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Articles 1 - 13 of 13
Full-Text Articles in Engineering
Seer: An Explainable Deep Learning Midi-Based Hybrid Song Recommender System, Khalil Damak, Olfa Nasraoui
Seer: An Explainable Deep Learning Midi-Based Hybrid Song Recommender System, Khalil Damak, Olfa Nasraoui
Faculty Scholarship
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning …
New Insights Into Anhydrobiosis Using Cellular Dielectrophoresis-Based Characterization, Mohamed Z. Rashed, Clinton J. Belott, Brett R. Janis, Michael Menze, Stuart J. Williams
New Insights Into Anhydrobiosis Using Cellular Dielectrophoresis-Based Characterization, Mohamed Z. Rashed, Clinton J. Belott, Brett R. Janis, Michael Menze, Stuart J. Williams
Faculty Scholarship
Late embryogenesis abundant (LEA) proteins are found in desiccation-tolerant species from all domains of life. Despite several decades of investigation, the molecular mechanisms by which LEA proteins confer desiccation tolerance are still unclear. In this study, dielectrophoresis (DEP) was used to determine the electrical properties of Drosophila melanogaster (Kc167) cells ectopically expressing LEA proteins from the anhydrobiotic brine shrimp, Artemia franciscana. Dielectrophoresis-based characterization data demonstrate that the expression of two different LEA proteins, AfrLEA3m and AfrLEA6, increases cytoplasmic conductivity of Kc167 cells to a similar extent above control values. The impact on cytoplasmic conductivity was surprising, given …
Evaluation Of Haptic Guidance Virtual Fixtures And 3d Visualization Methods In Telemanipulation—A User Study, Kevin Huang, Digesh Chitrakar, Fredrik Rydén, Howard Jay Chizeck
Evaluation Of Haptic Guidance Virtual Fixtures And 3d Visualization Methods In Telemanipulation—A User Study, Kevin Huang, Digesh Chitrakar, Fredrik Rydén, Howard Jay Chizeck
Faculty Scholarship
© 2019, The Author(s). This work presents a user-study evaluation of various visual and haptic feedback modes on a real telemanipulation platform. Of particular interest is the potential for haptic guidance virtual fixtures and 3D-mapping techniques to enhance efficiency and awareness in a simple teleoperated valve turn task. An RGB-Depth camera is used to gather real-time color and geometric data of the remote scene, and the operator is presented with either a monocular color video stream, a 3D-mapping voxel representation of the remote scene, or the ability to place a haptic guidance virtual fixture to help complete the telemanipulation task. …
Data-Driven I–V Feature Extraction For Photovoltaic Modules, Xuan Ma, Wei-Heng Huang, Jenny Brynjarsdottir, Jennifer L. Braid, Roger H. French
Data-Driven I–V Feature Extraction For Photovoltaic Modules, Xuan Ma, Wei-Heng Huang, Jenny Brynjarsdottir, Jennifer L. Braid, Roger H. French
Faculty Scholarship
In research on photovoltaic (PV) device degradation, current-voltage (I-V ) datasets carry a large amount of information in addition to the maximum power point. Performance parameters such as short-circuit current, open-circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the performance and degradation of solar cells and modules. To enable the scaling of I-V studies to millions of I-V curves, we have developed a data-driven method to extract I-V curve parameters and distributed this method as an open-source package in R. In contrast with the traditional practice of fitting the diode equation to I-V curves individually, …
Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders
Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders
Faculty Scholarship
Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered state-of-the-art in recommendation systems. Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of …
Debiasing The Human-Recommender System Feedback Loop In Collaborative Filtering, Wenlong Sun, Sami Khenissi, Olfa Nasraoui, Patrick Shafto
Debiasing The Human-Recommender System Feedback Loop In Collaborative Filtering, Wenlong Sun, Sami Khenissi, Olfa Nasraoui, Patrick Shafto
Faculty Scholarship
Recommender Systems (RSs) are widely used to help online users discover products, books, news, music, movies, courses, restaurants,etc. Because a traditional recommendation strategy always shows the most relevant items (thus with highest predicted rating), traditional RS’s are expected to make popular items become even more popular and non-popular items become even less popular which in turn further divides the haves (popular) from the have-nots (un-popular). Therefore, a major problem with RSs is that they may introduce biases affecting the exposure of items, thus creating a popularity divide of items during the feedback loop that occurs with users, and this may …
Meta-Network: Optimized Species-Species Network Analysis For Microbial Communities, Pengshuo Yang, Shaojun Yu, Lin Cheng, Kang Ning
Meta-Network: Optimized Species-Species Network Analysis For Microbial Communities, Pengshuo Yang, Shaojun Yu, Lin Cheng, Kang Ning
Faculty Scholarship
© 2019 The Author(s). Background: The explosive growth of microbiome data provides ample opportunities to gain a better understanding of the microbes and their interactions in microbial communities. Given these massive data, optimized data mining methods become important and necessary to perform deep and comprehensive analysis. Among the various priorities for microbiome data mining, the examination of species-species co-occurrence patterns becomes one of the key themes in urgent need. Results: Hence, in this work, we propose the Meta-Network framework to lucubrate the microbial communities. Rooted in loose definitions of network (two species co-exist in a certain samples rather than all …
Long-Term Trajectories Of Human Civilization, Seth D. Baum, Stuart Armstrong, Timoteus Ekenstedt, Olle Häggström, Robin Hanson, Karin Kuhlemann, Matthijs M. Maas, James D. Miller, Markus Salmela, Anders Sandberg, Kaj Sotala, Phil Torres, Alexey Turchin, Roman V. Yampolskiy
Long-Term Trajectories Of Human Civilization, Seth D. Baum, Stuart Armstrong, Timoteus Ekenstedt, Olle Häggström, Robin Hanson, Karin Kuhlemann, Matthijs M. Maas, James D. Miller, Markus Salmela, Anders Sandberg, Kaj Sotala, Phil Torres, Alexey Turchin, Roman V. Yampolskiy
Faculty Scholarship
Purpose: This paper aims to formalize long-term trajectories of human civilization as a scientific and ethical field of study. The long-term trajectory of human civilization can be defined as the path that human civilization takes during the entire future time period in which human civilization could continue to exist. Design/methodology/approach: This paper focuses on four types of trajectories: status quo trajectories, in which human civilization persists in a state broadly similar to its current state into the distant future; catastrophe trajectories, in which one or more events cause significant harm to human civilization; technological transformation trajectories, in which radical technological …
Towards Ai Welfare Science And Policies, Soenke Ziesche, Roman Yampolskiy
Towards Ai Welfare Science And Policies, Soenke Ziesche, Roman Yampolskiy
Faculty Scholarship
In light of fast progress in the field of AI there is an urgent demand for AI policies. Bostrom et al. provide “a set of policy desiderata”, out of which this article attempts to contribute to the “interests of digital minds”. The focus is on two interests of potentially sentient digital minds: to avoid suffering and to have the freedom of choice about their deletion. Various challenges are considered, including the vast range of potential features of digital minds, the difficulties in assessing the interests and wellbeing of sentient digital minds, and the skepticism that such research may encounter. Prolegomena …
An Explainable Autoencoder For Collaborative Filtering Recommendation, Pegah Sagheb Haghighi, Olurotimi Seton, Olfa Nasraoui
An Explainable Autoencoder For Collaborative Filtering Recommendation, Pegah Sagheb Haghighi, Olurotimi Seton, Olfa Nasraoui
Faculty Scholarship
Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unfortunately, like all black box machine learning models, they are unable to explain their outputs. Hence, while predictions from an Autoencoderbased recommender system might be accurate, it might not be clear to the user why a recommendation was generated. In this work, we design an explainable recommendation system using an Autoencoder model whose predictions can be explained using the neighborhood based explanation style. Our preliminary work …
Emergence Of Addictive Behaviors In Reinforcement Learning Agents, Vahid Behzadan, Roman Yampolskiy, Arslan Munir
Emergence Of Addictive Behaviors In Reinforcement Learning Agents, Vahid Behzadan, Roman Yampolskiy, Arslan Munir
Faculty Scholarship
This paper presents a novel approach to the technical analysis of wireheading in intelligent agents. Inspired by the natural analogues of wireheading and their prevalent manifestations, we propose the modeling of such phenomenon in Reinforcement Learning (RL) agents as psychological disorders. In a preliminary step towards evaluating this proposal, we study the feasibility and dynamics of emergent addictive policies in Q-learning agents in the tractable environment of the game of Snake. We consider a slightly modified version of this game, in which the environment provides a “drug” seed alongside the original “healthy” seed for the consumption of the snake. We …
Personal Universes: A Solution To The Multi-Agent Value Alignment Problem, Roman V. Yampolskiy
Personal Universes: A Solution To The Multi-Agent Value Alignment Problem, Roman V. Yampolskiy
Faculty Scholarship
AI Safety researchers attempting to align values of highly capable intelligent systems with those of humanity face a number of challenges including personal value extraction, multi-agent value merger and finally in-silico encoding. State-of-the-art research in value alignment shows difficulties in every stage in this process, but merger of incompatible preferences is a particularly difficult challenge to overcome. In this paper we assume that the value extraction problem will be solved and propose a possible way to implement an AI solution which optimally aligns with individual preferences of each user. We conclude by analyzing benefits and limitations of the proposed approach.
Quantum Metalanguage And The New Cognitive Synthesis, Alexey V. Melkikh, Andrei Khrennikov, Roman V. Yampolskiy
Quantum Metalanguage And The New Cognitive Synthesis, Alexey V. Melkikh, Andrei Khrennikov, Roman V. Yampolskiy
Faculty Scholarship
Problems with mechanisms of thinking and cognition in many ways remain unresolved. Why are a priori inferences possible? Why can a human understand but a computer cannot? It has been shown that when creating new concepts, generalization is contradictory in the sense that to be created concepts must exist a priori, and therefore, they are not new. The process of knowledge acquisition is also contradictory, as it inevitably involves recognition, which can be realized only when there is an a priori standard. Known approaches of the framework of artificial intelligence (in particular, Bayesian) do not determine the origins of knowledge, …