Reviewerly/exHarmony huggingface dataset:
Design of cognitive image filters for suppression of noise level in medical images

Currently, medical image processing is used for the diagnosis of various diseases. But current technologies for medical image processing needs brighter light of research, since medical images are obtained from the various devices are affected by the different noises which in turn affects the accuracy of diagnosis/detection. Hence we propose the intelligent and cognitive image filters which works on the principle of adaptive selection theorem in which filters are selected based on the powerful fuzzy rule engine. The proposed filters are then compared with the previous filters and results obtained are more pivotal when compared with other filters.
ROMAIN: Towards a BFO compliant reference ontology for industrial maintenance

In this paper, we present a domain-specific, open access, reference ontology (ROMAIN) for the maintenance management domain. We use a hybrid approach, based on a top-down alignment to an open source top-level ontology, the Basic Formal Ontology (BFO), and a bottom up focus on classes that are groun ded in maintenance practice. We constrain the scope of the ontology to the classes that are unique to the maintenance management practice, such as maintenance strategy, degradation, and work order management, rather than modeling the entire domain of maintenance. This approach reduces the scope of the development task and enables reasoning to be tested at a manageable scale. ROMAIN provides a unifying framework that can be used in conjunction with other BFO compliant sub-domain ontologies, such as planning and scheduling ontologies. The proposed ontology is validated using real-life data in the context of a use case related to evaluating the effectiveness of maintenance strategy.
ANALYZING THE DETERMINANTS OF E-COMMERCE IN SMALL AND MEDIUM-SIZED ENTERPRISES: A COGNITION-DRIVEN FRAMEWORK

The increasing use of information technology in enterprises’ daily operations has led to multiple innovative ways to run a business, including electronic commerce (hereafter, e-commerce). However, firms with fewer resources, such as small and medium-sized enterprises (SMEs), are more reluctant to use electronic channels during transactions. This aversion to contemporary business models is a result of these companies’ lack of knowledge and capabilities regarding e-commerce. To improve their businesses, SMEs’ managers and decision makers could benefit from a methodological framework that fosters a deeper understanding of the determinants of e-commerce. This study sought to explore the use of fuzzy cognitive mapping to address this need. The results are grounded in the knowledge and experience of a panel of experts in e-commerce. The fuzzy cognitive map (FCM) developed shows that entrepreneur profile, market, operational management, marketing and promotions, website and digital platform, and products present the highest centrality indices as determinants of SME e-commerce. The findings offer a better understanding of the cause-and-effect relationships between these determinants. The advantages, limitations, and shortcomings of our constructivist proposal are also discussed.
A keyword-based combination approach for detecting phishing webpages

In this paper, the Search & Heuristic Rule & Logistic Regression (SHLR) combination detection method is proposed for detecting the obfuscation techniques commonly used by phishing websites and improving the filtering efficiency of legitimate webpages. The method is composed of three steps. First, the title tag content of the webpage is input as search keywords to the Baidu search engine, and the webpage is considered legal if the webpage domain matches the domain name of any of the top-10 search results; otherwise, further evaluation is performed. Second, if the webpage cannot be identified as legal, then the webpage is further examined to determine whether it is a phishing page based on the heuristic rules defined by the character features. The first two steps can quickly filter webpages to meet the needs of real-time detection. Finally, a logistic regression classifier is used to assess the remaining pages to enhance the adaptability and accuracy of the detection method. The experimental results show that the SHLR can filter 61.9% of legitimate webpages and identify 22.9% of phishing webpages based on uniform/universal resource locator (URL) lexical information. The accuracy of the SHLR is 98.9%; thus, its phishing detection performance is high.
Bringing Design to the Privacy Table

In calls for privacy by design (PBD), regulators and privacy scholars have investigated the richness of the concept of "privacy." In contrast, "design" in HCI is comprised of rich and complex concepts and practices, but has received much less attention in the PBD context. Conducting a literature review of HCI publications discussing privacy and design, this paper articulates a set of dimensions along which design relates to privacy, including: the purpose of design, which actors do design work in these settings, and the envisioned beneficiaries of design work. We suggest new roles for HCI and design in PBD research and practice: utilizing values- and critically-oriented design approaches to foreground social values and help define privacy problem spaces. We argue such approaches, in addition to current "design to solve privacy problems" efforts, are essential to the full realization of PBD, while noting the politics involved when choosing design to address privacy.
The Impact of User Characteristics and Preferences on Performance with an Unfamiliar Voice User Interface

Voice User Interfaces (VUIs) are increasing in popularity. However, their invisible nature with no or limited visuals makes it difficult for users to interact with unfamiliar VUIs. We analyze the impact of user characteristics and preferences on how users interact with a VUI-based calendar, DiscoverCal. While recent VUI studies analyze user behavior through self-reported data, we extend this research by analyzing both VUI usage data and self-reported data to observe correlations between both data types. Results from our user study (n=50) led to four key findings: 1) programming experience did not have a wide-spread impact on performance metrics while 2) assimilation bias did, 3) participants with more technical confidence exhibited a trial-and-error approach, and 4) desiring more guidance from our VUI correlated with performance metrics that indicate cautious users.
Breast Cancer Classification using Capsule Network with Preprocessed Histology Images

Breast cancer is one of the most dangerous forms of cancer exists among women. The breast cancer is diagnosed using histology images. The purpose of this paper is to classify different types of breast cancer using histology images. The classification of histology images can be effectively done by image processing techniques. Among different image processing algorithms, deep learning gives the best performance for image classification applications. There are different convolutional neural network(CNN) architectures used for classification purpose such as AlexNet, Inception-Net, ResNet etc. Since conventional convolutional neural networks have lot of drawbacks, the architecture used for the current study is capsule networks, which captures the spatial and orientational information. The proposed work shows that the accuracy of Capsule Network model is improved due to the pre-processing of the histology images.
Challenges Using Head-Mounted Displays in Shared and Social Spaces

Everyday mobile usage of AR and VR Head-Mounted Displays (HMDs) is becoming a feasible consumer reality. The current research agenda for HMDs has a strong focus on technological impediments (e.g. latency, field of view, locomotion, tracking, input) as well as perceptual aspect (e.g. distance compression, vergence-accomodation ). However, this ignores significant challenges in the usage and acceptability of HMDs in shared, social and public spaces. This workshop will explore these key challenges of HMD usage in shared, social contexts; methods for tackling the virtual isolation of the VR/AR user and the exclusion of collocated others; the design of shared experiences in shared spaces; and the ethical implications of appropriating the environment and those within it.
Application of hierarchical clustering, singularity mapping, and Kohonen neural network to identify Ag-Au-Pb-Zn polymetallic mineralization associated geochemical anomaly in Pangxidong district

In order to identify Ag-Au-Pb-Zn polymetallic mineralization associated geochemical anomaly in Pangxidong district, hierarchical clustering (HC), singularity mapping (SM) and Kohonen neural network (KNN) methods have been employed to analysis stream sediment geochemical data in the study area. Firstly, the geochemical variables (i.e. 16 elements) were grouped by HC method, and Au, Ag, Pb, Zn, Hg, As and Sb were classified into the same group, which is in accordance with the fact that these elements are either the main ore-forming or accompanying elements that are associated with Ag-Au-Pb-Zn polymetallic mineralization. Then, the concentration values of the Au, Ag, Pb, Zn, Hg, As and Sb were processed by SM method to calculate the singularity indexes (i.e. α-values), which can be used to enhance the geochemical anomaly signature due to characterizing the local enrichment and depletion of these elements. Finally, α-values were classified by KNN method and a total of 9 groups of data were derived. The distributions of the samples in group 8 almost cover all the low α-values areas (i.e. anomaly areas with positive singularity). It can be thus inferred that the samples in group 8 indicate the enrichment of the mineralization associated elements Au, Ag, Pb, Zn, Hg, As and Sb. The main conclusions are: (1) HC method can be used as a data-driven approach to explore the proper association of ore-forming and accompanying elements linked with Ag-Au-Pb-Zn polymetallic mineralization in the study area; (2) the application of SM method has revealed the significant properties of local enrichment and depletion of the mineralization associated elements including Au, Ag, Pb, Zn, Hg, As and Sb; (3) KNN is a useful method to identify Ag-Au-Pb-Zn polymetallic mineralization associated geochemical anomaly by classifying α-values of these elements; and (4) the hybrid approach that combines HC, SM and KNN methods is an applicable tool for delineating target areas to prospect potential undiscovered deposits.
Performance boost of time-delay reservoir computing by non-resonant clock cycle

The time-delay-based reservoir computing setup has seen tremendous success in both experiment and simulation. It allows for the construction of large neuromorphic computing systems with only few components. However, until now the interplay of the different timescales has not been investigated thoroughly. In this manuscript, we investigate the effects of a mismatch between the time-delay and the clock cycle for a general model. Typically, these two time scales are considered to be equal. Here we show that the case of equal or resonant time-delay and clock cycle could be actively detrimental and leads to an increase of the approximation error of the reservoir. In particular, we can show that non-resonant ratios of these time scales have maximal memory capacities. We achieve this by translating the periodically driven delay-dynamical system into an equivalent network. Networks that originate from a system with resonant delay-times and clock cycles fail to utilize all of their degrees of freedom, which causes the degradation of their performance.
Independent Embedding Domain Based Two-Stage Robust Reversible Watermarking

Robustness is the most important factor that limits the practical application of reversible watermarking. To deal with this issue, several robust reversible watermarking (RRW) techniques have been proposed. Among them, the two-stage RRW framework proposed by Coltuc et al. is a promising one. In the first state of this framework, a robust watermark is embedded into the cover image to provide robustness, and then in the second stage, the information enabling revert the robust embedding is reversibly embedded into the already marked image to guarantee the reversibility. However, because of using the same area for these two embedding stages, the robustness in the first stage is seriously weakened by the reversible embedding. As a result, this elegant method is not effective as expected. Based on this consideration, this paper proposes an independent embedding domain (ED)-based two-stage RRW. The cover image is first transformed into two independent EDs, and then the robust and reversible watermarks are embedded into each domain separately. The carrier derived from the first embedding stage that carrying the robust watermark will not change after the reversible embedding, and thus, the robustness of the first stage is well preserved. By the proposed method, the embedding performance of the original two-stage RRW is significantly enhanced. Moreover, the proposed method is experimentally verified better than some other state-of-the-art RRW methods.
A high-capacity reversible data hiding method for homomorphic encrypted images

Recently, reversible data hiding in encrypted images has been developed to transmit useful data while the original images can be perfectly recovered when needed. In this paper, a new method is proposed for homomorphic encrypted images so that part of the hidden data can be extracted in encrypted domain and the rest are extractable after image decryption. Specifically, a plain-text image is preprocessed by reversibly embedding the bit values of some pixels into the image. The preprocessed image is encrypted in Paillier cryptosystem and two embedding algorithms are applied on the encrypted image in succession. Compared with the state-of-the-art schemes, higher embedding capacity can be achieved by applying the proposed method, respectively for data extraction before and after image decryption. Compared with the schemes with similar properties, better performances are achieved with the proposed method in terms of quality of directly decrypted image with respect to data hiding rate.
Needle Biopsy Adequacy in the Era of Precision Medicine and Value-Based Health Care

Needle biopsy of diseased tissue is an essential diagnostic tool that is becoming even more important as precision medicine develops. However, the capability of this modality to efficiently provide samples adequate for diagnostic and prognostic analysis remains quite limited relative to current diagnostic needs. For physicians and patients, inadequate biopsy frequently leads to diagnostic delay, procedure duplication, or insufficient information about tumor biology leading to delay in treatment; for health systems, this results in substantial incremental costs and inefficient use of scarce specialized diagnostic resources.To review current needle biopsy technology, devices, and practice with a perspective to identify current limitations and opportunities for improvement in the context of advancing precision medicine.PubMed searches of fine-needle aspiration and core needle biopsy devices and similar technologies were made generally, by tissue site, and by adequacy as well as by health economics of these technologies.Needle biopsy adequacy can be improved by recognizing the importance of this diagnostic tool by promoting common criteria for needle biopsy adequacy; by optimizing needle biopsy procedural technique, technologies, clinical practice, professional education, and quality assurance; and by bundling biopsy procedure costs with downstream diagnostic modalities to provide better accountability and incentives to improve the diagnostic process.
A Systematic Literature Review on Image Captioning

Natural language problems have already been investigated for around five years. Recent progress in artificial intelligence (AI) has greatly improved the performance of models. However, the results are still not sufficiently satisfying. Machines cannot imitate human brains and the way they communicate, so it remains an ongoing task. Due to the increasing amount of information on this topic, it is very difficult to keep on track with the newest researches and results achieved in the image captioning field. In this study a comprehensive Systematic Literature Review (SLR) provides a brief overview of improvements in image captioning over the last four years. The main focus of the paper is to explain the most common techniques and the biggest challenges in image captioning and to summarize the results from the newest papers. Inconsistent comparison of results achieved in image captioning was noticed during this study and hence the awareness of incomplete data collection is raised in this paper. Therefore, it is very important to compare results of a newly created model produced with the newest information and not only with the state of the art methods. This SLR is a source of such information for researchers in order for them to be precisely correct on result comparison before publishing new achievements in the image caption generation field.
Improved Faster R-CNN Traffic Sign Detection Based on a Second Region of Interest and Highly Possible Regions Proposal Network

Traffic sign detection systems provide important road control information for unmanned driving systems or auxiliary driving. In this paper, the Faster region with a convolutional neural network (R-CNN) for traffic sign detection in real traffic situations has been systematically improved. First, a first step region proposal algorithm based on simplified Gabor wavelets (SGWs) and maximally stable extremal regions (MSERs) is proposed. In this way, the region proposal a priori information is obtained and will be used for improving the Faster R-CNN. This part of our method is named as the highly possible regions proposal network (HP-RPN). Second, in order to solve the problem that the Faster R-CNN cannot effectively detect small targets, a method that combines the features of the third, fourth, and fifth layers of VGG16 to enrich the features of small targets is proposed. Third, the secondary region of interest method to enhance the feature of detection objects and improve the classification capability of the Faster R-CNN is proposed. Finally, a method of merging the German traffic sign detection benchmark (GTSDB) and Chinese traffic sign dataset (CTSD) databases into one larger database to increase the number of database samples is proposed. Experimental results show that our method improves the detection performance, especially for small targets.
Soft extreme learning machine for fault detection of aircraft engine

When extreme learning machine (ELM) is used to cope with classification problems, the ±1 is commonly used to construct the label vector. Since ELM adopts the square loss function, this means that it tends to force the margins of all the training samples exactly equaling one from the perspective of margin learning theory, which is unreasonable to some extent. To overcome this hard margin flaw, in this paper a soft extreme learning machine (SELM) is proposed, which flexibly sets a soft target margin for each training sample. Through solving a series of regularized ELMs (RELMs), SELM can be computed efficiently. Based on SELM, an improved SELM (ISELM) is proposed to deal with imbalanced classification problems, which can keep the same computational efficiency as SELM via solving a series of weighted RELMs. From the experimental results on benchmark data sets, the effectiveness and feasibility of SELM and ISELM are confirmed. More importantly, when they are applied to fault detection of aircraft engine, they are promising to be developed as the candidate techniques for it, and ISELM is especially in favor.
Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation

Interval type-2 fuzzy c-means (IT2FCM) clustering algorithm can describe more uncertainty than fuzzy c-means (FCM) clustering algorithm by using two fuzzifiers to construct a more inclusive boundary.How to obtain appropriate fuzzifiers and initialize cluster centers are essential tasks for the IT2FCM.To effectively solve these problems, this paper proposes an alternate particle swarm optimizationbased adaptive interval type-2 intuitionistic fuzzy c-means clustering algorithm (A-PSO-IT2IFCM) and applies this proposed method to color image segmentation.First, in order to further deal with the uncertainty, a novel interval type-2 fuzzy clustering objective function is constructed by utilizing the intuitionistic fuzzy information extracted from images.Then an alternate particle swarm optimization (PSO) scheme is designed to optimize fuzzifiers and cluster centers alternatively.In addition, a multiscale update strategy for the positions of particles is introduced into the A-PSO-IT2IFCM to increase the diversity of swarm and boost the convergence of optimization.The color image segmentation experiments on Berkeley and UC Merced Land Use datasets show that the proposed algorithm can adaptively determine fuzzifiers and cluster centers and achieve good segmentation results.
What drives the Bitcoin price? A factor augmented error correction mechanism investigation

This article aims to determine what drives the price of Bitcoin. To achieve this aim, a large set of data is analysed using VEC models augmented by factors representing unobservable economic forces. They have been obtained by means of principal component analysis. This method enables us to contribute to the existing literature on Bitcoin in two ways. First, we employ the dimension reduction technique to combine variables from several papers. Second, we estimate several unobservable economic concepts instead of utilizing proxy variables as is usually done. We find that the main factor driving the Bitcoin price is its popularity. Hence, our result not only confirms some previous findings but reinforces them by providing a better definition of popularity. Finally, we conclude that the Bitcoin price is not affected by supply and demand factors in the way that is natural for conventional currencies.