Search Results for author: Michael Fire

Found 8 papers, 0 papers with code

Malicious Source Code Detection Using Transformer

no code implementations16 Sep 2022 Chen Tsfaty, Michael Fire

Those attacks are categorized as supply chain attacks.

Co-Membership-based Generic Anomalous Communities Detection

no code implementations30 Mar 2022 Shay Lapid, Dima Kagan, Michael Fire

In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC), a novel and generic method that utilizes the information of vertices co-membership in multiple communities.

CompanyName2Vec: Company Entity Matching Based on Job Ads

no code implementations12 Jan 2022 Ran Ziv, Ilan Gronau, Michael Fire

Entity Matching is an essential part of all real-world systems that take in structured and unstructured data coming from different sources.

Data Visualization

Automatic Large Scale Detection of Red Palm Weevil Infestation using Aerial and Street View Images

no code implementations6 Apr 2021 Dima Kagan, Galit Fuhrmann Alpert, Michael Fire

The spread of the Red Palm Weevil has dramatically affected date growers, homeowners and governments, forcing them to deal with a constant threat to their palm trees.

How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning

no code implementations24 May 2020 Aviad Elyashar, Rami Puzis, Michael Fire

Searching for information about a specific person is an online activity frequently performed by many users.

It Runs in the Family: Searching for Synonyms Using Digitized Family Trees

no code implementations9 Dec 2019 Aviad Elyashar, Rami Puzis, Michael Fire

As a result, there is a need for an effective tool for improved synonym suggestion.

Exploring Online Ad Images Using a Deep Convolutional Neural Network Approach

no code implementations2 Sep 2015 Michael Fire, Jonathan Schler

Our study looks at new qualities of ads that can be determined before an ad is shown (rather than online optimization) and defines which ads are most likely to be successful.

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