1,782 research outputs found
A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins
Location check-ins contain both geographical and semantic information about the visited venues. Semantic information is usually represented by means of tags (e.g., ârestaurantâ). Such data can reveal some personal information about users beyond what they actually expect to disclose, hence their privacy is threatened. To mitigate such threats, several privacy protection techniques based on location generalization have been proposed. Although the privacy implications of such techniques have been extensively studied, the utility implications are mostly unknown. In this paper, we propose a predictive model for quantifying the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. We first study the usersâ motivations behind their location check-ins, based on a study targeted at Foursquare users (N = 77). We propose a machine-learning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for the utility implications of generalization. Based on the survey data, our results show that the model accurately predicts the fine-grained motivation behind a check-in in 43% of the cases and in 63% of the cases for the coarse-grained motivation. It also predicts, with a mean error of 0.52 (on a scale from 1 to 5), the loss of utility caused by semantic and geographical generalization. This model makes it possible to design of utility-aware, privacy-enhancing mechanisms in location-based online social networks. It also enables service providers to implement location-sharing mechanisms that preserve both the utility and privacy for their users
A personality aware recommendation system
Les systĂšmes de recommandation conversationnels (CRSs) sont des systĂšmes qui fournissent
des recommandations personnalisĂ©es par le biais dâune session de dialogue en langage
naturel avec les utilisateurs. Contrairement aux systĂšmes de recommandation traditionnels
qui ne prennent comme vérité de base que les préférences anciennes des utilisateurs, les
CRS impliquent aussi les préférences actuelles des utilisateurs durant la conversation. Des
recherches récentes montrent que la compréhension de la signification contextuelle des
préférences des utilisateurs et des dialogues peut améliorer de maniÚre significative les
performances du systÚme de recommandation. Des chercheurs ont également montré un
lien fort entre les traits de personnalité des utilisateurs et les systÚmes de recommandation.
La personnalité et les préférences sont des variables essentielles en sciences sociales. Elles
décrivent les différences entre les personnes, que ce soit au niveau individuel ou collectif.
Les approches récentes de recommandation basées sur la personnalité sont des systÚmes non
conversationnels. Par consĂ©quent, il est extrĂȘmement important de dĂ©tecter et dâutiliser les
traits de personnalitĂ© des individus dans les systĂšmes conversationnels afin dâassurer une
performance de recommandation et de dialogue plus personnalisée. Pour ce faire, ce travail
propose un systÚme de recommandation conversationnel sensible à la personnalité qui est
basé sur des modules qui assurent une session de dialogue et recommandation personnalisée
en utilisant les traits de personnalité des utilisateurs. Nous proposons également une
nouvelle approche de détection de la personnalité, qui est un modÚle de langage spécifique
au contexte pour détecter les traits des individus en utilisant leurs données publiées sur les
réseaux sociaux. Les résultats montrent que notre systÚme proposé a surpassé les approches
existantes dans différentes mesures.A Conversational Recommendation System (CRS) is a system that provides personalized
recommendations through a session of natural language dialogue turns with users. Unlike
traditional one-shot recommendation systems, which only assume the userâs previous
preferences as the ground truth, CRS uses both previous and current user preferences.
Recent research shows that understanding the contextual meaning of user preferences and
dialogue turns can significantly improve recommendation performance. It also shows a
strong link between usersâ personality traits and recommendation systems. Personality
and preferences are essential variables in computational sociology and social science.
They describe the differences between people, both at the individual and collective level.
Recent personality-based recommendation approaches are traditional one-shot systems, or
ânon conversational systemsâ. Therefore, there is a significant need to detect and employ
individualsâ personality traits within the CRS paradigm to ensure a better and more
personalized dialogue recommendation performance.
Driven by the aforementioned facts, this study proposes a modularized, personality-
aware CRS that ensures a personalized dialogue recommendation session using the usersâ
personality traits. We also propose a novel personality detection approach, which is a
context-specific language model for detecting individualsâ personality traits using their
social media data. The goal is to create a personality-aware and topic-guided CRS model
that performs better than the standard CRS models. Experimental results show that our
personality-aware conversation recommendation system has outperformed state-of-the-art
approaches in different considered metrics on the topic-guided conversation recommendation
dataset
A Survey on Differential Privacy with Machine Learning and Future Outlook
Nowadays, machine learning models and applications have become increasingly
pervasive. With this rapid increase in the development and employment of
machine learning models, a concern regarding privacy has risen. Thus, there is
a legitimate need to protect the data from leaking and from any attacks. One of
the strongest and most prevalent privacy models that can be used to protect
machine learning models from any attacks and vulnerabilities is differential
privacy (DP). DP is strict and rigid definition of privacy, where it can
guarantee that an adversary is not capable to reliably predict if a specific
participant is included in the dataset or not. It works by injecting a noise to
the data whether to the inputs, the outputs, the ground truth labels, the
objective functions, or even to the gradients to alleviate the privacy issue
and protect the data. To this end, this survey paper presents different
differentially private machine learning algorithms categorized into two main
categories (traditional machine learning models vs. deep learning models).
Moreover, future research directions for differential privacy with machine
learning algorithms are outlined.Comment: 12 pages, 3 figure
A review and comparison of ontology-based approaches to robot autonomy
Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.Peer ReviewedPostprint (author's final draft
Tensor Learning for Recovering Missing Information: Algorithms and Applications on Social Media
Real-time social systems like Facebook, Twitter, and Snapchat have been growing
rapidly, producing exabytes of data in different views or aspects. Coupled with more
and more GPS-enabled sharing of videos, images, blogs, and tweets that provide valuable
information regarding âwhoâ, âwhereâ, âwhenâ and âwhatâ, these real-time human
sensor data promise new research opportunities to uncover models of user behavior, mobility,
and information sharing. These real-time dynamics in social systems usually come
in multiple aspects, which are able to help better understand the social interactions of the
underlying network. However, these multi-aspect datasets are often raw and incomplete
owing to various unpredictable or unavoidable reasons; for instance, API limitations and
data sampling policies can lead to an incomplete (and often biased) perspective on these
multi-aspect datasets. This missing data could raise serious concerns such as biased estimations
on structural properties of the network and properties of information cascades in
social networks. In order to recover missing values or information in social systems, we
identify â4Sâ challenges: extreme sparsity of the observed multi-aspect datasets, adoption
of rich side information that is able to describe the similarities of entities, generation of
robust models rather than limiting them on specific applications, and scalability of models
to handle real large-scale datasets (billions of observed entries). With these challenges
in mind, this dissertation aims to develop scalable and interpretable tensor-based frameworks,
algorithms and methods for recovering missing information on social media. In
particular, this dissertation research makes four unique contributions:
_ The first research contribution of this dissertation research is to propose a scalable
framework based on low-rank tensor learning in the presence of incomplete information.
Concretely, we formally define the problem of recovering the spatio-temporal dynamics of online memes and tackle this problem by proposing a novel tensor-based
factorization approach based on the alternative direction method of multipliers
(ADMM) with the integration of the latent relationships derived from contextual
information among locations, memes, and times.
_ The second research contribution of this dissertation research is to evaluate the generalization
of the proposed tensor learning framework and extend it to the recommendation
problem. In particular, we develop a novel tensor-based approach to
solve the personalized expert recommendation by integrating both the latent relationships
between homogeneous entities (e.g., users and users, experts and experts)
and the relationships between heterogeneous entities (e.g., users and experts, topics
and experts) from the geo-spatial, topical, and social contexts.
_ The third research contribution of this dissertation research is to extend the proposed
tensor learning framework to the user topical profiling problem. Specifically,
we propose a tensor-based contextual regularization model embedded into a matrix
factorization framework, which leverages the social, textual, and behavioral contexts
across users, in order to overcome identified challenges.
_ The fourth research contribution of this dissertation research is to scale up the proposed
tensor learning framework to be capable of handling real large-scale datasets
that are too big to fit in the main memory of a single machine. Particularly, we
propose a novel distributed tensor completion algorithm with the trace-based regularization
of the auxiliary information based on ADMM under the proposed tensor
learning framework, which is designed to scale up to real large-scale tensors (e.g.,
billions of entries) by efficiently computing auxiliary variables, minimizing intermediate
data, and reducing the workload of updating new tensors
Grounded Semantic Reasoning for Robotic Interaction with Real-World Objects
Robots are increasingly transitioning from specialized, single-task machines to general-purpose systems that operate in unstructured environments, such as homes, offices, and warehouses. In these real-world domains, robots need to manipulate novel objects while adapting to changes in environments and goals. Semantic knowledge, which concisely describes target domains with symbols, can potentially reveal the meaningful patterns shared between problems and environments. However, existing robots are yet to effectively reason about semantic data encoding complex relational knowledge or jointly reason about symbolic semantic data and multimodal data pertinent to robotic manipulation (e.g., object point clouds, 6-DoF poses, and attributes detected with multimodal sensing).
This dissertation develops semantic reasoning frameworks capable of modeling complex semantic knowledge grounded in robot perception and action. We show that grounded semantic reasoning enables robots to more effectively perceive, model, and interact with objects in real-world environments. Specifically, this dissertation makes the following contributions: (1) a survey providing a unified view for the diversity of works in the field by formulating semantic reasoning as the integration of knowledge sources, computational frameworks, and world representations; (2) a method for predicting missing relations in large-scale knowledge graphs by leveraging type hierarchies of entities, effectively avoiding ambiguity while maintaining generalization of multi-hop reasoning patterns; (3) a method for predicting unknown properties of objects in various environmental contexts, outperforming prior knowledge graph and statistical relational learning methods due to the use of n-ary relations for modeling object properties; (4) a method for purposeful robotic grasping that accounts for a broad range of contexts (including object visual affordance, material, state, and task constraint), outperforming existing approaches in novel contexts and for unknown objects; (5) a systematic investigation into the generalization of task-oriented grasping that includes a benchmark dataset of 250k grasps, and a novel graph neural network that incorporates semantic relations into end-to-end learning of 6-DoF grasps; (6) a method for rearranging novel objects into semantically meaningful spatial structures based on high-level language instructions, more effectively capturing multi-object spatial constraints than existing pairwise spatial representations; (7) a novel planning-inspired approach that iteratively optimizes placements of partially observed objects subject to both physical constraints and semantic constraints inferred from language instructions.Ph.D
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