With the increasing popularity of social media, people has changed the way they access news. News online has become the major source of information for people. However, much information appearing on the Internet is dubious and even intended to mislead. Some fake news are so similar to the real ones that it is difficult for human to identify them. Therefore, automated fake news detection tools like machine learning and deep learning models have become an essential requirement. In this paper, we evaluated the performance of five machine learning models and three deep learning models on two fake and real news datasets of different size with hold out cross validation. We also used term frequency, term frequency-inverse document frequency and embedding techniques to obtain text representation for machine learning and deep learning models respectively. To evaluate models’ performance, we used accuracy, precision, recall and F1-score as the evaluation metrics and a corrected version of McNemar’s test to determine if models’ performance is significantly different. Then, we proposed our novel stacking model which achieved testing accuracy of 99.94% and 96.05 % respectively on the ISOT dataset and KDnugget dataset. Furthermore, the performance of our proposed method is high as compared to baseline methods. Thus, we highly recommend it for fake news detection.

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A Novel Stacking Approach for Accurate Detection of Fake News

Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic.

Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.

/pdf/a-fine-tuned-bert-based-transfer-learning-approach-for-text-3tj7rl4p.pdf

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally, and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus this paper on the trust component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three subtopics: 1) reviewing and positioning the trust modeling literature for the purpose of trusted autonomy; 2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and 3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enable trusted autonomous systems to become a reality. This paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.

A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus the paper on the `trust' component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three sub-topics: (1) reviewing and positioning the trust modelling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enables trusted autonomous systems to become a reality. The paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.

/pdf/a-review-of-theoretical-and-practical-challenges-of-trusted-1xx5aczynm.pdf

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites. It is harmful for the society to believe on the rumors and pretend to be a news. The need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and the methodology for fake news detection. With the help of Machine learning and natural language processing, author tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. The results of the proposed model is compared with existing models. The proposed model is working well and defining the correctness of results upto 93.6% of accuracy.

A smart System for Fake News Detection Using Machine Learning

In web information retrieval, every single user has a unique contextual objective when searching for information on web blogs. Based on the given query, the task of the web search engine is to fetch the most related information from the collection of web blogs. In order to enhance the searching ability, efficient semantic matching models, richer training, and evaluation resources are required. The conventional keyword-based search algorithms have a minimum efficiency in knowing users’ intentions compared to machine learning algorithms. Recently, neural networks are well-recognized in information retrieval due to the ability of vector representation learning. This paper proposes an adaptive fuzzy feedback recurrent neural network-based web blog searching technique which follows inverse filtering (IF) algorithm using Word2Vec representation. Initially, the user query is pre-processed, and then given to the IF for accelerating the search process. Inside the IF, the web blog content is labelled according to their blog information and stored in the hash table, and then relevant contents are extracted by TF–IDF; this result is given to the similarity estimation to obtain the similarity score. Finally, the proposed technique considers the feedback got from users and performs re-ranking by fuzzy-based RNN to achieve rich user intention satisfaction. The implementation is performed in Python, and statistical measures such as accuracy, precision, and recall are utilized for performance evaluation. The results demonstrate that the proposed technique has enhanced accuracy (94%) compared to conventional techniques, namely deep auto-encoder, deep neural networks, and artificial neural networks.

An intelligent personalized web blog searching technique using fuzzy-based feedback recurrent neural network

We are going towards the Web world and IoT with the speed of light. Personalization and Recommendation systems are the pillars of web world. Now a day’s users want to search the relevant content on web in minimum amount of time. It’s hard to make an efficient system which searches the relevant data to the user on a click. To show the curated results and recommends the best search results as per user’s interest is a typical task, and gets more complicated when we are going to apply on web data. In this paper, an approach and proposed model is discussed which is based on user’s personal interest and automatically recommends the relevant information as per his/her interest. Hybrid computing is used to make the algorithm and process the data to refine the results. Some existing personalized searching techniques and models are also compared as literature survey.

Analysis of Content Curation Algorithms on Personalized Web Searching

In this digital world, more than 90% of desktop and notebook computers have integrated Graphics Processing Units i.e. GPU's, for better graphics processing. Graphics Processing Unit is not only for graphics applications, even for non-graphics applications too. In the past few years, the graphics programmable processor has evolved into an increasingly convincing computational resource. But GPU sits idle if graphics job queue is empty, which decreases the GPU's efficiency. This paper focuses on various tact to overcome this problem and to make the CPU-GPU processing more powerful and efficient. The graphics programmable processor or Graphics processing unit is especially well suited to address problem sets expressed as data parallel computation with the same program executed on many data elements concurrently. The objective of this paper is to increase the capabilities and flexibility of recent GPU hardware combined with high level GPU programming languages: to accelerate the building of images in a frame buffer intended for output to a display, and, to provide tremendous acceleration for numerically intensive scientific applications. This paper also gives some light on major applicative areas where GPU is in use and where GPU can be used in future.

Efficient parallel processing by improved CPU-GPU interaction

Digital image processing is a mechanism for analysing and modifying the image in order to improve the quality and also to manage the unwanted involvement of noises. In image processing, noise is ch...

Harsh Khatter

Papers

A smart System for Fake News Detection Using Machine Learning

An intelligent personalized web blog searching technique using fuzzy-based feedback recurrent neural network

Analysis of Content Curation Algorithms on Personalized Web Searching

Efficient parallel processing by improved CPU-GPU interaction

Optimal reduction of noise in image processing using collaborative inpainting filtering with Pillar K-Mean clustering