Reviewerly/exHarmony huggingface dataset:
A Heterogeneous 1D Convolutional Architecture for Urban Photovoltaic Estimation

Global energy transition to renewable sources is among the substantial challenges facing humanity. In this context, the precise estimation of the renewable potential of given areas is valuable to decision-makers. This is particularly difficult for the urban case. In Chile, valuable data for solving this problem is available, however, standard machine learning algorithms struggle with their variable-length input. We take advantage of the ability of 1-D Convolutional Neural Network to fuse feature extraction and learning over a heterogeneous representation of the data. In the present manuscript, we propose an architecture to estimate the PV potential of Chilean cities and extract the relevant features over heterogeneous representations of available data. To this end, we describe and examine the performance of said architecture over the available data. We also extract its intermediate convolutional features and use them as inputs of other machine learning algorithms to compare performances. The network outperforms all other tested machine learning algorithms, while the intermediate learned convolutional representations improve the results of all non-linear algorithms explored.
Research on dynamic target tracking algorithm in mobile sensor networks

Abstract With the increasing complexity of networked cooperative detection environment, new targets appear and leave in the detection area, and the mobility of targets is increasing. The emergence of sensors makes the sensors in the sensor network have certain motion characteristics. These uncertainties increase the complexity of the problem. At the same time, the dynamic selection of sensor resources needs to consider the needs of different priority tasks, platform survival risk, sensor cost and other factors. In view of these problems to be solved, based on the qualitative synthesis theory of predictive control and stochastic dynamic programming optimization theory, the dynamic sensor selection decision theory and method based on multi-step forward decision are explored. This paper studies the dynamic sensor strategy based on multi-step forward decision scheduling, and carries out the research on the optimization decomposition method of sensor selection under different structural characteristics, the dynamic optimization method of sensor selection under complex and uncertain environment, and the multi-objective optimization method of dynamic sensor selection under heterogeneous targets, so as to explore a new way of dynamic sensor selection algorithm design and performance analysis under networked collaborative tracking A new way. The research of dynamic target tracking algorithm in mobile sensor networks has important theoretical and engineering significance to improve the resource efficient utilization and the overall target tracking performance of sensor networks.
Development of a Laboratory-Scale Pipeline Crack Inspection Robot

Development of society inevitably leads to increase in unforeseen problems. An example of this is the presence of piping system and its disadvantages. There are numerous usages of the piping system for instance the sewage at housing area of restaurant as well as offshore deep sea mining of oil. Some of the problems with these systems include blockage, cracking, etc. However, these can be solved with advancement of technology or more specifically, robot. By definition, a robot is a machine with the ability to carry out complex series of actions automatically. This is especially true for a programmable machine. By creating a laboratory-scale pipeline crack inspection robot, these problems can be tackled.
Towards Integrated Access Control for Software Product Line Engineering Web Development

The needs to reduce costs on cloning and adapting software leads to the emerging of Software Product Line Engineering (SPLE), which enables developers to create different software with common requirements and defined variabilities. To support web application generation based on the SPLE approach, a dedicated engine is necessary. We built the SPLELive Engine as our alternative approach to that problem. However, we also need to address another issue of SPLE web generation engine which is multi-product access control. This paper explains how the multi-product access control issue was tackled by doing experimental research using the single sign-on approach with external OAuth 2 authentication services and a decentralized authorization mechanism. This paper also explains and demonstrates how the SPLELive Engine handles authorization configuration variability using delta-oriented programming.
Inpainting method based on variational calculus and sparse matrices

Photo restoration is one of the most popular tasks in digital image processing, required when an image has stains, scratches or any unwanted object. Inpainting is the name given to this type of method, which is based on modifying the areas, where the unwanted information is, in an imperceptible way. The concept of Inpainting was born in the early twentieth century, due to the need to replace or remove an object from a photograph, this was possible through manual brushstrokes of an editor or painter. From the above and the theory of Poisson's image editing, a new technique based on variational calculus and the use of sparse matrices is developed. In this technique, a functional is proposed, which is subsequently minimized, thus achieving that the union between the filled region and the image to be repaired is visually imperceptible. The results obtained were compared with those of the bilinear interpolation, isophote, Orthogonal Matching Pursuit (OMP) and KSVD techniques, the latter two being techniques based on sparse models. Then, the difference between the original and the resulting image was calculated considering only the areas of interest to find the number of distinct pixels and the root mean square error (RMSE). The proposed method presents better results than bilinear interpolation, Orthogonal Matching Pursuit and K-SVD, and very similar to those obtained with the Isotope technique.
Street-crimes Modelled Arms Recognition Technique (SMART) : Using VGG

Open access of arms(weapons) is one of the very common threat to the peace. Criminal activities especially violence directly hits peace and economic status of the society which results in stress and downfall; one of the major reason for all this anarchy is the open access to arms. This disordered situation can be handled using automatic arms detection. CNN along with its variant models demonstrated to be the best among all other machineries. In this paper VGG is compared with LeNet and AlexNet and a system is proposed for arms detection from video surveillance and named it SMART (Street-crimes Modelled Arms Recognition Technique). Results shown the efficacy of the proposed model (SMART) as compared to other models.
Computer Artificial Intelligence Test System in the Internet Information Age

In recent years, due to the advancement of technology in the Internet information age, the use of computer artificial intelligence adaptive testing for evaluation has been rapidly developed. Based on Internet information, the article designs and develops an adaptive test system for multiple types of terminals. In the project selection process, it fully considers some of the existing algorithms' high exposure rate, low utilization rate of the question bank, and content balance, etc., and redesigned it. The project selection engine. Through this system, we can provide support for formative assessment, summative assessment and self-assessment.
A Novel Digital image encryption scheme using nonlinear mathematical model

In the recent communication systems it is very common to transmit digital images via wired or wireless medium. Irrespective of the transmission medium, the information which is embedded in the images have to be securely transmitted. Encryption is a preferable method to protect digital images during transmission. In this paper, an encryption scheme is proposed which ensures confidentiality, integrity and security of digital images. The proposed method is based on nonlinear mathematical model, which also improves the security of the system. The method uses nonlinear two dimensional Zaslavski map and DNA encryption. Performance of the proposed system is analyzed in detail using various analysis metrics.
Process Analysis of Internet of Things System Planning and Construction

Internet of things system planning and construction is an important issue in the design of Internet of things system. Aiming at the system design of the Internet of things, this paper uses the system engineering method and the mathematical model of the Internet of things to solve the practical problems in the engineering system of the Internet of things, discusses the mechanism, principle, type and application of the basic structure of the Internet of things system, discusses the composition and use of the Internet of things system, and explains the construction process and requirements of the Internet of things system.
An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing

In this study, a novel adaptive fuzzy weighted mean filter (AFWMF) model based on the directional median technique and fuzzy inference is presented for solving the restoring high-ratio random-valued noise in image processing. This study aims, not only to obtain information from each direction of the filtering window, but also to gain information from every pixel of the filtering windows completely. Thus, in order to implement preserving details and textures for better restoration in high-noise cases, this study utilizes the directional median to build the membership function in fuzzy inference dynamically, then calculates the weighted window corresponding to the filtering window using fuzzy inference to represent the importance of valuable pixels. Finally, the restoration pixel is calculated using the weighted window and the filtering window for the weighted mean. Subsequently, this new AFWMF model significantly improves performances in the measurement of the peak signal to noise ratio (PSNR) value for preserving detail and fixed image in noise density within the range of 20–70% for the five well-known experimental images. In extensive experiments, this study also shows the better performance of identifying the proposed peak signal-to-removal noise ratio (PSRNR) and evaluating psycho-visual tests than other listed filter methods. Furthermore, the proposed AFWMF model also has a better structural similarity index measure (SSIM) value of another indicator. Conclusively, two interesting and meaning findings are identified: (1) the proposed AFWMF model is generally the best model among the 10 listed filtering methods for image processing in terms of the measurement of two quantitative indicators for both the PSNR and SSIM values; (2) different impulse noise densities should be made for different filtering methods, and thus, this is an important and interesting issue when aiming to identify an appropriate filtering model from a variety of images for processing various noise densities.
The generalized covariance union fusion approach for distributed sensors with different fields of view

This paper proposes a generalized covariance union(GCU) approach to solve the distributed fusion problem of sensors with different fields of view (FoVs). It uses the fusion results within the intersection of the FoVs (IoF)to estimate the(target positioning) measurement error, and then employs this estimated error to correct the multitarget densities outside the IoF. Compared with the current approach, GCU approach is more robust to the sensor-related measurement error. Simulation experiments verified the effectiveness of the proposed approaches.
Restricted Boltzmann Machine-Driven Matchmaking Algorithm With Interactive Estimation of Distribution for Websites

In this chapter, restricted Boltzmann machine-driven (RBM) algorithm is presented with an enhanced interactive estimation of distribution (IED) method for websites. Indian matrimonial websites are famous intermediates for finding marriage-partners. Matchmaking is one of the most pursued objectives in matrimonial websites. The complex evaluations and full of zip user preferences are the challenges. An interactive evolutionary algorithm with powerful evolutionary strategies is a good choice for matchmaking. Initially, an IED is generated as a probability model for the estimation of a user preference and then two RBM models, one for interested and the other for not-interested, is generated to endow with a set of appropriate matches simultaneously. In the proposed matchmaking method, the RBM model is combined with social group knowledge. Some benchmarks from the matrimonial internet site are pragmatic to empirically reveal the pre-eminence of the anticipated method.
Ukrainian School Christmas Drama of the XVIIth–XVIIIth Centuries and Puppet Nativity Play Theatre: Problem of Adaptation and Interpretation of a Musical Factor

The article states that the Ukrainian baroque art has become differentiated into the high, middle and lower stylistic levels. There were certain connections between them, and new art genres appeared on the verges of these levels. The problem of the connection between distinct stylistic levels of the Ukrainian musical baroque has not yet attracted the attention of researchers. The study examines the links between the Ukrainian school Christmas drama of the XVIIth– XVIIIth centuries and the puppet Nativity play theatre. It is noted that for the first time a comparison of these two kinds of theatrical art is drawn in terms of the use of a musical factor in them. It is established that the first act of the Nativity play drama is related to the high style of Ukrainian Christmas school drama. This is revealed on the basis of analysing the dramatic functions of a Choir in both school drama and Nativity play drama. A choir in these spectacles took an active part in revealing the Christmas story, playing the role of a character. What they had in common was the genre of spiritual chant with the syllabic versification. Despite its association with the high style of school drama, the Nativity play drama was a quite new theatrical genre that belongs to the middle stylistic level. Focusing on the folk environment, authors of the Nativity play drama intelligibly conveyed to a wide audience the sacred plot. It is noted that in the second act of the Nativity scene were adapted interludes of school dramas, which represented the lower stylistic level of the Baroque. In this act, a funny line came to the fore, and a musical component is marked by influences of the Ukrainian musical folklore with a predominance of its dancing variety. The interaction of folklore with the lower version of the Baroque had a great potential for the further development of the Ukrainian national theatre. Due to the fact that music was an integral part of the puppet Nativity play drama and played an important dramatic function, this theatrical spectacle, like some school dramas, had the features of the drama with music genre. Thus, school drama and Nativity play drama created the foundation, on which in the XIXth century, the Ukrainian dramaturgy emerged, with a significant role of the musical factor in it, which was essentially a drama with music.
Deep Learning Approaches to Detect Real Time Events Recognition in Smart Manufacturing Systems – A Short Survey

When steam and water powered engines started the first industrial revolution, this increased production rate and gave way to large production facilities, which transitioned into mass production of identical products in an assembly line with the introduction of electricity in the second revolution. Subsequently, the processing of different product categories along the same assembly line was facilitated with the help of automation and robotics. This has led to the third industrial period aided by the introduction of computers and the advancement in electronics. Currently, we are during the process of transitioning into an autonomous and intelligent manufacturing system where cyber and physical systems connects through data analytics and machine learning. In this article, a short overview of deep learning approaches in the detection and recognition of real time events is discussed.
Augmented reality representation of virtual user avatars moving in a virtual representation of the real world at their respective real world locations

In this work we present an augmented reality (AR) application that allows a user with an AR display to watch another user, flying an airplane in the Microsoft Flight Simulator 2020 (MSFS), at their respective location in the real world. To do that, we take the location data of a virtual 3D airplane model in a virtual representation of the world of a user playing MSFS, and stream it via a server to a mobile device. The mobile device user can then see the same 3D airplane model at exactly that real world location, that corresponds to the location of the virtual 3D airplane model in the virtual representation of the world. The mobile device user can also see the avatar movement updated according to the 3D airplane movement in the virtual world. We implemented the application on both a cellphone and a see-through headset.
Blockchain and Privacy

Blockchain and privacy is a couplet that is not easy to comprehend. Apparently, their relation is conflictual and unidirectional, with the blockchain as a threat to the privacy of the users or in general of the people whose data are recorded in the ledgers. At a closer look, their correlation is more elaborate. On one hand, there are undoubtedly points of concern in terms of data protection when a blockchain is operating; on the other hand, the law can create the condition for making use of the blockchain technology in a way that is compliant with the privacy legislation, sometimes by restricting the applicability of this technology when the risks of data exposure are too high and the data are particularly sensitive, sometimes by regulating the data entries or exploiting the blockchain to reach a higher level of efficiency of data treatment (Millard in Comput Law Secur Rev 34:843–8462018; Steinbeck in How new EU privacy laws will impact blockchain: expert take 2018).
Concavity, Core-concavity, Quasiconcavity: A Generalizing Framework for Entropy Measures

We present a new generalising framework for conditional entropies, considering a limit construction over sequences of core-concave entropies, and prove that quasiconcave functions are the set of such limits. This generalising framework subsumes recently proposed frameworks for entropies in quantitative information flow, including entropies whose conditional form reflects the expected leakage and the leakage in the worst-case scenario. Thanks to the properties of the limits it is also shown that several important information theoretical properties can be proven for the generalised entropies satisfying the axioms.
A Modulation Classification Method for Communication Signals Using TFA and Two-Stream Convolutional Networks

Automatic Modulation Classification (AMC) is a key enabler of software radio systems for wireless communications and non-cooperative communications. In this paper, we propose a modulation classification method based on Time-Frequency Analysis (TFA) and Two-Stream Convolutional Networks. The time-dimension information of signals as well as the time-frequency distribution generated by TFA methods are fed into the network as the inputs. The Two-Stream Convolutional Networks utilizes a Two-Dimensional CNN and a One-Dimensional CNN to extract the local correlation information and track the In-phase and Quadrature component (IQ) changes of the time series, respectively. A feature-level fusion among the concatenation of extracted features is then carried out before the softmax layer performing modulation classification. Simulation results show that the proposed model offers a superior classification performance compared to the traditional algorithms and is significantly easier to train than that of the deeper networks.
Instructional competence and practices of teachers in the new normal: Basis for training program

This study aimed to determine the instructional competence and practices of teachers in the new normal.Specifically, this was intended to determine the learning environment, content of learning, teaching and learning strategy, resources of learning, communication in teaching and learning, sustaining interest in learning and evidence of learning of teachers.Descriptive survey method was used and the target respondents were thirty selected teachers under district of Buenavista.Questionnaire was the main instrument used for the data collection.Percentage distribution, weighted mean, were used to analyze the data.Findings revealed that the respondents belong to age bracket 26 to 30; majority were female with Master's units and teaching English for not more than five years as teacher 1 in Elementary school.They already attended more than ten seminars.The study further revealed that teacher respondents were highly competent in terms of getting evidence of learning and teaching-learning strategy.However, communication in teaching and learning was found out to be lowest in rank that needs to develop.It is therefore recommended that teachers should use online platforms for communication in teaching and learning in order to reach the students and parents in the new normal setting.Furthermore, the Training Program is recommended to train teachers for the less competence identified.
Design and implementation of control system for transport robot based on STM32 microcontroller

A kind of electric and pneumatic joint control system for transport robot was proposed, and the system implementation was given. A transport robot and a 3D model built by SolidWorks were provided to illustrate the control system. The control system adopted the driving method of motor and cylinder, and took STM32 microcontroller as the control core. In order to improve the control precision and compliance of the system, the position loop and speed loop were used to establish the double closed loop PID control. In order to verify the performance of the system, 25 handling experiments were carried out for four kinds of box workpiece with mass of 1kg, 2kg, 3kg and 4kg respectively. The results show that the system can control the transport robot to complete the handling of the box workpiece. The system has a fast response and it is stable.
Real-Time Instance Segmentation Tracking Algorithm in Mixed Reality

In a mixed reality environment, in order to complete the interaction of manipulating virtual items with physical wooden sticks, a real-time and accurate object tracking algorithm is needed. Therefore, we design a fast, pixel-wised object tracking model to quickly and accurately segment wooden sticks in each frame. The model consists of two parts: The first part is an object detection model, which is responsible for identifying and detecting the bounding box of the object in the first frame; The second part is the instance segmentation model, which uses the bounding box of the object obtained in the previous frame and the current frame's image features (extracted by the convolutional neural networks) to calculate the object boundary points in the current frame. In addition, we use dynamic convolutions in the CNNs to increase the representation capacity of the feature extraction parts without increasing the depth or width of the network. Experiments show that under the task of tracking stick objects in a mixed reality environment, our method achieves competitive performance in real-time running speed and segmentation quality.