Reviewerly/exHarmony huggingface dataset:
Power-Aware Mechanism for Scheduling Scientific Workflows in Cloud Environment

Cloud computing is a platform that hosts various services and applications for users and businesses to access computing as a service. Cloud provider offers two distinct types of plans: reserved service and on-demand service. Cloud resources need to be allocated efficiently, and task needs to be scheduled efficiently such that the performance can be enhanced. In this research work, the authors have proposed a novel mechanism named PAMP (performance aware mechanism for parallel computation) for scheduling scientific workflows. At first, the resources are allocated using the optimal resource allocation mechanism. Then tasks are scheduled in parallel utilizing the task scheduling algorithm. Further, they consider energy and time as constrained to makespan optimization. The evaluation is carried out by considering the scientific workflows cyber snake with its different variant, and the comparative analysis is carried out by varying the number of virtual machines. The proposed methodology outperforms the existing model.
Prototyping of Agricultural Education Website Using the Design Sprint Approach

Agricultural technology is developing in line with discoveries in soil science and plant nutrition. This technology enables increased tonnage of yields at lower costs. But unfortunately, this useful technological development cannot be directly applied by farmers due to the slow transformation of information to farmers. One thing that can be done is to create an appropriate agricultural technology information service website. For the information presented to suit the farmers' needs and easy for them to understand, a user interface design process is needed that involves the farmer directly. So this study uses Design Sprint as an approach to design and validate the user interface. This research succeeded in making a website template design and has been validated with a grade of 3.6 from a scale of 5. The grade is included in the category of very good and suitable for use as an educational medium.
Practical Approaches: Machine Learning Techniques for Image Processing and Text Processing

In modern era everyone is bound of using smartphones, smart TV and digital smart watches. Many in-build applications are used to unlock these devices by using face detection or text detection. There are numerous applications for Image and Text Processing. We are also proposing techniques using Machine Learning for image processing & text processing. In Image Processing, we are performing face detection, smile detection, face recognition, face filter, object detection and digit detection. Haarcascade XML Files are used for detecting face, smile and object. In face filter, application first detects the face and then puts the sunglasses image over the eyes. This all done by using OpenCV, K-Nearest Neighbors Algorithm, Convolutional Neural Network, Keras API is used because Keras API is an open source high-level neural network which is written in Python and can be run on the top of Tensorflow, Theano or CNTK. CIFAR 10 dataset which contains 10 classes with 6000 images per class is used for object detection. In Text Processing, Natural Language Processing (NLP) is used. NLP branch of Artificial Intelligence which can analysis and synthesis the human language, text and speech. Natural Language Toolkit is used for text summarization and classification which performs tokenization, stemming and lemmatization. In Text summarizer, important information is collected or extracted from the original text. In Sentiment Analysis, identifying and detecting the subjective information using NLP, text analysis and computational linguistics.
Shared Sample Projects (Advanced)

The lesson described in this chapter is meant to give students the opportunity to think about and develop their sonic imaginations, critical listening, decision making (including the skill of fighting <italic>option paralysis</italic>), ideas about living in a recording-saturated world, and authority and ownership of recorded music. Designed for undergraduate students, this activity requires all students to use the same short audio sample and audio manipulation software to create new full-length pieces of original music using the shared sample and nothing else. They can process and manipulate the sample as they see fit, but they may not use any additional sounds or instruments.
CAN-SIN: A Cross-Layer Heterogeneous Academic Network with Semantic Information

In this paper, we focus on incorporating the semantic information into the structure of academic networks to enrich the dimensionality of extracted features. We propose a cross layer scholar-paper network that can capture the characteristics of heterogeneous academic networks. In addition, we leverage the BERT model, which is widely used in the realm of natural language processing (NLP), to integrate the semantic information of the scholar papers. We also introduce a new concept, “close collaborator to tackle data leakage issues. This can be used in many downstream tasks such as automatic detection of conflict of interests among scholars. Extensive experiments on two datasets show that our enhanced cross-layer model is both effective and lightweight, and outperforms three strong baselines. Further analysis shows that our model successfully combines the semantic information and the topology of the whole network.
Hindi English Mixed Text on Twitter Along with Emoji Prediction

This paper, explains socialite platform like Facebook to develop a corpus of Hindi English mixed language. An approach is presented by us to essence socialite microbe like chirp on Facebook. This paper includes Hindi English as a language to collect data from tweets. Moreover, many NLP tasks are analyzed and performed, make use of the corpus to get amazing results. In this paper, the methodology is applicable for resource deficient language provided that appropriate accent are alive users on socialite podium.
Variation in Facial Index and Nasal Index in Western Maharashtrian Males- An Anthropometric Study

Introduction: Facial anthropometry had proved its great importance in the field of facial reconstructive surgery as well as in the field of forensic science. Specific face types and nose types provides us important information about specific types of races. Aim: To study the morphometric variation in facial index and nasal index as well as different types of faces and types of noses in the Western Maharashtrian males. Materials and Methods: An observational study was carried out in the five cities- Sangli, Kolhapur, Islampur, Karad and Satara of Western Maharashtra, India. Total 535 male students of different colleges between the age group of 18-20 years were selected and measurements were taken with the help of Digital Vernier Calliper in the sitting position after due approval from Institutional Ethical Committees and informed consent. The methodology adopted for the measurements was taken from the guidelines given by Farkas LG in his book-“Anthropometric facial proportions in Medicine”. Results: The mean facial index of Western Maharashtrian males was 89.51 with standard deviation 4.26 and dominant face type was leptoprosopic which was 46.73% and rare type of face was hypereuriprosopic which was 0.19%. Mean nasal index of Western Maharashtrian males was 72.84 with standard deviation 6.86 and dominant nose type was mesorrhine which was 63.74%. Conclusion: Certain facial and nasal features are common in particular racial group. Its detection can be helpful in reconstructive facial surgeries.
Use of computer technologies in primary schools

Information technology has a lot of dynamics. Technical software and algorithm tools are being updated and developed. New methods are introduced for information technology. During the transition to information society, it is necessary to prepare people for rapid adoption and processing of very large volumes of information. Key words: elementary class, computer, information, software, technology, training
Revisiting Explicit Regularization in Neural Networks for Reliable Predictive Probability

From the statistical learning perspective, complexity control via explicit regularization is a necessity for improving the generalization of over-parameterized models, which deters the memorization of intricate patterns existing only in the training data. However, the impressive generalization performance of over-parameterized neural networks with only implicit regularization challenges the importance of explicit regularization. Furthermore, explicit regularization does not prevent neural networks from memorizing unnatural patterns, such as random labels. In this work, we revisit the role and importance of explicit regularization methods for generalization of the predictive probability, not just the generalization of the 0-1 loss. Specifically, we analyze the possible cause of the poor predictive probability and identify that regularization of predictive confidence is required during training. We then empirically show that explicit regularization significantly improves the reliability of the predictive probability, which enables better predictive uncertainty representation and prevents the overconfidence problem. Our findings present a new direction to improve the predictive probability quality of deterministic neural networks, which can be an efficient and scalable alternative to Bayesian neural networks and ensemble methods.
Monitoring the quality of education in russian regions using information technologies

The article reveals the features of monitoring functions in education and its main models. In addition, the necessary conditions are considered not only for effective monitoring, but also for evaluating the quality of objective indicators. Special attention is paid to the intensive implementation of digital information technologies in the system of pedagogical measurements, including the possibility of studying educational processes in the online system. A number of positive aspects have been identified that facilitate measurement and control.
Intelligent Recognition Method of Human Motion Global Features Based on Kinect Skeleton Information

In order to solve the problems of low recognition accuracy and poor real-time performance in the existing training and recognition of human movement posture under complex background, an intelligent recognition method of human motion global features based on Kinect skeleton information is designed. The data of skeletal pose feature points is obtained from Kinect, and the pose feature vector is calculated. The current real-time pose feature vector is matched with the preset standard pose feature vector. The matching degree of the two postures is fed back in real time to achieve the purpose of pose recognition. The experimental results show that the method can effectively match and recognize human action posture, with high recognition accuracy and strong real-time performance Scalability.
MANAJEMEN PENGEMBANGAN KEGIATAN BELAJAR MELALUI KECERDASAN KINESTETIK

The title was made in this study about management development learning activities through the kinesthetic intelligence against the background of the problem based on the application curriculum of the 2013 demand the liveliness of the learners in the learning process, because physical motion turned out to have major contributions as revealed based on venon magnesen research say that brain of the human quick to get information from visual modality moving. Learning by doing must be a priority, because student learns 20% by reading, 30% by listening, 40% by looking, 50% by speaking, 60% by doing and 90% by looking, speaking and doing. The goal for obtaining an overview of the steps of planning, implementation, brominated and solutions in developing management development learning activities through the kinesthetic intelligence. Then was able to muster an effective category learning activities as well as efficient or can we take apart that learning it the right grind and grind it correctly. While the research conducted using qualitative methods descriptive observational field as well as supporting documentation. As for the results of the research obtained by the researchers are learning activities through the kinesthetic intelligence methods actually build a very productive learning atmosphere, during the development of the visible learning activities learning well and learners are very enthusiastic for the contribute in learning activities. Then it can be inferred that learning must have a principle of motion, change and evolve, kinesthetic intelligence help the whole body more responsive learning activities, class as a learning space should be one place of learning, and the teacher is able to realize the needs of every learner in the learning. Problems encountered based on explanation of the findings above, it is likely to be passive learners when learning activities are less than creative categories. The recommendations could be given for teachers on request able to conceptualize the active learning activities.
Faster and Smarter AutoAugment: Augmentation Policy Search Based on Dynamic Data-Clustering

Data augmentation tuned to datasets and tasks has had great success in various AI applications, such as computer vision, natural language processing, autonomous driving, and bioinformatics. However, most of the specific parameter-based augmentation strategies are inefficient in finding suitable augmentation parameters to improve the model performance whenever the dataset changes. We introduce a dynamic data augmentation strategy called Faster and Smarter AutoAugment (FSAA) that separates the data augmentation method through the initial policy search result. Based on our policy branching principle, the augmentation policy dynamically clusters data points within the dataset according to the degree of performance change at the data split stage. With extensive experimentation on various datasets for image recognition task, we show that FSAA dramatically reduces the GPU computation cost, a problem in the existing automatic data augmentation strategies, by up to over 90%. It also ensures diversity and generalization of the dataset augmentation by searching for more diverse policies than existing autonomous methods in less time, resulting in consistent performance gains and robustness across multiple models.
A Prototype Design of Water Filtration System and Recycling System for Flushing Toilet

Water is an invaluable source of nature, each resident needs water for their daily needs. Plus, water is a very important source in generating various economic income in areas such as agricultural, industrial, fisheries, and generation of electricity sources. However, people nowadays are being irresponsible with the use of water. The World Health Organization (WHO) has reported that every individual worldwide using water resources between 50 to 165 liters per day. In Malaysia, the population of wasting water exceeding international standards. Therefore, to minimize the water wastage, an initiative is taken to overcome the problem with the prototype design of a water filtration system and a recycling system for a flushing toilet to filter wastewater or greywater from the kitchen sink, toilet sink and floor trap in bathrooms. The purpose of this study is to design a prototype, produce, and test the effectiveness of the prototype design so that the water filtration system is veritable. This prototype uses natural ingredients and economical products to clean wastewater and it uses the gravity system. This prototype uses natural ingredients and economical products to clean wastewater and it uses the gravity system. Tests involved in the examination of the prototype of the water filtration system are leak detection test and refined water cleanliness quality test. The water hygiene quality test is to obtain the value of pH, TSS, TDS, and BOD. Besides, water hygiene test is done to ensure that the filtered water is not greasy, eliminate soap foam and solid waste such as hair. The prototype is set up and placed on a house sink to reduce water pollution. This project focuses on the filtered water from the kitchen sink, toilet, and floor trap for the flushing toilet system. The flushing toilet system usually uses direct water. Thus, by using this prototype, it can minimize water wastage and reduce the water tariff rate.
Research on Performance Appraisal Method of Grass Roots Staff in the Perspective of Complex Big Data

The traditional performance appraisal method of grass-roots staff is not strong enough to motivate employees, which leads to the problem of poor workload completion. To solve this problem, the paper designs the performance appraisal method of grass-roots employees from the perspective of complex big data. Sort out the evaluation procedures, determine the integrated employee performance appraisal indicators, and construct the index system. The weight of assessment index is calculated by the method of priority diagram. The index and index weight are integrated, and the competency model is constructed by combining fuzzy evaluation method. At the same time, set up the rules of appraisal grade division to complete the performance appraisal of grass-roots employees. This paper constructs the case analysis link, obtains the basic information of the enterprise, and realizes the comparison between this method and the traditional method. According to the comparison results, this method has a strong incentive effect on employees, and the completion degree of enterprise workload is better.
IMPLEMENTASI METODE ROUND TABLE PADA PEMBELAJARAN BAHASA INGGRIS DI SMA N MLONGGO JEPARA

Abstra ct This research was conducted to find out the application of the round table method in learning English in SMA N Mlonggo Jepara. Besides, this research also aimed to find out the improvement in how active students during English learning process by using the round table method. This research was a classroom action research (CAR), which is research conducted in the classroom. The class action research model used in this study was the CAR model according to Kemmis and Mc Taggart which consists of four stages: planning, action, observation, and reflection. This study implements the Round Table method in English learning material in SMA Negeri 1 Mlonggo Jepara. The population of this study was class X IPS. Meanwhile, for the sample the researcher took class X IPS 1 as the sample. To analyze the data researchers used quantitative descriptive analysis techniques. The results showed that the round table cooperative learning method can improve the learning outcomes of Jepara 1 Mlonggo High School students in learning English Introduction material. This method can also increase student activity in following the teaching and learning process. Each cycle has increased, in the first cycle the average class obtained was 60.83 and the classical completeness achieved 30.5%. In the second cycle has increased. The class average increased by 26.67, the average class in the first cycle was 87.5. The increase in classical completeness from cycle I to cycle II reached 52.8%, classical completeness in cycle II reached 83.3%. Keywords: Learning Method; Cooperative Learning; Classroom Action Research; Round Table
Review on IoT-aware, smart Healthcare automatic monitoring system of patients and biomedical devices within hospitals

Technology plays the vital role in healthcare not only for sensory devices but also in communication, recording and display device. It is very important to monitor various medical parameter and post operational days. Hence latest trend in Healthcare communication method using IOT is adapted. The IoT has a variety of application domains, including health care system. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects Among others, mainly radio frequency identification(RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a survey on novel, IoT-aware, smart system for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, a smart health care and management system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, Internet of things serves as a catalyst for the healthcare and plays important role in wide range of healthcare applications.
Quantum CFD Simulations for Heat Transfer Applications

Abstract In this paper, computational heat transfer (CHT) equations were solved using the state-of-art quantum computing (QC) technology. The CHT equations can be discretized into a linear equation set, which can be possibly solved by a QC system. The linear system can be characterized by Ax = b. The A matrix in this linear system is a Hermitian matrix. The linear system is then solved by using the HHL algorithm, which is a quantum algorithm to solve a linear system. The quantum circuit requires an Ancilla qubit, clock qubits, qubits for b and a classical bit to record the result. The process of the HHL algorithm can be described as follows. Firstly, the qubit for b is initialized into the phase as desire. Secondly, the quantum phase estimation (QPE) is used to determine the eigenvalues of A and the eigenvalues are stored in clock qubits. Thirdly, a Rotation gate is used to rotate the inversion of eigenvalues and information is passed to the Ancilla bit to do Pauli Y-rotation operation. Fourthly, revert the whole processes to untangle qubits and measure all of the qubits to output the final results for x. From the existing literature, a few 2 × 2 matrices were successfully solved with QC technology, proving the possibility of QC on linear systems [1]. In this paper, a quantum circuit is designed to solve a CHT problem. A simple 2 by 2 linear equation is modeled for the CHT problem and is solved by using the quantum computing. The result is compared with the analytical result. This result could initiate future studies on determining the quantum phase parameters for more complicated QC linear systems for CHT applications.
Realization of High-fidelity CZ and ZZ-free iSWAP Gates with a Tunable Coupler

High-fidelity two-qubit gates at scale are a key requirement to realize the full promise of quantum computation and simulation. The advent and use of coupler elements to tunably control two-qubit interactions has improved operational fidelity in many-qubit systems by reducing parasitic coupling and frequency crowding issues. Nonetheless, two-qubit gate errors still limit the capability of near-term quantum applications. The reason, in part, is the existing framework for tunable couplers based on the dispersive approximation does not fully incorporate three-body multi-level dynamics, which is essential for addressing coherent leakage to the coupler and parasitic longitudinal ($ZZ$) interactions during two-qubit gates. Here, we present a systematic approach that goes beyond the dispersive approximation to exploit the engineered level structure of the coupler and optimize its control. Using this approach, we experimentally demonstrate CZ and $ZZ$-free iSWAP gates with two-qubit interaction fidelities of $99.76 \pm 0.07$% and $99.87 \pm 0.23$%, respectively, which are close to their $T_1$ limits.