Reviewerly/exHarmony huggingface dataset:
Structuring of Unstructured Data from Heterogeneous Sources

Objectives: To develop a new data gathering processing under Big Data Perspectives.To convert unstructured text data into structured format by not missing out any text data available.Methods: The unstructured data is preprocessed using modified stemming and tokenization.From the stemming output, the proposed Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram features are derived.Unstructured data is considered from multiple sources like twitter, consumer complaints and news blog.Findings: The proposed model with extant TF-IDF features has exposed relatively high Mean Average Error (MAE) value which is 1.4325 when compared to the proposed model without optimization to be 0.5197.Novelty: The novelty of the research work is of the stemming process where dictionary checking process is added and the improved feature extraction, interclass dispersion coefficient is computed in TF-IDF features.
Highly Reliable Dielectric Interlayers for Flexible Displays and E‐paper

Abstract Flexible, foldable, and rollable displays aim to serve emerging user's needs.
Autocalibration for Creating 2D/3D Displays on Complex Geometric Surfaces

Abstract Projection mapping transforms the familiar through engaging displays.
Effective Method of Bike Starting Using Keypad for Security against Bike Thefts

Abstract: In this research we have designed a prototype for an enhanced method of bike starting. In regular course we start a bike using a key. It may have possibility of losing a key. There is also a danger of bike thefts. Therefore we have designed a prototype which allows the user to secure his bike. This system includes a two way security system i.e. a bike key and a keypad. When the user place a key on bike for starting the prototype will ask for a unique password. Then user must have to enter correct password if user fails to enter the correct password then system responds it by turning on a siren. After entering a correct password the bike will start. Although anyone having your bike key he will not able to start your bike without a unique password. Thus the system ensures the better security of bike.
Impact of AR and VR in Industrial Revolution

Abstract: Augmented Reality (AR) and Virtual Reality (VR) are state-of-the-art technologies for superimposing information onto the real world, has recently started to have an effect on our everyday lives. There has been an increase in the maturity and versatility of applications based on AR and VR technologies in recent years. Consequently, this improves user productivity and accuracy and offers economic benefits to companies. A wide range of augmented reality applications are now possible thanks to recent advances in the internet of things, smart sensors, and sophisticated algorithms. Currently, many research pieces are being done to expand the application of augmented reality and enhance its effectiveness in industrial production processes. The purpose of this paper is to analyze some use cases and the prospects associated with industrial augmented reality in order to highlight its critical role. This article explores the main future directions for industrial augmented reality applications in industry 4.0 through a systematic analysis. Various applications of this technology and their impact on improving production conditions are discussed in the article. In conclusion, this technology's challenges and research opportunities are discussed.
A Study of the Relationship Between the Weakening of the Social Mobility Function of Education and the Educational Equity in China

The social mobility function of education refers to the function of education to cultivate and screen individuals so that individuals can adopt different positions and give full play to their talents after stepping into society. This function is of great significance to the stability and development of social structures, and educational equity is a significant factor affecting the extent to which it can be performed. Based on the current situation of the weakening of the social mobility function of education in China, this paper first reviews the specific manifestations of the current weakening of this function and points out that the “Matthew effect” is the root of the problem, and the classification of school education is the direct cause of the situation. At the same time, this paper explores the causes of the weakening of this function from the perspective of educational equity, proposes the two-sided impact of educational equity on the function of social mobility, and proposes discusses the “Matthew effect” in the aspect of educational inequality. Finally, this paper puts forward three suggestions for improving educational inequality, seeking to provide references for improving the current situation of educational inequality in the future and alleviating the weakening of the social mobility function of education.
Web-Based Task Management System for Improving Group Work Collaboration

Group work collaboration in learning, such as carrying out group assignments or group projects, is a common practice in education.It can help group members to delegate tasks, develop communication skills and manage time.However, managing working in groups can be challenging as it is hard to keep track of the team member's work progress, difficult to manage the given group tasks and tend to forget the assignment or project due dates.Thus, a new method is necessary to help students to manage and coordinate group work collaboration better.This study proposed a web-based application system with work progress, group task management and email notification features.The application was developed using the Waterfall methodology that consists of requirement gathering, analysis, design, implementation and testing.The implementation used Xampp, Php, Sublime Text and Yii2 framework.Based on the five times of functional tests conducted, the system managed to achieve the study's objectives.In conclusion, the web-based system helps students in managing work group tasks when doing collaboration work.Future work for the application includes developing mobile platforms and uploading completed tasks or files to prove the work's progress.
Review on Region-Based Segmentation Using Watershed and Region Growing Techniques and their Applications in Different Fields

In digital image processing and computer vision, segmentation operation for an image refers to dividing an image into multiple image segments, and the significant purpose of segmentation operation is to depict an image in a way so that the analysis process of the objects of interest is easier and more accurate. The region-based segmentation scheme act for finding similarities between adjacent pixels to detect each region that constructs the image. Similarity scales have based on different features, in a grayscale image, the scale may be referred to as textures and other spatial appearances, and also the variance in intensity of a region and so on. Significantly, many applications in different fields involved region-based segmentation for instance remote sensing, medical application, and others for recognizing interesting objects in an image. In this paper, two techniques for segmentation operation in region-based which are region growing and watershed are reviewed.
A Qualitative Inquiry of Politeness Approaches through Gender Category: Evidences from University Students in Aceh

PurposeThis study examines the politeness strategies employed by male and female students and its differences across genders. MethodTo collect data for this qualitative study, 16 participants (with equally eight male and female students) were purposefully opted from the Department of English Language Education at UIN Ar-Raniry Banda Aceh. Eleven situations created to obtain responses from students using a semi-structured interview. The framework of analysis is based on Brown and Levinson’s politeness strategy. Results/FindingsThe study found that male and female students use four politeness strategies. They are (a) bald on record; (b) positive politeness; (c) negative politeness; and (d) off-record. The results indicated that positive politeness is the most prevalent strategy, while bald on record is the least utilized strategy. The politeness strategy was utilized when the participants wanted to express their view to classmates or lecturers. The students performed the strategy because they wanted to support their beliefs or statements unswervingly. Male students used more positive politeness and expresses ideas more directly than female students. ConclusionThe study concluded that both male and female students at the Department of English Language Education, UIN Ar-Raniry Banda Aceh exercised all varieties of politeness strategies. The most applied approaches were positive politeness strategy, followed by the negative politeness strategy, off-record and eventually bald on record as the least frequent politeness strategy.
Multi-Criteria Optimization Based VM Placement Strategy to Mitigate Co-Location Risks in Data Centers

Cloud providers generally run one or more Virtual Machine (VM) instances on the same physical machine.Though it increases data center utilization, it exposes VM to a co-location attack.VM placement and migration are the two strategies adopted for mitigating co-locations.Current methods for VM placement or VM migration consider only security as decision criteria and do not consider other factors like Quality-of-Service degradation, data center utilization, etc.This work proposes a placement and migration strategy for mitigation of co-location attacks with joint consideration of multi objectives like QoS, data center utilization, energy consumption, and security risks.A security-driven multi-criteria optimization -based VM placement policy is proposed.A joint consideration of multiobjective performance optimization along with co-location security risk minimization is done to design a novel VM placement policy based on user categorization.The policy can reduce the likelihood of co-location target VM with attacker VM without much degradation to the performance of VM and data center utilization.The solution mitigates co-location risks without much compromise to the performance of VM and data center resource utilization.The co-residence risk is mitigated by the categorization of users into three levels i.e. unlabeled, risky, and safe, and physical machines into two groups as safe and unsafe.The PMs available in data center is grouped into three different VM placement policies, they are undecided pool, safe pool and unsafe pool.
Improved Performance of Cloud Networks Using Chaotic Black Widow Optimization Algorithm

Nowadays, the speed of the internet becomes very high and users use a lot of data from a data center, so users need to access a lot of data from the nearest place to avoid any delay.The data will be stored without any interference from users.Due to the previous reasons, Cloud Computing (CC) plays an important role in finding the best route for data to reach users by general networks without a delay and with the least energy consumption.To achieve scalability, protocols are used and developed for networks for a suitable performance in the cloud computing networks.In this paper, a natural-inspired protocol is used in cloud networks to find a good routing, this protocol is called Chaotic Black Widow Optimization Algorithm (CBWOA).Some natural characteristics of the routing strategy are inspired by the black widow optimization algorithm.Taguchi's methods were used for tuning parameters of CBWOA based on orthogonal array experiments.CBWOA is applied with three constraints to measure two factors called energy consumption and throughput by finding best path direction in cloud Computing networks.CBWOA is evaluated in an experimental cloud using the current routing protocols against old routing protocols of the performance network.Finally, the best route in cloud networks is founded successfully and the performance of the developed protocol is associated with two factors, the first one is maximizing (the throughput with efficiency 93.4921% and the Packet delivery ratio with efficiency 90.3087%) and the second one is minimizing (the delay with efficiency 62.1788% and the energy consumed with efficiency 51.1406%).
Is Decentralized AI Safer?

Artificial Intelligence (AI) has the potential to significantly benefit or harm humanity. At present, a few for-profit companies largely control the development and use of this technology, and therefore determine its outcomes. In an effort to diversify and democratize work on AI, various groups are building open AI systems, investigating their risks, and discussing their ethics. In this paper, we demonstrate how blockchain technology can facilitate and formalize these efforts. Concretely, we analyze multiple use-cases for blockchain in AI research and development, including decentralized governance, the creation of immutable audit trails, and access to more diverse and representative datasets. We argue that decentralizing AI can help mitigate AI risks and ethical concerns, while also introducing new issues that should be considered in future work.
FPGA based secure and noiseless image transmission using LEA and optimized bilateral filter

In today’s world, the transmission of secured and noiseless image is a difficult task. Therefore, effective strategies are important to secure the data or secret image from the attackers. Besides, denoising approaches are important to obtain noise-free images. For this, an effective crypto-steganography method based on Lightweight Encryption Algorithm (LEA) and Modified Least Significant Bit (MLSB) method for secured transmission is proposed. Moreover, a bilateral filter-based Whale Optimization Algorithm (WOA) is used for image denoising. Before image transmission, the secret image is encrypted by the LEA algorithm and embedded into the cover image using Discrete Wavelet Transform (DWT) and MLSB technique. After the image transmission, the extraction process is performed to recover the secret image. Finally, a bilateral filter-WOA is used to remove the noise from the secret image. The Verilog code for the proposed model is designed and simulated in Xilinx software. Finally, the simulation results show that the proposed filtering technique has superior performance than conventional bilateral filter and Gaussian filter in terms of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).
Penentuan Guru Piket Dengan Metode Simple Additive Weighting (SAW

The problem of decision making is basically a form of selecting from a variety of alternative actions that may be chosen which process through a certain mechanism, with the hope that it will produce the best decision. The preparation of a decision method is a way to develop logical relationships that underlie decision issues into a mathematical method, which reflects the relationships that occur between the factors involved, so that the decision process must be taken through a gradual, systematic, consistent and endeavored process. in every step from the beginning has included and considered various factors. The model used in this decision support system is SAW, because this SAW was chosen because it can determine the weight value for each attribute, then proceed with a ranking process that will select the best alternative from a number of alternatives, in this case the alternative in question is the result of a system based on criteria. - specified criteria.&#x0D;
The CRINGE Loss: Learning what language not to model

Standard language model training employs gold human documents or human-human interaction data, and treats all training data as positive examples. Growing evidence shows that even with very large amounts of positive training data, issues remain that can be alleviated with relatively small amounts of negative data -- examples of what the model should not do. In this work, we propose a novel procedure to train with such data called the CRINGE loss (ContRastive Iterative Negative GEneration). We show the effectiveness of this approach across three different experiments on the tasks of safe generation, contradiction avoidance, and open-domain dialogue. Our models outperform multiple strong baselines and are conceptually simple, easy to train and implement.
Decoherence of a 2-Path System by Infrared Photons

We calculate the decoherence caused by photon emission for a charged particle travelling through an interferometer; the decoherence rate gives a quantitative measure of how much "which-path" quantum information is gained by the electromagnetic field. We isolate the quantum information content of both leading and sub-leading soft photons, and show that it can be extracted entirely from information about the endpoints of the particle's paths. When infrared dressing is properly incorporated, the leading order soft photons give no contribution to decoherence, and carry no quantum information. The sub-leading soft photons carry finite which-path information, and the sub-leading contribution to decoherence takes an extremely simple, time-independent form depending only on the size of the interferometer.
BLOCKCHAIN FOR STUDENT INFORMATION SYSTEM: NEED OF TECHNOLOGICAL ADOPTION

Decentralized and well maintained data transaction with new aspect of block reduces centralized administration burden in many aspects with automated security measures. Block chain involves series of tasks and transaction performed in decentralized form with high security, trust and reliability of transaction that creates meaningful information. Evolution of technology and life style demands more reliable and sharable framework for education system. Students’ information system creates information at different points of interaction leading in some useful transaction needs super centerlines of administration. Block Chain immutable solution provides more protected, trusted fully functional block of records which benefits to educational organizations or higher management institutes and creates value relationship with its stakeholders. An immutable block chain record with distributed ledgers, proof of Work and encryption crop thought run-through and impacts domain information. This raises a thirst for carrying out the study on mechanism, functions and elements of Blockchain framework for Student Information System.
Measuring Reliability of Large Language Models through Semantic Consistency

While large pretrained language models (PLMs) demonstrate incredible fluency and performance on many natural language tasks, recent work has shown that well-performing PLMs are very sensitive to what prompts are feed into them. Even when prompts are semantically identical, language models may give very different answers. When considering safe and trustworthy deployments of PLMs we would like their outputs to be consistent under prompts that mean the same thing or convey the same intent. While some work has looked into how state-of-the-art PLMs address this need, they have been limited to only evaluating lexical equality of single- or multi-word answers and do not address consistency of generative text sequences. In order to understand consistency of PLMs under text generation settings, we develop a measure of semantic consistency that allows the comparison of open-ended text outputs. We implement several versions of this consistency metric to evaluate the performance of a number of PLMs on paraphrased versions of questions in the TruthfulQA dataset, we find that our proposed metrics are considerably more consistent than traditional metrics embodying lexical consistency, and also correlate with human evaluation of output consistency to a higher degree.
Morphometric study of facial index in north indian population

The present study was conducted in Department of Anatomy, NIIMS and SMSR, Greater Noida out of which 200 subjects (100 males and 100 females) between age group of 18-35 years. Two measurements, the facial length, Facial breadth were taken by using Digital Vernier caliper. To calculate the facial Index in Males and Females &amp; measure the facial length and facial breadth of the subject. It was concluded that the Percentage of Very long face Shape (43%) is larger than long face shape (24%), Round face shape (23%), Broad face shape (10%) &amp; Very broad face shape (2%) in females. In males the Percentage of Very long face Shape (41%) is also larger than long face shape (36%), Round face shape (16%), Broad face shape (9%) and Very broad shape (2%).
Redefining Vastu Shastra Principles With Reference To The Contemporary Architectural Practices In India

This paper intends to look into Vastu Shastra, an antiquated Hindu science for building development, with current development procedures rehearsed in India. Vastu Shastra depends on the movement of the Sun with respect to the structure. The rooms were organized so that, contingent upon their capacity, every one of them should get regular light for the day. Because of technological advancement, the structures are built by the structure bye-laws outlined by local authorities. There exist contrasts just as similitudes in Vastu Shastra's ideas and developing structures in current occasions. So an endeavour has been made to join antiquated standards of Vastu Shastra in the current plan practices of private structures for accomplishing shared objectives of human wellbeing and worry for the climate. It is a short exploration that outlines the shared traits and offers guidelines that can be fused to develop rules for the improvement of society.
THEORETICAL AND PRACTICAL FOUNDATIONS OF THE COMPETENCE APPROACH TO THE PRIMARY EDUCATION SYSTEM

In this article, the importance of competencies in the organization of the primary education system, the theoretical and practical foundations of the formation of competencies, the study of standards in the field of education of developed foreign countries, the qualitative renewal of the primary education system, as well as the improvement of the teaching methodology, It is explained about the step-by-step implementation of the principles of individualization in the educational process, technological literacy, critical thinking and creativity competencies with the possibility of applying acquired competencies in independent life, and the wide introduction of effective forms, methods and tools to the educational process based on national, universal and spiritual values.
MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis

Multiple instance learning exhibits a powerful approach for whole slide image-based diagnosis in the absence of pixel- or patch-level annotations. In spite of the huge size of hole slide images, the number of individual slides is often rather small, leading to a small number of labeled samples. To improve training, we propose and investigate different data augmentation strategies for multiple instance learning based on the idea of linear interpolations of feature vectors (known as MixUp). Based on state-of-the-art multiple instance learning architectures and two thyroid cancer data sets, an exhaustive study is conducted considering a range of common data augmentation strategies. Whereas a strategy based on to the original MixUp approach showed decreases in accuracy, the use of a novel intra-slide interpolation method led to consistent increases in accuracy.
Paradigms Of Virtual Pedagogical Culture - In The Interpretation Of Parents And Young People

The article analyzes the concepts of parents and young people about paradigms of virtual-pedagogical culture from a theoretical and practical point of view. It is also stated that it is necessary to develop the concepts of conscious filtering in young people through the development of virtual-pedagogical culture in parents, to strengthen knowledge and skills in every field that promotes strong factors of the ideas of conscious filtering in them.