Reviewerly/exHarmony huggingface dataset:
Identification of Iron Oxide Inclusions

Abstract A hot rolled, low-carbon steel pot used to melt magnesium alloys leaked, releasing about 35 kg (80 lb) of molten magnesium onto the foundry floor and causing an extensive fire. Due to the fire, the original leakage hole could not be investigated. Samples of the failed pot were polished and etched and were found to be composed of ferrite and pearlite mixtures, as would be expected. However, the sample taken from a location about 75 mm (3 in.) from the hole contained a cluster of unusually large inclusions. By removing the beryllium window from in front of the detector, EPMA spectra were obtained from the inclusions and from the steel matrix. The inclusion spectrum contained primarily iron and oxygen, whereas the matrix spectrum contained primarily iron. X-ray maps were made to show the distribution of iron and oxygen. These results indicated that the inclusions were iron oxide. A similar inclusion at the failure site in the melting pot may have reacted violently with the molten magnesium, causing the leak.
Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks

Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Processing.Previous investigations prove that introducing a further pre-training phase between pre-training and fine-tuning phases to adapt the model on the domain-specific unlabeled data can bring positive effects.However, most of these further pre-training works just keep running the conventional pre-training task, e.g., masked language model, which can be regarded as the domain adaptation to bridge the data distribution gap.After observing diverse downstream tasks, we suggest that different tasks may also need a further pre-training phase with appropriate training tasks to bridge the task formulation gap.To investigate this, we carry out a study for improving multiple task-oriented dialogue downstream tasks through designing various tasks at the further pre-training phase.The experiment shows that different downstream tasks prefer different further pre-training tasks, which have intrinsic correlation and most further pre-training tasks significantly improve certain target tasks rather than all.Our investigation indicates that it is of great importance and effectiveness to design appropriate further pre-training tasks modeling specific information that benefit downstream tasks.Besides, we present multiple constructive empirical conclusions for enhancing task-oriented dialogues.
A Semantic Word Processing Using Enhanced Cat Swarm Optimization Algorithm for Automatic Text Clustering

Generally, Text mining indicates the process of extracting maximum-quality information from the text.Moreover, it is mostly exploited in applications such as text categorization, text clustering, and text classification and so forth.In recent times, the text clustering is considered as the facilitating and challenging task exploited to cluster the text document.Because of the few inappropriate terms and large dimension, accuracy of text clustering is reduced.In this work, the semantic word processing and Enhanced CSO algorithm are presented for automatic text clustering.At first, input documents are stated as input to the preprocessing step that provides the useful keyword for clustering and feature extraction.After that, the ensuing keyword is applied to wordnet ontology to discover the hyponyms and synonyms of every keyword.Then, the frequency is determined for every keyword used to model the text feature library.Since it comprises the larger dimension, the entropy is exploited to choose the most significant feature.Hence, the proposed approach is exploited to assign the class labels to generate different clusters of text documents.The experimentation outcomes and performance is examined and compared with conventional algorithms such as ABC, GA, and PSO.
Role of Artificial Neural Network in an Intelligent Educational System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Analysis of Entrepreneurship Education Model of Technology-Driven Agri-Food Industry Applying Line-by-line Coding Method of Grounded Theory

Background/Objectives: This paper investigated and analyzed the phenomena in implementing the curriculum and characteristics of an entrepreneurship education model existing technology-driven agri-food industry.&#x0D; Methods/Statistical analysis: The line-by-line coding method of grounded theory approach by Strauss &amp; Corbin was applied for this study and the collected data was analyzed with the NVIVO 12 program from QSR which is a tool for analyzing quality comparative analysis for better efficiency in open coding.
ALGERIA: Corruption Cases

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 57, Issue 9 p. 23146A-23146C Policy and Practice ALGERIA: Corruption Cases First published: 06 November 2020 https://doi.org/10.1111/j.1467-6346.2020.09710.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume57, Issue9November 2020Pages 23146A-23146C RelatedInformation
Text Generation

Text generation lets us auto-complete the sentences with appropriate words or phrases. In recent years, text generation with neural network models significantly improved. These models often benefited from recurrent neural networks or transformers. In this chapter, we will learn how to use one of the best text generation models (GPT-2) and build an iOS application using this model. Our application will use built-in OCR capabilities to capture text from camera and generate text based on scanned sentences.
Fine Tuning of Rank Based VM Placement and Scheduling Strategies on Opennebula Based Cloud

The HPC Clouds are good option over deploying actual physical infrastructure. This helps in running applications efficiently and in cost effective manner as applications leverage resources in pay as you go manner. But HPC clouds suffer performance issues due to Hypervisor layer. This work addresses the issue by coming up with a VM placement strategy considering the intensity of the applications and also resources available in the host machines and avoid performance degradation of parallel running applications. This placement strategy identifies the and ranks the available host machines and places the maximum possible VMs in highest ranking nodes. This avoids communication over the network since the VMs use shared memory for communication.
DishZle

Teaching students to learn new vocabularies and sequencing of ideas can be challenging to many students as they might find such tasks to be challenging boring. Thus, interactive teaching materials are necessary in grabbing learners’ attention span and interests, and in enhancing their involvement. DishZle is a self-designed 2-in-1 language game which makes learning English be more interactive and exciting with its colourful pictures. It also engages the players’ involvements via team play and cooperation. The theme of this game focuses on various traditional dishes in Malaysia. Students who play this game can enhance their pre-existing knowledge of local dishes, learn new words (local dishes and ingredients) andimprove their sequencing skills by using linkers. The game is suitable for beginner to intermediate learners of English in any ESL classrooms (as pre or post activities). To add, DishZle can also be introduced to foreign tourists who wish to know more about our local dishes. Travel agencies andhospitality services can offer enjoyable and rewarding activities for the tourists by displaying this game at tourism spots, hotels, homestays or holiday resorts. As the matter of fact, tourists can even purchase the game as souvenirs. The novelties of the game are it involves pictorial illustrations of famous yet authentic local dishes and the ingredients through magnetic board and its pieces, and jumbled arrangements of dish preparations in the form of flip charts. This game can be used in ESL classroom as it enhances fun learning, communicative competency, team work and cooperation as well as develops English language components.
Development and Implementation of Introduction to Mechanical Engineering Challenge-Based Instruction to Increase Student Retention and Engagement

This paper discusses a series of introduction to mechanical engineering challenges developed and implemented to increase student retention and engagement in a freshman engineering course.Studies have shown that freshman or sophomore intellectual experiences play a decisive role in Science, Engineering and Mathematics (SEM) student retention and that minority SEM students, among others, leave SEM undergraduate fields in part due to lack of real world connections to their classroom learning experiences.Introduction to Mechanical Engineering is a course that introduces mechanical and civil engineering students to the engineering college education and profession.The challenge-based instruction (CBI) curriculum developed for Introduction to Mechanical Engineering includes challenges, lecture and handout materials, hands-on activities, and assessment tools.CBI is a form of inquiry based learning which can be thought of as teaching backwards strategy.When implementing CBI, a challenge is presented first, and the supporting theory required to solve the challenge is presented second.CBI was built around the How People Learn (HPL) framework for effective learning environments and is realized and anchored by the STAR Legacy Cycle, as developed and fostered by the VaNTH NSF ERC for Bioengineering Educational Technologies.The CBI instruction was developed and implemented in the areas of reverse engineering, statics, dynamics, energy (including renewable energy), and forward engineering.Additionally, the paper describes the initial impact of the CBI curriculum on the students, including initial assessment results, and the impact on the faculty and the course.A controlled experiment was performed with a control group following a more traditional laboratory setup.From the initial positive results obtained in this project, it is argued that the VaNTH principles are effective in motivating and engaging freshman engineering students in mechanical and civil engineering majors and that the CBI materials and tools developed for this course could support other institutions' efforts in student attraction, retention, and engagement.
A Real time Application for Insurance fraud detection using Machine learning

Insurance is something people buy to protect themselves. Insurance fraud is an act of receiving the benefits to which they are not entitled or when a person makes false claims to receive the benefits. The type of insurance fraud are diverse and it occurs in all areas of insurance, among them Auto/vehicle insurance is the most noticeable one. Detecting insurance fraud is the major issue and it is hard through Traditional method as it is more time consuming and expensive, hence it needs an automatic system, so that one can detect whether the claim is genuine or fraud. Through the use of advanced technology like machine learning insurance fraud detection can be done in an efficient way. Here, we focus on identifying the auto/vehicle fraud through the use of machine learning algorithms. Likewise, the performance will be computed by the confusion matrix. Which in turn calculate the accuracy, precision, and recall
IOT Based Smart System for Servival Detection in Disasters Zones

Smart systems with high architecture and advance technology has been expected to mitigate losses during disasters especially in earthquake zones.An invention of robotic technology helps to search and rescue the survivals in disasters zones but unable to transmit real time information to base station on-time to expedite the rescue operations.The development of IOT based smart system modernize the survival detection technology to minimize the damages.The proposed system has been tested in controlled environment to evaluate the results and performance of system.The system has been installed in various place between different obstacles to verify and validate the survivals detection frequency and real time information dissemination frequency to its cross pounding base station as well as on handheld devices.Moreover, this research article encompasses various results of respiration, movements and heartbeats of the survival in any natural disaster zones.
Formation of Professional Competence of Students of Technical Universities through Interdisciplinary Integration

The article is devoted to one of the most pressing challenges facing higher education today - the formation of professional competence of university students. The competitiveness of the labor market demands of today’s students meet modern requirements of economic development, and subsequently formed the competence ensure readiness of students for their professional activities. Therefore, the authors believe that the professional competence of students of technical colleges can begin to form in the first stage of training through the context of learning and interdisciplinary integration, since contextual training constructed professional skills of students to carry out engineering activities, including many aspects, and interdisciplinary communication, in turn, It combines their knowledge and skills from different disciplines, directs the final result - the professional competence. Formed quality will help future technicians holistically apply knowledge of different fields of science in their professional engineering and be competitive specialists in the conditions of modernization of modern enterprises.
The Information System Of Name Card Sales Based On Digital Marketing To Improve Creativepreneur On College E-Commerce Website

Advances in technology and information have abundant benefits, especially in the business of business card services by utilizing e-commerce. When buying a business card, the buyer can order a business card by contacting the seller directly, then the order is processed to completion and finally reaches the buyer. However, this method is not in line with current technological advancements because it still uses conventional transaction processes. Therefore, to overcome this problem an e-commerce website is used that can process transactions accurately and quickly. In this study, there are 3 (three) problems that will be overcome with 2 (two) methods, and 3 (three) solutions are produced. The advantage of e-commerce website is that it can be accessed anytime and anywhere, so it is concluded that the use of e-commerce websites is able to overcome the problems found in business cards, business services and develop creativepreneur in Raharja Higher Education.
Video Saliency Detection for Visual Cryptography-Based Watermarking

The aim of this chapter is to review the application of the technique of Visual cryptography in non-intrusive video watermarking. The power of saliency feature extraction is also highlighted in the context of Visual Cryptography based watermarking systems for videos. All schemes in literature related to Visual cryptography based video watermarking, have been brought together with special attention on the role of saliency feature extraction in each of these schemes. Further a novel approach for VC based video watermarking using motion vectors (MVP Algorithm) as a salient feature is suggested. Experimental results show the robustness of proposed MVP Algorithm against various video processing attacks. Also, compression scale invariance is achieved.
Software Engineering Techniques for Block chain Technology

Blockchain science has determined an exceptional quantity of functions, from banking to the web of matters (IoT). Developed from the Merkle Tree, Blockchain technological expertise is a absolutely decentralized digital register which keeps a secure historical past of data exchanges. The decentralization part of Blockchain technological know-how does away the need of any valuable authority for managing it. On this paper we present a complete overview on blockchain technology. The block-chain technological expertise grants an extraordinarily progressive and at ease approach of managing transactions on-line. Hailed as one of the greatest inventions after the internet, it has taken the digital world by means of storm and is disrupting many industries. In the past few years, this technology has gained big value and its utility subject has evolved into a much broader context. The mass adoption of block-chain centered functions has accelerated dramatically and a plethora of such functions is actually available to be used.
Improved motorised spindle control using a biogeography-based optimisation algorithm

The BBO algorithm is introduced to estimate the equivalent stator resistance in the classical DTC method. BBO algorithm achieves the purpose of suppressing current harmonics and reducing torque by improving the online identification accuracy of the stator resistance of the motorised spindle, thus improving the performance of the motorised spindle control system. The vibrational speed of the electric spindle is significantly reduced at each operating frequency after using the BBO-DTC algorithm. The torque ripple of the electric spindle is obviously reduced, especially at 150 Hz and from 450~600 Hz. Analyses of the electromagnetic vibration speed of the electric spindle and the experimental input torque data indicate that the control performance of the BBO-DTC algorithm is improved. Not only is the dynamic performance of the system improved, but variations in the electromagnetic flux and the electromagnetic torque are also reduced. The control performance of motorised spindle is effectively improved.
Objects Working Together: Association, Aggregation and Composition

This chapter builds on the knowledge of classes and objects developed in the preceding chapters to create larger programs based on different objects communicating with one another. It illustrates different examples of the ways that objects can work together: association, where independent objects talk to each other, aggregation, where an object is made up of other objects that can vary, and composition, a very strong form of aggregation where the component objects are fixed. In the code examples, some design decisions are made, based on the concepts and syntax covered so far in the book, that might not be optimal. These serve as discussion points for ways that these design elements could be improved. For example, it considers the case of two classes that seem to have much in common and highlights how they might be better designed to share these common characteristics. This discussion serves as the foundation for the topics of inheritance and polymorphism, covered in the following chapter, that enable classes to build upon existing classes to extend and refine their behaviors.
Multi-Label Classification with PSO based Synthetic Minority Over-Sampling Technique (Psosmote) for Imbalanced Samples

Recently, the learning from unbalanced data has emerged to be a pre-dominant problem in several applications and in that multi label classification is an evolving data mining task, learning from unbalanced multilabel data is being examined. However, the available algorithms-based SMOTE makes use of the same sampling rate for every instance of the minority class. This leads to sub-optimal performance. To deal with this problem, a new Particle Swarm Optimization based SMOTE (PSOSMOTE) algorithm is proposed. The PSOSMOTE algorithm employs diverse sampling rates for multiple minority class instances and gets the fusion of optimal sampling rates and to deal with classification of unbalanced datasets. Then, Bayesian technique is combined with Random forest for multi-label classification (BARF-MLC) is to address the inherent label dependencies among samples such as ML-FOREST classifier, Predictive Clustering Trees (PCT), Hierarchy of Multi Label Classifier (HOMER) by taking the different metrics including precision, recall, F-measure, Accuracy and Error Rate.
Design and Implementation of Safety and Health Monitoring System for Women

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
libInterMAC: Beyond Confidentiality and Integrity in Practice

Boldyreva et al. (Eurocrypt 2012) defined a fine-grained security model capturing ciphertext fragmentation attacks against symmetric encryption schemes. The model was extended by Albrecht et al. (CCS 2016) to include an integrity notion. The extended security model encompasses important security goals of SSH that go beyond confidentiality and integrity to include length hiding and denial-of-service resistance properties. Boldyreva et al. also defined and analysed the InterMAC scheme, while Albrecht et al. showed that InterMAC satisfies stronger security notions than all currently available SSH encryption schemes. In this work, we take the InterMAC scheme and make it fully ready for use in practice. This involves several steps. First, we modify the InterMAC scheme to support encryption of arbitrary length plaintexts and we replace the use of Encrypt-then-MAC in InterMAC with modern noncebased authenticated encryption. Second, we describe a reference implementation of the modified InterMAC scheme in the form of the library libInterMAC. We give a performance analysis of libInterMAC. Third, to test the practical performance of libInterMAC, we implement several InterMAC-based encryption schemes in OpenSSH and carry out a performance analysis for the use-case of file transfer using SCP. We measure the data throughput and the data overhead of using InterMAC-based schemes compared to existing schemes in OpenSSH. Our analysis shows that, for some network set-ups, using InterMAC-based schemes in OpenSSH only moderately affects performance whilst providing stronger security guarantees compared to existing schemes.