Lihi Dery

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Verified

Lihi verified their affiliation via an institutional email.

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• Doctor of Engineering
• Senior Lecturer at Ariel University

Students’ decisions on whether to take a class are strongly affected by whether their friends plan to take the class with them. A student may prefer to be assigned to a course they like less, just to be with their friends, rather than taking a more preferred class alone. It has been shown that taking classes with friends positively affects academic...

Iterative peer grading activities may keep students engaged during in-class project presentations. Effective methods for collecting and aggregating peer assessment data are essential. Students tend to grade projects favorably. So, while asking students for numeric grades is a common approach, it often leads to inflated grades across all projects, r...

Earth observation satellites (EOS) are satellites equipped with optical sensors that orbit the Earth to take photographs of particular areas at the request of users. With the development of space technology, the number of satellites has increased continuously. Yet still, the number of satellites cannot meet the explosive growth of applications. Thu...

We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the...

Voters are usually asked to either rank or rate alternatives. However, reducing their task to just this or the other conceals essential information about their preferences. We propose a model consisting of two parts. First, we present an algorithm that elicits voter preferences: voters are asked to evaluate alternatives and respond to pairwise comp...

The problem of attacks on neural networks through input modification (i.e., adversarial examples) has attracted much attention recently. Being relatively easy to generate and hard to detect, these attacks pose a security breach that many suggested defenses try to mitigate. However, the evaluation of the effect of attacks and defenses commonly relie...

Electronic voting systems are essential for holding virtual elections, and the need for such systems increases due to the COVID-19 pandemic and the social distancing that it mandates. One of the main challenges in e-voting systems is to secure the voting process: namely, to certify that the computed results are consistent with the cast ballots, and...

The problem of attacks on neural networks through input modification (i.e., adversarial examples) has attracted much attention recently. Being relatively easy to generate and hard to detect, these attacks pose a security breach that many suggested defenses try to mitigate. However, the evaluation of the effect of attacks and defenses commonly relie...

Accurately tailored support such as advice or assistance can increase user satisfaction from interactions with smart devices; however, in order to achieve high accuracy, the device must obtain and exploit private user data and thus confidential user information might be jeopardized. We provide an analysis of this privacy–accuracy trade-off. We assu...

We propose a secure voting protocol for score-based voting rules, where independent talliers perform the tallying procedure. The protocol outputs the winning candidate(s) while preserving the privacy of the voters and the secrecy of the ballots. It offers perfect secrecy, in the sense that apart from the desired output, all other information – the...

Group decisions are often complicated by a deadline. For example, in committee hiring decisions the deadline might be the next start of a budget, or the beginning of a semester. It may be that if no candidate is supported by a strong majority, the default is to hire no one - an option that may cost dearly. As a result, committee members might prefe...

Consider a software application that pays a commission fee to be sold on an on-line platform (e.g., Google Play). The sales depend on the applications’ customer rankings. Therefore, developers have an incentive to dishonestly promote their application’s ranking, e.g., by faking positive customer reviews. The platform detects dishonest behavior (che...

We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the...

In the original publication of the article, the equation 1 − ((number of queries)/n × m) under the section “5 Experimental Setup” was published incorrectly.

Label ranking is a prediction task which deals with learning a mapping between an instance and a ranking (i.e., order) of labels from a finite set, representing their relevance to the instance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were develo...

Label ranking tasks are concerned with the problem of ranking a finite set of labels for each instance according to their relevance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking...

A voting center is in charge of collecting and aggregating voter preferences. In an iterative process, the center sends comparison queries to voters, requesting them to submit their preference between two items. Voters might discuss the candidates among themselves, figuring out during the elicitation process which candidates stand a chance of winni...

Consider an application sold on an on-line platform, with the app paying a commission fee and, henceforth, offered for sale on the platform. The ability to sell the application depends on its customer ranking. Therefore, developers may have an incentive to promote their applications ranking in a dishonest manner. One way to do this is by faking pos...

Committees are an important scenario for reaching consensus. Beyond standard consensus-seeking issues, committee decisions are complicated by a deadline, e.g., the next start date for a budget, or the start of a semester. In committee hiring decisions, it may be that if no candidate is supported by a strong majority, the default is to hire no one--...

A voting center is in charge of collecting and aggregating voter preferences. In an iterative process, the center sends comparison queries to voters, requesting them to submit their preference between two items. Voters might discuss the candidates among themselves, figuring out during the elicitation process which candidates stand a chance of winni...

Securing voters' privacy and ensuring the integrity of the voting process are major design goals in voting systems. We propose secure voting protocols for two families of voting rules -- score-based rules and order-based rules. This is the first study that considers the question of secure multiparty computation of election results that such voting...

Voting is a common way to reach a group decision. When possible, voters will attempt to vote strategically, in order to optimize their satisfaction from the outcome. Previous research has modelled how rational voter agents (bots) vote to maximize their personal utility in an iterative voting process that has a deadline (a timeout). However, it rema...

A group of people is often required to reach a joint decision and choose a single activity in which they will all participate. Members of such group often interact via online social networks. Group decision making requires knowledge of members’ preferences; however, in many cases the members’ preferences are not fully available. We consider a scena...

This paper addresses the issue of preference elicitation for group decision making using voting rules. We propose a general, domain-free framework for preference management, where the goal is to minimize the communication cost with the users. We introduce novel heuristics and show how they can operate under a ranking voting protocol, specifically u...

A group may appreciate recommendations on items that fit their joint preferences. When the members' actual preferences are unknown, a recommendation can be made with the aid of collaborative filtering methods. We offer to narrow down the recommended list of items by eliciting the users' actual preferences. Our final goal is to output top-N preferre...

Sometimes voters are required to reach a joint decision and find an item that best suits the group's preferences. Voters may wish to state preferences only when necessary, particularly in cases where there are many available options, therefore it is unpractical to assume that all voter preferences are known at all times. In order to elicit voter pr...

Groups engaged in a mutual activity often need assistance in order to reach a joint decision. However, the group members’ personal preferences are often unknown and need to be collected. Querying for preferences can annoy the users. We suggest employing a voting mechanism that finds a winning item under incomplete settings. We present a practical m...

Group Recommendation Systems (GRS's) assist groups when trying to reach a joint decision. I use probabilistic data and apply voting theory to GRS's in order to minimize user interaction and output an approximate or definite "winner item". Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

One of the challenges that companies face when launching a campaign to promote new services is selecting the 'right' customers for the campaign, i.e., customers with the highest probability of a positive response. Active learning can be used to efficiently identify this set of customers. It can also prevent approach to non-relevant customers and re...

Group Recommendation Systems (GRS) aim at recommending items that are relevant for the joint interest of a group of users. Voting mechanisms assume that users rate all items in order to identify an item that suits the preferences of all group members. This assumption is not feasible in sparse rating scenarios which are common in the recommender sys...

An intelligent model for campaign management was developed as a collaborative research effort between Deutsche Telekom and Ben-Gurion University. The model segments and filters potential customers for solicitation according to a novel algorithm. However, a mathematical model, however cleverly designed, doesn't encompass the human knowledge, experie...