Reviewerly/exHarmony huggingface dataset:
Descriptions of Part-of Relationships for Ontology Building and Its Deployment – A Representation Model for Part Structures based-on Role Theory

Ontological considerations about part-of relations have been extensively investigated because they are basic and important relationships for ontology building. Although there are various discussions on kinds of part-of and their ontological characteristics, there remains some room for discussing a couple of fundamental issues such as “What is a part?” and “When is a part-of relation composed?” This paper discusses ontology patterns of descriptions of part-of relationships on the basis of ontological theories in order to provide practitioners with useful guidelines for descriptions of part-of structurers. This paper focuses on ontology patterns which capture commonality and special characteristics of parts so that complicated structures of physical objects are described appropriately. We discuss four problems related to descriptions of parts. 1) interdependence between the whole and its parts, 2) kinds of parts such as components, portions and materials, 3) multiple inheritance according to substance and properties of parts, 4) the commonality and specificity of parts. To cope with these problems, this paper introduces a part representation model based on ontological theory of roles. The main idea of the part representation model is to distinguish between a part dependent on its whole and the context-independent properties of the part. The former is defined as the role-holder which plays roles and the latter is defined as the player of the role. The role defines properties of the part which is dependent on its whole. These three kinds of definitions enable to describe differences of various properties of parts according to their context dependence. We show how this model is used to describe various parts through practical examples of the anatomical structure of human body developed in the medical ontology project in Japan.
Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies

Optimal surface methods are a class of graph cut methods posing surface estimation as an n-ary ordered labeling problem. They are used in medical imaging to find interacting and layered surfaces optimally and in low order polynomial time. Representing continuous surfaces with discrete sets of labels, however, leads to discretization errors and, if graph representations are made dense, excessive memory usage. Limiting memory usage and computation time of graph cut methods are important and graphs that locally adapt to the problem has been proposed as a solution. Min-marginal energies computed using dynamic graph cuts offer a way to estimate solution uncertainty and these uncertainties have been used to decide where graphs should be adapted. Adaptive graphs, however, introduce extra parameters, complexity, and heuristics. We propose a way to use min-marginal energies to estimate continuous solution labels that does not introduce extra parameters and show empirically on synthetic and medical imaging datasets that it leads to improved accuracy. The increase in accuracy was consistent and in many cases comparable with accuracy otherwise obtained with graphs up to eight times denser, but with proportionally less memory usage and improvements in computation time.
Thermooptic reconfigurable Mach Zehnder quantum interference device

Integrated Mach-Zehnder (MZ) quantum optic circuit is one of most promising approaches for generation and reconfiguration of complex quantum interference. Here, multi photons quantum entanglement is formulated in terms of thermooptic phase change in MZ interferometer having 3 dB two mode interference couplers (MZ-TMI). The manipulation of quantum entanglement is demonstrated theoretically, opening a way to design silicon based quantum optic technology platform for quantum processing applications. Our results also provide multi-photon quantum interference with high fabrication tolerance and fidelity of 0.98 ± 0.01 within smaller dimension than previous implementation of MZ component.
On Involutive Nonassociative Lambek Calculus

Involutive Nonassociative Lambek Calculus (InNL) is a nonassociative version of Noncommutative Multiplicative Linear Logic (MLL) (Abrusci in J Symb Log 56:1403–1451, 1991), but the multiplicative constants are not admitted. InNL adds two linear negations to Nonassociative Lambek Calculus (NL); it is a strongly conservative extension of NL (Buszkowski in Amblard, de Groote, Pogodalla, Retoré (eds) Logical aspects of computational linguistics. LNCS, vol 10054. Springer, Berlin, pp 68–84, 2016). Here we also add unary modalities satisfying the residuation law and De Morgan laws. For the resulting logic InNLm, we define and study phase spaces (some frame models, typical for linear logics). We use them to prove the cut elimination theorem for a one-sided sequent system for InNLm, introduced here. Phase spaces are also employed in studying auxiliary systems InNLm(k), assuming the k-cyclic law for negation. The latter behave similarly as Classical Nonassociative Lambek Calculus, studied in de Groote and Lamarche (Stud Log 71(3):355–388, 2002) and Buszkowski (2016). We reduce the provability in InNLm to that in InNLm(k). This yields the equivalence of type grammars based on InNLm with ( $$\epsilon $$ -free) context-free grammars and the PTIME complexity of InNLm.
Overview of Machine Learning Approaches for Wireless Communication

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.
Evaluation of Group Convolution in Lightweight Deep Networks for Object Classification

Deploying a neural network model on a low-power embedded platform is a challenging task. In this paper, we present our study on the efficacy of aggregated residual transformation (defined in ResNeXt that secured 2nd place in the ILSVRC 2016 classification task) for lightweight deep networks. The major contributions to this paper include (i) evaluation of group convolution, (ii) study on the impact of skip connection and various width for lightweight deep network. Our extensive experiments on different topologies show that employing aggregated convolution operation followed by point-wise convolution degrades the accuracy significantly. Furthermore as per our study, skip connections are not a suitable candidate for smaller networks and width is an important attribute to magnify the accuracy. Our embedded friendly networks are tested on ImageNet 2012 dataset where 3D convolution is a better alternative to aggregated convolution because of the 10% improvement in classification accuracy.
An Enhanced Integration of Voice-, Face-, and Signature-Based Authentication System for Learning Content Management System

This chapter explores a novel learning content management system. This chapter presents a novel system based on integration of voice authentication, face recognition technique, and signature of a person to recognize in e-learning system. Voice-based authentication, face recognition, and signature of a person is most widely used to authenticate human identity. The main concern in an e-learning system is to demotivate unknown users from taking the examination in place of the learner. Different techniques have been introduced to stop this fraud if any unknown person wants to imitate person's identity. In order to avoid the fraudulent handling of e-learning systems, the authentication based on voice recognition is discussed as one of the efficient techniques in literature.
Efficient hardware structure for extended Euclidean‐based inversion over

In this study, an efficient hardware structure for implementation of extended Euclidean algorithm (EEA) inversion based on a modified algorithm is presented. In the proposed algorithm, one iteration performs the operations corresponding to two iterations in previously reported EEA-based inversion algorithm. The proposed structure is implemented based on a modified algorithm with low path-delay and hardware consumption. The circuit performs one iteration of the algorithm in one clock cycle. Furthermore, to increase speed and reduce the latency of the structure, the authors applied the loop unrolling technique by replicating the body of the loop. Loop unrolling replaces a loop body by several copies of the body. The authors chose the number of copies based on the trade-off between hardware resources, critical path delay and the number of clock cycles. Therefore, the time and area parameters can be adjusted by different choices. The proposed architecture is simple, low cost and also the number of clock cycles in the structure are reduced compared to existing EEA-based works. The structure over two binary finite fields and has been successfully verified and implemented on Virtex-5 XC5VLX110 field-programmable gate array. The comparison with other previous implementations of the inversion operation verifies that the proposed method has better improvement in terms of area × time complexity.
Multilevel Minimum Cross Entropy Threshold Selection Based on the Improved Bat Optimization

Thresholding is a simple and most commonly used method for image segmentation. It’s known that the minimum cross entropy thresholding (MCET) has been widely used in image threshold selection. The bat algorithm (BA) come from the social behavior of the swarm of bats, and it’s one of the popular techniques for optimization. This paper proposed an improved BA (IBA) by using time-varying inertia weights into the update formula, and six benchmark functions were selected for the simulation test. Then, the IBA was used for searching the optimal MCET thresholds. What’s more, three different methods that the improved particle swarm optimization (IPSO), the fuzzy-clustering method (FC) and basic BA are carried out for comparison with the proposed algorithm. The results demonstrate that the proposed IBA can obtain more fast and stable results.
Visibility restoration of single foggy images under local surface analysis

A variety of empirical methods, which are represented by dark channel prior, have been proved effective for haze removal. However, undesirable artifacts and color distortion are still left on some of dehazing results, which directly determines the performance of computer vision tasks. Different from traditional statistical methods, we apply Multi-dimensional theory that quickly predicts haze free images. To this purpose, the local manifold similarity is employed to reduce the error of initial estimation. Moreover, contrast-based Gaussian curvature is also introduced in order to obtain the smoothness transmission map. Compared with conventional methods, quantitative and qualitative comparisons have shown our approach improvement visual results.
Gaze estimation using a head-mounted single full-view camera

We present a gaze estimation method for a head-mounted full-view egocentric camera that can capture egocentric video together with users’ gaze cues. While the conventional gaze recording device has two cameras, an eye camera and a scene camera, the proposed gaze recording device has only a single spherical camera with a full field of view. To determine the point of gaze on full-view images, we present an eye-model-based gaze estimation method by means of three-dimensional iris projection. First, an eye model is built according to the precalibration process of an eyeball center and the eyeball biological parameters; then, we express the 3D iris contour in the eye model and project it back to the spherical camera model. Since 2D iris contours can be detected directly on images and can also be expressed under the spherical model, the relationship between the 3D iris contour and the 2D iris contour can be found. By solving this problem, the gaze direction under the camera model is determined; subsequently, the point of gaze on the images can be inferred. Since in the proposed method, a single spherical camera can play the role of the conventional two cameras, not only do the proposed method results have a simpler system structure but also the cumbersome operation of the calibration for the conventional two-camera gaze measurement devices becomes unnecessary. The effectiveness of the proposed method is shown by the experimental results.
IOT—Eye Drowsiness Detection System by Using Intel Edison with GPS Navigation

The number of traffic accidents continues to increase due to the driver’s fatigue has become a serious problem to the society especially for the driver who drove for long distance. Technology in digital computer system allows us to create a drowsiness detection system. Studies for drowsiness detector system have focused on development of computer vision algorithm and lack of Internet of Things (IoT) and notification system, either awake or sleep or might involve in accident, and current location. Thus, we decide to develop a drowsiness detection system with notification of accident and the location by using Global Positioning System (GPS) navigation. In this system, if the driver’s eyes are closed about more than 4 s, the driver considers as drowsy and an alarm system will be activated to warn the driver and notify the status and location to relative for further action via message (SMS).
Humanities Scholar Information-Seeking Behavior: Quantitative Approach

This study aims to investigate the humanities scholars’ information needs and behavior in an ICT environment as a case study at Yarmouk University, Jordan. Earlier researches showed that humanities scholars have an incompatible relationship with ICT use even despite their significant use of library facilities compared to other scholars. Not to mention, the lack of up-to-date studies, which considers the significant acceleration in ICT infrastructure and the emerging digital environment. However, the main objectives are to (a) understand humanities scholar’s behavior and their information need in an ICT environment (b) identify the barriers they encountered, the study employed survey questionnaire method. 280 Academic staff from four faculties of humanities were sampled and selected. Responses were received from 161 academician’s staff members. The finding indicated that the humanities scholars possessed various level of awareness in the availability of sources and services of information in their field of subject interest. Besides, they showed different motivations in in-formation-seeking which were influenced by the usage of appropriate format and preference languages. It was also discovered that barriers in information-seeking such as limited of library resources, inconvenient of university environment and personal incompetency had affect their information-seeking satisfaction. They overcame the barriers through personal experience and technological literacy. Lastly, the findings can greatly assist the library in facilitating the needed re-sources and services which could ease the information-seeking behavior. It also can increase the level of satisfaction among the library users particularly the electronic resources and services.
An Enhanced Hybrid Clustering Approach for Privacy Preservation (ECPS) in Big Data using Apache Spark Framework

Abstract In the era of big data mining, clustering is the organization of objects into groups based on applied similarity metrics. Among those metrics, the k-means approach is considered the most popular algorithm so far. As a result of the advancement in parallel processing and distributed computation, traditional techniques are not as efficient in computing the centroid when performing clustering. Consequently, this issue has an effect on privacy preservation when it comes to processing individuals’ sensitive information. Hence, an optimal clustering technique an enhanced hybrid clustering approach for privacy preservation in the context of big data perspective as well as in the context of the preservation of individual privacy protection from background knowledge attack is proposed in this article. The first approach depicts a combination of the ant colony optimization and firefly techniques for choosing the better centroid position with the data. The next approach is about combining the differential privacy algorithm, which uses the Laplace mechanism for augmenting additional noise to the individual’s data to make privacy preservation more robust. With the evolving trends and technologies, the amount of data being generated is increasing at an overwhelming rate. Thus, the proposed approaches are designed in such a way that they can be adapted to the changing needs of big data. The proposed algorithms are efficient when compared with the existing clustering algorithms and provide better performance by guaranteeing privacy. The implementation of the proposed works is done upon the Apache Spark with the big data framework.
Reliability, Fault Tolerance and Other Critical Components for Survivability in Information Warfare

The information revolution has caused many aspects of human activity to critically depend on a wide variety of physically existing or virtual technological achievements such as electronic devices, computer systems, algorithms, cloud resources, artificial intelligence hardware and software entities etc. Many of these systems are used in highly sensitive contexts, such as military applications. This implies the existence of an increasing number of unintentional disturbances or malicious attacks. Successful operation requires qualities such as robustness, fault tolerance, reliability, availability and security. All these may be summarized by the title of survivability. Survivability of critical systems working for sensitive applications involves the ability to provide uninterrupted operation under severe disturbances, gracefully degrade when limiting conditions are reached and maintain the ability to resume normal service once the disturbances have been removed. Survivability is an important, even - though non – functional, lifecycle property of many engineering systems. Further desirable elements of survivability include the ability of systems to recognize and resist attacks or accidents, adapt in order to avoid them and modify their behavior in order to diminish the effects of similar future occurrences. This chapter presents a quantitative approach to assessing survivability and an account of survivability in military systems. A scheme for survivability via replica diversity in the implementation of the AES algorithm is then presented. Following that, an algorithm for adaptive attack aversion in user authentication systems is presented that is based on Boolean transformations. An approach for increased survivability in Internet of Things (IoT) systems is then presented. Finally, an algorithm for secure data storage in cloud resources is presented that allows attack detection and avoidance.
Object-Oriented Logic Programming of Intelligent Visual Surveillance for Human Anomalous Behavior Detection

The idea of the logic programming-based approach to the intelligent visual surveillance is in usage of logical rules for description and analysis of people behavior. New prospects in logic programming of the intelligent visual surveillance are connected with the usage of 3D machine vision methods and adaptation of the multi-agent approach to the intelligent visual surveillance. The main advantage of usage of 3D vision instead of the conventional 2D vision is that the first one can provide essentially more complete information about the video scene. The availability of exact information about the coordinates of the parts of the body and scene geometry provided by means of 3D vision is a key to the automation of behavior analysis, recognition, and understanding. This chapter supplies the first systematic and complete description of the method of object-oriented logic programming of the intelligent visual surveillance, special software implementing this method, and new trends in the research area linked with the usage of novel 3D data acquisition equipment.
Building resilient community using social technologies

Natural disasters like cyclones are a common phenomenon in India, especially in coastal regions. During disaster, victims of a village/ locality normally take shelters in groups in some nearby safe areas (for example, Multi Purpose Cyclone Shelters). Despite of having access to physical infrastructure with basic facilities, disaster victims at cyclone shelters often find it difficult to communicate their exact situational information (like, resource needs and damage information) directly to the local/central authorities. Due to lack of coordination among relief workers and the remote central office, conflicting demands are often generated, causing mismanagement and delay in relief operation. This paper proposes innovative usage of social technology to build resilient community, a mandatory prerequisite to enhance disaster preparedness and coping up mechanisms among collectives inhabited in vulnerable areas. This paper presents an architecture of a Knowledge Informatics Hubs using social technologies at local multipurpose cyclone shelters (MPCS) in coastal areas that will empower the local community members to interact and collaborate with each other, establish communication with various supporting agencies during disaster, establish links with experts and participate in local governance. The aim of creating such a hub would be to create digitally empowered resilient communities who are not only prepared to prevent or minimize the risk of damage to life, property, and the environment due to disaster, but also have the ability to quickly reinstate their normal life and livelihood after a crisis, reopen businesses, and restore other essential services needed for the full economic recovery.
Voice Biometric for Learner Authentication

The objective of this chapter is to verify the identity of the claimed learner by extracting the prosodic features of the speech signal. TIMIT Acoustic-Phonetic Continuous Speech Corpus is used for learner verification using prosodic and articulation features such as energy, pitch, and formants. The prosodic feature includes pitch (F0), and articulation feature includes formants (F1-F7). From this database, for this project in the training phase, 200 learners were used and in the testing phase 160 learners were used. The pitch and formants were extracted using linear predictive analysis. The first seven formants were used for verification purpose. The feature set consists of eight features. The features are fed into the Guassian mixture model. In the Gaussian mixture model, parameters are estimated from the training and testing data using the iterative expectation-maximization. Log likelihood score is computed using these parameters, and then these scores are normalized to make decisions. The decision is made based on the threshold.