scholarly journals Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning

2021 ◽  
Vol 11 (15) ◽  
pp. 6912
Author(s):  
Jiaxin Tang ◽  
Yang Chen ◽  
Guozhen She ◽  
Yang Xu ◽  
Kewei Sha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Computer Science ◽  
Citation Analysis ◽  
Case Studies ◽  
Google Scholar ◽  
Manual Annotation ◽  
University Ranking

Google Scholar has been a widely used platform for academic performance evaluation and citation analysis. The issue about the mis-configuration of author profiles may seriously damage the reliability of the data, and thus affect the accuracy of analysis. Therefore, it is important to detect the mis-configured author profiles. Dealing with this issue is challenging because the scale of the dataset is large and manual annotation is time-consuming and relatively subjective. In this paper, we first collect a dataset of Google Scholar’s author profiles in the field of computer science and compare the mis-configured author profiles with the reliable ones. Then, we propose an integrated model that utilizes machine learning and node embedding to automatically detect mis-configured author profiles. Additionally, we conduct two application case studies based on the data of Google Scholar, i.e., outstanding scholar searching and university ranking, to demonstrate how the improved dataset after filtering out the mis-configured author profiles will change the results. The two case studies validate the importance and meaningfulness of the detection of mis-configured author profiles.

Applications of supervised deep learning for seismic interpretation and inversion

2019 ◽  
Vol 38 (7) ◽  
pp. 526-533 ◽  
Author(s):  
York Zheng ◽  
Qie Zhang ◽  
Anar Yusifov ◽  
Yunzhi Shi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Case Studies ◽  
Seismic Inversion ◽  
Seismic Interpretation ◽  
Elastic Model ◽  
Regression Problem ◽  
First Case ◽  
Seismic Image ◽  
Prestack Seismic Inversion

Recent advances in machine learning and its applications in various sectors are generating a new wave of experiments and solutions to solve geophysical problems in the oil and gas industry. We present two separate case studies in which supervised deep learning is used as an alternative to conventional techniques. The first case is an example of image classification applied to seismic interpretation. A convolutional neural network (CNN) is trained to pick faults automatically in 3D seismic volumes. Every sample in the input seismic image is classified as either a nonfault or fault with a certain dip and azimuth that are predicted simultaneously. The second case is an example of elastic model building — casting prestack seismic inversion as a machine learning regression problem. A CNN is trained to make predictions of 1D velocity and density profiles from input seismic records. In both case studies, we demonstrate that CNN models trained from synthetic data can be used to make efficient and effective predictions on field data. While results from the first example show that high-quality fault picks can be predicted from migrated seismic images, we find that it is more challenging in the prestack seismic inversion case where constraining the subsurface geologic variations and careful preconditioning of input seismic data are important for obtaining reasonably reliable results. This observation matches our experience using conventional workflows and methods, which also respond to improved signal to noise after migration and stack, and the inherent subsurface ambiguity makes unique parameter inversion difficult.

scholarly journals ProtTox: Toxin identification from Protein Sequences

2020 ◽  
Author(s):  
Sathappan Muthiah ◽  
Debanjan Datta ◽  
Mohammad Raihanul Islam ◽  
Patrick Butler ◽  
Andrew Warren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Synthetic Biology ◽  
Real World ◽  
Protein Sequences ◽  
Structural Diversity ◽  
Manual Annotation ◽  
Real World Applications ◽  
Automated Machine Learning

AbstractToxin classification of protein sequences is a challenging task with real world applications in healthcare and synthetic biology. Due to an ever expanding database of proteins and the inordinate cost of manual annotation, automated machine learning based approaches are crucial. Approaches need to overcome challenges of homology, multi-functionality, and structural diversity among proteins in this task. We propose a novel deep learning based method ProtTox, that aims to address some of the shortcomings of previous approaches in classifying proteins as toxins or not. Our method achieves a performance of 0.812 F1-score which is about 5% higher than the closest performing baseline.

Machine Learning Fundamentals

2021 ◽  
Author(s):  
Hui Jiang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Case Studies ◽  
Linear Algebra ◽  
Neural Nets ◽  
Supervised Machine Learning ◽  
Core Concepts ◽  
Big Picture ◽  
Basic Calculus ◽  
Probability And Statistics

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

Faculty Performance Evaluation Using Citation Analysis

2016 ◽  
Vol 37 (1) ◽  
pp. 83-94 ◽  
Author(s):  
Thomas W. Sanchez
Keyword(s):  
Performance Evaluation ◽  
Urban Planning ◽  
Citation Analysis ◽  
Google Scholar ◽  
Scholarly Activity ◽  
Faculty Performance ◽  
Citation Data ◽  
Valuable Source

This article provides a citation analysis for faculty from Association of Collegiate Schools of Planning (ACSP) member schools. The article argues that Google Scholar data is a particularly valuable source of citation data for urban planning because its coverage extends beyond traditional peer-reviewed publications. The analysis reports the level of scholarly activity within the urban planning discipline. The results show citation patterns for planning faculty, departments, and universities along with discussing the distribution of citation activity across the discipline. The article concludes by encouraging planning scholars and administrators to undertake more analysis of planning scholarship to understand scholarly performance and impact.

scholarly journals Artificial Intelligence in Business

2020 ◽  
pp. 1-38
Author(s):  
Amandeep Kaur ◽  
◽  
Anjum Mohammad Aslam ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Case Studies ◽  
Language Processing ◽  
Learning Abilities ◽  
The Core

In this chapter we discuss the core concept of Artificial Intelligence. We define the term of Artificial Intelligence and its interconnected terms such as Machine learning, deep learning, Neural Networks. We describe the concept with the perspective of its usage in the area of business. We further analyze various applications and case studies which can be achieved using Artificial Intelligence and its sub fields. In the area of business already numerous Artificial Intelligence applications are being utilized and will be expected to be utilized more in the future where machines will improve the Artificial Intelligence, Natural language processing, Machine learning abilities of humans in various zones.

Rule-Based Bibliographic Computer Science Indexing Review with COVID-19 Disease

2021 ◽  
Author(s):  
Andrianingsih Andrianingsih ◽  
Tri Wahyu Widyaningsih ◽  
Meta Amalya Dewi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Google Scholar ◽  
Link Strength ◽  
Learning Machine ◽  
Scientific Fields

A researcher in conducting his research usually uses a search through the homepage of the publication, based on expertise, collaboration in research, and research interests. Today, the COVID-19 pandemic is becoming a trending topic for researchers from various scientific fields. The study classified the case based on publications located in the homepage sources such as Scopus, Crossref, IEEE Xplore, and Google Scholar, by analyzing the following topics, namely Artificial Intelligence, Data Mining, Deep Learning, Machine Learning and the Internet of Things by using Named Entity Recognition to detect and classify named entities in text and using occurence and link strength methods. Based on this study, the results were obtained that Scopus has the most equitable percentage, which has a good occurrence and link strength among the five scientific fields, namely Artificial Intelligence 33.33%, Machine Learning 15.38%, Deep Learning 23.08%, Data Mining 12.82% and IoT 15.38%. The second-best are Google Scholar, then IEEE Xplore, and Crossref.

scholarly journals DL-LA: Deep Learning Leakage Assessment

2021 ◽  
pp. 552-598
Author(s):  
Thorben Moos ◽  
Felix Wegener ◽  
Amir Moradi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Case Studies ◽  
Success Probability ◽  
Correct Choice ◽  
Machine Learning Techniques ◽  
Time In Literature ◽  
Template Attacks ◽  
First Time ◽  
Leakage Assessment

In recent years, deep learning has become an attractive ingredient to side-channel analysis (SCA) due to its potential to improve the success probability or enhance the performance of certain frequently executed tasks. One task that is commonly assisted by machine learning techniques is the profiling of a device’s leakage behavior in order to carry out a template attack. At CHES 2019, deep learning has also been applied to non-profiled scenarios for the first time, extending its reach within SCA beyond template attacks. The proposed method, called DDLA, has some tempting advantages over traditional SCA due to merits inherited from (convolutional) neural networks. Most notably, it greatly reduces the need for pre-processing steps< when the SCA traces are misaligned or when the leakage is of a multivariate nature. However, similar to traditional attack scenarios the success of this approach highly depends on the correct choice of a leakage model and the intermediate value to target. In this work we explore, for the first time in literature, whether deep learning can similarly be used as an instrument to advance another crucial (non-profiled) discipline of SCA which is inherently independent of leakage models and targeted intermediates, namely leakage assessment. In fact, given the simple classification-based nature of common leakage assessment techniques, in particular distinguishing two groups fixed-vs-random or fixed-vs-fixed, it comes as a surprise that machine learning has not been brought into this context, yet. Our contribution is the development of the first full leakage assessment methodology based on deep learning. It gives the evaluator the freedom to not worry about location, alignment and statistical order of the leakages and easily covers multivariate and horizontal patterns as well. We test our approach against a number of case studies based on FPGA, ASIC and μC implementations of the PRESENT block cipher, equipped with state-of-the-art SCA countermeasures. Our results clearly show that the proposed methodology and network structures are robust across all case studies and outperform the classical detection approaches (t-test and X2-test) in all considered scenarios.

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