Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures

Numerical information plays an important role in various fields such as scientific, financial, social, statistics, and news. Most prior studies adopt unsupervised methods by designing complex handcrafted pattern-matching rules to extract numerical information, which can be difficult to scale to the open domain. Other supervised methods require extra time, cost, and knowledge to design, understand, and annotate the training data. To address these limitations, we propose QuantityIE, a novel approach to extracting numerical information as structured representations by exploiting syntactic features of both constituency parsing (CP) and dependency parsing (DP). The extraction results may also serve as distant supervision for zero-shot model training. Our approach outperforms existing methods from two perspectives: (1) the rules are simple yet effective, and (2) the results are more self-contained. We further propose a numerical information retrieval approach based on QuantityIE to answer analytical queries. Experimental results on information extraction and retrieval demonstrate the effectiveness of QuantityIE in extracting numerical information with high fidelity.

Thai Question-Answering System Using Pattem-Matching Approach

Teaching concepts in Thailand's universities have abruptly changed, due to the advancement of the COVID-19 pandemic, including changes in classroom to online formats, as well as administrative difficulties. The research herein, therefore, addresses these concerns, presenting a Thai question-answering system using the pattern-matching approach. Our case study covers course information, teaching timetable, teacher schedule, and course supplements. We classified the questions into six categories according to type and acknowledged typical expressions which matched to question patterns. We use RegEx® to match a defined pattern. When a response did not match, we used word embedding to transform the question into a vector and then calculated the cosine similarity to identify the most similar pattern. The system can then generate a corresponding SQL command to query the answer from the database. We evaluated the accuracy of the proposed system with the collected data resulted in an accuracy rate of 82%. © 2022 IEEE.

INTELLIBOT - Intelligent Voice Assisted Chatbot with Sentiment Analysis, COVID Dashboard and Offensive Text Detection

Chatbot has become an essential crowd puller in the world today and is used in various domains and professions. With increasing technologies and advancements in AI, components of voice assistance have been gaining prolific importance when integrated with chatbots. INTELLIBOT is a smart bot that not only interacts with its users through an interactive and aesthetic platform but also had added features for customized experience. It makes use of speech to text and text to speech processing to listen to the user and speak back to them. This paper would give insights on the various applications of chatbots and existing systems along with the system we have proposed to overcome and curb the challenges posed by them through the INTELLIBOT framework. Further, the paper would elucidate the use of Naïve-Bayes algorithm and pattern matching algorithms for the same. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

CheetahKG: A Demonstration for Core-based Top-k Frequent Pattern Discovery on Knowledge Graphs

Knowledge graphs capture the complex relationships among various entities, which can be found in various real world applications, e.g., Amazon product graph, Freebase, and COVID-19. To facilitate the knowledge graph analytical tasks, a system that supports interactive and efficient query processing is always in demand. In this demonstration, we develop a prototype system, CheetahKG, that embeds with our state-of-the-art query processing engine for the top-k frequent pattern discovery. Such discovered patterns can be used for two purposes, (i) identifying related patterns and (ii) guiding knowledge exploration. In the demonstration sessions, the attendees will be invited to test the efficiency and effectiveness of the query engine and use the discovered patterns to analyze knowledge graphs on CheetahKG. © 2022 IEEE.

A User Self-Design Collaborative Web System for Self-directed Health Learning: A Covid-19 Awareness Project

The system aims to direct the user to create their network system collaboratively for a case of Covid-19 health throughout a case study in a Web System and E-Commerce course. The framework of directed health learning is based on 7x2C content knowledge established criteria to assess the effectiveness and efficiency of the system. The self-design frame framework is plan-oriented and based on the concept of a plan and plans integrations and special relationships. The user is directed to break the system into plans and the design is to self-guide the user to build a system to comprehend, combat, coexist, cope, and trace COVID-19 with four layers of diagnostics, simulation, and pattern matching database. With the collaboration of users' systems, a fact from one user as output can be transferred to another user as an input in a circulation forming a general fact. Consequently, the transfer of learning from one system flows into another system resulting in a pattern to be found. Based on the pattern an algorithm will formulate to tackle a solution to COVID19. The implication of this study will be a guideline for others to initiate their own participant's system to find a pattern and formulate an algorithm for the pandemic. The idea of self-design and self-directed learning can be transferred to other fields of study covid-19 health. At present time, a parallel case study of goods and services on farming of Sunchoke plant has been directed with three plan themes of Grow, Eat, and Heal.) © 2021 IEEE.

A computational framework to support the treatment of bedsores during COVID-19 diffusion.

The treatment of pressure ulcers, also known as bedsores, is a complex process that requires to employ specialized field workforce assisting patients in their houses. In the period of COVID-19 or during any other non-trivial emergency, reaching the patients in their own house is impossible. Therefore, as well as in the other sectors, the adoption of digital technologies is invoked to solve, or at least mitigate, the problem. In particular, during the COVID-19, the social distances should be maintained in order to decrease the risk of contagion. The Project Health Management Systems proposes a complete framework, based on Deep Learning, Augmented Reality. Pattern Matching, Image Segmentation and Edge Detection approaches, to support the treatment of bedsores without increasing the risk of contagion, i.e., improving the remote aiding of specialized operators and physicians and involving inexperienced familiars in the process.

BioHD: An Efficient Genome Sequence Search Platform Using HyperDimensional Memorization

In this paper, we propose BioHD, a novel genomic sequence searching platform based on Hyper-Dimensional Computing (HDC) for hardware-friendly computation. BioHD transforms inherent sequential processes of genome matching to highly-parallelizable computation tasks. We exploit HDC memorization to encode and represent the genome sequences using high-dimensional vectors. Then, it combines the genome sequences to generate an HDC reference library. During the sequence searching, BioHD performs exact or approximate similarity check of an encoded query with the HDC reference library. Our framework simplifes the required sequence matching operations while introducing a statistical model to control the alignment quality. To get actual advantage from BioHD inherent robustness and parallelism, we design a processing in-memory (PIM) architecture with massive parallelism and compatible with the existing crossbar memory. Our PIM architecture supports all essential BioHD operations natively in memory with minimal modifcation on the array. We evaluate BioHD accuracy and efciency on a wide range of genomics data, including COVID-19 databases. Our results indicate that PIM provides 102.8× and 116.1× (9.3× and 13.2×) speedup and energy efciency compared to the state-of-theart pattern matching algorithm running on GeForce RTX 3060 Ti GPU (state-of-the-art PIM accelerator). © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.

In Search of a Pattern and Algorithmic Code for COVID-19: A Participant Self-Customized Awareness Systems for Diagnostics, Simulation, Tracing and Pattern Matching

The COVID-19 pandemic has become a challenge to our lives and affects us differently under certain circumstances. This project invites and assists participants in developing their awareness systems for COVID-19. The system comes with a set up consisting of standard information and the necessary coding explained to the participant. The findings are open and shared through an honor system. The awareness system consists of four embedded subsystems known as diagnostics systems, simulation, tracing, and pattern-matching systems. The diagnostic system uses inference rules to generate an outcome by diseases with the same symptoms related to CDVID-19. The skeleton for the simulation system works as a game, depicting how the virus enters and interacts with the body's cells with different four scenarios such as involved in defeating the body's cell, defeating the virus, neutral coexistence of the virus and enclosure, and at best, turning into a positive virus. At that stage of defeating the body's cell, the virus will replicate itself by an assigned degree with a recursive behavior. The tracing system traces the COVID-19 and the participant's health using red, green, and blue (RGB) colors. The combination of RGB creates more than 16 million using 24 bits in binary or hexadecimal. The red color reserves for COVID-19, with 16 shades of red for a symptom with a 16 degree of severity. The pattern-matching system's skeleton provides four databases for asymptomatic, mild, severe, and fatal cases. The digitized information will quickly identify the pattern. One database may compare with another database for similarities or differences. The transfer of learning from one system can flow into another, ultimately resulting in a solution pattern. This project's implication will guide others to initiate their participant system to find a way and formulate a solution.

Smart and Contactless Person Identification System in the COVID 19 Pandemic Process

The Covid 19 Pandemic has had an impact on many aspects of our daily lives such as Restricting contact through touch, wearing masks, practicing social distancing, staying indoors which has led to change in our behaviors and prioritized the importance of safety hygiene. We travel to different places such as Schools, Colleges, Restaurants, offices, and Hospitals. How do we adapt to these changes and refrain from getting the virus? Luckily, we have the technology to aid us. We are all used to biometric systems for marking our Presence/ Attendance in places like colleges, Offices, and Schools with fingerprint sensors, fingerprint sensors use our Fingerprint to mark our presence however Covid 19 has restricted the use of touch causing problems in marking attendance. One way to resolve the problem is using Artificial Intelligence by using a Recognizer to identify people with their face and iris features. We implement the Face Recognition and the Iris Recognition using two models which run concurrently, one to Recognize the Face by extracting the features of the face and passing the 128-d points to the Neural Network (Mobile net and Resnet Architecture). which gives the identity of the person whose image was matched with the trained database and the other by extracting iris features to recognize people. For extracting iris features we use the Gabor filter to extract features from the eyes which are then matched in the database for recognition using 3 distance-based matching algorithms city block distance, Euclidean distance, and cosine distance which gives an accuracy of 88.19%, 84.95%, and 85.42% respectively. The face Recognizer model yields an Accuracy of 98%, while Iris Recognizer yields an accuracy of 88%. When these models run concurrently it yields an accuracy of 92.4%. © 2021 IEEE.

Hamming Distance Tolerant Content-Addressable Memory (HD-CAM) for DNA Classification

This paper proposes a novel Hamming distance tolerant content-addressable memory (HD-CAM) for energy-efficient in-memory approximate matching applications. HD-CAM exploits NOR-type based static associative memory bitcells, where we add circuitry to enable approximate search with programmable tolerance. HD-CAM implements approximate search using matchline charge redistribution rather than its rise or fall time, frequently employed in state-of-the-art solutions. HD-CAM was designed in a 65nm 1.2V CMOS technology and evaluated through extensive Monte Carlo simulations. Our analysis shows that HD-CAM supports robust operation under significant process variations and changes in the design parameters, enabling a wide range of mismatch threshold (tolerable Hamming distance) levels and pattern lengths. HD-CAM was functionally evaluated for virus DNA classification, which makes HD-CAM suitable for hardware acceleration of genomic surveillance of viral outbreaks, such as Covid-19 pandemics. Author

A morphology-based radiological image segmentation approach for efficient screening of COVID-19.

Computer-aided radiological image interpretation systems can be helpful to reshape the overall workflow of the COVID-19 diagnosis process. This article describes an unsupervised CT scan image segmentation approach. This approach begins by performing a morphological reconstruction operation that is useful to remove the effect of the external disturbances on the infected regions and to locate different regions of interest precisely. The optimal size of the structuring element is selected using the Edge Content-based contrast matrix approach. After performing the opening by using the morphological reconstruction operation, further noise is eliminated using the closing-based morphological reconstruction operation. The original pixel space is restored and the obtained image is divided into some non-overlapping smaller blocks and the mean intensity value for each block is computed that is used as the local threshold value for the binarization purpose. It is preferable to manually determine the range of the infected region. If a region is greater than the upper bound then that region will be considered as an exceptional region and processed separately. Three standard metrics MSE, PSNR, and SSIM are used to quantify the outcomes. Both quantitative and qualitative comparisons prove the efficiency and real-life adaptability of this approach. The proposed approach is evaluated with the help of 400 different images and on average, the proposed approach achieves MSE 307.1888625, PSNR 23.7246505, and SSIM 0.831718459. Moreover, the comparative study shows that the proposed approach outperforms some of the standard methods and obtained results are encouraging to support the battle against the COVID-19.