Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Pathologist: The use of Artificial Intelligence in Digital Healthcare

Artificial intelligence is bringing revolutionary changes to so many industries, by introducing them to a new era, full of technological advancements. The healthcare industry has been one of the most beneficial to this change, by merging digital transformation and healthcare, to form digital healthcare. Thereby introducing digital pathology, which implements image processing algorithms to help pathologists analyze and examine a diagnosis faster and more efficiently. It not only reduces the long hours pathologists used to take in laboratory analysis but also reduces human error. Therefore, healthcare digitalization has allowed the integration of computer vision into the medical field, with the use of Artificial intelligence techniques such as deep learning and machine learning algorithms. However, past research work has been limited to using AI models to diagnosis one specific disease at a time. Whereas this research work aims to develop an AI model that will automatically perform pathological analysis, to determine the diagnosis for multiple diseases from a medical image, then provide the medical report, while securing the patient’s data, and assisting them with any questions they might have regarding the diagnosis. This research applies deep learning and machine learning algorithms for image classification via CNN architectures and feature extraction via Morphological properties. The model achieved great outcomes, with high accuracy and good F1-score results of 90.47% and 0.8332 respectively. The resultant model diagnoses 12 medical disorders, with an overall of 29 diagnostic cases, making it the only one of its kind in digitized healthcare applications.
Improved Mask R-CNN Method for Intelligent Monitoring of Helmet in Power Plant

Traditional helmet wearing detection algorithms frequently encounter the problems of low accuracy and poor robustness during the processing of power plants monitoring image with complex background, low image quality, target occlusion, and small targets. A helmet wearing detection algorithm based on improved Mask R-CNN was proposed. By improving the backbone network structure of Mask R-CNN algorithm and increasing the number of anchors, the detection accuracy and robustness were improved correspondingly. Meanwhile, Multi-NMS algorithm was proposed to remove redundant labels and masks; The helmet information was bound to the head as the detection target and the Soft-NMS was introduced to reduce the interference of occlusion and handheld the helmet. Through the analysis of 500 evaluation data sets, the results yield the Mean Average Precision (mAP) of the algorithm of 97.1&#x0025;, the Precision (P) of 98.0&#x0025;, and the Recall (R) of 97.4&#x0025;, which can meet the requirements of intelligent monitoring in different scenarios of power plants.
Stemming for Better Indonesian Text-to-Phoneme

Stemming, the process of getting a root from a derivative word by removing the affixes (which can be prefixes, infixes, and suffixes), plays an important role in computational linguistics. This paper investigates the use of a stemmer to boost a nearest neighbor-based text-to-phoneme (TTP) or phonemicization for Indonesian language, which has some problems regarding derivatives with prefixes ⟨ber⟩, ⟨me⟩, ⟨per⟩, and ⟨ter⟩ since the graphene ⟨e⟩ is sometimes incorrectly phonemicized. The confix-stripping-based stemming (CS) is added as the preprocessing procedure for a fuzzy nearest neighbor-based phonemicization. It is then investigated to see its impact in solving such errors. The 5-fold cross-validation on 50k words indicates that it is capable of eliminating most errors produced by the Indonesian phonemicization caused by the four prefixes. The averaged phoneme error rate (PER) is decreased from 1.34% to 1.04%. A deep investigation informs that CS can solve some errors from roots and derivatives consisting of graphene sequences ⟨ber⟩, ⟨me⟩, and ⟨per⟩. But, it still frequently fails to solve the errors caused by some words containing a graphene sequence ⟨ter⟩. Compared to a state-of-the-art deep learning-based phonemicization system that exploits a Transformer network, the proposed model gives lower PER and complexity.
A New Approach to an Achievement Motivation System for the Choice of an Engineering High School and Field of Study

Achievement motivation is an important prerequisite for students’ engagement, overcoming study difficulties and, ultimately, successful completion of their studies. The aim of this study is to find out the current level of performance motivation of the population of students of engineering high schools, to compare its level with the standard for high school population and to find out whether there is a relationship between its level and choice. Two questionnaires were used in the research. The first was the School Achievement Motivation Questionnaire for Pupils (Hrabal, Pavelková, 2011). The theory of achievement motivation is then based on the concept of independence of the need for successful performance and the need to avoid failure. The resulting orientation of a person in a performance situation then depends on the predominance of one or the other tendency. The second questionnaire was focused on finding personal data, questions of school choice, satisfaction with school choice. The research was carried out at the beginning of 2020 and the sample consisted of over 900 students. Main results of the study: First, in the component of performance motivation NACH (need to achieve) the performance motivation of the group is statistically different from the group norm. In the NAF (the need to avoid failure) component, the results are statistically identical. Second, schools do not differ in the results for the NACH component, but differ in the NAF component. Third, in both components, the value of performance motivation for beginners and for final years in both components is significantly different from other years. Fourth, the student's statement on the choice of study at an engineering school as a primary choice is not related to the values of NACH and NAF. Fifth, the level of student's achievement motivation in both components is related to the student's idea of studying.
Learning Motivation and Quality of the Educational Process

At vocational training schools, a low level of students’ learning motivation can be observed. There are some important questions to be considered from the point of view of the school’s effectiveness: what kinds of factors affect learning motivation, and how the efficacy of education can be enhanced. Self-regulated learning is an active, constructive process that includes students’ goal setting, monitoring, controlling and reflections. There has been no research on self-regulated learning among students in vocational training school/vocational grammar school. The research is based on Pintrich’s and De Groot’s Motivation Strategies for Learning Questionnaire (MSLQ) concerning students’ motivational beliefs and self-regulated learning strategies. The goal of the study is to point out why these students are unmotivated and in which domain it is worth planning an intervention. The second aim of this work is to show the importance of the expansion of teacher’s key competences, while also pointing out those other competences which are unavoidable in the pedagogical work and about the application of quality management in the teaching process, since it is one of the ways of increasing the quality of education and it is designing a system of quality management at vocational training schools.
Multi-staged Feature-Attentive Network for Fashion Clothing Classification and Attribute Prediction

In the visual fashion clothing analysis, many researchers are attracted with the success of deep learning concepts. In this work, we introduce a multi-staged feature-attentive network to attain clothing category classification and attribute prediction. The proposed network in this work brings out a landmark-independent structure, whereas the existing landmark-dependent structures take up a lot of manpower for landmark annotation and also suffers from inter- and intra-individual variability. Our focus on this work is intensifying feature extraction by incorporating low-level and high-level feature fusion within fashion network. We are aiming on multi-level contextual features which utilise spatial and channel-wise information to create contextual feature supervision. Further, we enclose a semi-supervised learning approach to escalate fashion clothes analysis that utilises knowledge sharing among labelled and unlabelled data. To the best of our knowledge, this is the first attempt to investigate the semi-supervised learning in fashion clothing analysis by adopting multitask architecture which simultaneously study the clothing categories as well as its attributes. We evaluated the proposed approach on large-scale DeepFashion-C dataset while unlabelled dataset obtained from six publicly available fashion datasets. Experimental results show that the proposed architectures for supervised and semi-supervised learning entailing deep convolutional neural network outperforms many state-of-the-art techniques considerably, in fashion clothing analysis.
Data Security in Cloud Computing Using an Improved Attribute-Based Encryption

Cloud processing is a virtual figuring approach in which we can get shared assets on request. It is a promising worldview for coordinating cell phones. Also, the reconciliation is cloud-based, various-leveled, multi-client information shared climate. The shared environment of distributed computing stimulates security problems such as client authenticity and information privacy. It is a fundamental requirement for the advancement of distributed computing. This paper presents a progressive access control approach for employing an enhanced quality-based encryption and an upgraded three-layer structure to provide a secure and safe environment for distributed computing operations. A large amount of data is obtained from a wide range of mobile phones, for example, personal digital assistants (PDAs), and working telephones, which may be monitored and limited by the framework in the distributed computing model. The strategy is primarily concerned with getting to, putting away, and handling information. It is designed to ensure that only authorized clients have access to the same information and to limit unauthorized client access.
Improving DASH Encoding with Scenes and Downscaling Techniques for VoD Streaming

Video content represents a high percentage of the traffic on Internet with an increasing number of platforms offering live and on demand video. Most platforms use HTTP Adaptive Streaming (HAS) to deliver their content. In HAS, the provider splits the videos in segments and each segment is offered in multiple representations. The player can dynamically request the appropriate representation for the next segments depending on the varying conditions. This alleviates problems such as initial delay or stalling, thus providing a better quality of experience to users. These segments can have a fixed duration or can be adapted to match scenes up to a maximum duration of typically 10 seconds. In this work, we study the effects in time, quality and size of the encoded videos when downscaling the video to obtain the scenes. The experiments are performed using two codecs: H.264 and VP9, and using 10 videos with 4K resolution with a duration of more than 150 seconds. The videos are encoded using fixed segments and variable segments based on scenes (obtained from the downscaled video). Results show that the use of downscaling to obtain the scenes has a small impact on the final quality, while reduces the total time, the consumption of computational resources, and the size of the encoded video.
Temporal logic for social networks

Abstract This paper introduces a logic with a class of social network models that is based on standard Linear Temporal Logic, which allows for leveraging the power of existing model checkers for the analysis of social networks. We provide a short literature overview, and then define our logic and its axiomatization, present some simple motivational examples of both models and formulas and show its soundness and completeness via a translation into propositional formulas. Lastly, we discuss model checking, time complexity analysis and a Susceptible–Infectious–Recovered model variation for infectious diseases.
Analysis of MP4 Videos in 5G Using SDN

Current video content providers require a huge demand for network resources to obtain the best quality of experience from the end-users perspective. This measure can be achieved if the network can anticipate itself to different existing issues (bandwidth problems, etc.). Due to traditional Internet Protocol networks cannot handle this problem due to the rigidity of its architecture, software defined networks is introduced as a potential answer for resolving the existing challenges in video content delivery decoupling the control plane and data plane. Utilizing the unified perspective of the network, it is possible to develop a monitoring tool obtaining the network metrics to be used afterwards. In this paper, we propose the application of different clustering algorithms for monitoring software defined networks to validate the number of statistics queries decrements using the data and error rates to improve the network traffic and to reduce the overload. The simulations demonstrate that the expectation-maximization algorithm highly reduces the monitoring queries maintaining the best data and error rate values in the video delivery.
Cricket Twitter Data Sentiment Analysis and Prediction Exerted Machine Learning

The aim of this project is to create an algorithm that can accurately classify Twitter messages as positive or negative, in relation to a query term. Our hypothesis is that to have high accuracy in separating emotions in Twitter messages using machine learning methods. We propose an approach that analyses feeling of cricket fans and correlate sentiment to match play. We use data collected on twitter in the message form. To predict the outcome of a cricket match we are not going to rely on a single machine learning algorithm we are using at least two machine learning algorithms to compare the accuracy. We have applied modern classification techniques -Logistics Regression and Random Forest, and conducted a comparative study based on the overall cricket tweets. The project outcome is given in form of webpage giving both analysis and prediction of live tweets using Logistic Regression and older tweets using Random Forest.
Moving Towards Blockchain-Based Solution for Ensuring Secure Storage of Medical Images

Over the last few years, the world has been moving towards digital healthcare, where harnessing medical data distributed across multiple healthcare providers is essential to achieving personalized treatments. Though the efficiency and speed of the diagnosis process have increased due to the digitalization of healthcare data, it is at constant risk of cyberattacks. Medical images, in particular, seem to have become a regular victim of hackers, due to which there is a need to find a feasible solution for storing them securely. This work proposes a blockchain-based framework that leverages the InterPlanetary File system (IPFS) to provide decentralized storage for medical images. Our proposed blockchain storage model is implemented in the IPFS distributed file-sharing system, where each image is stored on IPFS, and its corresponding unique content-addressed hash is stored in the blockchain. The proposed model ensures the security of the medical images without any third-party dependency and eliminates the obstacles that arise due to centralized storage.
Computer Vision Technology Based on Deep Learning

With the development of artificial intelligence, computer vision technology that simulates human vision has received widespread attention. Based on the current commonly used method of computer vision technology-deep learning, this paper outlines the development of deep learning models, and determines the inflection point of the development of the introduction of convolutional neural networks. Not only that, convolutional neural networks combined with deep learning have developed rapidly and are widely used in the field of computer vision. Therefore, the structure and development process of the convolutional neural network are further analyzed. Subsequently, the three major difficulties in the field of computer vision-image classification, object detection, and image segmentation are compared and analyzed, and their differences and connection analysis are drawn. Finally, an overview of the difficulties to be optimized in computer vision, and computer vision technology It will continue to develop as a research hotspot.
Quantum search of many vertices on the joined complete graph

The quantum search on the graph is a very important topic. In this work, we develop a theoretic method on searching of single vertex on the graph [ Phys. Rev. Lett. 114 110503 (2015)], and systematically study the search of many vertices on one low-connectivity graph, the joined complete graph. Our results reveal that, with the optimal jumping rate obtained from the theoretical method, we can find such target vertices at the time <?CDATA $O(\sqrt{N})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:msqrt> <mml:mi>N</mml:mi> </mml:msqrt> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , where N is the number of total vertices. Therefore, the search of many vertices on the joined complete graph possessing quantum advantage has been achieved.
Meanings of Rough Sex across Gender, Sexual Identity, and Political Ideology: A Conditional Covariance Approach

The purpose of this study was to investigate how various subgroups based on gender, sexual identity, and political ideology conceptualize rough sex. We used available data from a 2020 survey of randomly sampled undergraduate students at a large public university in the Midwestern U.S. (n = 4,989). The dichotomous responses to 13 items on the rough sex meaning scale were analyzed using a conditional covariance approach to better understand which behaviors commonly described as rough sex behaviors were prevalent. Our results showed that rough sex was conceptualized largely as a two-dimensional construct for vast majority of subgroups. Across all subgroups, nine of the 13 behaviors clustered along two dimensions in the same way. Specifically, four behaviors related to hair pulling, being pinned down, hard thrusting, and throwing someone onto a bed clustered together as one dimension (spanking and tearing clothes off being present along this dimension for vast majority of the subgroups). Being tied up, slapping, choking, punching, and making someone have sex behaviors formed a separate dimension of rough sex across all studied groups (with biting and scratching being associated with this dimension for vast majority of subgroups).
I Know You Better: User Profile Aware Personalized Dialogue Generation

Recently, the response generation for dialogue systems has become a research hotspot both in the academic and business communities. Existing personalized response generation methods mainly stand on the chatbot’s perspective, and focus on improving the conversation consistency according to the chatbot’s traits. However, for building an emotionally intelligent and human-like chatbot, it is essential to consider the user’s profile, such as interests, hobbies, and life experiences, and generate the personalized response from the user-oriented perspective. In this paper, we introduce the user profile aware personalized dialogue generation task. For sparse profile users, we extend Model-Agnostic Meta-Learning (MAML) method to quickly adapt to new profiles by leveraging only a few dialogue samples. Extensive experiments are conducted on a real-world dataset, and the results have validated the superiority of the proposed model over strong baseline methods.
Efficient learning for decomposing and optimizing random networks

In this study, we consider the problem of node ranking in a random network. A Markov chain is defined for the network, and its transition probability matrix is unknown but can be learned by sampling random interactions among nodes. Our objective is to decompose the Markov chain into several ergodic classes and select the best node in each ergodic class. We propose a dynamic sampling procedure, which gives a probability guarantee on correct decomposition and maximizes a weighted probability of correct selection of the best node in each ergodic class. Numerical experiment results demonstrate the efficiency of the proposed sampling procedure.
Evaluation and Optimization of College English Teaching Effect Based on Improved Support Vector Machine Algorithm

Improved SVM algorithm improves the efficiency of College English teaching effect evaluation and meets the requirements of College English teaching evaluation. Based on the relevant theories, this paper constructs the evaluation index system with teachers and students as the main body and takes the questionnaire survey results as the input samples of the LSSVM algorithm. Compared with the evaluation accuracy of an optimized BP neural network and the category weighted gray target decision-making method, the results show that the evaluation accuracy of optimized LSSVM algorithm is 96.26%. Taking SIT as an example, this paper uses the optimized LSSVM algorithm to evaluate its teaching effect and obtains that teachers’ literature and teaching contents are important factors to improve the effect of English teaching. Therefore, this paper introduces the intelligent voice system to optimize the English teaching design of SIT. The teaching design is optimized from the dimensions of teaching objectives, learning situation, teaching content, teaching media and curriculum materials, and teaching procedures.
Research on the control of quantitative economic management variables under the numerical method based on stochastic ordinary differential equations

Abstract This paper explores several differential equation models in the economic system and analyses the solution and stability of the differential equation models in order to better reflect the theoretical results in mathematics into reality. From a mathematical point of view, the analysis illustrates the important role of differential equation models in economic systems.
The Strong Tracking Innovation Filter

Sliding innovation filter (SIF) has recently been introduced as a robust strategy for estimation of linear systems. The SIF has been extended to nonlinear systems via analytical linearization. However, as the performance of the extended SIF (ESIF) degrades in the presence of severe nonlinearities, this article has initially developed a derivative-free cubature SIF (CSIF) that uses statistical linearization for the error propagation. In addition, the SIF gain has been reformed to incorporate the innovation covariance matrix, thus reducing the estimation error. Furthermore, the adaptive fading factor has been employed to strengthen the robustness and convergence properties of the CSIF against abrupt changes of state variables. Simulation results of the proposed estimation algorithm named the strong tracking innovation filter (STIF) have been compared to those of the ESIF and the CSIF in different conditions of the modeling error in the dynamic system and statistical characteristics of the system inputs. This comparison has demonstrated the superior performance of the STIF in terms of the convergence rate, estimation accuracy, and the chattering elimination. Integration of the STIF into a sliding mode controller for the concurrent estimation and control of a Mars lander has reconfirmed the robustness and accuracy of the proposed STIF.