Transfer Learning for Content-Based Recommender Systems using Tree Matching

In this paper we present a new approach to content-based transfer learning for solving the data sparsity problem in cases when the users' preferences in the target domain are either scarce or unavailable, but the necessary information on the preferences exists in another domain. We show that training a system to use such information across domains can produce better performance. Specifically, we represent users' behavior patterns based on topological graph structures. Each behavior pattern represents the behavior of a set of users, when the users' behavior is defined as the items they rated and the items' rating values. In the next step we find a correlation between behavior patterns in the source domain and behavior patterns in the target domain. This mapping is considered a bridge between the two domains. Based on the correlation and content-attributes of the items, we train a machine learning model to predict users' ratings in the target domain. When we compare our approach to the popularity approach and KNN-cross-domain on a real world dataset, the results show that on an average of 83$$ of the cases our approach outperforms both methods

Phish Phinder: A Game Design Approach to Enhance User Confidence in Mitigating Phishing Attacks

Phishing is an especially challenging cyber security threat as it does not attack computer systems, but targets the user who works on that system by relying on the vulnerability of their decision-making ability. Phishing attacks can be used to gather sensitive information from victims and can have devastating impact if they are successful in deceiving the user. Several anti-phishing tools have been designed and implemented but they have been unable to solve the problem adequately. This failure is often due to security experts overlooking the human element and ignoring their fallibility in making trust decisions online. In this paper, we present Phish Phinder, a serious game designed to enhance the user's confidence in mitigating phishing attacks by providing them with both conceptual and procedural knowledge about phishing. The user is trained through a series of gamified challenges, designed to educate them about important phishing related concepts, through an interactive user interface. Key elements of the game interface were identified through an empirical study with the aim of enhancing user interaction with the game. We also adopted several persuasive design principles while designing Phish Phinder to enhance phishing avoidance behaviour among users.Comment: 1

Semiclassical dynamics and time correlations in two-component plasmas

The semiclassical dynamics of a charged particle moving in a two-component plasma is considered using a corrected Kelbg pseudopotential. We employ the classical Nevanlinna-type theory of frequency moments to determine the velocity and force autocorrelation functions. The constructed expressions preserve the exact short and long-time behavior of the autocorrelators. The short-time behavior is characterized by two parameters which are expressable through the plasma static correlation functions. The long-time behavior is determined by the self-diffusion coefficient. The theoretical predictions are compared with the results of semiclassical molecular dynamics simulation.Comment: 12 pages, 3 figure

Entertainment Personalization Mechanism through Cross-Domain User Modeling

Abstract. The growth of available entertainment information services, such as movies and CD listings, or travels and recreational activities, raises a need for personalization techniques for filtering and adapting contents to customer's interest and needs. Personalization technologies rely on users data, represented as User Models (UMs). UMs built by specific services are usually not transferable due to commercial competition and models ' representation heterogeneity. This paper focuses on the second obstacle and discusses architecture for mediating UMs across different domains of entertainment. The mediation facilitates improving the accuracy of the UMs and upgrading the provided personalization. 1

Do I trust my machine teammate? An investigation from perception to decision

© 2019 Copyright held by the owner/author(s). In the human-machine collaboration context, understanding the reason behind each human decision is critical for interpreting the performance of the human-machine team. Via an experimental study of a system with varied levels of accuracy, we describe how human trust interplays with system performance, human perception and decisions. It is revealed that humans are able to perceive the performance of automatic systems and themselves, and adjust their trust levels according to the accuracy of systems. The 70% system accuracy suggests to be a threshold between increasing and decreasing human trust and system usage. We have also shown that trust can be derived from a series of users' decisions rather than from a single one, and relates to the perceptions of users. A general framework depicting how trust and perception affect human decision making is proposed, which can be used as future guidelines for human-machine collaboration design

Magnetic properties of colloidal suspensions of interacting magnetic particles

We review equilibrium thermodynamic properties of systems of magnetic particles like ferrofluids in which dipolar interactions play an important role. The review is focussed on two subjects: ({\em i}) the magnetization with the initial magnetic susceptibility as a special case and ({\em ii}) the phase transition behavior. Here the condensation ("gas/liquid") transition in the subsystem of the suspended particles is treated as well as the isotropic/ferromagnetic transition to a state with spontaneously generated long--range magnetic order.Comment: Review. 62 pages, 4 figure

Online engagement for a healthier you: A Case Study of Web-based Supermarket Health Program

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Obesity is a growing problem affecting millions of people. Various behavior change programs have been designed to reduce its prevalence. An Australian supermarket has recently run a web-based health program to motivate people to eat healthily and do more physical activity. The program offered discounts on fresh products and a website, HealthierU, providing interactive support tools for participants. The stakeholders desire to evaluate if the program is effective and if the supporting website is useful to facilitate behavior changes. To answer these questions, in this work we propose a method to: (1) model individual purchase rate from sparse recorded transactions through a mixture of Non-Homogeneous Poisson Processes (NHPP), (2) design criteria for partitioning participants based on their interactions with the HealthierU website, (3) evaluate the program impact by comparing behavior changes across different groups of participants. Our case study shows that during the program the participants significantly increased their purchases of some fresh products. Both the distribution of behavior patterns and impact scores show that the program imposed relatively strong impact on the participants who logged activities and tracked weights. Our method can facilitate the enhancement of personalized health programs, especially aiming to maximize the program impact and targeting participants through web or mobile applications

Nonlinear response of electrons to a positive ion

Electric field dynamics at a positive ion imbedded in an electron gas is considered using a semiclassical description. The dependence of the field autocorrelation function on charge number is studied for strong ion-electron coupling via MD simulation. The qualitative features for larger charge numbers are a decreasing correlation time followed by an increasing anticorrelation. Stopping power and related transport coefficients determined by the time integral of this correlation function result from the competing effects of increasing initial correlations and decreasing dynamical correlations. An interpretation of the MD results is obtained from an effective single particle model showing good agreement with the simulation results.Comment: To be published in the proceedings of the International Workshop on Strongly Coupled Coulomb Systems, Journal of Physics

Challenges of developing a digital scribe to reduce clinical documentation burden.

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms