Reviewerly/exHarmony huggingface dataset:
Textural and Shape Features for Lesion Classification in Mammogram Analysis

The efficient analysis of digital mammograms has an important role in the early detection of breast cancer and can lead to a higher percentage of recovery. The process of mammogram classification can be divided into two steps as follows: first, it has to be established if the tissue contains abnormalities, and, second, the nature of the lesion has to be determined. This second step of a computer-aided diagnosis system is important in order to select the best treatment for the patient and to achieve the highest chance of recovery. In general, digital mammogram analysis consists of preprocessing, feature extraction, feature selection and classification. Feature extraction is crucial in identifying informative characteristics that can differentiate between benign and malignant lesions. The two main types of feature extraction methods are shape features and texture features. In the current paper, we present several experiments in order to compare the performance of different feature extraction methods from the two types mentioned previously. As data, images from the Digital Database for Screening Mammography (DDSM) are used, which has precise ground truth for the cancerous tissue. For classification Decision Trees and Random Forest methods are used to evaluate the performance using the different extracted features. The experiments that were carried out show that shape features perform better than texture features to separate benign and malignant abnormalities. Also, some outliers were found causing a decrease in the accuracy of the system and achieving 66% test accuracy using shape features and Random Forest classifier.
Bisimulations Between Verbrugge Models and Veltman Models

Veltman semantics is the basic Kripke-like semantics for interpretability logic. Verbrugge semantics is a generalization of Veltman semantics. An appropriate notion of bisimulation between a Verbrugge model and a Veltman model is developed in this paper. We show that a given Verbrugge model can be transformed into a bisimilar Veltman model.
Sentiment Analysis for Vietnamese – Based Hybrid Deep Learning Models

Sentiment analysis of public opinion expressed in social networks has been developed into various applications, especially in English. Hybrid approaches are potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test some hybrid deep learning models' reliability in some domains' Vietnamese language. Our research questions are to determine whether it is possible to produce hybrid models that outperform the Vietnamese language. Hybrid deep sentiment-analysis learning models are built and tested on reviews and feedback of the Vietnamese language. The hybrid models outperformed the accuracy of Vietnamese sentiment analysis on Vietnamese datasets. It contributes to the growing body of research on Vietnamese NLP, providing insights and directions for future studies in this area.
Enhancing Evolutionary Optimization Performance Under Byzantine Fault Conditions

We evaluate the performance of panmictic evolutionary algorithms (EAs) in Byzantine environments, where fitness values are unreliable due to the potential presence of malicious agents. We investigate the impact of this phenomenon on the performance of the algorithm considering two different models of malicious behavior of different severity, taking the unreliability rate of the environment as a control parameter. We observe how there can be a significant toll in the quality of the results as the prevalence of cheating behavior increases, even for simple functions. Subsequently, we endow the EA with mechanisms based on redundant computation to cope with this issue, and examine their effectiveness. Our findings indicate that while a mechanism based on statistical averaging can be an effective approach under a relatively benign fault model, more hostile environments are better tackled via an approach based on majority voting.
Supporting Emotion Recognition in Human-Robot Interactions: An Experimental Italian Textual Dataset

In this study, we illustrate an ongoing work regarding building an Italian textual dataset for emotion recognition for HRI. The idea is to build a dataset with a well-defined methodology based on creating ad-hoc dialogues from scratch. Once that the criteria had been defined, we used ChatGPT to help us generate dialogues. Human experts in psychology have revised each dialogue. In particular, we analyzed the generated dialogues to observe the balance of the dataset under different parameters. During the analysis, we calculated the distribution of context types, gender, consistency between context and emotion, and interaction quality. With “quality” we mean the adherence of text to the desired manifestation of emotions. After the analysis, the dialogues were modified to bring out specific emotions in specific contexts. Significant results emerged that allowed us to reorient the generation of subsequent dialogues. This preliminary study allowed us to draw lines to guide subsequent and more substantial dataset creation in order to achieve increasingly realistic interactions in HRI scenarios.
A Novel Genetic Algorithm with Specialized Genetic Operators for Clustering

Clustering is an unsupervised learning task that groups objects in a multi-dimensional space based on similarity criteria. The goal is to make groups that contain objects that are similar to each other and different from other groups. This work proposes a novelty genetic algorithm to solve the clustering problem based on partitions and estimate automatically the number of clusters. The proposal, GASGO (Genetic Algorithm with Specialized Genetic Operators), includes a representation based on codebooks and the use of specialized and improvement mutation and crossover operators that achieve a high performance to solve clustering problems. The experimental study evaluates 10 clustering validation indexes, 46 data sets, and 8 previous proposals of GAs for clustering considered the state of the art in the area. Results show that GASGO improves the performance for all CVIs compared to the previous proposals.
Teaching Blockchain at School

A game has been invented in prototype form, consisting of 3D printed pieces that physically represent the blockchain. The game consists of building a blockchain of data (a simple sentence with some information), encoded with a simplified version of the hash function, and later trying to hack the stored information, in the shortest possible time before other players notice. The game aims to introduce blockchain to young students and raise a discussion around this emerging technology. The game has been tested in various schools and is enjoying considerable success.
A Fuzzy Logic Ensemble Approach to Concept Drift Detection

Concept drift occurs when the statistical properties of a data distribution change over time, causing the performance of machine learning models trained on prior data to degrade. This is a prevalent issue in many real-world applications where the data distribution can shift due to factors such as user behaviour alterations, environmental changes, or modifications in the data-generating system. Detecting concept drift is crucial for developing robust and adaptive machine learning systems. However, identifying excessive drifts may lead to decreased model performance. In this article, we present an ensemble method employing multiple concept drift detectors to detect concept drift. A fuzzy logic approach balances the outputs of the various drift detectors comprising the ensemble. The proposed framework can handle concept drift in regression problems. Experimental results demonstrate enhanced efficiency in detecting concept drifts under different conditions.
Conditional Obligations in Justification Logic

This paper presents a justification counterpart for dyadic deontic logic, which is often argued to be better than Standard Deontic Logic at representing conditional and contrary-to-duty obligations, such as those exemplified by the notorious Chisholm’s puzzle. We consider the alethic-deontic system (E) and present the explicit version of this system (JE) by replacing the alethic Box-modality with proof terms and the dyadic deontic Circ-modality with justification terms. The explicit representation of strong factual detachment (SFD) is given and finally soundness and completeness of the system (JE) with respect to basic models and preference models is established.
Hybrid Intelligent Parsimony Search in Small High-Dimensional Datasets

The search for machine learning models that generalize well with small high-dimensional datasets is a current challenge. This paper shows a specific hybrid methodology for this kind of problems combining HYB-PARSIMONY and Bayesian Optimization. The methodology proposes to use HYB-PARSIMONY with different random seeds and select those features that had the highest mean probability. Subsequently, with these features, a hyperparameter adjustment is performed with Bayesian Optimization. The results show that the methodology substantially improves the degree of generalization and parsimony of the obtained models compared to previous methods.
Image Classification Understanding with Model Inspector Tool

This paper proposes a novel method called U Analysis for interpreting the behavior of image classification models. The method allows the evaluation of the interdependence between patches of information in an image and their impact on the model’s classification performance. In addition, the paper introduces the Model Inspector tool that allows users to manipulate various visual features of an input image to understand better the model’s robustness to different types of information. This work aims to provide a more comprehensive framework for model interpretation and help researchers and practitioners better understand the strengths and weaknesses of deep learning models in image classification. We perform experiments with CIFAR-10 and STL-10 datasets using the ResNet architecture. The findings show that ResNet model trained with CIFAR-10 and STL-10 presents counter-intuitive feature interdependence, which is seen as a weakness. This work can contribute to developing even more advanced tools for analyzing and understanding deep learning models.
Improved Evolutionary Approach for Tuning Topic Models with Additive Regularization

The paper addresses a problem of tuning topic models with additive regularization by introducing a novel hybrid evolutionary approach that combines Genetic and Nelder-Mead algorithms to generate domain-specific topic models with better quality. Introducing Nelder-Mead into the Genetic Algorithm pursues the goal of enhancing exploitation capabilities of the resulting hybrid algorithm with improved local search. The conducted experimental study performed on several datasets on Russian and English languages shows noticeable increase in quality of the obtained topic models. Moreover, the experiments demonstrate that the proposed modification also improves the convergence dynamics of the tuning procedure, leading to a stable increases in quality from generation to generation.
Optimizing Video Analytics Deployment for In-Flight Cabin Readiness Verification

In this paper, we propose an approach to optimize the deployment of on-board video analytics for checking the correct positioning of luggage in aircraft cabins. The system consists of embedded cameras installed on top of the cabin and a heterogeneous embedded processor. Each camera covers multiple regions of interest (i.e., multiple seats or aisle sections) to minimize the number of cameras required. Each image region is processed by a separate image classification algorithm trained with the expected kind of visual appearance considering the effect of perspective and lens distortion. They classify these regions as correct or incorrect for cabin readiness by exploiting the hierarchical structure of classes composed of different configurations of passengers’ and objects’ presence or absence and the objects’ location. Our approach leverages semantic distances between classes to guide prototypical neural networks for multi-tasking between the main classification (i.e., correct or incorrect status) and auxiliary attributes (i.e., scene configurations), learning robust features from different data domains (i.e., various cabins, real or synthetic). The processing pipeline optimizes response delay and power consumption by leveraging embedded processors’ computing capabilities. We carried out experiments in a cabin mockup with a Jetson AGX Xavier, efficiently obtaining better-quality descriptive information from the scene to improve the system’s accuracy compared to alternative state-of-the-art methods.
JointPS: Joint Parameter Server Placement and Flow Scheduling for Machine Learning Clusters

To distill more information from training data, more parameters are introduced into machine learning models. As a result, communication becomes the bottleneck of Distributed Machine Learning (DML) systems. To alleviate the communication resource contention among DML jobs, which prolongs the time to train machine learning models, in machine learning clusters, JointPS is proposed in this paper. JointPS first minimizes the completion time of a single training epoch for each DML job via jointly optimizing the parameter server placement and flow scheduling, and predicts the number of remaining training epochs for each DML job by leveraging a dynamic model fitting method. Then, JointPS can estimate the remaining time to complete each DML job. According to such estimation, JointPS schedules DML jobs following the Minimum Remaining Time First (MRTF) principle to minimize the average job completion time. To the best of our knowledge, JointPS should be the first work that minimizes the average completion time of network-intensive DML training jobs by jointly optimizing the parameter server placement and flow scheduling without modifying the DML models and training procedures. Through both testbed experiments and extensive simulations, we demonstrate that JointPS can reduce the average completion time of DML jobs by up to 88% compared with state-of-the-art technology.
Improving the Quality of Quantum Services Generation Process: Controlling Errors and Noise

As the industry moves towards practical applications of quantum computing, it faces significant obstacles such as specific platform dependency and lack of mature tools. These obstacles make the creation of quantum applications a slow and complex process that requires specialized knowledge of quantum mechanics and computer science, which compromises the quality of quantum services. Therefore, the need to ensure an adequate level of quality in quantum software is fundamental. To address these challenges, this work proposes a process that enables developers to create high-quality quantum services in an automated and standardized way, using an extension of the OpenAPI specification. Furthermore, we analyze the challenges faced by NISQ devices, the most advanced quantum computers available today, due to errors and noise such as decoherence, gate errors, and readout errors. This process will make it possible to measure, at runtime, the stability and fidelity of the quantum circuits included in the generated quantum services.
Companion Classification Losses for Regression Problems

By their very nature, regression problems can be transformed into classification problems by discretizing their target variable. Within this perspective, in this work we investigate the possibility of improving the performance of deep machine learning models in regression scenarios through a training strategy that combines different classification and regression objectives. In particular, we train deep neural networks using the mean squared error along with categorical cross-entropy and the novel Fisher loss as companion losses. Finally, we will compare experimentally the results of these companion loss methods with the ones obtained using the standard mean squared loss.
Japanese Event Factuality Analysis in the Era of BERT

Recognizing event factuality is a crucial factor for understanding and generating texts with abundant references to possible and counterfactual events. Because event factuality is signaled by modality expressions, identifying modality expression is also an important task. The question then is howto solve these related tasks. On the one hand, while neural networks facilitate multi-task learning by means of parameter sharing among related tasks, the recently introduced pre-training/fine-tuning paradigm might be powerful enough for the model to be able to learn one task without indirect signals from another. On the other hand, ever-increasing model sizes make it practically difficult to run multiple task-specific fine-tuned models at inference time so that parameter sharing can be seen as an effective way to reduce the model’s size. Through experiments, we found: (1) BERT-CRF outperformed non-neural models and BiLSTM-CRF; (2) BERT-CRF did neither benefit from nor was negatively impacted by multi-task learning, indicating that BERT-CRF with multi-task learning is a practical solution.
Electricity Consumption and Environmental Impact Reduction Measures in Public Blockchain

Public blockchains are increasing proof-of-work (PoW) workloads and consume more electricity because of the growing use of products, such as cryptocurrencies, security tokens, and non-fungible tokens (NFTs). Companies are increasingly required to visualize the electricity consumption of the products and services they use to plan and promote measures to reduce Scope 3 emissions to achieve green transformation. However, the electricity consumption of public blockchain-based products and services is not well known to practitioners, and progress in this area has been slow. This study elucidates the electricity consumption of major public blockchains and the share of renewable energy they consumed. In addition, it describes the problems inherent in the blockchain mining process, which is the main cause of this problem, and how to address them. This study also examines possible methods that could be adopted to reduce the environmental impact from the perspective of the public blockchain, miners, and users.
Secure Decision Approach for Internet of Healthcare Things Smart-System-Based Blockchain

Modeling of Internet of Healthcare Things (IoHT) smart-system-based blockchain in terms of security and privacy requirements is considered a multicriteria decision-making (MCDM) problem. Even though literature reviews have evaluated IoHT smart-system-based blockchain, informational ambiguity, vagueness and uncertainty remain open issues. In addition, the early MCDM techniques used to model IoHT smart systems-based blockchain have substantial theoretical shortcomings. First, the tuning parameters used produce different modeling outcomes. Second, the use of optimization theory increases the complexity of the modeling and decreases the stability of the results when the alternative numbers increases. Third, distance measurement and binary decision matrix are incompatible. Fourth, the modeling method used cannot prioritize the evaluation criteria. Therefore, this article extends the fuzzy weighted with zero inconsistency (FWZIC) method with interval-valued spherical fuzzy sets (IvSFSs) for weighting security and privacy requirements. Then, the developed IvS-FWZIC method is integrated with the complex proportional assessment method to model IoHT smart-system-based blockchain. IoHT smart-system-based blockchains 15 and 11 are selected as alternatives under remote patient monitoring systems and telemedicine systems, respectively. These alternatives are evaluated by six security and privacy requirements (criteria). The stability and robustness of the proposed methods are assessed by conducting sensitivity analysis and Spearman’s rho and comparison analysis. Researchers and developers of IoHT smart-system-based blockchain can propose more secure, private solutions with the help of this work’s implications.
Deep Learning Attention-Ranked Media Space Generation for Virtual Reality Equirectangular Scene Augmentation

Virtual Reality has fastened its growth radicalising industries such as tertiary education, marketing, and entertainment. Developments in virtual world-building like the Metaverse yields challenges such as the prohibitive technical skill requirement. This work constructed a method of generating attention-ranked media spaces through deep learning as a solution to this issue mitigating unskilled demand for scene augmentation. Two segmentation tasks were addressed: true-perspective view-port media space inferencing, and gaze attention predictions for equirectangular 360-degree projections. Combining results produced ranked spaces providing multimedia implantation locations. Ablation studies assessed TranSalNet, a leading attention Transformer, for attention-saliency accounting for model pre-encoders. This was compared against U-Net for media space generation. Weak attention supervision and architecture overparameterisation limitations were addressed with modified Salient Object Subitizing and DT-Fixup algorithms respectively. These contributions yielded an overall improvement from second-best models demonstrating experimental success.
Distributed Integrity Auditing of Cloud data using Edge Computing and Blockchain

Auditing of outsourced data is typically managed by an untrusted third party, which poses a crucial data integrity issue for the data owner and end users as well. A distributed auditing scheme comprises three primary participants - data owner, storage server, and end users. One of the main concerns in a distributed auditing setting is that a registered end user may get involved in auditing data files on behalf of the data owner. The reason behind involving end users to audit data files is to reduce computational cost at data owner. However, the approach increases the communication cost, as the data owner may receive multiple failure reports corresponding to the same corrupt file from different authorized users. In this paper, a secure and efficient distributed auditing scheme for outsourced data is presented using blockchain technology and edge computing. The proposed scheme uses blockchain for storing distinct failure reports. Edge nodes are used to handle local auditing task. The scheme is compared and analyzed with the existing distributed data integrity auditing schemes. It is shown that the proposed scheme is secure and outperforms other schemes in terms of computational cost at the data owner and communication cost used by the scheme participants.
Optimizing the Efficiency of Winner-Takes-All Neuromorphic Circuit Optimization Using Self-Adaptive Multi-Population Quadratic Approximation Guided Jaya Algorithm

Metaheuristics are robust and sophisticated approaches to solving Electronic Design Optimization problems. However, due to the non-linearity of these optimization problems, the complexity increases and many of these algorithms do not deliver the global optimum. Additional difficulties include diverse constraints, inherent errors, conflicting objectives, and multiple local optima. Consequently, significant variations in the final results of these problems could be observed across multiple iterations, even after using traditional meta-heuristics. Therefore, proper tuning of the control parameters of these algorithms is very important, since it is proportional to their numerical cost and accuracy. The primary objective of this investigation is to enhance both the stability and quality of outcomes while optimizing the Winner-Takes-All neuromorphic circuit using a recently proposed parameter-free approach called Self-adaptive multi-population Quadratic Approximation guided Jaya algorithm. Extensive experimentations with promising outcomes confirm its efficiency compared to other state-of-the-art counterparts. Finally, validation is performed using the circuit design tool Cadence Virtuoso, further illustrating a close agreement with the algorithmic results.