Search Results for author: Bracha Shapira

Found 22 papers, 6 papers with code

Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation

no code implementations29 Oct 2021 Seffi Cohen, Niv Goldshlager, Lior Rokach, Bracha Shapira

Anomaly detection is a well-known task that involves the identification of abnormal events that occur relatively infrequently.

Anomaly Detection

Evolving Context-Aware Recommender Systems With Users in Mind

no code implementations30 Jul 2020 Amit Livne, Eliad Shem Tov, Adir Solomon, Achiya Elyasaf, Bracha Shapira, Lior Rokach

An empirical analysis of our results validates that our proposed approach outperforms SOTA CARS models while improving transparency and explainability to the user.

Recommendation Systems

A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach

no code implementations28 Jul 2020 Hagit Grushka-Cohen, Raphael Cohen, Bracha Shapira, Jacob Moran-Gilad, Lior Rokach

We find that individuals can be ranked for effective testing using a few simple features, and that ranking them using such models we can capture 65% (CI: 64. 7%-68. 3%) of the positive individuals using less than 20% of the testing capacity or 92. 1% (CI: 91. 1%-93. 2%) of positives individuals using 70% of the capacity, allowing reserving a significant portion of the tests for population studies.

Decision Making Multi-Armed Bandits

Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate

no code implementations26 Jul 2020 Amit Livne, Roy Dor, Eyal Mazuz, Tamar Didi, Bracha Shapira, Lior Rokach

Learning sophisticated models to understand and predict user behavior is essential for maximizing the CTR in recommendation systems.

Click-Through Rate Prediction Feature Engineering +1

Automatic Machine Learning Derived from Scholarly Big Data

no code implementations6 Mar 2020 Asnat Greenstein-Messica, Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach

One of the challenging aspects of applying machine learning is the need to identify the algorithms that will perform best for a given dataset.

BIG-bench Machine Learning

Sequence Preserving Network Traffic Generation

no code implementations23 Feb 2020 Sigal Shaked, Amos Zamir, Roman Vainshtein, Moshe Unger, Lior Rokach, Rami Puzis, Bracha Shapira

We examined two methods for extracting sequences of activities: a Markov model and a neural language model.

Language Modelling Two-sample testing

Diversifying Database Activity Monitoring with Bandits

no code implementations23 Oct 2019 Hagit Grushka-Cohen, Ofer Biller, Oded Sofer, Lior Rokach, Bracha Shapira

Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties.

Event Detection Recommendation Systems

Deep Context-Aware Recommender System Utilizing Sequential Latent Context

no code implementations9 Sep 2019 Amit Livne, Moshe Unger, Bracha Shapira, Lior Rokach

Recent research has shown that modeling contextual information as a latent vector may address the sparsity and dimensionality challenges.

Collaborative Filtering Recommendation Systems

Assessing the Quality of Scientific Papers

no code implementations12 Aug 2019 Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach

In this paper, we propose a measure and method for assessing the overall quality of the scientific papers in a particular field of study.

A difficulty ranking approach to personalization in E-learning

no code implementations28 Jul 2019 Avi Segal, Kobi Gal, Guy Shani, Bracha Shapira

EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions.

Collaborative Filtering

New Item Consumption Prediction Using Deep Learning

no code implementations5 May 2019 Michael Shekasta, Gilad Katz, Asnat Greenstein-Messica, Lior Rokach, Bracha Shapira

Our experiments show that PISA outperforms a well-known deep learning baseline when new items are introduced.

Recommendation Systems

Online Budgeted Learning for Classifier Induction

no code implementations13 Mar 2019 Eran Fainman, Bracha Shapira, Lior Rokach, Yisroel Mirsky

In online learning, the challenge is to find the optimum set of features to be acquired from each instance upon arrival from a data stream.

online learning

Personal Dynamic Cost-Aware Sensing for Latent Context Detection

no code implementations13 Mar 2019 Saar Tal, Bracha Shapira, Lior Rokach

Results show that by applying a dynamic sampling policy, our method naturally balances information loss and energy consumption and outperforms the static approach.% We compared the performance of our method with another state of the art dynamic sampling method and demonstrate its consistent superiority in various measures.

Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures

1 code implementation11 Mar 2019 Guy Shtar, Lior Rokach, Bracha Shapira

Finally, we create an ensemble-based classifier using AMF, AMFP, and existing link prediction methods and obtain an area under the receiver operating characteristic curve of 0. 814 and 0. 991 for the retrospective and the holdout analyses.

Graph Similarity Link Prediction

Attack Graph Obfuscation

1 code implementation6 Mar 2019 Rami Puzis, Hadar Polad, Bracha Shapira

Before executing an attack, adversaries usually explore the victim's network in an attempt to infer the network topology and identify vulnerabilities in the victim's servers and personal computers.

Combinatorial Optimization

Explaining Anomalies Detected by Autoencoders Using SHAP

2 code implementations6 Mar 2019 Liat Antwarg, Ronnie Mindlin Miller, Bracha Shapira, Lior Rokach

Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts.

Anomaly Detection Outlier Detection

Implicit Dimension Identification in User-Generated Text with LSTM Networks

no code implementations26 Jan 2019 Victor Makarenkov, Ido Guy, Niva Hazon, Tamar Meisels, Bracha Shapira, Lior Rokach

In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative.

Community Question Answering

Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems

1 code implementation8 Jan 2019 Victor Makarenkov, Lior Rokach, Bracha Shapira

We use a bidirectional Recurrent Neural Network (RNN) with LSTM for learning the proper word choice based on a word's sentential context.

Grammatical Error Correction Machine Translation +1

Wikibook-Bot - Automatic Generation of a Wikipedia Book

no code implementations28 Dec 2018 Shahar Admati, Lior Rokach, Bracha Shapira

A Wikipedia book (known as Wikibook) is a collection of Wikipedia articles on a particular theme that is organized as a book.

BIG-bench Machine Learning

Language Models with Pre-Trained (GloVe) Word Embeddings

1 code implementation12 Oct 2016 Victor Makarenkov, Bracha Shapira, Lior Rokach

In this work we implement a training of a Language Model (LM), using Recurrent Neural Network (RNN) and GloVe word embeddings, introduced by Pennigton et al. in [1].

Language Modelling Word Embeddings

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