Reviewerly/exHarmony huggingface dataset:
Classification of Rock Mineral in Field X based on Spectral Data (SWIR &amp; TIR) using Supervised Machine Learning Methods

Abstract The massive development of science and technology in the industrial era 4.0 includes artificial intelligence, which is purposed to produce research output in the field of geology more accurately and can be completed in a short time using large amounts of data. Machine learning is a part of artificial intelligence that can provide learning processes on computers independently without explicit programming. The process of identifying rocks through classification can be done using machine learning. The study area is in the Manjimup region, Western Australia which consists of Volcanogenic Massive Sulphide (VMS) deposits. This study purposed to determine the classification of rock minerals using accuracy values from the evaluation of models generated using supervised machine learning based on spectral data, namely Short-Wavelength Infrared (SWIR), and Mid or Thermal Infrared (TIR) acquired from electromagnetic spectrum measurements to identify rock mineral features. The spectral data comes from five rock drilling data in the study area. The supervised machine learning method used to determine the best accuracy consists of 5 types of methods, which are K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Multi-layer Perceptron (MLP). Machine learning is completed by supervised method because the research data contains information about label data, which is the type of rock mineral so that it can produce a classification based on the level of accuracy for each type of rock mineral data. The SVM method produces the best accuracy on SWIR data with 82.5% accuracy and the MLP method produces the best accuracy on TIR data with 82% accuracy for rock mineral classification.
Advantages of Using Factorisation Machines as a Statistical Modelling Technique

Factorisation machines originated from the field of machine learning literature and have gained popularity because of the high accuracy obtained in several prediction problems, in particular in the area of recommender systems. This article will provide a motivation for the use of factorisation machines, discuss the fundamentals of factorisation machines, and provide examples of some applications and the possible gains by using factorisation machines as part of the statistician’s model-building toolkit. Data sets and existing software packages will be used to illustrate how factorisation machines may be fitted and in what context it is worth being used.
A Reinforcement Learning Mission Supervisor with Memory for Human-multi-robot Coordination Systems

In this paper, a novel reinforcement learning mission supervisor (RLMS) with memory is proposed for human-multi-robot coordination systems (HMRCS). The existing HMRCS are known to suffer from long decision waiting time and large mission error caused by repeated human intervention, restricting the autonomy of multi-robot systems. The proposed supervisor elaborately integrates deep-Q-network (DQN) and long-short-term memory (LSTM) knowledge base within the null-space based behavioral control (NSBC) framework, so as to achieve optimal adjustment strategy of the behavioral priority in the presence of mission conflicts, and to reduce the frequency of human intervention. In particular, the proposed RLMS with memory first memorize human intervention history when robot systems are not confident in decision making when encountering emergencies, and then reload the history information when encountering the same situation that have been tackled by human previously. Simulation demonstrates the effectiveness of proposed RLMS with memory.
Nested Sequents for Intuitionistic Modal Logics via Structural Refinement

We employ a recently developed methodology -- called "structural refinement" -- to extract nested sequent systems for a sizable class of intuitionistic modal logics from their respective labelled sequent systems. This method can be seen as a means by which labelled sequent systems can be transformed into nested sequent systems through the introduction of propagation rules and the elimination of structural rules, followed by a notational translation. The nested systems we obtain incorporate propagation rules that are parameterized with formal grammars, and which encode certain frame conditions expressible as first-order Horn formulae that correspond to a subclass of the Scott-Lemmon axioms. We show that our nested systems are sound, cut-free complete, and admit hp-admissibility of typical structural rules.
Robustness of Supervised Learning Based on Combined Centroids

Recently, we proposed a novel sparse centroid-based supervised learning method, allowing to optimize a single centroid and its corresponding weights. The method is especially useful for localizing objects in images. Here, we extend the method to the task of joint localization of several objects in a 2D-image by means of combining several centroids. The novel approach, i.e. joint optimization of several centroids and a subsequent optimization of their weights, is illustrated on the task of localizing the mouth and both eyes in facial images. Because we are particularly interested in studying the robustness of the method to various modifications of the images, we evaluate the performance of the methods also over images artificially modified by additional noise, occlusion, changed illumination, or rotation. The novel centroid-based method is successful in the localization task, and the optimization turns out to ensure robustness with respect to the presence of noise or occlusion in the images. Moreover, combining the optimized centroids yields more robust results than a method using simple centroids with a highly robust correlation coefficient (with a high breakdown point).
DCT-based Fast Spectral Convolution for Deep Convolutional Neural Networks

Spectral representations have been introduced into deep convolutional neural networks (CNNs) mainly for accelerating convolutions and mitigating information loss. However, repeated domain transformations and complex arithmetic of commonly-used Fourier transform (DFT, FFT) seriously limit the applicability of spectral networks. In contrast, discrete cosine transform (DCT)-based methods are more promising owing to computations with merely real numbers. Hence in this work, we investigate the convolution theorem of DCT and propose a faster spectral convolution method for CNNs. First, we transform the input feature map and convolutional kernel into the frequency domain via DCT. We then perform element-wise multiplication between the spectral feature map and kernel, which is mathematically equivalent to symmetric convolution in the spatial domain but much cheaper than the straightforward spatial convolution. Since DCT only involves real arithmetic, the computational complexity of our method is significantly smaller than the traditional FFT-based spectral convolution. Besides, we introduce a network optimization strategy to suppress repeated domain transformations leveraging the intrinsically extended kernels. Furthermore, we present a partial symmetry breaking strategy with spectral dropout to mitigate the performance degradation caused by kernel symmetry. Experimental results demonstrate that compared with traditional spatial and spectral methods, our proposed DCT-based spectral convolution effectively accelerates the networks while achieving comparable accuracy.
Collective Knowledge Management in City Planning: Building Spatial-Cognition Ontologies from Literary Works

Due to the multiple dimensions of urban complexity, ontology-based multiagent models become widespread. These models seem to support complex relational and cognitive interactions in urban decision-making processes. In this context, simulations and experiments are today investigating ontologies applied to spatial planning, as a support in the real world.
An Overview of Video Super-Resolution Algorithms

Abstract We investigate some excellent algorithms in the field of video space super-resolution based on artificial intelligence, structurally analyze the network structure of the algorithm and the commonly used loss functions. We also analyze the characteristics of algorithms in the new field of video space-time super-resolution. This work helps researchers to deeply understand the video super-resolution technology based on artificial intelligence.
Characterizing Learning Dynamics of Deep Neural Networks via Complex Networks

In this paper, we interpret Deep Neural Networks with Complex Network Theory. Complex Network Theory (CNT) represents Deep Neural Networks (DNNs) as directed weighted graphs to study them as dynamical systems. We efficiently adapt CNT measures to examine the evolution of the learning process of DNNs with different initializations and architectures: we introduce metrics for nodes/neurons and layers, namely Nodes Strength and Layers Fluctuation. Our framework distills trends in the learning dynamics and separates low from high accurate networks. We characterize populations of neural networks (ensemble analysis) and single instances (individual analysis). We tackle standard problems of image recognition, for which we show that specific learning dynamics are indistinguishable when analysed through the solely Link-Weights analysis. Further, Nodes Strength and Layers Fluctuations make unprecedented behaviours emerge: accurate networks, when compared to under-trained models, show substantially divergent distributions with the greater extremity of deviations. On top of this study, we provide an efficient implementation of the CNT metrics for both Convolutional and Fully Connected Networks, to fasten the research in this direction.
Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning

This study proposes a framework for automatic high-frequency transactions based on a deep reinforcement learning model - proximal policy optimization. The framework creatively regards the transaction process as actions and returns as awards to align with the idea of reinforcement learning. It compares price prediction policies including long short-term memory (LSTM) and multi-layer perception (MLP) by applying them to the real-time bitcoin price. Then an automatically generating transaction strategy is constructed building on PPO with LSTM as the static policy. The approach is able to trade bitcoins in a simulating environment with synchronous data and achieves []% higher benefit than that of the average market. It is demonstrated that the approach can earn excess returns and be expanded to other financial products.
A Human Activity Recognition Approach Based on Skeleton Extraction and Image Reconstruction

Human Activity Recognition (HAR) has wide applications in surveillance, assisted living and robotic systems that interact with people. Traditional methods for HAR usually input source images directly. Various factors of these direct source images can affect the accuracy of recognition, such as heights, weights, poses, angles and whether being obscured or not. Most of the latest research on HAR has mainly focused on the use of 2D skeleton data, extracted from videos captured by standard cameras. Inspired by this trend, we propose a novel way of using skeleton data in this paper. As a pre-processing phase, we adopt the OpenPose library to extract 2D positions of human skeleton joints, then reconstruct, or synthesize, the skeleton image from these joints, together with coloring to encode the categories of different human parts. The reconstructed images are put into a Convolutional Neural Network (CNN) structure for classification, to obtain the recognition result. We implement the typical four activity classes, i.e., squat, stand, walk and work, in our experiments. Compared with feeding the original complex source images, our approach reduces the complexity of the neural network thus the time consumption, and improves the recognition accuracy significantly. All the images that we used for training and testing are collected from the real public places. We achieved a recognition accuracy of 97.3% in our experiments, indicating that our method has very good performance. Thanks to the lightning speed of the OpenPose library, our method can be carried out in real-time. The demonstration of using synthesized input to bridge existing technologies also has its general significance.
Detecting Three‐Dimensional Associations in Large Data Set

The associations detection among variables in the large dataset is recently important due to the rapid growth rate of data. The interested associations can provide references for solving the problems such as dimension reduction and feature selection. Many methods have done on the associations detection of pairwise variables. The multi-dimensional variables, especially three-dimensional variables, is rarely studied. The relationships among them cannot be revealed by the detection of pairwise variables methods. A new method of Maximal three-dimensional information coefficient (MTDIC) is proposed which is able to indicate the associations of three-dimensional variables. The correlation coefficient is calculated from the three-dimensional mutual information. The World Health Organization (WHO) data and the Tara data are selected to evaluate their associations. The experiment is verified by comparing the coefficient results with the Distance correlation (Dcor). The accurate association strength is obtained by an iterative optimization procedure on sorting descending order of coefficients. The MTDIC performs better than the Dcor in generality and equitability properties.
A Comparison of Machine Learning Techniques for Diagnosing Multiple Myeloma

Multiple myeloma is a type of bone marrow cancer. Patient’s blood samples are analysed from protein gel strips and densitometer graphs which are then interpreted by a pathologist to diagnose multiple myeloma. This manual process of diagnosis is slow which is problematic as patients need to be diagnosed as soon as possible in order to prevent the condition from worsening, hence the need to automate this process. Given the success of machine learning in diagnosing diseases from images, this study investigates the use of machine learning approaches for the diagnosis of multiple myeloma. This is the first study investigating the automation of this process and hence presents a novel application of machine learning. The study compares machine learning approaches, artificial neural networks, convolutional neural networks, random forests and support vector machines, for the diagnosis of multiple myeloma from gel strips and densitometer graphs. The study has revealed that convolutional neural networks, specifically VGG16, is the most suitable approach for the detection of multiple myeloma.
GSCAM: Global Spatial Coordinate Attention Module for Fine-Grained Image Recognition

Fine-grained image recognition is more difficult than conventional image classification tasks. Previous advanced network models require a large number of complex structure design and preliminary training. In this paper, a novel attention mechanism: Global Spatial Coordinate Attention Module (GSCAM) is proposed. This structure inherits the advantages of widely used SE channel attention and CBAM spatial attention, uses two 1D features for spatial position coding, and parallels global attention convergence blocks to extract context features, which can obtain the spatial location information and global context feature information of the region of interest of the image, so as to accurately locate the distinguishable region in the image. We apply this module to the mainstream classification network (ResNet50, ResNeXt50) pre-trained by ImageNet. Under the condition that the original network increases a very small number of parameters and computational cost, the accuracy of fine-grained image recognition is close to the SOTA level in different benchmark datasets, and there is no need to design the network structure from scratch, which is simple and fast.
Privacy Amplification via Iteration for Shuffled and Online PNSGD

In this paper, we consider the framework of privacy amplification via iteration, which is originally proposed by Feldman et al. and subsequently simplified by Asoodeh et al. in their analysis via the contraction coefficient. This line of work focuses on the study of the privacy guarantees obtained by the projected noisy stochastic gradient descent (PNSGD) algorithm with hidden intermediate updates. A limitation in the existing literature is that only the early stopped PNSGD has been studied, while no result has been proved on the more widely-used PNSGD applied on a shuffled dataset. Moreover, no scheme has been yet proposed regarding how to decrease the injected noise when new data are received in an online fashion. In this work, we first prove a privacy guarantee for shuffled PNSGD, which is investigated asymptotically when the noise is fixed for each sample size n but reduced at a predetermined rate when n increases, in order to achieve the convergence of privacy loss. We then analyze the online setting and provide a faster decaying scheme for the magnitude of the injected noise that also guarantees the convergence of privacy loss.
Transformer with Local-feature Extractor for Relation Extraction

Many pre-trained models recently achieved successful results in many NLP tasks. However, such models with heavy structure often led to problems such as a great amount of training time or too much computational cost. This paper presents a more light-weighted model based on Transformer and convolutional neural networks. The proposed model is able to capture important local information of different length by multi-head convolutional layer and effectively extracts long term dependency by position-enriched self-attention layer. These two structures together formed our Transformer with Local-feature Extractor (TLE) encoder. Comparing with previous CNN, RNN, and attention-based baseline models, our model achieves the best performance on multiple relation extraction datasets.
Experience Paper: sgx-dl

Trusted execution as offered by Intel's Software Guard Extensions (SGX) is considered as an enabler to protect the integrity and confidentiality of stateful workloads such as key-value stores and databases in untrusted environments. These systems are typically long running and require extension mechanisms built on top of dynamic loading as well as hot-patching to avoid downtimes and apply security updates faster. However, such essential mechanisms are currently neglected or even missing in combination with trusted execution.
A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning

Machine learning (ML) techniques are the backbone of Prediction and Recommendation systems, widely used across banking, medicine, and finance domains. ML technique’s effectiveness depends mainly on the amount, distribution, and variety of training data that requires varied participants to contribute data. However, it’s challenging to combine data from multiple sources due to privacy and security concerns, competitive advantages, and data sovereignty. Therefore, ML techniques must preserve privacy when they aggregate, train, and eventually serve inferences. This survey establishes the meaning of privacy in ML, classifies current privacy threats, and describes state-of-the-art mitigation techniques named Privacy-Preserving Machine Learning (PPML) techniques. The paper compares existing PPML techniques based on relevant parameters, thereby presenting gaps in the existing literature and proposing probable future research drifts.
Diagnosis of Digital Teaching Competence: Universidad Viña del Mar

This article aims to diagnose the level of digital teaching competence of the professors of Universidad Viña de Mar, who develop teaching in various faculties of the institution. The data obtained were collected through a heading to assess the digital competence of university teachers adapted to the Latin American context, an instrument composed of four dimensions, and four indicators on levels of development. Overall results show that teachers with less work experience according to their age show greater mastery of expert and transformative levels. On the other hand, those teachers with more experience in teaching self-evaluate at the middle level and even beginner with respect to the dimensions. Considering that the instrument allows to measure the self-perception of digital competence, it can be concluded that university teachers recognize their lacks in relational, ethical and security dimensions and in personal and professional dimensions, in which the scores are below average. It is also necessary to develop training programs to strengthen digital teaching competences, currently indispensable for university teaching.
A Pseudo-satellite Implementation Method Using High Precision Time Synchronization

In this paper, a GNSS-based multi-frequency pseudo-satellite implementation method is proposed. The analysis results show that the GNSS multi-frequency pseudo-satellite system (including Beidou and GPS) can be applied to the general receiver. The pseudo-satellite system can improve the positioning accuracy of the general receiver and improve the usability of the navigation system. Simulation analysis and test are carried out for the method. The experimental results show that the pseudo-satellite method is achievable and can significantly improve the availability and accuracy of the GNSS system.
Access to the banking sector and employment in Africa

African countries are desperate to tackle high level of unemployment, particularly among young people. In this paper, we examine the impact of access to the banking sector on employment in Africa using micro-level data from the sixth round of Afrobarometer surveys. The sample covers more than 53,000 people from 36 African countries. After controlling for several covariates and addressing the endogeneity issue, we find that having access to the banking sector increases the probability of being employed by nearly 15 percent. Our results are robust to alternative specifications. The findings of this paper support the view that African countries should implement policies aiming at improving the access to the banking sector to boost jobs creation and address the issue of unemployment in the continent.
An improved face recognition algorithm based on extended local binary pattern

This paper mainly studies the problem of face recognition, and an extended local binary pattern method is proposed to achieve face recognition. In this paper, we propose a further improvement on the original LBP operator, which is named extended LBP. We add 8 pixels to the original 8 pixels to improve the recognition abilities. The added 8 pixel can be divided into two groups, because the sampling radius is different. After extended local binary pattern, we use the SVM classifier to achieve face recognition. Based on the proposed algorithm, the performance of face recognition is good.
On Generalization of Graph Autoencoders with Adversarial Training

Adversarial training is an approach for increasing model’s resilience against adversarial perturbations. Such approaches have been demonstrated to result in models with feature representations that generalize better. However, limited works have been done on adversarial training of models on graph data. In this paper, we raise such a question – does adversarial training improve the generalization of graph representations. We formulate \(L_{2}\) and \(L_{\infty }\) versions of adversarial training in two powerful node embedding methods: graph autoencoder (GAE) and variational graph autoencoder (VGAE). We conduct extensive experiments on three main applications, i.e. link prediction, node clustering, graph anomaly detection of GAE and VGAE, and demonstrate that both \(L_{2}\) and \(L_{\infty }\) adversarial training boost the generalization of GAE and VGAE.