141 research outputs found
Profiling the news spreading barriers using news headlines
News headlines can be a good data source for detecting the news spreading
barriers in news media, which may be useful in many real-world applications. In
this paper, we utilize semantic knowledge through the inference-based model
COMET and sentiments of news headlines for barrier classification. We consider
five barriers including cultural, economic, political, linguistic, and
geographical, and different types of news headlines including health, sports,
science, recreation, games, homes, society, shopping, computers, and business.
To that end, we collect and label the news headlines automatically for the
barriers using the metadata of news publishers. Then, we utilize the extracted
commonsense inferences and sentiments as features to detect the news spreading
barriers. We compare our approach to the classical text classification methods,
deep learning, and transformer-based methods. The results show that the
proposed approach using inferences-based semantic knowledge and sentiment
offers better performance than the usual (the average F1-score of the ten
categories improves from 0.41, 0.39, 0.59, and 0.59 to 0.47, 0.55, 0.70, and
0.76 for the cultural, economic, political, and geographical respectively) for
classifying the news-spreading barriers.Comment: arXiv admin note: substantial text overlap with arXiv:2304.0816
ASSIGNING KEYWORDS TO DOCUMENTS USING MACHINE LEARNING
This paper describes the usage of machine learning techniques to assign keywords to documents. The large hierarchy of documents available on the Web, the Yahoo hierarchy, is used here as a real-world problem domain. Machine learning techniques developed for learning on text data are used here in the hierarchical classification structure. The high number of features is reduced by taking into account the hierarchical structure and using a feature subset selection based on the method used in information retrieval. Documents are represented as word-vectors that include word sequences (n-grams) instead of just single words. The hierarchical structure of the examples and class values is taken into account when defining the subproblems and forming training examples for them. Additionally, a hierarchical structure of class values is used in classification, where only promising paths in the hierarchy are considered
Political and Economic Patterns in COVID-19 News: From Lockdown to Vaccination
The purpose of this study is to analyse COVID-19 related news published
across different geographical places, in order to gain insights in reporting
differences. The COVID-19 pandemic had a major outbreak in January 2020 and was
followed by different preventive measures, lockdown, and finally by the process
of vaccination. To date, more comprehensive analysis of news related to
COVID-19 pandemic are missing, especially those which explain what aspects of
this pandemic are being reported by newspapers inserted in different economies
and belonging to different political alignments. Since LDA is often less
coherent when there are news articles published across the world about an event
and you look answers for specific queries. It is because of having semantically
different content. To address this challenge, we performed pooling of news
articles based on information retrieval using TF-IDF score in a data processing
step and topic modeling using LDA with combination of 1 to 6 ngrams. We used
VADER sentiment analyzer to analyze the differences in sentiments in news
articles reported across different geographical places. The novelty of this
study is to look at how COVID-19 pandemic was reported by the media, providing
a comparison among countries in different political and economic contexts. Our
findings suggest that the news reporting by newspapers with different political
alignment support the reported content. Also, economic issues reported by
newspapers depend on economy of the place where a newspaper resides
Analyzing Tag Semantics Across Collaborative Tagging Systems
The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance
A Capillary Computing Architecture for Dynamic Internet of Things: Orchestration of Microservices from Edge Devices to Fog and Cloud Providers
The adoption of advanced Internet of Things (IoT) technologies has impressively improved in recent years by placing such services at the extreme Edge of the network. There are, however, specific Quality of Service (QoS) trade-offs that must be considered, particularly in situations when workloads vary over time or when IoT devices are dynamically changing their geographic position. This article proposes an innovative capillary computing architecture, which benefits from mainstream Fog and Cloud computing approaches and relies on a set of new services, including an Edge/Fog/Cloud Monitoring System and a Capillary Container Orchestrator. All necessary Microservices are implemented as Docker containers, and their orchestration is performed from the Edge computing nodes up to Fog and Cloud servers in the geographic vicinity of moving IoT devices. A car equipped with a Motorhome Artificial Intelligence Communication Hardware (MACH) system as an Edge node connected to several Fog and Cloud computing servers was used for testing. Compared to using a fixed centralized Cloud provider, the service response time provided by our proposed capillary computing architecture was almost four times faster according to the 99th percentile value along with a significantly smaller standard deviation, which represents a high QoS.
Document type: Articl
Seeking information about assistive technology: Exploring current practices, challenges, and the need for smarter systems
Ninety percent of the 1.2 billion people who need assistive technology (AT) do not have access. Information seeking practices directly impact the ability of AT producers, procurers, and providers (AT professionals) to match a user's needs with appropriate AT, yet the AT marketplace is interdisciplinary and fragmented, complicating information seeking. We explored common limitations experienced by AT professionals when searching information to develop solutions for a diversity of users with multi-faceted needs. Through Template Analysis of 22 expert interviews, we find current search engines do not yield the necessary information, or appropriately tailor search results, impacting individuals’ awareness of products and subsequently their availability and the overall effectiveness of AT provision. We present value-based design implications to improve functionality of future AT-information seeking platforms, through incorporating smarter systems to support decision-making and need-matching whilst ensuring ethical standards for disability fairness remain
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