Reviewerly/exHarmony huggingface dataset:
TransCP: A Transformer Pointer Network for Generic Entity Description Generation With Explicit Content-Planning

We study neural data-to-text generation to generate a sentence to describe a target entity based on its attributes. Specifically, we address two problems of the encoder-decoder framework for data-to-text generation: i) how to encode a non-linear input (e.g., a set of attributes); and ii) how to order the attributes in the generated description. Existing studies focus on the encoding problem but do not address the ordering problem, i.e., they learn the content-planning implicitly. The other approaches focus on two-stage models but overlook the encoding problem. To address the two problems at once, we propose a model named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TransCP</i> to explicitly learn content-planning and integrate them into a description generation model in an end-to-end fashion. We propose a novel Transformer-based Pointer Network with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gated residual attention</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">importance masking</i> to learn a content-plan. To integrate the content-plan with a description generator, we propose a tracking mechanism to trace the extent to which the content-plan is exposed in the previous decoding time-step. This helps the description generator select the attributes to be mentioned in proper order. Experimental results show that our model consistently outperforms state-of-the-art baselines by up to 2% and 3% in terms of BLEU score on two real-world datasets.
View-dependent Projection Mapping Enhanced by Real Background

"Spatial augmented reality (SAR)" or "projection mapping" projects an image of a virtual object on the surface of a real object. A "view-dependent" display shows the virtual object with its correct appearance for an arbitrary viewer's position. If the virtual object and the real object have different shapes, the virtual object's image to project needs to be correctly distorted according to the viewer's position. Besides, the difference causes the real object's surface to have some empty areas onto which the virtual object is not projected. Such empty areas seriously degrade the viewer's feeling that the virtual object merges into the real world. We propose a method to eliminate undesired empty areas by projecting the real background behind the real object in a view-dependent way. Our method converts a real background's image captured by a fixed camera to an appropriate image for a viewer's position based on homography. This image conversion approximates the background's shape by a plane adjusted for an arbitrary viewer's position. The plane is determined by practical parameters interpolated on a 3D grid space. Consequently, the viewer does not feel the presence of the real object and feels the virtual object merging into the real world.
Design of Exergaming for Children and Adolescents

Exergaming are those physical activities that require participants to be physically active or exercise to play the game. To participate in virtual sports, group fitness exercises, or other interactive physical activities, the user must use full-body motion. This Chapter have complete study of several possible environments for the need of exergaming for the Children and Adolescents. A complete analysis of situations, scenarios and nature of interest of children and young adults has been considered in this chapter. As it begins with problems associated with young age group in modern scenario and Covid-19 is also considered as a case study. Along with some ideation, there is a sincere effort to solve these problems with creative and innovative approach. And at the end there is description of complete technology used, methodologies involved in it, description of processes as technical input for exergaming.
Recommender Algorithms Do No Harm ~90% But… An Exploratory Risk-Utility Meta-Analysis of Algorithmic Audits

We obtain a quantitatively coarse-grained, but wide-ranging evaluation of the frequency recommender algorithms provide ‘good’ and ‘bad’ recommendations. We found 146 algorithmic audits from 32 studies that report fitting risk-utility statistics from YouTube, Google Search, Twitter, Facebook, TikTok, Amazon, and others. The vast majority of algorithmic recommendations do no harm (around 90 %), while about a quarter of recommendations safeguard humans from self-induced harm (‘do good’). The frequency of ‘bad’ recommendations is around 7-10 % on average, which poses a potential risk that is notably higher than risks posed by other consumer products. This average is remarkably robust across the audits and neither depends on the platform nor on the kind of harm (bias/ discrimination, mental health and child harm, misinformation, or political extremism). Algorithmic audits find negative feedback loops that lock users into spirals of ‘bad’ recommendations (or being ‘dragged down the rabbit hole’), but they find an even larger probability of positive spirals of ‘good recommendations’. Our analysis refrains from any qualitative judgment of the severity of different harms. As concerns for ‘AI alignment’ with human values grow, necessitating more algorithmic audits, our study offers preliminary figures for quantitative comparison to other contemporary risks of modern life.
Learning attention-based strategies to cooperate for multi-agent path finding

Multi-agent path finding (MAPF) is a challenging multi-agent systems problem where all agents are required to effectively reach their goals concurrently with not colliding with each other and avoiding obstacles. In MAPF, it is a challenge to effectively express the observation of agents, utilize historical information, and effectively communicate with neighbor agents. To tackle these issues, in this work, we proposed a well-designed model that utilizes the local states of nearby agents and outputs an optimal action for each agent to execute. We build the local observation encoder by using residual attention CNN to extract local observations and use the Transformer architecture to build an interaction layer to combine local observations of agents. With the purpose of overcoming the deficiency of success rate, we also designed a new evaluation index, namely extra time rate (ETR). The experimental results show that our model is superior to most previous models in terms of success rate and ETR. In addition, we also completed the ablation study on the model, and the effectiveness of each component of the model was proved.
Data in CIB

This chapter addresses the three fundamental data objects needed for CIB analysis: descriptors, descriptor variants, and cross-impact data. The basic aspects of their role were described in the method description in Chap. 3 . These basic aspects will be deepened here by “dossiers” on the data objects. The dossiers discuss terminology, types, methodological requirements, and elicitation procedures commonly used in practice.
Toward Natural Language Processing Approaches for Text

Natural language processing is the field of study that includes multiples domains such as linguistic, informatics and artificial intelligence. In this paper, we explore the development of this field and introduce the different approaches used to deal with several task of natural language processing of the text. Using the tools offered by the field cited above and choosing the appropriate approach, the computers analysis texts, so this can be exploited in numerous use-cases. This field has become very important and popular in research in all its five levels: morphological, syntactic, semantic, discourse sentence and pragmatic. These researches had given us fundamental task such as part of speech tagging, named entity recognition. This leads us to make a comparison of these different approaches on different tasks. Also we evaluate their accuracy on different languages, and we show that the lack of data-sets influences negatively the results whatever the language is or the chosen approach especially the machine learning ones.
Emerging IoT Platforms Facilitate Entrepreneurship Businesses

In the current digital era, entrepreneurship has become a key differentiating reason in an extremely disruptive and competitive business environment to absorb constantly growing consumer demands. However, there is a need to comprehend the nuisances of digital entrepreneurship and its conceptual framework to navigate the associate businesses. IoT has a strong transformative potential to impact such entrepreneurship businesses. This paper discusses about the basic concept of IoT and briefly presents its security architecture. IoT connects diverse business entities and ‘things’ with real time data which is analyzed and combined with supplementary data in the cloud over varying platforms. This paper brings out briefly about some key platforms and their applications in entrepreneurship businesses. In this digital environment cognitive computing, artificial intelligence, machine learning and many other emerging technologies are driving new business capabilities with significant opportunities. Some prominent advantages of IoT platforms have been outlined in this paper. Different IoT application over distinct platforms are contributing to transform consumers’ lives and changing their entrepreneurship business operations. Yet, these platforms are beleaguered with certain challenges which have been briefly discussed in this paper. Today, almost 40 billion ‘things’ are connected over the Internet. IoT appliances are likely to generate approximately 180 zettabytes data annually by 2025. Massive entrepreneurship business activities in future will need powerful and sophisticated analytical engines to support it. By highlighting some significant future prospects of IoT supported entrepreneurship businesses, the paper has been concluded.
Correlative Exposition of Various Machine Learning Techniques for Urdu Handwritten Text Recognition

One of the most under-researched area in the field of natural language processing (NLP) has been the text classification. Text classification of languages like Urdu is more deprived of research since it is not among the top-most spoken language of the world. Despite that, almost 230 million people around the world speak Urdu. Researchers have worked with various machine learning and deep learning models for the identification of handwritten text of Urdu language. This paper is the comparative analysis of the various algorithms that have been used on the offline as well as online text recognition of Urdu language. From our analysis, CNN is the most widely adopted model for recognition of Urdu handwritten text due to its excellent properties of image classification as well as feature extraction. This paper gives a brief of various machine learning algorithms used for recognition of Urdu text and their comparative analysis.
Comparative Analysis of Transfer Learning Models in Classification of Histopathological Whole Slide Images

Histopathology is essential in medical imaging. Consequently, automatic histopathological computer vision significantly influences the overall affordability, reliability, and accessibility of healthcare. Histopathological examination is the diagnostic and research of tissue disorders. An integrated suite of histopathological samples has greatly aided doctors and researchers in the world of clinical research. Identifying cancerous tissues is critical for clinicians in providing appropriate oncology treatments. A whole slide image (WSI) is a digitized scanning of the tissues on the glass slide that allows the samples taken to be stored digitally on the computer system in the form of a digital picture. India is witnessing more than one million cases of breast cancer per year. WSI processing and storage have greatly aided professionals while encouraging researchers to develop more reliable and efficient fully automated analysis diagnosing models. ML, specifically DL-based models. DL models have outperformed in various disciplines, along with clinical applications and profound features in healthcare. In this study, we have exemplary tunned four models with a transfer learning approach to classify histopathical images. The validation accuracies achieved after fine-tuning these models are 91% for MobileNet V2, 90% for NasNet Large, 96% for Xception 94.43%, and a benchmark accuracy of 96.95% for EfficientNetV2L which is the latest model in Keras. We have plotted the confusion matrix for each model.
An Optimized Search-Enabled Hotel Recommender System

Nowadays, it is very challenging and confusing for the users to identify a well-suited hotel according to their requirements. Hotels are also increasing tremendously with a broader range of amenities. Many applications exist that recommend hotels based on various criteria such as location, rating, best offer and prices, amenities. In this paper, to minimize time and cost, we propose an optimized recommendation system using machine learning model based on multiple criterion and various filtering methods. We simulate the performance of different machine learning (ML) models and select the best model for hotel recommendation based on multiple performance metrics.
A Survey on Different Security Frameworks and IDS in Internet of Things

Internet of things has significantly revamped the whole dynamics of communication. It consists of multiple heterogeneous devices which exchange and receive data over the Internet with phenomenal ubiquitous connection. The heavy penetration of these devices into everyone’s life poses diverse nature of cyber security threats. IoT devices are susceptible to vast range of attacks due to their limited computation capabilities, low power and memory constraints. Even within one IoT standard, a device typically has multiple options to communicate with other devices. Various security measures have been proposed to counter these attacks from time to time. Intrusion detection system are excellent frameworks to save these devices from such vulnerabilities. This paper aims to conduct a deep, systematic and comprehensive survey on different IDS and security frameworks. Also, a novel security framework based on deep learning algorithms and blockchain platform has been proposed for this study.
Dark Channel Prior-Based Image Dehazing Algorithm for Single Outdoor Image

Hazed image is a critical issue for scene understanding which need to be mitigated properly by applying an image dehazing algorithm. Single image dehazing is a challenging preprocessing task of image vision for object detection. This paper presents a dark channel prior (DCP)-based image dehazing method to remove haze from a single outdoor input image. The DCP receives great attention in the field of dehazing/defogging because it takes advantage of the property that some pixels in at least one of the RGB channels have the lowest intensity which tends to zero resulting in the diversion toward dark or black. The presented algorithm divides a scene image into multiple regions guided by brightness estimation. All regions are processed separately for dehazing which includes normalization, transmission map estimation, depth estimation, and radiance recovery. All the regions are processed separately and combined using adaptive region merge to generate a haze-free image. The efficiency of the proposed method is evaluated in terms of MSE, PSNR, and SSIM and compared same with the result of the methods in the literature. The presented method is very much effective in fog removal from an input image captured outdoor containing trees, mountains, buildings, sky, etc. Experimental results indicate that the proposed method gives better results on Dense-Haze CVPR 2019, NH-HAZE, and O-HAZE datasets.
Analysis of Breast Cancer Prediction Using Machine Learning Techniques: Review Paper

Now-a-days machine learning plays an important role as there are a big number of human diseases detected which should be cured within the right time. So that the appropriate treatment or medicine is provided at the right time to save the life of a person. The result should be accurate and consume less time as well. Machine learning is a child of AI and it includes deep learning which mainly focuses on images. Consequently, these provide ultimate performance at the prediction/detection time. Machine learning provides the best solution in predicting or detecting breast cancer and evaluates the performance. Machine learning (ML) has been slowly entering every sector of our lives and its positive impact has been amazing. Today cancer is although a curable disease in early stages but most of the cancer patients are recognized in late stages. With the help of high-level specialists and machine learning techniques cancer can be detected. The aim of this review paper is to have an overview of thirteen studies predicting and then further classifying breast cancer. Through this article various machine learning algorithms and methodologies are being surveyed for breast cancer diagnosis. This paper presents theoretical and non-theoretical papers that are executed.
Container Division and Optimization (CDO) Algorithm for Secured Job Processing in Cloud

Generally, container resource allocation and job scheduling are the major concern in cloud computing. To produce proper cloud virtualization and to perform an exact allocation, migration process is opted. Several preceding methods opted for migration techniques to produce an appropriate cloud container allocation and resource scheduling. But, only migration is not enough to process a job scheduling and resource allocation. The system proposes a container division and optimization (CDO) algorithm that follows a three set of stage in job scheduling and processing. (1) ‘Entry-level/Bottleneck container’ to handle and processes allocation for user data. (2) ‘Processing container’ to perform the job scheduling and maintain the secrecy of the user job (3) ‘Exit-level container’ to cross-check both the container and user performance. Further, the system proposes a reduced migration model that performs migration only if highly needed (lack of space). Hence, the experimental results proves that the proposed algorithm is effectively performing a secured optimized job scheduling in the cloud.
Comparative Analysis of Performances of Convolutional Neural Networks for Image Classification Tasks

The main success of CNNs is in their capability to learn features automatically from domain-specific images unlike the other machine learning schemes. During training phase, CNN architecture utilizes transfer strategy of learned knowledge from a pre-trained network in one previous task to a new task. Traditional classification schemes have numerous weaknesses such as time consumption and subjectivity. Recently, a CNN built on deep learning holds prospects in the estimation and extraction features for improved image classification. This paper experiments the performances of CNNs classification using binary and RGB color images, which were collected from MINST and CIFAR-10. Two CNN-based models were proposed, trained and validated on Google Colaboratory simulator with 50,000 and 10,000 samples of binary and RGB color images, respectively. The results indicated that the CNN model with RGB color images outperformed CNN model with binary images in terms of average simulation time (14.20–29.00 ms). Conversely, the CNN with binary images was superior to the CNN with RGB images for loss function (3.20–88.30%) and accuracy (99.10% to 71.06%), respectively. These outcomes were better than comparable studies using the average duration and accuracy.
A Secure Authenticated Key Agreement Protocol Using Polynomials

Data communication is now an inseparable part of our day-to-day life. There are various aspects of data security, and authentication and key agreement are dominating among them in recent security work. Thus, key establishment and agreement is an important problem of study. Intruders always try various decoding methods to crack the security. Motivated from the same, it is now essential to present new authentication and key agreement protocols in order to surprise intruders. In this paper, a couple of innovative key agreement protocols are presented using the platform of non-commutative ring which is relatively a new platform and hence very lucrative for the development of new protocols. The correctness and working example are also given in order to verify the feasibility of the proposed protocols.
Ride-Sharing Service Based on Ethereum

A ride-sharing service is a centralized system in which riders share the rides. Such ride-sharing service provides tracking, fare calculation, and cancelation of rides. Such a centralized system suffers from an issue such as single point of failure, fairness of the system, surge pricing, reliance on a third party, and privacy disclosure. Such systems are even at the risk of Sybil and distributed denial of service (DDoS) attacks. In the past, breach of confidential data has also been an issue such as the UBER data attack. Ethereum-based ride-sharing services permit riders and drivers to use ride-sharing systems without depending upon arbitrators. Ethereum allows the creation of a decentralized ride-sharing service. All ride-sharing functions like fare assessment, route management, and rider and driver details are stored on Ethereum. It makes sure the reliability, confidentiality, and integrity of data are achieved by using public and private keys. Smart contracts are used to deploy the functions in the blockchain. The purpose of this paper is to develop an Ethereum-based decentralized system for ride-sharing where all the activity related to ride-sharing such as user, route, and ride-generated data will be stored on the Ethereum blockchain. This makes the ride-sharing network more dependable and unambiguous. As the data stored on Ethereum-based smart contract is immutable, that makes the stored ride-related information more secure and confirms user privacy.
Performance Analysis of User Authentication Schemes in Wireless Sensor Networks

Wireless sensor networks (WSNs) have a broad range of applications in most of the sectors which require a high level of security. These networks provide sensor data to the external users on a real-time basis; thus, it is a good idea to provide them immediate access. User authentication is an essential requirement to access the services of sensor areas to prevent unauthorized access. The network and data security are key concerns in WSNs for secure and authentic data communication. Thus, an analysis of a secure and efficient data transmission scheme is considered. In this paper, an analysis of existing user authentication schemes is discussed and provides comparative studies of user authentication schemes for network security. An explicitly simulated one of the user authentication schemes by the AVISPA tool is discussed, and its demonstration is also given which shows the resistant to the cryptographic attacks. The lower communication costs are one of the main parameters, i.e., considered to compared with the previous relevant techniques. Moreover, future directions are also discussed for the existing works.