Deep feature engineering using full-text publications

We have observed a rapid proliferation in scientific literature and advancements in web technologies has shifted information dissemination to digital libraries [1]. In general, the research conducted by scientific community is articulated through scholarly publications pertaining high quality algorithms along other algorithmic specific metadata such as achieved results, deployed datasets and runtime complexity. According to estimation, approximately 900 algorithms are published in top core conferences during the years 2005-2009 [2]. With this significant increase in algorithms reported in these conferences, more efficient search systems with advance searching capabilities must be designed to search for an algorithm and its supported metadata such as evaluation results like precision, recall etc., particular dataset on which an algorithm executed or the time complexity achieved by that algorithm from full body text of an article. Such advanced search systems could support researchers and software engineers looking for cutting edge algorithmic solutions. Recently, state designed to search for an algorithm from full text articles [3-5]. In this work, we designed an advanced search engine for full text publications that leverages the deep learning techniques to classify algorithmic specific metadata and further to improve searching capabilities for a search system

Deep Learning-based Extraction of Algorithmic Metadata in Full-Text Scholarly Documents

The advancements of search engines for traditional text documents have enabled the effective retrieval of massive textual information in a resource-efficient manner. However, such conventional search methodologies often suffer from poor retrieval accuracy especially when documents exhibit unique properties that behoove specialized and deeper semantic extraction. Recently, AlgorithmSeer, a search engine for algorithms has been proposed, that extracts pseudo-codes and shallow textual metadata from scientific publications and treats them as traditional documents so that the conventional search engine methodology could be applied. However, such a system fails to facilitate user search queries that seek to identify algorithm-specific information, such as the datasets on which algorithms operate, the performance of algorithms, and runtime complexity, etc. In this paper, a set of enhancements to the previously proposed algorithm search engine are presented. Specifically, we propose a set of methods to automatically identify and extract algorithmic pseudo-codes and the sentences that convey related algorithmic metadata using a set of machine-learning techniques. In an experiment with over 93,000 text lines, we introduce 60 novel features, comprising content-based, font style based and structure-based feature groups, to extract algorithmic pseudo-codes. Our proposed pseudo-code extraction method achieves 93.32% F1-score, outperforming the state-of-the-art techniques by 28%. Additionally, we propose a method to extract algorithmic-related sentences using deep neural networks and achieve an accuracy of 78.5%, outperforming a Rule-based model and a support vector machine model by 28% and 16%, respectively

Mining algorithmic complexity in full-text scholarly documents

Non-textual document elements (NTDE) like charts, diagrams, algorithms play an important role to present key information in scientific documents [1]. Recent advancements in information retrieval systems tap this information to answer more complex queries by mining text pertaining to non-textual document elements. However, linking between document elements and corresponding text can be non-trivial. For instance, linking text related to algorithmic complexity with consequent root algorithm could be challenging. These elements are sometime placed at the start or at the end of the page instead of following the flow of document text, and the discussion about these elements may or may not be on the same page. In recent years, quite a few attempts have been made to extract NTDE [2-3]. These techniques are actively applied for effective document summarization, to improve the existing IR systems. Generally, asymptotic notations are used to identify the complexity lines in full text. We mine the relevant complexities of algorithms from full text by comparing the metadata of algorithm with context of paragraph in which complexity related discussion is made by authors. In this paper, we presented a mechanism for identification of algorithmic complexity lines using regular expressions, algorithmic metadata compilation of algorithms, and linking complexity related textual lines to algorithmic metadata

Note Taking in the Digital Age – Towards a Ubiquitous Pen Interface

The cultural technique of writing helped humans to express, communicate, think, and memorize throughout history. With the advent of human-computer-interfaces, pens as command input for digital systems became popular. While current applications allow carrying out complex tasks with digital pens, they lack the ubiquity and directness of pen and paper. This dissertation models the note taking process in the context of scholarly work, motivated by an understanding of note taking that surpasses mere storage of knowledge. The results, together with qualitative empirical findings about contemporary scholarly workflows that alternate between the analog and the digital world, inspire a novel pen interface concept. This concept proposes the use of an ordinary pen and unmodified writing surfaces for interacting with digital systems. A technological investigation into how a camera-based system can connect physical ink strokes with digital handwriting processing delivers artificial neural network-based building blocks towards that goal. Using these components, the technological feasibility of in-air pen gestures for command input is explored. A proof-of-concept implementation of a prototype system reaches real-time performance and demonstrates distributed computing strategies for realizing the interface concept in an end-user setting

Parsing AUC Result-Figures in Machine Learning Specific Scholarly Documents for Semantically-enriched Summarization

Machine learning specific scholarly full-text documents contain a number of result-figures expressing valuable data, including experimental results, evaluations, and cross-model comparisons. The scholarly search system often overlooks this vital information while indexing important terms using conventional text-based content extraction approaches. In this paper, we propose creating semantically enriched document summaries by extracting meaningful data from the results-figures specific to the evaluation metric of the area under the curve (AUC) and their associated captions from full-text documents. At first, classify the extracted figures and analyze them by parsing the figure text, legends, and data plots – using a convolutional neural network classification model with a pre-trained ResNet-50 on 1.2 million Images from ImageNet. Next, we extract information from the result figures specific to AUC by approximating the region under the function’s graph as a trapezoid and calculating its area, i.e., the trapezoidal rule. Using over 12,000 figures extracted from 1000 scholarly documents, we show that figure specialized summaries contain more enriched terms about figure semantics. Furthermore, we empirically show that the trapezoidal rule can calculate the area under the curve by dividing the curve into multiple intervals. Finally, we measure the quality of specialized summaries using ROUGE, Edit distance, and Jaccard Similarity metrics. Overall, we observed that figure specialized summaries are more comprehensive and semantically enriched. The applications of our research are enormous, including improved document searching, figure searching, and figure focused plagiarism. The data and code used in this paper can be accessed at the following URL: https://github.com/slab-itu/fig-ir/

Automated Machine Learning implementation framework in the banking sector

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsAutomated Machine Learning is a subject in the Machine Learning field, designed to give the possibility of Machine Learning use to non-expert users, it aroused from the lack of subject matter experts, trying to remove humans from these topic implementations. The advantages behind automated machine learning are leaning towards the removal of human implementation, fastening the machine learning deployment speed. The organizations will benefit from effective solutions benchmarking and validations. The use of an automated machine learning implementation framework can deeply transform an organization adding value to the business by freeing the subject matter experts of the low-level machine learning projects, letting them focus on high level projects. This will also help the organization reach new competence, customization, and decision-making levels in a higher analytical maturity level. This work pretends, firstly to investigate the impact and benefits automated machine learning implementation in the banking sector, and afterwards develop an implementation framework that could be used by banking institutions as a guideline for the automated machine learning implementation through their departments. The autoML advantages and benefits are evaluated regarding business value and competitive advantage and it is presented the implementation in a fictitious institution, considering all the need steps and the possible setbacks that could arise. Banking institutions, in their business have different business processes, and since most of them are old institutions, the main concerns are related with the automating their business process, improving their analytical maturity and sensibilizing their workforce to the benefits of the implementation of new forms of work. To proceed to a successful implementation plan should be known the institution particularities, adapt to them and ensured the sensibilization of the workforce and management to the investments that need to be made and the changes in all levels of their organizational work that will come from that, that will lead to a lot of facilities in everyone’s daily work

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings