Reviewerly/exHarmony huggingface dataset:
Complexity-Driven Sampling for Bagging

Ensemble learning consists of combining the prediction of different learners to obtain a final output. One key step for their success is the diversity among the learners. In this paper, we propose to reach the diversity in terms of the classification complexity by guiding the sampling of instances in the Bagging algorithm with complexity measures. The proposed Complexity-driven Bagging algorithm complements the classic Bagging algorithm by considering training samples of different complexity to cover the complexity space. Besides, the algorithm admits any complexity measure to guide the sampling. The proposal is tested in 28 real datasets and for a total of 9 complexity measures, providing satisfactory and promising results and revealing that training with samples of different complexity, ranging from easy to hard samples, is the best strategy when sampling based on complexity.
Extracting Knowledge from Incompletely Known Models

In the age of Artificial Intelligence (AI), the use of trained models is a common practice to solve a huge amount of different problems. However, it is highly complicated to understand the decision-making process of these models and how the used training data affect them during the production phase. For this reason, multiple techniques related to model extraction have appeared. These techniques consist of analyzing the behavior of a (sometimes partially) unknown model and generating a clone that reacts similarly. This process is relatively simple with basic models, but it becomes arduous when complex models must be analyzed and replicated. This paper tackles this issue by presenting the Neural NetwOrk Models (VENNOM) system. It is a general framework architecture to extract knowledge and provide explainability to unknown models. The proposed approach uses low-capacity, high-explainability neural networks to produce flexible and interpretable models. The proposed framework offers several advantages, particularly in terms of obtaining explanations through visual and textual content for models that are otherwise opaque. The framework has been tested on tabular data sets, demonstrating its performance and potential.
The Vision

The idea that companies will develop into self-driving organizations has not yet become a topic of public discourse. There is extensive discussion of the ubiquitous digitization taking place, but there is currently no vision of a destination that goes beyond short-term developments. From individual developments, such as artificial intelligence and self-driving cars, a rather blurry picture may be emerging of what these developments could mean for our companies. One thing is certain: the successful ones will be quicker to seize these opportunities; they already have an idea of the enormous potential that will result from these technological advances. In order to open up these opportunities to everyone else, it is the goal of this book to create a clear, attractive, and fascinating picture of the future that will result from this technological progress.
Extracting Automatically a Domain Ontology from the “Book of Properties” of the Archbishop’s Table of Braga

During the last years, many researchers used ontology-based technologies for developing ontology-learning processes for acquiring knowledge included in textual information. In our case, we focused such processes to study the contents of Portuguese manuscripts from the 17th century, aiming to explore the formalities of the language and characterize the elements contained in the texts. In particular, we worked over a very specific manuscript. In this paper, we present and describe the implementation of a semi-automatic ontology learning process for extracting the knowledge contained in the “Book of Properties”. This manuscript includes a detailed inventory of assets belonging to the Archbishop’s Table of Braga at the beginning of the 17th century. Our goal is to provide a fundamental computational instrument for studying and learning the geography, culture, agriculture, history, and genealogy in Portugal at that time.
FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification

Class-incremental continual learning is an important area of research, as static deep learning methods fail to adapt to changing tasks and data distributions. In previous works, promising results were achieved using replay and compressed replay techniques. In the field of regular replay, GDumb [23] achieved outstanding results but requires a large amount of memory. This problem can be addressed by compressed replay techniques. The goal of this work is to evaluate compressed replay in the pipeline of GDumb. We propose FETCH, a two-stage compression approach. First, the samples from the continual datastream are encoded by the early layers of a pre-trained neural network. Second, the samples are compressed before being stored in the episodic memory. Following GDumb, the remaining classification head is trained from scratch using only the decompressed samples from the reply memory. We evaluate FETCH in different scenarios and show that this approach can increase accuracy on CIFAR10 and CIFAR100. In our experiments, simple compression methods (e.g., quantization of tensors) outperform deep autoencoders. In the future, FETCH could serve as a baseline for benchmarking compressed replay learning in constrained memory scenarios.
Domain Adaptation in Transformer Models: Question Answering of Dutch Government Policies

Automatic answering questions helps users in finding information efficiently, in contrast with web search engines that require keywords to be provided and large texts to be processed. The first Dutch Question Answering (QA) system uses basic natural language processing techniques based on text similarity between the question and the answer. After the introduction of pre-trained transformer-based models like BERT, higher scores were achieved with over 7.7% improvement for the General Language Understanding Evaluation (GLUE) score. Pre-trained transformer-based models tend to over-generalize when applied to a specific domain, leading to less precise context-specific outputs. There is a marked research gap in experiment strategies to adapt these models effectively for domain-specific applications. Additionally, there is a lack of Dutch resources for automatic question answering, as the only existing dataset, Dutch SQuAD, is a translation of the SQuAD dataset in English. We propose a new dataset, PolicyQA, containing questions and answers about Dutch government policies and use domain adaptation techniques to address the generalizability problem of transformer-based models. The experimental setup includes the Long Short-Term memory (LSTM), a baseline neural network, and three BERT-based models, mBert, RobBERT, and BERTje, with domain adaptation. The datasets used for testing are the proposed PolicyQA dataset and the existing Dutch SQuAD. From the results, we found that the multilanguage BERT-model, mBert, outperforms the Dutch BERT-based models (RobBERT and BERTje) on the both datasets. By introducing fine-tuning, a domain adaptation technique, the mBert model improved to 94.10% of F1-score, a gain of 226% compared to its performance without fine-tuning.
Comparing Ranking Learning Algorithms for Information Retrieval Systems

The growth of machine learning applications in various fields has enabled the advancement of Information Retrieval systems. As a result of this evolution, it has become possible to solve the well-known document classification problem. In the beginning, document positions within a result were given from a score, where each document receives an assigned value based on the terms used in the input query. The use of machine learning in this field is known as Learning to Rank, which allows the classification of documents to better meet user search requirements, taking into account aspects such as document preference, importance, and relevance. This paper presents a comparison of different algorithms for ranking documents using machine learning. It is observed that RankSVM presents relatively satisfactory results in smaller datasets, while algorithms that use Gradient Boosting obtain better results for larger datasets.
Unsupervised Online Event Ranking for IT Operations

Keeping high service levels of a fast-growing number of servers is crucial and challenging for IT operations teams. Online monitoring systems trigger many occurrences that experts find hard to keep up with. In addition, most of the triggered warnings do not correspond to real, critical problems, making it difficult for technicians to know which to focus on and address in a timely manner. Outlier and concept drift detection techniques can be applied to multiple streams of readings related to server monitoring metrics, but they also generate many False Positives. Ranking algorithms can already prioritize relevant results in information retrieval and recommender systems. However, these approaches are supervised, making them inapplicable in event detection on data streams. We propose a framework that combines event aggregations and uses a customized clustering algorithm to score and rank alarms in the context of IT operations. To the best of our knowledge, this is the first unsupervised, online, high-dimensional approach to rank IT ops events and contributes to advancing knowledge about associated key concepts and challenges of this problem.
Language Models for Automatic Distribution of Review Notes in Movie Production

During the several years of production of an animated movie, review meetings take place daily, where supervisors and directors generate text notes about fixes needed for the movie. These notes are manually assigned to artistic departments for them to fixed. Being manual, many notes are not properly assigned and are never fixed, lowering the quality of the final movie. This paper presents a proposal for automating the distribution of these notes using multi-label text classification techniques. The comparison of the results obtained by fine-tuning several transformer-based language models is presented. A highest mean accuracy of 0.776 is achieved assigning several departments to each of the review notes in the test set with a BERT Multilingual model. A mean accuracy of 0.762 was reached in just 10 epochs and 10 min of training on an RTX-3090 with a DistilBERT transformer model.
Threshold-Based Classification to Enhance Confidence in Open Set of Legal Texts

Machine Learning has revolutionized the categorization of vast legal documents, minimizing costs and improving evaluations. However, conventional models struggle with unseen data categories in real-world scenarios, a challenge termed Open Set Classification. Our study tackles the issue faced by the Court of Justice in São Paulo, Brazil, to identify recurring lawsuit themes from texts, as manual sorting is inefficient. We introduce a method to enhance confidence in text classification using an open dataset by converting multiclass challenges into binary ones with four confidence tiers. By testing various techniques, we found that combining doc2vec with the Support Vector Machine classifier delivers trustworthy results and robust performance. Ultimately, our method offers an effective solution for classifying legal texts confronting Open Set Classification issues in the legal sector.
On Mixed Fractional Lifting Oscillation Spaces

We introduce hyperbolic oscillation spaces and mixed fractional lifting oscillation spaces expressed in terms of hyperbolic wavelet leaders of multivariate signals on Rd, with d≥2. Contrary to Besov spaces and fractional Sobolev spaces with dominating mixed smoothness, the new spaces take into account the geometric disposition of the hyperbolic wavelet coefficients at each scale (j1,⋯,jd), and are therefore suitable for a multifractal analysis of rectangular regularity. We prove that hyperbolic oscillation spaces are closely related to hyperbolic variation spaces, and consequently do not almost depend on the chosen hyperbolic wavelet basis. Therefore, the so-called rectangular multifractal analysis, related to hyperbolic oscillation spaces, is somehow ‘robust’, i.e., does not change if the analyzing wavelets were changed. We also study optimal relationships between hyperbolic and mixed fractional lifting oscillation spaces and Besov spaces with dominating mixed smoothness. In particular, we show that, for some indices, hyperbolic and mixed fractional lifting oscillation spaces are not always sharply imbedded between Besov spaces or fractional Sobolev spaces with dominating mixed smoothness, and thus are new spaces of a really different nature.
How Easy Is It to Read Websites About Schizophrenia?

Unlike legibility, which is determined according to the formal characteristics of a text, such as typeface and page shape, 'readability' defines whether the text is easy to follow and understand by the reader. In addition, readability refers to the level of education necessary for comprehending a given text. The readability of a text can be objectively measured using mathematical formulas based on the relationships between the number of syllables, words and sentences it contains. In order for schizophrenia patients and their relatives to understand their current situation and what kind of process awaits them and to adapt to treatment, the informative texts on websites should be easy to read. Our study aimed to examine the texts about schizophrenia on websites in terms of Turkish readability levels. For the study, 'schizophrenia' was typed into the Google search engine, and the first 50 eligible websites were included. Web pages were analysed in three groups: hospitals, websites created by physicians specializing in psychiatry and other websites. In our study, formulas developed by Ateşman and Bezirci-Yılmaz were used to evaluate readability levels. Statistical analyses used analysis of variance (ANOVA) to compare normally distributed groups of three. There was no statistically significant difference between the readability of the three groups (p>0.05). Our research discovered that the initial 50 websites inspected concerning schizophrenia contained texts that were challenging to read, requiring a minimum of 12 years of education. The readability level of schizophrenia-related websites was observed to be much higher than the average education level in Turkey. This situation may pose difficulties for individuals with schizophrenia and their relatives to get information about schizophrenia online. In addition, if the general population has more accurate information about schizophrenia and can understand it correctly, it may reduce the wrong attitudes and stigmatization towards individuals with schizophrenia.
A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking

With the surge in college graduate numbers, a disparity has emerged where the supply of jobs falls short of demand, intensifying employment pressures annually. College graduates, due to their lack of historical employment data compared with job seekers in the broader society, encounter a ‘cold start’ issue in the job recommendation process. Additionally, the nature of job recommendations, which differs fundamentally from unilateral recommendations, requires consideration of reciprocity between both parties involved. This article introduces a new approach to job recommendations using college graduates as the object of study. In the screening stage, a semantic keyword iterative algorithm is applied to compute the similarity between the resume and recruitment texts. This algorithm enhances the intersectionality of keywords in the calculation process, maximizing the utilization of resume information to enhance the accuracy of text similarity calculations. The ranking phase utilizes in-school data to build a social network between college graduates and graduated students and solves the system’s cold-start problem using the social network to recommend jobs for college graduates where graduated students are employed. We introduce a dual-dimensional matching approach that incorporates both specialty and salary, building upon the amalgamated semantic keyword iterative algorithm and the social network job recommendation method, to enhance the reciprocity of job recommendations. The job recommendation method introduced herein outperforms other methods in terms of the average satisfaction rate (AR) and normalized discounted cumulative gain (NDCG), thereby confirming its superior ability to meet the job-seeking preferences of graduates and the recruitment criteria of employers. This job recommendation method offers effective assistance to graduates lacking employment experience and historical employment data, facilitating their search for more suitable job opportunities.
Revealing the Unseen: A Single-Stage Attention Based Occluded Object Detection Model in Remote Sensing Imagery

Object detection based on deep learning has achieved promising results on traditional datasets, but detecting objects in remote sensing imagery with diverse occlusions remains challenging. This is due to the fact that natural occlusions are common in real-world images, and there is a lack of adequate datasets and neglect of latent information that can be useful for identification. Our research endeavors to tackle this challenge by presenting an innovative single-stage image-adaptive YOLO-transformer framework that leverages attention-based mechanisms to enable the model to concentrate on important regions and extract more distinctive features. To optimize the model’s accuracy while maintaining its lightweight and suitability for real-time applications, we employed a depthwise convolution and SiLU activation function in lieu of the standard convolution. These modifications allow our framework to attain high levels of precision. Our approach is evaluated on synthetically generated occluded datasets from publicly available NWPU-VHR10 and demonstrated adaptive processing of images in both normal and three types of environmental occlusions (foggy, rainy, and cloudy). The experimental results are highly promising, as our approach achieved an inference speed of 6 ms with 11.9 GFLOPs on the NVIDIA Tesla T4 while maintaining effectiveness in all three occlusion types.
Scalable Centroid Based Fuzzy Min-Max Neural Network Ensemble Classifier Using MapReduce

Fuzzy Min-Max Neural Network (FMNN) Classifier has acquired significance owing to its unique properties of single-pass training, non-linear classification, and adaptability for incremental learning. Since its inception in 1992, FMNN has witnessed several extensions, modifications, and utilization in various applications. But very few works are done in the literature for enhancing the scalability of FMNN. In recent years, MapReduce framework is used extensively for scaling machine learning algorithms. The existing MapReduce approach for FMNN (MRFMNN) is found to be having limitations in load balancing and in achieving good generalizability. This work proposes MRCFMNN Algorithm for overcoming these limitations. MRCFMNN induces an ensemble of centroid-based FMNN Classifiers for achieving higher generalizability with load balancing. Four ensemble strategies are proposed for combining the individual classifier results. The comparative experimental results using benchmark large decision systems were conducted on Apache Spark MapReduce cluster. The results empirically establish the relevance of the proposed MRCFMNN by achieving significantly better classification accuracy in most of the datasets over MRFMNN.
RGB-D Fusion Based on Fuzzy Optimization for Salient Object Detection

The objective of RGB-D salient object detection is to identify visually distinct objects from both depth and RGB images. The fusion of depth information with RGB has gained significant research attention in this field since salient objects can appear in one or more modalities. This paper proposes a fusion scheme for detecting multiple salient objects from RGB-D data by integrating multiple prior maps using a fuzzy optimization framework. First, We generate several prior maps, which are considered to be fuzzy. A fuzzy divergence measure is then utilized to minimize the discrepancies between the different prior maps during the fusion process, ultimately maximizing the number of detected salient objects in the final fused map. Our proposed framework estimates the optimal fuzzy membership parameters to address the boundary ambiguity between salient regions and non-salient backgrounds. Experimental results on different databases demonstrate the efficiency of the proposed method compared to existing RGB-D salient object detection methods.
MSPIS: Multimodal Suspicious Profile Identification System in Online Social Network

Easy access to social media platforms and effortless profile creation have enabled users to create online profiles in large numbers. Apart from general interaction, user profiles can be created to spread fake news and target individuals, organizations, and communities by bullying, trolling, and spreading hate content. Identifying these suspicious profiles is one of the crucial research issues in online social media. Most research works in this domain focus on a single attribute, like either profile features or post content, thereby proposing unimodal models capable of processing numeric data or text. This paper presents a multimodal approach to identifying suspicious user profiles. The multimodal features in our experiments are extracted from profiles, posts, and their engagement. The baseline experiments and comparison with unimodal approaches show the effectiveness of the proposed multimodal model. The proposed MSPIS framework gives high accuracy of 93.6% in detecting suspicious users by using rich multimodal features to assess user credibility.
Unsupervised Discovery of Recurring Spoken Terms Using Diagonal Patterns

Spoken term discovery is a challenging task when a lot of spoken content is generated without annotation. The spoken term discovery task accomplished by pattern matching techniques resolves the challenge by directly capturing the resemblance of the spoken terms at the acoustic feature level. Despite feasibility, the pattern-matching approach generates more false alarms during the discovery task due to fluctuations that arise in natural speech; hence degradation in the performance was observed. In the proposed approach, the challenge that arises due to the variability is addressed in two stages. In the first stage, the RASTA-PLP spectrogram was used as an acoustic feature representation that reduces the variabilities among similar spoken contents. In the second stage, the novel Diagonal Pattern Search method unconstrainedly computes the pattern resemblance between the identical spoken terms at the segmental level. The proposed approach was evaluated using the IITKGP-SDUC speech corpus and inferred that a 10.11% improvement in the accuracy was achieved compared to other state-of-the-art systems in the spoken term discovery task.