Avinash Singh

Mayur Patidar, Prayushi Faldu, Avinash Singh et al.

When answering natural language questions over knowledge bases, missing facts, incomplete schema and limited scope naturally lead to many questions being unanswerable. While answerability has been explored in other QA settings, it has not been studied for QA over knowledge bases (KBQA). We create GrailQAbility, a new benchmark KBQA dataset with unanswerability, by first identifying various forms of KB incompleteness that make questions unanswerable, and then systematically adapting GrailQA (a popular KBQA dataset with only answerable questions). Experimenting with three state-of-the-art KBQA models, we find that all three models suffer a drop in performance even after suitable adaptation for unanswerable questions. In addition, these often detect unanswerability for wrong reasons and find specific forms of unanswerability particularly difficult to handle. This underscores the need for further research in making KBQA systems robust to unanswerability

Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter

Ransomware attacks have increased significantly in recent years, causing great destruction and damage to critical systems and business operations. Attackers are unfailingly finding innovative ways to bypass detection mechanisms, whichencouraged the adoption of artificial intelligence. However, most research summarizes the general features of AI and induces many false positives, as the behavior of ransomware constantly differs to bypass detection. Focusing on the key indicating features of ransomware becomes vital as this guides the investigator to the inner workings and main function of ransomware itself. By utilizing access privileges in process memory, the main function of the ransomware can be detected more easily and accurately. Furthermore, new signatures and fingerprints of ransomware families can be identified to classify novel ransomware attacks correctly. The current research used the process memory access privileges of the different memory regions of the behavior of an executable to quickly determine its intent before serious harm can occur. To achieve this aim, several well-known machine learning algorithms were explored with an accuracy range of 81.38 to 96.28 percents. The study thus confirms the feasibility of utilizing process memory as a detection mechanism for ransomware.

Yogesh M Chukewad, Johannes James, Avinash Singh et al.

Aerial robots the size of a honeybee (~100 mg) have advantages over larger robots because of their small size, low mass and low materials cost. Previous iterations have demonstrated controlled flight but were difficult to fabricate because they consisted of many separate parts assembled together. They also were unable to perform locomotion modes besides flight. This paper presents a new design of a 74 mg flapping-wing robot that dramatically reduces the number of parts and simplifies fabrication. It also has a lower center of mass, which allows the robot to additionally land without the need for long legs, even in case of unstable flight. Furthermore, we show that the new design allows for wing-driven ground and air-water interfacial locomotion, improving the versatility of the robot. Forward thrust is generated by increasing the speed of downstroke relative to the upstroke of the flapping wings. This also allows for steering. The ability to land and subsequently move along the ground allows the robot to negotiate extremely confined spaces, underneath obstacles, and to precise locations. We describe the new design in detail and present results demonstrating these capabilities, as well as hovering flight and controlled landing.