Reviewerly/exHarmony huggingface dataset:
Access to Learning Through Mobiles

The common myth that mobile learning cannot propel in a rural setting is null and void. The influx of modern ICTs like mobile technologies can revolutionize information access among the less privileged in many African communities. Using the Actor-Network Theory as a methodological tool, the chapter explores opportunities of increasing knowledge access through mobiles, by understanding the networks involved in farmer's mobile learning practice, with reference to Uganda. The chapter reveals that mobile technologies offer affordable individual and group learning opportunities to smallholder farmers. Learning is a socially constructed activity, where farmers with access to ICTs like mobile phones share knowledge among those with no access. Through a socio-technical discussion, technological initiatives ought to be pro-people where farmers' needs are key considerations in the mLearning actor-network. For sustainable impacts, all actors need to work collaboratively, negotiate different realities, and appreciate the local challenges within which mobile technologies can support learning.
Comparing Multi-issue Multi-lateral Negotiation Approaches for Group Recommendation

Recommender systems help users to deal with information overload when trying to find interesting items to consume in a certain domain (e-commerce, education, entertainment, tourism). Recommendations have been traditionally made to individuals, but in the last years recommender systems have been applied to group of users. Several aggregation techniques have been proposed to generate group recommendations, but most of them present some shortcomings when trying to satisfy the whole group evenly. In addition to recommendations to multiple users, it also arises the need of recommending multiple items to each group. In this context, this work proposes a multi-agent approach that uses a multi-agent system to make recommendations to a group of users using negotiation techniques. This approach also considers the recommendation of multiple items. Two alternatives within the approach were evaluated in the movies domain with promising results.
Adaptive Enhancement algorithm using image Decomposition for non-uniform illuminated images

The objectivity of illumination and reflectance has been the continuous thrust area in image analysis and enhancement. To achieve better quality, the researchers have focused on improving contrast enhancement and brightness preservation of mainly non- uniform images. This paper reviews the computational techniques evolved for improving the visual quality of images. The discussion has been focused on multi-scale decomposition approach to achieve the desired variation in contrast and brightness of the images. The multi-scale approach is termed as a combination of coarse and detail coefficients of the input image. So, it may be said that the detail characteristics are evaluated to study directional variations of the intensity in the images.
Tag Pollution Detection in Web Videos via Cross-Modal Relevance Estimation

In the era of big data, web videos are known for their astronomical volume and great difficulties to be understood by computers. Therefore it is challenging to detect and curb tag pollution on social networking platforms. Intuitively, the pollution in the tags of a video can be identified by exploiting the tags of visually similar videos. From this intuition, we develop a semi-supervised approach to estimate the relevance between Internet videos and user-provided labels accurately and detect polluted labels accordingly. To further enhance the accuracy of relevance estimation and pollution detection, we introduce three multi-view multi-label models, which employ the coherence and differences between various similarity relations of videos. Compared with two quintessential multi-view fusion models, the proposed models consistently outperform or achieve comparable performance.
Inducing the Document Array

The document array is a simple data structure commonly used together with the suffix array when indexing string collections. It determines to which document each suffix in the lexicographic order belongs. There exist algorithms to compute the document array in linear time from an existing suffix array, or alternatively, during suffix array construction. In this chapter we present algorithms gSAIS and gSACA-K (Louza et al., 2017) that construct the suffix array for a string collection, and we show how to modify them to also compute the document array, with the same theoretical bounds.
A Comparative Study of Image Features and Similarity Measurement Methods in Cross-modal Retrieval of Commodity Images

With the rapid development of information technology and the widespread popularity of mobile devices and e-commerce trading platforms, the number and scale of commodity images and corresponding description text and other multimedia data are growing rapidly, and the demand for cross-modal retrieval of commodity images is also increasing. Cross- mode hash method has been widely studied and applied. Based on the matrix three-factor decomposition hash cross mode retrieval method, the problem of mapping hash code with different modes is solved. In the cross-modal retrieval process, in order to study and analyze different similarity measurement methods, when searching for product image cross-modalities, in order to analyze the retrieval results of different similarity measurement methods, the potential characteristics of different images are The effect of the image is analyzed and the performance of cross-mode retrieval under the matrix three-factor decomposition hash framework is compared. The research results show the results of cross-modal retrieval of commodity images with different similarity measurement methods and different image underlying features.
ACO with Heuristic Desirability for Web Page Positioning Problem

Due to ever changing browsing patterns of the website users, the companies need to adapt the structure of their websites regularly. Webpage positioning problem (WPP) is about deciding the best locations of the webpages in the hyperlink graph structure of the website. Ant colony optimization (ACO) is among the prominent algorithms to solve the optimization problems. In this regard, this chapter aims to solve the webpage positioning problem by formulating it as quadratic assignment problem (QAP). The solution of this problem by ant colony optimization can already be found in the existing literature, subsequently in this chapter the solution to the problem is found by considering heuristic desirability parameter in ant colony optimization technique. In the end, a comparative analysis of both simple ACO and ACO with heuristic desirability as solutions to WPP problem is performed. The experimental results on artificial and real life datasets reveal that the ACO with heuristic desirability produced better results with most of the problem instances.
Implementation of Hybrid Optimization for Improvement in Efficiency of Clustering Techniques

In data tunneling and data analysis, clustering plays a vital role. Clustering is a technique wherein the resultant groups formed by including the similar data. Cluster analysis groups testimony based on correlation and diversity are observed among the data elements. These formed groups are called as clusters and it is also referred as an unsupervised approach. There are distinct algorithms that consider the data attributes and the crunch numbers to form clusters from the information. Based on the behavior of the algorithm, the centroids are preferred naturally by algorithm or the user can define it. The preferred algorithm for clustering is K-Means to split up the data based on the mark of compactness, but it also has some demerits like falling in local optimum. So, in order to avoid, that another algorithm used here is fuzzy clustering algorithm (FCM). FCM also has another face which describe that the Euclidean distance measures can consider unevenly weight underlying factors. Getting uplifted from the decorum of birds, particle swarm optimization (PSO) is a worldwide enhancement process. PSO is popularly used in many cluster analysis issues. So, in order to make algorithm gives better results, two algorithms are brought together to take advantage of twain design. This works set forth the hybrid combination of K-means and PSO called as Hybrid-PSO. The primary responsibility for linking two algorithms is that it gives preferable results in terms of speed and proves to be a strong clustering algorithm, which will be shell out for fitter optimization.
The PC Based Tongue Operated Mouse

The tongue-operated assistive technology, first described at Rehabilitation Engineering and Assistive Technology Society of North America (RESNA), Annual Conference at Washington, D.C. funded by the National Science Foundation and the Christopher and Dana Reeve Foundation along with the successful trial completed with six able-bodied individuals testing the Tongue Drive system. The technology reduces the need of individuals with severe disabilities to receive continuous assistance from family members or caregivers, thus significantly reducing healthcare and assistance costs. It makes easier for the disabled to work and communicate with others, such as friends and family. This work is intended to develop a minimally invasive, unobtrusive, easy-to-use, reliable, and low-cost Assistive Technology (AT) based device that could potentially substitute for the users' lost arm and hand functions.
Dongba Hieroglyphs Visual Parts Extraction Algorithm Based on MSD

Part-based representations (part-based representations) are widely used in the field of shape matching and classification. We can significantly improve the robustness of the recognition algorithm by using parts, so that the computer can analyze objects from different perspectives, such as global and local. Therefore, we use the theory of part-based representations in the field of shape matching, combined with multi-scale shape decomposition (MSD), to give a multi-scale decomposition based visual part extraction algorithm (MDVPE). The algorithm can accurately separate the visual parts from the feature curves of Dongba hieroglyphs, lay the foundation for designing an efficient Dongba hieroglyphs recognition method, and also provide technical support for researching radicals and other content.
Study of topic mining for microblog based on CBMB-HL model

Because of the immediacy and interactivity of microblog, it became particularly important to analysis micro-blog theme. Based on the characteristic of the microblog texts, the relationship between microblog users and the context of comments, this paper proposes a new topic mining model-CBMB-HL model, which is based on multiple features and topic state association model, and can mine the content and comments of Weibo blog simultaneously. The experiments show that CBMB-HL model is more accurately and effectively than CBMB-LDA model to do the topic mining and can effectively discover the relationship between subjects.
Prospects for the Development of ICO as an Alternative Financing Instrument

The article investigates problems of evaluating the economic effect of ICO (initial coin offering) process as an alternative financing instrument in the field of corporate innovation for all stakeholders taking into account their interests and potential benefits. To create an index for ICO’s assessment, the correlation and regression analyses were held. A sample of 234 projects was formed, which presented groups of successful and unsuccessful ICO projects. This index allows to evaluate the project by the criteria which are vital for investors and could be implemented in the practice of the companies within financial planning of their innovation activities. Also, the article pays attention to the overview of the ICO’s perspectives in Russia and worldwide: modifications of the classic ICO scheme, ways of legislation and technical development in the sphere of cryptocurrency.
Ways of Connecting Illiterates with the Security Mechanism: Case Study

The digital communication faces lots of security threats and attacks in the network. Thus, security mechanisms are used as a measure to ensure safety and to implement the security services for the text documents, audio, video and other types of data communication over the network. These security mechanisms are helpful to recognise, prevent and recover from security breaches, threats and attacks. Some of the security mechanisms are Cryptography, Public key certification, Authentication, Digital signatures etc. Obviously, these are mostly used by educated people in communication for their data to be remain integrated, confidential and safe.But, a bitter fact is that there are 30.90% people who are illiterate and have no education background living in our country and their data is also very important to them. So, we have to connect those illiterate people to these security mechanisms, so that they can also communicate without being in fear of the data loss or data manipulation.There are many ways to connect illiterate people with the security mechanisms such as by using facial recognition and fingerprint as the password in various systems or machines like ATMs, banks, government offices and also provide information and knowledge about the prevention from the possible fraud, theft and malicious activities. This paper discuss those different ways to connect the illiterate people with security mechanisms and enlighten their lives.
Rolling Stock for International Motor Trucking

The analysis of the international motor transportation in Russia is carried out, the main directions of cargo transportation, environmental requirements for rolling stock, rules for the vehicles admission for transportation under customs seals, peculiarities of vehicles inspection for international transportation by multilateral approvals and certificate issuance of roadworthiness are presented. The market of rolling stock for international road transportation is described; the limits on dimensions, total weight and thrust capacity of motor vehicles according to the standards of the European Community are mentioned.
Aerial Interaction Control in Outdoor Environments for a Micro Aerial Vehicle Equipped with a Robotic Arm

Physical contact with the surrounding environment is an essential and challenging task for unmanned aerial vehicles (UAV) to perform complex missions in the aerial manipulation field. Steady flight of an aerial manipulator is compromised due to the disturbances forces induced by the actuators of the manipulator, and the conditions of the indoor/outdoor environment. This paper focuses on the pose control performance of the aerial manipulator composed of an UAV and a 2-DOF arm for full contact of the system with a vertical surface in an outdoor scenario. The control technique is based on the Gain Scheduling (GS) approach incorporated into a proportional-integral-derivative (PID) algorithm to reduce the offset of the instability produced by the movement of the robotic arm, the wall effect and the external conditions like wind gusts. The visual simultaneous location and mapping (SLAM) method is implemented, exploiting the onboard sensing capabilities of the UAV without using another external caption motion method to estimate the pose of the system in the outdoor environment. The control design was implemented through extensive outdoor flight experiments to analyzed the behavior of the system in such conditions and obtained the gain value for the set of controllers. Experimental results of aerial contact with a surface show that the controller can suppress the disturbance mentioned above effectively, and make the system hover steadily with sufficient accuracy to complete aerial contact mission in an outdoor environment.
Artificial Intelligence, Machine Learning and Reasoning in Health Informatics—An Overview

As humans are intelligent, to mimic or models of human certain intelligent behavior to a computer or a machine is called Artificial Intelligence (AI). Learning is one of the activities by a human that helps to gain knowledge or skills by studying, practising, being taught, or experiencing something. Machine Learning (ML) is a field of AI that mimics human learning behavior by constructing a set of algorithms that can learn from data, i.e. it is a field of study that gives computers the ability to learn without being explicitly programmed. The reasoning is a set of processes that enable humans to provide a basis for judgment, making decisions, and prediction. Machine Reasoning (MR), is a part of AI evolution towards human-level intelligence or the ability to apply prior knowledge to new situations with adaptation and changes. This book chapter presents some AI, ML and MR techniques and approached those are widely used in health informatics domains. Here, the overview of each technique is discussed to show how they can be applied in the development of a decision support system.
Clustering by K-Means Method and K-Medoids Method

Data science and data analytics are becoming increasingly important. It is widely used in scientific and real-life applications. These methods enable us to analyze, understand, and interpret the data in every field. In this study, k-means and k-medoids clustering methods are applied to cluster the Statistical Regions of Turkey in Level 2. Clustering analyses are done for 2017 and 2018 years. The datasets consist of “Distribution of expenditure groups according to Household Budget Survey” 2017 and 2018 values, “Gini coefficient by equivalised household disposable income” 2017 and 2018 values, and some features of “Regional Purchasing Power Parities for the main groups of consumption expenditures” 2017 values. Elbow method and average silhouette method are applied for the determining the number of the clusters at the beginning. Results are given and interpreted at the conclusion.
Introduction to the automatization of measurement equipment with Python-GPIB

This article provides a quick introduction to the automation of measurements using a public domain language such as Python. We bring references for the used libraries and the basic commands for manipulating the GPIB port. Some examples are provided.
An Ensemble Learning Approach for Privacy–Quality–Efficiency Trade-Off in Data Analytics

Privacy is an issue of concern in the electronic era where data has become a primary source of investment for businesses and organizations. The value generated from data is put to use in a number of ways for economic benefit. Customer profiling is one such instance, where data collected is used for targeted marketing, personalized purchase recommendations and customized product deliveries. In such applications, the risk of individual sensitive information disclosure always prevails, affecting the privacy of individuals involved. Hence privacy preserving analysis demands suppressing or transforming data before it is published for analysis, thus curbing data leak. Subsequently, data quality degrades, and operative analytics is affected. With Big data, algorithms that offer a reasonable qualityprivacy trade off need enhancements in terms of efficiency and scalability. In this paper, the work proposed uses a privacy based composite classifier model to analyze the accuracy of classification. The diverse characteristics of algorithms in the composite classifier are found to balance the classification accuracy that is likely to get affected by privacy model. Further, the model's performance with respect to execution time is then evaluated using the parallel computing framework Spark.
Manifold-Based Classifier Ensembles

In this paper, we briefly present classifier ensembles making use of nonlinear manifolds. Riemannian manifolds have been created using classifier interactions which are presented as symmetric and positive-definite (SPD) matrices. Grassmann manifolds as some particular case of Riemannian manifolds are constructed using decision profiles. Experimental routine shows advantages of Riemannian geometry and nonlinear manifolds for classifier ensemble learning.
Evolution Mechanism of Traditional Residential Environment and Information Fusion Development

Evolution mechanism of the traditional residential building environment and the information fusion development is studied in this manuscript. The integrated management system based on BIM technology and Internet of Things technology,through application in the related technical fields of construction management and operation and maintenance management, can greatly improve the technical level, improve work efficiency, and overcome many problems in the current single BIM technology application. With respects to this trend, this paper gives the analysis of the integration of the information fusion. The novel models are presented to enhance the overall performance. The experimental results have proven their effectiveness.
Comparing Grammatical Evolution Survivor Selection Methods in Forecasting Tidal Level

Grammatical Evolution (GE) is a grammar-based form of Genetic Programming. One important operator in GE algorithms is the selection scheme. The objective of this study is a comparison of two models of survivor selection, the generalation replacement and steady state, for use in tidal level forecasting. The survivor selection schemes are compared and evaluated according to their properties. The results show the performence of steady state is 95,66 % and 94,39 % for generalation replacement.
Generating Automated Rules-Based Game Design Prototypes with MaruGen

Of all of the aspects of game development, Game Design is a long, complicated process that can only be solved via trial and error. Although Game Design can be understood with simple rulesets that define basic game mechanics, it can become a complex problem when many different rules get involved. Moreover, coming up with innovations in this field can be a daunting creative task. Other fields of game development like content creation and procedural generation have created automated tools to facilitate process, but this has not yet been materialized for basic Game Design and prototyping. MaruGen (Machinations Ruleset Generator) is a procedural L-System based tool that aims to fill this gap by creating complex rule-sets from a formal grammar, based on proved simple ones, in an attempt to automate design and encourage innovation via the emergent nature of the rulesets themselves.
Visualizing the Bitcoin's OP_RETURN operator

Bitcoin is undoubtedly the most used distributed ledger technology nowadays. Bitcoin's OP_RETURN operator allows for saving arbitrary data on the blockchain. This comes as an extension of Bitcoin's core usage (i.e. cryptocurrency) and opens up a multitude of use cases. These range from benign applications (e.g. ownership of a digital/physical asset) to illegal/malicious scenarios (e.g. blockchain-based botnets). In this paper, we present a system that provides advanced analytic and visual capabilities with regard to the OP_RETURN operator. Furthermore, we showcase a quantitative and qualitative analysis of the OP_RETURN along with a number of interesting findings.
Advanced structures on multiple configuration sensors

The theory of compound zoom system and its influence and control on aberration are the basis for the design of such systems and have practical value in engineering. The principle, modeling and error analysis are analyed. Based on the system configuration of the compound zoom system, moreover, this paper advances the algorithm analysis.