596 research outputs found
Mining the Relationship between Emoji Usage Patterns and Personality
Emojis have been widely used in textual communications as a new way to convey
nonverbal cues. An interesting observation is the various emoji usage patterns
among different users. In this paper, we investigate the correlation between
user personality traits and their emoji usage patterns, particularly on overall
amounts and specific preferences. To achieve this goal, we build a large
Twitter dataset which includes 352,245 users and over 1.13 billion tweets
associated with calculated personality traits and emoji usage patterns. Our
correlation and emoji prediction results provide insights into the power of
diverse personalities that lead to varies emoji usage patterns as well as its
potential in emoji recommendation tasks.Comment: To appear at The International AAAI Conference on Web and Social
Media (ICWSM) 201
A Multiagent Evolutionary Algorithm for the Resource-Constrained Project Portfolio Selection and Scheduling Problem
A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem
Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones
Medication for neurological diseases such as the Parkinson's disease usually
happens remotely away from hospitals. Such out-of-lab environments pose
challenges in collecting timely and accurate health status data. Individual
differences in behavioral signals collected from wearable sensors also lead to
difficulties in adopting current general machine learning analysis pipelines.
To address these challenges, we present a method for predicting the medication
status of Parkinson's disease patients using the public mPower dataset, which
contains 62,182 remote multi-modal test records collected on smartphones from
487 patients. The proposed method shows promising results in predicting three
medication statuses objectively: Before Medication (AUC=0.95), After Medication
(AUC=0.958), and Another Time (AUC=0.976) by examining patient-wise historical
records with the attention weights learned through a Transformer model. Our
method provides an innovative way for personalized remote health sensing in a
timely and objective fashion which could benefit a broad range of similar
applications.Comment: Accepted to ICDH-2023. Camera ready with supplementary materia
Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors
Visual localization is an attractive problem that estimates the camera
localization from database images based on the query image. It is a crucial
task for various applications, such as autonomous vehicles, assistive
navigation and augmented reality. The challenging issues of the task lie in
various appearance variations between query and database images, including
illumination variations, dynamic object variations and viewpoint variations. In
order to tackle those challenges, Panoramic Annular Localizer into which
panoramic annular lens and robust deep image descriptors are incorporated is
proposed in this paper. The panoramic annular images captured by the single
camera are processed and fed into the NetVLAD network to form the active deep
descriptor, and sequential matching is utilized to generate the localization
result. The experiments carried on the public datasets and in the field
illustrate the validation of the proposed system.Comment: Accepted by ITSC 201
The timing and cause of glacial activity during the last glacial in central Tibet based on Be-10 surface exposure dating east of Mount Jaggang, the Xainza range
Mountain glaciers are sensitive to climate change, and can provide valuable information for inferring former climates on the Tibetan Plateau (TP). The increasing glacial chronologies indicate that the timing of the local Last Glacial Maximum (LGM) recorded across the TP is asynchronous, implying different local influences of the mid-latitude westerlies and Asian Summer Monsoon in triggering glacier advances. However, the well-dated sites are still too few, especially in the transition zone between regions controlled by the two climate systems. Here we present detailed last glacial chronologies for the Mount Jaggang area, in the Xainza range, central Tibet, with forty-three apparent Be-10 exposure-ages ranging from 12.4 +/- 0.8 ka to 61.9 +/- 3.8 ka. These exposure-ages indicate that at least seven glacial episodes occurred during the last glacial cycle east of Mount Jaggang. These include: a local LGM that occurred at similar to 61.9 +/- 3.8 ka, possibly corresponding to Marine Isotope Stage 4 (MIS 4); subsequent glacial advances at similar to 43.2 +/- 2.6 ka and similar to 35.1 +/- 2.1 ka during MIS 3; one glacial re-advance/standstill at MIS3/2 transition (similar to 29.8 +/- 1.8 ka); and three glacial re-advances/standstills that occurred following MIS 3 at similar to 27.9 +/- 1.7 ka, similar to 21.8 +/- 13 ka, and similar to 15.1 +/- 0.9 ka. The timing of these glacial activities is roughly in agreement with North Atlantic millennial-scale climate oscillations (Heinrich events), suggesting the potential correlations between these abrupt climate changes and glacial fluctuations in the Mount Jaggang area. The successively reduced glacial extent might have resulted from an overall decrease in Asian Summer Monsoon intensity over this timeframe. (C) 2018 Elsevier Ltd. All rights reserved
Fuzzy matching template attacks on multivariate cryptography : a case study
Multivariate cryptography is one of the most promising candidates for post-quantum cryptography. Applying machine learning techniques in this paper, we experimentally investigate the side-channel security of the multivariate cryptosystems, which seriously threatens the hardware implementations of cryptographic systems. Generally, registers are required to store values of monomials and polynomials during the encryption of multivariate cryptosystems. Based on maximum-likelihood and fuzzy matching techniques, we propose a template-based least-square technique to efficiently exploit the side-channel leakage of registers. Using QUAD for a case study, which is a typical multivariate cryptosystem with provable security, we perform our attack against both serial and parallel QUAD implementations on field programmable gate array (FPGA). Experimental results show that our attacks on both serial and parallel implementations require only about 30 and 150 power traces, respectively, to successfully reveal the secret key with a success rate close to 100%. Finally, efficient and low-cost strategies are proposed to resist side-channel attacks
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