Avshalom Elmalech

Bar Ilan University | BIU · Department of Information Science

Many Library and Information Science (LIS) training programs are gradually expanding their curricula to include computational data science courses such as supervised and unsupervised machine learning. These programs focus on developing both “classic” information science competencies as well as core data science competencies among their students. Si...

With the rising popularity of user-generated genealogical family trees, new genealogical information systems have been developed. State-of-the-art natural question answering algorithms use deep neural network (DNN) architecture based on self-attention networks. However, some of these models use sequence-based inputs and are not suitable to work wit...

Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases pr...

Over the past few decades, large archives of paper-based documents such as books and newspapers have been digitized using Optical Character Recognition. This technology is error-prone, especially for historical documents. To correct OCR errors, post-processing algorithms have been proposed based on natural language analysis and machine learning tec...

Over the past few decades, large archives of paper-based historical documents, such as books and newspapers, have been digitized using the Optical Character Recognition (OCR) technology. Unfortunately, this broadly used technology is error-prone, especially when an OCRed document was written hundreds of years ago. Neural networks have shown great s...

This research studied people’s responses to requests that ask for accessing their personal information when using augmented reality (AR) technology. AR is a new technology that superimposes digital information onto the real world, creating a unique user experience. As such, AR is often associated with the collection and use of personal information,...

One of the key AI tools for textual corpora exploration is natural language question-answering (QA). Unlike keyword-based search engines, QA algorithms receive and process natural language questions and produce precise answers to these questions, rather than long lists of documents that need to be manually scanned by the users. State-of-the-art QA...

The main aim of this research is to gain understanding of the motives and factors that influence users’ willingness to share personal information, particularly in the realm of augmented reality (AR) apps. Within this context, we also examined the hot–cold empathy gap, i.e., the difference between how people estimate their reactions if faced with a...

Over the past few decades, large archives of paper-based historical documents, such as books and newspapers, have been digitized using the Optical Character Recognition (OCR) technology. Unfortunately, this broadly used technology is error-prone, especially when an OCRed document was written hundreds of years ago. Neural networks have shown great s...

With the rising popularity of user-generated genealogical family trees, new genealogical information systems have been developed. State-of-the-art natural question answering algorithms use deep neural network (DNN) architecture based on self-attention networks. However, some of these models use sequence-based inputs and are not suitable to work wit...

Aim: The aim of this study was to determine the safety of vaginal delivery with a non-vertex second twin when the first twin is in the vertex presentation. Materials and methods: A retrospective analysis was undertaken, utilizing a cohort of twin gestations in which the presenting twin was vertex and the second twin was either vertex (group A) o...

Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases pr...

Digitization of historical documents is a challenging task in many digital humanities projects. A popular approach for digitization is to scan the documents into images, and then convert images into text using Optical Character Recognition (OCR) algorithms. However, the outcome of OCR processing of historical documents is usually inaccurate and req...

Purpose Digitization of historical documents is a challenging task in many digital humanities projects. A popular approach for digitization is to scan the documents into images, and then convert images into text using optical character recognition (OCR) algorithms. However, the outcome of OCR processing of historical documents is usually inaccurate...

This paper presents methods for improving the attention span of workers in tasks that heavily rely on their attention to the occurrence of rare events. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting...

This paper studies a new paradigm for improving the attention span of workers in tasks that heavily rely on user's attention to the occurrence of rare events. Such tasks are highly common, ranging from crime monitoring to controlling autonomous complex machines, and many of them are ideal for crowdsourcing. The underlying idea in our approach is to...

This paper studies a new paradigm for improving the attention span of workers in tasks that heavily rely on user's attention to the occurrence of rare events. Such tasks are highly common, ranging from crime monitoring to controlling autonomous complex machines, and many of them are ideal for crowdsourcing. The underlying idea in our approach is to...

This paper studies to what extent agent development changes one’s own strategy. While this question has many general implications it is of special interest to the study of peer designed agents (PDAs), which are computer agents developed by non-experts. This latter emerging technology, has been widely advocated in recent literature for the purpose o...

This paper represents a paradigm shift in what advice agents should provide people. Contrary to what was previously thought, we empirically show that agents that dispense optimal advice will not necessary facilitate the best improvement in people's strategies. Instead, we claim that agents should at times suboptimally advise. We provide results dem...

This paper represents a paradigm shift in what advice agents should provide people. Contrary to what was previously thought, we empirically show that agents that dispense optimal advice will not necessary facilitate the best improvement in people's strategies. Instead, we claim that agents should at times suboptimally advise. We provide results dem...

Peer Designed Agents (PDAs), computer agents developed by non-experts, is an emerging technology, widely advocated in recent literature for the purpose of replacing people in simulations and investigating human behavior. Its main premise is that strategies programmed into these agents reliably reflect, to some extent, the behavior used by their pro...

In this paper we empirically investigate the feasibility of using peer-designed agents PDAs instead of people for the purpose of mechanism evaluation. This approach has been increasingly advocated in agent research in recent years, mainly due to its many benefits in terms of time and cost. Our experiments compare the behavior of 31 PDAs and 150 peo...

This paper proposes the use of restructuring information about choices to improve the performance of computer agents on recurring sequentially dependent decisions. The intended situations of use for the restructuring methods it defines are website platforms such as electronic marketplaces in which agents typically engage in sequentially dependent d...

Peer Designed Agent (PDA), computer agents developed by non-experts, is an emerging technology, widely advocated in recent literature for the purpose of replacing people in simulations and investigating human behavior. Its main premise is that the strategy programmed into these agents reliably reflect, to some extent, the behavior used by the progr...

Agents that interact with humans are known to benefit from modeling them. Therefore, when designing agents intended for interaction with automated agents, it is crucial to model the other agents. However, little is known about how to model automated agents and in particular non-expert agents. Are automated agents to be modeled the same way that an...

The blooming of comparison shopping agents (CSAs) in recent years enables buyers in today's markets to query more than a single CSA while shopping, thus substantially expanding the list of sellers whose prices they obtain. From the individual CSA point of view, however, the multi-CSAs querying is definitely non-favorable as most of today's CSAs ben...

The blooming of comparison shopping agents (CSAs) in recent years enables buyers in today's markets to query more than a single CSA while shopping, thus substantially expanding the list of sellers whose prices they obtain. From the individual CSA point of view, however, the multi-CSAs querying is definitely non-favorable as most of today's CSAs ben...

In this paper we empirically investigate the feasibility of using peer-designed agents (PDAs) instead of people for the purpose of mechanism evaluation. This latter approach has been increasingly advocated in agent research in recent years, mainly due to its many benefits in terms of time and cost. Our experiments compare the behavior of 31 PDAs an...

In many settings and for various reasons, people fail to make optimal decisions. These factors also influence the agents people design to act on their behalf in such virtual environments as eCommerce and distributed operating systems, so that the agents also act sub-optimally despite their greater computational capabilities. In some decision-making...