Reviewerly/exHarmony huggingface dataset:
Speckle noise reduction in echocardiography using a bank of filters based on oriented structuring elements

Speckle noise filtering has been investigated since at least fifty years, this multiplicative and granular interference may be found in any image, i.e, Synthetic Aperture Radar (SAR), optical coherence tomography, and of course, medical ultrasound imaging. Speckle noise is produced by structural characteristics of materials, in case of the ultrasound imaging case, by small structural irregularities. This work proposes a novel speckle noise filtering strategy using a bank of morphological multi-scale filters that captures anisotropic information and additionally preserves cardiac structures. This method is compared against commonly used filters, namely: Anisotropic Diffusion Filter (ADMSS), Non-Local Means Filter (NLMF) and Detail Preserving Anisotropic Diffusion Filter (DPAD).
Deterministic Broadcast-Based Controlled Remote State Preparation Scheme for Single-Qubit Real-Parameter State

In this paper, a novel scheme of broadcast-based controlled remote state preparation for single-qubit real-parameter state is proposed by using of three-qubit entangled states as quantum channel. The significant advantage of this scheme is that the successful probability is always equal to one. In our scheme, the single-qubit real-parameter state can be transmitted simultaneously from one sender to two remote receivers under the command of two controllers with the aid of suitable projective measurements and unitary operations. This protocol could be useful for multi-node quantum communication network.
Developing a radiographer-led breast boost volume definition pathway

Key words: Breast cancer, advanced practice, service improvement Introduction: Radiographer-led volume definition for whole breast irradiation has been established at the NNUH since 2013; however patients who require a photon breast boost plan have been traditionally excluded from this pathway as the consultant oncologists were required to delineate the clinical target volume (CTVtb). As the number of breast radiotherapy referrals per consultant have increased over recent years, the restriction for booking these patients during an individual clinical oncologist’s weekly planning clinic has also proved limiting upon the patient pathway. The advanced practitioner for breast cancer undertook training to enable radiographer-led target volume definition for patients referred for photon breast boost irradiation to reduce the time burden for the breast consultants and streamline the patient pathway. Method and Materials: An MSc module was undertaken to acquire the skills required; including research into histopathology, interpretation of pre-op imaging modalities including mammograms and MRI, and tailored training by breast consultants. CTVtb was defined by the location of surgical clips and evidence of post-surgical change. A total of 35 cases were delineated by the advanced practitioner and checked by the breast consultants in order to achieve competency, enabling independent practice. Results: Time saving has been significant; to date, 50 patients have been delineated by the advanced practitioner, saving 12.5 hours of consultant time. Knowledge gained through this role expansion has made problem-solving within dosimetry planning and on-treatment much more efficient. The increased flexibility to book patients across the entire working week has also ensured that the patient pathway is as condensed as possible. Conclusion and Discussion: Radiographer-led breast boost CTVtb delineation has been successful. Future plans include extending this role to partial breast localisation.
Text Data Analysis for Advertisement Recommendation System Using Multi-label Classification of Machine Learning

Everyone today can access the streaming content on their mobile phones, laptops very easily and video has been a very important and popular content on the internet. Nowadays, people are making their content and uploading it on the streaming platforms so the size of the video dataset became massive compared to text, audio and image datasets. So, providing advertisements on the video related to the topic of video will help to boost business. In this proposed system the title and description of video will be taken as input to classify the video using a natural language processing text classification method. Aim of Natural Language Processing is to solve the text classification problem by analyzing the contents of text data and decide its category. The proposed system would extract features from videos like title, description, and hashtags based on these extracted features we intend producing classification labels with the use of multi-label classification models. Analyzing produced labels concerning advertisement datasets we intend to provide advertisements on the video related to the topic of the video.
Hyperspectral face recognition based on SLRC for single sample problem

The hyperspectral imaging, adding many dimensions, has practical significance for robust face recognition. However, for hyperspectral face recognition, the main problems are small sample collection, low signal-to-noise ratio and inter band misalignment. In view of these problems, we propose a hyperspectral face recognition method based on SLRC (superposed linear representation classifier) for single sample problem. In the proposed method, one sample from each class is selected as training data, then the rest samples as test data. Since hyperspectral images have multiple bands, we average all bands as prototype dictionaries, and the difference between each band and the corresponding prototype dictionary as variation dictionary. Compared with other sparse representation classification methods, the proposed method can directly use a single sample to train hyperspectral face recognition and has no handcraft feature extraction. Experiments on the hyperspectral face database (PloyU-HSFD) validate that the proposed method can not only greatly increase the accuracy in single sample hyperspectral face recognition, but also improve the computation speed.
Expanded Dimensionality for Image Spectroscopy via Machine Learning

Earth and Space Science Open Archive posterOpen AccessYou are viewing the latest version by default [v1]Expanded Dimensionality for Image Spectroscopy via Machine LearningAuthors Fangcao Xu iD Guido Cervone Mark Salvador See all authors Fangcao XuiDCorresponding AuthorPennsylvania State University Main CampusiDhttps://orcid.org/0000-0001-5126-2885view email addressThe email was not providedcopy email addressGuido CervonePennsylvania State University Main Campusview email addressThe email was not providedcopy email addressMark SalvadorZi Incview email addressThe email was not providedcopy email address
Combination of 3des with dct algorithm in message security

Abstract In the digital era, communication through computer networks plays an important role. Through electronic communication, a person can make transactions or communication very quickly and practically. Sending data / messages from one place to another is much constrained by the issue of confidentiality. There are many ways you can hide data / messages that will be sent. First, using cryptographic techniques, namely by encoding data / messages using certain algorithms. Another technique is to insert a message that will be sent to other media, so that the message will be hidden and what will appear is other media used to insert messages.Here will be performed a Performance Analysis of the Combination of 3DES and DCT Algorithms in Securing Message Files into the Image.
Enhanced Semantic Features via Attention for Real-Time Visual Tracking

The key to balance the tracking accuracy and speed for object tracking algorithms is to learn powerful features via offline training in a lightweight tracking framework. With the development of attention mechanisms, it’s facile to apply attention to enhance the features without modifying the basic structure of the network. In this paper, a novel combination of different attention modules is implemented into a siamese-based tracker and boosts the tracking performance with little computational burden. In particular, by applying non-local self-attention and dual pooling channel attention, the extracted features tend to be more discriminative and adaptive due to the offline learning with tracking targets of different classes. Meanwhile, an Index-Difference-weight boosts the performance and reduces overfitting when full occlusion occurs. Our experimental results on OTB2013 and OTB2015 show that the tracker using the proposal to implement the attention modules can achieve state-of-the-art performance with a speed of 49 frames per second.
Motorbike Helmet with Heads-Up Display Navigation and Fall Detection System

Globally, there is an upward trend in the use of two-wheelers, both for transport and recreational purposes. This has led to an increase in the demand for a safety device that can act more than a cushioned wall between the wearer and the point of impact. The proposed solution is an Advanced Helmet with features like a Head-Up Display which would provide Distraction free Navigation and a Fall Detection system that could potentially reduce the number of deaths in road accidents by allowing faster Emergency Response through Accurate Location information being provided within minutes of the incident.
Disassembling the Dataset: A Camera Alignment Mechanism for Multiple Tasks in Person Re-identification

In person re-identification (ReID), one of the main challenges is thedistribution inconsistency among different datasets. Previous researchers havedefined several seemingly individual topics, such as fully supervised learning,direct transfer, domain adaptation, and incremental learning, each withdifferent settings of training and testing scenarios. These topics are designedin a dataset-wise manner, i.e., images from the same dataset, even fromdisjoint cameras, are presumed to follow the same distribution. However, suchdistribution is coarse and training-set-specific, and the ReID knowledgelearned in such manner works well only on the corresponding scenarios. Toaddress this issue, we propose a fine-grained distribution alignmentformulation, which disassembles the dataset and aligns all training and testingcameras. It connects all topics above and guarantees that ReID knowledge isalways learned, accumulated, and verified in the aligned distributions. Inpractice, we devise the Camera-based Batch Normalization, which is easy forintegration and nearly cost-free for existing ReID methods. Extensiveexperiments on the above four ReID tasks demonstrate the superiority of ourapproach. The code will be publicly available.
Texture Invariant Image Segmentation via Particle Swarm Optimization

Segmentation is a critical element in image processing since it can divide the image into various smaller regions for faster processing in later stages. Colour and shape, as the most distinguishable visual features in describing visual objects, have been adopted by many researchers as important information sources in image segmentation. In this work, an image segmentation approach is proposed that uses Particle Swarm Optimization (PSO) to find the appropriate combination of colour and shape information that can provide a proper volumetric description of image contents. The segmentation process is carried out via the computational friendly Otsu's multi-threshold. A spatial channel is computed and added to the PSO search space to address the issue of loss of spatial information led by the thresholding method.
Current Situation and Improvement Strategies of English Vocabulary Teaching in Senior High Schools under the New Curriculum

Vocabulary plays an important role in foreign language teaching. However, the current new curriculum has a large vocabulary, teachers'teaching methods are backward, and students' initiative and enthusiasm are poor, which seriously affects students'comprehensive application ability of English. This paper mainly discusses several feasible vocabulary teaching methods, aiming to help students really master the vocabulary they have learned and improve their language application ability.
Digital Humanities for the Study of Social Reading

Over the last decades, with technological advancements such as growing digitalization and the development of social media platforms, the act of reading has transformed into an interactive experience (Cordon-Garcia et al., 2013; Merga, 2015), where the Internet plays a key role (Murray, 2018). Social reading platforms like Goodreads and Wattpad are online environments where millions of people from all over the world come to share their love for the written word. Members come together to discuss what they have read and what they judge as good or bad literature, they recommend books to one another, and even try their hand at writing fiction. While a growing number of studies have been dedicated to this phenomenon (Nakamura, 2013; Ramdarshan Bold, 2016), so far only a few have adopted computational methods (Faggiolani and Vivarelli, 2016; Thelwall and Kousha, 2017) and none has combined these methods with empirical approaches to the study of literature and its effects. As the online environment is very different from the literary field as we know it, showing new types of complex interactions, we need to explore social reading and writing from both social and content perspectives. Social questions that should be investigated include interaction among users, questions of power (sources of literary authority), the effects on literacy and on reading behaviors, the changing system of social values (and of aesthetic evaluation). Content questions include questions about style, about the distribution and originality of comments, about the affective, reflective or social nature of content. With this panel, we will showcase the potential of studying social reading through the combination of multiple and interrelated approaches: from purely statistical, data-driven, and stylometric analyses, through qualitative and quantitative surveys of key users and a theory-driven qualitative taxonomy of reading valuation, towards a combination of the empirical and the computational, supported by a sound theoretical/methodological awareness. The substantial variety of case studies in four languages (English, German, Italian, and Dutch) will reflect the diversity of social reading, which can and should be studied from multiple points of view as well as with an array of methodological tools.
One of the most exciting and thrilling ...

One of the most exciting and thrilling experiences, as part of my office-based sedentary life, is upgrading the firmware of my devices over Bluetooth. Uploading
Minimum Classification Error Training with Speech Synthesis-Based Regularization for Speech Recognition

To increase the utility of Regularization, which is a common framework for avoiding the underestimation of ideal Bayes error, for speech recognizer training, we propose a new classifier training concept that incorporates a regularization term that represents the speech synthesis ability of classifier parameters. To implement our new concept, we first introduce a speech recognizer that embeds Line Spectral Pairs-Conjugate Structure-Algebraic Code Excited Linear Prediction (LSP-CS-ACELP) in a Multi-Prototype State-Transition-Model (MP-STM) classifier, define a regularization term that represents the speech synthesis ability by the distance between a training sample and its nearest MP-STM word model, and formalize a new Minimum Classification Error (MCE) training method for jointly minimizing a conventional smooth classification error count loss and the newly defined regularization term. We evaluated the proposed training method in an isolated-word, closed-vocabulary, and speaker-independent speech recognition task whose Bayes error is estimated to be about 20% and found that our method successfully produced an estimate of Bayes error (about 18.4%) with a single training run over a training dataset without such data resampling as Cross-Validation or the assumptions of sample distribution. Moreover, we investigated the quality of the synthesized speech using LSP parameters derived from the trained prototypes and found that the quality of the Bayes error estimation is clearly supported by the speech synthesis ability preserved in the training.
Information and teaching digital literacy: influence of the training branch

The digital competence has a great importance and boom in the European framework, given the recommendations given by this agency for lifelong learning. This fact requires some teachers with a consolidated base in said competence, so that they can transmit it to the students, and with it, comply with the recommendations marked from Europe. The purpose of this research is to describe and relate the branch of training of the students of the Master's Degree in Compulsory Secondary Education, Baccalaureate, Vocational Training and Language Teaching in the Campus of Ceuta with the area of digital competence of information and information literacy. The method applied in the study is quantitative, with descriptive and correlative scope, by non-experimental design, and longitudinal paradigm. The sample is 153 students. The instrument used is a questionnaire developed from the rubric created by the Common Framework of Digital Teaching Competence in 2017. The results show that the students' competence level is between A2 and B1 in the area of information and digital literacy. It is concluded that the training branch of future teachers is not an influential element in the competence level in the area named above
Epidemiology of Breast Cancer (BC) and Its Early Identification via Evolving Machine Learning Classification Tools (MLCT)–A Study

Now a day Breast cancer (BC) is very common and terrific disease in women, most detected and second leading cause of the ladies’ demise from the worldwide. Big number of people is passing their life or poor survival rate is because of this disease every year. Females are at high risk of BC, so it became quite essential and necessary for doctors to choose for an exact and suitable treatment for avoidance and remedy of cancer patients. So the basic motive is to find the cancer cells very correctly. Forecasting and categorization of BC using an effective and correct model of machine learning (ML) is essential for creation a new type of BC prognostic and diagnostic policies that really give a reduction push to the sufferer. Diversified technology, including Bayesian classifiers, Artificial Neural Networks and Decision Trees have been commonly applied in cancerous tumor. Undoubtedly methods used for Machine Learning may increase our understanding about breast cancer prediction and progression. It is important to consider these approaches in daily clinical practice. Neural networks are now a day’s very key and popular field in computational biology, chiefly in the area of radiology, oncology, cardiology and urology. In this study, we had summarized numerous ML techniques which could be used as an important tool by surgeons for timely detection, and prediction of cancerous cells has been studied and introduced.
Using persistent identifiers to track PhD outcomes

The British Library and STFC are working together to connect metadata relating to PhD theses to use of research facilities provided by STFC. Through the creation of a graph which connects metadata across EThOS, the UK national e-theses repository, the STFC insitutional repository ePubs and the Diamond Light Source repository, links between the theses, authors and institutions have been made. This pilot work has the potential to be extended to enhance the metadata stored within EThOS which is administered by The British Library. This work presents an exciting possibility for other institutions as this graph will present a method to enable the bi-directional updating of metadata held across different systems.
Visual Road Boundary Detection based Local Path Planner*

We propose a real-time visual road detection based local path planner for autonomous navigation in unstructured environments. The system consists of two parts: road detection and local path planning. Road boundary model based on convolution neural network is trained for local road detection in image frame. After perspective transformation, the result is transformed to the vehicle’s frame for path planning. The road midline generated from the road boundaries is used to represent the reference local path. Priori information with the global navigation topology map is combined to solve multi-roads selection. Finally, data fusion with historical detection results and predictive control of vehicle model is used for path optimization. In addition, obstacle avoidance combined with LIDAR perception is also taken into account to complete an autonomous navigation task in complex scenarios.A large number of experiments with various road conditions shows that the proposed method has a good adaptability and versatility for unstructured scenarios. The algorithm can effectively reduce the dependence on absolute positioning and LIDAR perception. So it is of great importance for autonomous navigation in scenarios without satellite or in unknown scenarios.
Hybrid Regularization with Elastic Net and Linear Discriminant Analysis for Zero-Shot Image Recognition

Zero-shot learning (ZSL) is the process of recognizing unseen samples from their related classes. Generally, ZSL is realized with the help of some pre-defined semantic information via projecting high dimensional visual features of data samples and class-related semantic vectors into a common embedding space. Although classification can be simply decided through the nearest-neighbor strategy, it usually suffers from problems of domain shift and hubness. In order to address these challenges, majority of researches have introduced regularization with some existing norms, such as lasso or ridge, to constrain the learned embedding. However, the sparse estimation of lasso may cause underfitting of training data, while ridge may introduce bias in the embedding space. In order to resolve these problems, this paper proposes a novel hybrid regularization approach by leveraging elastic net and linear discriminant analysis, and formulates a unified objective function that can be solved efficiently via a synchronous optimization strategy. The proposed method is evaluated on several benchmark image datasets for the task of generalized ZSL. The obtained results demonstrate the superiority of the proposed method over simple regularized methods as well as several previous models.
A Model for Developing Risk Prevention Management of Healthcare Service

The purpose of this study is to develop a new preventive risk management model for such activities as medical errors and accidents that occur in healthcare institutions. To conduct this study, the risk categories were classified by focusing on the medical and medical support departments through brainstorming with experts from the quality improvement (QI), nursing, and support departments of a comprehensive general hospital in Seoul during October 1-15, 2019. The results of the study indicate that the common factor of high risk items (RPN score) was the errors caused by poor management by the medical staff and support department providing the care service. This means that it can be fully preventable in advance of providing care services. It is also important to introduce advanced preventive systems to improve the care quality, in addition to reducing the risks that may result from lapse in good management practices. Thus, effective risk management in hospitals can be accomplished through preventive systems, staff training, and continuous monitoring of operations.