A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base

Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs). This paper targets at empowering the IR-based KBQA models with the ability of numerical reasoning for answering ordinal constrained questions. A major challenge is the lack of explicit annotations about numerical properties. To address this challenge, we propose a pretraining numerical reasoning model consisting of NumGNN and NumTransformer, guided by explicit self-supervision signals. The two modules are pretrained to encode the magnitude and ordinal properties of numbers respectively and can serve as model-agnostic plugins for any IR-based KBQA model to enhance its numerical reasoning ability. Extensive experiments on two KBQA benchmarks verify the effectiveness of our method to enhance the numerical reasoning ability for IR-based KBQA models.

ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models

Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning. In this paper, we present ReGen, a bidirectional generation of text and graph leveraging Reinforcement Learning to improve performance. Graph linearization enables us to re-frame both tasks as a sequence to sequence generation problem regardless of the generative direction, which in turn allows the use of Reinforcement Learning for sequence training where the model itself is employed as its own critic leading to Self-Critical Sequence Training (SCST). We present an extensive investigation demonstrating that the use of RL via SCST benefits graph and text generation on WebNLG+ 2020 and TekGen datasets. Our system provides state-of-the-art results on WebNLG+ 2020 by significantly improving upon published results from the WebNLG 2020+ Challenge for both text-to-graph and graph-to-text generation tasks. More details at https://github.com/IBM/regen.

NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases

Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence. Recently, much progress toward this goal has been made with automatic knowledge base (KB) construction techniques. However, such techniques focus primarily on the acquisition of positive (true) KB statements, even though negative (false) statements are often also important for discriminative reasoning over commonsense KBs. As a first step toward the latter, this paper proposes NegatER, a framework that ranks potential negatives in commonsense KBs using a contextual language model (LM). Importantly, as most KBs do not contain negatives, NegatER relies only on the positive knowledge in the LM and does not require ground-truth negative examples. Experiments demonstrate that, compared to multiple contrastive data augmentation approaches, NegatER yields negatives that are more grammatical, coherent, and informative---leading to statistically significant accuracy improvements in a challenging KB completion task and confirming that the positive knowledge in LMs can be re-purposed'' to generate negative knowledge.

Electric power Transformer Faults Detection Using Artificial Intelligence Techniques

Through our research we develop an Expert System called Transformer Fault Detection and abbreviation Exformer, to help engineers and technical's in detecting and diagnosis of oiled power transformer faults before it going out of service. We also use Fuzzy Logic in ambiguous data cases about gas ratios in transformer oil, which require use of fuzzy rules in knowledge base of expert system. We also discuss basis of using Artificial Neural Networks and choose number of layers, number of neurons and suitable neural network for power transformers faults analysis and compare.

Digital Control of Thyristors Bridges

In this paper, a theoretical study is done, and practical execution for a three phases, digitally controlled, multi-purposes, thyristor bridge is illustrated. and built this system as a laboratory type, easy to trace its every part.

Method of Detecting and Locating The Blood Vessels (arteries & veins)

The purpose of this research is to detect, locate, and define the blood vessels in the arm of any person who has a problem in taking samples of blood for laboratory testing in order to make it easier, not dangerous nor harmful. Depending on the properties of the scattering wave from the blood and the depth of the penetration, we have calculated the frequency which is necessary to choose the suitable transducer including the Geometric Dimension as well as the materials which is made from (we have taken the 5MHz Doppler –CW for 1.2 cm depth, 2.37 attenuation ratio and the 8MHz Doppler –CW for 0.74 cm depth , with the same attenuation ratio). Depending on the velocity's variation of the blood flow throughout the Biodynamic studies for important arteries in the upper limb, we have found the Doppler frequency which occurs when the acoustic wave passes across the blood red cell. We have designed a suitable electronic instrument which includes the transmitter circuit, receiver circuit, and the output unit - Audio graph.

Modeling the Blood Flow in the Arteries of the human arm by computational fluid dynamics (Medical – Engineering Studying )

The objective of this study was to model the Blood Flow into human arm’s arteries in order to define velocity profile. All steps were based on computational fluid dynamics .Simplified model for arm’s most important arteries were made, while primary data such as length, diameter, and velocity were collected for a healthy 40 years old, male , weight 64 Kg with pulse rate 62 bpm ,and his arteries ranges from 1.6 to 2.6 mm by using Doppler measurement.Bio dynami