Reviewerly/exHarmony huggingface dataset:
3D shape measurement with temporal phase unwrapping and 1-D continuous wavelet transform

The correct phase unwrapping in the presence of noise is a difficult problem and it has been of great interest since the last decades as observed in the literature. This is due to the spatial dependency that exists in traditional 2-D phase-unwrapping algorithms and, as a consequence, spreads errors in the reconstructed phase map. To solve this, algorithms known as temporal phase unwrapping (TPU) have been proposed; here the intensity of a fringe image sequence changes as a function of time, and therefore the elements of the phase-map are independent of each other. Thus, the 3-D measurement vision system by projection of fringe images presented here consists in capturing a sequence of images with sinusoidal fringes deformed by the height of the object. Then, the phase is processed by using a TPU technique based the one-dimensional Continuous Wavelet Transform (CWT). It should be noted that the use of CWT in one dimension is to analyze the intensity variations of the temporal sampled phase images, and the importance of this is in the detection of the frequency from which the phase is obtained as a linear function by means of an unwrapping in one dimension. We present simulated experiments and some real applications in digital archaeology and zoological morphometrics that validate the proposal.
The Case for a Cost-Effective General-Purpose Computer Cluster for Small Colleges

The computational capabilities of commercial CPUs and GPUs reached a plateau but soft-ware applications are usually memory-intense tasks and they commonly need/utilize most recent hardware developments. Computer clusters are an expensive solution, although reliable and versatile, with a limited market share for small colleges.&#x0D; Small schools would typically rely on cloud-based systems because they are more afford-able (less expensive), manageable (no need to worry about the maintenance), and easier to implement (the burden is shifted into the datacenter). Here we provide arguments in favor of an on-campus hardware solution, which, while providing benefits for students, does not present the financial burden associated with larger and more powerful computer clus-ters. We think that instructors of engineering/computer science faculties might find this a viable and workable solution to improve the computing environment of their school without incurring the high cost of a ready-made solution. &#x0D; At the basis of this proposal is the acquisition of inexpensive refurbished hardware and of a type1 VMware hypervisor with a free licensing, as well as of a custom-made web plat-form to control the deployed hypervisors. VMware is a global leader in cloud infrastruc-ture and software-based solutions. In particular, the adoption of a customized "Elastic Sky X integrated" as hypervisor together with Virtual Operating Systems installed in the very same datastore, would constitute an interesting and working proof-of-concept achieving a computer cluster at a fraction of the market cost.
Application of Data Mining Technology in Quality Evaluation of Online Teaching Based on 6G

With the popularization of online teaching, the trend of intelligent education has further increased the needs for communication technology.The Sixth Generation Mobile Communication Standard (Hereinafter referred to as 6G) is becoming an inevitable requirement.With the big data generated by communication technology, we started to thinking about how to make full use of data mining to serve the quality evaluation of online teaching in colleges and universities.In the context of 6G Communication, this paper puts forward some application scenes of data mining in the subjects and contents of online teaching quality control.Compared with the phased and result-oriented evaluation of traditional quality evaluation, the application of data mining technology based on 6G communication is more student-centered, more objectivity and more effective.For building a high quality education system, it will realize the specific and process-oriented quality evaluation in online teaching of colleges and universities, and achieve continuous improvement.
AMBA: Adaptive Monarch Butterfly Algorithm based Information of Transfer Scheduling in Cloud for Big Information Application

In present days cloud computing is most famous innovation and has a great deal of research potential in different zones like allocation of resource, scheduling of data transfer, security as well as privacy and so on.Data transfer Scheduling is one of the significant issues for improving the proficiency of all cloud based administrations.In cloud computing, data transfer scheduling is utilized to allot the task to best reasonable asset for execution.There are various types of data transfer scheduling algorithms.A few issues like execution time, execution cost, high delay time, complexity, and high data transfer cost as well as various optimization problems have been measured in existing papers.To tackle all the above problems, in this paper, a new Adaptive approach are introduced which is a combination of Monarch Butterfly and Genetic (AMBA) Algorithm based data transfer scheduling is proposed.So here the concept is to develop an optimal algorithm for scheduling the data transfer in an efficient way which helps in reducing the data transfer time.The performance of proposed methodology analyzed in terms of evaluation metrics.
Analysis of the Perceptual Quality Performance of Different HEVC Coding Tools

Each new video encoding standard includes encoding techniques that aim to improve the performance and quality of the previous standards. During the development of these techniques, PSNR was used as the main distortion metric. However, the PSNR metric does not consider the subjectivity of the human visual system, so that the performance of some coding tools is questionable from the perceptual point of view. To further explore this point, we have developed a detailed study about the perceptual sensibility of different HEVC video coding tools. In order to perform this study, we used some popular objective quality assessment metrics to measure the perceptual response of every single coding tool. The conclusion of this work will help to determine the set of HEVC coding tools that provides, in general, the best perceptual response.
THE MODELING OF "MUSTAHIQ" DATA USING K-MEANS CLUSTERING ALGORITHM AND BIG DATA ANALYSIS (CASE STUDY: LAZ)

There are a lot of Mustahiq data in LAZ (Lembaga Amil Zakat) which is spread in many locations today. Each LAZ has Mustahiq data that is different in type from other LAZ. There are differences in Mustahiq data types so that data that is so large cannot be used together even though the purpose of the data is the same to determine Mustahiq data. And to find out whether the Mustahiq data is still up to date (renewable), of course it will be very difficult due to the types of data types that are not uniform or different, long time span, and the large amount of data. To give zakat to Mustahiq certainly requires speed of information. So, in giving zakat to Mustahiq, LAZ will find it difficult to monitor the progress of the Mustahiq. It is possible that a Mustahiq will change his condition to become a Muzaki. This is the reason for the researcher to take this theme in order to help the existing LAZ to make it easier to cluster Mustahiq data. Furthermore, the data already in the cluster can be used by LAZ managers to develop the organization. This can also be a reference for determining the zakat recipient cluster to those who are entitled later. The research is "Modeling using K-Means Algorithm and Big Data analysis in determine Mustahiq data ". We got data Mustahiq with random sample from online and offline survey. Online data survey with Google form and Offline Data survey we got from BAZNAS (National Amil Zakat Agency) in Indonesia and another zakat agency (LAZ) in Jakarta. We conducted by combining data to analyzed using Big Data and K-Means Algorithm. K-Means algorithm is an algorithm for cluster n objects based on attributes into k partitions according to criteria that will be determined from large and diverse Mustahiq data. This research focuses on modeling that applies K-Means Algorithms and Big Data Analysis. The first we made tools for grouping simulation test data. We do several experimental and simulation scenarios to find a model in mapping Mustahiq data to developed best model for processing the data. The results of this study are displayed in tabular and graphical form, namely the proposed Mustahiq data processing model at Zakat Agency (LAZ). The simulation result from a total of 1109 correspondents, 300 correspondents are included in the Mustahiq cluster and 809 correspondents are included in the Non-Mustahiq cluster and have an accuracy rate of 83.40%. That means accuracy of the system modeling able to determine data Mustahiq. Result filtering based on Gender is “Male” accuracy 83.93%, based on Age is ”30-39” accuracy 71,03%, based on Job is “PNS” accuracy 83.39%, based on Education is “S1” accuracy 83.79%. The advantaged of research expected to be able to determine quickly whether the person meets the criteria as a mustahik or Muzaki for LAZ (Amil Zakat Agency). The result of modeling is K-Means clustering algorithm application program can be used if UIN Syarif Hidayatullah Jakarta want to develop LAZ (Amil Zakat Agency) too.
Can Emoticons Be Used to Predict Sentiment?

Getting a machine to understand the meaning of language is a largely important goal to a wide variety of fields, from advertising to entertainment.In this work, we focus on Youtube comments from the top twohundred trending videos as a source of user text data.Previous Sentiment Analysis Models focus on using hand-labelled data or predetermined lexicon-s.Our goal is to train a model to label comment sentiment with emoticons by training on other user-generated comments containing emoticons.Naive Bayes and Recurrent Neural Network models are both investigated and im-plemented in this study, and the validation accuracies for Naive Bayes model and Recurrent Neural Network model are found to be .548and .812.
Abstract PR-01: Real-time, point-of-care pathology diagnosis via embedded deep learning

Abstract There is an urgent need for widespread cancer diagnosis in low resource settings, especially in contrast to areas with developed healthcare systems. According to a study in The Lancet, in the U.S. there is one pathologist for every 20,000 individuals, while in Sub-Saharan Africa, there is only one for every million. In addition, current telepathology systems for cancer diagnosis mostly rely on pathologists performing remotely, which is low-throughput and requires more time and resources. With the growth of telepathology, remote diagnosis becomes a viable solution to address the lack of skilled pathologists in developing regions. Here, we present a cost-efficient device that incorporates embedded deep learning to achieve real time, point-of-care diagnosis of whole pathology slides. We achieve this with a low-cost, 3D-printable microscope that uses the Raspberry Pi and camera module to capture high-resolution images of slides. Then, using a weakly-supervised deep-learning model run on the NVIDIA Jetson Nano, the device is able to accurately classify the whole slide without any pixel-level annotations. Furthermore, the model’s attention-based approach to diagnosis allows us to generate human-interpretable heatmaps displaying the regions most influential to the model’s diagnosis. Our device also incorporates a touch screen and batteries to increase accessibility as an easy-to-use and low maintenance device while still maintaining an efficient runtime given the available resources. Overall, we demonstrate that the device is capable of achieving accurate, high-throughput, and interpretable cancer diagnoses in low resource settings. Citation Format: Bowen Chen, Max Lu, Jana Lipkova, Faisal Mahmood. Real-time, point-of-care pathology diagnosis via embedded deep learning [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-01.
Image to Image Translation using Deep Learning Techniques

This project aims to make image-to-image translation for different tasks using deep learning techniques.Using dataset generated by a combination of online images.We have generated images for different tasks.Our innovation is we train and test the cycle-consistent adversarial networks using dataset.We have tried to fine-tune hyper parameters such as batch size, learning rate, lambda in loss function and trying to add dropout to our network.The experiment outcomes show that our method can successfully transfer for disparate tasks while conserving the original content.
Mutual Information Kullback-Leibler Divergence based for Clustering Categorical Data

Clustering is a process of grouping a set of objects into multiple clusters, so that the collection of similar objects will be grouped into the same cluster and dissimilar objects will be grouped into other clusters. Fuzzy k-means Algorithm is one of clustering algorithm by partitioning data into k clusters employing Euclidean distance as a distance function. This research discusses clustering categorical data using Fuzzy k-Means Kullback-Leibler Divergence. In the determination of the distance between data and center of cluster uses mutual information known as Kullback-Leibler Divergence distance between the joint distribution and the product distribution from two marginal distributions. Extensive theoretical analysis was performed to show the effectiveness of the proposed method. Moreover, the proposed method's comparison results with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy were also performed employing several datasets from UCI Machine Learning. The experiment results show that the proposed Algorithm provides good results both from clustering quality and accuracy for clustering categorical data as compared to Fuzzy Centroid and Fuzzy k-Partition.
The Use of Digital Tools in Scholarly Activities. Empirical Findings on the State of Digitization of Science in Germany, Focusing on Saxony

Abstract Scholars are only beginning to understand what digitization means for their work, that is, the conduct of science. Taking a broad perspective on e-science, this paper provides empirical insights about two important aspects of the digitization of science, namely the use of digital tools in scholarly activities and scholars’ perceptions of the change such use entails. The results of a German-wide survey of scholars and supplementary qualitative interviews in the years 2012 and 2013 show that the majority of scholars have adopted digital tools and that scholarly practice is affected profoundly by the use of such tools. This does not apply to web 2.0 tools, which remain a niche medium for some scholars. Small but significant differences exist between disciplines, and decisions about individual tool use are utilitarian. Further research is needed to assess the changes from a longitudinal perspective.
Deviations of Eye Movements and Head Rotation during Response Tasks using Targets and HMD

In order to extract the relationship between head-eye movements and visual impressions while using Head-mounted displays (HMD) to view images, two types of response task experiments involving visual stimuli were conducted, using several visual angles. Both head movements and visual impressions increased with the visual angles during the observation of moving visual stimuli. Vestibulo-ocular reflex (VOR) gains as a ratio of eye and head movements decreased as the visual angle increased because the eye movement deviations were relatively small. When the viewer's reaction required an action, such as rotating the head or aiming at a target, the strength of the viewer's impression increased, though VOR gains remained at the same level.
CONSTRUCTING A STRUCTURAL-FUNCTIONAL MODEL OF SOCIAL EXPECTATIONS OF THE PERSONALITY

The purpose of the study is to construct and substantiate the structural-functional model of social expectations of the personality. A completely new approach to understanding and organizing the study of social expectations of the personality is proposed. Social expectations as a process and result of a person’s mental activity are theoretically substantiated. The methodology of the study was a systematic approach which allowed to solve the problem holistically. It was determined the verified system of principles on which the modeling of social expectations of the personality was based. It is stated that the following principles are based on the development of the structuralfunctional model: systematicity and integrity of the researched subject, complex approach to the research subject, reflection of the internal and external world of the research subject, interdependence of internal and external factors of the research, principle of functionality and social determinism of the researched subject, the principle of subjectivity. The structural and functional model of social expectations of personality was constructed, which allowed to study internal
The Phenomenon of Cyber Crime

With the rapid growth of computer and network systems in recent years, there has also been a corresponding increase in cyber-crime. Cyber crime takes many forms and has garnered much attention in the media, making information security a more urgent and important priority. With the advances in information technology (IT) criminals are using cyberspace to commit numerous cyber crimes. Cyber infrastructures are highly vulnerable to intrusions and other threats. Physical devices and human intervention are not sufficient for monitoring and protection of these infrastructures.
Construction of Genetic Algorithm Model for Fitness Program Optimization of Middle School Students

Genetic algorithm is one of the most important mathematical models to simulate biological evolution and recommend the best judgment for the development of things. It is widely used in many fields, such as engineering construction, medical diagnosis, economic management, daily management and so on. Therefore, the fitness running optimization program of middle school students based on genetic algorithm model was studied in this paper. The main process and structure of the genetic algorithm model were described. Based on the analysis of the model structure, the method of improving the genetic algorithm was proposed. Under the background of the rapid development of the Internet, big data, computer information technology and artificial intelligence, the improved algorithm was introduced to establish the optimized genetic algorithm model, so as to promote the fitness running optimization program more targeted and effective. Finally, the fitness running optimization scheme was tested and verified by genetic algorithm, so as to prove that the research has good practicability.
Countermeasures and Effects of the Big Data Marketing on the Business Operational Management

The popularization and application of the big data technology has been greatly changing the people's life and the business activities.The digitization of the consumption market is driving the enterprise to adjust all its operational sections.The big data marketing is a revolutionary change.It is also reforming the other sections of the business.How does it affect the other operational activities and what measures should be adopted?To answer these questions, the paper first explores the relationship between the big data marketing and the other business operational management, then analyzes the influence of the big data marketing to the other business activities such as that the establishment, implementation and change of the business strategic aim must be based on the precise information from the big data marketing and so forth, finally puts forward some tactics like the application of the big data technology in the business operation, digital of the marketing section, etc. since the big data have been penetrating the whole society either in the industry or in people's life or in the various marketplaces.
ESCAPE ROOM AS A TOOL TO IMPROVE THE MOTIVATION AND COMMITMENT OF STUDENTS

E. Martín-González, M. Godoy-Giménez, P. Fernández-Martín, J. León, A. González-Rodríguez, S. Mora, A. Sánchez-Kuhn, I. Carmona, Á. García-Pérez, P. Flores Cubos, A.F. Estévez, M. MorenoUniversity of Almería (SPAIN)
Influence of strategic planning and organizational resources on financial performance of state corporations: A case of Kenya Electricity Transmission Company Limited (KETRACO)

Abstract Influence of strategic planning and organizational resources on the financial performance of state corporations. The study aimed to establish the influence of strategic planning and organizational resources on the financial performance of state corporations a case study of KETRACO with specific objectives being to examine how strategic planning and organization resources affect financial performance. The findings will be used as a reference point to other researchers in the same field. The study findings will be beneficial to the foundation of future studies and provided a critical examination and the knowledge generated by this study will enable scholars to improve and develop a better understanding of the subject. The study was anchored on adaptive leadership theory and reinforced by trait leadership theory. The study adopted a descriptive research design with a target population of 385 respondents. Stratified proportion sampling was used to select 121 respondents. Questionnaires were used for data collection, and a pilot study was conducted on the questionnaires. Data were analyzed using descriptive statistics and inferential statistics. The study established a strong positive relationship between strategic planning and financial performance (r= 0.548, p=0.000), and that strategic planning significantly influences financial performance. The regression analysis revealed a relationship R = 0.302 which showed a significant correlation and revealed that organization resources and financial performance are significantly connected, the study established a strong positive relationship between the organization resources and financial performance (r= 0.302, p=0.004). The study concludes that strategic planning statistically and significantly affects financial performance and that there is a positive relation between organization resources and the variations in financial performance can be explained by other study variables. The study recommends that KETRACO management should develop and formulate guidelines, governing structure, and strategic plans for effective implementation of organizational goals and objectives. Keywords: Financial Performance, Strategic Planning, Organization Resources, Kenya Electricity Transmission Company Limited, State Corporations in Kenya.