Multilingual Cross-domain Perspectives on Online Hate Speech

In this report, we present a study of eight corpora of online hate speech, by demonstrating the NLP techniques that we used to collect and analyze the jihadist, extremist, racist, and sexist content. Analysis of the multilingual corpora shows that the different contexts share certain characteristics in their hateful rhetoric. To expose the main features, we have focused on text classification, text profiling, keyword and collocation extraction, along with manual annotation and qualitative study.Comment: 24 page

Corrigendum: an overview of MicroRNAs as biomarkers of ALS

A Corrigendum on An Overview of MicroRNAs as Biomarkers of ALS by Joilin, G., Leigh, P. N., Newbury, S. F., and Hafezparast, M. (2019). Front. Neurol. 10:186. doi: 10.3389/fneur.2019.00186 In the original article, there was a mistake in Table 1 as published. Some of the miRNAs listed in the table were incorrectly placed in the wrong column and/or row. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated

Smartphone sensing platform for emergency management

The increasingly sophisticated sensors supported by modern smartphones open up novel research opportunities, such as mobile phone sensing. One of the most challenging of these research areas is context-aware and activity recognition. The SmartRescue project takes advantage of smartphone sensing, processing and communication capabilities to monitor hazards and track people in a disaster. The goal is to help crisis managers and members of the public in early hazard detection, prediction, and in devising risk-minimizing evacuation plans when disaster strikes. In this paper we suggest a novel smartphone-based communication framework. It uses specific machine learning techniques that intelligently process sensor readings into useful information for the crisis responders. Core to the framework is a content-based publish-subscribe mechanism that allows flexible sharing of sensor data and computation results. We also evaluate a preliminary implementation of the platform, involving a smartphone app that reads and shares mobile phone sensor data for activity recognition.Comment: 11th International Conference on Information Systems for Crisis Response and Management ISCRAM2014 (2014

Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

Physical activity recognition (PAR) using wearable devices can provide valued information regarding an individual's degree of functional ability and lifestyle. In this regards, smartphone-based physical activity recognition is a well-studied area. Research on smartwatch-based PAR, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based PAR domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based PAR system for both personal and impersonal models. To further validate our hypothesis for both personal (The classifier is built using the data only from one specific user) and impersonal (The classifier is built using the data from every user except the one under study) models, we tested single subject validation process for smartwatch-based activity recognition.Comment: 15 pages, 2 figures, Accepted in CVC'1

Improving sensor network robustness with multi-channel convergecast

Intenerg

Characterization of multi-channel interference

Multi-channel communication protocols in wireless networks usually assume perfect orthogonality between wireless channels or consider only the use of interference-free channels. The first approach may overestimate the performance whereas the second approach may fail to utilize the spectrum efficiently. Therefore, a more realistic approach would be the careful use of interfering channels by controlling the interference at an acceptable level. We present a methodology to estimate the packet error rate (PER) due to inter-channel interference in a wireless network. The methodology experimentally characterizes the multi-channel interference and analytically estimates it based on the observations from the experiments. Furthermore, the analytical estimation is used in simulations to derive estimates of the capacity in larger networks. Simulation results show that the achievable network capacity, which is defined as the number of simultaneous transmissions, significantly increases with realistic interfering channels compared with the use of only orthogonal channels. When we consider the same number of channels, the achievable capacity with realistic interfering channels can be close to the capacity of idealistic orthogonal channels. This shows that overlapping channels which constitute a much smaller band, provides more efficient use of the spectrum. Finally, we explore the correctness of channel orthogonality and show why this assumption may fail in a practical setting