Miracle-dz/UniFAR-FTUs huggingface dataset:
Nueral Network and Deep Learning Analysis Using Backpropagation

: Any eccentric structure can be disengaged just, or perhaps dissected to its basic hypothetical parts. Multifaceted nature develops by the conglomeration of a couple of clear layer. The objective of this work be to clear up how neural frameworks job with the mainly direct reflection. We will endeavor to decrease the machine learning instrument within N-N near its fundamental hypothetical parts. Not in any way like diverse posts that elucidate neural frameworks, we will attempt to use the base possible proportion of numerical conditions and encoding code and centre just on top of the hypothetical thoughts. Neural frameworks and significant adjusting at present give the best responses to a few issues in picture affirmation, talk affirmation, and general tongue getting ready. This work shows a novel count in perspective of stochastic back spread standards through stochastic elements. The proposed estimation is versatile for inference and learning. KeywordSupervised neural network, deep learning, back
Improved testing procedures to assess the physical and mechanical properties of ceramic blocks

In Brazil, ceramic blocks have a significant share of the masonry construction market. Thus, an experimental testing program was devised to determine the physical and mechanical properties of ceramic blocks from different regions of Brazil. The experimental program included blocks from five Brazilian states and ten different producers, and the properties studied were absorption, material density, efflorescence, abrasion, compressive strength, and resistance towet-dry cycles. The purpose of determining these material properties is to set guidelines to standardize the performance of same-geometry blocks. The material density test method and the wet-and dry cycle test method are being proposed as new tests and are not usually specified in international codes. The objective of the wet and dry cycle test is to determine a durability parameter for blocks that are not subjected to freeze-thaw cycles. The analysis of the results identified important relationship between these properties.
Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.
FP Fused - Dot Product Add Unit based onBinary Signed Digit Representation

Fast Fourier transform (FFT) coprocessor, having a significant impact on the performance of communication systems, has been a hot topic of research for many years. The FFT function consists of consecutive multiply add operations over complex numbers, dubbed as butterfly units. Applying floating-point (FP) arithmetic to FFT architectures, specifically butterfly units, has become more popular recently. It offloads compute-intensive tasks from generalpurpose processors by dismissing FP concerns (e.g., scaling and overflow/underflow). However, the major downside of FP butterfly is its slowness in comparison with its fixed-point counterpart. This reveals the incentive to develop a high-speed FP butterfly architecture to mitigate FP slowness. This brief proposes a fast FP butterfly unit using a devised FP fused-dotproduct-add (FDPA) unit, to compute AB CD E, based on binarysigned-digit (BSD) representation. The FP three-operand BSD adder and the FP BSD constant multiplier are the constituents of the proposed FDPA unit. A carrylimited BSD adder is proposed and used in the three-operand adder and the parallel BSD multiplier so as to improve the speed of the FDPA unit. Moreover, modified Booth encoding is used to accelerate the BSD multiplier. The synthesis results show that the proposed FP butterfly architecture is much faster than previous counterparts but at the cost of more area.
Map Based Cloning of a Dominant Rust Resistance Gene and Mapping of its Duplicated Paralogues in Cultivated Groundnut (Arachis hypogaea L.)

Understanding the mechanism and nature of resistance genes in crop plants is essential for its use in new breeding techniques. Previously, a dominant rust resistance gene was fine-mapped within a 1.2 cM interval in chromosome A03 of groundnut. Here, the rust resistance gene, VG9514-Rgene was isolated through map based cloning. Sequencing of the gene from resistant and susceptible plants revealed non-synonymous mutations in the TIR, NBS and LRR region of R-protein. Genetic mapping of these SNPs-based markers confirmed the position of VG9514-Rgene in between FRS 72 and SSR_GO340445 markers in chromosome A03. Homology searching identified four homologous R-genes in groundnut genome. Of them, Arahy.R8KUIR, Arahy.T6DCA5 and Arahy.ZZ0VZ9 are paralogues. These paralogous genes had several small InDels. Mapping of these InDels-based markers revealed tandem duplication of these paralogous R-genes at distal portion of chromosome A03. Ka/Ks calculation revealed that this unique VG9514-Rgene had undergone positive selection. Homology based structure modelling of this R-protein revealed a typical consensus three dimensional folding of TIR-NBS-LRR protein. Non-synonymous mutations in susceptible version of R-protein were mapped in this protein model and found E268Q mutation in hhGRExE motif, Y309F in RNBS-A motif and I579T in MHD motif of NB-ARC domain are probable candidates for loss of function.
Fifty years in home computing, the digital computer and its private use(er)s

ABSTRACT The following chapter will discuss the relation between home computer history and computer programming – with a focus on game programming. The nurseries of the early 1980s are the origins of the later computer game industry and the private use of microcomputers becomes an essential part of the ‘playful’ exploration and emancipation of technology. This is why the contribution that home computing added to the history of digital economics, culture and even thought cannot be estimated high enough. After the definition of central terms and discussing the main problems of the historiography of technology and computing, the main topic separated in decades will be portrayed. The milestones of home computer technology will be linked to the scenes and (sub)cultures of their users. Some of these links will be highlighted. My presentation will not be exhaustive but will try to highlight some of the playful uses of computers – from the perspective of this technology.
A Near-Optimal Approach for Online Task Offloading and Resource Allocation in Edge-Cloud Orchestrated Computing

Due to the explosion of mobile devices and the evolution of wireless communication technologies, novel applications with intensive computation demands and low-latency requirements have arisen. Edge computing has been proposed as an extension of cloud computing, which moves computation workloads from remote cloud to network edge. Cooperating edge computing and cloud computing can significantly reduce the latency of computation tasks. However, considering the heterogeneity and stochastic arrivals of tasks and the limited computation and communication resources on the edge, task offloading and resource allocation are two joint crucial problems in an edge-cloud orchestrated computing system. In this paper, we propose an online task offloading and resource allocation approach for edge-cloud orchestrated computing, with the aim to minimize the average latency of tasks over time. We first build system models to analyze the latency and energy consumption incurred under different computing modes and formally formulate the joint problem as a mixed-integer optimal decision problem. Then, we employ Lyapunov optimization and duality theory to decompose the problem into a set of subproblems, which can be solved in a semi-decentralized way. We also formally analyze that our approach can achieve near-optimal performance. Extensive simulations are conducted to verify the superiority of our approach.
Simple and complex comparatives in Modern Standard Arabic

Modern Standard Arabic (MSA) has simple and complex comparatives, which look rather like their counterparts in many other languages. MSA simple comparatives are indeed like those of other languages, but MSA complex comparatives are quite different. They involve an adjective with a nominal complement, which may be an adjectival noun or an ordinary noun, and are rather like so-called ˋadjectival constructs'. Simple comparatives, complex comparatives, and adjectival constructs can all be analysed with lexical rules within HPSG.
Comparison of two classifiers based on neural networks and the DTW method of comparing time series to recognize maritime objects upon FLIR images

Infrared image recognition by means of FLIR cameras (forward-looking infrared) is one of the elements of the recognition of the maritime situation and it supports in many situations the creation of so-called maritime picture. This paper presents results of two FLIR image classifiers research. The first part presents the use of neural networks to classify images of maritime objects, while the second part presents the classifier using the time series comparison method called the DTW (data time warping). The neural network is a three-layered artificial neural network (feed forward). Both classifiers use the histograms of vertical projection of pre-processed FLIR images as input data. These histograms are created as a result of FLIR color images processing, including, among others, transformation of color images into grayscale images, grayscale images segmentation using the Otsu algorithm, rescaling, centering and leveling. In the final part of the paper preliminary test results of the both classification methods for a set of maritime objects FLIR images registered in the Baltic Sea are presented.
The Effectiveness of Artificial Credit Scoring Models in Predicting NPLs using Micro Accounting Data

In this paper we study the effectiveness of artificial credit scoring models in predicting SMEs default. We use a unique accounting dataset of small business loans granted by one of four systemic Greek Banks during expansion period. Comparing a neural network model (multilayer perceptron) and a decision tree model with the credit scoring model applied by the bank, we find that the bank’s model had the relative worse performance in predicting loans default. Moreover the effectiveness of all models decreased significantly during the recession, indicating that the loan performance is no longer depended only on the quality of the borrowers but also on the economic conditions of the country.
Coordinated Lot-Sizing Problem under Demand Uncertainty (Stochastic Programming approach)

Lot-sizing problems are production/procurement planning problems with different types of setups between production lots. These setups are often too costly so it is not reasonable to produce in every period. On the other hand, having fewer setups by producing/ordering large quantities to satisfy future demands will result in a high inventory holding costs. Thus, the objective is to determine the periods where production/order should take place, and the quantities to be produced/ordered to satisfy demands while minimizing setup and inventory holding costs. In this project we study an extension version, the Coordinated Lot-Sizing Problem (or Joint Replenishment Problem), where a family of different items shares a common production line, supplier, or a transportation mode etc. Hence a joint cost is incurred whenever at least one item of the family is replenished. We consider an uncapacitated variant of the problem, but the main and important issue we deal with in our study is uncertainty in items demand, so that planning decisions must be taken knowing only the demands in the first time period, while future demands have some degree of uncertainty. We firstly described the deterministic version of the problem, and evaluated the performance of one of the well-known heuristic solving procedures, Forward-Pass, in both static and rollinghorizon framework. Then by adding uncertainty, we implemented our problem as a mixed-integer multi-stage Stochastic Programming optimization model, taking into account demand uncertainty explicitly through a scenario tree. First we tried to solve the Deterministic Equivalent of the Stochastic Programming model by Gurobi, a state-of-the-art optimization solver, to compare the performance of different structure scenario trees and also to explore the maximum size tree (in both branching structure and number of items in the family) that is solvable in a reasonable amount of CPU time. We observed that even the best and most mature solvers will struggle even with not very large-size trees, so we may rely on alternative solving strategies such as scenario decomposition that enables large-scale problems to be efficiently solved. We applied the Progressive Hedging Algorithm (PHA) which is a general scenario-based decomposition method to cope with hard mixed-integer multi-stage stochastic programs, to our stochastic coordinated lot-sizing model. Our computational results show that the PH-based method we applied, is efficient and its solution quality is comparative to the solution obtained by Gurobi optimizer. Finally, we assessed the convenience and any competitive advantage of stochastic programming approach with respect to using the methods based on the approximated values or expected value of uncertain demand.
The Use of Hybrid Energy Storage Devices for Balancing the Electricity Load Profile of Enterprises

In this article, the authors consider the possibility of using a hybrid energy storage system to even out the load profile of the enterprise. Solving the problem of rational use of energy storage taking into account the initial variable load schedule will significantly reduce not only the cost of electricity consumption by the enterprise, but also the costs of its production. Detailed characteristics of batteries with various types of electrolytes and supercapacitors are given. A model of the active scheme of a hybrid electric energy storage system consisting of a lithium-ion battery and a supercapacitor unit with the corresponding characteristics is presented. The model was carried out by using the SimPowerSystems software in MatLab. During the simulation, the temperature and the aging effects and of the batteries were not taken into account. The selfdischarge parameter of the battery was also not presented. As a result of the simulation, discharge characteristics of supercapacitors and batteries were obtained based upon which the expediency of their combined use for leveling load profiles of various types was substantiated. The paper presents the results of the simulation of operating modes of a hybrid energy storage device, combining the advantages of two types of energy storage devices, as well as a diagram of delivered power to the network, corresponding to the specified parameters. The paper provides a mathematical description of the increasing power by hybrid storage system resulting from the combined use of supercapacitors and batteries. The paper presents the dependence of the power increase ratio on the frequency and the pulse current duty ratio, which proves that the maximum possible output power of the hybrid storage system can be several times greater than the power of a single battery having the same parameters.
Big data analytics in the health sector: challenges and potentials

Research Question: The introduction of the Big Data concept in the healthcare sector points to a major challenge and potential. Motivation: Our goal is to indicate the importance of analyzing and processing large amounts of data that go beyond the typical ways of storing and processing information. Тhе data have their own characteristics: volume, velocity and variety. There are different structures. Analysis of these data is possible with the Big Data concept. Its importance is most evident in the health sector, because the preservation of the health status of the population depends on adequate data analysis. Idea: The idea of the paper is that big health data analytics contributes to a better quality provision of health services. The process is more efficient and effective. Data: Health analytics suggests that more and more resources are being utilized globally. In order to achieve improvements, health analytics and Big data concepts play a vital role in overcoming the obstacles, working more efficiently and aiming at providing adequate medical care. Tools: The Big data concept will help identify patients with developed chronic diseases. Big data can identify outbreaks of flu or other epidemics in real time. In this way, they are managed by the healthcare system, reducing overall healthcare costs over time, and increasing revenues. Findings: A key policy challenge is to improve the outcomes of the healthcare system, data collection and analysis, security, storage and transfers. Big data are the potential to improve quality of care, improve predictions of diseases, improve the treatment methods, reduce costs. Contribution: This paper points to the challenges and potentials of Big Health Data analytics and formulates good reasons to apply the Big Data concept in healthcare.
Future cognitive network architecture with deterministic resource allocation techniques

Exponential growth of the wireless communications demands for efficient use of the limited available radio spectrum for network communications. Current fixed spectrum usage patterns are inefficient where some of the frequency bands are heavily congested while some other bands are rarely utilized. Cognitive radio has been proposed as a solution to the ever-increasing demand for the radio spectrum. Traditionally, channel sensing has been widely deployed by secondary users (SU) to determine the presence of primary users (PU). However, inherent limitations associated with channel sensing may make the secondary resource allocation unreliable and often energy-inefficient. Geolocation databases (GDB) have been proposed as an alternative to channel sensing, to overcome those limitations. This thesis proposes a GDB-based cognitive radio network (CRN) architecture to support different networks with heterogeneous radio access technologies. Novel algorithms have been proposed that exploit features of the GDB architecture to improve the QoS of the SU networks. Performance of the proposed algorithms is further improved by utilizing a Markov-based saturation throughput estimation mechanism and an auto regressive moving average-based traffic peak duration prediction mechanism. Simulation results show that the proposed algorithms yield better QoS levels compared to existing GDB-based resource allocation algorithms. QoS provision in CRNs is further discussed based on statistically guaranteed QoS provisioning. Effective capacity (EC) is utilized as the performance metric where the required QoS levels are expressed by means of the QoS exponent. Maximization of the total EC of a multi-user CRN, while meeting transmit power constraints and PU protection policies is considered. Two successive convex approximation based algorithms are proposed to solve the non-convex problem in an iterative manner. Using numerical analysis, it is shown that the two algorithms converge to the same
Smart additive manufacturing empowered by a closed-loop machine learning algorithm

Additive manufacturing (AM) is a crucial component of smart manufacturing systems that disrupts traditional supply chains. However, the parts built using the state-of-the-art powder-bed 3D printers have noticeable unpredictable mechanical properties. In this paper, we propose a closed-loop machine learning algorithm as a promising way of improving the underlying failure phenomena in 3D metal printing. We employ machine learning approach through a Deep Convolutional Neural Network to automatically detect the defects in printing the layers, thereby turning metal 3D printers into essentially their own inspectors. By comparing three deep learning models, we demonstrate that transfer learning approach based on Inception-v3 model in Tensorflow framework can be used to retrain our images data set consisting of only 200 image samples and achieves a classification accuracy rate of 100 % on the test set. This will generate a precise feedback signal for a smart 3D printer to recognize any issues with the build itself and make proper adjustments and corrections without operator intervention. The closed-loop ML algorithm can enhance the quality of the AM process, leading to manufacturing better parts with fewer quality hiccups, limiting waste of time and materials.
Preprocessing Techniques For Web Usage Mining

Web usage or log mining can be described as the discovery and analysis of access patterns of users through mining of log files. It is used to analysis Web log files and discovers user accessing patterns of Web pages; it can find user’s access patterns automatically and quickly from the vast Web log data, such as frequent access paths, frequent access page groups and user clustering. For analyzing the customer’s behavior, the data generated by the users visiting the website must be analyzed. The users’ accesses to Web sites are stored in server log files. This will provide foundation for decision making of organizations. But the data stored in these log files do not present an exact picture of the users’ accesses to the Web site. So preprocessing the web log data is a pre-requisite before this data can be used for mining tasks. It is a key technology in this mining activity. This preprocessed web data then will be suitable for web mining. Once the data preprocessing is done, the invalid data will be removed. Here we present various steps involved in preprocessing of web log files.