Reviewerly/exHarmony huggingface dataset:
Deep Signal-Dependent Denoising Noise Algorithm

Although many existing noise parameter estimations of image signal-dependent noise have certain denoising effects, most methods are not ideal. There are some problems with these methods, such as poor noise suppression effects, smooth details, lack of flexible denoising ability, etc. To solve these problems, in this study, we propose a deep signal-dependent denoising noise algorithm. The algorithm combines the model method with a convolutional neural network. We use the noise level of the noise image and the noise image together as the input of the convolutional neural network to obtain a wider range of noise levels than the single noise image as the input. In the convolutional neural network, the deep features of the image are extracted by multi-layer residuals, which solves the difficult problem of training. Extensive experiments demonstrate that our noise parameter estimation has good denoising performance.
A computational analysis of accessibility, readability, and explainability of figures in open access publications

Abstract Figures are an essential part of scientific communication. Yet little is understood about how accessible (e.g., color-blind safe), readable (e.g., good contrast), and explainable (e.g., contain captions and legends) they are. We develop computational techniques to measure these features and analyze a large sample of them from open access publications. Our method combines computer and human vision research principles, achieving high accuracy in detecting problems. In our sample, we estimated that around 20.6% of publications contain either accessibility, readability, or explainability issues (around 2% of all figures contain accessibility issues, 3% of diagnostic figures contain readability issues, and 23% of line charts contain explainability issues). We release our analysis as a dataset and methods for further examination by the scientific community.
Gesture-to-Text Translation Using SURF for Indian Sign Language

This research paper focuses on developing an effective gesture-to-text translation system using state-of-the-art computer vision techniques. The existing research on sign language translation has yet to utilize skin masking, edge detection, and feature extraction techniques to their full potential. Therefore, this study employs the speeded-up robust features (SURF) model for feature extraction, which is resistant to variations such as rotation, perspective scaling, and occlusion. The proposed system utilizes a bag of visual words (BoVW) model for gesture-to-text conversion. The study uses a dataset of 42,000 photographs consisting of alphabets (A–Z) and numbers (1–9), divided into 35 classes with 1200 shots per class. The pre-processing phase includes skin masking, where the RGB color space is converted to the HSV color space, and Canny edge detection is used for sharp edge detection. The SURF elements are grouped and converted to a visual language using the K-means mini-batch clustering technique. The proposed system’s performance is evaluated using several machine learning algorithms such as naïve Bayes, logistic regression, K nearest neighbors, support vector machine, and convolutional neural network. All the algorithms benefited from SURF, and the system’s accuracy is promising, ranging from 79% to 92%. This research study not only presents the development of an effective gesture-to-text translation system but also highlights the importance of using skin masking, edge detection, and feature extraction techniques to their full potential in sign language translation. The proposed system aims to bridge the communication gap between individuals who cannot speak and those who cannot understand Indian Sign Language (ISL).
Baugh-Wooley Multiplier design using Multiple Control Toffoli and Multiple Control Fredkin reversible logic gates

Abstract Most error-resilient media processing applications use multipliers as their basic building blocks. These are power-consumption and computationally intensive modules. In the existing works, several types of the multipliers were used to improve the hardware capacity, but those methods did not provide sufficient results. Therefore, in this manuscript, a Baugh-Wooley Multiplier design using Multiple Control Toffoli (MCT) and Multiple Control Fredrick gate (MCF) Reversible Logic gate (BWM-MCT-MCF) will be analyzed. Initially, Reversible Full Adder (RFA) is designed using Multiple Control Toffoli and Multiple Control Fredrick gate Reversible Logic gates. Then the proposed Reversible Full Adder is used in Baugh-Wooley Multiplier. By this, it reduces the hardware complexity with higher speed, lower area, lower power consumption. The proposed BWM-MCT-MCF multiplier is implemented in MATLAB, its performance shows lower Garbage output 22.78%, 24.88%, 20.95% compared with the existing designs, like BWM-FG-FRG, BWM-RL-TG, BWM-TG-FG respectively. Then the designed BWM-MCT-MCF is implemented using Xilinx ISE tool with the Virtex 5 device. From this, the performance of the proposed FPGA-BWM-MCT-MCF method shows lower delay 23.77%, 16.86% compared with the existing designs, like FPGA-BWM-RL-TG, FPGA-BWM-TG-FG respectively.
Data Clustering Methods for Recognition of Endoscopic Images in the Problems of Computer Medical Diagnosis

This paper presents the results of the analysis of existing methods for clustering data obtained during endoscopy of a larynx. A modification of the Viola-Jones method for image recognition using the flexible exit criterion is proposed. The Viola-Jones method explores all areas in the image and decides whether the recognized area belongs to the desired one by passing through a classified cascade. Endoscopic images have a large number of features, such as flare, noise, etc., which degrade the quality of recognition. To improve the quality of recognition, clustering with a flexible exit criterion was proposed, which satisfies the scalability criteria: changing the decision of the solution, instead of moving to another recognition area. It has been established that the proposed modification of the Viola-Jones method shows higher recognition results for endoscopic images.
Efficient Framework for Video Communication in IoT Applications

This paper is mainly concerned with video watermarking as a tool to secure the video transmission process over wireless channels. In addition, the relatability of the communication process is guaranteed through a hybrid error control scheme. The watermarking depends on applying a hybrid structure of Block-based Singular Value Decomposition (B-SVD) and SVD schemes. The rationale behind the utilization of SVD for video watermarking is the fact that singular values of a frame are not severely affected by noise or disturbance induced on that frame. The error control is performed with an efficient hybrid post-processing scheme, which comprises Spatial Circular-Scan Order Interpolation Algorithm (CSOIA), temporal Partitioning Motion Compensation Algorithm (PMCA) and Bayesian Kalman Filter (BKF). In the proposed framework, cornea and infrared frames are watermarked using two stages of SVD watermarking. The watermarking scheme includes embedding and extraction stages. Two watermark images are embedded in the cornea and infrared frames in the embedding stage using the hybrid structure of B-SVD and SVD schemes. Next, the watermarked cornea or infrared frames are transmitted through the erroneous wireless channel. The received corrupted cornea or infrared frames are recovered using the proposed post-processing error control schemes. Finally, the inverse process of hybrid SVD and B-SVD is employed in the watermark extraction stage. Simulation results for several cornea and infrared frames show that the proposed framework has extremely adequate subjective and objective video quality metrics compared to the traditional methods. In addition, the watermark robustness, security, and detectability are enhanced. Moreover, the experimental results clarify that the proposed watermarking scheme is superior and more secure than the other previous schemes for embedding and extracting watermarks efficiently in the presence of attacks.
LastPass breach underlines critical cyber trends

Headline INT: LastPass breach underlines critical cyber trends
Security Analysis on LUC-type Cryptosystems Using Common Modulus Attack

LUC-type cryptosystems are asymmetric key cryptosystems based on the Lucas sequence that is extended from RSA. The security challenge is comparable to RSA, which is based on the intractability of factoring a large number. This paper analysed the security of LUC, LUC3, and LUC4,6 cryptosystems using a common modulus attack. For a common modulus attack to be successful, a message must be transmitted to two distinct receivers with the same modulus. The strengths and limitations of the LUC, LUC3, and LUC4,6 cryptosystems when subjected to a common modulus attack were discussed as well. The results reveal that the LUC4,6 cryptosystem provides greater security than the LUC and LUC3.
Citra Perempuan dalam Novel Imperfect Karya Meira Anastasia dan Implikasinya terhadap Pembelajaran Bahasa Indonesia

This study discusses Imperfect, a novel written by Meira Anastasia which tells about how the female figure is one of God's creatures who has privileges and also has a very noble position. The charm of female beauty is a gift that can make men fascinated. However, on the other hand, women are often referred to as a disaster, and are considered weak by some people. The image of women is an illustration that discoveries are often made by finding gender, but it is not uncommon for self-confidence to emerge. Therefore, it becomes an interesting thing to discuss by raising stories about the image of women. The purpose of this study was to determine the image of women in Imperfect, a novel written by Meira Anastasia and its implications in Indonesian language learning. This study used descriptive qualitative method. There are five studies on the image of women in Meira Anastasia's novel called Imperfect, namely Citra Pigura with 30 findings equal to 60%, Citra Pilar with 9 findings equal to 18%, Citra Peraduan with 4 findings equal to 9%, Citra Pinggan 1 finding equal to 2% and Citra Pergaulan 6 findings equal 12%. The dominant female image in Meira Anastasia's novel, Imperfect is the Citra Pigura and Citra Pilar.
Reform of Craftsmanship Spirit Cultivation Integrated in Automation Major Curriculums

To solve abrupt, superficial and unsystematic problems in the cultivation of craftsmanship spirit in Automation Major Curriculums，the curriculum content and craftsmanship spirit were deeply integrated, taking the curriculum of “Automatic Detection Technology” as an example，it can effectively improve students' professional ability and professional quality. It emphasizes the relationship between the curriculum content and craftsmanship spirit, and integrate the cultivation of craftsmanship spirit into the curriculum teaching in the four dimensions of ideology and morality, will quality, professional skills and thinking quality, and by online and offline blending active and immersivity learning. Practice has proved that deep integration of professional curriculum and craftsmanship spirit cultivation can comprehensively improve students' professional ability, ethics and quality.
Metrological support of video measuring systems

Test objects of various types are used when creating and using video measuring systems which are part of the robots` technical vision, television sights, triangulation measuring systems, as well as 3D scanners. At the same time, an individual test object is developed for each type of device. This paper considers the possibility of development and construction of test objects for calibration (verification) of 2D and 3D video measuring systems, which are used to measure the geometric parameters of twodimensional (2D) and three-dimensional (3D) objects, respectively. We propose to use a measure based on a liquid crystal monitor to calibrate 2D systems. The calibration of such measure is based on the use of a high-precision displacement measurement devices. They are a laser interferometer and subpixel image resolution algorithms. The measure corresponds to the standard of the 3rd category according to the State verification scheme for measuring instruments of length in the range of 1·10 –9 –100 m and wavelengths in the range of 0.2–50 microns. The measure can be used to measure the modulation transfer function (MTF) of a video system and the point spread function (PSF). We also propose to use gauge block and move it in space according to the specific program using a translator and an external linear encoder. We introduce the technical specifications of the developed measures that can be used as working standards for calibration (verification) of a wide class of video measuring systems.
Detection of Phishing URLs Using Heuristics-Based Approach

Phishing is one of the types of cybercrime in which the attacker poses as a trustworthy entity with a view to obtaining sensitive information or data from the victim, this occurs usually through email. In the process, the victim may release information such as login credentials, credit card details, and other personally identifiable information that normally should not be revealed. The existing approaches used for phishing detection, therefore, need to be enhanced to effectively detect phishing. This study proposed a novel method for detecting phishing based on some heuristic features by extracting some relevant attributes, filtering these attributes, and classifying the same according to their impact on a website. The data explored for this study was retrieved from PhishTank and Alexa, which was later preprocessed for smooth model creation in python. The model created was evaluated and consistently gives a true positive rate of 85% based on the threshold set and an accuracy of 95.52%. The resulting output of this study has shown its reliability in the detection of phishing and could serve as a good benchmark for similar studies.
Sentiment Rating Prediction using Neural Collaborative Filtering

The main purpose of this study is to examine the working of neural collaborative filtering by utilizing the user reviews. We have done this research using the Amazon Reviews dataset. E-commerce is the most well-known application of recommendation algorithms. Websites that use information about a customer's tastes to provide a list of recommended things. Sentiment analysis can help you better understand the emotions, attitudes, and views of your users. Collaborative-filtering in a recommender system is beneficial to improve suggestion reliability as it focuses on the interaction between items and users, and the similarity of two items is determined by the similarity of their ratings by users. In the paper, sentiment rating for each user review has been calculated using the Vader lexicon approach to get the polarity score and a general framework of Neural Collaborative Filtering with a neural architecture has been studied that can learn any function from the input by replacing the inner product. NCF (Neural Collaborative Filtering) is a generic framework that can express and generalize matrix factorization, train user–item interaction function and introduce a multi-layer perceptron to boost NCF modeling with non-linearity. The Mean absolute error results obtained for different models claim for the NCF to be the best approach.
Research on Volume Measurement of Logs Based on Embedded Application

Spurred by the worldwide concern for forest protection and the increased log sales, most countries have standardized log volume calculations to avoid excessive timber and protect buyers.However, log volume is currently manually measured, suffering from high labor costs, low measurement progress, and imposing significant measurement errors.Thus, automatically obtaining the volumetric data of logs is a convenient and quick solution.Therefore, this work proposes a Mask Region Convolutional Neural Networkbased (R-CNN) algorithm for Logs volume measurement, named Wood Mask (WM) R-CNN.Specifically, we employ the Res2Net structure as the backbone to obtain receptive fields that exceed the input feature size, thus improving our model's multi-scale information fusion ability.Additionally, WM R-CNN relies on the Path Aggregation Feature Pyramid Network's (PAFPN's) path enhancement structure, shortening the low-level feature map's propagation path and improving the wood contour segmentation accuracy.Extensive experiments on the Vehicle-mounted Dense Logs (VMDL) dataset demonstrate that WM R-CNN affords a highly appealing segmentation accuracy for small, medium, and large wood, improving the corresponding mAP indicators against current methods by 2.0%, 1.2%, and 4.4%, respectively.Furthermore, a quantitative method based on TensorRT compresses the proposed model to deploy the WM R-CNN to mobile embedded devices.However, to compensate for the quantization loss, we introduce the expansion convolution operation method to manipulate the mask map and control the volume calculation error of all logs on a vehicle within 1%.The experiments reveal that the proposed method offers an appealing performance, verifying the algorithm's effectiveness and implementation ability on mobile terminals.
Accelerating Semi-Supervised Text Classification by K-Way Projecting Networks

The state of the art semi-supervised learning framework has greatly shown its potential in making deep and complex language models such as BERT highly effective for text classification tasks when labeled data is limited. However, the large size and low inference speed of such models may hinder their application on resources-limited or real-time use cases. In this paper, we propose a new approach in semi-supervised learning framework to distill large complex teacher model into a fairly lightweight student model which has the ability of acquiring knowledge from different layers of teacher with the usage of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -way projecting networks. Across four English datasets in text classification benchmarks and one dataset collected from an Chinese online course, our experiment shows that this student model achieves comparable results with the state of the art Transformer-based semi-supervised text classification methods, while using only 0.156MB parameters and having an inference speed 785 times faster than the teacher model.
Anytime Lifelong Multi-Agent Pathfinding in Topological Maps

This study addresses a lifelong multi-agent path finding (lifelong MAPF) problem that continuously solves an MAPF instance online according to newly assigned goals. Specifically, we focus on lifelong MAPF in a topological map. This problem is challenging because the movement of the agent is restricted to narrow corridors in a topological map, rather than the entire map area. Bypasses may be limited or farther away in corridors, significantly complicating the computation of paths. Furthermore, low-quality solutions may cause traffic congestion or even deadlock in a corridor. Therefore, we propose a novel lifelong MAPF method that effectively resolves conflicts in corridors based on an anytime strategy. This method gradually improves the solution quality by updating sub-paths with heavy traffic congestion. Furthermore, we adopt several improvement steps to effectively resolve corridor conflicts in a conflict-based search (CBS). This method significantly reduces the search space and computation time of CBS. We conducted extensive experiments on various topological maps in warehouse and railway environments. The results show that the proposed method outperforms state-of-the-art methods in terms of throughput and success rate. In particular, the proposed method can resolve collisions with a longer time horizon than existing methods, considerably improving throughput on a topological map with long-range corridors and heavy traffic congestion.
A Personalized Deep Neural Network to Recognize Human Activities in Healthy Subjects

Human Activity Recognition (HAR) using smartphone sensors is among the most demanding Artificial Intelligence (AI) topics for its growing applications in clinical healthcare monitoring. Although implementing an accurate HAR model is essential, developing a personalized one and adapting it to the end-user is challenging in real-life applications. There are various personalization methods in HAR. This research combines two personalization methods to adapt the proposed HAR model to individual subjects. Accordingly, we developed an Android application to capture the accelerometers of fifteen subjects during six predefined daily activities, such as standing, sitting, lying down, walking, going upstairs, and going downstairs. Subjects were instructed to carry the phone in predetermined positions in fixed or free modes. These positions include locations where people mostly carry their smartphones. The proposed personalized HAR model consisted of two main steps: (1) the dataset was divided into multiple groups based on the subjects' physical similarities, e.g., such as weight and height. Then, (2) based on multiple data splitting configurations (user-independent, user-dependent, and hybrid), the HAR model is generalized to the end-user in each group using a Long Short-Term Memory (LSTM). After normalization and windowing, the collected data were fed to the LSTM network. The model was trained and validated by 80% and 20% of the dataset, respectively. Finally, we evaluated our proposed model using the Human Activity Sensing Consortium (HASC) corpus 2011, a large, publicly available dataset. The proposed LSTM network with the user-dependent personalization method produced an average F-measure of 0.99 and 1 for our dataset and the HASC dataset, respectively.
Particle swarm optimizer based epithelial layer segmentation in CIElab color space

The study of epithelial tissue is critical for the pathologist to determine its grade and the presence of various diseases. Proper segmentation of the epithelial layer plays a significant role in different disease detection like Oral Sub-mucous Fibrosis (OSF), multi-class grading of Oral Squamous Cell Carcinoma (OSCC), and Epithelial Biopsy Image Classifier, etc. In this study, our main aim is automatic segmentation of epithelial layer from pathology images with is very helpful for Computer-Aided Diagnosis (CAD) based system. Here author used particle swarm optimization (PSO) and k-means clustering (KM) methods in CIElab color space for oral epithelial layer segmentation and also used different color spaces and its component for comparative study. Experimental results showed that the CIElab color space gave the best result among all other color spaces. The segmented images are compared with ground truth images and PSO with CIElab color space outperformed all other methods with an accuracy of 98.43%.