Reviewerly/exHarmony huggingface dataset:
Combination Of Modern Science And Artificial Intelligence

This article deals with the scientific basis of the interrelationship between science and artificial intelligence at a time when scientific innovations are evolving rapidly. Besides there is also concept of research and future prospects in the field of artificial intelligence. How the revolutionary changes in science today will use the potential of artificial intelligence in the future shows the interdependence and prospects for the development of technology and human thinking.
Marine Environment Monitoring Based on Virtual Reality and Fuzzy C-Means Clustering Algorithm

In modern society, with the rapid increase of population and the serious shortage of resources, the marine environment has been destroyed; there are also many people who go out to sea without permission, regardless of the legal constraints, fishing. This kind of behavior leads the marine environment to get worse and worse, so the real-time monitoring of the marine environment is very necessary. The main article marine environment monitoring, virtual reality technology, and fuzzy C-means clustering algorithm combine to improve the efficiency of monitoring and processing power of the data information. Through the application of virtual reality technology in the marine environment monitoring base and real-time simulation of the dynamics of the ocean, the monitoring personnel can understand the emergencies on the sea in time; the fuzzy C-means clustering algorithm is applied to the server receiving the data to classify the data. It is found in the experiment that when virtual reality technology and fuzzy C-means clustering algorithm are not used, the data of marine environment monitoring takes more than 1.3 s to return to the server, but, after applying two advanced technologies, the return efficiency is greatly improved, and the time consumed is less than 0.82 s. The results show that virtual reality technology and fuzzy C-means clustering algorithm can improve the efficiency of environmental monitoring, and through virtual reality technology, real-time monitoring of the marine environment can be achieved; in the absence of people out to sea, the actual situation on the sea can be clearly understood; and fuzzy C-means clustering algorithm can improve the speed of data processing, so that the monitoring personnel can solve the problem faster.
Cloud Technology and Management

Cloud is a buzzword nowadays. With the advent of cloud technology, the computing paradigm has changed drastically. It has given us new insight to see the computing world with new characteristics like on-demand, virtualization, elasticity, and many more. There are many definitions of cloud computing, but one definition that has been widely accepted by the IT industry is by NIST. NIST published two definitions, one in 2009 and the revised definition in 2011. NIST included all the characteristics of cloud computing in one concise framework. Cloud computing is provisioning of resources to end users. In order to control and orchestrate the resources, cloud management plays a vital role. The cloud possesses resources like infrastructure stack, servers, networking components, and storage devices. In addition to infrastructure, the cloud provisions services to end users in the form of platforms, applications, and data. Management of these resources in a proper and optimized manner is mandatory. With this, customers can leverage optimum and efficient use of resources in the cloud. Cloud management provides administrative control to resources in the cloud, like infrastructure, platforms, and applications. It is a combination of software, automation, policies, governance, and people that determine how those cloud computing services are made available.
Subgraph Matching on Attributed Multiplex Networks with Applications to Knowledge Graphs

Author(s): Tu, Thomas Kahn-Ming | Advisor(s): Bertozzi, Andrea L | Abstract: For applications involving graph-structured data, a natural problem is to identify subgraphs that match a given template. In this thesis, we present several approaches for solving this subgraph matching problem, including approaches for both exact and inexact subgraph matching. Additionally, we define several problems related to subgraph matching in order to obtain a better understanding of the entire solution space for the subgraph matching problem. We analyze a variety of synthetic and real-world multiplex datasets, including Sudoku, transportation networks, Twitter, the DBPedia knowledge graph, and several datasets from the DARPA-MAA program for modeling adversarial activity. We then show how our exact subgraph matching approach can be extended to handle errors in network alignment, the process of merging multiple networks together to form a single multiplex network. Finally, we address the closely related problem of matching variable length path-based graph templates, and apply our method to identify potential activation pathways within a biological knowledge graph extracted from scientific publications on COVID-19.
Prosthetic Visualizations for a Smart Heritage

The development of ICT has favoured the spread of real-time, pervasive and ubiquitous applications.In particular, VR and AR visualizations allow a close interrelation between people, data, environments and objects.Consequently, it is possible to enrich cultural heritage with information by visually superimposing multimedia content, in absolute respect of their physical consistency.In this way, it is possible to create a 'smart heritage' dimension that combines the potential of the 'Phygital' with the protection and enhancement of assets often characterized by important elements of fragility.An important role is played by AI applications, which automatically direct the processes of 'Interpretation' and 'Presentation' of the heritage.Based on the experience of the 3D reconstruction of the no longer existing Baroque configuration of the Basilica of Collemaggio in L'Aquila, aim of the paper is a theoretical-methodological reflection on the concept of VR / AR / MR for cultural heritage.
Synchronous Binocular Visible Light Parallax Map for Face Detection

In the binocular vision system, the parallax image is different when the real face of the three-dimensional structure is viewed from different viewing angles, while the face in photos and videos is flat or the planar structure is the same when viewed from different angles, so it is proposed A new method that uses a manual face binocular disparity map made by a synchronized binocular visible light camera combined with a convolutional neural network (convolutional neural network, CNN) to achieve two classifications. First, the Harr cascade classifier is used to extract faces in binocular images at the same time, and the extracted partial faces are made into a data set; then, the disparity map form that meets the experimental hypothesis is proposed and analyzed; finally, the ResNet18 network is used to compare different methods Binocular live body detection solution for human face. The accuracy rate of 99.12% is achieved on the self-made data set in the laboratory, and it has a good resistance to facial attacks on photos and videos. The results show that the method has a small amount of data, low hardware cost and good robustness to human faces due to changes in light and angle.
Controlling the Shapes of the Moiré Patterns Generated Using the Interferometry and a Spatial Light Modulator

Abstract: Moiré patterns are generated by the superposition of two periodic patterns of close frequencies. This present paper proposes a simple method to bridge the conventional gap between interferometry and moiré patterns by generating moiré patterns using straight-line interference fringes from a Michelson interferometer like setup and a circular grating pattern generated on a Spatial Light Modulator (SLM). The visible variation in the geometrical shapes of the moiré patterns by varying the ratio of the periods of the two periodic patterns has also been presented. The experimental results match with the simulation results. Keywords: Moiré, Interferometry, Spatial Light Modulator, Optics, Engineering physics
Romanoff's theorem for polynomials over finite fields revisited

Let $g$ be a given polynomial of positive degree over a finite field. Shparlinski and Weingartner proved that the proportion of monic polynomials of degree $n$ which can be represented as $h+g^k$ has the order of magnitude $1/\mathrm {deg}&nbsp;g$, where
Grouping Districts/Cities in Kalimantan Island Based on The People's Welfare Indicators Using Fuzzy C-Means and Subtractive Fuzzy C-Means Methods

Cluster analysis has the aim of grouping several objects of observation based on the data found in the information to describe the objects and their relationships. The grouping method used in this research is the Fuzzy C-Means (FCM) and Subtractive Fuzzy C-Means (SFCM) methods. The two grouping methods were applied to the people's welfare indicator data in 42 regencies/cities on the island of Kalimantan. The purpose of this study was to obtain the results of grouping districts/cities on the island of Kalimantan based on indicators of people's welfare and to obtain the results of a comparison of the FCM and SFCM methods. Based on the results of the analysis, the FCM and SFCM methods yield the same conclusions, so that in this study the FCM and SFCM methods are both good to use in classifying districts/cities on the island of Kalimantan based on people's welfare indicators and produce an optimal cluster of two clusters, namely the first cluster consisting of 10 Regencies/Cities on the island of Kalimantan, while the second cluster consists of 32 districts/cities on the island of Borneo.
Abusive Language and Hate Speech Detection for Javanese and Sundanese Languages in Tweets: Dataset and Preliminary Study

Indonesia's demography as an archipelago with lots of tribes and local languages added variances in their communication style.Every region in Indonesia has its own distinct culture, accents, and languages.The demographical condition can influence the characteristic of the language used in social media, such as Twitter.It can be found that Indonesian uses their own local language for communicating and expressing their mind in tweets.Nowadays, research about identifying hate speech and abusive language has become an attractive and developing topic.Moreover, the research related to Indonesian local languages still rarely encountered.This paper analyzes the use of machine learning approaches such as Naï ve Bayes (NB), Support Vector Machine (SVM), and Random Forest Decision Tree (RFDT) in detecting hate speech and abusive language in Sundanese and Javanese as Indonesian local languages.The classifiers were used with the several term weightings features, such as word n-grams and char n-grams.The experiments are evaluated using the F-measure.It achieves over 60 % for both local languages.
Homeroom Symposium and Calamity Response Level of Senior High School Learners

The second semester of the school year 2019 -2020 was bombarded with different calamities and pandemic that hamper school days.This paper sought to determine the significant difference of calamity response level of senior high school learners of Talangan Integrated National High School, Nagcarlan, Laguna, Philippines, when measured across sex groups, family income status and academic performance.With the conduct of monthly homeroom symposium that dealt with non-academic topics such as reaching out to people, continuing life in the midst of adversities, and developing full potential as God"s stewards, the researcher also measured the relationship of homeroom symposia application and calamity response level of learners.The writing of this research study revolved around the concept of a qualitative approach, in which data came from responses through survey questions provided to 32 Grade 11 learners through social media, in the midst of the quarantine period battling against COVID-19.The questionnaire has the following parts: learners" profile, homeroom symposium application, and calamity response level with 3 indicators: reaching out to people, continuous study at home in the midst of calamity, and maximizing time to do productive tasks.It was found out that there is a significant difference in calamity response level in terms of reaching out when checked across family income status.Those who have average family income tends to reach out more than those with below average and above average family income status.Also, it came out that there is a significant relationship between homeroom symposia application and calamity response level in terms of continuous study and maximizing time.Through homeroom symposium, learners" desire to continue studying at home and develop their potentials is sustained.Recommendations were given to classroom advisers, school heads, district supervisors and future researchers.
Deep Active Learning for Dual-View Mammogram Analysis

Supervised deep learning on medical imaging requires massive manual annotations, which are expertise-needed and time-consuming to perform. Active learning aims at reducing annotation efforts by adaptively selecting the most informative samples for labeling. We propose in this paper a novel deep active learning approach for dual-view mammogram analysis, especially for breast mass segmentation and detection, where the necessity of labeling is estimated by exploiting the consistency of predictions arising from craniocaudal (CC) and mediolateral-oblique (MLO) views. Intuitively, if mass segmentation or detection is robustly performed, prediction results achieved on CC and MLO views should be consistent. Exploiting the inter-view consistency is hence a good way to guide the sampling mechanism which iteratively selects the next image pairs to be labeled by an oracle. Experiments on public DDSM-CBIS and INbreast datasets demonstrate that comparable performance with respect to fully-supervised models can be reached using only 6.83% (9.56%) of labeled data for segmentation (detection). This suggests that combining dual-view mammogram analysis and active learning can strongly contribute to the development of computer-aided diagnosis systems.
Client-Server Approach for Managing Visual Attention, Integrated in a Cognitive Architecture for a Social Robot

This paper proposes a novel system for managing visual attention in social robots. This system is based on a client/server approach that allows integration with a cognitive architecture controlling the robot. The core of this architecture is a distributed knowledge graph, in which the perceptual needs are expressed by the presence of arcs to stimuli that need to be perceived. The attention server sends motion commands to the actuators of the robot, while the attention clients send requests through the common knowledge representation. The common knowledge graph is shared by all levels of the architecture. This system has been implemented on ROS and tested on a social robot to verify the validity of the approach and was used to solve the tests proposed in RoboCup @ Home and SciROc robotic competitions. The tests have been used to quantitatively compare the proposal to traditional visual attention mechanisms.
New Approach towards Generalizing Feistel Networks and Its Provable Security

This paper proposes a new approach to generalizing Feistel networks, which unifies the classical (balanced) Feistel network and the Lai–Massey structure. We call the new structure extended Feistel (E-Feistel) network. To justify its soundness, we investigate its indistinguishability using Patarin’s H-coefficient technique. As a result, it is proved that the 4-round key-alternating E-Feistel (KAEF) cipher with adequately derived keys and identical round functions is secure up to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mrow> <mi>n</mi> <mo>/</mo> <mn>2</mn> </mrow> </mrow> </msup> </math> queries, i.e., birthday-bound security. In addition, when adjacent round keys are independent and independent round functions are used, the 6-round KAEF is secure up to beyond-birthday-bound <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mrow> <mrow> <mn>2</mn> <mi>n</mi> </mrow> <mo>/</mo> <mn>3</mn> </mrow> </mrow> </msup> </math> queries. Our results indicate that the E-Feistel structure is secure and reliable and can be adopted in designing practical block ciphers.
Continual Learning for Food Recognition Using Class Incremental Extreme and Online Clustering Method: Self-Organizing Incremental Neural Network

Nowadays, standard intake of healthy food is necessary for keeping a balanced diet to avoid obesity in the human body.Literature has indicated that accurate dietary assessment is very important for assessing the effectiveness of weight loss interventions.However, most of the existing dietary assessment methods rely on memory.With the help of pervasive mobile devices and rich cloud services, it is now possible to develop new computer-aided food recognition system for accurate dietary assessment.But Food Recognition does not allow data incremental learning and often suffer from catastrophic interference problems during the class incremental learning.This is an important issue in food recognition since real-world food datasets are open-ended and dynamic, involving a continuous increase in food samples and food classes.Model retraining is often carried out to cope with the dynamic nature of the data, but this demands high-end computational resources and significant time.This paper proposes a new open-ended continual learning framework by employing transfer learning on deep models for feature extraction, Relief F for feature selection, and a novel adaptive reduced class incremental kernel extreme learning machine (ARCIKELM) for classification.Relief F reduces computational complexity by ranking and selecting the extracted features.The novel ARCIKELM classifier dynamically adjusts network architecture to reduce catastrophic forgetting.It addresses domain adaptation problems when new samples of the existing class arrive.Results show that the proposed framework learns new classes incrementally with less catastrophic inference and adapts domain changes while having competitive classification performance.
Image Preprocessing Using OTSU Method On Pap Smear Images For Accurate Segmentation

Cervical cancer is the second most common gynaecologic cancer worldwide. The most essential and challenging task in Medical field is image processing, early detection and analysis of Cervical cancer which is a time consuming process. So there is a need of a computer-assisted image screening system that aims early diagnosis of cervical cancer which is done by the accurate segmentation of cells. The process of partitioning the image into multiple segments refers to Image segmentation. This paper address the image thresholding using Otsu technique  approach to cell segmentation in Pap smear test images of cervical cancer   with the challenges of inconsistent discoloration, poor contrast cells. The proposed method shows how implementation of Otsu’s image thresholding method is best for image segmentation by performing comparative analysis of other image thresholding methods like simple thresholding and adaptive thresholding technique along with focusing on competitive performances of Otsu’s image thresholding method and its results. The python open CV library is used for implementation of all the image thresholding techniques. The computation of black pixel and white pixel ratio is carried out on each image thresholding technique .
Assessment of the Efficacies of Inverted Classroom Approach and Powerpoint Presentation on Students’ Achievement and Self-Efficacy in Biology

In this paper, the effect of inverted classroom approach and PowerPoint presentation on students' achievement and self-efficacy in biology has been discussed. Two research questions and two null hypotheses were formulated to guide the study. A Quasi-experiment of 2 x 2 factorial design was adopted to guide the study. 4,408 senior secondary two (SSII) Biology students in Agbani education zone of Enugu state composed the population. The sample size for the study consisted of 150 SSII Biology students. The sample was be drawn using a multistage sampling procedure. the Biology Achievement Test (BAT) and Biology Self-efficacy Scale (BSS) were used as an instrument for data collection. Three experts all from the Department of Science Education, University of Nigeria, Nsukka, validated the instruments. The reliability of BAT was determined using Kuder Richardson (K-R-21) formula which gave a reliability index of 0.79. The reliability of BSS gave an index of 0.81 using Cronbach alpha formula. Data obtained from the pre-test and post-test were quantitatively analysed. All the research questions were answered using mean and standard deviation while Analysis of Covariance (ANCOVA) was used to test all the hypotheses at 0.05 level of significance. Results from the study revealed that there is a significant effect of approaches on the achievement and self-efficacy of students taught biology using inverted classroom approach, and those taught using the PowerPoint presentation. Based on the findings, it was recommended among others that biology teachers should try to develop and adopt the use of PowerPoint presentation in their instructional delivery as it would enhance the overall achievement and self-efficacy of students in biology. Keywords: Inverted Classroom, PowerPoint Presentation, Achievement, Self-efficacy, Biology DOI: 10.7176/JEP/12-23-09 Publication date: August 31 st 2021
Blockchain Voting: WY Not?

Class of 2021.I would like to thank Professor George Mocsary for being my introduction into this emerging field of blockchain and the law,
Gender Specific Preferences Towards Anthropomorphic AI Devices and Robots

In recent years, artificial intelligence has changed the way people interact with each other or with technology.The discussion on the spectacular evolution of this phenomenon is more actual and present than ever because of the fact that devices equipped with artificial intelligence are more frequently present in lives of consumers, impacting their daily activities.The objective of this paper is to examine the gender specific perceptions and preferences towards anthropomorphic and empathetic characteristics of AI devices and robots.Anthropomorphic characteristics of an AI device or robot can have a positive influence on consumer's perception towards them and towards the decision of buying these products.This paper presents the way in which AI impacts the emotional perception of consumers and the differences of attitude between men and women towards the interaction with robots and AI devices.The empirical part of this paper includes the results of a research regarding the differences of perception and preference of consumers, towards the anthropomorphic characteristics of AI devices and robots, depending on gender.Using the discriminant analysis in SPSS 20, we have shown that men consider AI devices and robots more entertaining, while women appreciate more their empathetic human-like interaction and behavior.This result is important for the marketing communication when AI devices and robots are marketed.In the marketing communication addressed to men, features like the entertaining role of AI devices should be addressed, while in women specific communication channels the empathetic human-like interaction should be emphasized.