Reviewerly/exHarmony huggingface dataset:
Relative Research on Psychological Character and Plot Design Preference for Audiences of VR Movies

VR movies have been a new trend in recent years, and interactive VR films are getting popular among young audiences. The high-tech environment, sense of immersion and participation and decision in making choices at fork points in the story are all so appealing the audience are part of the movie, and their decision may change the flow and even the ending of the plot. Outstanding interactive VR movies can provide chances that can go up to a hundred choices, and more than 10 different endings, which would be heavy workload and huge budget for the producing team, including the screenwriters. The thesis reviewed and collected the development of VR movies and relevant theories on personality test and screenwriting, to find the barriers that hinder VR movie script and plot development. The purpose is to investigate the audience’s intuitive feelings and expectations of watching VR movies, as well as their understanding and acceptance of the story, to explore the relationship between personality and decision making at turning points of each fork of the pitchfork bifurcation structure plot. The authors hope to find an efficient way to lead the audiences to an ending which seems to be chosen by themselves rather than the writers, although it is within the designer’s expectation.
Utilizing the State-of-The-Art Application of Machine Learning for Behind Casing Opportunity (Bco) Analysis Using Multi-Output Regression

Generally, Behind Casing Opportunity (BCO) provides a good alternative to maximize brownfield production by enabling access to the remaining oil targets behind existing completion. Proper BCO maturation which includes minor reservoir unit analysis may offer a high return, low-risk item in terms of obtaining cheap oil with low cost. Unfortunately, lack of proper resources such as possible manpower and budget constraints may pose a challenge for proper BCO analysis. A novel application of multioutput regression Random Forest algorithm to predict both BCO and fluid code for BCO determination marks a good start for further implementation of machine learning to tackle this problem. The multi-output model enables two or more variables to be predicted hence allowing uniformed prediction rules and time-saving alternatives. Exploratory data analysis (EDA) and required data preprocessing were carried out to provide excellent inputs for the algorithm. The algorithm produced two outputs of predicted BCO and fluid code with root mean squared error (RMSE) of 0.0933 and R2 of 0.9619. To properly support the logic of the model prediction, the log curve of both predicted values was plotted and the rationale behind the predicted result was observed. Besides, a good cross-plot correlation of both predicted and actual value of the outputs also aided in further validating the result. The research potentially can help to further enhance BCO analysis by giving robust and very effective methods for both BCO and fluid detection. Furthermore, predicting fluid code helps for proper reservoir analysis hence providing a time-saving alternative for better drilling decisions.
Unfolding Handcrafted Features Contribution in Cnns for Valence and Arousal Estimation in Images

In this paper, we shed some light on the impact of combining handcrafted with learned features for the estimation of valence and arousal values. First, we identified 30 discriminant handcrafted visual and semantic features for the estimation of valence and arousal using statistical analysis. Then, we analysed the impact of using the discriminant handcrafted features on three well-known CNN architectures (VGG16, InceptionV3, ResNet50) using transfer learning, and a Multilayer Perceptron (MLP). Experiments conducted on nine benchmark datasets revealed that the use of color and semantic information with the ResNet50 has yielded promising results for valence. For arousal, deep features from the ResNet50 achieved better results than its combination with the handcrafted features. The MLP achieved superior results than the ResNet50 for both valence and arousal using only one semantic feature.
Classification of Cervical Squamous Cells

Cancer is considered second most cause of mortality, accounting for one out of every six deaths worldwide. As per the statistics around 9.6 million deaths were caused in 2018. 70% death in low and middle-income countries. Almost 0.3 million deaths per year in India. Pap tests, is the procedure to test for cervical cancer in women. It examines the cervix for the existence of cancerous or precancerous cells. Cervix is the inferior portion of the uterus that leads to the vaginal opening of the uterus. Human Papillomavirus (HPV) the sexually transmitted disease place the role causing most in cervical cells. It also detect changes in the cervical cells that propsed cancer may growth in future. Basically, a pap test can be done in two ways, one is liquid-based and the other is a conventional pap smear. Different algorithms can be used to identify the cancerous cells from a microscopic image. Image division is considered among the most significant image processing processes. The technique of dividing or partitioning an image into bits, called segments, is known as image segmentation. It's mainly useful for image compression and object recognition applications since processing the entire image is wasteful in these cases. As a result, image segmentation is used to separate the image's parts for further processing. There are many other strategies for image segmentation, dividing the imaged into so many components dependent on image characteristics including pixel intensities, texture, colour etc. The segmentation approach is used to categories both of these techniques.
Prioritization of Crowdsourced Testing Reports

In Chaps. 8 and 9 , we have mentioned that it is often time-consuming and tedious to manually inspect all received test reports. Besides automatic classification of test reports introduced in Chap. 8 and duplicate detection of test reports introducted in Chap. 9 , this chapter seeks for other alternatives for managing these test reports, i.e., to prioritize test reports for manual inspection.
Fostering Childcare E-Service

A novel and vibrant mobile computing application has been designed and developed that facilitates a well-organized far-off active interaction amongst its stakeholders constantly, mainly a parent and babysitter/nanny who heeds on updating about the current situation of a child. Basically, the key idea to provide an online childcare service aims towards constructing an app that supports the mothers to virtually monitor, track, manage and timely take sound care of their children from a distant place. On duty parents or guardians feel connected on having up-to-date information on the child status literally in the palm of a hand and can do the job tasks with utmost focus, contentment and peace of mind. Performance evaluation of the embedded features and functionality of the proposed strategy confirms its ease and utility on a regular day-to-day basis towards fostering quality parenthood for little kids. Furthermore, this research work serves as a usable reference in varied domains for the students engineering their own software applications.
Robust Segmentation of Nodules in Ultrasound-B Thyroid Images Through Deep Model-Based Features

Ultrasound imaging is used to perform diagnosis on organs of the human body. Segmentation of thyroid nodules has been a pressing problem in recent time. The application of deep learning has served as a solution. This research work uses ultrasound-B images of the thyroid obtained from an open-sourced database by the National University of Colombia. Images from 389 patients and their subsequent masks annotated by experts were collected and served to a modified U-Net style deep learning model with a 70:30 train–validation split. Robust segmented masks were obtained with a pixel accuracy of 93.3% and an intersection over union of 0.56 on the validation set. The proposed model outperformed the state-of-the-art U-Net-plus-plus model which yielded around 45% validation pixel accuracy and 0.22 intersection over union. Image similarities over 99.7% were obtained between the predicted masks and the labels of the validation set. The proposed model was deployed at http://thy-seg.herokuapp.com/ as a Web application. It is proposed that the model could potentially aid medical professionals for localization of thyroid nodules in ultrasound images.
Animated and Unmoderated: A Usability Evaluation Case Study of A/B Designs with SAS Analytics Customers

Designing complex interactions with prompt user feedback is crucial in fast-paced product development processes such as agile or CI/CD. This case study presents an effective method for obtaining rapid user feedback on a complex interaction in a desktop software interface using embedded animations in a remote and unmoderated setting. The study examined the interaction between single-selection, multiple-selection and a property panel that displays key metadata about the selected asset. The interactions were animated for both the keyboard and the mouse groups. Results from pilot studies informed the design of the final studies to better evaluate the differences between an existing Design A and a newly proposed Design B. All studies used a total of 4 simulated animations to display the A and B designs for both the keyboard and mouse groups. The participants could view these animations as many times as needed to select their preferred designs and provide comments on their preferences. The results showed that most users preferred Design B for the keyboard group. Users also preferred Design B with the mouse group, however the difference was not as strong as with the keyboard group. Findings did show that a segment of analytics-focused users did have a stronger preference for Design B with the mouse group. Future studies may serve to evaluate the mouse patterns in more detail. The animated simulations illustrate complex interactions more effectively than static text or images. Embedding such animations for remote and unmoderated evaluations has the potential to reach a larger global audience much faster than it would with evaluations that rely on discrete prototypes or developed code.
Collecting Preference Rankings under Local Differential Privacy

With the deep penetration of the Internet and mobile devices, preference rankings are being collected on a massive scale by diverse data collectors for various business demands. However, users&#x2019; preference rankings in many applications are highly sensitive. Without proper privacy protection mechanisms, it either puts individual privacy in jeopardy or hampers business opportunities due to users&#x2019; unwillingness to share their true rankings. In this paper, we initiate the study of collecting preference rankings under local differential privacy. The key technical challenge comes from the fact that the number of possible rankings could be large in practical settings, leading to excessive injected noise. To solve this problem, we present a novel approach SAFARI, whose main idea is to collect a set of distributions over small domains which are carefully chosen based on the riffle independent (RI) model to approximate the overall distribution of users&#x2019; rankings, and then generate a synthetic ranking dataset from the obtained distributions. By working on small domains instead of a large domain, SAFARI can significantly reduce the magnitude of added noise. In SAFARI, we design two transformation rules, namely Rule I and Rule II, to instruct users to transform their data to provide the information about the distributions of the small domains. In particular, we propose a method called LADE to precisely estimate the required distributions used for the structure learning of RI model. We also propose a new LDP method called SAFA for frequency estimation over multiple attributes that have small domains. We formally prove that SAFARI guarantees <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula>-local differential privacy. Extensive experiments on real datasets confirm the effectiveness of SAFARI.
Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks

We introduce Dynatask: an open source system for setting up custom NLP tasks that aims to greatly lower the technical knowledge and effort required for hosting and evaluating state-of-the-art NLP models, as well as for conducting model in the loop data collection with crowdworkers.Dynatask is integrated with Dynabench, a research platform for rethinking benchmarking in AI that facilitates human and model in the loop data collection and evaluation.To create a task, users only need to write a short task configuration file from which the relevant web interfaces and model hosting infrastructure are automatically generated.The system is available at https:
Optimization Design of Privacy Protection System Based on Cloud Native

In order to meet the needs of group enterprises for data privacy protection processing in the internal private network environment, this paper proposes an optimization design method of Privacy Protection System (PPS) based on cloud native. The main goal of this method is to transform structured data using data anonymization techniques to mitigate attacks that could lead to privacy breaches. From the perspective of logical architecture, this method supports the anonymization of data using the privacy protection model and related parameters selected by the user. First, the identifiers are removed from the dataset to be processed, and constraints are imposed on the quasi-identifiers. Further, the algorithms for protecting sensitive properties that require certain assumptions about the attacker’s goals and background knowledge are also supported. In particular, in the process of anonymization, the scheme introduces the methods of utility analysis and risk analysis, so that the anonymization results can be accurately evaluated. Finally, the proposed method allows users to iteratively update the privacy-preserving model and related parameters according to the results of the anonymization evaluation. From the perspective of technical architecture, the proposed method uses Spring Boot as the back-end framework and MyBatis as the persistence layer framework. At the same time, in order to ensure system security requirements, Json Web Token is also used for user authentication. Finally, when designing the system deployment scheme, cloud native technology is introduced to encapsulate system functions into microservice containers, and cluster management tools are used to dynamically manage microservice containers to ensure high availability of the system.
An Estimation of Distribution Algorithm for Selecting Hand Colors

A dynamic estimation of distribution algorithm is presented for the problem of selecting hand colors which is formulated as a dynamic multi-objective optimization model. The proposed algorithm takes full advantage of the correlation among the CbCr (Cb stands for chrominance blue and Cr stands for chrominance red) vectors of hand colors and their dynamic law, which is divided into the following two stages, with each having its unique algorithmic characteristic. In the first stage, an interval method is employed to obtain a part of hand CbCr vectors which are in a rectangular region. Then new candidates are achieved by sampling around the non-dominated solutions in the second stage to form an offspring population for the subsequent evolutions. The advantage of the proposed algorithm lies in that the obtained non-dominated solutions contain CbCr vectors of a hand region and the hand region is segmented accurately from its background region by the CbCr vectors. The proposed optimization model and algorithm are applied to actual segmentation of dynamic hand-region, and the proposed algorithm is compared with two existing algorithms. The experimental results demonstrate that the proposed algorithm is effective and superior to the compared counterparts.
Model Construction of “Kawaii Characters” Using Deep Learning

In recent years, “kawaii” has been attracting attention as an affective value in manufacturing for various purposes. One example is the use of “kawaii characters” in marketing, PR and advertisement for several target groups especially young people. However, those “kawaii characters” have been designed and used intuitively without systematically evaluating their kawaii degree for target group. Since different target groups might have different preferences for kawaii characters, only intuitive design of kawaii characters might not fulfil their satisfaction and attract enough attention as expected. Therefore, this study proposes a systematic method to evaluate kawaii characters by constructing a model to classify kawaii characters with different physical attributes. To construct “kawaii character” dataset, we firstly prepared ten standard characters as images. Then, for each standard character, we prepared four different variations for each of these six physical attributes: eyebrows, eyes, mouth, facial (cheek) redness, clothing, and hair accessories. Next, we conducted a questionnaire to evaluate the kawaii degree of each kawaii character, and calculated it as “kawaii score”. Using the questionnaire results, we built a dataset containing a total of 120 images of kawaii characters and their corresponding kawaii scores. The dataset was used to construct a model using Deep Convolutional Neural Network (CNN) algorithm, which is a binary classification of kawaii characters into “kawaii” and “not-kawaii” group. Finally, we evaluated the classification performance of the model to confirm its performance for evaluating kawaii characters.
NetImmerse - Evaluating User Experience in Immersive Network Exploration

The increasing amount of interconnected data has given rise to a need among researchers and practitioners to develop new approaches to visualizing network structures. The intricacy of such structures vastly exceeds the capacity of most conventional approaches to network visualization in terms of dimensional and resolution restrictions, as they are mostly presented as two-dimensional with on a limited size screen. An additional limitation of traditional network visualization tools from a human–computer interaction standpoint is the limited interaction itself where immersion and “deep-diving” into high-dimensional data is not possible. We built NetImmerse, an application to visualize network data in a virtual environment with the ability to overview, zoom, and request details on-demand. Within the virtual space, users can either walk around the 3D data representation or rotate and move the representation using the two controllers. We tested the application with users and simulated a representative use case. NetImmerse enabled the participants to gain accurate insights based on the defined task. Participants indicated a PU of 5.25 and a PEOU of 5.46. We believe that NetImmerse is an engaging platform for multi-dimensional data exploration and may result in better insights and enhanced network data exploration.
Technological Interventions During the COVID-19 Pandemic in India

As COVID-19 pandemic has afflicted countries the world over, and governments have taken a variety of steps to curb the spread of the virus. India like many other countries had taken very stringent measures early on to contain and curb the spread of the virus. This chapter examines the technological interventions implemented in India at national, state, and local levels in the government’s “war” against COVID-19. In order to contain and curb the spread of the virus, various geospatial, information, communication, and surveillance technologies have been used. These technologies are being used by the government for contact tracing, tracking, monitoring, and managing crowds for strict enforcement of quarantines and lockdowns. A comprehensive examination of the technological interventions raises social and ethical questions about the efficacy of technology use during the pandemic. The measures exacerbate social inequities and deepen digital divide. Moreover, the measures taken in the war against COVID-19 further the government agenda of building a Digital India that has promoted instituting a system that actively seeks to create a “geo-coded” landscape that enables government authorities to surveil, trace, track, delegate, and govern from a distance.
A Secure ‘e-Tendering’ Application Based on Secret Image Sharing

Since an ‘e-Tendering’ application allows bidders to submit e-tenders which are confidential documents, the security of these documents is the highest requirement of any ‘e-Tendering’ system. The party inviting the e-tender (may be an organization or government) or any entities administrating the ‘e-Tendering’ system (known as e-tender committee members) has the ability to see every e-tender submitted to the system. In the presence of multi-authority, any dishonest authority may compromise the confidentiality of the e-tenders, and identification of the dishonest authority is difficult. Traditional cryptographic approaches are not enough to provide complete security to the ‘e-Tendering’ systems. A secret sharing-based approach can be utilized to provide a cost-effective solution. If the e-tenders are submitted in the form of images, a type of secret sharing known as secret image sharing may be applied, which ensures that the authorities individually or in collusion with a certain number of authorities cannot compromise the confidentiality of the e-tenders. In this paper, we propose a mobile application, i.e., an ‘e-Tendering’ application based on an (n, n) secret image sharing scheme. We consider that the bidders upload their tender images through the app as per the requirement of the tender inviting organization. The application is implemented to encode each secret tender-image to n number of share images (none of the share images discloses any information stored in the secret image). Thus, each of the n authorities of the organization receives exactly one share image. The recovery of the secret tender image is possible in the presence of all the n share images. Thus, it requires all the authorities to be involved in retrieving the e-tender details. The underlying secret image sharing scheme of the system is based on basic Boolean operations, which guarantees high performance.
Network Embedding with Dual Generation Tasks

We study the problem of Network Embedding (NE) for content-rich networks. NE models aim to learn efficient low-dimensional dense vectors for network vertices which are crucial to many network analysis tasks. The core problem of content-rich network embedding is to learn and integrate the semantic information conveyed by network structure and node content. In this paper, we propose a general end-to-end model, <b>D</b>ual <b>GE</b>nerative <b>N</b>etwork <b>E</b>mbedding (DGENE), to leverage the complementary information of network structure and content. In this model, each vertex is regarded as an object with two modalities: node identity and textual content. Then we formulate two dual generation tasks, Node Identification (NI) which recognizes nodes&#x2019; identities given their contents, and Content Generation (CG) which generates textual contents given the nodes&#x2019; identities. We develop specific Content2Node and Node2Content models for the two tasks. Under the DGENE framework, the two dual models are learned by sharing and integrating intermediate layers. Extensive experimental results show that our model yields a significant performance gain compared to the state-of-the-art NE methods. Moreover, our model has an interesting and useful byproduct, that is, a component of our model can generate texts and nodes, which is potentially useful for many tasks.