Technology Assisted Review of Legal Documents

A legal prediction-based approach will help judges and solicitors to take judicial decisions on current cases, which are going on in courts, and make predictions on new cases on the basis of existing references and judgments. This model also helps law students learn about legal references. This application was developed specifically for the “Supreme Court of Pakistan (SCP)” and the “Pakistan Bar Council (PBC)” to expedite their judgments and provide legal guidance to lawyers based on historical data and constitutions

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity

THE APPLICATION OF COMPUTER VISION, MACHINE AND DEEP LEARNING ALGORITHMS UTILIZING MATLAB

MATLAB is a multi-paradigm proprietary programming language and numerical computing environment developed by MathWorks. Within MATLAB Integrated Development Environment (IDE) you can perform Computer-aided design (CAD), different matrix manipulations, plotting of functions and data, implementation algorithms, creation of user interfaces, and has the ability to interface with programs written in other languages1. Since, its launch in 1984 MATLAB software has not particularly been associated within the field of data science. In 2013, that changed with the launch of their new data science concentrated toolboxes that included Deep Learning, Image Processing, Computer Vision, and then a year later Statistics and Machine Learning. The main objective of my thesis was to research and explore the field of data science. More specifically pertaining to the development of an object recognition application that could be built entirely using MATLAB IDE and have a positive social impact on the deaf community. And in doing so, answering the question, could MATLAB be utilized for development of this type of application? To simultaneously answer this question while addressing my main objectives, I constructed two different object recognition protocols utilizing MATLAB_R2019 with the add-on data science tool packages. I named the protocols ASLtranslate (I) and (II). This allowed me to experiment with all of MATLAB data science toolboxes while learning the differences, benefits, and disadvantages of using multiple approaches to the same problem. The methods and approaches for the design of both versions was very similar. ASLtranslate takes in 2D image of American Sign Language (ASL) hand gestures as an input, classifies the image and then outputs its corresponding alphabet character. ASLtranslate (I) was an implementation of image category classification using machine learning methods. ASLtranslate (II) was implemented by using a deep learning method called transfer learning, done by fine-tuning a pre-trained convolutional neural network (CNN), AlexNet, to perform classification on a new collection of images

An Approach for Improving Automatic Mouth Emotion Recognition

The study proposes and tests a technique for automated emotion recognition through mouth detection via Convolutional Neural Networks (CNN), meant to be applied for supporting people with health disorders with communication skills issues (e.g. muscle wasting, stroke, autism, or, more simply, pain) in order to recognize emotions and generate real-time feedback, or data feeding supporting systems. The software system starts the computation identifying if a face is present on the acquired image, then it looks for the mouth location and extracts the corresponding features. Both tasks are carried out using Haar Feature-based Classifiers, which guarantee fast execution and promising performance. If our previous works focused on visual micro-expressions for personalized training on a single user, this strategy aims to train the system also on generalized faces data sets

Pando: Personal Volunteer Computing in Browsers

The large penetration and continued growth in ownership of personal electronic devices represents a freely available and largely untapped source of computing power. To leverage those, we present Pando, a new volunteer computing tool based on a declarative concurrent programming model and implemented using JavaScript, WebRTC, and WebSockets. This tool enables a dynamically varying number of failure-prone personal devices contributed by volunteers to parallelize the application of a function on a stream of values, by using the devices' browsers. We show that Pando can provide throughput improvements compared to a single personal device, on a variety of compute-bound applications including animation rendering and image processing. We also show the flexibility of our approach by deploying Pando on personal devices connected over a local network, on Grid5000, a French-wide computing grid in a virtual private network, and seven PlanetLab nodes distributed in a wide area network over Europe.Comment: 14 pages, 12 figures, 2 table