Reviewerly/exHarmony huggingface dataset:
Librarian in charge of social media: Volunteering or staffing position?

Тhe problems of library services integration into social media are considered. The main activities of the library specialist working in social media are revealed. The opinion is expressed that the library account in social networks has not only a marketing function, but also an educational and service one. The example of applied research "Activities of municipal public libraries in Belgorod region on the grounds" held in 2016, Belgorod state universal scientific library, lists the shortcomings of the mission of the municipal libraries of the region in social networks, among them the lack of system and low frequency, incorrect processing of accounts, poor navigation, insufficient efficiency in answering queries of users and the support of dialogue. It is proved that the insufficient realization of the potential of social networks as a resource for promoting libraries and a platform for full library and information services is due to human resources. It is concluded that the library, the leading representation of the library in social networks, requires an employee at a separate rate for work in social networks.
Speckle reduction in laser illuminated endoscopy using adversarial deep learning

Endoscope size is a major design constraint that must be managed with the clinical demand for high-quality illumination and imaging. Existing commercial endoscopes most often use an arc lamp to produce bright, incoherent white light, requiring large-area fiber bundles to deliver sufficient illumination power to the sample. Moreover, the power instability of these light sources creates challenges for computer vision applications. We demonstrate an alternative illumination technique using red-green-blue laser light and a data-driven approach to combat the speckle noise that is a byproduct of coherent illumination. We frame the speckle artifact problem as an image-to-image translation task solved using conditional Generative Adversarial Networks (cGANs). To train the network, we acquire images illuminated with a coherent laser diode, with a laser diode source made partially- coherent using a laser speckle reducer, and with an incoherent LED light source as the target domain. We train networks using laser-illuminated endoscopic images of <i>ex-vivo</i>, porcine gastrointestinal tissues, augmented by images of laser-illuminated household and laboratory objects. The network is then benchmarked against state of-the-art optical and image processing speckle reduction methods, achieving an increased peak signal-to-noise ratio (PSNR) of 4.1 db, compared to 0.7 dB using optical speckle reduction, 0.6 dB using median filtering, and 0.5 dB using non-local means. This approach not only allows for endoscopes with smaller, more efficient light sources with extremely short triggering times, but it also enables imaging modalities that require both coherent and incoherent sources, such as combined widefield and speckle ow contrast imaging in a single image frame.
A Method of 3D Hand Movement Recognition by a Leap Motion Sensor for Controlling Medical Image in an Operating Room

A problem of surgery is an infection of patient. In this paper, we presented a method to control medical image in an operating room by using only a hand. The proposed methods consisted of five steps, namely 1) data acquisition from Leap Motion sensor 2) hand visualization with color mapping 3) close and open hand classification 4) hand gesture recognition and 5) command interpretation. The system has total ten commands divided to six commands on program screen, namely, waiting, selection, searching next image in left and right direction, and increasing and decreasing brightness, and four commands on button; namely, zoom in, zoom out, clockwise rotation, and counterclockwise rotation. An accuracy rate average is 95.83%. The results show that the proposed method performs effectively. The advantage of the proposed method is one hand can control the image.
Automatic Extraction of SBVR Based Business Vocabulary from Natural Language Business Rules

In the early phases of software development, both of business analysts and IT architects collaborate to define the business needs in a consistent and unambiguous format before exploiting them to produce a software solution to the problem have been defined. Given the divergence of the interest areas of each intervenor, the natural language remains the most adequate format to define the business needs in order to avoid misunderstanding. This informal support suffers from ambiguity leading to inconsistencies, which will affect the reliability of the final solution. Accordingly, the Object Management Group (OMG) has proposed the “Semantic Business Vocabulary and Rules” (SBVR) standard which offers the opportunity to gather business rules in a natural language format having a formal logic aspect, letting the possibility to be understood by not only the different stakeholders but also directly processed by the machine. Since the SBVR standard is born to represent business rules by combining business vocabulary, it would be wise to give a great attention to the latter. In this paper we present an approach to extract business vocabulary according to SBVR Structured English as one of possibly notation that can map to the SBVR Meta-Model, with a view to provide a relevant resource for the next software deployment steps.
Optimization of Hyperparameters and Feature Selection for Random Forests and Support Vector Machines by Artificial Bee Colony Algorithm

Hyperparameters optimization for learning algorithms and feature selection from given data are key issues in machine learning, would greatly affect classification accuracy. Random forests and support vector machines are among some of the most popular learning algorithms. Random forests that have relatively few hyperparameters, can perform more accurate classification by optimizing these parameters without requirement of feature selection. Same as random forests, support vector machines also have a few hyperparameters. However, whether or not to perform feature selection at the same time as optimization of these parameters greatly affects classification accuracy. Usually, grid search method is used to optimize hyperparameters. However, since this search method is performed on predetermined grids, the detailed optimization cannot be realized. In this paper, we thus introduce an artificial bee colony (ABC) algorithm to optimize hyperparameters and to perform more accurate feature selection. ABC algorithm is one of the swarm intelligence algorithms used to solve optimization problems which is inspired by the foraging behaviour of the honey bees. Using KDD Cup 1999 Data that is a benchmark of network intrusion detection classification, experimental results demonstrate the effectiveness of our method. The proposed method is superior in classification accuracies to existing methods for the same data, where swarm intelligence is used to hyperparameters optimization and feature selection. Our method also shows better performance than classification accuracies of random forests and SVM that are learned using default parameters values provided by scikit-learn, an open source machine learning library for Python.
Application of augmented and virtual reality technologies in semi-trailer’s lifecycle

This paper presents an application of 3D models and modern visualization techniques – virtual and augmented reality in the lifecycle of semi-trailers, starting from the process of its development through market launching and exploitation. In the process of development of a semi-trailer different visualization techniques together with 3D models are used in communication between members of the project team and for carrying out various types of analysis, in particular: functional, ergonomic, kinematic, collisions. In the later stages, they allow us to aid the sales and marketing departments in a more effective product presentation to customers. Finally, at the exploitation stage they support users in activities realised by operators (drivers) and technicians in services. The application of 3D models and visualization techniques at various stages of a trailer’s lifecycle brings many benefits that are discussed in this article.
Studying the Effect of Using E-Learning Through Secure Cloud Computing Systems

Cloud computing is a set of IT services offered to users over the web on a rented base. Recently, cloud computing is considered as a new internet based paradigm for hosting and providing hardware and software services the consumers. The main services models for cloud computing includes: software, platform and infrastructure (as a service), to satisfy the needs of different kinds of organizations. Security in cloud computing is critical when developing services, especially in the education area, cloud computing will be the basic platform of the future E-learning environment. It provides secure data storage, favorable and easy to use internet services and strong computing infrastructure for e-learning environment. This paper will focus on the effectiveness of using e-learning through cloud computing systems. The research study shows that the cloud computing model is extremely valued for both students and instructors to achieve the course objective. The paper presents the advantage, limitation and the challenges of using cloud computing services (IaaS, SaaS, PaaS) as a good platform for e-learning. The descriptive statistics, i.e. of the selected dependent and independent variables that related to the research samples, will be presented by the authors. Arithmetic mean, standard deviation, coefficient of standard variation, etc., will be include in the descriptive statistics. The results of the research study are mainly based on data from questionnaires of survey that have been held by the authors from to February 25, to May 20, 2018, among a sample of adults.
Public libraries in Switzerland: RDA and the FRBRization watershed

Purpose The purpose of this paper is to investigate how Swiss public libraries are experiencing a normative revolution connected to new cataloging standards, such as RDA and the FRBRization of catalogs. Design/methodology/approach Thanks to semi-structured interviews, the paper analyzes the current positioning of Swiss public libraries on the “bibliographic transition” issue by using a case study of the network of municipal libraries in Geneva. Findings In Switzerland, the federal and multi-linguistic structure of the library networks increases the organizational obstacles to the adoption of new cataloging principles and formats. At the local level, the Swiss municipal libraries have to cope with this complexity to transform their structures and continue to offer competitive and effective services to their users. Practical implications The paper proposes six scenarios of technology watershed for the analyzed case study and their consequences for cataloging standards and rules. Social implications The paper shows how the adoption of technological and conceptual innovations has to be done in the face of real organizational and administrative constraints, especially in the case of public lending libraries. Originality/value The paper analyzes at the empirical and theoretical levels how, especially in Switzerland, the variety of governance levels and linguistic areas have made strategizing more complex for public lending libraries.
Data-driven two-layer visual dictionary structure learning

An important issue in statistical modeling is to determine the complexity of the model based on the scale of data so as to effectively mitigate the model’s overfitting problems without big data. We adopt a data-driven approach to automatically determine the number of components of the model. In order to better extract robust features, we propose a framework of data-driven two-layer structure visual dictionary learning (DTSVDL). It works by dividing the visual dictionary structure learning into two levels: the attribute layer and the detail layer. In the attribute layer, the attributes of the image dataset are learned, and these attributes are obtained by a data-driven Bayesian nonparametric model. Then, in the detail layer, the detailed information over attributes is further explored and refined, and the attributes are weighted by the number of effective observations associated with each attribute. Our proposed approach has three main advantages: (1) the two-layer structure makes our building visual dictionary be more expressive; (2) the number of components in the attribute layer can be determined automatically from the data; (3) the components are automatically determined based on the scale of visual words; therefore, our model can well mitigate the overfitting problem. In addition, by comparing with stacked autoencoders, stacked denoising autoencoders, LeNet-5, speeded-up robust features, and pretrained deep learning model ImageNet-VGG-F algorithms, we find that our approach achieves satisfactory image categorization results on two benchmark datasets. Specifically, higher categorization performance is achieved than by the classical approaches on 15 scene categories and action datasets. We conclude that the resulting DTSVDL possesses a good generality derived from attribute information as well as an excellent distinction derived from detailed information. In other words, the visual dictionary learned by our algorithm is more expressive and discriminatory.
An Ontological Representation of ITIL Framework Service Level Management Process

IT Service Management (ITSM) is an information system management approach that represents the information system in the form of a set of capabilities that bring value to customers in the form of services. ITIL (Information Technology Infrastructure Library) framework has positioned itself as a generic solution to tackle a broad range of ITSM issues and try to guide IT managers in their endeavors. Research on the subject has been mostly restricted to the implementation of the incident, problem and change management processes. Such approaches, however, have failed to address the deployment of the service level management (SLM) process. Yet, the SLM process has been criticized as the most important process in ITIL framework and that is highly dependent on the other processes. In this paper, an ontological metamodel of ITIL SLM process explicating its core concepts is presented; the goal is to provide a machine-readable document for modeling SLM domain knowledge according to ITIL V3 Framework. The proposed metamodel could be used for: (i) a deeper evaluation for comparing it to other ITSM models, and (ii) the integration of different frameworks and standards of IT governance such as Cobit and ISO 27001.
Third workshop on exploratory search and interactive data analytics (ESIDA)

This is the third edition of the Workshop on Exploratory Search and Interactive Data Analytics (ESIDA). This series of workshops emerged as a response to the growing interest in developing new methods and systems that allow users to interactively explore large volumes of data, such as documents, multimedia or specialised collections, such as biomedical datasets. There are various approaches to supporting users in this interactive environment ranging from the development of new algorithms through visualisation methods to analysing users' search patterns. The overarching goal of ESIDA is to bring together researchers working in areas that span across multiple facets of exploratory search and data analytics to discuss and outline research challenges for this novel area.
Personal user interfaces for recommender systems

In this Student Consortium submission, I outline the motivation, the current situation and the research questions of my PhD research on user interfaces for interacting with recommender systems, and particularly the interplay of different personal characteristics and different visualisation and interaction techniques.
Is there a safety-net effect with computer-aided detection (CAD)?

Computer-Aided Detection (CAD) systems are used to aid readers interpreting screening mammograms. An expert reader searches the image initially unaided, and then once again with the aid of CAD which prompts automatically detected suspicious regions. This could lead to a ‘safety-net’ effect, where the initial unaided search of the image is adversely affected by the fact that it is preliminary to an additional search with CAD, and may, therefore, be less thorough. To investigate the existence of such an effect, we created a visual search experiment for non-expert observers mirroring breast screening with CAD. Each observer searched 100 images for microcalcification clusters within synthetic images in both prompted and unprompted (no-CAD) conditions. Fifty-two participants were recruited for the study, 48 of whom had their eye movements tracked in real-time; four participants could not be accurately calibrated so only behavioural data was collected. In the CAD condition, before prompts were displayed, image coverage was significantly lower than coverage in the no-CAD condition (t(47)=5.48, p&lt;0.001). Observer sensitivity was significantly greater for targets marked by CAD than the same targets in the no-CAD condition (t(51)=11.67, p&lt;0.001). For targets not marked by CAD, there was no significant difference in observer sensitivity in the CAD condition compared to the same targets in the no-CAD condition (t(51)=0.88, p=0.382). These results suggest that the initial search may be influenced by the subsequent availability of CAD; if so, CAD efficacy studies should account for the effect when estimating benefit.
Urgent image-to-video person reidentification by cross-media transfer cycle generative adversarial networks

Recently, image-to-video person reidentification (IVPR) has attracted enormous research interest, and various models are proposed. IVPR is often applied to urgent situations, such as suspect tracking and lost-human locating. Existing IVPR models are under supervised frameworks, which require a large number of labeled image-to-video pairs. This severely limits their real-time efficiency in urgent situations, because annotation is much more time-consuming. To solve the urgent image-to-video person reidentification (UIVPR) problem, we propose a cross-media transfer cycle generative adversarial networks (CTC-GAN) network. Our model aims to alleviate the “media-gap” between image-to-video pairs without newly labeled pairs. We make an existing completely labeled dataset as guidance for CTC-GAN to achieve domain adaptation and make urgent image-to-video matching easier for person reidentification. We introduce cycle GANs for image(video)-to-video(image) translation and extract cross-media features using a triplet constraint in the source domain for different media features. Furthermore, we train the model in the labeled source domain by reconstructing the image (video) as its related video (image). Then, train the model in the unlabeled target domain by reconstructing itself along with source data, so as to ensure that the discriminative model can be used in target domain. Through CTC-GAN, our network can retain pedestrian discriminative information as much as possible, to ensure the matching rate in the target domain. To validate the effectiveness of our approach, we implement substantial experiments on two large-scale person reidentification datasets compared with six existing state-of-the-art unsupervised revised person reidentification models, and experimental results demonstrate that our method can solve UIVPR effectively.
Fast preparation of entangled states of two qutrits in cavity or circuit QED

We present a scheme for generating entangled states of two qutrits, by using a 4-level system and a 3-level system coupled to a single cavity. Because of only employing resonant interactions, the entangled state can be fast created. This scheme is easy to implement in experiments since only a single cavity is needed and none of measurement, auxiliary system and identical system-cavity coupling constant is required. Furthermore, two-qutrit partially or maximally entangled states can be created by using this scheme. The proposal is quite general, which can be used to prepare entangled states of two qutrits in various physical systems, such as two natural or artificial atoms of the Λ-type or Δ-type three lowest levels, coupled to an optical or microwave cavity.
Fast Approximation of Frequent k-mers and Applications to Metagenomics

Estimating the abundances of all k-mers in a set of biological sequences is a fundamental and challenging problem with many applications in biological analysis. While several methods have been designed for the exact or approximate solution of this problem, they all require to process the entire dataset, that can be extremely expensive for high-throughput sequencing datasets. While in some applications it is crucial to estimate all k-mers and their abundances, in other situations reporting only frequent k-mers, that appear with relatively high frequency in a dataset, may suffice. This is the case, for example, in the computation of k-mers’ abundance-based distances among datasets of reads, commonly used in metagenomic analyses. In this work, we develop, analyze, and test, a sampling-based approach, called SAKEIMA, to approximate the frequent k-mers and their frequencies in a high-throughput sequencing dataset while providing rigorous guarantees on the quality of the approximation. SAKEIMA employs an advanced sampling scheme and we show how the characterization of the VC dimension, a core concept from statistical learning theory, of a properly defined set of functions leads to practical bounds on the sample size required for a rigorous approximation. Our experimental evaluation shows that SAKEIMA allows to rigorously approximate frequent k-mers by processing only a fraction of a dataset and that the frequencies estimated by SAKEIMA lead to accurate estimates of k-mer based distances between high-throughput sequencing datasets. Overall, SAKEIMA is an efficient and rigorous tool to estimate k-mers abundances providing significant speed-ups in the analysis of large sequencing datasets.
Managing Your Kleptographic Subscription Plan

In the classical kleptographic business models, the manufacturer of a device D is paid either in advance or in installments by a malicious entity to backdoor D. Unfortunately, these models have an inherent high risk for the manufacturer. This translates in high costs for clients. To address this issue, we introduce a subscription based business model and tackle some of the technical difficulties that arise.
Word Sense Disambiguation Studio: A Flexible&#x0D; System for WSD Feature Extraction

The paper presents a flexible system for extracting features and creating training and testexamples for solving the all-words sense disambiguation (WSD) task. The system allowsintegrating word and sense embeddings as part of an example description. The system possessestwo unique features distinguishing it from all similar WSD systems—the ability to construct aspecial compressed representation for word embeddings and the ability to construct training andtest sets of examples with different data granularity. The first feature allows generation of data setswith quite small dimensionality, which can be used for training highly accurate classifiers ofdifferent types. The second feature allows generating sets of examples that can be used for trainingclassifiers specialized in disambiguating a concrete word, words belonging to the samepart-of-speech (POS) category or all open class words. Intensive experimentation has shown thatclassifiers trained on examples created by the system outperform the standard baselines formeasuring the behaviour of all-words WSD classifiers.