Miracle-dz/UniFAR-FTUs huggingface dataset:
Microservices based Linked Data Quality Model for Buildings Energy Management Services

During the production, distribution, and consumption of energy, a large quantity of data is generated. For efficiently using of energy resources other supplementary data such as building information, weather, and environmental data etc. are also collected and used. All these energy data and relevant data is published as linked data in order to enhance the reusability of data and maximization of energy management services capability. However, the quality of this linked data is questionable because of wear and tears of sensors, unreliable communication channels, and highly diversification of data sources. The provision of high-quality energy management services requires high quality linked data, which reduces billing cost and improve the quality of the living environment. Assessment and improvement methodologies for the quality of data along with linked data needs to process very diverse data from highly diverse data sources. Microservices based data-driven architecture has great significance to processes highly diverse linked data with modularity, scalability, and reliability. This paper proposed microservices based architecture along with domain data and metadata ontologies to enhance and assess energy-related linked data quality.
A General Scoring Rule for Randomized Kernel Approximation with Application to Canonical Correlation Analysis

Random features has been widely used for kernel approximation in large-scale machine learning. A number of recent studies have explored data-dependent sampling of features, modifying the stochastic oracle from which random features are sampled. While proposed techniques in this realm improve the approximation, their application is limited to a specific learning task. In this paper, we propose a general scoring rule for sampling random features, which can be employed for various applications with some adjustments. We first observe that our method can recover a number of data-dependent sampling methods (e.g., leverage scores and energy-based sampling). Then, we restrict our attention to a ubiquitous problem in statistics and machine learning, namely Canonical Correlation Analysis (CCA). We provide a principled guide for finding the distribution maximizing the canonical correlations, resulting in a novel data-dependent method for sampling features. Numerical experiments verify that our algorithm consistently outperforms other sampling techniques in the CCA task.
Component Mismatches Are a Critical Bottleneck to Fielding AI-Enabled Systems in the Public Sector

The use of machine learning or artificial intelligence (ML/AI) holds substantial potential toward improving many functions and needs of the public sector. In practice however, integrating ML/AI components into public sector applications is severely limited not only by the fragility of these components and their algorithms, but also because of mismatches between components of ML-enabled systems. For example, if an ML model is trained on data that is different from data in the operational environment, field performance of the ML component will be dramatically reduced. Separate from software engineering considerations, the expertise needed to field an ML/AI component within a system frequently comes from outside software engineering. As a result, assumptions and even descriptive language used by practitioners from these different disciplines can exacerbate other challenges to integrating ML/AI components into larger systems. We are investigating classes of mismatches in ML/AI systems integration, to identify the implicit assumptions made by practitioners in different fields (data scientists, software engineers, operations staff) and find ways to communicate the appropriate information explicitly. We will discuss a few categories of mismatch, and provide examples from each class. To enable ML/AI components to be fielded in a meaningful way, we will need to understand the mismatches that exist and develop practices to mitigate the impacts of these mismatches.
Tidal flows with convection: frequency-dependence of the effective viscosity and evidence for anti-dissipation

Tidal interactions are important in driving spin and orbital evolution in planetary and stellar binary systems, but the fluid dynamical mechanisms responsible remain incompletely understood. One key mechanism is the interaction between tidal flows and convection. Turbulent convection is thought to act as an effective viscosity in damping large-scale tidal flows, but there is a long-standing controversy over the efficiency of this mechanism when the tidal frequency exceeds the turnover frequency of the dominant convective eddies. This high frequency regime is relevant for many applications, such as for tides in stars hosting hot Jupiters. We explore the interaction between tidal flows and convection using hydrodynamical simulations within a local Cartesian model of a small patch of a convection zone of a star or planet. We adopt the Boussinesq approximation and simulate Rayleigh-Bénard convection, modelling the tidal flow as a background oscillatory shear flow. We demonstrate that the effective viscosity of both laminar and turbulent convection is approximately frequency-independent for low frequencies. When the forcing frequency exceeds the dominant convective frequency, the effective viscosity scales inversely with the square of the tidal frequency. We also show that negative effective viscosities are possible, particularly for high frequency tidal forcing, suggesting the surprising possibility of tidal anti-dissipation. These results are supported by a complementary high-frequency asymptotic analysis that extends prior work by Ogilvie & Lesur. We discuss the implications of these results for interpreting the orbital decay of hot Jupiters, and for several other astrophysical problems.
Reliable and Low-Complexity MIMO Detector Selection using Neural Network

In this paper, we propose to dynamically select a MIMO detector using neural network for each resource element (RE) in the transport block of 5G NRLTE communication system. The objective is to minimize the computational complexity of MIMO detection while keeping the transport block error rate (BLER) close to the BLER when dimension-reduced maximum-likelihood (DR-ML) detection is used. A detector selection problem is formulated to achieve this objective. However, since the problem is high dimensional and NP-hard, we first decompose the problem into smaller problems and train a multi-layer perceptron (MLP) network to obtain the solution. The MLP network is trained to select a low-complexity, yet reliable, detector using instantaneous channel condition in the RE. We first propose a method to generate a labeled dataset to select a low-complexity detector. Then, the MLP is trained twice using quasi-Newton method to select a reliable detector for each RE. The performance of online detector selection is evaluated in 5G NR link level simulator in terms of BLER and the complexity is quantified in terms of the number of Euclidean distance (ED) computations and the number of real additions and multiplication. Results show that the computational complexity in the MIMO detector can be reduced by ~10× using the proposed method.
Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT

Various deep learning algorithms have been developed to analyze different types of clinical data including clinical text classification and extracting information from 'free text' and so on. However, automate the keyword extraction from the clinical notes is still challenging. The challenges include dealing with noisy clinical notes which contain various abbreviations, possible typos, and unstructured sentences. The objective of this research is to investigate the attention-based deep learning models to classify the de-identified clinical progress notes extracted from a real-world EHR system. The attention-based deep learning models can be used to interpret the models and understand the critical words that drive the correct or incorrect classification of the clinical progress notes. The attention-based models in this research are capable of presenting the human interpretable text classification models. The results show that the fine-tuned BERT with the attention layer can achieve a high classification accuracy of 97.6%, which is higher than the baseline fine-tuned BERT classification model. In this research, we also demonstrate that the attention-based models can identify relevant keywords that are strongly related to the clinical progress note categories.
Model Predictive Tracking Control for Invariant Systems on Matrix Lie Groups via Stable Embedding Into Euclidean Spaces

For controller design for systems on manifolds embedded in Euclidean space, it is convenient to utilize a theory that requires a single global coordinate system on the ambient Euclidean space rather than multiple local charts on the manifold or coordinate-free tools from differential geometry. In this article, we apply such a theory to design model predictive tracking controllers for systems whose dynamics evolve on manifolds and illustrate its efficacy with the fully actuated rigid body attitude control system.
Educational data mining for students’ academic performance analysis in selected Ethiopian universities

Universities are working in a very dynamic and powerfully viable environment today. Due to the advent of information technology, they gather large volumes of data related to their students in electronic form in various formats like records, files, documents, images, sound, videos, scientific data and many new data formats. This study focuses on predicting performance of student at an early stage of the degree program, in order to help the university not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. The aim of this paper is to use data mining methodologies to design and develop a Data Mining model to predict academic performance of students at the end of first year degree program in selected Ethiopian higher educational institutions (universities). The data of different undergraduate students has been mined with decision tree classifiers. A model is built using C4.5 Decision tree learning algorithm – generates five classification rule set classifiers (predictors) in an experiment. The experiment using a test data set produces 81.4% accuracy.Keywords: Educational Data, Educational Data mining, Decision tree, Classification rule, C4.5
Facial Expression Recognition Using Human to Animated-Character Expression Translation

Facial expression recognition is a challenging task due to two major problems: the presence of inter-subject variations in facial expression recognition dataset and impure expressions posed by human subjects. In this paper we present a novel Human-to-Animation conditional Generative Adversarial Network (HA-GAN) to overcome these two problems by using many (human faces) to one (animated face) mapping. Specifically, for any given input human expression image, our HA-GAN transfers the expression information from the input image to a fixed animated identity. Stylized animated characters from the Facial Expression Research Group-Database (FERGDB) are used for the generation of fixed identity. By learning this many-to-one identity mapping function using our proposed HA-GAN, the effect of inter-subject variations can be reduced in Facial Expression Recognition(FER). We also argue that the expressions in the generated animated images are pure expressions and since FER is performed on these generated images, the performance of facial expression recognition is improved. Our initial experimental results on the state-of-the-art datasets show that facial expression recognition carried out on the generated animated images using our HA-GAN framework outperforms the baseline deep neural network and produces comparable or even better results than the state-of-the-art methods for facial expression recognition.
Variation-aware Binarized Memristive Networks

The quantization of weights to binary states in Deep Neural Networks (DNNs) can replace resource-hungry multiply accumulate operations with simple accumulations. Such Binarized Neural Networks (BNNs) exhibit greatly reduced resource and power requirements. In addition, memristors have been shown as promising synaptic weight elements in DNNs. In this paper, we propose and simulate novel Binarized Memristive Convolutional Neural Network (BMCNN) architectures employing hybrid weight and parameter representations. We train the proposed architectures offline and then map the trained parameters to our binarized memristive devices for inference. To take into account the variations in memristive devices, and to study their effect on the performance, we introduce variations in $R_{\text{ON}}$ and $R_{\text{OFF}}$. Moreover, we introduce means to mitigate the adverse effect of memristive variations in our proposed networks. Finally, we benchmark our BMCNNs and variation-aware BMCNNs using the MNIST dataset.
Situation-aware Re-configuration of Production Processes

Manufacturing of products, nowadays, due to the advances in Information Technologies (IT) that turn products into smart devices, and the evolution in device connectivity, becomes increasingly complex. Connected product networks (CPN) and cyber-physical systems (CPS) are bringing new challenges to manufacturing companies. These challenges, among others things, lead to increased demands for optimisation and personalisation of production processes. This move, which as a result drives the market of machines and products, poses new challenges to the manufacturing domain that needs flexible solutions to replace the rigid traditional methodologies and tools. The current work suggests a cloud-based platform for optimisation of machines, products and processes, allowing for situational awareness on the users' site, involving technologies for event prediction and optimisation.
The Novikov conjecture and extensions of coarsely embeddable groups

Let $1 \to N \to G \to G/N \to 1$ be a short exact sequence of countable discrete groups and let $B$ be any $G$-$C^*$-algebra. In this paper, we show that the strong Novikov conjecture with coefficients in $B$ holds for such a group $G$ when the normal subgroup $N$ and the quotient group $G/N$ are coarsely embeddable into Hilbert spaces. As a result, the group $G$ satisfies the Novikov conjecture under the same hypothesis on $N$ and $G/N$.
Image Generation and Recognition (Emotions)

Generative Adversarial Networks (GANs) were proposed in 2014 by Goodfellow et al., and have since been extended into multiple computer vision applications. This report provides a thorough survey of recent GAN research, outlining the various architectures and applications, as well as methods for training GANs and dealing with latent space. This is followed by a discussion of potential areas for future GAN research, including: evaluating GANs, better understanding GANs, and techniques for training GANs. The second part of this report outlines the compilation of a dataset of images `in the wild' representing each of the 7 basic human emotions, and analyses experiments done when training a StarGAN on this dataset combined with the FER2013 dataset.
Networks of monetary flow at native resolution

People and companies move money with every financial transaction they make. We aim to understand how such activity gives rise to large-scale patterns of monetary flow. In this work, we trace the movement of e-money through the accounts of a mobile money system using the provider's own transaction records. The resulting transaction sequences---balance-respecting trajectories---are data objects that represent observed monetary flows. Common sequential motifs correspond to known use-cases of mobile money: digital payments, digital transfers, and money storage. We find that each activity creates a distinct network structure within the system, and we uncover coordinated gaming of the mobile money provider's commission schedule. Moreover, we find that e-money passes through the system in anywhere from minutes to months. This pronounced heterogeneity, even within the same use-case, can inform the modeling of turnover in money supply. Our methodology relates economic activity at the transaction level to large-scale patterns of monetary flow, broadening the scope of empirical study about the network and temporal structure of the economy.
Using [C ii] 158 μm Emission from Isolated ISM Phases as a Star Formation Rate Indicator

The brightest observed emission line in many star-forming galaxies is the 158 μm line, making it detectable up to z ∼ 7. In order to better understand and quantify the emission as a tracer of star formation, the theoretical ratio between the 205 μm emission and the 158 μm emission has been employed to empirically determine the fraction of emission that originates from the ionized and neutral phases of the interstellar medium (ISM). Sub-kiloparsec measurements of the 158 μm and 205 μm lines in nearby galaxies have recently become available as part of the Key Insights in Nearby Galaxies: a Far Infrared Survey with Herschel (KINGFISH) and Beyond the Peak programs. With the information from these two far-infrared lines along with the multi-wavelength suite of KINGFISH data, a calibration of the emission line as a star formation rate (SFR) indicator and a better understanding of the deficit are pursued. emission is also compared to polycyclic aromatic hydrocarbon (PAH) emission in these regions to compare photoelectric heating from PAH molecules to cooling by in the neutral and ionized phases of the ISM. We find that the emission originating in the neutral phase of the ISM does not exhibit a deficit with respect to the infrared luminosity and is therefore preferred over the emission originating in the ionized phase of the ISM as an SFR indicator for the normal star-forming galaxies included in this sample.
Comparative Study of Inspired Algorithms for Trajectory-Following Control in Mobile Robot

This paper is devoted to the design of a trajectory-following control for a differentiation nonholonomic wheeled mobile robot. It suggests a kinematic nonlinear controller steer a National Instrument mobile robot. The suggested trajectory-following control structure includes two parts; the first part is a nonlinear feedback acceleration control equation based on backstepping control that controls the mobile robot to follow the predetermined suitable path; the second part is an optimization algorithm, that is performed depending on the Crossoved Firefly algorithm (CFA) to tune the parameters of the controller to obtain the optimum trajectory. The simulation is achieved based on MATLAB R2017b and the results present that the kinematic nonlinear controller with CFA is more effective and robust than the original firefly learning algorithm; this is shown by the minimized trackingfollowing error to equal or less than (0.8 cm) and getting smoothness of the linear velocity less than (0.1 m/sec), and all trajectoryfollowing results with predetermined suitable are taken into account. Stability analysis of the suggested controller is proven using the Lyapunov method.
A New Design of Predictive Functional Control Strategy for Batch Processes in the Two-Dimensional Framework

To cope with the control of batch processes under uncertainty, a novel two-dimensional (2-D) predictive functional iterative learning control (ILC) scheme is developed. By introducing a new model formulation and error compensation approach, the proposed strategy in which predictive functional control and ILC are combined through the 2-D framework compensates for the influence caused by various uncertainty gradually between cycles in the batch processes. Meanwhile, the improved state-space model is also employed effectively to enhance the control performance. Through the independent weighting on the state variables and the set-point tracking error in the performance index, additional degrees of freedom can be offered for the controller design. The effectiveness of the proposed 2-D method is tested on the injection velocity regulation in an injection molding process.
Spikey: A Search for Lensing Flares from SMBH Binaries

We examine the light curves of two quasars, motivated by recent suggestions that a supermassive black hole binary (SMBHB) can exhibit sharp lensing spikes. We model the variability of each light curve as due to a combination of two relativistic effects: the orbital relativistic Doppler boost and gravitational binary self-lensing. In order to model each system we extend previous Doppler plus self-lensing models to include eccentricity. The first quasar is identified in optical data as a binary candidate with a 20-yr period (Ark 120), and shows a prominent spike. For this source, we rule out the lensing hypothesis, because if the observed sinusoid-like variability is attributed to Doppler shifts caused by the orbital motion, it would require a total BH mass in excess of $10^{12}$ solar masses. The second source, which we nickname Spikey, is the rare case of an active galactic nucleus (AGN) identified in Kepler's high-quality, high-cadence photometric data. For this source, we find a model, consisting of a combination of Doppler modulation and a narrow symmetric lensing spike, consistent with an eccentric SMBH binary with mass $M_{\text{tot}} = 5.4\times10^{7}$ solar masses, rest-frame orbital period $T=422$ days, eccentricity $e=0.6$, and seen at an inclination $9^{\circ}$ from edge-on. This interpretation can be tested by monitoring Spikey for periodic behavior and recurring flares in the next few years. In preparation for such monitoring we present the first X-ray observations of this object taken by the Neil Gehrels Swift observatory.
Hyperbolic flowers

ABSTRACT Crochet models of a hyperbolic plane are very popular as they help to visualize complicated objects in hyperbolic geometry. We present another way to make crochet models as a part of a triangulated plane and also provide an example of a cylinder in hyperbolic space. This approach assists in understanding of various properties of hyperbolic geometry such as the relation between edges and angles in a hyperbolic triangle, tiling of a hyperbolic plane or Nash–Kuiper embedding theorem. GRAPHICAL ABSTRACT