Reviewerly/exHarmony huggingface dataset:
Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling

This thesis addresses the environmental uncertainty in satellite images as a computer vision task using semantic image segmentation. We focus in the reduction of the error caused by the use of a single-environment models in wireless communications. We propose to use computer vision and image analysis to segment a geographical terrain in order to employ a specific propagation model in each segment of the link. Our computer vision architecture achieved a segmentation accuracy of 89.41%, 86.47%, and 87.37% in the urban, suburban, and rural classes, respectively. Results indicate that estimating propagation loss with our multi-environment model reduced the root mean square deviation (RMSD) with respect to two publicly available tracing datasets.
Learning Smooth and Fair Representations

Organizations that own data face increasing legal liability for its discriminatory use against protected demographic groups, extending to contractual transactions involving third parties access and use of the data. This is problematic, since the original data owner cannot ex-ante anticipate all its future uses by downstream users. This paper explores the upstream ability to preemptively remove the correlations between features and sensitive attributes by mapping features to a fair representation space. Our main result shows that the fairness measured by the demographic parity of the representation distribution can be certified from a finite sample if and only if the chi-squared mutual information between features and representations is finite. Empirically, we find that smoothing the representation distribution provides generalization guarantees of fairness certificates, which improves upon existing fair representation learning approaches. Moreover, we do not observe that smoothing the representation distribution degrades the accuracy of downstream tasks compared to state-of-the-art methods in fair representation learning.
Fit to Measure: Reasoning about Sizes for Robust Object Recognition

Service robots can help with many of our daily tasks, especially in those cases where it is inconvenient orunsafe for us to intervene – e.g., under extreme weather conditions or when social distance needs to bemaintained. However, before we can successfully delegate complex tasks to robots, we need to enhancetheir ability to make sense of dynamic, real-world environments. In this context, the first prerequisite toimproving the Visual Intelligence of a robot is building robust and reliable object recognition systems.While object recognition solutions are traditionally based on Machine Learning, augmenting them withknowledge-based reasoners has been shown to improve their performance. In particular, based on ourprior work on identifying the epistemic requirements of Visual Intelligence, we hypothesise that knowl-edge of the typical size of objects can significantly improve the accuracy of an object recognition system.To verify this hypothesis, in this paper we present an approach to integrating knowledge about objectsizes in a ML-based architecture. Our experiments in a real-world robotic scenario show that this hybridapproach ensures a significant performance increase over state-of-the-art Machine Learning methods.
Active Faults: Geomatics and Soft Computing Techniques for Analysis, Monitorig and Risk Prevention in Central Tyrrhenian Calabria (Italy)

Geodynamic phenomena monitoring is constantly evolving; however, earthquake prediction is still impossible. The acquired big data over time availability allows us to create specific models to simulate these phenomena. Generally, earthquakes happen in clusters, and major aftershocks are preceded by other small aftershocks. Applying mathematical models to the swarm measurement data provides the seismic event probability of a given magnitude in a given region. Predictive systems of seismological phenomena and soft computing techniques can therefore help to obtain good choices for the citizens’ safety when a given danger threshold is exceeded. In this regard, the possibility to have significant and reliable displacement data of network points repeated over time deriving from GPS monitoring networks set up across the monitored faults, as well as the use and implementation of dynamic GIS that also use “predictive” layers based on the use of neural networks and soft computing, can provide on one hand databases useful for the implementation of predictive models (soft computing techniques that use displacements as input data) and on the other hand valid information on propagation of the isoseismal (starting from information relating to the study area, the hypocenter of the considered earthquakes and the seismic intensity determined according to standard procedures). The objective of the following work is therefore to present and analyze the results of a prototypal predictive system developed by the Reggio Calabria Geomatics Laboratory. This prototype use a GIS systems and soft computing techniques. It allows on one hand to calculate the probability of seismic event's occurrence (event of known intensity that follow another also known) and on the other to identify and predict the isoseismal's propagation. The Gis system incorporate and implement rigorous methodologies for displacements computing on GPS networks repeated over time, while the soft computing uses the surface's displacements points monitored by a GPS network and the events that took place in their surroundings. The methodology was tested in the central Tyrrhenian area of Calabria (where there are a series of active faults). focusing in particular on the Falerna -Fuscaldo fault (Italy).
Learning with Gradient Descent and Weakly Convex Losses

We study the learning performance of gradient descent when the empirical risk is weakly convex, namely, the smallest negative eigenvalue of the empirical risk's Hessian is bounded in magnitude. By showing that this eigenvalue can control the stability of gradient descent, generalisation error bounds are proven that hold under a wider range of step sizes compared to previous work. Out of sample guarantees are then achieved by decomposing the test error into generalisation, optimisation and approximation errors, each of which can be bounded and traded off with respect to algorithmic parameters, sample size and magnitude of this eigenvalue. In the case of a two layer neural network, we demonstrate that the empirical risk can satisfy a notion of local weak convexity, specifically, the Hessian's smallest eigenvalue during training can be controlled by the normalisation of the layers, i.e., network scaling. This allows test error guarantees to then be achieved when the population risk minimiser satisfies a complexity assumption. By trading off the network complexity and scaling, insights are gained into the implicit bias of neural network scaling, which are further supported by experimental findings.
Autonomous Reusing Policy Selection using Spreading Activation Model in Deep Reinforcement Learning

This paper describes a policy transfer method of a reinforcement learning agent based on the spreading activation model of cognitive psychology.This method has a prospect of increasing the possibility of policy reuse, adapting to multiple tasks, and assessing agent mechanism differences.In the existing methods, policies are evaluated and manually selected depending on the target-task.The proposed method generates a policy network that calculates the relevance between policies in order to select and transfer a specific policy that is presumed to be effective based on the current situation of the agent while learning.Using a policy network graph structure, the proposed method decides the most effective policy while repeating probabilistic selection, activation, and spread processing.In the experiment section, this study describes experiments conducted to evaluate usefulness, conditions of use, and the usable range of the proposed method.Tests using CartPole and MountainCar, which are classical reinforcement learning tasks, are described and transfer learning is compared between the proposed method and a Deep Q-Network without transfer.As the experimental results, usefulness was suggested in the transfer learning of the same task without manual compared with previous method with various conditions.
IMPLEMENTING THE THEORY OF MULTIPLE INTELLIGENCES INTO PROJECT-BASED MULTIMEDIA LEARNING AT PRIMARY SCHOOL

The article substantiates the relevance of the use of project-based multimedia learning at primary school. Feasibility of implementing H. Gardner’s theory of multiple intelligences for optimization and increase of efficiency of such work is presented. According to this theory, each person possesses a certain number of relatively autonomous intelligences: verbal-linguistic, logical-mathematical, visual-spatial, kinaesthetic, musical, interpersonal, intrapersonal, naturalistic. Each person has his or her own intellectual profile which is determined by heredity and life experience. The essential characteristics of project-based multimedia learning are determined, in particular, the importance of a teacher’s role during the organization of project-based multimedia learning at primary school is emphasized. Critical analysis of theoretical and practical research has proven the need to study the influence of the information and communication technologies on primary school children with different types of intelligence. The empirical study has identified the correlation between types of intelligence and ICT tools for teaching young students. The authors of the study conclude that the use of multimedia educational projects increases interest and motivates students to engage in learning activities taking into account their psychological features, helps participants in the educational process to rationally plan their activities and better perceive and remember information. The article substantiates the technology of project-based multimedia learning of primary school children in accordance with the theory of multiple intelligences, which determines the sequence of a teacher’s steps for effective organization of project-based multimedia learning based on H. Gardner’s theory of multiple intelligences. The steps or stages of the proposed technology include determining the dominant types of intelligences of each student in the class; organizing students into homogeneous or heterogeneous groups according to their types of intelligences, taking into consideration the goals and objectives of the multimedia project; dividing responsibilities in the group and supporting each student’s work with the appropriate software or Internet resource; conducting general control over the work of the project groups and assisting in the presentation of the multimedia project results.
Formation of motivation to learn the vocation-related language

The current situation on the global education market bears the need for new approaches to learning Russian as a foreign language. The innovation concerns both professors and students’ activity. The success in comprehension of scientific language is largely dependent on students’ motivation to study the vocation-related language. The article covers the methodological basis of the approach which may improve preparatory department students’ motivation to study.
A multi-grained aspect vector learning model for unsupervised aspect identification

Unsupervised aspect identification is a challenging task in aspect-based sentiment analysis. Traditional topic models are usually used for this task, but they are not appropriate for short texts such as product reviews. In this work, we propose an aspect identification model based on aspect vector reconstruction. A key of our model is that we make connections between sentence vectors and multi-grained aspect vectors using fuzzy k-means membership function. Furthermore, to make full use of different aspect representations in vector space, we reconstruct sentence vectors based on coarse-grained aspect vectors and fine-grained aspect vectors simultaneously. The resulting model can therefore learn better aspect representations. Experimental results on two datasets from different domains show that our proposed model can outperform a few baselines in terms of aspect identification and topic coherence of the extracted aspect terms.
Distance Learning for New Students in the Era of Pandemic Coronavirus Disease (Covid-19): Implementation and Barriers

The implementation of distance learning during a pandemic era faces various obstacles.This study explores information about how the implementation of distance learning to first semester of freshment students (new students) and what obstacles they face.This research is a descriptive qualitative research.The data sources consisted of new students, lecturers, and learning result documents.The data were collected by distributing questionnaires using Google Forms, interviews, and document studies.The study results show that the massive application of distance learning in the State Islamic University of Maulana Malik Ibrahim Malang began in March 2020.It coincides with the spread of the coronavirus in Malang City.The data reveals that, the implementation of distance learning can be grouped into five categories, namely effective, moderately effective, casual, less effective, and ineffective.The six most frequently complained problems were the low quality of the internet connections, the much spending of buying internet data, the fatigue due to staring at the devices' screen for too long, the difficulty in accessing references, the poor communications, and the lack of materials comprehension explained by lecturers.However, these six obstacles do not entirely affect the respondents' motivation to learn.As a consequence, improvements in the quality of infrastructure and services have to be carried out to maximize the quality of distance learning.
IA-GCN: Interpretable Attention Based Graph Convolutional Network for Disease Prediction

Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in general in computer vision; yet, in the medical domain, it requires further examination. Most of the interpretability approaches for GCNs, especially in the medical domain, focus on interpreting the output of the model in a post-hoc fashion. In this paper, we propose an interpretable attention module (IAM) that explains the relevance of the input features to the classification task on a GNN Model. The model uses these interpretations to improve its performance. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. The main novelty lies in the IAM, which directly operates on input features. IAM learns the attention for each feature based on the unique interpretability-specific losses. We show the application of our model on two publicly available datasets, Tadpole and the UK Biobank (UKBB). For Tadpole we choose the task of disease classification, and for UKBB, age, and sex prediction. The proposed model achieves an increase in an average accuracy of 3.2% for Tadpole and 1.6% for UKBB sex and 2% for the UKBB age prediction task compared to the state-of-the-art. Further, we show exhaustive validation and clinical interpretation of our results.
Histopathological Image Classification Using Deep Neural Networks with Fine-Tuning

The role of deep learning is predominant in classifying medical images. Histopathological image classification involves classifying tumor and cancer tissue subtypes, but it has many challenges like complexity of image, labeled data availability. Overcoming these challenges and accurately classifying the images require better approach than traditional one. In this work, we use fine-tuning-based deep neural network in classifying the histopathological image into eight different classes. The obtained results are compared with different other CNN and machine learning models. In addition, we trained a model using early stopping function which monitors the validation accuracy during training and prevents overfit, which makes the model work trained efficiently. This approach is tested on colorectal cancer histology image dataset which contains 5000 labeled images belonging to eight different classes. In our result, fine-tuning with early stopping performed better than other methods with accuracy of 91.2%.
A method for fault recognition in the last three rounds of Advanced Encryption Standard

A large number of studies are there for Advanced Encryption Standard (AES) fault attack analysis, but less for fault recognition. This paper presents a recognition method for single-byte fault which is induced in the last three rounds of AES. Studying the differential characteristics of Sbox, the single byte fault induced in ninth round or tenth round will be identified respectively with 9.3 and 9.1 ciphertexts. For the fault induced in eighth round, the fault value can be obtained with 188.5 ciphertexts by analyzing the differential features of two Sboxes and MixColumns. As an auxiliary means for fault attacks, this method is used to realize the byte or bit level physical positioning of confidential data in the encryption chip, which is beneficial to reduce the blindness of the attacker's experiments and obtain the sensitive area of fault attack.
Peer Review: Implementation of Fuzzy-based Model for Prediction of Thalassemia Diseases

Abstract: Thalassemia is known as one of the blood disorder diseases that is inherited by parents. There are several types of Thalassemia, namely as Thalassemia major, minor, and intermedia. Among them, Thalassemia major is the most dangerous and needs more attention. Generally, it can be detected since the child is one year old. Late detection of this disease can have adverse consequences and various complications. This study aims to develop a new model for the prediction of thalassemia for children. The model adopts a fuzzy-based rule. The novelty in this article is that our model has 4 outputs, namely thalassemia major, intermedia, minor and not thalassemia. In the previous article it only had 3 outputs. In this study, we intend to implement a model that we developed using a fuzzy-based approach to classify thalassemia diseases based on CBC data. This article describes how to build a model and implement it in software. We compare the test results with the opinion of pediatricians regarding thalassemia. The final results of testing 4 CBC data show that our proposed model has successfully identified the type of thalassemia.
Zero divisors of support size 3 in group algebras and trinomials divided by irreducible polynomials over GF(2)

A famous conjecture about group algebras of torsion-free groups states that there is no zero divisor in such group algebras. A recent approach to settle the conjecture is to show the non-existence of zero divisors with respect to the length of possible ones, where by the length we mean the size of the support of an element of the group algebra. The case length $2$ cannot be happen. The first unsettled case is the existence of zero divisors of length $3$. Here we study possible length $3$ zero divisors in rational group algebras and in the group algebras over the field with $p$ elements for some prime $p$.
Patient-Specific Modeling with Personalized Decision Paths.

Predictive models can be useful in predicting patient outcomes under uncertainty. Many algorithms employ methods, which optimize a single model to perform well on average over an entire population, but the model may perform poorly on some patients. Personalized methods optimize predictive performance for each patient by tailoring the model to the individual. We present a new personalized method based on decision trees: the Personalized Decision Path using a Bayesian score (PDP-Bay). Performance on eight synthetic, genomic, and clinical datasets was compared to that of decision trees and a previously described personalized decision path method in terms of area under the ROC curve (AUC) and expected calibration error (ECE). Model complexity was measured by average path length. The PDP-Bay model outperformed the decision tree in terms of both AUC and ECE. The results support the conclusion that personalization may achieve better predictive performance and produce simpler models than population approaches.
FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizer by Strong Convexity.

The AdaBelief algorithm demonstrates superior generalization ability to the Adam algorithm by viewing the exponential moving average of observed gradients. AdaBelief is proved to have a data-dependent $O(\sqrt{T})$ regret bound when objective functions are convex, where $T$ is a time horizon. However, it remains to be an open problem on how to exploit strong convexity to further improve the convergence rate of AdaBelief. To tackle this problem, we present a novel optimization algorithm under strong convexity, called FastAdaBelief. We prove that FastAdaBelief attains a data-dependant $O(\log T)$ regret bound, which is substantially lower than AdaBelief. In addition, the theoretical analysis is validated by extensive experiments performed on open datasets (i.e., CIFAR-10 and Penn Treebank) for image classification and language modeling.
A Real-Time Compression Method of Power System Waveform Data Based on 2-D Lifting Wavelet Transform and Deflate Algorithm

In this paper, based on the two-dimensional lifting wavelet transform and Deflate algorithm, this paper proposes a power system waveform data compression method. This method converts the waveform data of the power system to be compressed from I-D to 2-D, and then the 2-D lifting wavelet transform is used to more effectively reduce the redundancy in the periodic waveform data, and uses the Deflate algorithm to losslessly compress the waveform data processed by the 2-D lifting wavelet transform. After performing a compression test on the data collected by the waveform measuring device on site, a compression ratio close to 4 was achieved. It is verified that the method is suitable for real-time compression of the massive waveform data continuously recorded by the power system dynamic monitoring device.
Quantifying the privacy risks of learning high-dimensional graphical models

Models leak information about their training data. This enables attackers to infer sensitive information about their training sets, notably determine if a data sample was part of the model’s training set. The existing works empirically show the possibility of these membership inference (tracing) attacks against complex deep learning models. However, the attack results are dependent on the specific training data, can be obtained only after the tedious process of training the model and performing the attack, and are missing any measure of the confidence and unused potential power of the attack. In this paper, we theoretically analyze the maximum power of tracing attacks against high-dimensional graphical models, with the focus on Bayesian networks. We provide a tight upper bound on the power (true positive rate) of these attacks, with respect to their error (false positive rate), for a given model structure even before learning its parameters. As it should be, the bound is independent of the knowledge and algorithm of any specific attack. It can help in identifying which model structures leak more information, how adding new parameters to the model increases its privacy risk, and what can be gained by adding new data points to decrease the overall information leakage. It provides a measure of the potential leakage of a model given its structure, as a function of the model complexity and the size of the training set.