SRI Blog

By - Siyang Lu, Xiang Wei, Yandong Li, Liqiang Wang Department of Computer Science, University of Central Florida, Orlando, FL, USA School of Software Engineering, Beijing Jiaotong University, China Paper Link Previous week we did a scan of the survey of Anomaly detection   , that covers breath of the topic (Types of Anomalies, Types of Models and their applications). In this summary paper, we look in to the core Heavy lifting part of Detecting Anomaly in Big Data System Logs Using Convolutional Neural Network. Abstract Nowadays, big data systems are being widely adopted by many domains for offering effective data solutions, such as manufacturing, healthcare, education, and media. Big data systems produce tons of unstructured logs that contain buried valuable information. However, it is a daunting task to manually unearth the information and detect system anomalies. A few automatic methods have been developed, where the cutting-edge machine learning

-By  Raghavendra Chalapathy  University of Sydney,  Capital Markets Co-operative Research Centre (CMCRC)  Sanjay Chawla  Qatar Computing Research Institute (QCRI),  HBKU  Paper Link Anomaly detection also known as outlier detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions Hawkins defines an outlier as an observation that deviates so significantly from other observations as to arouse suspicion that it was generated by a different mechanism. Aim of this paper is two-fold, First is a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore the adoption of these methods

By - Jayashree Aanand Gajjam, Diptesh Kanojia,Malhar Kulkarni IIT Bombay, India IITB-Monash Research Academy, India Paper Link Abstract The Sanskrit grammatical tradition which has commenced with Pāṇini’s Aṣṭādhyāyī mostly as a Padaśāstra has culminated as a Vākyaśāstra, at the hands of Bhartṛhari. The grammarian-philosopher Bhartṛhari and his authoritative work ‘Vākyapadīya’ have been a matter of study for modern scholars, at least for more than 50 years. The notions of a sentence and a word as a meaningful linguistic unit in the language have been a subject matter for the discussion in many works that followed later on. While some scholars have applied philological techniques to critically establish the text of the works of Bhartṛhari, some others have devoted themselves to exploring philosophical insights from them. Some others have studied his works from the point of view of modern linguistics, and psychology. Few others have tried to justify the vie

 -By Abhishek Singh, Manish Gupta, Vasudeva Varma Paper Link Centre for Language Technologies Research Centre  International Institute of Information Technology  Hyderabad - 500 032,  INDIA summary can can be defined as: ”A summary is a text produced from one or more texts that contains a significant portion of the information in the original text which is no longer than half of the original text.” ”Automatic text summarization is the process of reducing text document(s) with a computer program in order to create a summary that retains the most important points of the original document(s)” Summarization categories based on type of generated summary: • Extractive Summarization : is the most common approach for text summarization. It focuses on selecting a subset of existing textual units (words/phrases/sentences) in the original text by assigning a score to each textual unit followed by picking the most informative units in order to create a summar