Reviewerly/exHarmony huggingface dataset:
Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities

Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering and safety will undoubtedly follow suit. There is already a large but fragmented literature on ML for reliability and safety applications, and it can be overwhelming to navigate and integrate into a coherent whole. In this work, we facilitate this task by providing a synthesis of, and a roadmap to this ever-expanding analytical landscape and highlighting its major landmarks and pathways. We first provide an overview of the different ML categories and sub-categories or tasks, and we note several of the corresponding models and algorithms. We then look back and review the use of ML in reliability and safety applications. We examine several publications in each category/sub-category, and we include a short discussion on the use of Deep Learning to highlight its growing popularity and distinctive advantages. Finally, we look ahead and outline several promising future opportunities for leveraging ML in service of advancing reliability and safety considerations. Overall, we argue that ML is capable of providing novel insights and opportunities to solve important challenges in reliability and safety applications. It is also capable of teasing out more accurate insights from accident datasets than with traditional analysis tools, and this in turn can lead to better informed decision-making and more effective accident prevention.
Textual Summarization of Text and Multimedia Data using LDA Algorithm

To generate a summary lots of efforts have been taken in past years for the events such as Meetings, Sports-clips, Pictorial Storylines, Movies, Social media contents.Natural Language Processing (NLP) is a basic Automatic text summarization application which goals to summarize a given text into a compressed form.Over the year the fast growth in multimedia data across the internet, demands summarization from the asynchronous data that is the combination of image, text, video, and audio.We have describe an multi-modal summarization framework that uses the techniques of OCR, NLP and speech processing examine the information contained in the statics and to enhance the aspect of multimedia summarization.
Uncertainty-aware Self-supervised 3D Data Association

3D object trackers usually require training on large amounts of annotated data that is expensive and time-consuming to collect. Instead, we propose leveraging vast unlabeled datasets by self-supervised metric learning of 3D object trackers, with a focus on data association. Large scale annotations for unlabeled data are cheaply obtained by automatic object detection and association across frames. We show how these self-supervised annotations can be used in a principled manner to learn point-cloud embeddings that are effective for 3D tracking. We estimate and incorporate uncertainty in self-supervised tracking to learn more robust embeddings, without needing any labeled data. We design embeddings to differentiate objects across frames, and learn them using uncertainty-aware self-supervised training. Finally, we demonstrate their ability to perform accurate data association across frames, towards effective and accurate 3D tracking. Project videos and code are at https://jianrenw.github.io/Self-Supervised-3D-Data-Association.
Digital learning: Realities and Perspectives of a New Trend

The relevance of the research is determined by the need to study the problem of education digitalization in order youth to self-identify themselves in the modern society, as well as by insufficient development of theoretical, content and scientific aspects of this issue. The article is to highlight the current state and future prospects of education digitalization. The purpose of the article is to study the features of education digitalization at the current stage and to define its perspectives. The main research methods used are theoretical ones, since they provide the opportunity to consider the modern problem of education digitalization as a holistic process and to reveal its future prospects. The article presents the analysis of the current state of education digitalization and future prospects of this process. The overview of the research issue is oriented to the study of education digitalization prospects in order youth to self-identify themselves in the modern society. The research is also focused on the elaboration of methodological recommendations concerning this problem.
The Effect Of Applying Picture Word Inductive Model (PWIM) on the Students’ Achievement in Writing Descriptive Text

The objective of this research was to find out the effect of applying picture word inductive model (PWIM) on students' achievement in writing descriptive text.The method of research was the experimental research method.The population of this research was taken from the eight grade and the second year students of junior high school at MTs. Islamiyah Medan Jl.Suluh No. 71 D, Sidorejo Hilir, Medan Tembung, Medan, Sumatera Utara.at academic 2017/2018 with the total population were 84 students.The sample consisted of 30 students were taken by using purposive sampling.The classes were divided into one group, namely experimental group used in one-group pretest-posttest.Therefore, the research was taken one class as the experimental class.The experimental group was taught by applying picture word inductive model (PWIM).The instrument of research was written test (pretest-postest).The researcher were given treatment by PWIM strategy.The instrument of collecting data was written test which was given picture to make a descriptive text as the test items.The data analysis was using T-test.The result showed that the students mean in teaching descriptive text before taught applying Picture word inductive model (PWIM) is only 47.5 while the students mean in teaching descriptive text after the being taught applying picture word inductive model (PWIM) is 85.5 it was improved.The data were analyzed by using t-test formula.with the t-test analysis that used by researcher, with the significant level 0.05.The result of the data showed that 5.71 > 2.04 with degree of freedom (df) = N-1 (30-1).The final hypothesis showed that Hₒ was rejected and Hₐ was accepted.It proves that Picture Word Inductive Model (PWIM) significantly effect to the students' achievement in writing descriptive text.
Beyond Monolingual Vision-Language Navigation

Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics. But the dominant language is English, according to previous studies on vision-language navigation (VLN). To go beyond English and serve people speaking different languages, we collect a bilingual Room-to-Room (BL-R2R) dataset, extending the original benchmark with new Chinese instructions. Based on this newly introduced dataset, we study how an agent can be trained on existing English instructions but navigate effectively with another language under a zero-shot learning scenario. Without any training data of the target language, our model shows competitive results even compared to a model with full access to the target language training data. Moreover, we investigate the transferring ability of our model when given a certain amount of target language training data.
Organizing Virtual Conferences through Mirrors: The ACM e-Energy 2020 Experience

The emergence of the world-wide COVID-19 pandemic has forced academic conferences to be held entirely in a virtual manner. While prior studies have advocated the merits of virtual conferences in terms of energy and cost savings, organizers are increasingly facing the prospect of planning and executing them systematically, in order to deliver a rich conference-attending-experience for all participants. Starting from March 2020, tens of conferences have been held virtually. Past conferences have revealed numerous challenges, from budget planning, to selecting the supporting virtual platforms. Among these, two special challenges were identified: 1) how to deliver talks to geo-distributed attendees and 2) how to stimulate social interactions among attendees. These are the two important goals of an academic conference. In this paper, we advocate a mirror program approach for academic conferences. More specifically, the conference program is executed in multiple parallel (mirrored) programs, so that each mirror program can fit a different time zone. This can effectively address the first challenge.
La tecnología DLT en el mercado de valores, en especial respecto de las acciones de las sociedades cotizadas. Aspectos generales y análisis de una posible desintermediación del mercado

espanolEl presente trabajo tiene como punto de partida el creciente desarrollo de la aplicacion de la tecnologia DLT (distributed ledger technology, generalmente conocida como blockchain) en el mercado de valores y, en especial, en el ambito de las sociedades cotizadas. Este tipo de tecnologia se asienta sobre la existencia de una base de datos distribuida de forma descentralizada y encriptada mediante algoritmos de consenso. Los datos incluidos, en el caso de las sociedades cotizadas, podrian comprender la titularidad de las acciones, los derechos conferidos sobre las mismas, asi como las transacciones que se realizaran con estas. En este caso, la tecnologia DLT permitiria que estos datos permanezcan inalterados y ordenados cronologicamente; cualquier cambio en los mismos se registraria automaticamente por medio de smart contracts. Todo esto permite dotar de seguridad al sistema, haciendo innecesaria, en cierta medida y en un planteamiento general, la participacion de terceros que tradicionalmente ejercen dicha funcion. No obstante lo anterior, tal y como se estudia a lo largo del trabajo, el Derecho del mercado de valores actual y algunos limites de la propia tecnologia parecen indicar que a corto y medio plazo sera necesaria la participacion de las entidades que forman parte de la infraestructura del mercado, asi como, entre otros intermediarios, de los custodios de valores. EnglishThe baseline of this paper is the increasing development of the DLT technology (generally known as blockchain) implementation in the stock market and, especially, in the field of listed companies. This type of technology is based on the existence of a decentralized and encrypted distributed database using consensus algorithms. The data contained, in the case of listed companies, could include the ownership of the shares, the rights over them, as well as the transactions undertaken with regards those shares. In this case, DLT technology would allow these data to remain unchanged and ordered chronologically, any change regarding such data would be automatically recorded through smart contracts. All this allows to provide security to the system, making unnecessary, to a certain extent, the participation of third parties that traditionally exercise such purpose. Notwithstanding the foregoing, as studied throughout this paper, the current stock market law and some limits of the technology itself seem to indicate that in the short and medium term the participation of the market infrastructure, as well as, among other intermediaries, of the security safekeepers (custodians) will be necessary.
Using LHC to Discover the Bilepton

We discuss a minimal and simple extension of the standard electroweak model and discuss its uniqueness properties.Its most readily testable prediction is the existence of |Q| = |L| = 2 gauge bosons, bileptons, which decay into like-sign lepton pairs.We discuss how a discovery could be made at the LHC.
CLIENTES PAGANTES VS TRIAL: EXPLORANDO A IDENTIFICAÇÃO EM UM AMBIENTE SAAS

oftware as a Service (SaaS) is a software licensing and de-livery model in which software is licensed on a subscription basis and is centrally hosted, generally, in the web. In this software delivery model, users have a period for freely tes-ting the software before buying it, called trial. This work aims to extract features from a speciﬁc SaaS product (named Belasis) to build a classiﬁcation model to identify whether or not a trial customer becomes a payer one. To accomplish that we use three classiﬁcation algorithms: Support Vector Machine, K-Nearest Neighbours and Random Forest. We conduct some experiments using some existing and new fe-atures from Belasis, and the results are promissing. Based on 𝐹1-score metric, Random Forest gets the best results.
FIU Libraries: Return to Campus: Contactless Material Pickup

A guide to new policies/procedures as we repopulate the FIU Libraries. How to request contactless pickup of library materials.
Blended Learning; Integrating Moodle to Facilitate Reading Comprehension Activities

This article examines the integration of Moodle to support face-to-face reading class learning activities since the management of learning reading comprehension as a single activity in the classroom is not sufficient. Students need to read outside the classroom and the teacher monitors students' reading comprehension activities. This can be realized by integrating Moodle in teaching and learning activities as a form of blended learning. More specifically, this best practice article illustrates three things, namely: (1) learning planning, (2) implementation strategies for integrating Moodle into face-to-face learning reading comprehension activities, and (3) The results of utilizing moodle to facilitate students’ reading comprehension activities in blended learning context. Students at one of senior high schools in Pangkalpinang are participants in this study. Furthermore, in this best practice, there are two data sources; document analysis and interview. The document analysis is taken from the learning activities where Moodle is used to visualize the picture of reading comprehension narrative text activities. Data from interviews are used to describe students' attitude in integrating Moodle in learning reading comprehension of narrative texts. It was described that Moodle, forum and quiz feature, were developed to support reading comprehension activities. It is also illustrated that students' attitude on the use of Moodle is positive; Moodle motivated students to work on Moodle questions so that they learn more actively, effectively, and reflectively.
Semantic Classification of Images in Hierarchical Manner using Fuzzy Rules and HSVM Classifier

Hierarchical structure classification is more effective in concept based classification. The feature vector is varying with the different concepts, though it belongs to the same class. Images which are similar in appearance are not correlated semantically. Hence an author made an attempt to classify the images in the dataset in concept wise in a hierarchical manner. For this, an author manually clustered the same concept images in a layer by layer to explore the semantic correlation of the concepts. A number of nodes in the hierarchy tree represent the number of classes in the dataset. Each node is classified into sub-nodes by the fuzzy classifier using fuzzy association rules. Fuzzy inference system trained by the fuzzy rules base, which is generated by the fuzzy feature vector. The performance of the fuzzy classifier is validated with multiclass support vector machine classifier and hierarchical support vector machine classifier. The dataset used here is COREL dataset; an author selected 8 concepts for the classification. Keywords: Fuzzy classifier, fuzzy rule, hierarchical SVM, multiclass SVM, semantic concepts Cite this Article: Thirumala Lakshmi K, Usha Kingsly Devi K. Semantic Classification of Images in Hierarchical Manner using Fuzzy Rules and HSVM Classifier. Journal of Image Processing &amp; Pattern Recognition Progress. 2020; 7(2): 33–54p.
Awareness of Consortium and Cloud-based Online Resources in VTU Engineering College Libraries

Modern academic libraries should well be equipped with online resources along with print resources. Libraries are facing a transition period from print to electronic. The younger generation prefers to refer to the digital form of resources quickly. Consortium is a blessing for readers, who are studying/working in technical institutes. To meet the industrial requirements, one who updates his/her knowledge up to global and industrial standards. It can be achieved only by referring to current journals. Consortium has given that chance to library users to access world-class eJournals and eBooks on their campuses just by clicking the mouse. This study focuses on the usage of electronic resources subscribed through consortium by students and teaching fraternity and how those resources being utilized by users. This paper gives the idea of promoting these resources at the college level.
Smart System’s Potential

In this chapter, we introduce the concept of parametric algebraic potential in line with the algebraic formalization of the system.
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards

As the operations of autonomous systems generally affect simultaneously several users, it is crucial that their designs account for fairness considerations. In contrast to standard (deep) reinforcement learning (RL), we investigate the problem of learning a policy that treats its users equitably. In this paper, we formulate this novel RL problem, in which an objective function, which encodes a notion of fairness that we formally define, is optimized. For this problem, we provide a theoretical discussion where we examine the case of discounted rewards and that of average rewards. During this analysis, we notably derive a new result in the standard RL setting, which is of independent interest: it states a novel bound on the approximation error with respect to the optimal average reward of that of a policy optimal for the discounted reward. Since learning with discounted rewards is generally easier, this discussion further justifies finding a fair policy for the average reward by learning a fair policy for the discounted reward. Thus, we describe how several classic deep RL algorithms can be adapted to our fair optimization problem, and we validate our approach with extensive experiments in three different domains.
A procedure for Web Service Selection Using WS-Policy Semantic Matching

In general, Policy-based approaches play an important role in the management of web services, for instance, in the choice of semantic web service and quality of services (QoS) in particular. The present research work illustrates a procedure for the web service selection among functionality similar web services based on WS-Policy semantic matching. In this study, the procedure of WS-Policy publishing in the UDDI registry was also described. The approach, which is used to represent the policies, is thus represented as semantic trees, and in this representation, measurable quality attributes are considered; and the certain matching operations are used to identify the similarity match via match function or similarity distance function. The illustration of semantic concepts and rules during policy matching, which is not possible by using a mere semantic concept, leads to better web service matches. The proposed approach has been validated through various tests that can evaluate the similarity of large and arbitrary sets of measurable quality attributes. We also compared the proposed procedure with the other ones. The proposed procedure for web service choose, which uses WS-Policy semantic matching, can be more effective to solve different problems like selection, composition, and substitution of services.
The Influence of Using Group Work and Reading Attitude Towards Reading Comprehension Achievement

This study was to investigate the influence of using group work and reading attitude towards the eleventh grade students’ reading comprehension achievement on narrative text. The population of this study was the eleventh grade students at Senior High School Puspita Air Kumbang. The sample was 60 students taken by using cluster random sampling. In carrying out of the research, the writer used an experimental method; factorial design. The writer distributed pretest, posttest, and attitude questionnaire in collecting the data. Then, in analyzing the data the writer used t-test and two-way ANOVA. The result of this study analysis indicated that; first, there was significant influence of group work technique towards the eleventh grade students’ reading comprehension achievement between those who had positive attitude and those who had negative attitude. Second, there was significant influence of conventional teaching technique towards the eleventh grade students’ reading comprehension achievement between those who have positive attitude and those who have negative attitude. Third, there was significant influence of group work technique and conventional teaching technique towards the eleventh grade students’ reading comprehension achievement between those who have positive attitude and those who have negative attitude. Forth, there is significant interaction effect of group work technique and reading attitude towards the eleventh grade students’ reading comprehension achievement between those who have positive attitude and those who have negative attitude.
Taking into Account Semantic Peculiarities of English Verbs of Thinking When Teaching Russian as a Foreign Language

The paper aims to identify semantic peculiarities of English verbs of thinking, to reveal a methodological potential of the author’s techniques when teaching Russian as a foreign language. The article proposes a classification of Russian verbs of thinking considering the level of difficulty of their acquisition by English-speaking students, justifies efficiency of the suggested vocabulary teaching techniques. Scientific originality of the study lies in the fact that the author identifies the basic difficulties experienced by English-speaking students when studying Russian verbs of thinking and develops techniques to introduce this group of verbs to English-speaking students. The research findings are as follows: the main differences between the Russian and English linguistic worldviews are revealed; the experiment results justify efficiency of the proposed methodology when forming foreign students’ Russian-language lexical competence.
Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-Based Explanations

Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert's level, interpretability (highlight how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification. We formalize the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context. We aim at explaining how the decision is made based on tile level scoring, how these tile scores are decided and which features are used and relevant for the task. After training two WSI classification architectures on Camelyon-16 WSI dataset, highlighting discriminative features learned, and validating our approach with pathologists, we propose a novel manner of computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances by more than 29% for AUC.