Reviewerly/exHarmony huggingface dataset:
Statistical Model Based Cloud Resource Management

In this paper, we present a statistical model based VM placement approach for Cloud infrastructures. The model is motivated by the fact that more and more resource demanding applications are deployed in Cloud Infrastructures and in particular, communication data rate and latency bound applications are suffering from common placement algorithms. Based on a requirements analysis from the use cases of the CloudPerfect Project and the bwCloud production infrastructure, the need for a network-aware VM placement is motivated. The solution approach is inspired from the data source modelling applied for statistical multiplexer components in ATM networks. For each VM deployed in the Cloud Infrastructure, a probability for data rate distributions is derived from the collected data traces and the overall network resource consumption is estimated by overlaying the individual data rate probability distributions. The second part of the paper outlines a possible integration into a cloud infrastructure using OpenStack as an example. The paper concludes with a discussion on the stability of the model and initial results derived from collected data traces along with the future work.
Data Encryption Key Sharing Using Image Pixel Color Value

Security is required to transmit confidential information over the network. Security is also demanding in wide range of applications. Cryptographic algorithms play a vital role in providing the data security against malicious attacks. Massage communication over internet facing problems like data security, copyright control, data size capacity, authentication etc. A new idea is to apply reversible data encoding algorithms images by wishing to remove the embedded data after data receiving with the help of image pixel color value.
Bayesian Estimation of Gini-Simpson’s Index Under Mainland-Island Community Structure

The mainland-island community structure is an ecological transposition of a popular model in population genetics in which a fixed number of subpopulations (islands) are connected, through differing immigration rates, to a single metapopulation (mainland) where diversity is generated through speciation. It has been recently shown that a large class of neutral models with this particular structure converges in the large population limit to the Hierarchical Dirichlet process. This finding provides the analogous, in the multipopulation setting, of the Ewens sampling formula for the single population neutral hypothesis. Here we apply some recent results for conditional moments of diversity measures under Gibbs-type priors to derive a Bayesian nonparametric estimator of Gini-Simpson’s index under the Hubbell Unified Neutral Theory of Biodiversity and Biogeography. Potential applications are also illustrated.
Patent services system in sci-tech libraries

The need for efficient support of innovative activities of research institutes and universities is substantiated. The author examines patent activities of science and academic Ubraries’ patent divisions providing the wide range of services - from offering access to specialized information resources and generating analytics to user training and methodological support. As a case study, the experience of Pushchino Central Library Patent Sector is discussed. The library is the branch of RAS Library for Natural Sciences. The Sector offers information and analytical data based on bibliometrical analysis and patent information analysis, as well as user consulting and methodological support. The author concludes that today libraries’ patent divisions are among the most demanded - they analyze prospective vectors in science and technology, develop strategies for patent research; consult researchers in patent activities and intellectual property protection issues. He also suggests, that through facilitating patent information support, the possible risks can be minimized for science-intensive researchers while science intensive research makes the foundation for the country’s economic prosperity.
Usability Evaluation of Online Flight Reservation Systems

Online flight reservation systems are the major source for booking flights in contemporary world. However, these systems lack consistency as well as a fixed UI pattern so a thorough usability evaluation was important. It has been investigated how users use flight booking systems and how long does it take to book a flight. Usability evaluation of state-of-the-art flight booking systems has been conducted. The findings indicate that existing systems still have a major room for improvement.
Modeling and recognition of human limbs cooperative interaction based on Random Increased Hybrid Learning Machine

Abstract In this paper, we study the identification problem of human limbs synergistic interaction based on the application of natural human-computer interaction. We proposed an identification model of human limbs cooperative interaction based on the Random Increased Hybrid Learning Machine for the unique requirements of natural human-computer interaction applications. The model combines the motion region image, the Hu invariant moment model and the RIHLM. In this paper, the algorithm theory, the model rationality and the implementation of the human motion recognition model based on the RIHLM is described. Finally, by comparing the experimental results, we verified that the model presented in this paper has better robustness, timeliness and accuracy in the application of natural human-computer interaction.
Quantifying Visual Abstraction Quality for Computer-Generated Illustrations

We investigate how the perceived abstraction quality of computer-generated illustrations is related to the number of primitives (points and small lines) used to create them. Since it is difficult to find objective functions that quantify the visual quality of such illustrations, we propose an approach to derive perceptual models from a user study. By gathering comparative data in a crowdsourcing user study and employing a paired comparison model, we can reconstruct absolute quality values. Based on an exemplary study for stippling, we show that it is possible to model the perceived quality of stippled representations based on the properties of an input image. The generalizability of our approach is demonstrated by comparing models for different stippling methods. By showing that our proposed approach also works for small lines, we demonstrate its applicability toward quantifying different representational drawing elements. Our results can be related to Weber--Fechner’s law from psychophysics and indicate a logarithmic relationship between number of rendering primitives in an illustration and the perceived abstraction quality thereof.
Image super-resolution based on multi-grained cascade forests

Although neural networks are most commonly used in the field of image super-resolution (SR), methods based on decision trees are still discussed. These kinds of algorithm need less time to compute than others because of their simple structure but still yield high quality image SR. In this paper, we propose an SR algorithm using the multi-grained cascade forest (SRGCF) method. Our algorithm first uses multi-grained scanning to process the spatial relationships of image features, thus the representational learning ability is improved. During the reconstruction process, the image obtained by cascade forest training is used as the input of the next training, therefore, the image features are continuously emphasized. The training of the cascade forest ends when the evaluation value is optimal. Because the decision tree uses a divide-and-conquer strategy, the SR of an image is improved in an iterative manner simply and quickly. Compared with existing methods, our method not only avoids the tradeoff between reconstruction quality and run time, but also has a good generalization capability. It can be quickly applied to the many cases of image SR.
Semantic Validation Based Massive Image Composition

Creative composited results could be achieved by composition technique using target and candidate images. However, with the advent of big data era, how to composite a huge database of images gathered from different sources has become one non-negligible challenge. Traditional methods have the obvious drawback by ignoring semantic validation of massive images. Even though some algorithms have considered this point, the accuracy of semantic matching is not realistic. Aiming at the problem above, we proposed one semantic validation based composition method. On the one hand, optimized VGG16 model was used for retrieving semantically valid candidate images which ensures the semantic validation between candidate images and target images. On the other hand, Poisson blending and some related algorithms contribute to achieve the final composited results, especially for the boundary. At last, a database concluding a large number of images was built, based on it, sufficient experiments indicate that our method could achieve realistic multi-option composited results, especially for considering semantic validation fully.
Simple computation of st-edge- and st-numberings from ear decompositions

We propose simple algorithms for computing st-numberings and st-edge-numberings of graphs with running time O(m). Unlike previous serial algorithms, these are not dependent on an initially chosen DFS-tree. Instead, we compute st-(edge-)numberings that are consistent with any open ear decomposition D of a graph in the sense that every ear of D is numbered increasingly or decreasingly. Recent applications need such st-numberings, and the only two linear-time algorithms that are known for this task use a complicated order data structure as black box. We avoid using this data structure by introducing a new and light-weight numbering scheme. In addition, we greatly simplify the recent algorithms for computing (the much less known) st-edge-numberings.
A fall detection method based on multi-threshold value and EML learning machine

In recent years, fall injuries have not only caused serious harm to the health of the elderly in China, but also occupied a large amount of social medical resources. The most important reason for the fall injury is the rescue not timely after the fall. In view of this phenomenon, this paper realizes the fusion of multi-threshold detection algorithm and EML learning machine classification algorithm, and proposes a two-layer fall detection algorithm based on weight discrimination. Experiments show that the algorithm has high accuracy of fall alarm detection and low false alarm rate.
SoTesTeR: Software Testing Techniques’ Recommender System Using a Collaborative Approach

Software testing is a key factor on any software project; testing costs are significant in relation to development costs. Therefore, it is essential to select the most suitable testing techniques for a given project to find defects at the lower cost possible in the different testing levels. However, in several projects, testing practitioners do not have a deep understanding of the full array of techniques available, and they adopt the same techniques that were used in prior projects or any available technique without taking into consideration the attributes of each testing technique. Currently, there are researches oriented to support selection of software testing techniques; nevertheless, they are based on static catalogues, whose adaptation to any niche software application may be slow and expensive. In this work, we introduce a content-based recommender system that offer a ranking of software testing techniques based on a target project characterization and evaluation of testing techniques in similar projects. The repository of projects and techniques was completed through the collaborative effort of a community of practitioners. It has been found that the difference between recommendations of SoTesTeR and recommendations of a human expert are similar to the difference between recommendations of two different human experts.
Deep Learning in Big Data and Internet of Things

Due to constant escalation in the pace of information technology, the potential of Big Data and Internet of Things (IoT). Data induce high focus in data science field. As in IoT, large number of smart devices generate or collect enormous amount of data, over the period of time for various domain. However, IoT are basically one of the main source for the generation of big data. This massive volume of data contains valuable information upon which many organizations applying analytics for bang into future technology and favorable for business analysis and decision-making. Deep Learning is the high focus of advanced machine learning and facilitating analytics in various territories of Big Data and IoT by extracting complex feature abstraction or representation from different forms of data through hierarchical process. This review paper putting a focus on the overview of unique and modern technique of machine learning i.e. Deep Learning followed by a detailed consideration on models and algorithms. We also try to cover frameworks, opted for implementing Deep Learning and their use in various Big Data and IoT applications. We also investigate various Deep Learning applications in the realm of Big Data and IoT. Further try to incorporate challenges in several areas of Big Data and IoT. Finally, we conclude this work along with the future work.
User Experience Study of 360° Music Videos on Computer Monitor and Virtual Reality Goggles

360° videos are increasingly used for media and entertainment, but the best practices for editing them are not yet well established. In this paper, we present a study in which we investigated the user experience of 360° music videos viewed on computer monitor and VR goggles. The research was conducted in the form of a laboratory experiment with 20 test participants. During the within-subject study, participants watched and evaluated four versions of the same 360° music video with a different cutting rate. Based on the results, an average cutting rate of 26 seconds delivered the highest-quality user experience both for computer monitor and VR goggles. The cutting rate matched with participants' mental models, and there was enough time to explore the environment without getting bored. Faster cutting rates made the users nervous, and a video consisting of a single shot was considered to be too static and boring.
Towards General Scheme for Data Sharing Agreements Empowering Privacy-Preserving Data Analysis of Structured CTI

This paper proposes an extension to the standard STIX representation for Cyber Threat Information (CTI) which couples specific data attributes with privacy-preserving conditions expressed through Data Sharing Agreements (DSA). The proposed scheme allows, in fact, to define sharing and anonymization policies in the form of a human-readable DSA, bound to the specific CTI. The whole scheme is designed to be completely compatible with the STIX 2.0 standard for CTI representation. The proposed scheme will be implemented in this work by defining the complete scheme for representing an email, which is more expressive than the standard one defined for STIX, designed specifically for spam email analysis. Hence, an application to an email is presented, together with DSA definition and inclusion in a STIX record. Finally, a set of experiments will show the performance improvement related to data access, brought by the adoption of the proposed scheme.
The Role of Recommender System of Tags in Clinical Decision Support

The widespread use of the Electronic Health Records EHRs has increasingly emerged in the healthcare industry. The structured and unstructured forms of EHRs are implemented in a Clinical Decision Support System CDSS. The CDSS is an health information technology system designed to provide healthcare professionals with clinical decision support. In this article, we aim to enhance the computer-aided diagnosis in medical imaging by recommending diseases for each patient’s medical image. We propose a recommender system of tags based on the tags co-occurrence, the graph of tags and the graph of the community of patients. The proposed approach is called MedicalRecomTags. The tags are the commonly used diseases or pathologies terms. The graphs, namely, the graph of tags and the graph of the community of patients, are derived by analyzing the annotated medical images. The experimental results show the effectiveness of the tag recommendation approach. In future works, the suggested tags will be evaluated by healthcare providers to affirm their relevancy. The intended online evaluation will enrich and enhance the recommender system of tags.
Spam User Detection Through Deceptive Images in Big Data

Image mining has a very emerging sub-domain, namely, web image mining, and researchers are warmly excited towards it. This study presents a deep detail of former studies and ideas that we come up with, i.e. what is Image Mining and what are Web Image Mining techniques. In the domain of web image mining, this study also proposes an idea to recognize and cope with the deceptive images found on the web. It further helps in banning the fraudulent and annoying web users along with solutions in enhancing the users’ behavior in social networking websites like Facebook, Twitter, Tumbler, etc., in blogs, and in e-shopping websites like eBay, Amazon, Daraz.pk, Kaymu.pk, etc. Apart from this, the study also mentions nearly of the conceivable future directions for the researchers in aforementioned domain.
Generation of Dynamical S-Boxes via Lag Time Chaotic Series for Cryptosystems

Advances in computing and electronic transactions have led to the need to develop new techniques to ensure information security. In this sense, cryptographic algorithms protect information against any attack. Substitution boxes for block ciphers play an important role as they ensure the security of the algorithms. The aim of this work is to present an algorithm for generating substitution boxes based on series with delay of logistic mapping. By analyzing the dynamics of the logistic mapping, the amount of delays to eliminate the trace of the logistic mapping and the characteristic probability distribution of this system are determined. The performance of the proposed substitution boxes is evaluated using the criteria of a good substitution box, mainly defined by Webster and Tavares. Finally, the proposed substitution boxes are implemented in the image coding to obtain a uniform distribution of pixels.
A weight-based optimization algorithm of histogram equalization

Histogram equalization is a simple and effective image enhancement technique that adjusts the contrast through the histogram of the image. In order to optimize the histogram equalization and improve the conventional mapping method, we propose the Histogram Equalization of Weighted Gray-Level Difference (HEWGLD) algorithm, which utilizes the quantity of pixels at each gray level as weight and adjusts the image gray levels based on the conventional histogram equalization results. The whole problem is modelled as a linear programming problem, and solved by a greedy method, which can lead to the global optimal value. The experimental results show that compared with the conventional histogram equalization algorithm, the optimization algorithm has obvious contrast enhancement effect for grayscale images with histogram peaks, and the visual effects of the edges between foreground and background in the image are improved efficiently.
TMAP Design Cards for Technology-Mediated Audience Participation in Live Music

Historically, audiences have had various ways to participate in live music performances, including clapping, dancing, swaying, whistling, and singing. More recently, mobile and wireless devices, such as smartphones have opened up powerful new opportunities for audience participation. However, design for technology-mediated audience participation (TMAP) can be challenging: musicians and audiences have different demands, as does the coherence of the music, and group needs can vary widely. Thus, effective TMAP design requires the balancing of knowledge from diverse perspectives and must take into account the needs of diverse roles in creating and supporting performances. This chapter focuses on the process of creating and evaluating a set of design cards to support the interaction design and evaluation of TMAP systems. The cards are based on a previously created descriptive framework for supporting interaction design and evaluation in this challenging area. We discuss the conception and development of the TMAP design cards in some detail, and present an empirical study to evaluate their practical usefulness. Particular attention is paid to the ability of the cards to support finding ideas, changing ideas, and examining ideas from different perspectives.
A Novel Approach to Fuzzy Model Identification Based on Bat Algorithm

The identification of a fuzzy model is a complex and nonlinear problem. This can be formulated as a search and optimisation problem and many computing approaches are available in the literature to solve this problem. This research paper is focused on using a new nature inspired approach for fuzzy modeling based on Bat Algorithm which is derived from the behaviour of micro-bats to search for their prey. The bat algorithm approach has been implemented and validated successfully on a rapid battery charger fuzzy controller problem. Currently, the key requirement is real-time solutions to complex problems at a blazing speed. Bat algorithm evolved the optimised fuzzy model within a few seconds as compared to other approaches.
Taxonomy-Augmented Features for Document Clustering

In document clustering, individual documents are typically represented by feature vectors based on term-frequency or bag-of-word models. However, such feature vectors intrinsically dismiss the order of the words in the document and suffer from very high dimensionality. For these reasons, in this paper we present novel taxonomy-augmented features that enjoy two promising characteristics: (1) they leverage semantic word embeddings to take the word order into account, and (2) they reduce the feature dimensionality to a very manageable size. Our feature extraction approach consists of three main steps: first, we apply a word embedding technique to represent the words in a word embedding space. Second, we partition the word vocabulary into a hierarchy of clusters by using k-means hierarchically. Lastly, the individual documents are projected to the hierarchy and a compact feature vector is extracted. We propose two methods for generating the features: the first uses all the clusters in the hierarchy and results in a feature vector whose dimensionality is equal to the number of the clusters. The second uses a small set of user-defined words and results in an even smaller feature vector whose dimensionality is equal to the size of the set. Numerical experiments on document clustering show that the proposed approach is capable of achieving comparable or even higher accuracy than conventional feature vectors with a much more compact representation.
Metrics for Real-Time Mono-VSLAM Evaluation Including IMU Induced Drift with Application to UAV Flight

Vision based algorithms became popular for state estimation and subsequent (local) control of mobile robots. Currently a large variety of such algorithms exists and their performance is often characterized through their drift relative to the total trajectory traveled. However, this metric has relatively low relevance for local vehicle control/stabilization. In this paper, we propose a set of metrics which allows to evaluate a vision based algorithm with respect to its usability for state estimation and subsequent (local) control of highly dynamic autonomous mobile platforms such as multirotor UAVs. As such platforms usually make use of inertial measurements to mitigate the relatively low update rate of the visual algorithm, we particularly focus on a new metric taking the expected IMU-induced drift between visual readings into consideration based on the probabilistic properties of the sensor. We demonstrate this set of metrics by comparing ORB-SLAM, LSD-SLAM and DSO on different datasets.