Reviewerly/exHarmony huggingface dataset:
Lessons from Prof. K. S. Deshpande

Late Prof. Krishna Shripad Deshpande taught us many lessons, not necessarily in a classroom. Some lessons he, no doubt, taught us in the classroom. In addition, he taught us some lessons as a working librarian, some as writer/editor, some as a social worker, many as a head of a family and many more by effortlessly inspiring those who came across him in routine conversation by setting an example. He was, and he is, a lighthouse providing right orientation and guidance. He played many roles to spread a mission comprising several lessons. Here is an attempt to recapture a select few lessons learned by us.
Paperless Society: The Vision of An Oasis or Mirage: Implications for Libraries and Librarians

It’s unlikely that we Information Scientist will ever fully get rid from dependence of paper. The research paper discuss the proliferation of electronic resources, digital libraries, Internet technology, ICT etc., which adds to the complexity of organizing these new types of information. By combining the various printed/nonprinted, manuscripts, maps and all other types of documents into one electronic repository, information becomes easier to access. Thus saving of our Time, Money and Space is rule “a paperless society.” By making responsible choices for saving environment and joining hands together we would certainly be able to fulfill our vision of “Paper less Society”.
School Teachers Reading Habits for Lifelong Learning:A Study

The present study aimed to examine how the teachers inculcate the reading habits in doing their job in an effective manner. The article describeresults of 150 questionnaire obtained by the school teachers in Tumakuru. 92% of the teachers spend 30 minutes to 2 hours per week in the library for reading. 47.3% of the respondents feel that authors and 35% feel Subject is the main influence for them to read a book. Further, 32% read to prepare class work, 22.7% read for personality development, 20.7% read for pleasure or entertainment and 11.3% respondents only read books to gain knowledge. The teachers when read an interesting / stimulating book, 38% discuss it with their friends or colleagues, 26% share the joy of reading with their respective families and 24% would discusses it in the class. We feel this aspect is much useful to the students. The article also discuss on the ways to improve the reading habit among the teachers. The article also discusses Aliteracy and Peter Effect.
Use of Web Technology in Engineering and Technological Institution Libraries:A Study:Special Reference to Google Services

The research paper extracts the information on use of Google Services by engineering college library professional. The study is based on a survey research method. The paper cover four VTU regional divisions affiliated college as random samples. The questionnaire is distributed through mail IDs with Google Form design. 40 college libraries are selected for research sample. The study is limited to use of Google Services and its use by the professionals and awareness. The SPSS statistical software is used to analyse and design the graph for the received data.
Reference Management (RM):A Science of Referencing

The referencing is a science of showing authenticity of the content. It is reflection of honesty and acknowledging the original source and its creator. The development of referencing and its importance begin with footnotes. The growth of science of literature particularly the advent of concept like publish and perish well supported by electronic information, software's and networks furthered this referencing science as another scientific revolutions. The article has sketched and overview of reference management with a model.
Harnessing of DOAB Resources and it Utility Behavior of Teaching and Learning Community of Rajiv Gandhi Prodyogiki University Bhopal, M.P.

Library and Information Centres play pivotal role to fulfil the thirst knowledge of knowledge seekers. We are living in revolutionary era of Information and Communication Technology (ICTs). The revolution of ICTs emerged as a boon for library and information science professions which opened a door for accessing digital resources i.e. free and open source platform. The key objective of the study is to explain about prospecting of Free and Open Access Journals special reference of Directory of Open Access Books (DOAB) by the Intellectual Community (ICs) of Rajiv Gandhi Prodyogiki University (RGPV), Bhopal, and M.P. The study used structured questionnaires method as a tool of data collection to attain the motto of the study, 221 questionnaires distributed among ICs and 86 per cent respondents given effective responses which have been explained in data and interpretation section. The study revealed that DOAB resources helped to fulfil the academic and research needs of the intellectual community and suggested that there is need to promotion of these resources which provide more accessibility of library resource & set a trend for accessing of open and free resources platforms & tools.
Petrography, Geochemistry and Tectonic Settings of Granitic Gneisses from Swarnagadde Plateau of Western Ghats, Karnataka

The granitic gneisses of the Swarnagadde plateau located on the southwestern margin of Uttar Kannada district forming a basement complex of peninsular gneiss, have been examined in the field, mapped and interpreted on the basis of petrological and geochemical data.This was meant to categorize the rocks and to understand the tectonic setting in order to evaluate the crustal evolution.The chemical analyses were done using inductively-coupled plasma mass spectrometer.From the results obtained, the major element contents of granitic gneisses reflect their fairly homogeneous composition.Mineralogy of rock is dominated by plagioclase (49-62%), quartz (29-42%), K-feldspar (1-5%) and /or hornblende is in decreasing order.The granitic gneisses formed from medium to low grade metamorphism of granite and granodiorite witnessed at a late stage in the Precambrian records of the area.Tectonically most of the rock samples plotted in the field of volcanic arc granite+Syn-collisional granite rocks, which indicated that the protolith granite and granodiorite are volcanic arc magmas predominantly post orogenic development in island-arc to continental and collision zone.
Perkembangan Jaringan Kerjasama Perpustakaan

&#x0D; In order to realize library services that are carried out in an excellent and user-oriented manner, library collaboration is carried out. This library collaboration serves to provide wider access to collections, improve user and technical services, and increase activities in various resources. Technological developments have an impact on document products and the content of information contained in them varies. In addition to the library network, there is also an information network whose members do not limited to the library, but also information units outside the library such as documentation centers, data banks, information analysis centers, and referral centers. Here we discuss the UISU library collaboration network.&#x0D; Keywords: library collaboration, technological developments
Experimental Psychology Analysis of TOPSIS Methods

Experimental psychologyExperimental psychologists use scientific methods to gather information and behaviour research. Often, their work builds to one important discovery or conclusion, one have a look at a time. Few researchers have dedicated their complete careers to answering a complicated studies question. These psychologists work in a variety of settings, consisting of universities, research centres, government agencies, and private agencies. Experimental psychologists, but, often consciousness their complete interest on research its design, implementation, evaluation, and dissemination. An example of experimental psychological research could be to conduct a look at to see if sleep deprivation impairs performance on a riding take a look at. Experimental psychology. The oft-heard criticism that this original work belies its title because it fails to emphasize experimental topics as much as it emphasizes movements TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis using the Experimental Psychology Cognitive and affective measures, Experience, Behavioral measures Speed, Performance, Choice Alternative value and Goal activation, Memory, accessibility, inhibition of goal-related constructs, Different Dimensions of Motivation Evaluation Parameters in value. A Result Experimental Psychology Choice is got the first rank whereas is the Performance is having the lowest rank.
‘Knowing-that’ vs. ‘Knowing-wh’

Though there is a huge amount of the so-called epistemic logics that deal with propositional attitudes, i.e., sentences of the form “a knows that P”, their ‘wh-cousins’ of the form “a knows who is a P”, “a knows what the P is”, “a knows which Ps are Qs”, etc., have been, to the best of my knowledge, almost neglected. A similar disproportion can be observed between the analysis of Yes-No questions, which has been under scrutiny of many erotetic logics, and Wh-questions which have been dealt with just by a few authors. To fill this gap, we have analysed Wh-questions in Transparent Intensional Logic (TIL) and adjusted Gentzen’s system of natural deduction to TIL natural language processing; thus, our TIL question-answering system can answer not only Yes-No questions but also derive answers to Wh-questions. In this paper, I am going to apply these results to the analysis of sentences containing a ‘knowing-wh’ constituent. In addition, I will analyse the relation between ‘knowing-that’ and ‘knowing-wh’. For instance, if a knows that the Mayor of Ostrava is Mr Macura, can we logically derive that a knows who is the Mayor of Ostrava? Or, vice versa, if a knows who is the Mayor of Ostrava and the Mayor of Ostrava is Mr Macura, do these assumptions logically entail that a knows that the Mayor of Ostrava is Mr Macura? Though in case of rational human agents the answers seem to be a no-doubt YES, perhaps a rather surprising answer is in general negative. We have to specify rules for deriving the relation between knowing-that and knowing-wh, and if a software agent is rational but resource bounded, it does not have to have in its ontology the rules necessary to derive the answer. The goal of the paper is the specification of these rules. Hence, when applying these results into the design of a multi-agent system composed of software resource-bounded agents, we have to compute their inferable knowledge, which accounts not only for their explicit knowledge but also for their inferential abilities.
Amphibious Vehicles

An amphibious vehicle is a transport able of moving both on land and on water. In these last years, the ability of autonomous and semi-autonomous amphibious vehicles to operate in harsh environments is becoming increasingly important for military and civilian operations. Apart from the size of the vehicles, they can be divided into two broad categories: Hovercraft and all those vehicles that can operate not only on water, but on multiple terrains (all-terrain) such as ice, snow and mud. Some of them are based on tracked or wheeled movements; there is also a “screw” configuration used for muddy soils. In some cases, when vehicles are heavier than water, therefore unable to float, rigid or inflatable floats are applied. For propulsion in water, propellers or ducted propellers are used, or the rotation of the wheels themselves or of the tracks is exploited. In this chapter some amphibious vehicles, both civil and military will be presented.
SECURE FILE STORAGE ON CLOUD USING ELLIPTIC CURVE CRYPTOGRAPHY (ECC) ALGORITHM

Cloud is used in various fields like industry, military, college, etc. for various services and storage of huge amount of data.Data stored in this cloud can be accessed or retrieved on the users request without direct access to the server computer.But the major concern regarding storage of data online that is on the cloud is the Security.This Security concern can be solved using various ways, the most commonly used techniques are cryptography and steganography.But sometimes a single technique or algorithm alone cannot provide high-level security.Cloud storage services may be accessed through a web service API through a Web-based user interface.The cloud storage architectures build a single virtual cloud storage system or cloud of clouds system.
Neighborhood Preserving Embedding via Capsules for Collaborative Filtering

Collaborative filtering (CF) has proved to be effective for predicting users’ preferences. Embedding-based CF models have been the most popular ones for over a decade on very large datasets, among which the basic matrix factorization (MF) is the simplest but effective model. Latent factor models can capture global structure of the rating matrix but usually ignore local structure, which can be easily captured by neighborhood-based CF methods. Thus, a MF-based model that captures neighbor-entity spatial relationships between entities (users or items) should get better performance. Inspired by the recently developed capsule network which can capture part-whole spatial relationships, we incorporate capsules into CF to capture both local and global structure of the matrix. Specifically, we adopt the same way as MF to model the user-item interaction to capture global structure. To directly capture local structure in a similar way to neighborhood-based CF methods, we devise two types of routing mechanisms between capsules: pairwise routing and linear routing. These two types of routing algorithms can capture different levels of neighborhood relationships between entities and can be combined to further improve the performance of our models. Experimental results on Douban, MovieLens and Netflix datasets demonstrate that the proposed model achieves state-of-the-art performance.
MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.
Sentiment Analysis of Movie Reviews Based on LSTM-Adaboost

With the improvement of public living standards and the rapid development of the Internet, film works as cultural carriers have become an integral part of people's cultural and spiritual life. The booming movie industry has also given rise to a large number of user online reviews. How to effectively identify the positive and negative emotional tendencies of movie reviews is of great significance to the online word-of-mouth research and publicity marketing of movies. Based on the machine learning perspective of deep learning, this paper proposes a LSTM-Adaboost text sentiment analysis method which combines LSTM neural network and Adaboost boosting method. And the experiments are compared with CNN and LSTM models on the IMDB movie review dataset. The results show that the proposed method in this paper has higher accuracy compared with CNN and LSTM methods. In terms of sentiment classification accuracy, the LSTM-Adaboost method improves by 6 percentage points.
ComBiNet: Visual Query and Comparison of Bipartite Multivariate Dynamic Social Networks

Abstract We present ComBiNet, a visualization, query, and comparison system for exploring bipartite multivariate dynamic social networks. Historians and sociologists study social networks constructed from textual sources mentioning events related to people, such as marriage acts, birth certificates and contracts. We model this type of data using bipartite multivariate dynamic networks to maintain a representation faithful to the original sources while not too complex. Relying on this data model, ComBiNet allows exploring networks using both visual and textual queries using the Cypher language, the two being synchronized to specify queries using the most suitable modality; simple queries are easy to express visually and can be refined textually when they become complex. These queries are used for applying topological and attribute‐based selection on the network. Query results are visualized in the context of the whole network and over a geographical map for geolocalized entities. We also present the design of our interaction techniques for querying social networks to visually compare the selections in terms of topology, measures and attribute distributions. We validate the query and comparison systems by showing how they have been used to answer historical questions and by explaining how they have been improved through a usability study conducted with historians.
A Combination of Embedded Markov Chain and Binomial Probability As A Tool in Sedimentary Facies Analysis

DOI:10.17014/ijog.9.3.291-302 The methodology of facies analysis may include statistical examination to establish a local facies model. Without such examination, it is very difficult to predict the facies succession and to interpret the changes in depositional conditions from thick or complex stratigraphic sections. Based on the survey of sedimentary geologic literature in Indonesia, it is suggested that the aforementioned statistical method has relatively seldom been used in Indonesia. Such a situation may reflect a gap in information on the methodology and, accordingly, it seems imperative that there should be a re-exposition of the method. This paper presents a concise exposition of the combination of the embedded Markov Chain and binomial probability as one of the most successful statistical methods which are used in facies analysis. The results of previous studies of The Citalang Formation are used as a case study to illustrate its applications to the real geologic world.
A Cross-Level Requirement Trace Link Update Model Based on Bidirectional Encoder Representations from Transformers

Cross-level requirement trace links (i.e., links between high-level requirements (HLRs) and low-level requirements (LLRs)) record the top-down decomposition process of requirements and support various development and management activities (e.g., requirement validation). Undoubtedly, updating trace links synchronously with requirement changes is critical for their constant availability. However, large-scale open-source software that is rapidly iterative and continually released has numerous requirements that are dynamic. These requirements render timely update of trace links challenging. To address these problems, in this study, a novel deep-learning-based method, deep requirement trace analyzer fusing heterogeneous features (DRAFT), was proposed for updating trace links between various levels of requirements. Considering both the semantic information of requirement text descriptions and the process features based on metadata, trace link data accumulated in the early stage are comprehensively used to train the trace link identification model. Particularly, first, we performed second-phase pre-training for the bidirectional encoder representations from transformers (BERT) language model based on the project document corpus to realize project-related knowledge transfer, which yields superior text embedding. Second, we designed 11 heuristic features based on the requirement metadata in the open-source system. Based on these features and semantic similarity between HLRs and LLRs, we designed a cross-level requirement tracing model for new requirements. The superiority of DRAFT was verified based on the requirement datasets of eight open-source projects. The average F1 and F2 scores of DRAFT were 69.3% and 76.9%, respectively, which were 16.5% and 22.3% higher than baselines. An ablation experiment proved the positive role of two key steps in trace link construction.
Review of Machine Learning in Logic Synthesis

Electronic design automation tools have multiple options that need to be tuned for specific designs and technology nodes. Traditionally, the tuning process is done by teams of expert engineers and demands a large amount of computational resources.In recent years, there has been an increased effort to apply machine learning techniques in electronic design automation problems, attempting to increase the design flow correlation and predictability, hence reducing the time spent on tuning.In this work, we revise modern approaches in electronic design automation and machine learning techniques applied during logic synthesis. We categorize and discuss their core technologies, such as transforming data into images. Machine learning techniques are as good as the available data. Thus, we present existing learning datasets for logic synthesis and strategies such as data augmentation to overcome the lack of specific data for logic synthesis problems.To cope with these problems, we discuss how research is shifting from traditional supervised learning techniques to reinforcement learning-based methods.
Techniques for automatic landmark detection of human tissue

Landmarks are like salient elements of a tissue. They help to register different parts of a patient and enable a better comprehension of them. It also allows us to identify pathologies, to compare with a previous state of the patient or to check its symmetric counterpart. In the end, landmark detection favors storing and managing digital information of the patient before and after a given treatment. It could become a patient-specific digital atlas. The research community has developed different approaches to automatically detect landmarks. Some of them take as input 3D medical images, being able to detect some diseases or features by employing machine learning algorithms. Others follow a geometrical approach by using curvatures or boundary detection to identify their goals. In this chapter, we review these techniques and analyze their main characteristics as well as their advantages and shortcomings.