Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Classification of Power Load Data Based on LIBSVM

In order to effectively classify the increasingly massive load data, the method of applying support vector machine to load classification is studied. By comparing and selecting the C-SVC classification and the RBF kernel function, the optimization method of the grid search is used to find the optimal parameter combination of the LIBSVM classifier and establish the model. The visualization accuracy and index function are used to observe the classification accuracy of each class. Operations such as weighting the unbalanced samples are conducted to adjust the accuracy gap between large and small samples. Experimental results show that the method is feasible in load classification.
The deployment of Machine Learning in eBanking: A Survey.

Thanks to the machine learning algorithms revolution, several organizations will be able to transform their services, automate functions and predict their customer’s behaviors. The fact that digital has begun a huge change in the world of finance such as the traditional bank with its physical breaches and advisers; the digital bank is one of the financial organizations integrates machine learning into its services. In order to properly guide future research and development, it will be extremely beneficial to carry out an integral and up-to-date study, focusing on banking fields integrating (or able to integrate) the machine learning techniques. This paper will allow managers and developers to carefully evaluate and embrace machine learning techniques to solve e-baking issues.
A new interpretation technique of traffic signs, based on Deep Learning and Semantic Web

In this paper, we present a new robust interpretation and recognition technique of traffic signs, this work is part of a smart cities project, a solution for road traffic. Our technique is based on two technologies: Semantic Web technology to interpret a message sent by a traffic sign and Deep Learning technology to recognize its image. General strategy is composed of three main steps. First of these consists to implement traffic signs ontologies by one of Semantic Web languages. Second step consists to use SPARQL language queries to query these ontologies in order to recuperate necessary synonyms to interpret a message sent by a traffic sign. For final step consists to use Deep Learning technology to recognize different objects in traffic sign image. Multi-agents Systems (MAS) will be used as simulation environments with two agent categories: agents that represent traffic signs and agent that represent vehicles.
INA: Incremental Network Approximation Algorithm for Limited Precision Deep Neural Networks

Approximate computing is a promising paradigm to deal with large computing workloads in fault-tolerant applications, providing opportunities to improve hardware efficiency of Deep Neural Networks (DNNs). However, it is still difficult to apply highly approximate arithmetics (e.g., multipliers) to DNNs due to the effect of error accumulation and the convergence problem in re-training phase. To tackle this limitation, we propose a hardware-software co-design algorithm, namely Incremental Network Approximation (INA). By addressing the convergence problem, INA promotes fault tolerance of DNNs, and yields more tradeoffs between accuracy and implementation cost. Experiments show that the approximate inference models re-trained by INA could achieve up to 80% hardware reduction in various hardware design level, while the classification accuracy degradation is less than 2%. Moreover, the experiments also exhibit the generality of INA algorithm for applying to various approximate multiplier design.
Computer Generated Algorithm of Sand Painting Based on Convolutional Neural Network

The neural style transfer of sand painting is to transform the style with the artistic image of sand painting into another image, and to create a new art image with appreciation while maintaining the semantic content of the image. This paper introduces a sand painting image simulation algorithm based on convolutional neural network. The algorithm utilizes the output of the VGG16 network as an advanced feature of the image to achieve the content of the target image by separating and recombining the content features and style features of the input image. The fusion of sand painting form and high quality computer art works proves the correctness and effectiveness of the algorithm.
A Comprehensive Rearranging Priority Based Method To Accelerate the Reconstruction of RAID Arrays

With the development of cloud computing, the reliability of disk arrays are increasingly concerned. Data centers usually use erasure codes to provide high reliability. However, most of reconstruction methods on disk arrays focus on single/ multiple disk(s) recovery, which ignores how to efficiently reconstruct the lost data such as Latent Sector Errors (LSEs), etc. In real situations, local stripe errors are much more common than disk failures. It has become an urgent problem that how to improve reconstruction efficiently for stripes. This paper proposes a comprehensive rearranging priority reconstruction(CRPR), which combines temporal locality, spatial locality and coding characteristics together. CRPR divides different blocks into various priorities and recovers them sequentially. To demonstrate the effectiveness of CRPR, we conduct several simulations via disksim. The simulations results show that, the comprehensive rearranging priority reconstruction method keeps up with previous methods and can save up to 63.9% in terms of waiting time.
Classification of Sleep Stages for Healthy Subjects and Patients with Minor Sleep Disorders

Sleep stage classification is one of the most critical steps in the effective diagnosis and treatment of sleeprelated disorders. Classic approaches involve trained human sleep scorers, utilizing a manual scoring technique, according to certain standards. This paper examines the implementation of an algorithm for the automation of the sleep scoring process. EEG recordings data are acquired from three different groups comprising of healthy subjects and people with minor sleep disorders. A mixture of time domain and frequency domain features are extracted. Temporal feature changes are utilized in order to capture contextual information of the EEG signal. Multiple classifiers are tested, culminating in a voting classifier, achieving a maximum accuracy of 90.8% for the healthy subjects' group. The main novelty introduced by the proposed solution is the algorithm's high accuracy when tested on a mixed dataset of healthy and patient subjects. The promising capabilities that derive from the successful implementation of this solution are discussed in the conclusions.
An Enhanced Network Learning Method for Dynamic Probabilistic LCF Evaluation of Turbine Blisk

To reasonably predict the blisk LCF life, an enhanced network learning method (ENLM) was proposed by integrating neural network of regression with extremum thought. The developed ENLM was mathematically modeled and the method of reliability analysis with the ENLM were investigated. Then the LCF life reliability of a turbine blisk were evaluated utilizing the proposed ENLM by considering thermal-structural interaction and the stochastic characteristics of parameters. It is illustrated in the analysis to the reliability degree of blisk (0.998,5) and blisk LCF life (9,419 cycles) under the allowable value 6,000 cycles, which ensures some life margin for the blisk relative to the deterministic analysis. The comparison of methods reveals that the proposed ENLM has highly-computing efficiency and precision. The advantages of this method will more outstand with the increasing simulations. The outcome of this paper is to develop a promising tool for the reliability-based design optimization of gas turbine blisk LCF life in future work.
Forecasting the Electric Network Frequency Signals on Power Grid

The power grid, which is one of the important infrastructures, has a very challenging issue to stably manage electric power. The Electrical Network Frequency (ENF) which is the supply frequency on the power grid, however, has small variations near a constant frequency over time. In this paper, we studied the feasibility of predicting ENF values to operate the power grid reliably. To forecast ENF values, we analyzed the ENF signals by using auto-correlation and correlation coefficient. Based on the analysis results, we employed two approaches to forecast ENF values using a kernel regression model with correlation coefficient and autoregressive moving average model. To evaluate the accuracy of the proposed prediction algorithm, we experimented ENF data for 29 days in three power grids of the United States; the Eastern, the Western, and the Texas power grid. The results of our suggested methods presented the remarkable performance in forecasting ENF signals.
MLP and CNN-based Classification of Points of Interest in Side-channel Attacks

A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation. © 2020 The Authors . Published by Atlantis Press SARL. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Maximum Power Point Tracking with PV fed Switched Inductor Topology DC-DC Converter

The considerable implementation and increase in installation of photovoltaic systems is being implemented in the world during the last 10 years. This provides easy availability of electrical power in suitable to environment aspects. The operation at point where maximum of power point facilitates the user to utilize the maximum of electrical energy generated from photovoltaic system during stochastically changing solar insolation and ambient temperature conditions. This paper proposes photovoltaic module fed Switched inductor dc–dc converter with higher boost gain to track maximum power point by using perturbation and observation algorithm. Switched inductor converter topology gives higher step-up voltage gain with nominal duty ratio. Conventional boost converters usually in-capable to result in higher voltage gains due to effect of rectifier diodes, switches, and ESR values of energy storing elements. Only one power stage is used. This results in increment in overall efficiency of the PV energy production system. The operational characteristics of PV based MPPT and steady-state analyses of converter are discussed in details.
Financial Web Calculators

Different types of financial web calculators' support web users in financial decision-making and similar finance products comparison. This paper illustrates the general characteristics of financial web calculators from IT perspective, and discusses possible calculation, interface, decision-making solutions, flaws and opportunities. The financial web calculators have a huge potential in the financial advisory industry as standalone solutions or implemented in robo-advisors, even though there is still a lot of potential yet to be exploited.
Physical Workout Classification Using Wrist Accelerometer Data by Deep Convolutional Neural Networks

Recently, the deep learning algorithm has received considerable attention and is influencing different fields including human-computer interaction (HCI). The purpose of this study is to maximize accuracy by applying deep learning to the classification of body movements. An experiment was performed to collect acceleration information on the wrist while performing seven workouts: pull up, row-barbell, bench press, dips, squat, deadlift, and military press. Participants were asked to perform each workout for ten sets repeated ten times per set. Experimental results confirm that one-dimensional convolutional neural network was the best among different algorithms including support vector machine, multi-layer perceptron, long short-term memory, and other deep convolutional neural networks. The accuracy was extremely high, 96%. The results of this experiment are applicable not only to the classification of fitness activities but also to the classification of different motions in numerous sporting events.
Atherosclerotic Plaque Growth Prediction in Coronary Arteries using a Computational Multi-level Model: The Effect of Diabetes

Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for its treatment. This study is aiming to investigate the role of diabetes in the atherosclerotic plaque growth mechanisms through the utilization of a multi-level numerical model. To accomplish this, we developed a proof-of-concept mathematical model of the diabetes effect to plaque growth, that has been coupled to a stateof-the-art multi-level numerical model of plaque growth. Diabetes main effect is the increase of the average blood glucose concentration, which causes the decrease of the endothelial nitric oxide production rate by affecting several biologic pathways. Nitric oxide is a signaling molecule that regulates the endothelial flow rates, and any abnormal alteration leads to endothelial dysfunction, the major culprit of atherosclerosis. The derived model considers the modeling of blood flow in lumen and of species transport and reactions in the arterial wall. The considered factors include: (i) LDL, (ii) HDL, (iii) oxidized LDL, (iv) monocytes, (v) macrophages, (vi) cytokines, (vii) smooth muscle cells (contractile & synthetic), and (viii) collagen. The model is validated using 10 patients' reconstructed arterial data in two time-points. More specifically, baseline geometries are used as an input to our model, while follow-up geometries are used as benchmark for our model's output. The results presented a high coefficient of determination between the simulated with diabetes effect and the real follow-up geometries of 0.634.
A Comparative Secrecy Study of Flying and Ground Eavesdropping in UAV Based Communication Systems

In this paper, we study the strictly positive secrecy outage probability (SOP) and the ergodic secrecy capacity of a wiretap unmanned aerial vehicle (UAV) system where a UAV transmitter (Alice) is trying to send a message to a ground receiver (Bob) while an eavesdropper (Eve) is attempting to eavesdrop on the legitimate communication link. We study the performance of the system in two cases, first, when Eve is on the ground and an Air-to-Ground channel model is adopted between Alice, Bob and Eve. In the second case, Eve is a UAV drone that is located at the same altitude as Alice and the channel between Alice and Eve follows an Air-to-Air channel model. We derive closed form expressions of the SOP and ergodic secrecy capacity for the two scenarios. Simulation results verify the analytical derivations, where the results highlight the importance of the derived secrecy metrics, especially the impact of the distance between Alice and Eve on the overall performance.
A Biped Walking Control Model Based on Reinforced Learning

Traditional character animation often relies on kinematic-based methods (key frame, motion capture, etc.), resulting in less flexibility and authenticity. To solve this problem, we propose a walking control method based on the reinforcement learning algorithm. Compared with the traditional control method, besides using motion capture data as a reference, we adopt a multi-reward function training to generate more realistic motion. At the same time, the step update problem of the learning process is avoided, and the training is more stable. For verification, we employ the bullet physics engine to build a human motion model called RLM, which combines motion data with reinforcement learning to generate fast and stable motion. The experiment shows that the learning efficiency and the final reward are evidently improved; the RLM model achieves a stable walking in the physical simulation, being practically consistent with the real captured trajectory.
Information and Communication Technologies Acceptance Models in Universities

TAM (Technology Acceptance Model) is used in Computational Science research to collect user feedback for Information Systems (IS). Carrying out this research it was found that the TAM is one of the most significant methodological individualist approaches related to adoption of Information and Communication Technologies (ICT). Different modifications have been incorporated into this model. A systematic review method was used in order to identify studies related to intention to use a technology in universities. 61 primary studies were selected from the different scientific databases. The results show that TAM has been applied in studies related to adoption of e-learning, web 2.0 and ICT. Although TAM has proven to be a coherent model, it still has limitations that can be perceived by other intellectuals as opportunities for future research.
Visualising Learning Pathways in MOOCs

It is difficult to get an understanding of learner behaviour within MOOCs. Commonly used metrics are not well suited for this. This paper describes a visualisation tool based on modified Sankey diagrams to explore learner paths through edX MOOCs. The tool visualises learners’ progression through the learning material and their interaction with discussion forums. These diagrams can be filtered based on user demographies and behaviours. Automated clustering of users based on behaviour metrics is available to provide suggestions of different learner behaviours. Statistical tests are also included to allow hypothesis exploration by researchers. The tool was developed using a devops development method to improve the usability of the tool for MOOC learning designers and researchers.
Recurrent Highway Networks With Grouped Auxiliary Memory

Recurrent neural networks (RNNs) are challenging to train, let alone those with deep spatial structures. Architectures built upon highway connections such as Recurrent Highway Network (RHN) were developed to allow larger step-to-step transition depth, leading to more expressive models. However, problems that require capturing long-term dependencies still cannot be well addressed by these models. Moreover, the ability to keep long-term memories tends to diminish when the spatial depth increases, since deeper structure may accelerate gradient vanishing. In this paper, we address these issues by proposing a novel RNN architecture based on RHN, namely the Recurrent Highway Network with Grouped Auxiliary Memory (GAM-RHN). The proposed architecture interconnects the RHN with a set of auxiliary memory units specifically for storing long-term information via reading and writing operations, which is analogous to Memory Augmented Neural Networks (MANNs). Experimental results on artificial long time lag tasks show that GAM-RHNs can be trained efficiently while being deep in both time and space. We also evaluate the proposed architecture on a variety of tasks, including language modeling, sequential image classification, and financial market forecasting. The potential of our approach is demonstrated by achieving state-of-the-art results on these tasks.
Indonesian Lyrics Classification using Feature Level Fusion

The Lyrics classification is one of remain were challenging topics in Music Information Retrieval. In this research, we build Indonesian Lyrics Dataset (ILD) collecting derived more than a thousand popular Indonesian song from 1970 until 2010. The data was crawling from various offline sources or online sources. We use pre-processing techniques like stopword, Indonesian stemming, dictionary checking, spell correction, and others. We adopt the Feature Level Fusion method that combining bottleneck features of Neural Networks. We tested a model into the genre, mood, and year label of Indonesian lyrics. Experiments show that Feature level Fusion model outperforms single Long ShortTerm Memory and Recurrent Neural Networks for Indonesian lyrics classification.
LRNeuNet: An attention based deep architecture for lipreading from multitudinous sized videos

Speech Perception is considered as a sound-related aptitude, which is characteristically multimodal on the grounds that delivers speech requires the movements of the lips, teeth, and tongue of the speakers which are normally noticeable in vis--vis correspondence. Though sign language has been developed and deaf people are usually quite adept at lipreading, it can become a very difficult task for varying speakers with different accents and speaking speeds. A lot of models have been proposed to capture the speaker’s lip movements and predicting the sentences spoken by them by observing the movements and contours. This too is a difficult task as a lot of consonants have the same lip movement which leads to the necessity of having sequential models, which take into account the words that have been spoken previously and the words that are spoken afterwards. Speakers’ mouths may additionally look of varying kinds and so might their appearances in images. We propose a model for this purpose. Our LRNeuNet operates using SpatioTemporal Convolutional Neural Network (STCNN), ResNet, Bi-directional Gated Recurrent Unit (Bi-GRU), SpatioTemporal Pyramidal Pooling(STPP) and Hybrid Connectionist Temporal Classification (CTC)/ Attention. The STCNN incorporates Maxout layer. We use STPP to give a fixed-length representation for training by pooling the features in arbitrary regions. In the hidden neural layer, Hybrid CTC/ Attention model partially removes the error of the assumption of conditional independence of CTC. Our proposed model LRNeuNet achieved 1.2% CER and 2.7% WER on the GRID Corpus dataset.
Ship Motion Control Based on AMBPS-PID Algorithm

Intelligent motion control is one of the key technologies of ships. This paper studies the application of Adaptive Mutation Beetle Particle Swarm (AMBPS)-PID algorithm in ship motion control. Firstly, the ship MMG model is established. Then the BAS algorithm is introduced, and AMBPS algorithm is improved and designed on this basis. Secondly, ship heading and path following controllers are designed according to the algorithm, and rudder turning rate constraint is introduced to limit the rudder angle. Thirdly, through the test function effect analysis of AMBPS and other similar algorithms, the improved effect of this algorithm is verified. Finally, from manual tuning PID parameters to off-line and on-line optimizing parameters based on AMBPS algorithm, the optimal control parameters are obtained step by step, and the optimal heading and path following simulation results are achieved. Compared with the results of traditional PID, AMBPS-PID algorithm has a better adaptive control effect on ship motion control, reduces the error of manual tuning parameters and improves efficiency.