Reviewerly/exHarmony huggingface dataset:
Learning in Reproducing Kernel Hilbert Spaces

This chapter is dedicated to nonparametric modeling of nonlinear functions in reproducing kernel Hilbert spaces (RKHS). The basic definitions and concepts behind RKHSs are presented, including positive definite kernels, reproducing kernels, kernel matrices, and the kernel trick. Cover's theorem and the representer theorem are introduced. Then, kernel ridge regression, support vector regression, and support vector machines are studied. The concept of random Fourier features for kernel approximation is introduced and its application to online and distributed learning is discussed. The notion of multiple kernel learning is presented and a discussion on sparse modeling for nonparametric models in the context of additive models is provided. The chapter closes with a case study for text authorship identification via the use of string kernels.
An Intelligent Knowledge Treasure for Military Decision Support

Information integration is great for military operations because the range of pertinent information sources is significantly distinct and dynamic. This article develops an intelligent knowledge treasure comprised of military resource ontology and procedures, as a learning model for better interoperability of heterogeneous resources of the Indian military. This model can interpret and learn the context of military information automatically, thereby facilitating the military commanders with decision making in several operations, such as command and control, teaching and training, military coalition, situation awareness and many more. To design the military resource ontology, this article specifies the core concepts of the ontology based on terms derived from heterogeneous resources. WWW standard ontology language, OWL has been used to codify the ontology. This article develops an intelligent tool—“QueryOnto”—as an interface to the military resource ontology that provides a commander decision support service and demonstrates how to apply the military ontology in practice. The developed ontology has been verified and validated with the best known approaches and metrics available. The presented model is helpful for military commanders to train their juniors in a systematic way and will provide an efficient web-based learning of different military tasks in future.
Self-Adaptive QoS-Aware Web Service Discovery Using Ontology Approach

In recent years, although semantic has been widely used in service discovery mechanisms, it still needs to exploit all semantic aspects included in service documents so that the discovered service can highly be relevant with user request. Moreover, it also needs to consider self-adaptability in discovering the services which can adapt to searching conditions or parameters in order to find other suitable and potential services if no feasible solution could exactly satisfy user QoS requirements. Therefore, this paper proposes a novel self-adaptive QoS-based service discovery mechanism which can adapt the discovery process with the help of semantically structured ontology trees if unexpected results are encountered. The discovery process matches the equivalences between service advertisement and requirement using three similarity evaluation criteria namely concept, attribute and constraint similarity. This discovery process is repeated until feasible solution is found and a set of most suitable services are returned to the users. The authors prototype their system called SQoSD to evaluate the efficiency and adaptability compared with OWLS-CPS and RQSS. The experimental results prove that our mechanism is superior to the other compared mechanisms.
Performance of Reinforcement Learning on Traditional Video Games

Reinforcement learning is a model structure that learns and updates the model based on receiving the reward and feedback of certain environments. In recent researches, reinforcement learning has been shown to be very effective in a well- defined or simulated environment to identify optimal strategies for solving given problems. This work test the performance of several Deep Reinforcement Learning model structures on a classic video game, Flappy bird. This project wants to further analyze the performance of different settings of deep Q networks and policy gradient models, to see how different model structures affect the training speed, accuracy, and efficiency. The author has found that the Double DQN model can achieve the best score among all deep learning models, whereas the Q-table model can achieve a better score with a proper feature extraction method, as the environment is rather simple.
EDRS: an Automatic System to Recognize Electrical Drawings

Electrical drawings, consisting of electrical symbol elements, connection lines and text annotations, describe the layout of electrical equipment. However, power system technicians often have to manually redraw them from image format to digital files, which is challenging. Here we propose EDRS—a deep learning based automatic Electrical Drawings Recognition System for electrical drawings. Specifically, we first develop a Faster- RCNN based model to accomplish the subtask of electrical symbols automatic recognition. Then we provide a text detection and recognition model to recognize the content of electrical text annotations, while the electrical connection lines are also recognized by a pixel-level digital image processing approach. Finally, we carry out an association analysis on electrical elements, with which we identify the topological relationship of electrical elements and group the electrical elements for determining their types. Extensive experiments on several real-world power systems demonstrate that our EDRS can effectively identify the electrical elements and their relationship in drawings.
Mobile Health Applications and Cloud Computing in Cytopathology

Modern cytopathology labs offer an outstanding portfolio of important testing services that must be of high quality and credibility. Over the last decade, clinical cytopathology was influenced by the wide implementation of computer sciences. Cytopathology labs wishing to achieve a seamless workflow process have successfully adopted automation and information management systems. Innovative information technologies, including e-health, constitute a valuable tool for interlaboratory collaboration and quality improvement. Cloud computing will enhance the opportunities in cytological data management and sharing. The authors present thorough research of mobile applications related to cytopathology and try to foresee applications that may benefit modern cytopathology. Also, the feasibility of such applications for inter-laboratory comparisons, proficiency testing, and diagnostic accuracy validation is examined. Finally, the role of mobile applications for providing and/or enhancing the laboratory capabilities through educational training and other research activities is investigated.
Electronic Theses and Dissertations (ETDs) Metadata Policies, Workflows, and Practices: A Survey of the ETD Metadata Lifecycle at United States Academic Institutions

Academic libraries and graduate schools have a variety of workflows and practices for creating and disseminating metadata for electronic theses and dissertations (ETDs). A survey of United States academic institutions in the fall of 2017 addressed the lifecycle of ETDs and their metadata, including policy setting, metadata creation, and access. Participants from 137 institutions, both public and private, were sent the 23-question survey that contained a combination of qualitative and open-ended questions. This survey helped to identify trends and will expand the ETD literature with its comparison of policies, workflows and practices of ETD metadata.
Analysis of Audio Steganography combined with Cryptography for RC4 and 3DES Encryption

In this digital era, most of the real-time applications makes use of digital data. At the same time, security of this data becomes important and essential. Different aspects to be considered while dealing with such digital data. They are preserving authenticity and make sure that the data is not modified in transit and no one else other than intended user can read this data. To fulfil these requirements, many techniques are available to protect this data. Cryptography is to jumble the original information so that unauthorized person can’t see it and that of steganography is to hide the original information. Depending on the cover medium used, steganography has many types such as image, audio, and video. Audio steganography deals with the noise, echo, samples etc present in the audio. LSB, Echo hiding, phase coding, Tone insertion are some of the types of audio steganography techniques. These techniques have their respective pros and cons. The main intention of the presented technique is to reconciliate cryptography with steganography and give better protection.
Security and Privacy Requirements Engineering

Security requirements engineering identifies security risks in software in the early stages of the development cycle. In this chapter, the authors present the SQUARE security requirements method. They integrate privacy requirements into SQUARE to identify privacy risks in addition to security risks. They then present a privacy elicitation technique and subsequently combine security risk assessment techniques with privacy risk assessment techniques. The authors discuss prototype tools that have been developed to support SQUARE for security and privacy as well as recent workshops that have focused on additional results in the security and privacy requirements area. Finally, the authors suggest future research and case studies needed to further contribute to early lifecycle activities that will address security and privacy-related issues.
The Application of Artificial Intelligence in Computer Network Technology

Today’s era is an era of rapid development. With the continuous improvement of the national economy, China has higher requirements and standards for the development of Internet information technology. Based on this social context, "artificial intelligence" came into being. Artificial intelligence has also brought unprecedented changes to our lives, enriched the world we live in, and made the application of computer network technology more convenient. The needs of the social masses are also constantly improving, and people begin to pursue a higher quality of life. As a kind of high and new technology, computer network technology gradually appears in our life, closely related to the people’s daily life, and has become an advanced industry in the new era. This paper introduces the application of contemporary artificial intelligence technology in computer network technology, through the discussion of artificial intelligence, analyzes the application of artificial intelligence in classification and identification services, network security, computer network management, and can better understand the application strategy of computer network technology under today’s artificial intelligence.
Looking to the future: Learning from experience, averting catastrophe

As humans go through life sifting vast quantities of complex information, we extract knowledge from settings that are more ambiguous than our early homes and classrooms. Learning from experience in an individual's unique context generally improves expert performance, despite the risks inherent in brain dynamics that can transform previously reliable expectations. Designers of twenty-first century technologies face the challenges and responsibilities posed by fielded systems that continue to learn on their own. The neural model Self-supervised ART, which can acquire significantly new knowledge in unpredictable contexts, is an example of one such system.
Minimizing Interference Potential Among Moving Entities

We consider the problem of monitoring the interference among a collection of entities moving with bounded speed in d-dimensional Euclidean space.Uncertainty in entity locations due to unmonitored and unpredictable motion gives rise to a space of possible entity configurations at each moment in time, with possibly very different interference properties.We define different measures of what we call the interference potential of such spaces to describe the interference that might actually occur.We study the extent to which restricted monitoring frequency impacts interference potential, through the analysis of a clairvoyant scheme (one that knows the trajectories of all entities) subject to the same monitoring frequency restriction.This forms a benchmark for the analysis of uninformed schemes.In this framework, we describe and analyse an adaptive monitoring scheme for minimizing interference potential over time that is competitive (to within a constant factor) with any other scheme (in particular, a clairvoyant scheme) over modest sized time intervals.As a natural application, imagine that the entities are transmission sources, with associated broadcast ranges, moving in three dimensions.Two such entities, transmitting on the same channel, interfere if their broadcast ranges intersect.Uncertainty in the location of a transmission source effectively expands its broadcast range to a potential broadcast range.The chromatic number of the intersection graph of these potential broadcast ranges, one of our interference potential measures, gives the minimum number of broadcast channels required to avoid interference.Our scheme provides the foundation of an adaptive, locally updated, channel assignment algorithm that is competitive over time, in terms of the number of broadcast channels used with a fixed monitoring frequency, with any other such scheme.
Application of Artificial Intelligence in Libraries

Artificial intelligence (AI) has shown great promise for applications with large breakthroughs in pattern recognition, knowledge mapping, computer vision, and human-machine interaction. In this paper, firstly, the key technical features of AI in library application are studied, and its application in library stack building, stack management and information retrieval service is analyzed; then the existing problems of artificial intelligence in library application are analyzed; and finally the corresponding application service countermeasures are put forward, which can provide reference for related researchers and staff.
A Perspective on Using Blockchain for Ensuring Security in Smart Card Systems

Due to the momentous growth in the field of Internet of Things (IoT), various commercial and government organizations are exploring possibilities of mass issuance of smart cards in different applications. Widespread deployment of smart card-based systems in heterogeneous environment would facilitate card holders to participate in these applications in a personalized manner. Despite the security features, valuable data and access to decisive services make these systems prime target for attackers. These systems can be subjected to a range of security attacks – from hardware exploitation to exploitation of software bugs, from unauthorized data access to social engineering, and so forth. In the future, where many sectors will be trying to adopt the concept of Blockchain, it will create new opportunities for benefiting citizens with enhanced security over their data. In this chapter, the author performs in-depth analysis over the role of Blockchain in securing the smart card ecosystem.
Using Machine Learning Method to Predict Stroke Risk

To prevent the potential incidence of the stroke and avoid any side effects which further leads to the stroke, it becomes essential to predict whether the individual is at high risk of stroke based on his/her basic information. Previously published most of the studies in stroke prediction used Cox proportional hazards model or other novel integrated models. These models are difficult for individuals who probably do not possess sufficient background knowledge to implement and use those models. Therefore, the main objective of the present study is to identify whether it is feasible to use traditional machine learning models, including logistic regression, K-Nearest Neighbor, and random forest in stroke prediction. Among these models, the random forest outperformed other two models by classifying the sample correctly from the test dataset into binary categories. Our approach proved that simple machine learning models, especially random forest model performed exceptionally well in stroke prediction. Moreover, the classical machine learning model can also be easily implemented compared to other integrated models for stroke prediction.
Multi-Floor Indoor Trajectory Reconstruction Using Mobile Devices

An indoor trajectory is the path of an object moving through corridors and stairs inside a building. There are various types of technologies that can be used to reconstruct the path of a moving object and detect its position. GPS has been used for reconstruction in outdoor environments, but for indoor environments, mobile devices with embedded sensors are used. An accelerometer sensor and a magnetometer sensor are used to detect human movement and reconstruct the trajectory on a single floor. In an indoor environment, there are many activities that will create the trajectory similar to an outdoor environment, such as passing along the corridor, going from one room to another, and other activities. We need to analyse trajectories to obtain the movement patterns, understand the most frequently visited places or paths used as well as the least frequented ones. Furthermore, we can utilize movement patterns to obtain a better building design and layout. The latest studies focus on reconstructing the trajectory on a single floor. However, actual indoor environments are comprised of multi-floors and multi-buildings. The purpose of this paper is to reconstruct a trajectory in an indoor multi-floor environment. We have conducted extensive experiments to evaluate the performance of our proposed algorithms in a campus building. The result of our experiment shows that the height of the building can be detected using a barometer sensor that gives an atmospheric pressure reading which is then transformed by setting the range value according to the number of floors, enabling the sensors to detect activity in a multi-floor building. The readings obtained from the magnetometer sensor can be used to reconstruct the trajectory similar to the real path based on the direction and degree of direction. The system accuracy in recognizing steps in a multi-floor building is about 84%.
Non-Conventional Factors for Keystroke Dynamics as a Support Factor for Authenticating User

Keystroke Dynamics Are Most Frequently UsedIn Situation Where Password Security Is The Major Concern. In The Event That The User’s Secret Word Is Disclosed, AndThe Keystroke Rhythm Of The Genuine Client Is Known, The Application Will Not Accept The Fake User In Spite Of Having Valid Credentials. This Paper Focuses On Strengthening The Keystroke Dynamics By Introducing Non-Conventional Features For Authenticating A User Using Static Keystroke Dynamics. The Paper Also Discusses The Improvement In Far, Frr, Err, And Accuracy After The Application Of The Proposed Non-Conventional Feature Set Along With The Existing Timing Feature Set.
Voice-quality Features for Deep Neural Network Based Speaker Verification Systems

Jitter and shimmer are voice-quality features which have been successfully used to detect voice pathologies and classify different speaking styles.In this paper, we investigate the usefulness of such voice-quality features in neural-network based speaker verification systems.To combine these two sets of features, the cosine distance scores estimated from the two sets are linearly weighted to obtain a single, fused score.The fused score is used to accept/reject a given speaker.The experimental results carried out on Voxceleb-1 dataset demonstrate that the fusion of the cosine distance scores extracted from the melspectrogram and voice quality features provide a 15% relative improvement in Equal Error Rate (EER) compared to the baseline system which is based only on mel-spectrogram features.
Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.
A Single-pass Noise Covariance Estimation Algorithm in Adaptive Kalman Filtering for Non-stationary Systems

Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of applications. This paper presents a single-pass stochastic gradient descent (SGD) algorithm for noise covariance estimation for use in adaptive Kalman filters applied to non-stationary systems where the noise covariances can occasionally jump up or down by an unknown magnitude. Unlike our previous batch method or our multi-pass decision-directed algorithm, the proposed streaming algorithm reads measurement data exactly once and has acceptable root mean square error (RMSE). The computational efficiency of the new algorithm stems from its single-pass nature, recursive fading memory estimation of the sample cross-correlations of the innovations, and the RMSprop accelerated SGD algorithm. The comparative evaluation of the proposed method on a number of test cases demonstrates its computational efficiency and accuracy.
Leveraging High-Frequency Components for Deepfake Detection

In the past years, RGB-based deepfake detection has shown notable progress thanks to the development of effective deep neural networks. However, the performance of deepfake detectors remains primarily dependent on the quality of the forged content and the level of artifacts introduced by the forgery method. To detect these artifacts, it is often necessary to separate and analyze the frequency components of an image. In this context, we propose to utilize the high-frequency components of color images by introducing an end-to-end trainable module that (a) extracts features from high-frequency components and (b) fuses them with the features of the RGB input. The module not only exploits the high-frequency anomalies present in manipulated images but also can be used with most RGB-based deepfake detectors. Experimental results show that the proposed approach boosts the performance of state-of-the-art networks, such as XceptionNet and EfficientNet, on a challenging deepfake dataset.