Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a High-precision Campus Intelligent Blackboard Inspection System Based on AI

Abstract At present, traditional campus smart blackboard detection generally uses manual detection as the main method and equipment detection as the auxiliary method to determine the fault information encountered, and it is still limited to relying on the experience of the campus smart blackboard management and maintenance personnel to judge the fault. Solve the problem. In this paper, aiming at the problems of low efficiency of traditional campus blackboard detection technology, high manual detection cost, and low classroom enthusiasm due to untimely detection, this paper proposes a campus intelligent blackboard monitoring system based on AI technology, and establishes a chain-type intelligent classroom education system. Smart classroom does not affect the development of social education level because of some disadvantages brought about by technological development.
Dissecting Neural Networks Filter Responses for Artistic Style Transfer

Current developments in the field of Artistic Style Transfer use the information encoded in pre-trained neural networks to extract properties from images in an unsupervised process. This neural style transfer works well with art and paintings but only produces limited results when dealing with highly structured data. Characteristics of the extracted information directly define the quality of the generated artifact and traditionally require the user to do manual fine-tuning. This paper uses current methods of deep learning to analyze the properties embedded in the network, group filter responses into semantic classes and extract an optimized layer set for artistic style transfer, to improve the artifact generation with a potentially unsupervised preprocessing step.
Potency of Virtualization Technology for Getting Energy Potent Data Center

An atmosphere of virtualization allows green data-center on it which is such an idea that has a significant part on the energy potency of data-center and server. For the augmentation of the potency of the data-center, an eminent and fruitful method implemented, is via virtualization. It is constituted for making a sole part of hardware operation like several parts, and such mechanization is able to assist to reduce power as well as expenditure of energy. We have found that there is the presence of intricacy of the data center. Besides, profuse servers result in such issues that inflate exertion of energy regarding power as well as cooling. It is nothing but server virtualization that is integrated with amalgamation is able to assist data-center controllers in terms of potency and expense factors. This research allows us to establish a business profit to make the expense of server power as well as cooling reasonable for more than three years. This is the evidence of the mechanization being fruitful for curtailing costs as well as diminishing consumption of power, fabricating a data-center which is energy potent.
The comparison between extreme learning machine and artificial neural network-back propagation for predicting the dengue incidences number in DKI Jakarta

Abstract The existence of COVID-19 in Indonesia is not the only disease which we must be aware of. The Health Ministry has said that Dengue Hemorrhagic Fever is as dangerous as COVID-19 and must also be treated with caution. Based on data, until July 2020, there are 71,633 dengue cases in Indonesia and DKI Jakarta has the sixth-highest dengue incidence number. One of the factors that affects the spread of dengue vector is weather. It is necessary to predict the number of dengue incidences so that the dengue handling and prevention efforts can be done optimally. In this study, the number of dengue incidences will be predicted by involving weather factors (rainfall, temperature, and humidity) using Extreme Learning Machine and Artificial Neural Network-Back Propagation and also comparing the both of their performance. The result shows that Extreme Learning Machine can give the dengue incidence prediction in DKI Jakarta with the best RMSE testing result of 0.04584, which is more accurate than the dengue incidence prediction that is given by using Artificial Neural Network-Back Propagation with 100 epochs. Moreover, Extreme Learning Machine can do the training process faster than Artificial Neural Network-Back Propagation.
Evaluating the Performance of Facial Expression Recognition Model via Various Classifiers

Facial expression recognition plays a major role in various application areas such as medical science, security, computer vision, etc. The face was detected and aligned using the ensemble of regression tree technique. The face detection and face alignment challenges such as pose variations, the camera distance from the face, and the lighting changes were analyzed. The aligned face region was connected and cropped with the boundary of the landmark points using Delaunay triangulation for extracting facial features. Two types of feature fusion methods are used to derive the distinctive aspect of the cropped face images. These features are fed into three classifiers to recognize six facial expressions. The error-correcting output code model achieves promising predictions in terms of accuracy.
Low Resource English to Nepali Sentence Translation Using RNN—Long Short-Term Memory with Attention

In natural language processing, machine translation is an automated system that analyzes text from a source language (SL), applies some computation on that input, and generates equivalent sentences in the desired destination language (DL) without any human engagement. Recurrent neural network (RNN), a deep learning paradigm has been proved as an effective technique in applications like natural language processing and computer vision. This research work aims to develop a model for the English to Nepali sentence translation using long short-term memory (LSTM) cells in its encoder and decoder with attention. Bilingual evaluation understudy (BLEU) score method is adapted to evaluate the efficacy of the model. The model is trained and tested with different parameters such that the neural network layer considered are 2 and 4, and the hidden units considered are 128, 256. The performance and effectiveness of the model were compared with different parameters, the LSTM cells in encoder and decoder with attention with two layers of neural network and 256 hidden units found to be better in translating English to Nepali sentence with the highest BLEU score 8.9.
Autoencoder for ecg signal outlier processing in system of biometric authentication

A novel method for ECG signal outlier processing based on autoencoder neural networks is presented in the article. Typically, heartbeats with serious waveform distortions are treated as outliers and are skipped from the authentication pipeline. The main idea of the paper is to correct these waveform distortions rather them in order to provide the system with better statistical base. During the experiments, the optimum autoencoder architecture was selected. An open Physionet ECGID database was used to verify the proposed method. The results of the studies were compared with previous studies that considered the correction of anomalies based on a statistical approach. On the one hand, the autoencoder shows slightly lower accuracy than the statistical method, but it greatly simplifies the construction of biometric identification systems, since it does not require precise tuning of hyperparameters.
Novel Method for Measuring the Grounding Resistance of Towers With Short Electrode Wire

The three-electrode method is the most commonly used grounding resistance measurement method. But the electrode wire of the three-electrode method is too long to be used in mountainous areas or densely built areas. Therefore, it is of great application value to effectively shorten the electrode wire under the condition of ensuring measurement accuracy. In this article, a novel method for measuring the grounding resistance of towers with short electrode wire is proposed. Based on the connectivity of soil inside and outside the tower foundation, the limited space inside the tower foundation is fully used to arrange the electrodes. The principle of arranging electrodes inside the tower foundation to measure the grounding resistance is deduced in detail. The calculation method of the compensation point position and measurement error is studied. The best way of arranging electrodes is proposed. Through simulation experiments and field tests, we confirmed that the measurement accuracy of this novel method is similar to that of the three-electrode method. Its electrode wire length is shorter than the three-electrode method and other improved methods. Only the length of electrode wire needs to be the same as the length of the grounding conductor, and the measurement error can be controlled at about 3%. The novel grounding resistance measurement method has a wider application range and higher measurement efficiency.
Modelling the Structure of Terrestrial Landscapes in Urban Areas

Abstract The study of internal and external factors in the formation of an urban geosystem is determined by its complex structure and multiple connections. Based on geoinformation modelling, an analysis of the landscape structure of the city territory is carried out, which can be a basis for further geoecological research. Morphometric indicators, which make it possible to determine the elementary geochemical landscapes, are indicated according to the data of the digital elevation model. A standardised topographic position index (TPI) is used to determine locations. Spatial zoning according to the type of land use reflects the qualitative features of the external load and technogenic impact. The data on the composition of the lithogenic base show the properties of the depositing medium and determine the natural background. Number of categories of landscapes identified are 58, characterised by a homogeneous geological composition, technogenic load and conditions for the migration of matter. The ratios of various landscape zones have been calculated. The study area as a whole is characterised by the predominance of migration processes over accumulation.
Magnifier: A Compositional Analysis Approach for Autonomous Traffic Control

Autonomous traffic control systems are large-scale systems with critical goals. To satisfy expected properties, these systems adapt themselves to possible changes in their environment and in the system itself. The adaptation may result in further changes propagated throughout the system. For each change and its consequent adaptation, assuring the satisfaction of properties of the system at runtime is important. A prominent approach to assure the correct behavior of these systems is verification at runtime, which has strict time and memory limitations. To tackle these limitations, we propose Magnifier, an iterative, incremental, and compositional verification approach that operates on an actor-based model where actors are grouped in components, and components are augmented with a coordinator. The Magnifier idea is zooming on the area (component) affected by a change and verifying the correctness of properties of interest of the system after adapting the component to the change. Magnifier checks if the change is propagating, and if that is the case, then it zooms out to perform adaptation on a larger area to contain the change. The process is iterative and incremental, and considers areas affected by the change one by one. In Magnifier, we use the Coordinated Adaptive Actor model (CoodAA) for traffic control systems. We present a formal semantics for CoodAA as a network of Timed Input-Output Automata (TIOAs), and prove the correctness of our compositional reasoning. We implement our approach in Ptolemy II. The results of our experiments indicate that the proposed approach improves the verification time and the memory consumption compared to the non-compositional approach.
Hilbert’s Tenth Problem for Fixed d and n

Hilbert’s 10th problem, stated in modern terms, is Find an algorithm that will, given p 2 Z [ x 1 , . . . , x n ] , determine if there exists a 1 ,..., a n 2 Z such that p ( a 1 ,..., a n ) = 0 . Davis, Putnam, Robinson, and Matiyasevich showed that there is no such algorithm. But what if we bound the degree of the polynomial? The number of variables? This paper surveys what is known for these cases.
Robotic Tool as Support in Teaching Processes During COVID 19 Pandemic

The pandemic caused by COVID 19 has led to the transition from traditional education to other modalities that use technological resources as their primary tool. This work proposes a robotic theater as a reinforcement mechanism in education and transmission of knowledge in various areas for regular education in children. The robotic platform is made up of an automatic curtain and 3 NAO social robots. The elements are controlled from a user interface (GUI) that easily and intuitively allows the creation of theatrical scripts. As part of the investigation, the adaptation of the play "Planeta" by the author Alan Rejón was staged. This was presented to three groups of people. The first scenario places professionals in the area of education: teachers of regular primary education, students of initial education, and an educational psychologist. A second scenario is a group of students between the ages of 8 and 9. The results obtained show a 92% acceptance of research as an innovative tool, and 90% of those surveyed consider that robotic plays can awaken children's awareness in education. Possible areas in education for applying the robotic platform could be history or language and communication are also exposed.
Lambda calculus with algebraic simplification for reduction parallelisation: Extended study

Abstract Parallel reduction is a major component of parallel programming and widely used for summarisation and aggregation. It is not well understood, however, what sorts of non-trivial summarisations can be implemented as parallel reductions. This paper develops a calculus named λ AS , a simply typed lambda calculus with algebraic simplification. This calculus provides a foundation for studying a parallelisation of complex reductions by equational reasoning. Its key feature is δ abstraction. A δ abstraction is observationally equivalent to the standard λ abstraction, but its body is simplified before the arrival of its arguments using algebraic properties such as associativity and commutativity. In addition, the type system of λ AS guarantees that simplifications due to δ abstractions do not lead to serious overheads. The usefulness of λ AS is demonstrated on examples of developing complex parallel reductions, including those containing more than one reduction operator, loops with conditional jumps, prefix sum patterns and even tree manipulations.
Efficient Ensemble Sparse Convolutional Neural Networks with Dynamic Batch Size

In this paper, an efficient ensemble sparse Convolutional Neural Networks (CNNs) with dynamic batch size is proposed. We addressed two issues at the heart of deep learning—speed and accuracy. Firstly, we presented ensemble CNNs with weighted average stacking which significantly increases the testing accuracy. Secondly, we combine network pruning and Winograd-ReLU convolution to accelerate computational speed. Motivated by electron movement in electrical fields, we finally propose a novel, dynamic batch size algorithm. We repeatedly increase the learning rate and the momentum coefficient until validation accuracy falls, while scaling the batch size. With no data augmentation and little hyperparameter tuning, our method speeds up models on FASHION-MINST, CIFAR-10, and CIFAR-100 to 1.55x, 2.86x, and 4.15x with a testing accuracy improvement of 2.66%, 1.37%, and 4.48%, respectively. We also visually demonstrate that our approach retains the most distinct image classification features during exhaustive pruning.
Deep Learning Approach for Prediction of Handwritten Telugu Vowels

Telugu is an ancient language in which many historical and valuable scripts are written. Understanding and digitalization of theses scripts is difficult task. Hence, here we are proposing a machine learning approach which recognizes the handwritten Telugu vowels (Achulu). We employed convolutional neural network (CNN) approach to find the features automatically than the handicraft features. Dataset has been built and deployed in IEEE dataport. The results are impressive and interesting. Handwritten character recognition could be a part of Optical Character Recognition System (OCRS). OCRS can be applied to each printed text and handwritten documents. This paper describes the handwritten Telugu vowels recognition by employing a CNN approach. The dataset is pre-processed and also extracted the features using deep neural network system for training. The model is validated on test dataset, and ~98% of training accuracy ~92% of test accuracy are observed.
Technology implementation to promote digital learning

Abstract Interactive learning environment (ILE) is one of the keys of successful learning; one of which is triggered by an effective selection of technology implemented. This paper aims to investigate technology implementation promoting digital learning to be able to create ILE. A variety of digital platforms are identified. It has been found that such platforms as virtual environment, digital games, web-based learning platforms, virtual labs/simulations, mobile devices, robots, and social networks are effective technology to promote effective digital learning and ILE. It is implied that effective digital learning and ILE is most likely to meet the requirements of the industry.
Read, spot and translate

We propose multimodal machine translation (MMT) approaches that exploit the correspondences between words and image regions. In contrast to existing work, our referential grounding method considers objects as the visual unit for grounding, rather than whole images or abstract image regions, and performs visual grounding in the source language, rather than at the decoding stage via attention. We explore two referential grounding approaches: (i) implicit grounding, where the model jointly learns how to ground the source language in the visual representation and to translate; and (ii) explicit grounding, where grounding is performed independent of the translation model, and is subsequently used to guide machine translation. We performed experiments on the Multi30K dataset for three language pairs: English-German, English-French and English-Czech. Our referential grounding models outperform existing MMT models according to automatic and human evaluation metrics.
Android application innovation as the indonesian basic spices learning media

Abstract Education is a significant aspect in the development of a nation. Currently, Indonesia is entering the industrial revolution 4.0 which brings a great impact in various fields, including the field of education as the learning process must continuously evolve by embracing the applications of digital media. However, the learning process in formal education at the moment in Indonesia, especially in vocational high school, tends to use conventional-type media and methods. As a result, students are less enthusiastic and experience difficulties in understanding the lessons and absorbing the materials in the class. For this reason, this study develops a learning media innovation that addresses current student needs by developing an Android application innovation. The method used was the research and development (R&amp;D) method, while the objective of the study was to examine the development process and feasibility of the Android application learning media for Indonesian Basic Spices. The research findings suggest that (1) the development process of the application uses the 4D research method (define, design, develop, and disseminate) and results in a 26.25 MB application using the Adobe Animate software.
Circuit Breakers and Biopolitical Strategies

Abstract Taking as a starting point the challenge of containing the spread of epidemics, this article provides an oblique critique of the connections between biopolitics and contact tracing. Aligning the question of biopolitical strategies with epidemiology, the article follows the lines of continuity between containment strategies, contact-tracing technology, and circulations and networks. The uptake of mobile application surveillance by government entities to trace the spread of SARS-CoV-2 has seamlessly supplemented containment measures. Singapore's deployment of TraceTogether, an application developed by the Ministry of Health and Government Technology Agency, circumvents the use of geolocation tracking: formulating a network of infected bodies using proximity data, the population undergoes a topological change. Drawing on a tradition that acknowledges the transformative quality of technology and its implications on information societies, the article frames the enquiry within the parameters of Martin Heidegger's and Gilles Deleuze's deliberations on the ways in which technology is brought to bear on the biopolitical imaginary of a population. The technological rationality that, according to Heidegger, has gripped the entire horizon of thought is opened up for interruption wherever technology fails. In these slippages emerge spaces in which a critique of society's faults may be advanced. This article proposes a critical reading of application surveillance with a view to the biopolitical and philosophical implications of overdetermined network structures against the backdrop of contagion-related phenomena.
Patient Identity Management Maturity Model (PIM3) for Imaging Information Technology Systems

In Consolidated Enterprises, there are often more than one set of patient identities or some amount of historic records, such as imaging exams that are still identified by more than one patient identity (also known as a Patient Identifier or Medical Record Number) per person. Information technology systems often need some capability to cross-reference records for the same patient so that the records are linked to the one, correct person. If not, it may create a risk for the patient. Historically, each independent facility or organization managed its own patient identity information, including the unique identifier/Medical Record Number. This can result in a fractured view of a patient's records. To present a longitudinal, unified-view record of a patient, it is necessary to have functions to manage these multiple domains. Without this capability, multiple patient identity domains result in a broken imaging record for the patient and often prevents the discovery of, access to, and comparison of a patient's imaging exams. Even worse, without a method to manage patient identity across records where more than one patient identity domain is involved, the records for two different people can be linked to one patient, resulting in a potentially serious risk for harm. This paper proposes a maturity model to assess and categorize the capabilities of different imaging information technology systems, such as Picture Archiving Communication and Archiving System, Vendor Neutral Archive, and other image management and viewing applications.