Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy analysis and energy planning for kindergartens based on data analysis

The aim of the study was to utilize different building data for prediction of development in energy use of a typical building type. In this study, energy use and its future development for kindergartens in Trondheim, Norway, were analyzed. The energy use data were retrieved from the energy monitoring platform of Trondheim Municipality. The total area of all the kindergartens was about 76 000 m2, where the area of each kindergarten was ranging from 100 – 4 471 m2. Firstly, typical heat and electricity duration curves per m2 of kindergartens in Trondheim within six years were identified. Secondly, the kindergartens were divided into two cohorts based on their connection to district heating (DH). The average total annual energy use was 177 kWh/m2 for kindergartens without DH, and 168 kWh/m2 for kindergartens connected to DH. The peak load values were similar for both cohorts, about 140 W/m2. Analysis of the duration curves showed a bigger electricity load variation for the kindergartens without DH connection. Within the building cohort with DH, three cases were found depending on the energy share from DH; i.e. DH high share, DH average share, and DH low share. By following different background data for CO2 factors of electricity and local DH, the kindergarten with DH high share had almost the lowest annual CO2 emission. Contrarily, the annual CO2 emission of a kindergarten with lower share of DH, or without DH, usually had a wider range of emissions due to its dependence of the electricity production mix. Finally, a prediction was made by assuming 14.2 % growth rate of kindergartens on the ground of the average six-year total kindergarten area. The result showed that if more than 50-67 % of the new building area would be connected to DH, a smaller increase of CO2 emission from the projected area could be achieved, depending on the relevant CO2 factors. This proved that buildings with DH were more robust than the one without DH concerning CO2 emission. The suggested analysis method and identified duration curves could be used to as a reference example for defining energy profiles of other building types. These profiles are necessary for diversifying and upgrading local energy supply pathways, infrastructure sizing, and improving urban energy planning.
Deep Video‐Based Performance Synthesis from Sparse Multi‐View Capture

We present a deep learning based technique that enables novel‐view videos of human performances to be synthesized from sparse multi‐view captures. While performance capturing from a sparse set of videos has received significant attention, there has been relatively less progress which is about non‐rigid objects (e.g., human bodies). The rich articulation modes of human body make it rather challenging to synthesize and interpolate the model well. To address this problem, we propose a novel deep learning based framework that directly predicts novel‐view videos of human performances without explicit 3D reconstruction. Our method is a composition of two steps: novel‐view prediction and detail enhancement. We first learn a novel deep generative query network for view prediction. We synthesize novel‐view performances from a sparse set of just five or less camera videos. Then, we use a new generative adversarial network to enhance fine‐scale details of the first step results. This opens up the possibility of high‐quality low‐cost video‐based performance synthesis, which is gaining popularity for VA and AR applications. We demonstrate a variety of promising results, where our method is able to synthesis more robust and accurate performances than existing state‐of‐the‐art approaches when only sparse views are available.
Machine Learning Algorithms for Soil Properties Prediction with Treated Vis–NIR Spectrums from the Itatiaia National Park

Visible and near-infrared reflectance (Vis–NIR) techniques are a plausible method to soil analyses. The main objective of the study was to investigate the capacity to predicting soil properties Al, Ca, K, Mg, Na, P, pH, total carbon (TC), H and N, by using different spectral (350–2500 nm) pre-treatments and machine learning algorithms such as Artificial Neural Network (ANN), Random Forest (RF), Partial Least-squares Regression (PLSR) and Cubist (CB). The 300 soil samples were sampled in the upper part of the Itatiaia National Park (INP), located in Southeastern region of Brazil. The 10 K-fold cross validation was used with the models. The best spectral pre-treatment was the Inverse of Reflectance by a Factor of 104 (IRF4) for TC with CB, giving an averaged R² among the folds of 0.85, RMSE of 1.96; and 0.67 with 0.041 respectively for H. Into the K-folds models of TC, the highest prediction had a R² of 0.95. These results are relevant for the INP management plan, and also to similar environments. The good correlation with Vis–NIR techniques can be used for remote sense monitoring, especially in areas with very restricted access such as INP.
A Survey On Auto-Image Colorization Using Deep Learning Techniques With User Proposition

An approach based on deep learning for automatic colorization of image with optional user-guided hints. The system maps a gray-scale image, along with, user hints” (selected colors) to an output colorization with a Convolution Neural Network (CNN). Previous approaches have relied heavily on user input which results in non-real-time desaturated outputs. The network takes user edits by fusing low-level information of source with high-level information, learned from large-scale data. Some networks are trained on a large data set to eliminate this dependency. The image colorization systems find their applications in astronomical photography, CCTV footage, electron microscopy, etc. The various approaches combine color data from large data sets and user inputs provide a model for accurate and efficient colorization of grey-scale images.
Algorithms for computing the sets of stochastic matrices with predefined properties, based on automaton models

The formed sets of ergodic stochastic matrices are focused on solving the problem of classifying probabilistic automaton models by certain criteria/parameters of similarities or differences in the structures of ergodic stochastic matrices using the methods of applied multivariate mathematical statistics. The algorithms developed allow forming a variety of the sets of stochastic matrices through changing the transition and random entry function in a probabilistic automaton. Transition function of the autonomous probabilistic automaton allows using the probabilistic automaton operation algorithm proposed to form the sets of ergodic stochastic matrices that differ in their potencies and structures, based on implementing state set permutations with repetitions and changing the random input variable probability distributions. Defining various autonomous deterministic automaton output functions, we can use the algorithm developed to form the sets of ergodic stochastic matrices that differ in their potencies, with a specified limiting vector, based on implementing the permutations of a set of output letters with repetitions. We are also presenting the evaluations of the potencies of the sets of ergodic stochastic matrices with rational elements, represented by autonomous probabilistic automata under the given constraints.
Tweaks and Tricks for Word Embedding Disruptions

Word embeddings are established as very effective models used in several NLP applications. If they differ in their architecture and training process, they often exhibit similar properties and remain vector space models with continuously-valued dimensions describing the observed data. The complexity resides in the developed strategies for learning the values within each dimensional space. In this paper, we introduce the concept of disruption which we define as a side effect of the training process of embedding models. Disruptions are viewed as a set of embedding values that are more likely to be noise than effective descriptive features. We show that dealing with disruption phenomenon is of a great benefit to bottom-up sentence embedding representation. By contrasting several in-domain and pre-trained embedding models, we propose two simple but very effective tweaking techniques that yield strong empirical improvements on textual similarity task.
Parallel Sentence Retrieval From Comparable Corpora for Biomedical Text Simplification

Parallel sentences provide semantically similar information which can vary on a given dimension, such as language or register. Parallel sentences with register variation (like expert and non-expert documents) can be exploited for the automatic text simplification. The aim of automatic text simplification is to better access and understand a given information. In the biomedical field, simplification may permit patients to understand medical and health texts. Yet, there is currently no such available resources. We propose to exploit comparable corpora which are distinguished by their registers (specialized and simplified versions) to detect and align parallel sentences. These corpora are in French and are related to the biomedical area. Manually created reference data show 0.76 inter-annotator agreement. Our purpose is to state whether a given pair of specialized and simplified sentences is parallel and can be aligned or not. We treat this task as binary classification (alignment/non-alignment). We perform experiments with a controlled ratio of imbalance and on the highly unbalanced real data. Our results show that the method we present here can be used to automatically generate a corpus of parallel sentences from our comparable corpus.
Multi-Classification Method for Determining Coastal Water Quality based on SVM with Grid Search and KNN

To address the problem of multi-classification of coastal water quality, this work envisioned the establishment of a multi-classification model of coastal water quality that uses an improved support vector machine. Inorganic nitrogen, active phosphate, chemical oxygen demand, pH, and dissolved oxygen were the input parameters of the model. The parameters of the support vector machine (SVM) model were optimized by cross-validation and the grid search optimization method, and the optimal parameters of the classifier were obtained. Subsequently, the KNN method was combined, and the optimized model was used to classify the water quality. The optimal parameters for the classifier were finally obtained. The experimental results showed that compared with SVM before optimization, the accuracy of the optimized model was improved by up to 10%, and the sample size was less.
Software Fault Tolerance: A Theoretical Overview

- The progression of software systems implementation and development is always associated with the emergence and evolution of new requirements and services, which in turn will increase the complexity of these systems. It is almost impossible to build error-free software for highly complex systems because it is challenging to detect design errors and to measure the accuracy of the functionality and output for these systems. In some systems such as critical systems, the safety of the software overcomes the reliability of that software,- so Software Fault Tolerance (SFT) techniques will be implemented with these systems to avoid catastrophes. The SFT techniques we overview in this paper are divided into dual groups: Traditional Methods and New Techniques. In turn, the traditional techniques are classified into Single-version and Multi-version software fault tolerance techniques, where Single version SFT include techniques such as: “system structuring and closure”, “atomic actions”, “inline fault detection” and “exception handling”. While Multi-version SFT will include methods such as: "Recovery blocks", "N-version programming", "N self-checking programming" and "consensus recovery blocks". On other hand the new techniques will include: "Adaptive n-version Systems", "Fuzzy Voting", "Abstraction", "Rejuvenation" and "Parallel Graph Reduction".
Poster: design of survivable SDN/NFV-enabled networks with bandwidth-optimal failure recovery

ISP networks are taking a leap forward thanks to emerging technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). Efficient algorithms considered too hard to be put in practice on legacy networks now have a second chance to be considered again. In this context, we rethink the ISP network dimensioning problem with protection against Shared Risk Link Group (SLRG) failures. We consider a path-based protection scheme with a global rerouting strategy in which, for each failure situation, we may have a new routing of all the demands. Our optimization task is to minimize the needed amount of bandwidth. We develop a scalable mathematical model that we handle using the Column Generation technique. We show the effectiveness of our methods and demonstrate the feasibility of our approach using Mininet.
Development of algorithms for processing and transmitting remote sensing data from unmanned aerial vehicles

Currently, various types of robotic systems are used to collect primary data. For example, UAVs can be used to collect various types of melon required for analyzing the effectiveness of agricultural operations and predicting the yield of certain types of crops. The problem of the transfer of information accumulated by the UAV, its analysis and making management decisions remains an urgent task. As a rule, wireless sensor networks are used to transfer data to a UAV, the main problem of which is the low bandwidth of the channel. To improve the efficiency of data transmission over low-speed communication channels and the subsequent processing of the information flows of the UAVs, it is necessary to develop traffic routing methods. As part of the study, an approach has been developed that allows taking into account not only the state of the communication channel at the current time but also to predict its characteristics ahead of time.
Empirical study of dynamics of amoebiasis transmission in mobile ad hoc networks (MANETs)

We present a study of the novel mathematical model, CISER, in this work. The proposed model is a more general variant of the SIR epidemic model for representing information spread. The work provides an analysis of the mathematical model, in particular, the inclusion of carrier and exposed states in providing a more generalized representation toward SIR. The model also provides an accurate description of the disease amoebiasis and relates the disease characteristics to message propagation. We derive various properties of importance and depict the model representation of various states of spread (message propagation) through numerical results. Based on this model, we then provide an epidemic controlled flooding mechanism for information spread in a mobile ad hoc network (MANET). This is accomplished through a reactive IPv4‐based routing protocol built on top of the MAC 802.11b layer. We further present our simulated results and perform a comparative analysis against other epidemic routing protocols such as SIR and SEIR. We discover our model performing better in terms of most performance metrics.
BER analysis of FSO communication link over UAE weather conditions for UAV applications

Tracking the moving objects in a video sequence is a critical problem in wide area airborne surveillance systems due to its cyber-physical nature. The large amount of imagery data generated by the unmanned aerial vehicle (UAV) requires high data rate link to handle real-time video streaming to the ground control station. Free space optics (FSO) technology is a promising data link for UAV due to its easy installation process, long range and duplex operation, and high bandwidth with more security. The main issue using FSO communication is the beam attenuation due to the various atmospheric impairment. In this work, the characteristics of FSO communication links with various data rate and different wavelength from UAV to the ground station has been investigated with Polarization Shift Keying (PolSK) and On-Off Keying (OOK) modulation techniques to improve the performance of data communication under various UAE weather conditions. The proposed system’s degradation model considered the rain, foggy and haze and height of UAV from the ground and a case study is experimented on the effects of FSO based UAV communication system in UAE weather conditions. The results show the performance of the mobile optical link in the presence of atmospheric effects is compared in UAE weather conditions and the graphs plotted against bit error rate (BER), attenuation and distance which highlights the benefits of FSO.
EnLSTM-WPEO: Short-Term Traffic Flow Prediction by Ensemble LSTM, NNCT Weight Integration, and Population Extremal Optimization

Accurate and stable short-term traffic flow prediction is an indispensable part in current intelligent transportation systems. In this paper, a novel short-term traffic flow forecasting model termed as EnLSTM-WPEO is proposed based on ensemble learning of long short term memory neural network (LSTM), no negative constraint theory (NNCT) weight integration and population extremal optimization (PEO) algorithm. In the first stage, a cluster of LSTMs is constructed to separately forecast with different time lag, which is a significant element to affect the prediction performance. In the second stage, the PEO-based NNCT weight integration strategy is introduced to determine the weight coefficients of the ensemble model. The simulation results for six different datasets from highways of Seattle have testified the superiority of the proposed EnLSTM-WPEO to other six popular traffic flow forecasting models in terms of two commonly used performance indices and three statistical tests.
Spectral sensitivity estimation of color digital camera based on color checker

Spectral sensitivity is an important parameter of color digital camera, which can be used for color correction and spectral reflectance reconstruction. In this paper, a spectral sensitivity estimated method for color digital camera based on standard color checker is studied. Under standard light source A, using the measured camera to take an image of a standard color checker, the spectral sensitivity is estimated from the camera response values of the color checker in the image, repeat the experiment under different illumination level of the same source and compare the estimated results. The experimental results show that compared with the traditional measurement methods, the estimated method in this paper can obtain higher precision spectral sensitivity and the illumination level of the ambient light source will affect the estimated results.
System Identification and Model-Based Control of Quadcopter UAVs

Szabo, Andrew P. M.S.Egr. Department of Electrical Engineering, Wright State University, 2019. System Identification and Model-Based Control of Quadcopter UAVs. As control systems become more sophisticated, more accurate system models are needed for control law design and simulation. In this research, a nonlinear dynamic model of a quadcopter UAV is presented and model parameters are estimated off-line using in-flight experimental data. In addition, a model-based classical control law for the quadcopter UAV is designed, simulated, and then deployed in UAV flight tests. The intent of this research is to identify a model which may be simple enough to easily use for control law design, and accurate enough for simulation. In addition, a model-based classical control law is designed to for flight control. The parameters of the nonlinear dynamic model are estimated with the Linear Least Squares Error method. In-flight disturbances are introduced in flight tests to ensure frequency rich data. The performances of different models are compared using validation flight test data to select an accurate model. This model is used as the simulation model and the design model. Model-based control law design techniques are used to create a flight control law which provides good performance both in the simulator, as well as when deployed to the quadcopter. To perform these tests, the Real-Time Marseille Grenoble Project software is used for the creation of ground station programs and flight control algorithms in Simulink. This test environment integrates a VICON camera systems, QuaRC Real Time system, a 3DR APM 2.6 micro-controller unit, and a Gumstix Overo AirSTORM micro-controller unit to create a low-cost quadcopter research platform.
What impact will immersive technologies such as augmented and virtual reality have on the retail sector

A new wave of technology is set to transform the retail sector. This includes immersive technologies such as augmented reality (AR) and virtual reality (VR) that could revolutionize how people shop. This paper aims to study the impact that these immersive technologies will have on the retail sector from the perspective of the consumer. To do this, it goes in depth into the background and evolution of the AR/VR field before looking at the response of the user when using the technology, the current and potential uses within a retail setting as well as what is required for the proper implementation of the technologies. The literature review then discusses the potential negatives of the technology and whether these they will be a long-term success or if they are simply a novelty. The methodology section then seeks to continue this discussion by examining how other researchers in the field conducted their research to help justify this paper’s methodological approach of a survey. The survey will be conducted to help explore the attitudes and perceptions of consumers towards these technologies in a retail setting. After the survey data is collected, it is analysed using SPSS to see its relationships and significance. The implications of these results will then be discussed before concluding with the limitations of the study and the possibility of future studies. The results found that consumers believe these technologies will indeed have an impact on retail and enhance the shopping experience.
Attention Mechanism in Machine Translation

Machine translation is an important field in machine learning. In recent years, the way to improve the effect of machine translation through attention mechanism has been well introduced and developed, and has become an important research part in machine translation. However, how to use the attention mechanism to ensure the translation speed and accuracy at the same time has also become an important problem for researchers to solve. In this article, we will introduce the development process of attention mechanism in the field of machine translation, and introduce in detail the algorithm changes of four papers on the development of Attention in Machine translation. Through the analysis of their experimental data, the development level of attention mechanism in the field of machine translation is shown.
Analysis on Cross-Correlation Coefficient for Survivability of Chirp Spread Spectrum Systems

Since chirp spread spectrum (CSS) system has robustness to reduce multipath fading effect and to extend spectrum bandwidth with low energy consumption, the CSS system has attracted much attention not only for radio detection and ranging systems but also for commercial wireless communication systems. In the spread-spectrum systems, low probability of detection (LPD) and anti-jamming (AJ) abilities are two key issues. In this paper, a multiple linear chirp (MLC)-based binary orthogonal keying (BOK)-CSS system considering cross-correlation coefficient for improving the LPD and the AJ performances is proposed. In order to analyze the performance of the MLC-based BOK-CSS system, the cross-correlation coefficient between two different chirps is analytically derived according to the time-bandwidth separation product, and it is numerically compared to a Monte Carlo simulation. From the results, the proposed MLC-based BOK-CSS system guarantees higher survivability than the conventional schemes by considering the cross-correlation coefficient according to the bandwidth separation.