the weakness of the SSD algorithm. The proposed algorithm combines the top-to-down structure of the FPN with the SSD algorithm. Experimental results verify that the proposed method performs better than SSD in remote sensing images without increasing the amount of calculation.