Reviewerly/exHarmony huggingface dataset:
Assessment of Image Information Capacity Based on the Analysis of Brightness Increments Distribution

The problem of assessing the information capacity of images is rather complicated and has many different aspects. This paper addressed the problem of quantifying the information capacity of images. The low accuracy of estimating the information capacity of the image based on the use of the classical approach to assessing the informativeness of signals using Shannon's information entropy is demonstrated. The purpose of this work is to enhance the accuracy of assessing the information capacity of images. To this end, a new approach to assessing the information capacity of the image based on the provisions of information theory and theory of contrast is proposed. The basis of the proposed approach is the analysis of the joint distribution of image brightness and the distribution of brightness increments. The proposed approach is based on measuring the average amount of information about the joint distribution of brightness, about the magnitude of brightness increments, as well as about the location of intervals of increment in the brightness scale. The proposed assessment and its components can be used to analyze various aspects of image quality and estimate the information capacity of the image. The proposed approach increases the accuracy and reliability of the assessment of statistical characteristics and information potential of the image.
Work-in-Progress: Accelerated Matrix Factorization by Approximate Computing for Recommendation System

Matrix factorization (MF) is widely used in collaborative filtering-based recommendation systems, but the computational complexity greatly increases for larger scaled recommendation systems. We propose to accelerate MF by performing approximate matrix multiplications, considering the joint sparsity of the decomposed matrices. We show our method realizes a more than 1.1 speedup with a minimal error, and the speedup can be higher for the recommendation systems with larger scales.
Work-in-Progress: A Browser-Driven Sensor Service for Embedded IoT

The classic approach to the Web of Things in embedded systems consists of the use of embedded web servers that transmit data from sensors to external entities (web clients). In this paper we present a browser-driven sensor service, in which an embedded web browser shares data with external entities: collects them, pre-processes them and sends them to their recipients. The paper shows the framework software of such a system and a simple, prototype implementation with the use of this framework.
Undergraduate perceptions of remote teaching in Computer-Aided Design courses

This research analyzes the undergraduate student's perceptions and challenges of the mandatory transition of six computer-aided design courses, from a blended modality to a completely remote one, in the context of the coronavirus pandemic. We analyzed a School of Architecture in Peru, which has implemented the blended learning in these six courses since 2015, to acquire technical skills in drawing, design, BIM, visualization, information graphic, video editing, computational design, and digital fabrication. We used an anonymous and self-administered survey between November and December 2021 to 1,045 students and received 435 responses for analysis. The results reveal the different ways in which the student interacts with the CAD software for each course. We concluded that the experience of going through the stages of face-to-face, blended, and online, prepares us to migrate into learning environments in the format of MOOCs. The study allowed us to identify challenges and strengths, such as usage preferences to consider for future transition, taking into account the characteristics of these types of courses as a basis for the creation of MOOCs for the inevitable demand for the technologies of the Fourth Industrial Revolution.
A hybrid approach based on dynamic trajectories to predict mortality in COVID-19 patients upon steroids administration

Since the World Health Organization (WHO) has declared Artificial Intelligence (AI) as a powerful tool in the fight against COVID-19, multiple studies have been launched aiming to shed light into risk factors for ICU admission and mortality. None of the existing studies, however, have captured the dynamic trajectories of hospitalized COVID-19 patients who receive steroids nor have explored trajectory-based mortality indicators. In this work, we present a novel, hybrid approach to address this need. Latent Growth Mixture Modelling (LGMM) was used to analyze the trajectories of patients who received steroids. The patients were then grouped into clusters based on the similarity of their dynamic trajectories. State-of-the art machine learning classifiers are trained on the original dataset with and without dynamic trajectories to assess whether their inclusion can enhance the prediction of mortality. Our results highlight the importance of trajectories for predicting mortality in patients who receive steroids yielding 4% and 5% increase in the sensitivity (0.84) and specificity (0.85). The FiO2 and percentage of neutrophils at day 5, along with the percentage of lymphocytes at day 7, were identified as the main causes for mortality in patients who receive steroids, where the SatO2 levels showed significant alterations in the dynamic trajectories.
Understanding the Generalization Power of Overfitted NTK Models: 3-layer vs. 2-layer (Extended Abstract)

Neural tangent kernel (NTK) models [1] have been recently used as an important intermediate step to understand the exceptional generalization power of overparameterized deep neural networks (DNNs). Compared to linear models with simple Gaussian or Fourier features, NTK models can capture the nonlinear features inherent in neural networks. Indeed, the work in [2] has shown that, for a 2-layer NTK model, the generalization error of an overfitted solution decreases as the number of neurons increases. Further, this descent behavior is qualitatively different from that of linear models with simple Gaussian and Fourier features, and closer to that of an actual neural network.
Classifying Incomplete Data with a Mixture of Subspace Experts

Performing inference on data with missing values is a highly common yet challenging task in machine learning, and is often addressed by imputing missing values before performing inference on the filled dataset. However, the two parts of this paradigm may be at odds with one another in that optimal imputation may not yield data that is optimal for inference. In this paper, we propose an ensemble approach to classification with missing values where base learners are trained on embeddings of the data, not on imputed versions of the data. To accomplish this, MoSEs first uses a novel subspace clustering algorithm which characterizes an input space as a hierarchy of localized affine subspaces, each of which produces a low-dimensional embedding of samples with missing data. Then, an expert for each subspace is trained on the associated embedding, with final predictions being a weighted sum of all expert predictions. The degree to which experts are localized to a subspace of data, as well as the degree to which final predictions are influenced by the nearest subspaces are controlled by a pair of tunable hyper-parameters. We perform experiments demonstrating the state-of-the-art performance of this method across a variety of reference datasets.
Multi-Message Private Information Retrieval: A Scalar Linear Solution

In recent years, the Multi-message Private In-formation Retrieval (MPIR) problem has received significant attention from the research community. In this problem, a user wants to privately retrieve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> messages out of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> messages whose identical copies are stored on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> remote servers, while maximizing the download rate. The MPIR schemes can find applications in many practical scenarios and can serve as an important building block for private computation and private machine learning applications. The existing solutions for MPIR require a large degree of subpacketization, which can result in large overheads, high complexity, and impose constraints on the system parameters. These factors can limit practical applications of the existing solutions. In this paper, we present a methodology for the design of scalar-linear MPIR schemes. Such schemes are easy to implement in practical systems as they do not require partitioning of messages into smaller size sub-messages and impose less stringent constraints on the minimum required size of the messages. Focusing on the case of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=D+1$</tex> , we show that when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> divides <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> , our scheme achieves the capacity, where the capacity is defined as the maximum achievable download rate. When the divisibility condition does not hold, the performance of our scheme is the same or within a small additive margin compared to the best known scheme that requires a high degree of subpacketization.
Covering Properties of Sum-Rank Metric Codes

The sum-rank metric can be seen as a generalization of both, the rank and the Hamming metric. It is well known that sum-rank metric codes outperform rank metric codes in terms of the required field size to construct maximum distance separable codes (i.e., the codes achieving the Singleton bound in the corresponding metric). In this work, we investigate the covering property of sum-rank metric codes to enrich the theory of sum-rank metric codes. We intend to answer the question: what is the minimum cardinality of a code given a sum-rank covering radius? We show the relations of this quantity between different metrics and provide several lower and upper bounds for sum-rank metric codes.
Degree Tables for Private Information Retrieval

We present a general technique for constructing schemes for a broad class of private information retrieval (PIR) problems. Our technique is inspired by polynomial code constructions utilized for secure distributed matrix multiplication and is applicable to a broad range of PIR problems. We showcase our technique by reducing the problem of scheme constructions for PIR from coded storage and colluding servers to a combinatorial problem of designing a degree table that needs to satisfy certain properties. We then present simple schemes for PIR from coded storage and colluding servers which, for certain cases, achieve the best known rates in the asymptotic regime. Our results show that the degree table is a powerful tool for constructing PIR codes for a variety of settings of practical interest.
Class Activation Maps for the disentanglement and occlusion of identity attributes in medical imagery

Deriving patients' identity from medical imagery threatens privacy, as these data are acquired to support diagnosis but not to reveal identity-related features. Still, for many medical imaging modalities, such identity breaches have been reported. To cope with this, some de-identification methods based on the generation of synthetic data have been explored in the literature. However, in this paper, we try to perform, instead, an occlusion of the personal identifiers directly on the data by means of Class Activation Maps, in such a way that diagnosis related features do not get altered.
Improve the trustworthiness of medical text interpretations

Currently, how to make a concrete and correct disease prediction is a popular research trend. Researchers made more efforts to develop various models to provide interpretations of medical area, however, there is still lack of human understandable explanations provided due to the non-transparency structure of some machine learning and deep learning models. According to this work, there is one combined model application we would like to adopt. After comparison experiments of classification and interpretation, it is found the combination model can address the issues from the latest interpretation models, and try to improve the trustworthiness of medical text interpretations.
Optimal Discrimination Between Two Pure States and Dolinar-Type Coherent-State Detection

We consider the problem of discrimination between two pure quantum states. We present a unified approach which finds the optimal measurement under any distortion measure that satisfies a convexity relation with respect to the Bhattacharyya distance. The well-known results for the error probability, log-loss and “erasure-distortion” criteria are obtained as special cases. We also treat a special setting of great practical interest, the discrimination between two coherent-light waveforms. Dolinar showed that a simple detector, with an optimal local signal, obtains the quantum-optimal error probability. By applying the convexity approach, we obtain in a unified manner the optimal local signal for the same criteria as in the general setting.
Interpretability with Relevance Aggregation in Neural Networks for Absenteeism Prediction

The lack of attendance of employees is called absenteeism and occurs for various reasons, such as vigorous physical activity, advanced age and high psychological demands of the work. The absenteeism affects the direct and indirect costs of the companies, and may reach 15% of the payroll. Therefore, it is fundamental to know its main causes and contribute to control and mitigation strategies. Neural networks have been successfully applied in the classification of several problems, but they are black boxes, because they do not explain which aspects are considered in their decisions. This aspect is very important in health applications, in which it is necessary to explain and clearly interpret the results. In this context, this work presents an approach to classify absenteeism through neural networks and Layer-wise relevance propagation (LRP) aggregation in order to identify the most relevant features and to assign relevance scores individually per class and among all classes. The proposed approach was assessed by considering a dataset widely used as a benchmark and compared to the existing literature methods. The proposed approach presented the highest assertiveness rates among the compared methods, reaching an average accuracy of 0.83, identifying the most relevant features for the classification of absenteeism through a relevance score. Therefore, the results allow the interpretability of the causes of each class of absenteeism, which contribute to the management of human resources, occupational medicine and the development of strategies for its mitigation.
Development and Independent Validation of Energy Expenditure Models Using SmartStep

In this work we developed and validated a method to capture the activities of daily living (ADL), transitions between ADL, and the associated Energy Expenditure (EE) using a novel insole based wearable system (SmartStep). A 15-participant study was conducted in a controlled laboratory environment while participants wore the SmartStep and performed various ADL. Machine learning models were developed using 4-branched and 8-branched steady-state activities to estimate the total energy expenditure (TEE) and physical activity energy expenditure (PAEE). Additional models accounting for transitions between activities were also developed. These models were validated in an independent study with 8-participants, performed in a whole room indirect calorimeter. In the controlled study, the 8-branched models had a lower root mean square error (RMSE, 0.58 vs. 0.67 kcal/min) and lower total error (−1.5% vs. 3%). In the validation study, the 8-branched models also had a lower RMSE (0.9 kcal/min vs. 1.2 kcal/min) and lower total error (−4.5% vs 11%). Accounting for activity transitions reduced the total error in the EE estimation to −1.3%. The results suggested that SmartStep can be used to accurately monitor the EE of the wearers in their daily living. The validation study results suggested that 8-branched models more accurately predict EE than 4-branched models and that accounting for activity transitions improves the estimation of EE in daily living.
Multi-label Neural Model for Prediction of Myocardial Infarction Complications with Resampling and Explainability

With myocardial infarctions accounting for the largest percent of cardiovascular-related deaths, the need for machine learning tools in prediction and prevention has never been clearer. Specifically, in the case of in-hospital complications following acute myocardial infarction (AMI), even with decreased in-hospital mortality rate due to improved hospital care, patients who survive the acute phase of MI remain at risk for MI-associated complications or recurrent AMI such as bundle branch blocks and angina. In this paper, we propose a multi-label framework to predict the occurrence of 5 complications following admission of 1,700 patients after suffering an AMI episode. We evaluate the models using several multi-label prediction metrics as a test of robustness of our method beating numerous other alternatives and comment on the balance of cost-effectiveness of a compact deep learning model versus shallow machine learning in the multi-label context. Our neural network outperformed 13 other algorithms across all metrics, except Hamming loss. We also implement Shapley value analysis to this multi-label problem and observe interesting behaviour such as the duration of arterial hypertension and time elapsed from the beginning of the attack to the hospital being key predictive features of lethal outcome. This framework presents a novel approach in using multi-label learning, and especially compact cost-effective deep learning, simultaneous for prediction of several AMI complications which has not been explored previously.
A Kernel Analysis of Feature Learning in Deep Neural Networks

Deep neural networks learn useful representations of data, yet the nature of these representations has not been fully understood. Here, we empirically study the kernels induced by the layer representations during training by analyzing their kernel alignment to the network's target function. We show that representations from earlier to deeper layers increasingly align with the target task for both training and test sets, implying better generalization. We analyze these representations across different architectures, optimization methods and batch sizes. Furthermore, we compare the Neural Tangent Kernel (NTK) of deep neural networks and its alignment with the target during training and find that NTK-target alignment also increases during training.
Performance vs. Privacy: Evaluating the Performance of Predicting Second Primary Cancer in Lung Cancer Survivors with Privacy-preserving Approaches

Deep learning has been widely used in the medical field to support medical decision making. Simultaneously, with the rise of data privacy protection, accessing clinical records across different institutions has become a possible challenge. Several approaches, such as federated and transfer learning, have been proposed to train models without accessing all the records from each institution, but the performance of these privacy-preserved models may not be as good as centralized approaches, which aggregate all records to build a centralized model. To explore the potential of privacy-preserving second primary cancer (SPC) prediction of lung cancer survivors using real-world data, we evaluated the performance of federated learning, transfer learning, learning with a single institution, and traditional centralized learning. We trained machine learning models using data from four hospitals and compared the model performances of learning from a single institution, centralized learning, federated learning, and transfer learning. The results show that federated learning outperformed other learning strategies in three of the four sites (AUROC from 0.733 to 0.777). However, only Site 6 showed that federated learning significantly outperformed all the other learning strategies (P < 0.05). In summary, federated learning can develop a unified model for the multiple institutions while maintaining data security.
CoBAAF: Controlled Bayesian Air Aggregation Federated Learning from Heterogeneous Data

Federated learning (FL) is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. A major challenge in FL is to reduce the bandwidth and energy consumption due to the repeated transmissions of large volumes of data by a large number of users over the wireless channel, and to handle statistical heterogeneity of users data. In this paper we present a novel algorithm, dubbed Controlled Bayesian Air Aggregation Federated-learning (CoBAAF), that handles statistical heterogeneity in noisy networks using a joint design of three main steps in FL: Model distribution, local training, and global aggregation. Specifically, CoBAAF controls the drift in local updates using a correction term, and allows users to transmit their data signal simultaneously over MAC. Second, it adopts a Bayesian approach to average properly the channel output, thus mitigating the effect of the noise and fading induced by the channel. We analyze the convergence of CoBAAF to the loss minimizing model theoretically, showing its ability to achieve a convergence rate similar to that achieved over error-free channels. Extensive simulation results demonstrate the improved convergence of CoBAAF for training in machine learning problems.
AI Methods for Personalized Suggestions on Smart Glasses Based on Human Activity Recognition

Smart wearables are becoming an irreplaceable part of daily living by supporting their users to maintain or adopt healthier lifestyles and monitor their current status. While the trend is increasing, little has been accomplished in the field of personalized solutions. In the present study, two models derived from distinct conceptual themes were developed, and the performance was evaluated utilizing a wearable prototype in the form of smart glasses. A statistical and a reinforcement learning approach were adopted to construct a personalization layer in terms of a predefined system reaction upon specific user behavior. The settings of the present study involve the user behavior derived from Artificial Intelligence (AI) based human activity recognition, among others, and the system reaction being a supportive Augmented Reality (AR) based functionality. Each approach yielding different benefits and drawbacks, imminently leads to a comparative analysis based on the efficiency offered by assessing the inference, update, and trend handling time. Both models are built upon the user's previous data, resulting in a data driven approach that is entirely different for each user and tailored to the user preferences. The results derived from the comparative analysis indicate that both approaches offer the personalization seeked, with the reinforcement learning approach to adapt faster.