Reviewerly/exHarmony huggingface dataset:
Attention-Based Feature Decomposition-Reconstruction Network for Scene Text Detection

Recently, scene text detection has been a challenging task. Texts with arbitrary shape or large aspect ratio are usually hard to detect. Previous segmentation-based methods can describe curve text more accurately but suffer from over segmentation and text adhesion. In this paper, we propose attention-based feature decomposition-reconstruction network for scene text detection, which utilizes contextual information and low-level feature to enhance the performance of segmentation-based text detector. In the phase of feature fusion, we introduce cross level attention module to enrich contextual information of text by adding attention mechanism on fused multi-scaled feature. In the phase of probability map generation, a feature decomposition-reconstruction module is proposed to alleviate the over segmentation problem of large aspect ratio text, which decomposes text feature according to their frequency characteristic and then reconstructs it by adding low-level feature. Experiments have been conducted on two public benchmark datasets and results show that our proposed method achieves state-of-the-art performance.
Optimal scheme for dynamic adjustment of active mirror in FAST system

This paper mainly studies the optimal scheme of dynamic adjustment of active mirror in FAST system by establishing mathematical model. According to the constraints such as distance variation range, a mathematical model describing the dynamic trajectory of the position and angle of the active mirror is established, and the correctness of the solution of the model is verified by simulation. The optimal surface model is selected from all the results. When the measured object is directly above the fast system, it is modeled based on the ideal parabola to ensure that the expansion constraints and threshold conditions of the actuator are met. Because the distribution of actuator and cable in the whole fast system is not a uniform sphere, the change of radial distance of cable can be considered as the expansion of actuator; In addition, the model is optimized from two aspects: adjusting the position of focus on the horizontal plane and changing the size of zoom alignment. From these two aspects, the parabolic focusing range satisfying the expansion range is obtained. Finally, the optimization strategy of dynamic adjustment of active mirror in fast system is given.
An Efficient Analysis of Wavelet Techniques on Image Compression in MRI Images

Compression can be defined as an art form that involves the representation of information in a reduced form when compared to the original information. Image compression is extremely important in this day and age because of the increased demand for sharing and storing multimedia data. Compression is concerned with removing redundant or superfluous information from a file to reduce the size of the file. The reduction of the file size saves both memory and the time required to transmit and store data. Lossless compression techniques are distinguished from lossy compression techniques, which are distinguished from one another. This paper focuses on the literature studies on various compression techniques and the comparisons between them.
In-Depth Analysis and Prediction of Coupling Metrics of Open Source Software Projects

This research was conducted to perform an in-depth analysis of the coupling metrics of 10 Open Source Software (OSS) projects obtained from the Comets dataset. More precisely, we analyze the dataset of object-oriented OSS projects (having 17 code related metrics such as coupling, complexity, and size metrics) to (1) examine the relationships among the coupling and other metrics (size, complexity), (2) analyze the pattern in the growth of software metrics, and (3) propose a model for prediction of coupling. To generalize the model of coupling prediction, we have applied different machine learning algorithms and validated their performance on similar datasets. The results indicated that the Random forests algorithm outperforms all other models. The relation analysis specifies the existence of strong positive relationships between the coupling, size, and complexity metrics while the pattern analysis pinpointed the increasing growth trend for coupling. The obtained outcomes will help the developers, project managers, and stakeholders in better understating the state of software health
Training of Offline Handwritten Text Recognisers Using Computer-Generated Text

In order to convert physical documents to digital form, handwritten text recognisers are needed. The typical modern approach to do this is to train a neural network using Machine Learning to recognise text in physical documents one character or word at a time and produce the corresponding digital equivalent. To train these networks, a lot of data is required in the form of digital images of characters and/or words that have been written by humans. To gather and preprocess these images can be very tasking and error prone. This study provides an alternative way of acquiring training data for the handwritten text recognisers by training a Generative Adversarial Network (GAN) to generate images of handwritten text automatically and which can then be fed to the neural networks for the recognisers. Images from the Extended MNIST dataset were used to train a GAN which was then able to generate as many new images as required. The software was built with Python, and the Keras library was used in building the neural networks. After training for 5,000 epochs, the GAN was able to generate letters with up to 77.8% recognisability (by a human). It was also able to generate letters that were rotated and inverted, properties that are desirable for training handwriting recognisers.
Conditional Drive Environment Translation using StarGAN with CBIN

Automatic driving without human's control requires a lot of training to be able to adopt any situation. About situation, the environment at driving has the variety such as dark at night, wet road because of rain and the stack of snow. Preparing the drive image dataset with a lot of environment situations is the difficult task in collecting. This study tried solving the preparing problem by constructing deep learning model with limited data and translating single image to multi environment situations. For accomplishing this subject, we employed N domains translation model called StarGAN. In this paper, we investigated the StarGAN with enough conditional translation performance through comparing visualized results and FID score. We trained StarGANs including original to translate single image to 6 kinds of drive environment domain: daytime & clear, daytime & rainy, daytime & snowy, night & clear, night & rainy and night & snowy. Through experiments, we found the StarGAN employing “CBIN: Central Biasing Instance Normalization” and “AdaLIN: Adaptive Layer-Instance Normalization” at Generator, and “the adversarial loss of CAM Logit” at Discriminator could mark the lower FID score than original StarGAN.
Medical image de-identification using cloud services

Purpose: Patient privacy rules require removal of Protected Health Information (PHI) before sharing images publicly. Manual de-identification is no longer scalable due to the rapid increase in imaging data volume. Our goal was to configure and test the efficacy of an automated medical image de-identification (MIDI) pipeline using cloud services. Materials and Methods: Training and test datasets for validation of image de-identification, specifically prepared by placement of synthetic PHI in DICOM headers and image pixel data, were prepared by The Cancer Imaging Archive (TCIA). These datasets included 1,836/14,372 images from 21/93 patients, respectively. Answer keys based on TCIA de-identification conventions were made available for the two datasets. The MIDI pipeline was configured using the Google Cloud Platform Healthcare API, which is based on Google’s Data Loss Prevention API for sensitive information detection. Performance was also measured by monitoring throughput. Results: For DICOM header data elements, 99.8% of expected actions were performed correctly. The two incorrect actions included one false-positive case (information removed incorrectly), and one false-negative case (PHI not removed). For the image pixel data, one false-positive was noted. There were no false negatives; all sensitive information was correctly removed from all image pixel data. Throughput averaged at 58.4 images per second. Conclusion: The current implementation of the MIDI pipeline holds great promise for automated de-identification at scale. However, verification by a human expert is currently recommended. Optimization of the underlying algorithm could further increase accuracy.
Unranked Nominal Unification

In this paper we define an unranked nominal language, an extension of the nominal language with tuple variables and term tuples. We define the unification problem for unranked nominal terms and present an algorithm solving the unranked nominal unification problem.
Human Activity Recognition with IMU and Vital Signs Feature Fusion

Combining data from different sources into an integrated view is a recent trend taking advantage of the Internet of Things (IoT) evolution over the last years. The fusion of different modalities has applications in various fields, including healthcare and security systems. Human activity recognition (HAR) is among the most common applications of a healthcare or eldercare system. Inertial measurement unit (IMU) wearable sensors, like accelerometers and gyroscopes, are often utilized for HAR applications. In this paper, we investigate the performance of wearable IMU sensors along with vital signs sensors for HAR. A massive feature extraction, including both time and frequency domain features and transitional features for the vital signs, along with a feature selection method were performed. The classification algorithms and different early and late fusion methods were applied to a public dataset. Experimental results revealed that both IMU and vital signs achieve reasonable HAR accuracy and F1-score among all the classes. Feature selection significantly reduced the number of features from both IMU and vital signs features while also improved the classification accuracy. The rest of the early and late level fusion methods also performed better than each modality alone, reaching an accuracy level of up to 95.32%.
Opinion Spamming: Fake Consumer Review Detection

This research paper gives a review of our research, which intend implement machine learning model that can recognize whether the user/customer reviews on dataset are true or fake. For this, we have applied and compared many machine learning classifications to see which one giving the best result. Brief descriptions for each of the classification techniques are provided to aid understanding of why some methods are better than others in some cases. These days, people rely on content accessible on social media to make their own decisions (e.g., feedbacks and reviews on an entity). In this research paper for detecting opinion spamming, we have implemented three different techniques first one is Naïve Bayes, second one is Logistic Regression and third one is Support Vector Machine (SVM). There are chances that anyone can write feedback gives a brilliant chance to spammers to compose spam surveys about items and administrations for various interests. Distinguishing spammers and spam content is an intriguing subject to explore and albeit an extensive number of studies has been done as of late, however so far, the philosophies set forth still scarcely recognize spam audits, and none show the significance of each separated element type.
Hand Gesture Recognition using Convolutional Neural Network

The implicit message is usually brought to the spectators through activities involving various body parts like hands, face and arms. This is prominently known as Gesture and many such gestures are generally performed through hands in an involuntary manner. Smartness is to keep tabs on these hand gestures and derive purposeful details out of it. Convolutional neural networks (CNN) track these complex movements and help in extracting prime features. In this paper, training and testing were done consecutively with the aid of images to check the effectiveness of CNN and results are presented.
Assessing the Chaotic Map Population Initializations for Sine Cosine Algorithm Using the Case Study of Pairwise Test Suite Generation

Sine Cosine Algorithm (SCA) is a new population based meta-heuristic algorithm that exploits both the sine and cosine functions for its update operators. The main strength of SCA is its simplicity and straightforward implementation as well as provides no parameter control adjustment. For these reasons, SCA can be adopted in many optimization problems quickly and without much tuning. Despite the aforementioned advantages, SCA convergence can still be problematic depending on the initial starting positions of initial populations. In this work, we propose to assess the effectiveness of pseudo random (i.e., Random) as well as three chaotic map initializations (i.e., sine map, circle map, and logistic map) for SCA using the pairwise test case generation as our case study. The original SCA with random initialization (R_SCA) is outperformed on the adopted experiments by the proposed logistic map SCA (LM_SCA), circle map SCA (CM_SCA) and singer map SCA (SM_SCA).
Implementation of Spline-Wavelet Robust Bent Code in Code-Division Multiple Access

This paper presents the application of spline wavelet robust bent codes based on bent functions in communication systems with code-division multiple access (CDMA). The scheme of a spline wavelet robust bent code provides higher robust parameters and gives better protection from side-channel attacks in comparison with existing solutions. The comparison was made with the codes used in CDMA protocols. As a result, schemes have been proposed for introducing robust codes into the CDMA communication system. Implementation of these code constructions in CDMA can present a better level of providing information security.
Research on Teaching Mode of Intelligent Learning in Universities Based on 5G Mobile Network

With the rapid development of Internet technology, 5G communication technology and multimedia technology, the development of educational informatization to intelligence has become a trend. This paper proposes the research on teaching mode based on intelligent learning, which takes constructivism and pyramid learning theory as the theoretical support, intelligent cloud learning platform and WeChat platform as the technical support, 5G mobile network as the data transmission support, highlights the core concept of student-centered, and improves students' information literacy, Help students master the basic knowledge and skills of information technology, enhance students' information awareness, pay attention to the development of students' computing thinking, pay attention to the cultivation of digital learning ability and innovative thinking ability, enable students to establish correct values and sense of responsibility in the information society, and promote the cultivation of intelligent talents.
Facial Expression Recognition Using Keras in Machine Learning

The paper aims at the extraction of expression features in facial expression recognition. Facial expression identification has become increasingly significant in identifying the emotions of those who are confronted with it, as well as in picture processing. In this paper dataset is used namely the FER2013 dataset, it is a popularly used dataset for exploration and practices. The FER2013 dataset is used to detect facial expressions efficiently. AFERS is applied in this case. Facial detection, face feature extraction, and facial expression recognition are the three stages of this method for detecting facial expressions. And some other methods are also used as Pre-processing and Emotion classification. This paper outlines the efficiency of various widely spread machine learning algorithms for recognizing facial expressions, namely Support Vector Machine (SVM), Naïve Bayes.
Task Scheduling in Cloud Using Improved ANT Colony Algorithm

In recent years, with the in-depth development of power informatization, more and more power applications and tasks are deployed in the cloud. Due to the dynamic heterogeneity of cloud resources and power applications, realizing resource division and task scheduling is a challenging problem in cloud computing systems. Power applications need to achieve rapid response and minimum completion time, and the scheduler must consider the load of each cloud computing node to ensure the reliability of cloud computing. A task scheduling algorithm based on improved ant colony algorithm is proposed to solve the task scheduling problem in virtual machines. Through the improvement of the standard ant colony algorithm, the task scheduling time reduction and load balancing are realized while minimizing the overall completion time. The research results show that the algorithm effectively shortens the task scheduling time and realizes the load balancing of cloud nodes, providing a technical basis for the optimization of power cloud computing.
Coordination Control of the Automated Fiber Placement System Using Vision-Based Leader-Follower Approach

The use of multiple robots in automated fiber placement system (AFPS) makes it possible to manufacture composite components with complex shapes. The robots in such systems usually adopt independently trajectory planning and control methods. This makes AFPS lack coordination, and rely heavily on high-precision calibration of the system when completing complex fiber placement tasks. In this article, a novel vision-based leader-follower (VBLF) approach is proposed for a particular AFPS to improve its accuracy and coordination. The approach is composed of an offline trajectory planning module, a leader module, and a follower module. Within the approach details, the trajectory planning algorithm in offline trajectory planning module solves the problem of robot workspace constraints and mandrel occlusion in fiber placement tasks for complex component. Besides, a novel vision-based dynamic pose tracking method is proposed to make the follower robot track its desired relative trajectory with respect to the moving reference frame and keep the coordination of the two robots. The theoretical stability of the proposed approach is proved. The results of simulation and experiments indicate that the VBLF approach can effectively improve the coordination and accuracy of fiber placement processes.
Anti-Occlusion and Anti-deformation Hand Tracking Based on Kernel Correlation Filter

With the rapid development and wide application of digital technology and multimedia technology, somatosensory games, smart TV, sign language teaching and so on often need hand tracking. Tracking the hand with the Kernel Correlation Filtering(KCF) target tracking algorithm, when the target is blocked or deformed, the success rate will decrease. To solve these problems, an anti-occlusion and anti-deformation hand tracking algorithm based on KCF is proposed. Firstly, the extracted Histograms of Oriented Gradients(HOG) and Color Name(CN) features are directly fused and adaptively fused. In the training stage, the multi-template kernel correlation filter is used for training, and the responses of the two filters are fused adaptively in the detection stage. Secondly, the size of the response matrix and the positional relationship of specific values in the response matrix are used to judge the occlusion and deformation. Finally, if it is determined that occlusion or deformation occurs, the target is re-detected by skin color detection and some parameter adjustment. The analysis of experimental results on LaSOT data set and randomly intercepted data set shows that the performance of this algorithm is better than many existing algorithms, and the tracking accuracy and overlapping success rate are 8.7% and 2.6% higher than those of the corresponding algorithm ranked second.
Examining the Critical Success Factors in IT Projects

Many companies experience IT project failures in relatively new areas such as Big Data, Data Science, Enterprise Systems, Blockchain, Cloud Computing, IT security, and IoT. Because of inadequate research in identifying critical success factors for these projects, we conducted a Delphi study employing separate panels for each of two kinds of project implementations, those using a predictive lifecycle approach and those using an adaptive lifecycle approach. We found common critical success factors: user/client involvement, senior management support, effective monitoring and control, effective communication and feedback, good change and configuration management, having a competent project manager, proper project leadership, and excellent vendor performance. Both predictive and adaptive lifecycle IT projects had certain unique critical success factors. The findings provide guidance to IT Project Managers.
Sahlqvist correspondence theory for second-order propositional modal logic

Abstract Modal logic with propositional quantifiers (i.e. second-order propositional modal logic ($\textsf {SOPML}$)) has been considered since the early time of modal logic. Its expressive power and complexity are high, and its van Benthem–Rosen theorem and Goldblatt–Thomason theorem have been proved by ten Cate (2006, J. Philos. Logic, 35, 209–223). However, the Sahlqvist theory of $\textsf {SOPML}$ has not been considered in the literature. In the present paper, we fill in this gap. We develop the Sahlqvist correspondence theory for $\textsf {SOPML}$, which covers and properly extends existing Sahlqvist formulas in basic modal logic. We define the class of Sahlqvist formulas for $\textsf {SOMPL}$ step by step in a hierarchical way, each formula of which is shown to have a first-order correspondent over Kripke frames effectively computable by an algorithm $\textsf {ALBA}^{\textsf {SOMPL}}$. In addition, we show that certain $\varPi _2$-rules correspond to $\varPi _2$-Sahlqvist formulas in $\textsf {SOMPL}$, which further correspond to first-order conditions, and that even for very simple $\textsf {SOMPL}$ Sahlqvist formulas, they could already be non-canonical.
Complete Blood Count (CBC) Analysis Mobile Application

Complete Blood Count is one of the most commonly performed medical laboratory procedure today. It is required to detect various types of diseases. Presently, some small-scale clinics in the country still does the tedious, manual method of counting the blood cell. With Complete Blood Count Analysis System through Image Processing, automated CBC can be performed by mounting the smart phone camera on the viewer of the microscope. The input image will go through several image processing algorithms such as: Binary Thresholding, Clustering, and Hough Circle Technique. The result will be computed through the formulas used in the manual method of the CBC process. Experimental results show the developed system gains 94% of accuracy for counting the Hematocrit, Hemoglobin, Red Blood Cell, and White Blood Cell values
Combining Word Embeddings for Portuguese Named Entity Recognition

Named Entity Recognition (NER) is the task of identifying textual elements and categorizing them into predefined classes, such as names of people, locations, organizations, and others. Recently, NER systems based on neural networks have obtained state-of-the-art results. One of the main components of this kind of system is the word embeddings representation adopted that can be static (traditional) or contextual trained in language models. This work analyzes the strategy of combining traditional and contextual embeddings to obtain richer representations for Portuguese NER. We adopt a Bidirectional Long Short-Term Memory (BiLSTM) as a classification model integrated with the Conditional Random Fields (CRF) algorithm. Experiments were carried out in different corpora, and the results obtained demonstrate that the combination strategy of word embeddings models is viable and led to results comparable with state-of-the-art NER systems in Portuguese.