Reviewerly/exHarmony huggingface dataset:
Machine Learning Classifiers for Efficient Spammers Detection in Twitter OSN

Online Social Networking (OSN) is one of the fastest-growing areas of communication over the internet. The popular social networking sites Facebook, LinkedIn, Instagram, Twitter etc. have attracted public helping them connected to family, friends and relatives. People share videos, texts, pictures, opinions, ideas, knowledge and some confidential information knowingly or unknowingly through these sites and thus, OSNs have become the main source of targets for cyber attackers. Cyberattacks have been increasing for the last few decades throwing a serious threat to the internet world. This paper first discusses various OSN threats such as misuse of identity, malware, phishing attacks etc. and also recommends some of the threats preventive measures. Since OSNs activities occur online, it is difficult to trust the users at the other end. Some users misguide genuine users (non-spammers). The one who misguides is known as spammers. Hence, our research work focuses on spammers detection in Twitter using machine learning models. Twitter data is used to carry out this research. For detection, three machine learning models are used, namely, Naive Bayes, Support Vector Machine and Random Forest. Performance of the models is evaluated using Precision, Recall and F measure metrics.
A Bi-directional, Multi-modality Framework for Segmentation of Brain Structures

Careful delineation of normal-tissue organs-at-risk is essential for brain tumor radiotherapy. However, this process is time-consuming and subject to variability. In this work, we propose a multi-modality framework that automatically segments eleven structures. Large structures used for defining the clinical target volume (CTV), such as the cerebellum, are directly segmented from T1-weighted and T2-weighted MR images. Smaller structures used in radiotherapy plan optimization are more difficult to segment, thus, a region of interest is first identified and cropped by a classification model, and then these structures are segmented from the new volume. This bi-directional framework allows for rapid model segmentation and good performance on a standardized challenge dataset when evaluated with volumetric and surface metrics.
Research of Deep Learning on Gastric Cancer Diagnosis

Gastroscopy is the first method to check gastrointestinal cancer and related diseases. Traditional manual methods have the disadvantages of time-consuming, high missed diagnosis and misdiagnosis rates. Image recognition technology based on deep learning has great potential in improving diagnosis efficiency and accuracy. We summarize the latest advances in deep learning in gastric cancer diagnosis from the aspects of data sets, image preprocessing, and classification algorithms. Most research teams cooperate with hospitals to build small data sets, and preprocess the images using threshold-based filters and other methods. In terms of gastric cancer and gastric disease classification, the DenseNet model has the highest ACC, F1 score and specific SP.
Abnormal event detection algorithm based on dual attention future frame prediction and gap fusion discrimination

Rapid and accurate detection of crowd abnormal events, such as stampedes and violent attacks in public places, has great research significance and application value. Due to the diversity and uncertainty of abnormal events, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for all abnormal events. According to the idea that “normal events can be predicted, abnormal events cannot be predicted,” we proposed a future frame prediction-based anomaly detection algorithm. First, the generative adversarial network (GAN) is trained by the normal videos to predict normal future frames. Then, it can determine the existence of abnormal events by identifying the difference between the ground truth and predicted video frame. In the design of the GAN, the attention module is introduced to improve the prediction level of the network. At the same time, the optical flow information is added for motion constraint to improve the constraint ability on the appearance characteristics. In the testing stage, the appearance gap and optical flow gap between the ground truth and the predicted video frame are fused to determine whether the frame is abnormal. The experimental results on the datasets of CUHK Avenue, UCSD, and ShanghaiTech show that the proposed algorithm is superior to that of the current mainstream anomaly detection algorithms.
Dynamic Iris Authentication by High-speed Gaze and Focus Control

Iris is an advantageous feature of biometric authentication that can be measured without contact and low possibility of damage and change. Conventional iris authentication systems, however, require approach to measurement devices or stationary of users due to necessity of the iris image with sufficient resolution, which reduces time efficiency of the authentication. In this paper, we propose a dynamic iris authentication system using high-speed gaze and focus control by high-speed image processing. We control high-speed rotational mirrors and liquid-based variable focus lens by triangulation with a wide-angle camera. We also control the liquid lens with additive modulation of sine wave to get the most focused iris image. We have experimentally evaluated the sufficient focus measure around eyes of static and dynamic users, and confirmed successful iris authentication.
Electronic information and education environment as a basis for ensuring education in pandemic settings

In order to ensure safe conditions for the education and upbringing of students, guided by the orders of the Ministry of Education and Science of Russia and Roszheldor from March 17, 2020, the implementation of all educational programs of higher, secondary professional education, additional educational programs (programs for advanced training, professional retraining, etc.) implemented in the university complex, including the completion of intermediate and state final certificates by students, as well as the completion of educational, industrial (including pre-diploma) practices for relevant educational programs are provided using remote technologies — in the Blackboard electronic information and educational environment, which is one of the elements of digitalization of the university complex. To optimize the educational process, the university developed a set of measures to improve and systematize work in an electronic information and educational environment, one of which was the transition from a traditional paper student’s record-book to an electronic one that functionally fully corresponds to a paper analogue. The purpose of introducing an electronic student’s record-book is to improve the quality of the provision of educational services through the integration and automation of individual information on the results of training in electronic form. The results of the development of educational programs by students in the electronic information and educational environment are automatically displayed in the electronic student’s record-book, which is especially relevant in pandemic conditions. The entire university complex is ready to continue the implementation of all educational programs of both of higher, secondary professional education, in full exclusively in remote mode using the electronic information and educational environment of Blackboard University.
A novel approach for dynamic information integration

With tremendous growth of data, in recent past, NoSQL databases have emerged and become the popular choices amongst top companies. Scalability can be easily achieved with these databases which are suitable for big companies working with streaming or social data. The consistency offered by these databases has always remained a concern for companies working with crucial and especially financial transactional data. Moreover, the entirely new architecture and new set of query language makes it more expensive and tedious to migrate the existing architecture on this new platform. The proposed architecture in this paper tries to eliminate these business issues by implementing the powers of NoSQL databases using the traditional RDBMS model. With this proposed architecture, the query language remains the same, the ACID properties can be maintained where required and more scalability and reliability can be achieved.
Study on Blended Teaching in Principles of Chemical Engineering Based on Cloud Platform

Abstract Aiming at the questions existing in the teaching of “Principles of Chemical Engineering”, in order to cultivate students’ engineering application abilities, new technologies are integrated into the “three-transfer” theory (momentum transfer, heat transfer, and mass transfer), experimental projects are renovated, and virtual simulation experiments are added. Then, a new teaching system for chemical principles and experimental teaching on the basis of “one platform and three clients” (one cloud platform, mobile client, teacher client, and administration client) is constructed. It is a student-oriented blended teaching system driven by problems; develop abundant teaching resources, further consummate multimedia courseware, computer-aided teaching, and develop a three-dimensional teaching system with textbooks, multimedia courseware, chemical simulation software, and online classroom. To realize a student-oriented interactive process for learning and teaching with the guidance of teachers, and give full play to students’ initiative, activity, and creativity during their learning process; focus on strengthening the assessment for the experimental courses of chemical principles, strengthen classroom discussion and creative practical work, and improve students’ engineering application ability, as well as abilities of analyzing problems, solving problems, and raising problems by applying comprehensive knowledge.
Transformational barriers in adapting the online learning for chemistry during COVID-19 pandemic

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Halimatus Syakdiyah, Tuti Iriani, Basuki Wibawa; Transformational barriers in adapting the online learning for chemistry during COVID-19 pandemic. AIP Conf. Proc. 2 April 2021; 2331 (1): 040023. https://doi.org/10.1063/5.0041845 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Secure and Efficient Certificateless Provable Data Possession for Cloud-Based Data Management Systems

Cloud computing provides important data storage, processing and management functions for data owners who share their data with data users through cloud servers. Although cloud computing brings significant advantages to data owners, the data stored in the cloud also faces many internal/external security attacks. Existing certificateless data provider schemes have the following two common shortcomings, i.e., most of which use plaintext to store data and use the complex bilinear pairing operation. To address such shortcomings, this scheme proposes secure and efficient certificateless provable data possession for cloud-based data management systems. In our solution, the data owners and cloud servers need to register with the key generation center only once. To ensure the integrity of encrypted data, we use the public key of the cloud server to participate in signature calculation. Moreover, the third-party verifier can audit the integrity of ciphertext without downloading the whole encrypted data. Security analysis shows that our proposed scheme is provably secure under the random oracle model. An evaluation of performance shows that our proposed scheme is efficient in terms of computation and communication overheads.
Picture fuzzy labelling graphs with an application

The main objective of this paper is to introduce the idea of picture fuzzy labelling of graphs and the concepts of strong arc, partial cut node, bridge of picture fuzzy labelling graphs, picture fuzzy labelling tree and cycle along with their properties and results. In addition, an application of the picture fuzzy graph labelling model for the human circulatory system has been discussed.
AI Based Cancer Detection Models Using Primary Care Datasets

Cancer is one of the most common and serious medical conditions with more than 144 000 Australians having been diagnosed with cancer in 2019. The non-specific nature of cancer symptoms and its low prevalence make cancer diagnosis particularly challenging, especially for primary care physicians/general practitioners (GPs). Ongoing research in cancer diagnosis places a heavy focus on understanding the epidemiology of cancer symptoms. With GPs being the first point of contact for most patients, prediction models using the patient’s medical history from primary care data can be a useful decision tool for early cancer detection. Our work both investigates the opportunities to use primary care data, specifically pathology data, for developing such decision tools and tackles the challenges coming from uncertainty in the data such as irregular pathology records. We present opportunities using the results within the frequently ordered full blood count to determine relevance to a future cancer diagnosis. By using several different pathology metrics, we show how we can generate features suitable for AI models that can be used to detect cancer 3 months earlier than current practices. Though the work focuses on patients with lung cancer, the methodology can be adjusted to other types of cancer and other data within the medical records. Our findings demonstrate that even when working with incomplete or obscure patient history, hematological measures contain valuable information that can indicate the potential of cancer diagnosis for up to 8 out of 10 patients. The use of the proposed decision tool presents a way to incorporate pathology data in the current cancer diagnosis practices and to incorporate various pathology tests or other primary care datasets for similar purposes.
Current Program States of Healthy Family &amp; Multicultural Family Support Centers and Related Factors to the Intention to Continue Online Programs During the COVID-19 Pandemic: Focusing on Experiences of Family Specialists

This study aims to explore how Healthy Family & Multicultural Family Centers have been running programs and how the family specialists have been experiencing online programs development during the COVID-19 pandemic. Using the survey data of 147 family specialists we found that 67.38% of the respondents reported that their centers have been operating online programs. The family specialists experienced difficulties in program development motivating participants and preparing for online programs. However, 51.80% of the respondents have the intention to continue online programs even after the COVID-19 pandemic ends. We found that the family specialists who had less difficulties recruiting instructors suitable for online services, or had experiences of higher job demands are more likely to intend to continue online programs afterwards. The results imply that the centers will provide more online programs after the COVID-19 pandemic and family specialists need to prepare for the changes.
Automatic braking system using fuzzy logic method

Abstract This research is design an autonomous car control system that can reduce the rate of traffic accident. Car prototype is designed by using Fuzzy Logic Control. Fuzzy logic is as main component of artificial intelligence has significantly influence the design controlled system. Fuzzy logic system is integrated with the arduino Mega 2560 microcontroller as the central control car prototype. In this paper, the Fuzzy logic used Mamdani method and 28 rule base. The fuzzy system was made based on fuzzy mechanism which are fuzzification, inference and defuzzification. Fuzzy logic has a variable input and output variables. Input variables used in this research is the distance between car and obstacle; the speed of car prototype and the output variable is the brake angle. The test is done with 5 variations of distance that is 100 cm, 125 cm, 150 cm, 175 cm, and 200 cm and two variations of speed that are 54 cm/s, and 63 cm/s. The test results showed that the prototype of the car was able to stop with a range of 22 cm - 35 cm from obstacle. The highest deceleration value at the speed of 63 cm/s is - 49.22 cm/s2 and the accuracy of braking on a system that has been designed is between 99.91% -100%.
Deep Learning for Medical Image Segmentation

Pixel accurate 2-D, 3-D medical image segmentation to identify abnormalities for further analysis is on high demand for computer-aided medical imaging applications. Various segmentation algorithms have been studied and applied in medical imaging for many years, but the problem remains challenging due to growing a large number of variety of applications starting from lung disease diagnosis based on x-ray images, nucleus detection, and segmentation based on microscopic pictures to kidney tumour segmentation. The recent innovation in deep learning brought revolutionary advances in computer vision. Image segmentation is one such area where deep learning shows its capacity and improves the performance by a larger margin than its successor. This chapter overviews the most popular deep learning-based image segmentation techniques and discusses their capabilities and basic advantages and limitations in the domain of medical imaging.
EDKT: An Extensible Deep Knowledge Tracing Model for Multiple Learning Factors

Knowledge Tracing (KT) refers to the problem of predicting learners’ future potential performance given their past learning history in e-learning systems. In order to better trace the learners’ knowledge, KT tasks have become increasingly complicated recently, and various factors related to learning (such as skill, exercise, hint, etc.) have been incorporated into the modeling of KS of the learner, which renders it inadequate for the traditional KT definition to formalize these tasks. Therefore, this paper first gives a more general formal definition of KT tasks, and then proposes an Extensible Deep Knowledge Tracing model for multiple learning factors based on this general definition, named EDKT. EDKT can integrate various different learning factors by extending or ablating factors in two plug-ins on the basis of minor modifications. To demonstrate the effectiveness of the proposed model, we conduct extensive experiments on three real-world benchmark datasets, and the results show that EDKT comprehensively outperforms the state-of-the-art KT models on predicting future learner responses.
Semantic-Based Image Retrieval Using Balanced Clustering Tree

In this paper, we propose a model for semantic-based image retrieval (SBIR) on the clustering balanced tree, C-Tree, and ontology to analyze the semantics of an image and extract a similar set of images, in which the input is a query image. This structure is constructed rely on separating the nodes from the leaf node and growing towards the root to create a balanced tree. A set of similar images are searched on the C-Tree to classify the query image based on the k-NN (k-Nearest Neighbor) algorithm. Then, the SPARQL query is generated to query the semantics of the image on ontology. We experimented with image datasets such as COREL (1000 images), Wang (10,800 images), ImageCLEF (20,000 images). The results are compared and evaluated with the relevant projects published recently on the same datasets.
Development Mobile Learning For Vegetable Farming In Indonesia Based On Mobile Cloud Computing

In vegetable production, farmers face many obstacles, such as the problems related to seeds, pest and disease control, commodity prices, and marketing of produces. There is almost no useful information and technology easily accessible to improve the situation. With the better penetration of the Internet to the villages and the wide availability of inexpensive mobile devices, mobile learning provides a good solution. This study is aimed to create a mobile learning that provides information and interactive communication about vegetable production needed by farmers using internet and mobile cloud computing concept, for better communication, sharing of information and profitability in agriculture. Based on the assessment by experts, 87.3% of them agreed that the mobile learning based on cloud computing for vegetable farming provide learning information about vegetable production.
English Language Accent Classification and Conversion using Machine Learning

Language is considered an integral part of human communication. Sometimes, a non-native speaker who is as fluent as the native speaker may not be comprehended suitably because of dissimilitude in accents. Also, detecting a person’s accent itself poses an arduous task due to subtle heterogeneity in pronunciation. In order to curtail these communication barriers arising because of accents distinctness, we have proposed a system to detect and convert speech which can conveniently differentiate one accent from another which has achieved an accuracy of 68.67%. The main motivation was solving the difficulty of Indians to understand the foreign accents and of foreigners to understand the Indian accent. The scope of this project would be limited to the English language. Detection of accent would involve identifying the speaker’s native language. We introduced a novel method of augmenting the traditionally exercised techniques by using Convolutional Neural Network (CNN) fused with Deep Neural Network (DNN) along with Recurrent Neural Networks (RNN) to subsequently enhance the accuracy. For accent conversion, audio features mainly consist of Mel-Frequency Cepstral Coefficients (MFCCs), Aperiodicity (AP) and Fundamental Frequency (F0). These features once extracted are processed by a Generative Adversarial Network (GAN) to convert source accent to target accent.
Distribution of Discourse Markers Elements or Discourse Particle as an Entity Relationship in Discourse

The process of determining groups, features and functions of discourse markers or discourse particle are debateable among language researchers. Various definitions and classification of elements have developed confusion of its status. The efforts to rearrange various opinions related to discourse markers or discourse particles according to certain functions are able to create systematic categories and expose its functions clearly. This article aimed to examine a number of discourse markers concepts or discourse particles and its functions according to the context of grammar and pragmatics as well as the features and domains of discourse markers or discourse particles from views from past studies. The methodology used was the examination of discourse markers or discourse particles into a theory, a model which explained the functions of discourse markers or discourse particles. This explained the research findings which listed the inventory concept of discourse markers or discourse particles, the functions, features and domain of discourse markers or discourse particles in detailed. Distribution of concepts, functions and domain of discourse markers or discourse particles from the views from past studies indicated that discourse markers or discourse particles is a meaningful entity in communication.