Reviewerly/exHarmony huggingface dataset:
Deploying an Application using Google Cloud Platform

Cloud computing is an emerging technology being used in every area. Conventional organization use IT infrastructure, which is not scalable according to their requirement. Organizations shifting their workload to cloud for enhancing their performance, scalability and also for reducing cost. Cloud computing is used for deploying the hospital management system available anywhere and at any time. Here, the administrator performs the action on three modules i.e. doctor, patient and rooms allocation, where the administrator can view and access the details. Generally, there are many public cloud computing providers such as AWS, IBM Smart Cloud, GCP, and many others. This proposed model uses GCP as it is rising cloud computing platform with varieties of services like storage technologies, various kind of databases, secure networking technologies, machine learning platforms, computing capabilities and hosting of application.
StainCNNs: An efficient stain feature learning method

Color variation in stained histopathology images prevents the development of computer-assisted diagnosis (CAD) algorithms for whole slide imaging systems. Therefore, stain normalization methods are studied to reduce the influence of color variation combined with digital image processing algorithms. The Structure Preserve Color Normalization (SPCN) method is a promising stain normalization method, utilizing the sparse non-negative matrix factorization to estimate the stain feature appearance matrix. However, the SPCN method suffers from the high computational complexity of dictionary learning, and its official implementation relies on Matlab and CPU. This research proposes the StainCNNs method to simplify the process of stain feature extraction, and imply a GPU-enabled realization to accelerate the learning of stain features in the Tensorflow Framework. What’s more, the StainCNNs method is able to perform the stain normalization quickly in dataset level, more efficient than the SPCN method which is unable to make use of the stain feature distribution in dataset. Stain normalization experiments are conducted on the Camelyon16 dataset and the ICPR2014 dataset, evaluated by the QSSIM score and the FSIM score. Results demonstrate that the proposed StainCNNs method achieves a state-of-the-art performance compared with many conventional stain normalization methods.
On Dubins paths to a circle

This paper is concerned with characterizing the shortest path of a Dubins vehicle from a position with a prescribed heading angle to a target circle with the final heading tangential to the target circle. Such a shortest path is of significant importance as it is usually required in real-world scenarios, such as taking a snapshot of a forbidden region or loitering above a ground sensor to collect data by a fixed-wing unmanned aerial vehicle in a minimum time. By applying Pontryagin’s maximum principle, some geometric properties for the shortest path are established without considering any assumption on the relationship between the minimum turning radius and radius of the target circle, showing that the shortest path must lie in a sufficient family of 12 types. By employing those geometric properties, the analytical solution to each type is devised so that the length of each type can be computed in a constant time. In addition, some properties depending on problem parameters are found so that the shortest path can be computed without checking all the 12 types. Finally, some numerical simulations are presented, illustrating and validating the developments of the paper.
Convolutional prototype learning for zero-shot recognition

Zero-shot learning (ZSL) has received increasing attention in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. The key to ZSL is to transfer knowledge from the seen to the unseen classes via auxiliary class attribute vectors. However, the popularly learned projection functions in previous works cannot generalize well since they assume the distribution consistency between seen and unseen domains at sample-level. Besides, the provided non-visual and unique class attributes can significantly degrade the recognition performance in semantic space. In this paper, we propose a simple yet effective convolutional prototype learning (CPL) framework for zero-shot recognition. By assuming distribution consistency at task-level, our CPL is capable of transferring knowledge smoothly to recognize unseen samples. Furthermore, inside each task, discriminative visual prototypes are learned via a distance based training mechanism. Consequently, we can perform recognition in visual space, instead of semantic space. An extensive group of experiments are then carefully designed and presented, demonstrating that CPL obtains more favorable effectiveness, over currently available alternatives under various settings.
Augmented Reality for Future Research Opportunities and Challenges in the Shipbuilding Industry: A Literature Review

Augmented reality (AR) systems blend the real world environment with virtual information and provide an expanded and information-rich view of the user surroundings, generating a greater understanding of—and interaction with—datasets, manufacturing processes, and abstract concepts. At present, learning factories aim to implement this technology into diverse industrial cases and applications. This paper reviews peer-reviewed empirical studies on AR in relation to the shipbuilding industry and examines the state of current research on the topic, presenting various concrete AR applications in maritime engineering, production, design, operation, and maintenance. Finally, the findings of the review provide an outlook for further research and experimental opportunities aligned with the development of the learning factory concept (Idealab) at the Norwegian University of Science and Technology (NTNU) in Ålesund in Norway. This will ensure the effective use of this Industry 4.0 technology as a learning tool and enabler of cross-collaboration with the maritime industry.
Multi-resident type recognition based on ambient sensors activity

With the development of sensing and intelligent technologies, ambient sensor-based activity recognition is attracting more attention for a wide range of applications. One of the technology challenges is the recognition of the activity performer in a multi-occupancy scenario. This paper proposes a multi-label Markov Logic Network classification method to recognize resident types based on their activity habits and preference. The activity preference mainly includes time sequence preference, duration and period preference, and the location preference of a basic entity or action events. According to the resident type (gender, age bracket, job), the further reasoning work is the family role (mother, father, daughter and so on.) recognition. We have designed simple and combined preferences to test and evaluate our proposed method. Initial experiments have produced good performance in many cases proving this solution is an efficient and feasible method for resident type recognition which could be applied to real-world scenarios.
Image descattering and absorption compensation in underwater polarimetric imaging

Underwater image contrast degrades and color distorts due to wavelength attenuation caused by scattering and absorption, and the attenuation rate of different wavelengths is significant different in variety of underwater environment, which leads to the satisfying underwater image restoration very difficult. We propose a novel underwater polarimetric image restoration method that consists of descattering and absorption compensation, which particularly takes into account wavelength- and scenedepth- dependent absorption existed in underwater signal light that is ignored by current polarimetric descattering methods. In the descattering step, an automatic attenuation difference map based background region selection strategy is presented, which can estimate background light without considering the no-object region exists or not. In the absorption compensation step, a novel absorption compensation constraint strategy is introduced into the color restoration algorithm for the detected signal light. The strategy relies on well-preserved color component and an absorption prior constraint. Extensive experiments on real-world underwater images demonstrate that the proposed method is more effective than several previous underwater image restoration or enhancement works.
It is not my job: exploring the disconnect between corporate security policies and actual security practices in SMEs

Purpose This paper aims to present empirical results exemplifying challenges related to information security faced by small and medium enterprises (SMEs). It uses guidelines based on work system theory (WST) to frame the results, thereby illustrating why the mere existence of corporate security policies or general security training often is insufficient for establishing and maintaining information security. Design/methodology/approach This research was designed to produce a better appreciation and understanding of potential issues or gaps in security practices in SMEs. The research team interviewed 187 employees of 39 SMEs in the UK. All of those employees had access to sensitive information. Gathering information through interviews (instead of formal security documentation) made it possible to assess security practices from employees’ point of view. Findings Corporate policies that highlight information security are often disconnected from actual work practices and routines and often do not receive high priority in everyday work practices. A vast majority of the interviewed employees are not involved in risk assessment or in the development of security practices. Security practices remain an illusory activity in their real-world contexts. Research limitations/implications This paper focuses only on closed-ended questions related to the following topics: awareness of existing security policy; information security practices and management and information security involvement. Practical implications The empirical findings show that corporate information security policies in SMEs often are insufficient for maintaining security unless those policies are integrated with visible and recognized work practices in work systems that use or produce sensitive information. The interpretation based on WST provides guidelines for enhancing information system security. Originality/value Beyond merely reporting empirical results, this research uses WST to interpret the results in a way that has direct implications for practitioners and for researchers.
Fabriccio: Touchless Gestural Input on Interactive Fabrics

We present Fabriccio, a touchless gesture sensing technique developed for interactive fabrics using Doppler motion sensing. Our prototype was developed using a pair of loop antennas (one for transmitting and the other for receiving), made of conductive thread that was sewn onto a fabric substrate. The antenna type, configuration, transmission lines, and operating frequency were carefully chosen to balance the complexity of the fabrication process and the sensitivity of our system for touchless hand gestures, performed at a 10 cm distance. Through a ten-participant study, we evaluated the performance of our proposed sensing technique across 11 touchless gestures as well as 1 touch gesture. The study result yielded a 92.8% cross-validation accuracy and 85.2% leave-one-session-out accuracy. We conclude by presenting several applications to demonstrate the unique interactions enabled by our technique on soft objects.
Quantum Advantage in Simulating Stochastic Processes

We investigate the problem of simulating classical stochastic processes through quantum dynamics, and present three scenarios where memory or time quantum advantages arise. First, by introducing and analysing a quantum version of the embeddability problem for stochastic matrices, we show that quantum memoryless dynamics can simulate classical processes that necessarily require memory. Second, by extending the notion of space-time cost of a stochastic process $P$ to the quantum domain, we prove an advantage of the quantum cost of simulating $P$ over the classical cost. Third, we demonstrate that the set of classical states accessible via Markovian master equations with quantum controls is larger than the set of those accessible with classical controls, leading, e.g., to a potential advantage in cooling protocols.
Transformed k-nearest neighborhood output distance minimization for predicting the defect density of software projects

Software defect prediction is one of the most important research topics in software engineering. An important product measure to determine the effectiveness of software processes is the defect density (DD). Cased-based reasoning (CBR) has been the prediction technique most widely applied in the software prediction field. The CBR involves k-nearest neighborhood for finding the number (k) of similar software projects selected to be involved in the prediction process. To propose the application of a transformed k-nearest neighborhood output distance minimization (TkDM) algorithm to predict the DD of software projects to compare its prediction accuracy with those obtained from statistical regression, support vector regression, and neural networks. Data sets were obtained from the ISBSG release 2018. A leave-one-out cross validation method was performed. Absolute residual was used as the prediction accuracy criterion for models. Statistical significance tests among models showed that the TkDM had the best prediction accuracy than those ones from statistical regression, support vector regression, and neural networks. A TkDM can be used for predicting the DD of new and enhanced software projects developed and coded in specific platforms and programming languages types.
Content-based Union and Complement Metrics for Dataset Search over RDF Knowledge Graphs

RDF Knowledge Graphs (or Datasets) contain valuable information that can be exploited for a variety of real-world tasks. However, due to the enormous size of the available RDF datasets, it is difficult to discover the most valuable datasets for a given task. For improving dataset Discoverability , Interlinking , and Reusability , there is a trend for Dataset Search systems. Such systems are mainly based on metadata and ignore the contents; however, in tasks related to data integration and enrichment, the contents of datasets have to be considered. This is important for data integration but also for data enrichment, for instance, quite often datasets’ owners want to enrich the content of their dataset, by selecting datasets that provide complementary information for their dataset. The above tasks require content-based union and complement metrics between any subset of datasets; however, there is a lack of such approaches. For making feasible the computation of such metrics at very large scale, we propose an approach relying on (a) a set of pre-constructed (and periodically refreshed) semantics-aware indexes, and (b) “lattice-based” incremental algorithms that exploit the posting lists of such indexes, as well as set theory properties, for enabling efficient responses at query time. Finally, we discuss the efficiency of the proposed methods by presenting comparative results, and we report measurements for 400 real RDF datasets (containing over 2 billion triples), by exploiting the proposed metrics.
Can the Number of Clusters Be Determined by External Indices?

External indices have been used in the literature for determining the number of clusters. The idea is to measure the stability of clustering results using an external validity index when adding randomness to the clustering process. The hypothesis is that the clustering results are more stable when the correct number of clusters is used. The goal of this paper is to provide an answer to the research question stated in the paper title. We conduct a systematic study of the main components of the stability-based approach. We will discuss how to add randomness to the process, how to perform the cross-validation, and which external index to use. We will show that the number of clusters can be reliably determined only when the type of clusters is known and all the components of the approach are carefully chosen. Inferior algorithms like k-means, too high or low subsampling rate, null reference for normalization, and ineffective validation indices can all cause the stability-based approach to break. We recommend better design choices for all these components, which leads to better results compared to existing stability-based methods. However, even with the best choices, there are pathological cases where the stability-based method fails.
Towards Agile Standardization: Testbeds in Support of Standardization for the IIoT

The Industrial Internet of Things (IIoT) poses multiple challenges to traditional standardization, due to the complexity, dynamics, and accelerating speed of technological progress.The need for a timely availability of standards calls for new approaches and tools to enhance standardization processes for smart manufacturing.Industry and standards development organizations worldwide are seeking new solutions.Testbeds have been acknowledged in innovation policy as a powerful tool for knowledge transfer and the further development of emerging technologies.Lately, they have also attracted increasing attention from the standardization perspective as a promising tool for a more agile standards development process.Proponents of such testbeds expect them to support standardization by providing validated solutions and accelerating the processes to meet the growing demands for faster standardization without any detriment to quality.In an explorative, qualitative approach this article is the first to investigate the operation and impact of testbeds in standardization processes based on a multiple case study on testbeds implemented worldwide in the context of the IIoT.
VisBERT: Hidden-State Visualizations for Transformers

Explainability and interpretability are two important concepts, the absence of which can and should impede the application of well-performing neural networks to real-world problems. At the same time, they are difficult to incorporate into the large, black-box models that achieve state-of-the-art results in a multitude of NLP tasks. Bidirectional Encoder Representations from Transformers (BERT) is one such black-box model. It has become a staple architecture to solve many different NLP tasks and has inspired a number of related Transformer models. Understanding how these models draw conclusions is crucial for both their improvement and application. We contribute to this challenge by presenting VisBERT, a tool for visualizing the contextual token representations within BERT for the task of (multi-hop) Question Answering. Instead of analyzing attention weights, we focus on the hidden states resulting from each encoder block within the BERT model. This way we can observe how the semantic representations are transformed throughout the layers of the model. VisBERT enables users to get insights about the model's internal state and to explore its inference steps or potential shortcomings. The tool allows us to identify distinct phases in BERT's transformations that are similar to a traditional NLP pipeline and offer insights during failed predictions.
Robotic Arm Assistance System Based on Simple Stereo Matching and Q-Learning Optimization

This study presents a stereo vision robotic arm assistance system, in which five degrees of catching can be performed by the robot arm in a single instance. The algorithm of the control system is built for population-based optimization and specifically aimed to assist people with disabilities. The proposed stereo vision-based robot arm system enables users to manipulate objects based on the robot's ability to aim at objects by using computer vision. The stereo vision system counts the parameters by focusing on the real-world position of the instance in the coordinate system. A trained deep fully connected network is then adopted to compensate the location measurement errors incurred by the inaccurate parameters measured from the deep learning procedure. Subsequently, the proposed Q-learning-based swarm optimization algorithm is adopted to solve the forward kinematics problem and count the angles of each servo. The performance of the robot arm is compared with several real-life experiments to test its ability to grip a target object in different positions.
AMIL

Human pose estimation has an important impact on a wide range of applications, from human-computer interface to surveillance and content-based video retrieval. For human pose estimation, joint obstructions and overlapping upon human bodies result in departed pose estimation. To address these problems, by integrating priors of the structure of human bodies, we present a novel structure-aware network to discreetly consider such priors during the training of the network. Typically, learning such constraints is a challenging task. Instead, we propose generative adversarial networks as our learning model in which we design two residual Multiple-Instance Learning (MIL) models with identical architecture—one is used as the generator, and the other one is used as the discriminator. The discriminator task is to distinguish the actual poses from the fake ones. If the pose generator generates results that the discriminator is not able to distinguish from the real ones, then the model has successfully learned the priors. In the proposed model, the discriminator differentiates the ground-truth heatmaps from the generated ones, and later the adversarial loss back-propagates to the generator. Such procedure assists the generator to learn reasonable body configurations and is proved to be advantageous to improve the pose estimation accuracy. Meanwhile, we propose a novel function for MIL. It is an adjustable structure for both instance selection and modeling to appropriately pass the information between instances in a single bag. In the proposed residual MIL neural network, the pooling action adequately updates the instance contribution to its bag. The proposed adversarial residual multi-instance neural network that is based on pooling has been validated on two datasets for the human pose estimation task and successfully outperforms the other state-of-the-art models. The code will be made available on https://github.com/pshams55/AMIL.