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A Deep Learning based approach for MultiModal Sarcasm Detection

A Deep Learning based approach for MultiModal Sarcasm Detect...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Aruna Bhat Aditya Chauhan Department of Computer Science and Engineering Delhi Technological University(formerly DCE) New Delhi India

Sarcasm detection is used to single out natural language statements where intended meaning differs from what the surface meaning implies. A number of tasks in natural language processing areas like sentiment analysis and also mining of opinions use sarcasm detection underneath. Many of the key research in the area of sarcasm detection primarily focus on only text-based input. In the present day scenario , there has been a sudden explosion in the amount of multimodal data mainly due to social media. As a result of that, users these days are not just limited to text while expressing themselves , but also make heavy use of visuals like in images and videos. the objective of our paper is to incorporate multimodal data so as to enhance the performance of present sarcasm detection algorithms. Multiple methods that leverage data in the form of image and text, and also as a combination of both, have been presented thus *** present a unique architecture which works on the Robustly Optimized BERT pre-training approach(RoBERTa) which is a facebook modified version of well known model BERT with a co¬attention layer on the top for including the context incongruency between input text and attributes of the image. Using Gated Recurrent Unit(GRU), the text-based features are extracted and the features of the image are retrieved by conditioning the image through Feature-wise Linearly Modulated ResNet Blocks. these combined with CLS token from the model RoBERTa are used to obtain the final predictions. In the end, we show performance enhancements from our model when it is stacked against the other latest models with competitive results which make use of the same publicly available Twitter dataset.

关键词： Visualization Sentiment analysis Social networking (online) Computational modeling Bit error rate Predictive models Feature extraction

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Investigating Opthalmic images to Diagnose Eye diseases using Deep Learning Techniques

Investigating Opthalmic images to Diagnose Eye diseases usin...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Shalini Agarwal Aruna Bhat Dept. of Computer Science & Engineering Delhi Technological University Delhi India

In Ophthalmology, several eye image related diagnostic methodologies started offering meaningful insight about eye disease based on huge set of data points and morphological datasets. the common causes of various eye abnormalities are any trauma, aging and diseases, particularly diabetes. Glaucoma, retinal detachment, Cataract, macular degeneration, diabetic retinopathy, hypertensive Retinopathy, and diabetic macular edema are the most common causes of blindness all around the world. All these diseases are manually suspected and diagnosed by ophthalmologists with clinical practice. Lack of eye care centers and scarcity of trained ophthalmologists are very common in rural and remote areas in developing countries like India. Detection at early stage, followed by appropriate and timely clinical treatment for a varieties of eye diseases, can solve problem of blindness to a large extent. Ophthаlmоlоgist adopt a computerized analysis of ophthalmic imaging modalities for faster clinical screening and accurate diagnosis of these diseases. Latest deep learning models, when compared with traditional machine learning algorithms, speed up the diagnosis of disease and improve accuracy with less false positive rate. this paper investigates all latest deep learning methodologies used in automatic retinal disease diagnosis process concerning different image modalities. this paper also lists some of the most common eye diseases, limitations with the latest developments and potential challenges for future enhancements.

关键词： Deep learning Cataracts Retinopathy Process control Blindness Retina Nonhomogeneous media

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ProProv: A Language and Graphical Tool for Specifying data Provenance Policies

ProProv: A Language and Graphical Tool for Specifying Data P...

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IEEE international conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Kevin Dennis Shamaria Engram Tyler Kaczmarek Jay Ligatti Department of Computer Science and Engineering University of South Florida Tampa USA Secure Resilient Systems and Technology Group MIT Lincoln Laboratory Lexington USA

the Function-as-a-Service cloud computing paradigm has made large-scale application development convenient and efficient as developers no longer need to deploy or manage the necessary infrastructure themselves. However, as a consequence of this abstraction, developers lose insight into how their code is executed and data is processed. Cloud providers currently offer little to no assurance of the integrity of customer data. One approach to robust data integrity verification is the analysis of data provenance—logs that describe the causal history of data, applications, users, and non-person entities. this paper introduces ProProv, a new domain-specific language and graphical user interface for specifying policies over provenance metadata to automate provenance *** evaluate the convenience and usability of the new ProProv interface, 61 individuals were recruited to construct provenance policies using both ProProv and the popular, general-purpose policy specification language Rego—used as a baseline for comparison. We found that, compared to Rego, the ProProv interface greatly increased the number of policies successfully constructed, improved the time taken to construct those policies, and reduced the failed-attempt rate. Participants successfully constructed 73% of the requested policies using ProProv, compared to 41% using Rego. To further evaluate the usability of the tools, participants were given a 10-question questionnaire measured using the System Usability Scale (SUS). the median SUS score for the graphical ProProv interface was above average and fell into the “excellent” category, compared to below average and “OK” for Rego. these results highlight the impacts that graphical domain-specific tools can have on the accuracy and speed of policy construction.

关键词： Cloud computing data integrity Sociology Metadata Particle measurements Real-time systems Security

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Music Recommendation System Using Machine Learning

Music Recommendation System Using Machine Learning

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Rajesh Kumar Rakesh Department of Computer Science Chandigarh University Mohali PB India

the Internet plays a crucial role in society, and many apps use suggestion structure. It has created a wide range of applications, a global local area, and built for different types of data. Today's proposal framework has altered how we currently view the things that are to our advantage. Additionally, it is possible due to the data sifting method that is used to predict the customer's propensity. the most well-known applications of the recommender system are in the areas of books, news, articles, music, records, movies, and other media. the outline of the machine learning calculations and tactics developed for the proposal framework are covered in this *** have suggested a music suggestion framework dataset in this article. three categories make up the proposal framework: collaborative filtering, content-based, and approach based on half a breed. Recommender frameworks are widely used these days, especially in informal groups and electronic industry, as communitarian sifting methods become more mature. this paper organises the community-based, i-based separating process that uses the information provided by clients, analyses it, and then offers the songs that are most suitable for the client at that particular *** paper describes this methodology, its steps, and its limitations. the recommended music list is arranged using the K-means algorithm and is based on the ratings that previous listeners have given the music. Additionally, this helps customers find the music of their choice based on the cinema insights of other customers efficiently and effectively without wasting much time in meaningless *** newly introduced recommender framework generates recommendations using a variety of information, including information about customers, the items that are available, and previous transactions stored in modified data sets. the customer could then efficiently browse the choices and find the music of their choice.

关键词： Machine learning algorithms Filtering Collaborative filtering Music Machine learning Media Motion pictures

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Artificial Intelligence Meets Tactical Autonomy: Challenges and Perspectives

Artificial Intelligence Meets Tactical Autonomy: Challenges ...

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IEEE international conference on Cognitive Machine Intelligence (CogMI)

作者： Danda B. Rawat Department of Electrical Engineering and Computer Science (EECS) DoD Center of Excellence in Artificial Intelligence & Machine Learning Howard University Washington DC 20059 USA

Artificial Intelligence (AI) enabled systems have shown tremendous impact in our national defense and in our society due to recent advances in artificial neural networks, deep learning, machine learning, and Internet of things, big data, computing and communications. New AI capabilities can improve efficiency, trust, and efficacy for mission critical applications for tactical autonomy with minimal supervision from human operators in multi-domain battlefield (MDB) environments that are complex, contested and unpredictable. Although AI-enabled tools have been responsive to people and complementary to human capabilities, in order to realize its full potential in tactical applications, there are several challenges to be addressed for making trustworthy, ethical, fair, real-time explainable AI-enabled autonomous systems. Collaborations between platforms/systems as well as joint human-machine learning/teaming could address many of these issues to provide trusted and shared understanding and delivering cost-effective and adaptive systems to assist operations across military domains (space, air, land, maritime, and cyber) at combat speed using a shared set of resources. In this paper, we present some challenges and perspectives for AI enabled tactical autonomy.

关键词： Deep learning Ethics Mission critical systems Collaboration Big data Real-time systems Internet of things

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Expression of Concern for: Heart Attack Prediction using Machine Learning

Expression of Concern for: Heart Attack Prediction using Mac...

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international conference on Inventive Research in computing Applications (ICIRCA)

作者： Janaraniani N Divya P Madhukiruba E R. Santhosh R. Reshma D. Selvapandian Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497084

In the recent years, the coronary artery disease (CAD) becomes leading cause of mortality worldwide both in high-income nations and in poor countries to a rising extent. Studies of human genetics can provide useful information for the development of such enhanced techniques by enhancing our ability to identify those who are at a higher risk for CAD. this work explores artificial intelligence technologies that analyze raw clinical data for the study and assessment of heart disease severity. Further utilizes machine learning algorithms to predict various state of CAD in individuals, such as naive bayes (NB), decision tree (DT), and Weighted Associative Rule Mining (WARM). this research study has tested these algorithms on the clinical data obtained from the Cardiology department, which include highlights and distinct cases. the decision tree achieves the highest prediction accuracy of 99.5 percent with the fastest rate of exactness over other algorithms.

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DevFlair: A Framework to Automate the Pre-screening Process of Software Engineering Job Candidates

DevFlair: A Framework to Automate the Pre-screening Process ...

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Advancements in computing (ICAC), international conference on

作者： R.T.R Jayasekara K.A.N.D Kudarachchi K.G.S.S.K Kariyawasam Dilini Rajapaksha S.L Jayasinghe Samantha thelijjagoda Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka School of Computing Technologies Royal Melbourne Institute of Technology Melbourne Australia SLIIT Business School Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

the HR department of a technology company receives hundreds of job applications for each Software Engineering related vacancy. Evaluating a candidate by looking at the curriculum vitae may appear to be easy during the pre-screening process. However, an automated pre-screening process using Natural Language Processing and Machine Learning methodologies would help the recruiter to obtain a more accurate and deeper understanding of the candidate. In this paper we propose “DevFlair”, a framework to automate pre-screening Software Engineering job candidates. DevFlair uses data from social media, Github, and open-ended questionnaires to predict the Big-Five personality traits, analyze technical skill expertise, and analyze the experience in using industry-related online platforms. After analysis, the candidates are ranked according to their personality and technical skill levels. We conduct the personality prediction experiments using a social media posts dataset annotated with gold-standard Big-Five personality labels. We train FastText classification models and compare their accuracy against other state of the art classification models. the comparisons conclude that the FastText classification models substantially outperform the state of the art classification models when predicting Openness, Conscientiousness, and Agreeableness personality traits.

关键词： Social networking (online) Machine learning Companies Predictive models Natural language processing Software engineering Software development management

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Intelligent Analysis System of University Stadium Governance Based on Big data Era 4th

Intelligent Analysis System of University Stadium Governance...

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4th international conference on Innovative computing, IC 2021

作者： Ma, Rufei School of Physical Education Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9789811642579

Gymnasium resources are an important material basis for the development of sports, and the management of gymnasiums is also an important part of the sports industry. there are many gymnasiums in colleges and universities, which not only have a large amount of exercise, but also have complete facilities, high standards, good quality and good environment. they are ideal places for exercise. But the stage is a very old problem that plagues us. Good high school management methods can solve the problem of using fitness resources. this article uses interview method, field research method, literature method, mathematical statistics method, logical analysis method and other research methods. Understand the resource situation in Kunming area and the operation and management of university sports facilities, including resource quantity, usage, open status, management status, etc. Analyze the internal and external factors that affect the operation and management of the university gymnasium, as well as good operating and management conditions. Promote the operation and management of university sports facilities. I hope to explore a reasonable way to manage and manage college sports facilities and provide a theoretical basis for the rational development and utilization of college sports facilities resources. through the method of this article, the flow of people in the gym has increased by 12%. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stadiums

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A Simple Dashboard for OpenStack Horizon 2021

A Simple Dashboard for OpenStack Horizon

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2021 4th international conference on computing and Big data

作者： Hongbin Wang Shaoxu Li Dan Deng Leixiao Li Jing Gao Jie Li College of Data Science and Application Inner Mongolia University of Technology China and Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center based on Big Data China College of Computer and Information Engineering Inner Mongolia Agricultural University China

ISBN: (纸本)9781450387194

OpenStack is an open source cloud computing management project, which allows flexible resources applying. the traditional OpenStack dashboard is inflexible, thus a simple dashboard is designed. the simple dashboard integrates keystone, horizon, grace, nova, neutron, swift and other components, communicates with the traditional OpenStack dashboard and runs in the form of docker container. We implemented simple dashboard on a small cloud platform. Compared with the traditional dashboard, the interface of simple dashboard is simpler, more flexible and easy to use.

关键词： Cloud computing Horizon OpenStack Simple dashboard

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Predicting Loan Grant using Feed Forward Neural Nets and SGD optimizer

Predicting Loan Grant using Feed Forward Neural Nets and SGD...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ankit Sharma Vinod Kumar Department of Computer Science & Engineering Delhi Technological University Delhi India

In our day to day life, as we can witness there are innumerable amount of people who are seeking for loan from some or the other banking organizations/institutions to achieve or fulfill their demands for some or the other things. Hence with the scope with increasingly good amount of giving loan to individuals/groups it becomes a tedious job for all the institutions to lend their precious assests to them, so this study/research is based on to curb these issues/efforts from the banking Ins or Org. this would help them to pick who should be the best candidate to pick for loan worthiness. How this could be achieved is by assessing the candidates past records based on what all could be the factors which would affect them to predict the most accurate result. this research would be beneficial for the BIns. So that they would know already whether the candidate would be able to repay the said amount on a proper schedule and also not forgetting to mention which would also preserve the dignity and reputation. the most important, tremendously crucial and critical aspect for these banking Ins, largely depends on the profit and the net revenue. there already exists a significant amount of under performing anomalies which are responsible for keeping across the reputation of Bins. At stake. For this research we have described a full fledged way to forecast and predict the mortgage loan risk, appropriate model has been devised using the NN model. How the data is being assessed largely depends on what all personal and financial information that the candidate has provided, nothing as far as the important factors based on to analyse the loan defaulters are taken into consideration and other personal information is discarded and unasked. the data set has been used to train and analyse the NN model which would be used in the research. Depending upon the outcomes, it is evident that this model outdoes most of the other models which are widely used by the financial institutions for the sam

关键词： Schedules Loans and mortgages Analytical models Computational modeling Banking Artificial neural networks Predictive models

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