Reviewerly/exHarmony huggingface dataset:
K-Same-Siamese-GAN: K-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, “how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?” becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also Justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters.
Applicability of colour transfer techniques in Twelve fringe photoelasticity (TFP)

Colour adaptation techniques are useful in extending the method of Twelve fringe photoelasticity (TFP) to solve problems in industries as one can minimise the generation of specific calibration tables for each experiment. Image artefacts affect the functioning of existing colour adaptation techniques affecting the fringe order demodulation. Colour transfer is a popular technique in the field of image processing to tackle colour mismatch between images. In this paper, the applicability of two colour transfer strategies in the context of TFP is explored, of which one is selected for implementation. A comparative study on the performance of the novel colour transfer method with the existing colour adaptation techniques is carried out.
Occlusion Problem-Oriented Adversarial Faster-RCNN Scheme

In the practical scene, object detection faces a very complicated situation. The occlusion problem always occurs in actual scene, which may affect the accuracy of object detection, especially for the occluded objects. For the deep models, a larger dataset with sufficient occlusion samples will improve the performance of the object detection models. However, the sample with occlusion problem is too hard to obtain. Therefore, a global average pooling(GAP) based adversarial Faster-RCNN is proposed to generate the hard samples and enhance the performance of object detection algorithm. Sufficient hard samples can be generated with the help of this model. Therefore, the object detection model can be trained adequately for the occluded objects. The hard sample generation is carried out in the space of image feature instead of image generation directly. The class-dependent part is obtained by the GAP network, and it is obscured to generate the feature map of hard sample for model reinforcement training. Therefore, the better object detection model can be trained using a conventional dataset. The Faster-RCNN is adopted as the baseline. The Faster-RCNN and GAP have a joint training to improve the performance of the proposed model. The simulation results exhibit the validation of the proposed algorithm.
Computational Governance and Violable Contracts for Blockchain Applications

We propose a sociotechnical, yet computational, approach to building decentralized applications that accommodates and exploits blockchain technology. Our architecture incorporates the notion of a declarative, violable contract and enables flexible governance based on formal organizational structures, correctness verification without obstructing autonomy, and a basis for trust.
Mobile robot visual navigation based on fuzzy logic and optical flow approaches

This paper presents the design of mobile robot visual navigation system in indoor environment based on fuzzy logic controllers (FLC) and optical flow (OF) approach. The proposed control system contains two Takagi–Sugeno fuzzy logic controllers for obstacle avoidance and goal seeking based on video acquisition and image processing algorithm. The first steering controller uses OF values calculated by Horn–Schunck algorithm to detect and estimate the positions of the obstacles. To extract information about the environment, the image is divided into two parts. The second FLC is used to guide the robot to the direction of the final destination. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.
Software Metrics and tree-based machine learning algorithms for distinguishing and detecting similar structure design patterns

Design patterns are general reusable solutions for recurrent occurring problems. When software systems become more complicated due to the lack of documentation of design patterns in software and the maintenance and evolution costs become a challenge. Design pattern detection is used to reduce the complexity and to increase the understandability of the design in the software. In this paper, we propose a design pattern detection approach based on tree-based machine learning algorithms and software metrics to study the effectiveness of software metrics in distinguishing between similar structural design patterns. We build our datasets using P-MARt repository by extracting the roles of design patterns and calculating the metrics for each role. We used parameter optimization techniques based on the Grid search algorithm to define the optimal parameter of each algorithm. We used two feature selection methods based on a genetic algorithm to find features that influence the most in the distinguishing process. Through our experimental study, we showed the effectiveness of machine learning and software metrics when distinguishing similar structure design patterns. Moreover, we extracted the essential metrics in each dataset that supported the machine learning model to take its decision. We presented the detection conditions for each role in the design pattern by extracting them from the decision tree model.
Blockchain-Based Identity Verification System

In any registration process, it is a hassle to always bring physical documents. Not only that, the process is elongated if they are lost, and will contribute to identity theft if unauthorized persons have access to those documents. Therefore, the objective of this paper is to generate a decentralized system, similar to the concept of blockchain, to allow registered persons to access user’s personal records. To further elaborate, this system is designed to be used by three consumers, which are: user, authority, and third-party (requester). Nowadays, majority of systems are susceptible to massive data breaches. However, some researches have theorized that blockchain technology may solve this problem, such as one that uses a real-world example, Aadhaar. In conclusion, this project enhances that the area of blockchain identity is vital in helping society gain control of their personal details. Since most of researches are focused on storage system of businesses using blockchain, personal identities of the people should be digitized on the blockchain as well. An identity verification system that is entirely owned by the individual will increase trust that the data is genuine and reliable.
Data Security, Data Breaches, and Compliance

: This chapter explores the attributes of compliance in the context of data breaches. First, it identifies the sort of corporate governance problem that data breaches create. Then, it approaches the empirical work related to data breaches and to the organization of compliance-based responses in terms of risk assessment, training, and compliance, both preemptively and after a breach. Next, the chapter discusses the extant theoretical and empirical evidence about the short- and long-term impacts of IT security events on breached firms as well as corporate governance issues relating to data breaches. It also examines studies that evaluate the impact of different types of event on various types of firm and stakeholder. The chapter also explores how data breaches impact broader issues of corporate governance and compliance. In the end, it identifies potential research questions and avenues for future researchers on how firms or governments might have to think about their IT security investments and the necessary measures that have to be in place to respond effectively if such events occur.
Implementation of Critical Path Method in Project Planning and Scheduling

Abstract Scheduling in a project is a tool to determine the activities needed to complete a project in a certain order and time frame, in which every activity must be carried out so that the project is completed on time at an economical cost. The purpose of this study is to find out the critical path in project implementation and find out which projects are postponed and which cannot be delayed in their completion which can affect the entire project work process so that the project can be completed on time. The method used in this study is the Critical Path Method (CPM), which is a network method that has a series of activity components with the longest total amount of time and shows the fastest time period of completion. The results of this study indicate that the use of CPMs is able to get the longest total amount of time with the fastest project completion period, project scheduling and project critical paths can be seen more clearly so that the project can be completed on time.
Secure Quantum Extraction Protocols

Knowledge extraction, typically studied in the classical setting, is at the heart of several cryptographic protocols. The prospect of quantum computers forces us to revisit the concept of knowledge extraction in the presence of quantum adversaries. We introduce the notion of secure quantum extraction protocols. A secure quantum extraction protocol for an NP relation $$\mathcal {R}$$ is a classical interactive protocol between a sender and a receiver, where the sender gets as input the instance y and witness $$\mathbf {w}$$ while the receiver only gets the instance y as input. There are two properties associated with a secure quantum extraction protocol: (a) Extractability: for any efficient quantum polynomial-time (QPT) adversarial sender, there exists a QPT extractor that can extract a witness $$\mathbf {w}'$$ such that $$(\mathbf{y},\mathbf {w}') \in \mathcal {R}$$ and, (b) Zero-Knowledge: a malicious receiver, interacting with the sender, should not be able to learn any information about $$\mathbf {w}$$ . We study and construct two flavors of secure quantum extraction protocols. Both the above extraction protocols are constant round protocols. We present an application of secure quantum extraction protocols to zero-knowledge (ZK). Assuming quantum hardness of learning with errors, we present the first construction of ZK argument systems for NP in constant rounds based on the quantum hardness of learning with errors with: (a) zero-knowledge against QPT malicious verifiers and, (b) soundness against classical PPT adversaries. Moreover, our construction satisfies the stronger (quantum) auxiliary-input zero knowledge property and thus can be composed with other protocols secure against quantum adversaries.
Deep Learning for Classifying of White Blood Cancer

Cell classification by computer assistant technique has become a popular topic recently. It is crucial to do early disease diagnosis with less cost. In order to find efficient, high accuracy computer assistant methods, The International Symposium on Biomedical Imaging (ISBI) held the classification of normal vs malignant cells (C-NMC) challenge 2019 for classifying B-ALL white blood cancer microscopic images. Our team deploys several state-of-the-art CNN architectures and uses an ensemble method named stacking to boost the final prediction performance, which helped to get the eighth place in the preliminary phase and tenth place in the final phase. Weighted F1 scores evaluated on our model were 0.8674 and 0.8552, for the preliminary and final phase, respectively. It is a relatively reasonable result in such an expertise required task.
A Novel Ambiguous Set Theory to Represent Uncertainty and its Application to Brain MR Image Segmentation

This article presented a new set theory to deal with ambiguousness, which was entitled as an “Ambiguous Set Theory”. The proposed ambiguous set theory can represent any feature into four degrees of memberships, viz., true, false, ambiguous-true and ambiguous-false. This kind of representation provides granular visualization of features, and helps to model uncertainties very effectively. In this article, initially we discussed the motivation to introduce the theory of ambiguous set. Then, we proposed methodology of ambiguous set by: 1) defining it in a precise way, 2) presenting a mathematical representation for the set, and 3) giving various mathematical definitions for the set. Applications of the proposed ambiguous set were demonstrated in human brain MRI segmentation. Various comparison results demonstrated the effectiveness of the theory over existing well-known approaches of the image segmentation.
Re-introducing Cognitive Complexity: A Meta-analysis and Agenda for Future Research

Cognitive complexity represents the extent to which individuals cognitively structure and process their environment in a multidimensional manner. Despite over 60 years of research, gaps remain in our understanding of the construct. In this meta-analytic review (k = 74, n = 18,973), we addressed a set of research questions regarding the nomological network surrounding cognitive complexity as well as the incremental validity and relative weight of cognitive complexity over traditional predictors of job outcomes. Cognitive complexity exhibited moderate correlations with constructs like cognitive ability (ρ = .19) and task complexity (ρ = − .18), but performed poorly in relative weights and incremental validity analyses. Given our findings, we suggest provide avenues for future research to explore and present a measure of cognitive complexity to advance the literature.
Comparison of development methodologies in web applications

Web applications development is at its peak due to the advance of technological trends and the constant dependence of the Internet. As a result of the needs of developers, new development methodologies have emerged. However, that does not mean that companies always implement an optimal development process; instead, there are several disadvantages presented by an inadequate and not versatile methodologies. The aim is to compare web development methodologies based on dynamic features presented during the life cycle to identify their use, relevance, and characteristics. The process employing is an SLR and field research to Ecuadorian development companies. The method used is a systematic literature review (SLR) for the identification of characteristics and processes of development methodologies. Additionally, a survey of Ecuadorian web application developers was implemented to assess the importance of using a method during the project. The literature review exhibited as a result that UWE and OOHDM have greater flexibility than other methodologies before dynamic environments during the web development process. On the other hand, within field research was obtained that companies use different software development methods than those assessed in the study (hybrid methodologies). However, within the range of companies using the compared methodologies, UWE is the most selected. Each methodology holds particular features and employment environment, which makes them useful in specific conditions. Through the field research, it is possible to conclude that most of the companies use different methodologies than the evaluated ones; thus, the process is guided by hybrids methods or models based on experience. On the other hand, through the SLR, we identified UWE as the most suitable methodology for web development under dynamic environments, such as the size of the company, the need to modify the requirements, or the knowledge that the development team has about the process.
Spatio-Temporal Attention Model for Foreground Detection in Cross-Scene Surveillance Videos

Foreground detection is an important theme in video surveillance. Conventional background modeling approaches build sophisticated temporal statistical model to detect foreground based on low-level features, while modern semantic/instance segmentation approaches generate high-level foreground annotation, but ignore the temporal relevance among consecutive frames. In this paper, we propose a Spatio-Temporal Attention Model (STAM) for cross-scene foreground detection. To fill the semantic gap between low and high level features, appearance and optical flow features are synthesized by attention modules via the feature learning procedure. Experimental results on CDnet 2014 benchmarks validate it and outperformed many state-of-the-art methods in seven evaluation metrics. With the attention modules and optical flow, its F-measure increased 9 % and 6 % respectively. The model without any tuning showed its cross-scene generalization on Wallflower and PETS datasets. The processing speed was 10.8 fps with the frame size 256 by 256.
Image and Video Super Resolution using Recurrent Generative Adversarial Network

Recently, the convolutional neural network with residual learning models achieves high accuracy for single image super-resolution with different scale factors. With adversarial learning model, effective learning of transformation function for the low-resolution input image to a high-resolution target image can be achieved. In this paper, we propose a method for image and video super-resolution using the recurrent generative adversarial network named SR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN. In the proposed model (SR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN) we use recursive learning for video super-resolution to overcome the difficulty of learning transformation function for synthesizing realistic high-resolution images. This recursive approach helps to reduce the parameters with increasing depth of the model. An extensive evaluation is performed to examine the effectiveness of the proposed model, which shows that SR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN performs better in terms of peak signal to noise ratio (PSNR) and structural self-similarity index (SSIM) as compared to the state-of-the-art methods for super-resolution. For source code and supplementary material visit: https://github.com/OmkarThawakar/SR2GAN/.
Modeling Data-Driven Dominance Traits for Virtual Characters Using Gait Analysis

We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominance labels. We use our dominance mapping to generate walking gaits for virtual characters that exhibit a variety of dominance traits while interacting with the user. Furthermore, we extract gait features based on known criteria in visual perception and psychology literature that can be used to identify the dominance levels of any walking gait. We validate our mapping and the perceived dominance traits by a second user study in an immersive virtual environment. Our gait dominance classification algorithm can classify the dominance traits of gaits with ˜73 percent accuracy. We also present an application of our approach that simulates interpersonal relationships between virtual characters. To the best of our knowledge, ours is the first practical approach to classifying gait dominance and generate dominance traits in virtual characters.
Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density

Breast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume. Its histology is incompletely characterized. Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density. We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n = 852 patients). Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n = 588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). Using extracted features, regression models predicted global (r = 0.94) and localized (r = 0.93) fibroglandular volume, with fat and non-fatty stromal content representing the strongest correlates, followed by epithelial organization rather than quantity. For predicting cancer among high and low fibroglandular volume, the classifier achieved AUCs of 0.92 and 0.84, respectively, with epithelial organizational features ranking most important. These results suggest non-fatty stroma, fat tissue quantities and epithelial region organization predict fibroglandular volume. The model holds promise for identifying histological correlates of cancer risk in patients with high and low density and warrants further evaluation.
Enhanced Vendor-managed Inventory through Blockchain

Vendor-managed inventory (VMI) is a commonly used collaborative inventory management policy in which manufacturers/vendors manage the inventory of retailers and take responsibility for making decisions related to the timing and extent of inventory replenishment. Several prerequisites exist for successfully implementing a VMI strategy like information sharing, trust, systems integration and long-term collaboration. However, in nowadays supply chain networks are becoming more complex, highly disjointed and geographically spread. As a consequence, the implementation of a VMI strategy may be a difficult task. In this paper, we propose a new interaction mechanism between retailers and vendors, which aims to improve their supply chain strategy and inventory policies based on a trustless and distributed mechanism. In particular, we use an autonomous trustless framework based on smart contracts and blockchain technology for governing the relationship between multiple vendors and multiple retailers. Finally, a use-case VMI scenario is presented along with several functional smart contracts. Tests performed using a local private blockchain illustrate the applicability of the proposed architecture along with the significant benefits for each participant.