Reviewerly/exHarmony huggingface dataset:
Supporting Shared Sense of History Within a Rural Village Community

In this chapter we present our longitudinal study of a community photo display system known as the Wray Photo Display (Taylor and Cheverst in Int J Hum Comput Stud 67(12):1037–1047 , in IEEE Comput 45(5):26–32, ) and how members of the community used this display to interact with their past (and each other). Our development of the Wray Photo Display commenced in 2006 as part of a research project which set out to investigate how situated displays could support rural communities, and in particular how such displays could support notions of community. Our analysis of the user generated content (in the form of images and associated comments) submitted to the system reveals a significant proportion related to cultural heritage. The current focus of our work with the Wray community is to provide residents with more sophisticated tools (including mobile tools) to support the shared collection and curation of narratives relating to local history.
Enhanced Cooperative Vehicular Communication for larger-scale Distributed Edge Computing

Given the vast advancements in mobile, fog, and edge computing, addressing network related challenges became a desperate demand. Vehicular clouds changed the conventional computing architectures to a more distributed, cooperative, and loosely-coupled construction. However, the current network and communication infrastructures are not designed to support such construction. Vehicular networks must be designed to tolerate fast movements and sudden changes to efficiently support to the hosted services. In this paper, we investigate the diversity gain order of the transmission mode over the smart cooperative vehicle network. Moreover, we formulate an advanced vehicular relaying technique to enhance the network connectivity to better serve the hosted cloud services. Extensive simulations and analysis showed that the presented wireless communication model presents a enabled a considerable enhancement in diversity gain orders which leads to a more stable and resilient communication links.
A Learning-Based Approach for Perceptual Models of Preference

This paper introduces a novel data-driven approach based on subjective constraints and feature learning for training perceptual models of preference. Fuzzy evaluation is applied to describe the subjective opinions from a large set of data collected from user study. Combined with the objective attributes of the training models and the subjective preferences, an optimization method is developed successfully for training and learning perceptual models. Two applications are given in details for the selection of “best” viewpoint of 3D objects and the optimized direction of 3D printing, which verify the effectiveness of our approach. This work also demonstrate a good human-computer interaction practice that draws supporting knowledge from both the machine side and the human side.
Structure-preserving public-key encryption with leakage-resilient CCA security

Structure-preserving primitives are important building blocks in cryptographic protocols. Up to now, the only structure-preserving public-key encryption (SP-PKE) with CCA security over asymmetric pairing groups is based on the SXDH assumption, due to Libert et al. [18]. In this work, we propose a general framework of constructing SP-PKE with leakage-resilient CCA security (which implies the IND-CCA2 security). The corresponding instantiations result in the first leakage-resilient CCA secure SP-PKE from the Matrix Decision Diffie-Hellman (MDDH) assumption (including the SXDH and k-Linear assumptions) over asymmetric pairing groups. The ciphertext of our SP-PKE also enjoys the publicly verifiable property.
Detecting and Counting Eyes Blinking Using Haar Cascade—A Handy Way to Diagnose Dry Eyes Disease

This paper presents the design of the program, called Eye Blinking Detection Program, to define eye state as well as eye blinking by using Haar Cascades. Based on the result of the opened-eye detection process, eye state is determined and the eye blinking can be counted. All the results are displayed on a friendly user interface. By checking eyes state, the program determines if the object is asleep or awake and activate the alert. In addition, the program also has dry-eyes disease diagnostic feature and an eye health-training program. This program is very useful for drivers by helping them stay awake or apply to patients who has eye-related disease.
Reconstructing Bifurcation Diagrams of a Chaotic Neuron Model Using an Extreme Learning Machine

In recent years, an extreme learning machine that has a simple structure and good generalization has been applied to various problems. We have applied this extreme learning machine to reconstruction of bifurcation diagrams (BDs). We have reconstructed the BDs of various systems using the extreme learning machine. However, we have noticed that the original extreme learning machine, whose range of the synaptic weights of hidden neurons is $$\left[ -1,1 \right] $$ , cannot predict time series of the chaotic neuron model, although it is important for the reconstruction of bifurcation diagram to predict time series of target systems. In this paper, we show that the extreme learning machine can predict time series of chaotic neuron models by adjusting the synaptic weights of hidden neurons. In addition, we reconstruct the BDs of chaotic neuron models.
Discerning cyber threatening incidents from ordinary events using sentiment analysis and logistic regression

Many organizations allow incident reports from the general public. Some of these reports may contain information about threatening incidents, while others may describe ordinary events. Incident classification is the process of distinguishing between incidents and events. We describe an automated incident classification system, which uses logistic regression and sentiment analysis to estimate the likelihood that an event is an incident using its textual description. We trained and validated two different models on one dataset and used a different dataset for testing purposes. The model that performed better utilized sentiment analysis at the sentence level as well as at the level of individual verbs, nouns, and adjectives. It achieved 99% accuracy on the validation set and 100% accuracy on the test set over 50% baseline. Overall, we found that using sentiment score increased the model's accuracy, precision, and recall by at least 10% especially when it is applied on several levels of the text. The difference between our approach and the typical human approach is that in our approach we train the system to recognize incidents before any incident actually takes place and our system can recognize incidents even if their descriptions do not include keywords the system previously encountered.
The Role of Productive Structures in the Attractiveness of African Economies: A “Lucas Paradox” Approach

The purpose of this article is to analyze the paradox of atrophy of capital flows to African countries, while economic theories predict that such flows of investment should be large enough because of higher returns on capital in countries where its presence is relatively low in production factors.The GMM estimate of a system of two equations for a sample of 25 African countries over the period 2004-2014 gives the following results: the low investment flows are due to the production structures of African economies, which lack efficiency and attractiveness.Also, by improving the structural elements of the economy that render production ineffective, African countries can thus increase their potential to raise larger flows of foreign direct investment.
Study of the Chinese-English Translation of Computer Network Terms

With the continuous development of the computer science, the computer network terms play an increasingly important role in the understanding of computer English. This paper makes a general study of the Chinese translation of the English computer network terms by analyzing the characteristics of the computer network terms, the comparison of the English and Chinese word-formation, and the translation principles and methods and so on. Although the history of the computer development is short, it has a wide range of applications. The computer terminology has its own characteristics. The computer network terms produce a large number of the new words through infiltration, derivation and transformation, which is a significant feature of it.
Enhancing Interaction with Social Networking Sites for Visually Impaired People by Using Textual and Visual Question Answering

Question Answering (QA) is an all-around inquired issue in Natural Language Processing (NLP). This paper expands the boundaries of Question Answering by including textual and visual aspects, then further combining it with the SNS for enhancing its interaction with visually impaired people. In our proposed work, a text supported with an image is fed into the hybrid model which is a combination of CNN and LSTM, producing the most accurate result with the highest probability. Both questions and answers are open ended visual and textual queries in specifically targeted diverse regions of a picture including the subtle elements of a text. Subsequently, we created a framework that required a point by point comprehension of the picture which is more complex than the framework delivering just pictorial inscriptions. The model achieved better results than other models. By using this model, we enhanced interaction with the SNS with greater efficiency.
Privacy Preserving Collaborative Agglomerative Hierarchical Clustering Construction

Sharing information brought by governments, companies, and individuals, has created fabulous opportunities for knowledge-based decision making. However, the main challenge in collaborative data analysis returns back to the privacy of sensitive data. In current study, we propose a general framework which can be exploited as a secure tool for constructing any agglomerative hierarchical clustering algorithm over partitioned data. We assume that data is distributed between two (or more) parties either horizontally or vertically, such that for mutual benefits the participated parties are interested in obtaining the clusters’ structure on whole data, but for privacy concerns, they are not willing to share the original datasets. To this end, in this study, we propose general algorithms based on secure scalar product and secure hamming distance to securely compute the desired criteria for shaping the clusters’ scheme. Our proposed approach covers the private construction of all possible agglomerative hierarchical clustering algorithms on distributed datasets, including both numerical and categorical data.
Genetic Algorithms and Their Ethical Degeneration in Simulated Environment

There is one very popular topic – not only for scientists, but for the community as a whole. Can machines easily learn something, which is not part of their programs? Can the machines revolt? If we are thinking only simple programming like functional or imperative, it is hard to imagine. But there are different kinds of programming, like setting the base program and rules, and let the programs teach themselves. These are the principles are used in Artificial neural networks and in Genetic/Evolution programming. US scientists have proven the potential of degeneration in ANN. This topic is focusing on the Evolution programming and its Ethical Degeneration in a Simulated Environment.
Introducing Managed Software Evolution

This chapter introduces the book and motivates managed software evolution. Moreover, the structure of the book is outlined.
SOCIAL TECHNOLOGY OF UNIVERSITY INTERACTION MANAGEMENT WITH STAKEHOLDERS AS A TOOL FOR INCREASING COMPETITIVENESS

The article has been devoted to the possibilities of using an interdisciplinary approach of sociology of management to adapt the management system of an organization of high education to the challenges of the «external» and «internal» environment using the potential of the theory of stakeholders in order to harmonize interests and increase competitiveness. The necessity of using the basic elements of this theory in management practice has been substantiated. As an experiment, an experience in the development and implementation of social technology for the organization of interaction of Lipetsk State Technical University with stakeholders has been adduced. The algorithm, principles and tasks of interaction of the educational organization with stakeholders and its place in the strategic management of the University have been described. The possibilities of using the theory of stakeholders in the strategic management of the University have been considered.
Self-Tuned Descriptive Document Clustering using a Predictive Network

Document network is defined as a collection of documents that are connected by links. Document clustering become ubiquitous nowadays due to the widespread use of online databases, such as academic search engines. Topic modeling has become a widely used tool for document management because of its superior performance. However, there are few topic models differentiate the importance of documents on different topics. In this survey, can implement text rank algorithms of documents to improve topic modeling and propose to incorporate link based ranking into topic modeling. Text summarization provides an important role in information retrieval. Snippets generated by web search engines for every query result is an application of text summarization. Existing text summarization techniques shows that the indexing is done on the basis of the words present in the document and consists of an array of the posting lists. Document features such as term frequency, text length are used to allocate indexing weight to words. Specifically, topical rank is used to compute the subject stage rating of files, which indicates the significance of documents on special topics. By taking flight the topical ranking of a file as the opportunity of the record concerned in corresponding subject matter, a generalized relation is created between ranking and subject matter modeling. In this thesis, can implement topic discovery model for large number of medical database. The datasets are trained and extract the key terms based text mining and fuzzy latent semantic analysis (FLSA), a novel approach in topic modeling using fuzzy perspective. FLSA can maintain health &amp; medical corpora redundancy problem and provides a new method to estimate the number of topics.
Toward Shareable Multi-abstraction-level Feature Extractor Based on a Bayesian Network

In this study, we propose a Multi-abstraction-level Feature extractor with a Bayesian network (MFB) that can output intermediate patterns as feature values and can be shared across different abstraction level classifiers. To leverage the patterns from intermediate layers, we implemented a bidirectional network based on a Bayesian network to accurately calculate posterior probabilities. Experimental testing confirmed that a MFB could be constructed successfully on an actual computer to achieve pattern extraction.
Using Neural Networks and Hough Transform for Leukocytes Differentiation in Blood Count Images

Automated equipments for blood count have facilitated the clinical routines in medical laboratories. However, when grave anomalies are identified, some samples still need to be analyzed in a traditional way using a microscope. This paper proposes a method to classify the five most common types of leukocytes, present in the human blood using blood smear images. This allows reducing the amount of work, the time spent in the blood test as well as cost of such a test. The results are satisfactory and should contribute positively to the development of an automatic system and/or a smart electronic device, which would come handy for this kind of applications.
Dynamic optimization of a dual-axis solar tracker for PV modules

Abstract This paper deals with the dynamic optimization of the dual-axis tracking mechanism, which is used for increasing the energetic efficiency of a photovoltaic module, by maximizing the rate of incident solar radiation. The study is based on the optimal design algorithm included in the commercial MBS (Multi-Body Systems) software solution ADAMS, through the parameterization of the virtual model, which offers the possibility to perform a series of studies aiming at identifying the design parameters that decisively influence the dynamic behavior of the tracking systems. The global coordinates of the design points (i.e. the mounting points of the actuating sources) are defined as design variables for the optimization process, while the design objectives refer to the power consumptions of the actuating sources.
Representation Learning of Taxonomies for Taxonomy Matching

Taxonomy matching aims to discover categories alignments between two taxonomies, which is an important operation of knowledge sharing task to benefit many applications. The existing methods for taxonomy matching mostly depend on string lexical features and domain-specific information. In this paper, we consider the method of representation learning of taxonomies, which projects categories and relationships into low-dimensional vector spaces. We propose a method to takes advantages of category hierarchies and siblings, which exploits a low-dimensional semantic space to modeling categories relations by translating operations in the semantic space. We take advantage of maximum weight matching problem on bipartite graphs to model taxonomy matching problem, which runs in polynomial time to generate optimal categories alignments for two taxonomies in a global manner. Experimental results on OAEI benchmark datasets show that our method significantly outperforms the baseline methods in taxonomy matching.
A Tour of Franz Baader’s Contributions to Knowledge Representation and Automated Deduction

This article provides an introduction to the Festschrift that has been put together on the occasion of Franz Baader’s 60th birthday to celebrate his fundamental and highly influential scientific contributions. We start with a brief and personal overview of Franz’s career, listing some important collaborators, places, and scientific milestones, and then provide first person accounts of how each one of us came in contact with Franz and how we benefitted from his collaboration and mentoring. Our selection is not intended to be complete and it is in fact strongly biased by our own personal experience and preferences. Many of Franz’s contributions that we had to leave out are discussed in later chapters of this volume.
Blockchain-Based Insurance

To date, the insurance industry's interest in the blockchain has focused largely on the possibility of recording insurance entitlements in a transparent way. While the blockchain may produce significant efficiencies of this sort, it has considerably greater transformative potential. Smart contracts could serve as a substitute for insurance companies, conventionally conceived. Such contracts could perform the function of deciding whether claims should paid, without the need for or possibility of judicial intervention. The blockchain and smart contracts are difficult to regulate, because ownership and decisionmaking can be decentralized. Blockchain-based insurance may successfully provide a means of avoiding expensive regulation and could have a competitive advantage over regulated insurance. This article will discuss how blockchain-based insurance might work and will identify some technical challenges and other obstacles that it may face.
A cascaded multitask network with deformable spatial transform on <i>person search</i>

This article introduces a cascaded multitask framework to improve the performance of person search by fully utilizing the combination of pedestrian detection and person re-identification tasks. Inspired by Faster R-CNN, a Pre-extracting Net is used in the front part of the framework to produce the low-level feature maps of a query or gallery. Then, a well-designed Pedestrian Proposal Network called Deformable Pedestrian Space Transformer is introduced with affine transformation combined by parameterized sampler as well as deformable pooling dealing with the challenge of spatial variance of person re-identification. At last, a Feature Sharing Net, which consists of a convolution net and a fully connected layer, is applied to produce output for both detection and re-identification. Moreover, we compare several loss functions including a specially designed Online Instance Matching loss and triplet loss, which supervise the training process. Experiments on three data sets including CUHK-SYSU, PRW and SJTU318 are implemented and the results show that our work outperforms existing frameworks.
Bedrock Phase Composition of Kamenny Ambar and Konoplyanka Bronze Age Fortified Settlements and Neplyuevsky Burial Ground (Southern Urals)

Among the main archaeological sites of the Late Bronze Age are the fortified settlements of Kamenny Ambar and Konoplyanka and Nepluyevsky burial ground, located in the basin of the Karagaily-Ayat river (Chelyabinsk region, Southern Urals). The studies of cultural heritage objects collected from these sites are impossible without a comprehensive study of the surrounding geological and ecological environment, representing some reference points or background values for comparison. The region under consideration appears to be poorly studied, particularly regarding isotopic and geochemical characteristics of complex interactions between the underlying rocks and soils, groundwater and plants. The present work is devoted to the study of phase composition of rocks characterising the surrounding geological environment in the area of the major Bronze Age archaeological monuments by X-ray diffraction. Using the SiroQuant software, the phase composition of bedrocks characterising the surrounding geological environment in the region of the major Bronze Age archaeological sites of the Southern Urals is was determined.
Automatic Academic Paper Rating Based on Convolutional Neural Network

As an ever increasing number of academic papers are being submitted to journals and conferences, assessing every one of these papers by experts is tedious and can cause imbalance because of the personal factors of the reviewers. In this system, in order to help professionals in assessing academic papers, here propose a task: Automatic Academic Paper Rating (AAPR), which automatically determine whether to accept academic papers. We build a convolutional neural network (CNN) model to achieve automatic academic paper rating task. It has two phases, first phase is identifying abstract part of source paper and generate rating score using CNN model and second phase is taking decision based on the score to accept or decline papers. This model takes word embedding of the abstracts as the input and learns useful features. The word embedding used for training the model is a semantically enriched set of Word2Vec word embedding. After the training phase, the proposed model will be able to generate the score of a new abstract. And find that the title and abstract parts have the most influence on whether the source paper quality when setting aside the other part of source papers. The proposed system outperforms the state-of-art technique.