Reviewerly/exHarmony huggingface dataset:
Research on Human Action Recognition Based on Improved Pooling Algorithm

With the rapid development of deep learning, human behavior recognition has become a focus of research. When using various artificial neural network training models to recognize human behavior, the problem of overfitting has always been a difficult point that hinders the improvement of the accuracy of recognition. This paper combines stochastic pooling and Dropout, proposes an improved stochastic pooling method based on Dropout, and applies it to a 3D convolutional neural network for human behavior recognition to solve the problem of overfitting. In the training phase, the Dropout method is introduced into the pooling layer of the 3DCNN, and the unit values of the pooling layer are randomly suppressed, so that more sub-models are generated on the pooling layer. During the testing phase, the idea of double probability weighting method is introduced The idea of combining the weight of the activation value of the selected neuron and the activation value of the selected neuron is introduced to obtain an approximate model of all sub-models. Finally, the trained model was input into the UCF101 data set for verification, and an accuracy rate of 70.09% was obtained. Experimental results show that this method can extract more general features, enhance generalization ability, effectively suppress overfitting, and improve accuracy.
Noise reduction by spatio-temporal filtering on parallel phase-shifting interferometry

Visualizing sound field is important to fully understand the acoustic behavior and phenomena. Recently, sound field visualization by using parallel phase-shifting interferometer (PPSI) has been proposed. PPSI can observe sound field quantitively and instantaneously without placing any object inside the field. However, the system has introduced some unwanted noise to the output fringe image. There is continuous development in signal processing method to reduce the noise in PPSI system. In this paper, different type of spatio-temporal filter has been tested to improve noise reduction in PPSI system upon establishing.
Time-Frequency Analysis of Digital Broadband Station Based on Padang Lawas Earthquake

Time-Frequency Analysis of the earthquake signals has long been an important issue for some reason. However, the impact of the Padang Lawas earthquake upon the environment is a range out within minute affectation fatalities that may last for a long time. Signal identification generated by a seismic waveform had been analyzed based on time-frequency analysis using wavelet transform. The goal of this study to classify and detect the waveform of earthquake signals from Padang Lawas event 2020. The result is then discussed in the context of the potential use of the proposed for a new perspective for the classification of earthquake phases in vertical channel signals data collected within broadband recording stations in Northern Sumatera. The result indicated the high energy was concentrated in 1 - 0.1 Hz for BKNI, GSI, KCSI, and MNSI. The spectral decomposition of the high energy still concentrated in 0.1-1 Hz for KUSI, MTSI and TSI.
NOVA Mobility Assistive System: Developed and Remotely Controlled with IOPT-Tools

In this paper, a Mobility Assistive System (NOVA-MAS) and a model-driven development approach are proposed to support the acquisition and analysis of data, infrastructures control, and dissemination of information along public roads. A literature review showed that the work related to mobility assistance of pedestrians in wheelchairs has a gap in ensuring their safety on road. The problem is that pedestrians in wheelchairs and scooters often do not enjoy adequate and safe lanes for their circulation on public roads, having to travel sometimes side by side with vehicles and cars moving at high speed. With NOVA-MAS, city infrastructures can obtain information regarding the environment and provide it to their users/vehicles, increasing road safety in an inclusive way, contributing to the decrease of the accidents of pedestrians in wheelchairs. NOVA-MAS not only supports information dissemination, but also data acquisition from sensors and infrastructures control, such as traffic light signs. For that, it proposed a development approach that supports the acquisition of data from the environment and its control while using a tool framework, named IOPT-Tools (Input-Output Place-Transition Tools). IOPT-Tools support controllers’ specification, validation, and implementation, with remote operation capabilities. The infrastructures’ controllers are specified through IOPT Petri net models, which are then simulated using computational tools and verified using state-space-based model-checking tools. In addition, an automatic code generator tool generates the C code, which supports the controllers’ implementation, avoiding manual codification errors. A set of prototypes were developed and tested to validate and conclude on the feasibility of the proposals.
A Security Vehicle Network Organization Method for Railway Freight Train Based on Lightweight Authentication and Property Verification

With the development of railway transportation, it has put forward higher requirements for the safety of freight train. The present ground vehicle safety monitoring system, due to technical limitations, can’t meet the security requirement for real time information report of the train. Vehicular sensor can collect real-time vehicle information, to guarantee the safety of the train. Vehicle network is the basic of the communications between vehicular sensors. Because of the particularity of the freight train, a security vehicle network organization algorithm is proposed for the railway freight train based on lightweight authentication and property verification. Distance measured by distance sensor is the trigger event of the algorithm. The lightweight authentication makes the node trusted. Authenticating with speed and direction make the nodes belong to the same train. And ultimately vehicle network is established. Through the vehicle network, vehicle sensors collect data in real time and transmit to the ground processing system. Finally, ensure the safety of railway freight train.
Variable selection in Gamma regression model using binary gray Wolf optimization algorithm

Abstract In the real life applications, large amounts of variables have been accumulated quickly. Selection of variables is a very useful tool for improving the prediction accuracy by identifying the most relative variables that related to the study. Gamma regression model is one of the most models that applied in several science fields. Gray Wolf optimization algorithm (GWO) is one of the proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this paper, chaotic GWO is proposed to perform variable selection for gamma regression model. The simulation studies and a real data application are used to evaluate the performance of our proposed procedure in terms of prediction accuracy and variable selection criteria. The obtained results demonstrated the efficiency of our proposed methods comparing with other popular methods.
Semi-supervised Generative Adversarial Networks Based on Scalable Support Vector Machines and Manifold Regularization

Generative adversarial networks (GANs) are potential models in semi-supervised learning because of the excellent performance of GANs. However, most GAN-based semi-supervised models are sensitive to local perturbation, which means those models are not stable enough. Besides, Softmax classifier is the first choice of those models. In this paper, a novel method is proposed by introducing a discriminator using scalable SVM classifier with manifold regularization. Scalable SVM classifiers typically perform better in small sample data sets compared with other classifiers, which is consistent with the feature that semi-supervised learning consists of a few labeled data and a large number of unlabeled data. Manifold regularization forces discriminator to keep invariable to local perturbations. By incorporating into feature-matching GAN architecture, the proposed GANs-based semi-supervised learning algorithm has advantages over other methods on the Cifar-10, SVHN and Cifar-100 datasets. The results show that the proposed model SSVM-GAN has good robustness and strong generalization ability.
SeFAct2: Selective Feature Activation for Energy-Efficient CNNs Using Optimized Thresholds

This work presents a framework for dynamic energy reduction in hardware accelerators for convolutional neural networks (CNNs). The key idea is based on the early prediction of the features that may be important, with the deactivation of computations related to unimportant features and static bitwidth reduction. The former is applied in late layers of the CNN, while the latter is more effective in the early layers. The procedure includes a methodology for automated threshold tuning to detect feature activation. For various state-of-the-art neural networks, the results show that energy savings of up to about 30% are achievable, after accounting for all implementation overheads, with a small loss in the accuracy.
Calculus CL as a Formal System

In recent years CL diagrams inspired by Lange’s Cubus Logicus have been used in various contexts of diagrammatic reasoning. However, whether CL diagrams can also be used as a formal system seemed questionable. We present a CL diagram as a formal system, which is a fragment of propositional logic. Syntax and semantics are presented separately and a variant of bitstring semantics is applied to prove soundness and completeness of the system.
Image Classification based on CBIR

Abstract In this paper, we present a new way to classify four types of images (Car accidents, Fire, Abnormal objects in street and Digs) which will be sent to four government places; Civil Defence, police station and Municipal. The classification method depends on the Content-Based Image Retrieval (CBIR), where we use a new method. In this method, we use a combination of three methods to extract features from an image; Single Value Decomposition (SVD), Edge Histogram Descriptor (EHD) and Color Auto-Correlogram for Extraction Features. You will use these features to find the closest similarities to the query image from the database images by selecting the closest 3 images, then choosing the class to which the closest two images belong to the retrieved. The combined method showed 100% accuracy in training phase and 100% test phase accuracy.
Generative Adversarial Network for Image Deblurring Using Content Constraint Loss

The research of image deblurring plays an important role in the digital image processing. In order to reduce image blurring problems, a content constraint loss (CCL) function in the generative adversarial network (GAN) is proposed. The SSIM loss and the perceptual loss constitute the CCL function, which makes the trained generative model stable. The CCL function as the content constraint loss component and the adversarial loss component constitute the total loss. The total loss is optimized by the iterative training to further improve the stability of the network model, and the image blurring will be reduced. In the test experiment of the open source image dataset MNIST, CIFAR10/100 and CELEBA, the CCL function is used as the content constraint loss component of the generative adversarial network, the effect of image deblurring has obvious promotion in the structural similarity measure and visual appearance.
Deep Multi-scale Convolutional Neural Network Method for Depth Estimation from a Single Image

In this paper, a depth estimation method combined with monocular vision is proposed. This method uses the coding-decoding structure as the inspired basic network of depth estimation, and combines the convolution neural network unit as the basic network unit. The concept of multi-scale network is applied to the deep convolution network for depth distance estimation. Experiment results show that the combination of multi-scale network method can effectively improve the accuracy of depth estimation.
Fine-Grained Sentiment Rating of Online Reviews with Deep-RNN

Increasing volume of customer reviews over the commercial Web sites have created a demand for the construction of automated content analysis systems. However, present techniques mainly focus on traditional bag-of-words (BOW) and statistical language models, ignoring semantic compositions. In contrast, deep neural networks (DNN) have exhibited greater stability in equipping on-scale sentiment prediction. Particularly, deep recursive neural networks (Deep-RNN) have been consistently used for capturing semantic compositions in natural language content when represented with structured formats (e.g., parse trees). Improved word spaces (word-embeddings) on the other hand proved to be efficient in comprehending fine-grained semantic regularities. In this paper, a fine-grained sentiment rating of online reviews based on Deep-RNN is proposed. The performance of the proposed model is evaluated through the conduction of experiments over Stanford sentiment treebank (SST) dataset. Furthermore, the effect of tuning hyper-parameters on the performance of the network is studied. The experimental results reveal that Deep-RNN exhibits better prediction accuracy compared to the traditional shallow counterparts.
From Trivium to Smart Education

Rethinking the classics for thinking the future. This could be the compendium of the present article, in which we propose a revision of the immediate future of education based on the classic project of the Trivium. We will analyze, first, the transformation of education in the perspective of smart education, determined by the impact of technology and by the reflection on the competences of the 21st century; secondly, we will review the strategic and methodological proposals in accordance with this transformation, based on the theory of generative learning; thirdly, from the point of view of contents, we will analyse the importance of core digital skills as programming and computational thinking. On this basis, the paper offers a proposal from a dual perspective. Firstly, by rethinking the main issues of education in the light of the history of the Trivium and the epistemological principles that shaped it. Secondly, by proposing the recovery of the Trivium disciplines (Grammar, Rhetoric and Logic) having in mind the debate on the competences of the 21st century, as the best instrument to enrich the current educational systems, especially in view of the challenges of digitalization.
Intuitionistic Euler-Venn Diagrams

We present an intuitionistic interpretation of Euler-Venn diagrams with respect to Heyting algebras. In contrast to classical Euler-Venn diagrams, we treat shaded and missing zones differently, to have diagrammatic representations of conjunction, disjunction and intuitionistic implication. Furthermore, we need to add new syntactic elements to express these concepts. We present a cut-free sequent calculus for this language, and prove it to be sound and complete. Furthermore, we show that the rules of cut, weakening and contraction are admissible.
An Intelligent Machine-Driven Perspective to Archaeological Pottery Reassembly

Information and communication technologies (ICT) have been at the centre of most innovations. With applications in science, technology, engineering, and mathematics fields, it has become prevalent in business, art, and humanities disciplines, among others, as well. Among the potential applications of ICT in social sciences and digital humanities, documentation and reconstruction of archaeological artefacts have garnered interest and resulted in several studies. This is because of the potential inherent in these artefacts for archaeological and historical studies. However, regarding pottery reassembly, challenges are experienced in implementing an optimal solution entailing high standards. Although existing studies attempted to solve these challenges, a high standard solution is still elusive. This article presents an approach to a machine-driven solution that intends to use computer vision and machine learning, whose potential is yet to be felt in pottery reassembly. This investigation, still at an early stage, has profound implications for future studies in pottery studies in general.
The Decentralized Voting Model Using the Hyperledger Platform Paper

It is presented theoretical statements about decentralized systems technology, blockchain networks, their structure as well as main functioning principles, structure and material about Hyperledger platform. It is examined Hyperledger services architecture, spheres of use and such important aspects as smart-contracts and their application, as well as advantages of using this platform. It is also modelled and explained the algorithm and schematic diagram of voting network built with Hyperledger platform.
An Image Hidden Transmission Method of Networked Inverted Pendulum Visual Servo Control System

In the real-time control of Networked Inverted Pendulum Visual Servo Control Systems (NIPVSCSs), how to achieve safe and efficient image transmission is an important and challenging task. Generally, the inverted pendulum image is directly transmitted so that the security of images during the network transmission can not be guaranteed in the NIPVSCS. To solve this problem, our contributions of the paper include: 1) A Chaos-based Image Hiding (CIH) algorithm to hide the inverted pendulum image is used to improve the security of the image during the network transmission; 2) The effectiveness and feasibility of the proposed method is confirmed by simulations and real-time control experiments.
Simulator of mobile robots controlled by Artificial Neural Networks to learning courses in robotics

A mobile robot simulator was developed in a computer graphics environment as a didactic tool able to assist the teaching and learning in disciplines that involve mobile robotics. It is also useful in validating control algorithms before testing on a real robot. The developed interface allows the user to adjust the parameters of the robot, the controller and exchange maps with different obstacles. In addition to the kinematics and 2D modeling of the virtual mobile robot with wheels, three sensors were used that indicates the robot's distance to obstacles and walls. The robot controller uses a Multilayer Perceptron Neural Network (MLPNN) for autonomous navigation avoiding the collision with obstacles.
Expelliarmus: Semantic-centric virtual machine image management in IaaS Clouds

Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management requires dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Unfortunately, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages), with either restricted possibility to identify and extract reusable functionalities or with higher VMI publishing and retrieval overheads. In this paper, we propose Expelliarmus, a novel VMI management system that helps to minimize VMI storage, publishing and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it models VMIs as semantic graphs to facilitate their similarity computation. Second, it provides a semantically-aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, it assembles VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on a real test-bed. Experimental results show that our semantic-centric approach is able to optimize the repository size by 2.3−22 times compared to state-of-the-art systems (e.g. IBM’s Mirage and Hemera) with significant VMI publishing and slight retrieval performance improvement.
English Transliteration of Kannada Words with Anusvara and Visarga

Kannada is one of the frequently spoken languages of state Karnataka. It is descendents of Brahmi and one among the five Dravidian languages. Resources in Kannada are used by only those people who can read Kannada. Kannada language is spoken by most of the people in southern Indian state of Karnataka and in Andhra Pradesh, Maharashtra, Kerala, Tamil Nadu and Goa. Type of writing system in Kannada is called alphasyllabary and very less amount of work is done in this area. Kannada literature remained limited only for those who know it, and we improved the access to Kannada literature by proposing a method for Kannada to English transliteration. Our input language is Kannada and the output language is English. Here, input word is divided into transliteration units and then mapped to output language. We proposed an approach which takes Kannada words as input and produces exact transliterations from Kannada to English with Anusvara (“O”) and Visarga (“:”) and gives results with 100% accuracy including numbers.
Speckle Noise Reduction In Sar Images Using Information Theory

In this work, a new nonlocal means filter for single-look speckled data using the Shannon and Renyi entropies is proposed. The measure of similarity between a central window and patches of the image is based on a statistical test for comparing if two samples have the same entropy and hence have the same distribution.The results are encouraging, as the filtered image has better signal-to-noise ratio, it preserves the mean, and the edges are not severely blurred.
On the iterations of the maps $$ax^{2^k}+b$$ and $$(a x^{2^k} + b)^{-1}$$ over finite fields of characteristic two

The maps $$x \mapsto ax^{2^k}+b$$ defined over finite fields of characteristic two can be related to the duplication map over binary supersingular elliptic curves. Relying upon the structure of the group of rational points of such curves we can describe the possible cycle lengths of the maps. Then we extend our investigation to the maps $$x \mapsto (ax^{2^k}+b)^{-1}$$ . We also notice some relations between these latter maps and the polynomials $$x^{2^k+1} + x +a$$ , which have been extensively studied in literature.
Pricing Mechanisms for Fair Bills and Profitable Revenue Share in Cloud Federation

Cloud Federation is the coalition of multiple Cloud Providers (CPs) to surmount the constraint of finite physical resources of individual CPs. The collaboration enables them to share resources with each other at a certain price based on Federation Level Agreement (FLA). The pricing mechanism used for the resources requested by customer’s acts as an important factor in the calculation of their bills and in the generation of revenues for CPs, in such an environment. Consequently, it performs a key role in the selection of a CP by customers and in incentivizing the CPs to remain in Federation. Considering this, the present paper aims to propose mechanisms of pricing to charge internal users and peer CPs. The proposed mechanisms are evaluated in a simulated environment. The simulation results illustrate that the proposed pricing mechanisms ensure fair bills for customers and profitable share of revenues for the collaborating CPs.