Reviewerly/exHarmony huggingface dataset:
The Interactive Applications (IAs) in Academic Libraries: Challenges and Opportunities

Presentation tools of academic content are increasing in popularity for educators in Higher Education Institutions (HEI) who want to share ideas and information in a more creative and interactive environment using more effective tools and demand to involve. Interactive Applications are becoming lot more common and is more integrated into our everyday activities, like using mobile apps. The features of the Fourth Industrial Revolution (4IR) began to emerge through Interactive Applications (IAs) such as the applications of Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR). Information resources development is no longer restricted and residing within the realm of speculative fiction. By using AR, VR and MR, academic libraries could already deliver a massive revolution in information retrieval. However, the biggest challenge that need to be tackled perhaps remains in how we could tune between these resources and the users so that the greatest possible benefit could be achieved in the light of accelerated technological development. This chapter uncovers the challenges and opportunities in using Interactive Applications (IAs) technologies and should be an eye opener for academic libraries that Interactive Applications technology are important to transform the use of traditional resources to interactive resources.
I’m Not a Chatbot: An Empirical Investigation of Humanized Profiles of Social Media Customer Service Representatives

While artificial intelligence is robotizing customer service at an unprecedented pace, there is great concern that robotized customer service could undermine customer satisfaction.This study searches for a solution that humanizes customer service to address this concern.Aiming to increase humanization, U.S. telecom giant T-Mobile recently added personal identities to its customer service representatives' profiles on Twitter.Here, we examine the effect of humanized profiles on customers' expressions of emotion or complaints via public tweets.The study provides novel insight explaining why customers are more likely to express positive emotions and fewer complaints if they are interacting with customer service representatives with a humanized profile on a social media platform.Interestingly, this effect is stronger among female users.We also discuss the implications for research and practice.
The Effect of Learning Motivation, Learning Discipline and Learning Media on Learning Readiness

This research was made to find out and analyze the factors that influence Learning Readiness in the form of Learning Motivation, Learning Discipline, and Learnin Media. The technique used in data retrieval is done by the method of data collection by distributing questionnaires to respondents online with a total sample of 217 respondents using SPSS software version 23 and Amos version 22. This study supports previous research which states that previous research has been carried out by Ayu Fitri Yanida and Hengky Pramusinto (2014) Effects of Learning Motivation, Learning Discipline, and Learning Media on Learning Readiness of Class X Students in Office Administration where the results of this study indicate that there is an influence of learning motivation, learning discipline.
Effects of SMS Texting on Academic Writing Skills of Undergraduate Students at a Public Sector University in Pakistan

The growing concern about the use of texting endangering the standard forms in language prompted the present research to determine the presence or absence of SMS features in the academic writing of the participants.Triangulation was used for data collection i.e. questionnaires for learners and educators and samples of the learners' English written work were examined for SMS features.Simple average and ratio were used for descriptive analysis of the data.Contrary to the expectation, there were no significant evidences of these features in the sample.It seems being proficient in standard forms, these learners are context conscious and can switch to the appropriate register or style when writing formally .Thus the present study has demystified the popular belief about texting adversely affecting writing and thus destroying Standard English.Moreover, the evidences of one punctuation mark used in place of another indicate there can be other factors like carelessness or lack of knowledge of students and the lack of training, feedback or emphasis by educators or the system.So the matter of concern should be the general neglect of punctuation even out of the context of texting.It is found that the higher the exposure to the SMS, more the negative effect on the writing skills of the university students.The excessive use of this medium is leading students towards writing wrong spellings and using SMS language's short abbreviations that are not standard in examinations and daily academic work that is very harmful in academia.
Imperceptible manipulation of lateral camera motion for improved virtual reality applications

We measure detection thresholds for lateral translation gains of virtual camera motion in response to the corresponding head motion under natural viewing. We propose three applications for our method, addressing three key problems in virtual reality, and validate their improvement when our manipulation technique is applied.
What can be improved? Identifying actionable items from patient narratives

Recent literature suggests the potentiality of patient narratives to evaluate the quality of healthcare services, but little research has investigated the “actionability” of such narratives in driving or reshaping the practices. Moreover, the colloquial and implicit nature of natural language expressions poses challenges to automatic actionable item identification (AII) from large-scale patient narratives. In this paper, we propose a Content Sense and Inference BiLSTM (CSI-BiLSTM) model to address the challenges, and the model includes the element-aware network (EAN) and the aspect indicator network (AIN). The EAN is designed to sense and capture the mentioned degree of the key elements of input sentences to generate effective element-aware sentence representations. The AIN is endowed with reasoning ability for implicit expressions by inferring whether given opinion words are aspect indicators, to further enhance the accuracy and robustness of the model. To the best of our knowledge, this is the first study to automatically identify actionable items from patients' online narratives. Experiment results on two real-world datasets demonstrate the performance of the proposed model outperforms the competitive baselines and extensive analysis reveals the effectiveness of our model in identifying actionable items.
PROFESIONALITAS PUSTAKAWAN DAN TANTANGANNYA PADA ERA REVOLUSI INDUSTRI 4.0

Abstract: The professionalism of a librarian and the challenges in the era of the industrial revolution 4.0, which in this era really demands the skills of a librarian in utilizing the sophistication of existing technology. Also, being able to minimize risks and face the challenges posed by this era of the industrial revolution 4.0 - so that the functions and objectives of the library can be achieved. Keyword: Librarian Professionalism, Industrial Revolution Era 4.0 Abstrak: Profesionalitas seorang pustakawan dan tantangannya pada era revolusi industri 4.0 yang mana era ini sangat menuntut kemahiran seorang pustakawan dalam memanfaatkan kecanggihan teknologi yang ada. Serta, mampu meminimalisir resiko dan menghadapi tantangan yang dimunculkan oleh era revolusi industri 4.0 ini—sehingga fungsi dan tujuan perpustakaan dapat tercapai. Kata Kunci : Profesionalitas Pustakawan, Era Revolusi Industri 4.0
Ontological Machinery of the Artistic Process: the Becoming-Technical and Art

This article pursues dual purpose. On the one hand, the author analyzes the general and the most important in the technical being since only therewith it is possible to conceptualize the sphere of technical objects and to determine the place of technics with respect to the art. So the article is devoted to new ontological opportunity of the technics’ entity perception by its connection with art. This relationship we consider in the context of “technological revolution” in the art. Moreover, it has been tried to be revealed possible mechanisms of technics emancipation with the help of the art and to be shown that technology can emancipate the art using both artistic techniques and inartistic technological processes. On the other hand, the aim of our article is to provide with a presentation of some onto technological key ideas, along with some conceptual approaches (for example, Levis Brуant’s onto-cartography) which we can distinguish by their further connection with the modern art. Conception of ontology in the terms of technics by all means has become the urgent task for philosophy, and one of the ways to solve this task is to investigate the relationship between ontology and technics. The author of the article argues that the confrontation between nature and technology is an illusion. We tried to complete this argument combining the ontology of nature and of technology in one concept – the concept of machine. Recently most works of art have become technological objects revealing the problem of “technical” art. In the article it is proposed the analysis of some modern works of art and is demonstrated that “the nature” and technics cannot be no more opposed and differentiated since one gives birth to another. Moreover, it is existed only techno-nature and its production (in the difference of repetition and creation) is the final question of techno-art.
Don't be picky, all students in the right family can learn from good teachers

State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models. However, widespread use is constrained by device hardware limitations, resulting in a substantial performance gap between state-of-the-art models and those that can be effectively deployed on small devices. While Knowledge Distillation (KD) theoretically enables small student models to emulate larger teacher models, in practice selecting a good student architecture requires considerable human expertise. Neural Architecture Search (NAS) appears as a natural solution to this problem but most approaches can be inefficient, as most of the computation is spent comparing architectures sampled from the same distribution, with negligible differences in performance. In this paper, we propose to instead search for a family of student architectures sharing the property of being good at learning from a given teacher. Our approach AutoKD, powered by Bayesian Optimization, explores a flexible graph-based search space, enabling us to automatically learn the optimal student architecture distribution and KD parameters, while being 20x more sample efficient compared to existing state-of-the-art. We evaluate our method on 3 datasets; on large images specifically, we reach the teacher performance while using 3x less memory and 10x less parameters. Finally, while AutoKD uses the traditional KD loss, it outperforms more advanced KD variants using hand-designed students.
Implementasi Algoritma Colussi Pencocokan Kata Berbahasa Inggris Pada Dokumen Microsoft Office Word

English words are often used in various documents including one of them is in Microsoft Office Word documents. A data or document may experience errors, namely in foreign words that do not match the true meaning or meaning so that the document cannot be used. To get the correct document results and foreign words that match the meaning, this is done by using the Colossi algorithm by matching these foreign words. The colossi algorithm shifts the text from left to right. The colossi algorithm has two stages of its completion. First Comparisons are made from left to right on text which is located in the same position as the pattern position where the value of the function kmp ext is slightly greater than -1. These positions are called nohole (s), second Comparing the remaining positions (other names are hole (s)) from the left to the right. With this algorithm it is expected to solve the problem of foreign words in Microsoft office Word documents. , then the application is built using Microsoft Visual Basic 2008, and it is expected that the results of the application can produce the right document
Study on the Construction of Personnel Training System with Flexible Dynamic for Road and Bridge Engineering Specialty in Cold Area

Road and Bridge Engineering Specialty is a comprehensive and practical major. In the course system of higher vocational education, it mainly takes the project of Road and Bridge Engineering as the main teaching line to carry out the teaching of Road and Bridge Engineering related technology and theory, so as to meet the needs of the society for the personnel of Road and Bridge Engineering . But in fact, in the teaching of Road and Bridge Engineering in higher vocational colleges, the teaching of Road and Bridge Engineering is universal, but the teaching content of Road and Bridge Engineering development in special areas is insufficient, which leads to the cold areas in the society and the shortage of construction talents in the extreme environment. In addition, the seasonal influence of road and bridge construction in cold area is great. Based on this, this paper puts forward the concept of flexible and dynamic personnel training, which aims to realize the cultivation of road and bridge engineering technical talents for cold areas and ensure the overall and coordinated development of our economy.
MIT-AVT Clustered Driving Scene Dataset: Evaluating Perception Systems in Real-World Naturalistic Driving Scenarios.

Solving the driving scene perception problem for driver-assistance systems and autonomous vehicles requires accurate and robust performance in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier scenarios (termed “edge cases”). We propose an automated method for clustering common cases and detecting edge cases based on the visual characteristics of the external scene using deep learning. We apply this approach to develop a large-scale real-world video driving scene dataset of edge cases and common cases. This dataset consists of 1,156,592 10-second video clips, including 450 clusters of common cases, and 5,601 edge cases. We assign human-interpretable metadata labels (e.g., weather, lighting conditions) to the clusters through manual annotation. We further propose two automated methods for large-scale evaluation of scene segmentation models on naturalistic driving datasets that can capture potential system failures without human inspection. Video illustrations of select clusters will be made available to help with future research.
ANALYSIS OF ADVANCED AND DEVELOPING COUNTRIES USING CLUSTER ANALYSIS METHOD: Asian Countries

The position of a country can determine the views of investors to determine the investment to bemade. Currently, the grouping of countries is usually based only on economic factors, this makes otheraspects outside the economy unimportant. The purpose of this research is to see the position of a country byusing economic and non-economic aspects. It is hoped that using other aspects besides the economy canbetter position the country. The data used is data from the World Bank in 2018 by taking a sample of 43countries in the Asian region. Some of the indicators used include Population Growth, Incident ofTuberculosis, Death and Birth Rate, and so on. Grouping is done using 3 scenarios, namely grouping into 2,3, and 4 groups. The method used is clustering with the K-Means Clustering method. The results obtainedshowed that using 4 groups resulted in a clustering accuracy rate of 88.37%. These results certainly cannotbe used as an absolute benchmark for grouping a country, but it can describe the country's position quitewell.
ارتقای عملکرد نوآورانه و فروش از طریق مدیریت دانش مشتری

Background and Aim: Today, knowledge resources of organizations are is considered as a powerful driver for promoting innovation and performance enhancement. Accordingly, this research is to investigate the effects of customer knowledge management on sale performance and innovative performance. Methodology: The research method is descriptive and correlational. The population of the research consists of 900 sellers of Beh pakhsh Corporation. A sample of 270 subjects was selected as statistical sampling. The required Data are collected through questionnaire. analyzed through Structural equation model by means of LISREL and SPSS software. Results: all studied aspects of customer knowledge have positive effects on the innovative and sale performance of the corporation. In addition, innovative performance can improve the sale performance of the distribution corporations. Conclusion: Recognizing the needs, issues, ideas, and knowledge of customers is a valuable resource. Effective management of the knowledge can help promote sustainable productivity of organizations in competitive environments.
Continual Invariant Risk Minimization

Empirical risk minimization can lead to poor generalization behaviour on unseen environments if the learned model does not capture invariant feature represen- tations. Invariant risk minimization (IRM) is a recent proposal for discovering environment-invariant representations. It was introduced by Arjovsky et al. (2019) and extended by Ahuja et al. (2020). The assumption of IRM is that all environ- ments are available to the learning system at the same time. With this work, we generalize the concept of IRM to scenarios where environments are observed se- quentially. We show that existing approaches, including those designed for contin- ual learning, fail to identify the invariant features and models across sequentially presented environments. We extend IRM under a variational Bayesian and bilevel framework, creating a general approach to continual invariant risk minimization. We also describe a strategy to solve the optimization problems using a variant of the alternating direction method of multiplier (ADMM). We show empirically us- ing multiple datasets and with multiple sequential environments that the proposed methods outperforms or is competitive with prior approaches.
Various Techniques for De-noise Image

&lt;p&gt;Wavelet decomposition has a great role in eliminating noise, the aim of this work on different noise removal techniques by analyzing the color image. Based on the analysis of different image compression techniques, this paper provides a survey of existing research papers. Different types of method for noise are analyzed from the necessary image, where the disturbance was removed using wavelets with basic theories, and the most important details that will be presented in this work, which clarifies the proposed smooth and effective theory in terms of accuracy in our results. By creating new algorithms that explain how to use the proposed theory, some medical applications were used Discrete Wavelet Transform (DWT) where the results were satisfactory obtained, our proposed theory has been proven to be effective, and examples used will demonstrated this method.&lt;/p&gt;
Are the Sri Lankan Cyber Laws Sufficient to Safe Guard the IT Professionals and Victims of Cyber Attacks

This study is conducted in conjunction with the computer crimes act of Sri Lanka an it evaluates the whether computer crimes act of srilanka is able to safeguard IT proffessionals and cybers crime victims in comparison to European union act for computer crimes, and provides and indepth comparison between the computer crimes act of srilanka and the European union act as well.This leads for the evaluation of the computer crimes act of Sri Lanka an identifies the pro's and con's of the act as well.
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions.

Active learning generally involves querying the most representative samples for human labeling, which has been widely studied in many fields such as image classification and object detection. However, its potential has not been explored in the more complex instance segmentation task that usually has relatively higher annotation cost. In this paper, we propose a novel and principled semi-supervised active learning framework for instance segmentation. Specifically, we present an uncertainty sampling strategy named Triplet Scoring Predictions (TSP) to explicitly incorporate samples ranking clues from classes, bounding boxes and masks. Moreover, we devise a progressive pseudo labeling regime using the above TSP in semi-supervised manner, it can leverage both the labeled and unlabeled data to minimize labeling effort while maximize performance of instance segmentation. Results on medical images datasets demonstrate that the proposed method results in the embodiment of knowledge from available data in a meaningful way. The extensive quantitatively and qualitatively experiments show that, our method can yield the best-performing model with notable less annotation costs, compared with state-of-the-arts.
핵심역량 강화를 위한 고전소설교육 방법

This study emphasized that education is now shifting to core competencies.And This study examines what kind of education method is needed for...
Methods for effective and reliable resource allocation in service-oriented systems

Many life and infrastructure critical service-oriented systems require rigid compliance of their execution parameters values with the defined Service Level Agreement (SLA). In such systems reliable and proper execution of provided functionalities is an important part of their security assurance procedures. To meet these demands, the majority of system architects overprovision systems' infrastructures to ensure proper operability even during high load scenarios. In this article, a proposition of methods for effective and reliable resource allocation methods for service-oriented systems is proposed. The main foundation of this research lays in the user request processing procedure. This paper proposes a just-intime resource provisioning procedure for composite service with optimization methods ensuring proper resource allocation, compliance with the defined SLA and optimization of composite service delivery cost. The paper describes the concept of such request processing procedure, proposition of resource allocation methods and their experimental verification results.
Meningkatkan Motivasi dan Hasil Belajar Peserta Didik dengan Mengoptimalkan Penerapan Model Discovery Learning di SD Negeri 2 Cakranegara

The purpose of this study was to determine the effectiveness of the application of the Discovery Learning model in an effort to improve motivation and learning outcomes of grade VI students of SD Negeri 2 Cakranegara. This research method uses classroom action research. This research was conducted in two cycles, each cycle of activities is; planning, implementation, observation and reflection. The final results of the action in the second cycle showed that the observations of teachers at the second meeting obtained an average score (4.57) and the results of observations of the students of the second meeting reached an average score (4.35). While student learning outcomes reach an average value (90.46), meaning that indicators of success (> 4.0) and learning outcomes (> 75.00) have been exceeded. Because the indicators of success have been proven, the research was declared successful and stopped in the second cycle.
Plasma Therapy as a Last Resort in Combating COVID-19?

This study aims to provide some insights into the current pandemic situation of COVID-19 and its proposed cure in the form of convalescent plasma (CP) therapy. This article is prepared using information from various research papers available online. The findings are concluded with the point that the convalescent plasma therapy is although functional in the early stages, is much important in critical cases where the patient is suffering from SARS CoV in the final stage along with drug administration, convalescent plasma therapy cannot alone cure the disease but can help subside the lethality.