Reviewerly/exHarmony huggingface dataset:
Entropy and Divergence

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Convergence Analysis III: Stochastic Proximal Algorithms

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Nonconvex Optimization

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Optimizing UHD Production with Super Resolution Techniques

Super resolution (SR) is a technique that is getting increased attention. It was first deployed in high-end TVs to upconvert content, and now content providers are looking at how it can be used on the production side to guarantee the same high quality on all devices. Different techniques can be used to apply SR to standard definition (SD) and high definition (HD) content for transmitting ultra high definition (UHD) resolution. During a recent trial with France TV at the French Open, SD content with a 16:9 aspect ratio was processed offline to create UHD content, using a hybrid pipeline of deep restoration and SR methods. A pretrained deep neural network was used to produce an intermediate upscaled and deblurred HD version, which was then passed on to a faster, less memory-intensive, and machine-learning (ML)-based SR filter to produce the final UHD 4K resolution output. The result is a significantly better subjective viewing experience in terms of details and sharpness compared with classical filters used in live systems. Adequately addressing analog tape recordings, which have been digitized, is a significantly bigger technical challenge, which will need to be resolved in the future. While a one-model-fits-all approach for video enhancement sounds attractive due to its simplicity, the best visual quality is produced by applying purpose-built models trained for addressing the general types of degradation found in the source video.
Impact of the Use of Virtual SEDI Platform in the Reeducation of Children with Dyslexia in a Primary Educational Center

This article presents the result of an investigation on the impact of the Educational Software for Dyslexia (SEDI) platform. It was designed based on the analysis of the traditional process carried out by specialists in learning difficulties (DIFA) in Panama, to achieve the reeducation of the difficulties presented by children with dyslexia. The study was conducted in a primary school located in the province of Veraguas in Panama, with the objective of evaluating the impact on the development of re-educational activities within the SEDI platform. For this purpose, a sample of 14 students was taken, of which 7 were from the control group and 7 were from the experimental group. The results of this research will greatly contribute to the improvement of the software product that directly and indirectly benefits specialists in learning difficulties, grade teachers, parents, non-dyslexic children, and students of Psychology, Speech Therapy, and Education with an emphasis on Learning Difficulties.
Data Visualization Rules

We are done with the practice – now let's move on to the theoretical part. Usually people do the opposite, but I use this approach on purpose. Firstly, so as not to limit you to theory right away. Secondly, because there are exceptions in every visualization rule – I will also show their examples in this part.
Multi-view branch-shared subway platform crowd counting method

With the development of society, single-view crowd counting can no longer meet the existing social needs, in order to solve the existing problems, multi-view crowd counting emerged. The multi-view crowd counting is suitable for situations where a single view cannot cover the entire scene. In the rail transit industry, multi-view crowd counting can be used to count passengers on the platform, organize driving activity more scientifically and rationally, improve driving efficiency. In this paper, we propose a multi-view branch-shared crowd counting model for subway platforms, which uses a single branch to extract features and select scales for multi-perspective views. Compared with the existing multi-branch model, the structure of a single branch is more concise, and the feature redundancy is reduced. Considering the relationship between pixels, this paper introduces a combination of the local consistency loss function and Euclidean loss function. The performance of this model is tested on two public datasets (PETS2009 and CityStreet), and better results are obtained compared with the existing five methods.
Improved TOPSIS key intersection recognition based on fusion improved CRITIC weight method and independent weight method

Aiming at the problems such as the lack of consideration of space and other factors in the traditional key intersection identification model and the lack of objectivity in the weight distribution of intersection evaluation indicators in the urban traffic road network, an improved CRITIC (Criteria Importance Though Intercrieria Correlation)-independent weight method based on fusion is proposed. The improved TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. Aiming at the defects of the CRITIC method, the Theil index and the grey correlation degree are introduced to improve the CRITIC method; the independent weight method is used to eliminate the collinearity between the evaluation indexes and assign the weights of the evaluation indexes; in order to overcome the defects of using Euclidean distance as the distance measurement in TOPSIS , the weighted Mahalanobis distance is introduced to replace the Euclidean distance, and the key intersection identification is realized according to the closeness. The simulation results show that compared with the traditional key intersection identification model, the model proposed in this study can identify key intersections more accurately and comprehensively, which fully verifies the validity and accuracy of the model proposed in this study.
Impact Of Iot Adoption And Application For Smart Healthcare

The Internet of Things (IoT) is a rapidly growing technology that is transforming the healthcare industry. IoT devices and applications are enabling the collection and analysis of patient data, facilitating remote monitoring and diagnosis, and improving the efficiency and effectiveness of healthcare delivery. This abstract explores the impact of IoT adoption and application for smart healthcare. The study highlights the various applications of IoT in healthcare, such as remote patient monitoring, real-time patient tracking, and medication management. The study reviews the benefits of IoT in healthcare, such as improved patient outcomes, reduced costs, and increased efficiency. The study also examines the challenges of implementing IoT in healthcare, such as data privacy and security concerns, interoperability issues, and regulatory compliance. The study concludes that IoT is transforming the healthcare industry, enabling a new era of smart healthcare delivery. The adoption of IoT in healthcare has the potential to improve patient outcomes, reduce costs, and increase the efficiency and effectiveness of healthcare delivery. However, the challenges of implementing IoT in healthcare cannot be ignored, and the industry must work to address these challenges while harnessing the benefits of this emerging technology. Overall, the potential impact of IoT adoption and application for smart healthcare is significant and holds great promise for the future of healthcare delivery.
A Critical Review on Location Based Hybrid Filtering Recommender Systems

A recommender system is basically a type of information filtering system that suggests/recommends items based on the factors that constitute what the user is most interested in. The recommendations are typically provided in relation to different decision-making processes. Tourism is a social phenomenon where people deliberately travel in search of recreation, well-being, cultural exploration or get themselves softened up. But, the amount of information available online keeps expanding at exponential rates and thus the users have expressed their feeling of frustration at how challenging it is to find the appropriate information. This problem is called information overload. This is where the recommendation system comes into play which helps in solving the information overload problem. The hybrid system addresses the disadvantages where location-based recommendation systems are used individually, of which the most notable is the cold start issue. Furthermore, in order to improve the accuracy of the prediction to recommend items, these systems search for the ideal fusion of different approaches. Thus, the hybrid recommendation method solves the challenges like ‘cold start problem’, inability to capture changes in user behavior, sparsity and selecting correct choices for users. This paper explores the hybrid recommendation systems and other filtering techniques used in various fields, their challenges, how they can also be used for tourism recommender systems based on the longitudes and latitudes.
Multiplication is to addition as addition is to what?

When children are first taught about multiplication of the natural numbers, it is usually presented as repeated addition. Later, learning about raising to an exponent is presented as repeated multiplication. Then the following analogy is obvious: addition is to multiplication as multiplication is to raising to an exponent. An interesting question is to ask what happens if we go in the other direction. That is, multiplication is to addition as addition is to what? In this paper, we will answer this question, and show that there are several possible operations that could be used to answer the question. Some of them will be presented in connection with algebraic semirings. A semiring is a set, together with two operations, semiring addition and semiring multiplication, satisfying certain properties. We will define several semirings, such that the semiring multiplication is ordinary addition. In each case, the semiring addition will then be an answer to the question in the title of this paper.
Computer vision-based detection model for passenger flows inside urban rail transit stations

The study of short-term passenger flow of rail transit is based on the passenger flow data, usually taking the station as the smallest unit and can only grasp the passenger flow law between stations macroscopically. In order to study the microscopic passenger flow law in different scenes within the station, it is necessary to obtain the passenger flow sequence of the corresponding scene. Therefore, we propose an end-to-end refined short-term passenger flow identification model based on computer vision named PAX-Detect. The structure of the model are as follows: 1). Take pictures of the scene in the station and manually mark the passenger's head; 2). Using annotated datasets to train the YOLOv5 algorithm and pruning optimization;3). YOLOv5 and Deep SORT algorithm are used to detect and track passengers in the video, and the statistical results of the number of passengers are output in real-time. The experiment is carried out on the video data of the escalator entrance of a subway station in Beijing. The experimental results show that the accuracy of the real-time statistical algorithm for passenger numbers reaches 99.4% in the test scenario. The proposed model can identify the microscopic passenger flow in the scene of the rail transit station in real-time and has a certain application prospect.
Enabling scalable clinical interpretation of machine learning (ML)-based phenotypes using real world data

Background: Large and deep electronic health record (EHR) datasets have the potential to increase understanding of real-world patient journeys, and to identify subgroups of patients currently grouped with a common disease label but differing in outcomes and medical need. However, working with EHRs is still relatively new and challenging due to the heterogeneous nature of data. Increasing interest in machine learning (ML)-based EHR aggregation is mostly method-driven, i.e., building on available or newly developed methods. These methods, input requirements, and output are frequently difficult to interpret, especially without data science or statistical training. This endangers the ultimate aim of such analyses: generating actionable and clinically meaningful interpretation. Methods: We conducted stratification and sub-phenotyping of cardiovascular patients from combination of NHS EHR datasets with encounters from March 2014 to August 2020 from Oxford University Hospital (OUH) and from February 2014 to March 2020 from Chelsea Hospital and Westminster Hospital (ChelWest). The dataset contained diagnoses, laboratory tests, medications, and procedures from 1,480 and 918 patients from OUH and ChelWest respectively. Different clustering and preprocessing resulted in more than 100 clinical reports which would require significant time to be clinically interpreted. Results: We have developed a new framework facilitating clinical evaluation and interpretation of unsupervised patient stratification. For each new step within this framework we present example methods—pattern screening, meta clustering, surrogate modeling, and curation—which can be used at different stages within the analysis. Compared to a standard approach, we demonstrate the ability to condense results and optimize analysis time. For meta clustering, we show an example where the number of patient clusters are reduced from 72 to 3. With surrogate models, we quickly identified “blood sodium” as a stratification measure in heart failure patients, a likely identification of data bias as this is a routine measurement. By using cohort and feature curation, these and other irrelevant features were removed, increasing clinical meaningfulness. Conclusions: This study investigates approaches to perform patient stratification analysis at scale using large EHR datasets and multiple clustering methods for clinical research. We show examples on the effectiveness of the methods and hope to encourage further research in this field.
A Comparative Study of Bitcoin’s Price Fluctuations and Twitter Sentiments

This study is going to determine whether the sentiments in Twitter discourse about Bitcoin have an influence on the overall market and pricing of Bitcoin trades. The study aims to reveal the correlation between the volatility of the price of Bitcoin and how it relates to the sentiments in Twitter discourse. A large dataset of tweets mentioning Bitcoin has been amassed from the Twitter API between the months of February and July 2021, which will then be analysed against the Bitcoin trade performance. For this study, different predictive and descriptive models were applied that are equally important for data analysis. The sentiment for overall daily discourse has been categorised into three polarities, i.e., positive, neutral and negative using the VADER sentiment analysis. The overall polarity is then derived from a compounded and normalised dataset of each of the above-mentioned valencies for any given day. By utilising this study, it is hoped that potential Bitcoin buyers may make better and informed decisions about their purchases and that this study could be generalised to a broader analysis of social media discourse and how such means of communication could be used to drive consumer choice. Our results unequivocally prove that Twitter sentiments have a slight impact on the price and trading volumes of Bitcoin but the cryptocurrency has a unique way of sailing the storm of negative sentiments
Homograph Language Identification Using Machine Learning Techniques

Multilingual systems often have to deal with base language texts containing inclusions of multiple other languages in form of phrases, words, or even parts of words. In such settings, the system should be capable enough to classify the language of each piece of text effectively. This task becomes more complex in case of mixed-lingual sentences when the mono scripted corpora contain a considerable number of foreign language words. The present language identification systems accurate in identifying the language of mixed-lingual texts, however, they lack robustness to identify the language of homographs. In this work, we present a supervised learning framework using word sense disambiguation in the perspective of language identification to effectively deal with homographs. The dataset from Twitter is proposed in respect of significant homographs and to distinguish their language, a supervised learning framework based on word sense disambiguation is proposed. The framework is evaluated using various classification methods and results show that random forest algorithm performs the best among all with the F1-measure of 97.94.
Methods and Techniques of Professional Education in Professional Mobility Formation among Vocational School Students

The aim of the study is to substantiate the expediency of using methods and techniques of professional education that contribute to professional mobility formation among vocational school students. The paper presents the rationale for professional mobility formation among vocational school students, discusses the most effective methods and techniques of professional education that affect professional mobility formation and also reveals the results of testing the effectiveness of their application in the educational process. The scientific novelty of the study lies in selecting and substantiating methods and techniques of professional education for professional mobility formation among vocational school students taking into account the existing Russian experience. As a result of analysing the data obtained, it has been proved that the presented methods and techniques of professional education contributed to students’ awareness of the variability of future professional activities, their understanding of the need for development and self-development for successful employment and career growth, motivated them to further master the specialty and obtain additional qualifications.
What and Why? Interpretability in Colon Cancer Detection

Colorectal Cancer (CRC) is treated as one of a most frequent cancer, and it is the third leading cancer ended with death. Recent progress in deep neural network makes it possible to detect such cancer automatically and accurately from histopathological images. The main challenges of such neural network based model are that we cannot understand why it is making such decision and that leads to a question of trust. Histopathological whole slide images are typically large volume, and manual analysis of such data is tedious. In this paper, we have shown a traditional convolutional neural network (CNN) can be used to answer what (cancer or non-cancer) and attention-based pixel highlighter can highlight the important regions and can be helpful for human expert to validate the decision (why). We have used LC25000 dataset to evaluate the proposed method. The results shown a state-of-the-art accuracy with ability to explain the decision.
A BBF, RANSAC, and GWO-Based Hybrid Model for Forensic Image Investigation

Time consumption is a major issue in forensic image processing and there exists immense scope for an efficient system that could save time consumed in the detection of graphical content’s features. The research being reported here has investigated the role of BBF, RANSAC, and GWO in forensic graphical content processing and a hybrid forensic investigation model has been proposed that employs: (i) the BBF algorithm to search matched key points, (ii) the RANSAC to reject mismatched key points, and (iii) the GWO for optimization of results. The focus of the research was on forensic image processing systems and the loopholes in currently popular systems were considered.