Reviewerly/exHarmony huggingface dataset:
Blockchain Based Solar Energy Trading

Depletion of non-renewable energy resources is having an adverse effect on the planet. Therefore, various governments have begun enforcing use of renewable energy to meet the rising electricity demands. Due to environmental and resiliency benefits, distributed energy resources are a potential solution for meeting future electricity demand. Combining the flow of the energy along with networks forms the Energy Internet. The survey provides a concise review of smart grid, blockchain architecture and their role in distributed energy trading. Solar energy has been the primary focus as the renewable energy resource for implementation purposes. Smart grid ensures an efficient distribution of the power, hold low losses, maintain degree of quality, and the safety of power supply. Such a setup has nodes including power sources and arrangements for transferring power to other nodes as well. It has various parameters that are of primary concern like power supply control, power flow execution, limiting the electricity as per the top layer transactions with help of monitoring devices, etc. Microcontrollers are responsible for interacting with the network and accessing data on it. Blockchain technology has been largely confined to finance and cryptocurrency. Recently, the technology has emerged as a distributed ledger technology which allows exchanges among participants without the need for a central market entity. The blockchain gives immutability of the transactions, which make sure each transaction among peers will be executed. It additionally gives immutability to transaction history, which may be used for audit or fixing a transaction dispute. Transaction executions are performed using smart contracts. Synchronization of the logical and physical layer between a pair of nodes forms the crux of the survey.
Software Maintainability Estimation in Agile Software Development

Agile methodologies have gained wide acceptance for developing high-quality products with a quick and flexible approach. However, until now, the quality of the agile process has not been validated quantitatively. Quality being important for the software system, there is a need for measurement. Estimating different quality factors will lead to a quality product. Also, agile software development does not provide any precise models to evaluate maintainability. Therefore, there is a need for an algorithmic approach that can serve as the basis for estimation of maintainability. The article proposes an adaptive neuro-fuzzy inference system (ANFIS) model for estimating agile maintainability. Maintainability is one of the prominent quality factors in the case of agile development. The proposed model has been verified and found to be effective for assessing the maintainability of agile software.
Colorizing Black and White Videos with Damping Factor and Pixel Tolerance

Colored videos are more attractive to audiences compared to black and white videos. This can be seen through recent adaptions of old black and white movies into their colored versions. Given the relevance in the industry, this project tackles the problem of colorizing videos effectively. Previous attempts on the same topic could not do this efficiently as they always encountered the vital issue of a “flickering effect.” The said flickering effect is observed when the frames are colored individually and then reassembled to form a video. To solve this issue, we introduce two parameters, namely, a damping factor and a pixel tolerance. We have also fine-tuned the parameters for the most optimal result through experimentation. Finally, using custom metrics, we showcase the efficiency and effectiveness of this method.
Document-level Event Argument Extraction via Multispan Semantic Reinforcement

The challenge of document-level event argument extraction (DEAE) is to extract scattered event information beyond individual sentences, which generally requires a deeper semantic understanding of a document. This paper proposes a DEAE approach that incorporates multispan semantic reinforcement, which can better obtain complete event information by capturing and integrating the contextual semantics from the word span, the sentence span and the paragraph span in the document. Specifically, to extract the word-span contextual semantics optimally, we present the word-span semantic reinforced entity (WSRE) embedding approach and investigate the effects of its fusion ratio on the DEAE model. We also creatively extract the sentence- and the paragraph-span contextual semantics with a contextual attention mechanism and a gated attention mechanism. Finally, we use the sequence annotation strategy to extract the document-level event argument and match its role type simultaneously. We performed extensive experiments with the MUC-4 event extraction dataset, and experimental results with comprehensive analyses show that the proposed model outperforms other existing methods.
Activity Recognition in Industrial Environment using Two Layers Learning

Action and activity recognition is essential in the world of cobots to ensure the best efficiency and a safety collaboration between a robot and the human-being. The approach of the article is the creation of a new activity dataset for an industrial context with cobots for recognition. We proposed to use LSTM (Long Short-Term Memory) to analyse and recognize the activities and we also proposed to model the action using the Principal Component Analysis (PCA) and then recognize the activity using LSTM. Using this two level approaches on the dataset we collected, we obtained high recognition level : 96.826 (+/-0.383) %.
The effect of participation on economic development emphasizing social cohesion

In political and social institutions, in developed countries, participation is a great of importance. According to, institutional contexts and the level of socio-political cohesion, political participation indicates different results in developing countries. However, political participation is the most general form of democracy and political develop of a country, it leads to development in developing countries when is subject to socio-political cohesion. The present article, using panel data, aims to find the effect of political participation on economic development, emphasizing the effect of socio-political cohesion over a period of time 2006- 2016 for 123 developing countries. Research results show that participation increase, causes economic development subject to socio-political cohesion. Therefore, based on the facts of present study, economic development needs to be endogenous and based on internal capabilities that it is provided by creating useful contexts in order to more relationship and correlation between society and politicians.
Weightless Neural Models: An Overview

This paper presents an overview of research in Weightless Neural Models. Weightless Neural Networks (WNNs) do not have weighted connections between nodes. They use a different kind of neuron, usually based on RAM memory devices. The weightless (or RAM-based) neural networks are based on variations of the RAM node proposed by Aleksander. Recent works in the literature, such as the quantum weightless neuron, provide a novel perspective to this area. The paper describes classical and quantum weightless models and important recent works found in the literature, pointing out the challenges and future directions in the area.
Information System of English Learning Platform Based on Feature Extraction Algorithm

With the trend of globalization, learning and understanding English has become more and more important. Due to its limitations, traditional English teaching methods can no longer meet the current growing demand for English learning. Although the current English teaching system can meet the needs of use to a certain extent, there are still many problems. Learning English is a three-dimensional process, including vocabulary memory, grammar learning, listening practice, etc. The best learning outcome should be their integration, not their independence. In addition, the process of learning English is also a highly personalized process: students of different English proficiency may face different learning tasks, which is a very important problem. In response to this problem, the original English learning system platform is being improved, and some new methods are adopted, such as applying feature extraction algorithms to the English learning platform, which has certain practical significance. This article aims to study an English learning platform based on feature extraction algorithms, and apply feature extraction algorithms to design and implement an effective English learning platform that can meet the needs of different users for significant learning difficulties and learning English. I'm learning. Experimental research shows that based on the feature extraction algorithm proposed in this paper and other model training and testing algorithms, the F value reaches more than 74%, which is in line with the expected results of the experiment. It proves that the model and calculation method established in this article are practical in practical applications.
Location Based Events Management System Using Ensemble Learning Techniques

Now a day’s events are becoming core and important part of life. According to surveys, events are planned according to the management in an accurate time. But still, some ensemble techniques are used according to the learning patterns, sensor data is mostly used in high-level activity inference. There are some labeled person’s activities for significant places that are used as traces in GPS data. In contrast with ensemble learning techniques, this approach is constantly detecting and classifying some significant locations. This system uses Markov relational networks in a hierarchical model that performs some encoding operations in complex GPS readings. There are various deep learning architectures that are designed for various tasks that are performed in the past few decades. At present this research ensemble deep learning frameworks to decrease the issues of overfitting caused in single models.Through learning patterns, the analysis of the algorithms can be done that are applied for this research. The system contains Markov relational networks to implement the hierarchical activities which need to encode the complex relations. In recent researches, some experiments have shown some improvements in the latest techniques that are implemented. Some issues in this are not solved. The first issue is that work is decoupled this shows issues to determine the locations. This issue is simply handled by assuming the location but in some ways, locations are recorded for the user who is using this system. It also contains a lower threshold; some of it contains too many locations that are significant.The second concern is about computational efficiency that prevents the approaches from tackling event activities. It cannot differentiate between the events and places. There are no modular approaches that represented the activities.
Cbir-Gan: A Triplet Generative Adversarial Network for Content-Based Image Retrieval

Content-based image retrieval systems have become popular in various fields such as computer vision and artificial intelligence. Deep neural networks, especially CNNs, have frequently been employed in visual representations; however, they require large amounts of labeled data, which are hard, costly, and sometimes impossible to obtain. Moreover, since these methods rely only on semantically discriminative representations, they fail to yield significant outputs in instancelevel image retrieval systems. Therefore, this paper proposes a triplet generative adversarial network (GAN) based on the idea of integrating deep metric learning methods with GANs to benefit from the advantages of both at the same time. In this model, three generator networks that use a triplet loss function are responsible for learning a similarity measure over objects and embedding images in an appropriate vector space. In these networks, a CNN-based perceptual loss function is also employed to force the generators to adhere a certain type of structural features in intermediate layers. Since only triplets must be used as network inputs in the proposed method, the learning process is performed in a semi-supervised way. According to the results of comprehensive experiments conducted on four datasets in comparison with several state-of-the-art methods, the proposed method was efficient in terms of precision and computational complexity. For the real-life implementation of the proposed method, a distributed large-scale fashion image retrieval platform, called SnapMode1, has been developed through big data tools such as Apache Storm, Kafka, Solr, and Milvus.
Mapping the Changing Contours of Electronic Evidence in India

The courts have gone on a discourse starting from admissibility of CDs as evidence to the latest being that of WhatsApp Chats. The author of this chapter will map the changing discourse of electronic evidence in India and its evolution in India. The author will discuss the normative discourse with respect to electronic evidence and its applicability, which will be followed by the legal dynamics during such evolution and in the third part argue the current trend of decisions. The author will further provide for the current policy and future changes which need to be imbibed in order to make the law more robust.
An Efficient Exponentiation Algorithm in &lt;i&gt;GF&lt;/i&gt;(2&lt;i&gt;&lt;sup&gt;m&lt;/sup&gt;&lt;/i&gt;) Using Euclidean Inversion

We introduce a new type of exponentiation algorithm in GF(2m) using Euclidean inversion. Our approach is based on the fact that Euclidean inversion cost much less logic gates than ordinary multiplication in GF(2m). By applying signed binary form of the exponent instead of classic binary form, the proposed algorithm can reduce the number of operations further compared with the classic algorithms.
Comparing Different Stopping Criteria for Fuzzy Decision Tree Induction Through IDFID3

Decision trees are commonly used as classification models in data mining whose induction methods recursively partition the instance space for generating tree-structured models. The recursion is completed when the stopping condition is satisfied. In these tree structures, leaves represent classifications and branches represent conjunctions of attributes that lead to those classifications. Decision trees are comprehensible classifiers that cannot handle language uncertainties and are noise sensitive. In this chapter, an FDT induction algorithm is described that has the ability to control the tree’s growth via dynamically setting the threshold value of the stopping criterion.
Handwriting digits recognition in real scenario based on deep network and recurrent network

research-article Share on Handwriting digits recognition in real scenario based on deep network and recurrent network Author: Zhipeng Guo Beihang University, China Beihang University, ChinaView Profile Authors Info & Claims ICASIT 2021: 2021 International Conference on Aviation Safety and Information TechnologyDecember 2021 Pages 808–814https://doi.org/10.1145/3510858.3511393Published:04 March 2022Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Analysis of Object Detection Under Different Weather Conditions in Simulated and Real Environment

In modern robotic applications, like autonomous driving in an outdoor environment, a very important task is robust object detection. The object detection model tends to perform poorly under weather conditions including rain. Therefore, in order to have good performance, the data required for the training of these models has to be from a wide variety of conditions, like different lighting, weather, locations, etc. Any model not trained on a different variety of data is more likely to overfit on the dataset it is trained on and hence have poor performance over other situations. To avoid this situation often simulation data is used. The main goal of this paper is to analyse the performance of the object detection model on the various simulated and real weather conditions in order to achieve good performance under the real rain condition with a model trained only on simulated rain data.
Human Activity Detection from Still Images using Deep Learning Techniques

Human activity detection is an active research topic now, the difficult problem of fine-grained activity detection is often ignored. This paper proposes a method to detect human activity from still images. Iterative detection of human activity in a scene is another tough and exciting area of computer vision research. In our day to day life, we have seen implementations of automated cars, speech recognition, and various machine learning models. Unlike action detection in videos that have spatio-temporal features, still images can't be considered similarly, making the problem more complex. The current work solely comprises activities that involve objects to reach a simple answer. Based on semantics, a complicated human activity is broken down into smaller components. The significance of each of these elements in action recognition is investigated in depth. This system is based on detecting an individual's action or behaviour with the help of a single frame (image). Activity detection consists of various tasks like object recognition, pose estimation, video action recognition, and image recognition. Since the current paper is focused only on actions that involve objects, a dataset with specified classes is created. Images for this dataset will be chosen from different sources. This study aims at the development of computational algorithms for activity detection in still images.
Pengembangan Sistem Pakar Menggunakan Metode Papi Kostick Untuk Tes Kepribadian

This paper is a report on the results of developing an expert system using the Papi Kostick method which is used to test the personality of employees in a company. Papi Kostick method to evaluate individual behavior and work style in relation to work situations. The researcher's system development process uses the Software Development Life Cycle (SDLC) method. This expert system is implemented web-based. The tests used are validation testing and expert system accuracy testing. The test results are 100% which shows that the functionality of the system can run well according to the list of requirements. The results of the accuracy test show that the expert system using the Papi Kostick method can function properly. The results of the assessment, the company can see the progress and ability of its employees in doing certain jobs well and the suitability of employees with the workplace through personality tests. Assessment of employee behavior and work style is also useful for companies in providing feedback to employees and can be used as a basis for companies to conduct training, transfer, rotation and promotion of employees.
Learn Fine-grained Sharing Network for Multiple Tasks

Conventional Multi-Task Learning (MTL) models, such as hard sharing, adopt handcrafted network architecture, which shares entire layers for all tasks, and thus have two shortcomings: 1) negative transfer phenomenon and 2) low parameter efficiency. This paper proposes a novel neural network model, which allows different tasks to share a network at the parameter level. Specifically, the model defines a subnet for each task by adopting task-specific binary masks. The masks are trainable and can be learned together with network weights using standard back-propagation. Benefit from the fine-grained sharing mechanism, the negative transfer phenomenon can be alleviated, and the parameter efficiency is greatly improved. According to the experiments on a public dataset, our model outperforms the single-task baseline model even when only 0.8% of parameters remained in the subnets. Compared with the multi-task baseline model using fixed masks, our model is much more robust to changes in network sparsity.
Denoising Score Matching via Random Fourier Features

The density estimation is one of the core problems in statistics.Despite this, existing techniques like maximum likelihood estimation are computationally inefficient in case of complex parametric families due to the intractability of the normalizing constant.For this reason, an interest in score matching has increased, being independent on the normalizing constant.However, such an estimator is consistent only for distributions with the full space support.One of the approaches to make it consistent is to add noise to the input data called Denoising Score Matching.In this work we build computationally efficient algorithm for density estimate using kernel exponential family as a model distribution.The usage of the kernel exponential family is motivated by the richness of this class of densities.To avoid calculating an intractable normalizing constant we use Denoising Score Matching objective.The computational complexity issue is approached by applying Random Fourier Features-based approximation of the kernel function.We derive an exact analytical expression for this case which allows dropping additional regularization terms based on the higher-order derivatives as they are already implicitly included.Moreover, the obtained expression explicitly depends on the noise variance, so that the validation loss can be straightforwardly used to tune the noise level.Along with benchmark experiments, the method was tested on various synthetic distributions to study the behavior of the method in different cases.The empirical study shows comparable quality to the competing approaches, while the proposed method being computationally faster.The latter one enables scaling up to complex highdimensional data.
High-speed time series prediction and classification on an all-optical neural network

We experimentally demonstrate high-speed time series prediction and binary classification tasks using an all-optical integrated SiN-based nonlinear photonic node in a time-delay based reservoir architecture.
Intelligent Disabled Passenger Support System

In the modern world, technologies associated with systems for generating and processing signals in the field of communication are ubiquitous. They are used for different purposes. In Russia, as in the rest of the world, much attention is paid to social innovation. Many people with disabilities participate independently in public life without assistance. but sometimes they can get into critical situations while on board an aircraft or any ground vehicle. A person may become ill and need urgent help from people around him who do not speak sign language. In this case, a sign language recognition system and its translation into text or sound, located on a smartphone, are required. The article describes the development of a software package for recognizing static and dynamic dactyls of the Russian language using neural network methods of machine learning, provides the architecture of the developed neural network of direct distribution, the results of training and test experiments on recognizing all dactyls of the Russian sign language, both static and dynamic.