Reviewerly/exHarmony huggingface dataset:
Optimizing Feature Selection and Oversampling Using Metaheuristic Algorithms for Binary Fraud Detection Classification

Identifying fraudulent transactions and preventing unauthorized individuals from revealing credit card information are essential tasks for different financial entities. Fraud detection systems are used to apply this task by identifying the fraudulent transactions from the normal ones. Usually, the data used for fraud detection is imbalanced, containing many more instances of normal transactions than fraudulent ones. This causes diminished classification task results because it is hard to train a classifier that distinguishes between them. Another problem is caused by many features under study for the fraud detection task. This paper utilizes different metaheuristic algorithms for feature selection to solve the problem of unneeded features and uses the Synthetic Minority Oversampling TEchnique (SMOTE) to solve the imbalance problem of the data using different classification algorithms. The metaheuristic algorithms include Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), and A Multi-Verse Optimizer (MVO), whereas the classification algorithms include Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The results show that applying the oversampling technique generated better results for the G-Mean and Recall values, while the feature selection process enhanced the results of almost all the classification algorithms.
Overview of Blockchain-Based Seafood Supply Chain Management

Seafood products are among the most commonly traded food products. Unfortunately, this industry is suffering from many malpractices and unsustainable resource management. It is also suffering from operational inefficiencies and mistrust among supply chain stakeholders. At the same time, the demand for transparency is increasing not only to boost operating efficiency in the supply chain but also to ensure that accurate product information is accessible to end consumer. In order to address this issue, Blockchain is being regarded as a promising tool to efficiently manage a supply chain. This paper focuses on Blockchain-based seafood supply chain management. It brings out an exhaustive survey of such works and presents a comparative analysis of the studied approaches. Finally, it outlines some critical challenges for further research.
Comparing Vectorization Techniques, Supervised and Unsupervised Classification Methods for Scientific Publication Categorization in the UNESCO Taxonomy

A comparison of classification strategies for scientific articles using the UNESCO taxonomy for categorization is presented. An annotated set of articles were vectorized using TF-IDF, Doc2Vec, BERT y SPECTER and it was established that among those options SPECTER provided the best separability properties using quantitative metrics as well as qualitative inspection of 2D projections using t-SNE. When pairing the best performing vectorization strategy with classical machine learning strategies for the classification task, such as multiple layer perceptron and support vector machines, comparable results are found, concluding that the choice of text representation strategy exerts a greater impact over the choice of classifier. The most problematic areas for classification were identified and a cascading classification strategy was implemented and evaluated. Unsupervised methods were also tested to consider the case when annotated data is not readily available and test their suitability. Two different unsupervised methods were used and it was determined that k-means yielded the best results when considering 3 times the number of categories as the optimal number of clusters.
3D Attention Based YOLO-SWINF for Real-Time Video Object Detection

Video object detection has a lot of applications that require detections in real-time, but these applications are unable to leverage the high accuracy of current SOTA video object detection models due to their high computational requirements. A popular approach to overcome this limitation is to reduce the frame sampling rate, but this comes at the cost of losing important temporal information from these frames. Thus, the most widely used object detection models for real-time applications are image-based single-stage models. Therefore, there is a need for a model that can capture the temporal information from the other frames in a video to boost detection results while still staying real-time. To this end, we propose a YOLOX based video object detection model YOLO-SWINF. Particularly, we introduce a 3D-attention based module that uses a three-dimensional window to capture information across the temporal dimension. We integrate this module with the YOLOX backbone to take advantage of the single-stage nature of YOLOX. We extensively test this module on the ImageNet-VID dataset and show that it has an improvement of 3 AP points over the baseline with just less than 1 ms increase in inference time. Our model is comparable to current real-time SOTA models in accuracy while being the fastest. Our YOLO-SWINF-X model achieves 80.4% AP at 38FPS on NVIDIA 1080Ti GPU.
Fusion of Learned Representations for Multimodal Sensor Data Classification

Time-Series data collected using body-worn sensors can be used to recognize activities of interest in various medical applications such as sleep studies. Recent advances in other domains, such as image recognition and natural language processing have shown that unlabeled data can still be useful when self-supervised techniques such as contrastive learning are used to generate meaningful feature space representations. Labeling data for Human Activity Recognition (HAR) and sleep disorder diagnosis (polysomnography) is difficult and requires trained professionals. In this work, we apply learned feature representation techniques to multimodal time-series data. By using signal-specific representations, based on self-supervised and supervised learning, the channels can be evaluated to determine if they are likely to contribute to correct classification. The learned representation embeddings are then used to process each channel into a new feature space that serves as input into a neural network. This results in a better understanding of the importance of each signal modality as well as the potential applicability of newer self-supervised techniques to time-series data.
OF-AE: Oblique Forest AutoEncoders

We propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, which is an extension of the eForest encoder introduced in [14]. By employing oblique splits, we will devise an auto-encoder method through the computation of a sparse solution of a set of linear inequalities consisting of feature values constraints. The code for reproducing our results is available at https://github.com/CDAlecsa/Oblique-Forest-AutoEncoders .
Exploring Pairwise Spatial Relationships for Actions Recognition and Scene Graph Generation

Visual scene understanding is a fundamental problem and a complex task in computer vision, which not only requires identifying objects in isolation, but also the ability to understand and recognize the relationships between them. These relationships can be abstracted into a semantic representation of $$<subject, predicate, object>$$ , resulting in a scene graph that captures much of the visual information and semantics in the scene. In recent years, scene graph generation with message-passing mechanism [1] has been an active area of research, as it has the potential to capture global dependencies between objects and their relationships. Inspired by these developments, this paper introduces a novel scene graph generation approach based on spatial relationships. Our approach performs a classification of the spatial relationship between each pair of objects to generate the initial scene graph. Then, based on the semantic features, the model detects action relationships in the scene and updates the scene graph by applying the message-passing mechanism. We conclude this paper by comparing the proposed method with the state-of-the-art approaches [1–7] and demonstrate the effectiveness of our method over the Visual Genome [1] dataset.
Implicit Directed Acyclic Graphs (DAGs) for Parallel Outlier/Anomaly Detection Ensembles

We present a methodology to automatically parallelize outlier detection ensemble models using directed acyclic graphs embedding the MapReduce paradigm. The DAGs are built implicitly such that naive sequential computations can be transformed into efficient parallel computations without changing the underlying implementation. We show that the proposed parallelization approach is an effective strategy to combat the computational complexity inherent to ensemble learning models, leading to a near-optimal speedup in a theoretical setting, and a substantial speedup in a practical setting.
Utilizing AR and Hybrid Cloud-Edge Platforms for Improving Accessibility in Exhibition Areas

The technological advancements in the areas of mobile and edge computing provide nowadays the required levels of performance to handheld commodity devices to carry out computational intensive tasks which are required for an effective human-computer interaction. This enables advanced Augmented Reality - AR and Computer Vision approaches to operate on edge and mobile devices offering an immersive experience to the users by augmenting the foreground scene without the need of additional and expensive hardware. In this work we demonstrate the capabilities of AR and Computer Vision technologies for object and scene identification when deployed in a hybrid cloud-mobile environment. The prototype addresses the requirements of a real-world usage scenario for improving the accessibility for hearing and mild vision impaired visitors in museums and exhibition areas. This project is part of the implementation of the SignGuide project, an interactive museum guide system for deaf visitors, which can automatically recognize an exhibit, and create an interactive experience including the provision of content in sign language content using an avatar or video. The proposed system introduces a novel Multitenant Cloud AR-based platform and a client library for mobile apps capable of effectively identifying points of interest and creating new opportunities of interactivity between the real and digital worlds.
VAE-Based Generic Decoding via Subspace Partition and Priori Utilization

Generic decoding is a challenging problem in visual neural decoding. The existing methods based on generative models ignore the application of prior knowledge, which leads to poor interpretability, and few pay attention to fMRI (functional Magnetic Resonance Imaging) processing. To tackle these problems, a novel framework for generic decoding has been proposed named GD-VAE. GD-VAE is based on Variational Auto-Encoder (VAE) which is capable of meaningful latent space, and contains four modules: feature extractor, feature VAE, Prior Knowledge Network (PKN) and Latent Space Disentangling Network (LSDN). The feature extractors extract features of raw visual and cognitive data, and feature VAE implements decoding with a shared latent space for both modalities. The PKN and LSDN constrain the latent space of VAE with delicate structure, in order to apparently reveal the information in the subspace. Benefiting from these modules, the alignment between visual and cognitive modality can be achieved, and greater interpretability can be acquired. Experiments on Generic Decoding Dataset validate the effectiveness and interpretability of the proposed method.
Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction

Flight Take-Off Time (TOT) delay prediction is essential to optimizing capacity-related tasks in Air Traffic Management (ATM) systems. Recently, the ATM domain has put afforded to predict TOT delays using machine learning (ML) algorithms, often seen as “black boxes”, therefore it is difficult for air traffic controllers (ATCOs) to understand how the algorithms have made this decision. Hence, the ATCOs are reluctant to trust the decisions or predictions provided by the algorithms. This research paper explores the use of explainable artificial intelligence (XAI) in explaining flight TOT delay to ATCOs predicted by ML-based predictive models. Here, three post hoc explanation methods are employed to explain the models’ predictions. Quantitative and user evaluations are conducted to assess the acceptability and usability of the XAI methods in explaining the predictions to ATCOs. The results show that the post hoc methods can successfully mimic the inference mechanism and explain the models’ individual predictions. The user evaluation reveals that user-centric explanation is more usable and preferred by ATCOs. These findings demonstrate the potential of XAI to improve the transparency and interpretability of ML models in the ATM domain.
GCN-based Reinforcement Learning Approach for Scheduling DAG Applications

Applications in various fields such as embedded systems or High-Performance-Computing are often represented as Directed Acyclic Graphs (DAG), also known as taskgraphs. DAGs represent the data flow between tasks in an application and can be used for scheduling. When scheduling taskgraphs, a scheduler needs to decide when and on which core each task is executed, while minimising the runtime of the schedule. This paper explores offline scheduling of dependent tasks using a Reinforcement Learning (RL) approach. We propose two RL schedulers, one using a Fully Connected Network (FCN) and another one using a Graph Convolutional Network (GCN). First, we detail the different components of our two RL schedulers and illustrate how they schedule a task. Then, we compare our RL schedulers to a Forward List Scheduling (FLS) approach based on two different datasets. We demonstrate that our GCN-based scheduler produces schedules that are as good or better than the schedules produced by the FLS approach in over 85% of the cases for a dataset with small taskgraphs. The same scheduler performs very similar to the FLS scheduler (at most 5% degradation) in almost 76% of the cases for a more challenging dataset.
Towards Automatic Evaluation of NLG Tasks Using Conversational Large Language Models

Evaluating the quality of machine generated open-ended texts is a long-standing challenge in Natural Language Processing (NLP). Even though there have been dramatic advancements in the machine learning technologies that propelled the research work concerning Natural Language Generation (NLG), a subdivision of NLP that focuses on text generation, a promising and widely adopted automatic evaluation technique for NLG tasks is yet to be developed. In this paper, we propose leveraging conversational Large Language Models (LLMs) as automatic evaluators for several open-ended NLG tasks. Our experiments with a recently released conversational LLM named ChatGPT demonstrate the viability of our proposal.
Towards Autonomous Developmental Artificial Intelligence: Case Study for Explainable AI

State-of-the-art autonomous AI algorithms such as reinforcement learning and deep learning techniques suffer from high computational complexity, poor explainability ability, and a limited capacity for incremental adaptive learning. In response to these challenges, this paper highlights the TMGWR-based algorithm, developed by the present authors, as a case study towards self-adaptive unsupervised learning in autonomous developmental AI, and makes the following contributions: it presents and reviews essential requirements for today’s autonomous AI and includes analysis for their potential for Green AI; it demonstrates that, unlike these state-of-the-art algorithms, TMGWR possesses explainability potentials that can be further developed and exploited for autonomous learning applications. In addition to shaping researchers’ choice of metrics for selecting autonomous learning strategies, this paper will help to motivate further innovative research in autonomous AI.
Exclusive Topic Model

Digital documents are generated, disseminated, and disclosed in books, research papers, newspapers, online feedback, and other content containing large amounts of information, for which discovering topics becomes important but challenging. In the current field of topic modeling, there are limited techniques available to deal with (1) the predominance of frequently occurring words in the estimated topics and (2) the overlap of commonly used words in different topics. We propose exclusive topic modeling (ETM) to identify field-specific keywords that typically occur less frequently but are important for representing certain topics, and to provide well-structured exclusive terms for topics within each topic. Specifically, we impose a weighted LASSO penalty to automatically reduce the dominance of frequently occurring but less relevant words and a pairwise Kullback–Leibler divergence penalty to achieve topic separation. Numerical studies show that the ETM can detect field-specific keywords and provide exclusive topics, which is more meaningful for interpretation and topic detection than other models such as the latent Dirichlet allocation model.
Efficient Approaches for the Discovery of Sensitive Information by Using Natural Language Processing Techniques

The quantity of data and documents created on a huge scale has recently increased. This signifies a rise in unstructured textual information. Extracting sensitive data is usually done manually, following certain rules, which means additional time costs and elevated chances of errors and non-performance. There is a growing need to automatically solve these types of problems, leading to the need of utilizing methodologies that are more intelligent than those previously used. The likelihood of automating this kind of assignment might significantly facilitate compliance with safety policies and imposed regulations. This work aims to highlight the status of Named Entity Recognition (NER) by evaluating models and presenting their overall presentation. It also describes the conflicts and factors that affect the perception of a given entity.
A Difference Measuring Network for Few-Shot Learning

Few-Shot Learning (FSL) aims to distinguish novel categories for which only a few labeled samples are accessible. Due to significant advances in metric learning over the past decade, several metric-based approaches have been investigated for FSL to classify unseen categories with the k-nearest neighbor strategy. However, the existing metric-based FSL methods are typically based on the instance-level or class-level representations that might suffer from the adaptation problem because of the gap between seen and unseen classes. A fact is that human beings can quickly acquire new concepts by comparing similarities and differences between instances. Inspired by this fact, we explore a novel idea that learns difference-level representations instead of instance-level representations. This paper proposes a Difference Measuring Network (DMNet) that learns the patterns of difference from input paires of query-support. The DMNet converts both seen and unseen categories into a binary space that only contains the Same and the Different categories. Therefore, it concentrates on mining the patterns of differences between any two samples rather than the patterns of an individual instance. Finally, the DMNet distinguish whether a pair of inputs belongs to the same category or not. Experiments on five benchmark datasets demonstrate that the proposed DMNet is conducive to generalization ability of FSL.
Active Learning Query Strategy Selection Using Dataset Meta-features Extraction

In the age of information, data abundance has enabled scientists to create models that have great positive impact in our life and society. However, many times the rate of data production is much bigger than the rate of classifying them in the appropriate label due to complexity, personnel or cost of equipment for the labeling task. For this reason, Active Learning techniques have been developed with the Uncertainty Sampling being one of the most popular techniques for querying the unlabeled data. However, selecting the correct Query Strategy for ranking the uncertainty in order to create the best possible model is a time and cost consuming task and most of the times the Active Learning process needs to be repeated multiple times during training. In this work, we exploit the Meta-Features extracted by 123 datasets and select the winning Query Strategy among Least Confidence, Smallest Margin and Entropy for each dataset. In the sequence, we create a dataset with a subset of the extracted Meta-Features and the winning Query Strategy for each dataset and train it in order to create a Decision Tree that can be used in order to select the most suitable Query Strategy.
Copula Bounds for Circular Data

We propose an extension of the Fréchet–Hoeffding copula bounds for circular data. The copula is a powerful tool for describing the dependency of random variables. In two dimensions, the Fréchet–Hoeffding upper (lower) bound indicates the perfect positive (negative) dependence between two random variables. However, for circular random variables, the usual concept of dependency may not be accepted because of their periodicity. In this paper, we redefine the Fréchet–Hoeffding bounds and consider modified Fréchet and Mardia families of copulas for modelling the dependency of two circular random variables. Simulation studies are also given to demonstrate the behaviour of the model.
Group Theory and Sage

At present, almost no mathematician works without using software. Software such as MATLAB and MATHEMATICA are among the best tools for engineers and mathematicians. Moreover, there are computer software as Sage (SageMath) and CoCalc (SageMathCloud) that can help explore, compute, solve a variety of algebraic problems, and address group theory problems. Sage and CoCalc were founded by William Stein and were hosted by Sage. CoCalc is an online version that uses Sage, Latex, and Jupyter. In this chapter, we learn how to use Sage to solve problems (typically complicated problems) in group theory.
Simple Multiple Target Tracking

This chapter develops a basic multiple target tracker which will be useful in relatively simple multiple target tracking situations. The difficulties in multiple target tracking arise when the number of targets is uncertain and there is ambiguity in deciding which target generated which measurement or whether, in fact, the measurement was generated by a false target. This chapter presents a simplified non-linear Joint Probabilistic Data Association (JPDA) algorithm that assumes measurements arrive one at time. Section 4.2 defines association probability, i.e., the probability that a given target generated a measurement and shows how to calculate it in a Bayesian fashion. Section 4.3 defines hard and soft association in terms of association probabilities. Section 4.4 uses soft association to develop a simplified JPDA tracker and provides an example of its use. It then discusses feature aided tracking and shows how features can aid in associating measurements to targets and improve track estimates. Section 4.5 briefly discusses multiple target tracking methods designed for more complicated problems.