Sentiment Analysis of Spanish Words of Arabic Origin Related to Islam: A Social Network Analysis

With the arrival of Muslims in 711 till their expulsion in the 1600s, Arabic language was present in Spain for more than eight centuries. Although social networks have become a valuable resource for mining sentiments, there is no previous research investigating the layman’s sentiment towards Spanish words of Arabic etymology related to Islamic terminology. This study aim at analyzing Spanish words of Arabic origin related to Islam. A random sample of 4586 out of 45860 tweets was used to evaluate general sentiment towards some Spanish words of Arabic origin related to Islam. An expert-predefined Spanish lexicon of around 6800 seed adjectives was used to conduct the analysis. Results indicate a generally positive sentiment towards several Spanish words of Arabic etymology related to Islam. By implementing both a qualitative and quantitative methodology to analyze tweets’ sentiments towards Spanish words of Arabic etymology, this research adds breadth and depth to the debate over Arabic linguistic influence on Spanish vocabulary

Application of machine learning techniques at the CERN Large Hadron Collider

Machine learning techniques have been used extensively in several domains of Science and Engineering for decades. These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for years already. Accelerator physics, however, has not started exploiting machine learning until very recently. Several activities are flourishing in this domain, in view of providing new insights to beam dynamics in circular accelerators, in different laboratories worldwide. This is, for instance, the case for the CERN Large Hadron Collider, where since a few years exploratory studies are being carried out. A broad range of topics have been addressed, such as anomaly detection of beam position monitors, analysis of optimal correction tools for linear optics, optimisation of the collimation system, lifetime and performance optimisation, and detection of hidden correlations in the huge data set of beam dynamics observables collected during the LHC Run 2. Furthermore, very recently, machine learning techniques are being scrutinised for the advanced analysis of numerical simulations data, in view of improving our models of dynamic aperture evolution.peer-reviewe

What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization

Knowledge graphs (KGs) store highly heterogeneous information about the world in the structure of a graph, and are useful for tasks such as question answering and reasoning. However, they often contain errors and are missing information. Vibrant research in KG refinement has worked to resolve these issues, tailoring techniques to either detect specific types of errors or complete a KG. In this work, we introduce a unified solution to KG characterization by formulating the problem as unsupervised KG summarization with a set of inductive, soft rules, which describe what is normal in a KG, and thus can be used to identify what is abnormal, whether it be strange or missing. Unlike first-order logic rules, our rules are labeled, rooted graphs, i.e., patterns that describe the expected neighborhood around a (seen or unseen) node, based on its type, and information in the KG. Stepping away from the traditional support/confidence-based rule mining techniques, we propose KGist, Knowledge Graph Inductive SummarizaTion, which learns a summary of inductive rules that best compress the KG according to the Minimum Description Length principle---a formulation that we are the first to use in the context of KG rule mining. We apply our rules to three large KGs (NELL, DBpedia, and Yago), and tasks such as compression, various types of error detection, and identification of incomplete information. We show that KGist outperforms task-specific, supervised and unsupervised baselines in error detection and incompleteness identification, (identifying the location of up to 93% of missing entities---over 10% more than baselines), while also being efficient for large knowledge graphs.Comment: 10 pages, plus 2 pages of references. 5 figures. Accepted at The Web Conference 202

EvLog: Evolving Log Analyzer for Anomalous Logs Identification

Software logs record system activities, aiding maintainers in identifying the underlying causes for failures and enabling prompt mitigation actions. However, maintainers need to inspect a large volume of daily logs to identify the anomalous logs that reveal failure details for further diagnosis. Thus, how to automatically distinguish these anomalous logs from normal logs becomes a critical problem. Existing approaches alleviate the burden on software maintainers, but they are built upon an improper yet critical assumption: logging statements in the software remain unchanged. While software keeps evolving, our empirical study finds that evolving software brings three challenges: log parsing errors, evolving log events, and unstable log sequences. In this paper, we propose a novel unsupervised approach named Evolving Log analyzer (EvLog) to mitigate these challenges. We first build a multi-level representation extractor to process logs without parsing to prevent errors from the parser. The multi-level representations preserve the essential semantics of logs while leaving out insignificant changes in evolving events. EvLog then implements an anomaly discriminator with an attention mechanism to identify the anomalous logs and avoid the issue brought by the unstable sequence. EvLog has shown effectiveness in two real-world system evolution log datasets with an average F1 score of 0.955 and 0.847 in the intra-version setting and inter-version setting, respectively, which outperforms other state-of-the-art approaches by a wide margin. To our best knowledge, this is the first study on tackling anomalous logs over software evolution. We believe our work sheds new light on the impact of software evolution with the corresponding solutions for the log analysis community

Enhancing Text Annotation with Few-shot and Active Learning: A Comprehensive Study and Tool Development

The exponential growth of digital communication channels such as social media and messaging platforms has resulted in an unprecedented influx of unstructured text data, thereby underscoring the need for Natural Language Processing (NLP) techniques. NLP-based techniques play a pivotal role in the analysis and comprehension of human language, facilitating the processing of unstructured text data, and allowing tasks like sentiment analysis, entity recognition, and text classification. NLP-driven applications are made possible due to the advancements in deep learning models. However, deep learning models require a large amount of labeled data for training, thereby making labeled data an indispensable component of these models. Retrieving labeled data can be a major challenge as the task of annotating large amounts of data is laborious and error-prone. Often, professional experts are hired for task-specific data annotation, which can be prohibitively expensive and time-consuming. Moreover, the annotation process can be subjective and lead to inconsistencies, resulting in models that are biased and less accurate. This thesis presents a comprehensive study of few-shot and active learning strategies, systems that combine the two techniques, and current text annotation tools while proposing a solution that addresses the aforementioned challenges through the integration of these methods. The proposed solution is an efficient text annotation platform that leverages Few-shot and Active Learning techniques. It has the potential to assist the field of text annotation by enabling organizations to process vast amounts of unstructured text data efficiently. Also, this research paves the way for inspiring ideas and promising growth opportunities in the future of this field