no code implementations • 21 Apr 2024 • Shai Meital, Lior Rokach, Roman Vainshtein, Nir Grinberg
Branching may occur for multiple reasons -- from the asynchronous nature of online platforms to a conscious decision by an interlocutor to disengage with part of the conversation.
no code implementations • 24 Feb 2024 • Seffi Cohen, Lior Rokach
This paper introduces BagStacking, a novel ensemble learning method designed to enhance the detection of Freezing of Gait (FOG) in Parkinson's Disease (PD) by using a lower-back sensor to track acceleration.
1 code implementation • Information Fusion 2023 • Seffi Cohen, Dan Presil, Or Katz, Ofir Arbili, Shvat Messica, Lior Rokach
Social media platforms have become an essential means of communication, but they also serve as a breeding ground for hateful content.
no code implementations • 7 Feb 2023 • Bar Vered, Guy Shtar, Lior Rokach, Bracha Shapira
Adverse drug interactions are largely preventable causes of medical accidents, which frequently result in physician and emergency room encounters.
1 code implementation • 5 Jan 2023 • Tzvi Lederer, Gallil Maimon, Lior Rokach
Backdoor poisoning attacks pose a well-known risk to neural networks.
no code implementations • 29 Dec 2022 • Moti Cohen, Lior Rokach, Rami Puzis
Our approach uses network embedding techniques to leverage CDN information without having to build the metrics manually.
1 code implementation • 16 Jul 2022 • Yarden Rotem, Nathaniel Shimoni, Lior Rokach, Bracha Shapira
In this paper, we propose an innovative Transfer learning for Time series classification method.
1 code implementation • 19 Jun 2022 • Gallil Maimon, Lior Rokach
We achieve this by learning a single search policy over a predefined set of semantics preserving text alterations, on many texts.
1 code implementation • 16 Mar 2022 • Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach
This paper presents Deepchecks, a Python library for comprehensively validating machine learning models and data.
1 code implementation • 29 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.
1 code implementation • 10 Feb 2021 • Yael Mathov, Lior Rokach, Yuval Elovici
We use the framework to create a patch for an everyday scene and evaluate its performance using a novel evaluation process that ensures that our results are reproducible in both the digital space and the real world.
1 code implementation • 16 Nov 2020 • Noy Cohen-Shapira, Lior Rokach
The widespread adoption of machine learning (ML) techniques and the extensive expertise required to apply them have led to increased interest in automated ML solutions that reduce the need for human intervention.
no code implementations • 25 Oct 2020 • Nir Regev, Lior Rokach, Asaf Shabtai
We use LSTM network to learn the relationship between queries and their results, and to provide a rapid inference layer for predicting query results.
no code implementations • 15 Sep 2020 • Victor Makarenkov, Lior Rokach
One of the challenges in the NLP field is training large classification models, a task that is both difficult and tedious.
no code implementations • 30 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.
no code implementations • 28 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.
no code implementations • 26 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.
no code implementations • 5 Jul 2020 • Ihai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach
In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security.
1 code implementation • 9 Jun 2020 • Eli Simhayev, Gilad Katz, Lior Rokach
Improving the robustness of neural nets in regression tasks is key to their application in multiple domains.
no code implementations • 6 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.
1 code implementation • 23 Feb 2020 • Sigal Shaked, Lior Rokach
Since in many cases the researcher does not need the data as is and instead is only interested in the patterns that exist in the data, we propose PrivGen, an innovative method for generating data that maintains patterns and characteristics of the source data.
no code implementations • 23 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.
1 code implementation • IEEE Access 2020 • Amiel Meiseles, Lior Rokach
They then select the most similar source task and use the model trained on it for transfer learning.
no code implementations • 31 Oct 2019 • Doron Laadan, Roman Vainshtein, Yarden Curiel, Gilad Katz, Lior Rokach
In this study, we propose RankML, a meta-learning based approach for predicting the performance of whole machine learning pipelines.
no code implementations • 31 Oct 2019 • Yuval Heffetz, Roman Vainstein, Gilad Katz, Lior Rokach
The second challenge is the computational cost of training and evaluating multiple pipelines.
no code implementations • 23 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.
no code implementations • 9 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.
no code implementations • 12 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.
no code implementations • 5 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.
1 code implementation • 21 Mar 2019 • Adam Kubany, Shimon Ben Ishay, Ruben-sacha Ohayon, Armin Shmilovici, Lior Rokach, Tomer Doitshman
However, these APIs are often trained on different datasets, which, besides affecting their performance, might pose a challenge to their performance evaluation.
no code implementations • 13 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.
no code implementations • 13 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.
1 code implementation • 11 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.
2 code implementations • 6 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.
no code implementations • 28 Jan 2019 • Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach
Using our methods we were able to decrease the effectiveness of such attack from 99. 9% to 15%.
Cryptography and Security
no code implementations • 26 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.
1 code implementation • 8 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.
no code implementations • 28 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.
no code implementations • 23 Apr 2018 • Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach
In this paper, we present a generic, query-efficient black-box attack against API call-based machine learning malware classifiers.
no code implementations • 14 Aug 2017 • Hagit Grushka-Cohen, Oded Sofer, Ofer Biller, Michael Dymshits, Lior Rokach, Bracha Shapira
Data leakage and theft from databases is a dangerous threat to organizations.
no code implementations • 19 Jul 2017 • Ishai Rosenberg, Asaf Shabtai, Lior Rokach, Yuval Elovici
In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e. g., printable strings) that will be misclassified by the classifier without affecting the malware functionality.
1 code implementation • 12 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].
no code implementations • 6 Jan 2016 • Gilad Katz, Lior Rokach
We present a new concept - Wikiometrics - the derivation of metrics and indicators from Wikipedia.
no code implementations • 6 Jul 2013 • Lior Rokach, Alon Schclar, Ehud Itach
In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels.
no code implementations • 19 May 2013 • Alon Schclar, Lior Rokach, Amir Amit
These versions are obtained by applying dimensionality reduction to the original training set using different values of the input parameters.
no code implementations • 15 May 2013 • Naseem Biadsy, Lior Rokach, Armin Shmilovici
In the next step we find a correlation between behavior patterns in the source domain and behavior patterns in the target domain.
no code implementations • 9 Sep 2012 • Eitan Menahem, Alon Schclar, Lior Rokach, Yuval Elovici
XML transactions are used in many information systems to store data and interact with other systems.
no code implementations • 22 Dec 2011 • Eitan Menahem, Lior Rokach, Yuval Elovici
In particular, we propose two new one-class classification performance measures to weigh classifiers and show that a simple ensemble that implements these measures can outperform the most popular one-class ensembles.