Reviewerly/exHarmony huggingface dataset:
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding

Spoken language understanding (SLU) plays a crucial role in the performance of dialogue systems. It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances. At present, researchers focus mainly on English SLU tasks, while such investigations on Chinese utterances are not sufficient. In this paper, we first propose a reading comprehension based multi-source information fusion network, called ReCoMIF for Chinese SFID tasks by transforming the SLU task into a multi-turn question answering procedure comprising multiple choice for intent detection and span extraction for slot filling. Moreover, we present a TCM_CLS module with a concise architecture composed of TextCNN, MaxPooling, and feed forward network plus the [CLS] representation. Such three TCM_CLS modules are stacked in the proposed ReCoMIF network that can serve as sufficient integration of multi-source information originating from contexts, reading comprehension based queries, and hidden representations concerning intent and slot semantics. Finally, experimental results and ablation studies on three Chinese SLU datasets show that our proposed model can effectively fuse intent and slot information achieved by\ state-of-the-art performance compared with other baseline methods.
A hybrid deep learning framework for privacy preservation in edge computing

The number of connected devices in our world is continuously increasing at a rapid rate. The Internet of Things(IoT) civilization has resulted in the generation of enormous amounts of data. Analytics on this data pays off in various sectors like health care, manufacturing, and transportation. However, the data generated in the IoT environment is often sensitive, and hence, the need to address the privacy concerns of the data owners. Existing approaches incur a huge computation cost and there is also a gap between privacy preservation and data utility. In this work, a genetic algorithm is coupled with a deep learning network based on adversarial training to build a utility-privacy balanced, low computation solution. The proposal aims to prevent inference of implicit privacy labels present in the data while maintaining data utility. The first part of the proposed work leverages an optimized encoder architecture to learn latent space representation of the input and the second part is the incorporation of adversary for training the framework to prevent unintended sensitive inference. Both parts are governed by a genetic algorithm to output a fitting encoder. Numerical results carried on a benchmark dataset exhibit the capability to protect sensitive data by keeping the accuracy level of the adversary within 23%, and producing a maximum inference accuracy of 95% for the intended task.
The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning

In order to improve the obstacle avoidance and endurance capability of quadrotor UAVs performing tasks such as forest inspection and rescue search, this paper proposes improvements to address the problems of too many traversed nodes, too many redundant corners, too-large turning angles and unsmooth generated paths in the traditional A* algorithm in path planning. The traditional A* algorithm is improved by using a segmented evaluation function with dynamic heuristic and weighting processing, a steering cost heuristic function based on heading angle deviation control, a redundant turning points removal strategy, and a quasi-uniform cubic b-spline. Through the test comparison of different complexity map scenarios, it is found that the improved A* algorithm reduces the number of traversed nodes by 64.87% on average, the total turning angle by 54.53% on average, the path search time by 49.64% on average, and the path length by 12.52% on average compared to the traditional A* algorithm, and there is no obvious turning point in the path. The real-world applicability of the improved A* algorithm is confirmed by comparing the effect of different algorithms on obstacle avoidance in a map of a real plantation forest environment.
Regularization oversampling for classification tasks: To exploit what you do not know

In numerous binary classification tasks, the two groups of instances are not equally represented, which often implies that the training data lack sufficient information to model the minority class correctly. Furthermore, many traditional classification models make arbitrarily overconfident predictions outside the range of the training data. These issues severely impact the deployment and usefulness of these models in real life. In this paper, we propose the boundary regularizing out-of-distribution (BROOD) sampler, which adds artificial data points on the edge of the training data. By exploiting these artificial samples, we are able to regularize the decision surface of discriminative machine learning models and make more prudent predictions. Next, it is crucial to correctly classify many positive instances in a limited pool of instances that can be investigated with the available resources. By smartly assigning predetermined nonuniform class probabilities outside the training data, we can emphasize certain data regions and improve classifier performance on various material classification metrics. The good performance of the proposed methodology is illustrated in a case study that consists of both benchmark balanced and imbalanced classification data sets.
Multi-Attention Network for Sewage Treatment Plant Detection

As an important facility for effectively controlling water pollution discharge and recycling waste water resources, accurate sewage treatment plant extraction is very important for protecting quality, function, and sustainable development of the water environment. However, due to the presence of rectangular and circular treatment facilities in sewage treatment plants, the shapes are diverse and the scales are different, resulting in the poor performance of conventional object detection algorithms. This paper proposes a multi-attention network (MANet) for sewage treatment plants using remote sensing images. MANet consists of three major components: a light backbone used to obtain multi-scale features, a channel and spatial attention module that realizes the feature representation of the channel dimension and spatial dimension, and a scale attention module to obtain scale-aware features. The results from the extensive experiments performed on the sewage treatment plant dataset suggest that our proposed MANet exhibits a superior performance compared with other competing methods. Meanwhile, we used a well-trained model to predict the sewage treatment plant from the GF-2 data for the Beijing area. By comparing the results with the data of manually obtained sewage treatment plants, our method can achieve an accuracy of 80.1% while maintaining the recall rate at a high level (90.4%).
Webapp service for providing Information about ngos using Html5, css3, javascript, node js, Mongodb

In recent years, the existence of non-governmental organisations (NGOs) has proven to be an essential requirement rather than a luxury in the community all over the contemporary world. A non-governmental organisation (NGO) is an organisation that addresses a wide range of social needs and problems that is neither part of authority nor a conventional profitable line of work. [1] NGOs reflect the diverse community through their work in a wide range of fields – environment, wellbeing, poverty alleviation, culture & the arts, schooling, and so on. If a person is unfortunate enough to encounter an issue, it can be difficult to locate an NGO at the right time and in the right place. As a result, this online website makes it easier to find an NGO with a single click. It is a website that showcases collective information about several NGO’s or organizations from different parts of India which provide free services to people and are actually working for the benefit of others. It is a comprehensive platform for all the people who are looking to connect with an NGO. It has several filter options (according to cities and services) and sections to ease out the process of searching. The information of the organizations are represented in the form of a Blogpost which contains an image or the logo of the Organization for identification along with their basic required information such as name, place, services provided, contact number , direct google map link, website link , etc. It also has a feature where NGO’s can register their organization and after verification they can be listed on the website. The comment box is for personal interaction with the user and for their valuable suggestions for improvement.
Power and Performance Issues and Management Approaches in Cloud Computing

Cloud computing is a relatively new developing technology that allows users to pay for services on a pay-as-you-go basis. It provides internet-based information and communication technology services, while virtualization enables it to deliver computer resources. Datacenters are the backbone of this technology, consisting of networked computers, cables, power supply units, and other components that host and store corporate data. In cloud datacenters, high performance has always been the most important consideration, but it comes at the expense of power consumption. The key problem is finding a balance between the system's performance and power consumption by lowering power usage without compromising the quality and performance of services. As a result, several attempts are now being done to reduce datacenter energy usage. All concerns relating to energy efficiency are explored and researched in this work. This paper also presents an overview of various power and performance management strategies in cloud computing.
Robustness of k-Anonymization Model in Compliance with General Data Protection Regulation

The advancement in technology and the emergence of big data and the internet of things (IoT), individuals (data subjects) tend to suffer from privacy breach of various types that has led to a lot of damages to both data subjects and brands. These and other issues about data privacy breach led the European Union to come up with a much stringent regulations that will serve as a deterrent to businesses or organizations that handle data. This gave birth to the General Data Protection Regulation (GDPR) in 2018 which replaced the previous 1995 Data Protection Directive in Europe. This research examined the robustness of k-anonymity in compliance with GDPR regulations at varying k-values (5,10,50, and 100) using the 1994 USA Census Bureau Data referred to as the adult dataset. Various measures were used to determine which k-value meets the GDPR criteria and the findings revealed the best anonymizing threshold complies with the GDPR criteria that prevents information loss (which determines data utility), prosecutor re-identification risk percentage and attacker models (prosecutor, journalist and marketer model).
A Model to Detect Face Mask Using Deep Learning

Coronavirus pandemic started spreading in 2019 and is still spreading until now in 2021 all over the world. Due to this the healthcare sectors are going on crisis all over the world. One basic protective measure that we can implement in our daily life is wearing a face mask. Wearing a mask properly can control the spread of this virus to a great extent. Various regions have made wearing face mask mandatory to prevent spread of this virus. In this paper we have proposed a deep learning-based model to detect face mask using python, OpenCV, TensorFlow and it can be used in our health care sectors.
Blockchain-Based Electronic Voting System

Creating a vote counting plan that suits legislators’ legal criteria has been a difficult task for a long time. In the era of global statistics, allocated ledger software is a remarkable technological innovation. Blockchain technology may be applied to an almost infinite number of sharing economy-related applications. The purpose of this paper is to assess the value of blockchain as little more than a vendor for establishing authorized e - government applications. The paper examines the requirements for creating digital votes casting frameworks and analyzes the technical and legal constraints of using blockchain to determine these structures. The research begins with a review of the most well blockchain frameworks that make use of the technology.
Improve Accuracy in Prediction of Credit Card Approval using a Novel Xgboost compared with Decision Tree Algorithm

The purpose of this investigation is to evaluate the Xgboost Classifier in contrast to the Decision Tree in order to make an accurate forecast regarding credit card approval (DT). The approval of a credit card is accomplished through the use of Xgboost Classifier with a number of samples equal to ten (N = ten), as well as Decision Tree (N = ten). The credit card approval dataset includes 19 characteristics that are taken into consideration when deciding whether or not to grant a person a credit card. According to the statistical analysis performed by SPSS, the accuracy of the Xgboost Classifier is 87.96%, which appears to be superior to the accuracy of the Decision Tree, which is 79.38% correspondingly, with a significant difference of p=0.001 (p0.05, 2-tailed). According to the findings, the accuracy of the Xgboost Classifier appears to greatly outperform that of the Decision Tree (DT) when it comes to predicting whether or not a credit card application will be approved.
Artificial Intelligence in Information Retrieval

This paper discusses the relationship between information retrieval (IR) and AI. Checking retrieval of texts, summarizes its key features and demonstrates the state of its art by introducing it one model that may have details, and other test results that show its value. The paper then analyzes this model and effective methods related to, focusing on and forgiving their weak use, unwanted representation and thinking. This paper describes some of the most effective ways uses intelligence-acquiring information retrieval (IR). Recovery of information is an important information management technology. It works together by searching for information and referencing, storing and categorizing information.
Voice based translation in fitness band for tourists

People all over the world speak in a variety of languages. Despite the fact that English appears to be the most suited language for most people and is taught all around the globe, many individuals find it difficult to learn, and some think that working or studying one's home language is more pleasurable than learning other languages. As a result, communication is tough while travelling throughout the world. As we see in our every-day lives, accomplishing activities, managing things, and so on, technology has the capacity to affect the world in new ways. It has been proved to make things easier for travellers. There are a variety of technologies that might help travellers who travel a long distance. We have included a new feature in today's fitness watches/bands that will assist travellers or anybody else who travels from one location to another and is having difficulty with language translation. Apart from language translation, it has many other features that other smart watches have, and they don't have to pay more for these services because they are included in their budget and are easy to use for everyone. For this, we employed voice recognition and a variety of other modules.
Data Streams Management: Multidimensional Summary with Big Data Tools

Data streams are large, fast, varied and sometimes multidimensional. These characteristics are the reason why their processing and storage are a real challenge. In addition, they reduce the possibilities of querying them a posteriori. It therefore becomes necessary to set up summaries on these data streams for an analysis on the data already unloaded from the system. In this sense, many multidimensional summaries of data streams have been proposed but with some limits. Thus, this article aims to present a new multidimensional summary approach for big data streams called StreamCubeCascadeMode as well as functions that create, load, and update the summary when new events arrive in the system. This proposal is realized by the construction of data storage structures called cubes. These cubes are calculated at the expiration of a time window taken from a Tilted-time window. The solution is implemented with the help of Big data tools which allows it to optimize storage and processing resources by having the ability to scale.
Decentralized Voting System

The evolution of Blockchain has given way to a Smart World where there is improved security and integration of devices, systems, and processes with humans through all-pervasive connectivity. There are numerous secure applications using Blockchain like smart cities, Cloud Computing, Smart Management of the Environment and Healthcare, etc.A decentralized voting system is an option for the paper ballot system and EVM (Electronic Voting Machines). Democracies need a decentralized voting system that offers security, integrity, immutability, transparency, and privacy to voters. Blockchain is an emerging technology that offers integrity, immutability, and decentralization of data. Moving our traditional voting system to Blockchain technology can increase voter confidence. This paper describes an attempt to influence the advantages of Blockchain, such as cryptography and transparency, to accomplish an efficient scheme for a decentralized voting system using the Ethereum network. Smart contracts are profound chunks of codes, which are included in the Blockchain and then execute written code as planned in each stage of Blockchain updates. Decentralized voting is one of the trending topics, but is yet to be significant, compared to the other e-services.
Hybrid Neuro-Particle Swarm Optimization Model for Predicting Depression in Asphalt Pavements

Roads are critical to the economic development of a country. Pavement distresses affects the serviceability of the roads and road maintenance significantly disrupts the traffic flow and consequently the economy in the surrounding area. The application of machine learning techniques coincides with the shift of several industries to Industry 4.0. The objective of this study is to forecast the depression % occurrence in an asphalt pavement using an artificial neural network (ANN)-particle swarm optimization (PSO) algorithm. The network was developed using the temperature, precipitation, pavement age, and average annual daily traffic (AADT) as the input parameters (IP). The governing model developed using the ANN-PSO algorithm has an architecture of 4-9-1 (input-hidden-output). The governing model has the highest R and lowest Mean Squared Error (MSE). The mean absolute percentage error (MAPE) of the governing model is 6.38%. Using the connection weights (CW) of the governing model, the variable significance of the IP was obtained utilizing the Garson's algorithm (GA) and the AADT is the most influential parameter to the depression % occurrence. Moreover, the governing ANN-PSO model was compared to other prediction modeling methods including ensemble of trees, linear regression, regression trees, and support vector machine (SVM) and it was seen that the ANN-PSO is the superior model among the methods observed.
Semantic Approach for the classification of Knowledge representation and reasoning analysis

A form of artificial intelligence (AI) that is advanced and involves rules that are applied to data to simulate a person's thought process in a particular area, knowledge engineering can be described as an advanced form of artificial intelligence (AI). Knowledge engineering had evolved from its original form when it focused on transferring and analyzing knowledge from human problem-solvers into computer programs that could do the same. .It is essential to realize that transfer processing has limitations because humans make decisions differently than machines do. Non-linear thinking and analogous reasoning, often not logical, have not been considered in this case.It is becoming increasingly common today to use a modeling process for knowledge engineering, which creates a system that can reach the same conclusions as experts without following the same path or acquiring the same information from the same sources. In this paper, we aim to represent Knowledge engineering to transform knowledge into software that makes decisions similar to those made by human experts, such as financial advisors, whose decisions are based on their learning.Eventually, it is expected that human experts will be replaced by knowledge engineering in decision support software
Face Detection And Recognization System

A typical issue in biometry analysis could be face recognition from an images or videos. Since face recognition doesn’t require the cooperation of the object, it plays a crucial role in closed circuit television. The advantages of face-based identification over other biometry are individualism and acceptance as a face can be a dynamic object. Many public spaces regularly have police investigation cameras for video capture. These cameras have their important worth for security. This paper’s objective is to judge the various face detection and recognition techniques and provide a comprehensive solution for image based face detection and recognition with high accuracy and a higher response rate as a first step in video police investigation. The solution is projected supported by tests that were carried out on various face to rich databases in terms of subjects’ ability to evoke emotions and their weight and race.
Canny Edge Detection Techniques for Image Segmentation

Edge identification alludes to the most common way of recognizing and finding sharp discontinuities in a picture. This paper, we scale the increase technique is dissected in the Canny edge obtaining structure. The capacity of reiteration scales is characterized as the result of the locater reaction scales for two scales. Edge maps are developed as the size of the area by restricting the impacts of copying the scale. Terms of procurement and limitation of scale increase are gotten. With a little misfortune in the obtaining factor, the state of creating the site can be incredibly worked on by the augmentation of the scale. The result of the two-layered states of duplication is more noteworthy than that of a solitary scale, bringing about better execution of the edge identification. Test results are introduced. This work is then reached out to isolate pictures by different scales utilizing our anisotropic dispersion framework. Shrewd edge recognition calculation is delicate to clamor, thusly, it's not difficult to lose feeble edge data while sifting through the commotion, and its decent boundaries show unfortunate flexibility. Because of these issues, this paper proposed a superior calculation in light of Canny calculation. This calculation presented the idea of gravitational field power to supplant picture angle and got the gravitational field force administrator. Two versatile limit choice strategies in view of the mean of picture inclination greatness and standard deviation were advanced for two sorts of average pictures (one has less edge data, and different has rich edge data) separately. The superior Canny calculation is basic and simple to understand.
Argument Mining using BERT and Self-Attention based Embeddings

Argument mining automatically identifies and extracts the structure of inference and reasoning conveyed in natural language arguments. To the best of our knowledge, most of the state-of-the-art works in this field have focused on using tree-like structures and linguistic modeling. But, these approaches are not able to model more complex structures which are often found in online forums and real world argumentation structures. In this paper, a novel methodology for argument mining is proposed which employs attention-based embeddings for link prediction to model the causational hierarchies in typical argument structures prevalent in online discourse.