Reviewerly/exHarmony huggingface dataset:
Detection of Anomaly User Behaviors Based on Deep Neural Networks

In order to predict the user's anomaly operation behaviors, we perform deep learning modeling on the user's UNIX command line operation sequence. The use of deep neural networks in anomaly detection is to build a model through the training set, enabling the model to predict the user's next action or command based on the given first <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> actions or commands. The network trains the command set commonly used by users. After a period, the network can match the real commands according to the existing user characteristic files in the network, and any mismatched events or commands are regarded as anomalies. The detection of anomaly user behaviors is an imbalanced classification problem. To address imbalanced classification problem, we propose an imbalanced self-paced sampling method to improve the efficiency of anomaly user behavior detection. The results show that the DNNs model can usually find anomaly user behaviors that are not easily detectable by other models in anomaly detection.
Cloud Build Methodology

The cloud build methodology chapter provides an introduction to the build methods for hybrid clouds. It does this by first introducing the concept of a hybrid cloud and the different types of services provides by clouds. It then overviews the components of hybrid clouds and how these components get incorporated into the design. It takes a brief look at the cost drivers with building a cloud to provide background with design decisions to be made. With the background on the design, it takes the reader through the build of a hybrid cloud and how automation can be used to reduce the cost. Lastly, it takes a brief look at a possible direction of cloud builds.
Digital Forensic and Machine Learning

Digital Forensic as it sounds coerce human mind primarily with exploration of crime. However in the contemporary world, digital forensic has evolved as an essential source of tools from data acquisition to legal action. Basically three stages are involved in digital forensic namely acquisition, analysis and reporting. Digital Forensic Research Workshop (DFRW) defined digital forensic as “Use of Scientifically derived and proven method towards the identification, collection, analysis, interpretation, documentation and presentation of digital evidence derived from digital sources for the purpose of facilitating or furthering the reconstruction of event to be criminal”. The hard problem in digital forensic is such that the acquired data need to be cleaned and is required to be intelligible for reading by human. As a solution to this complexity problem a number of tools are present which may be repeated until relevant data is obtained.
An Integration between Information Systems Engineering and Software Engineering Theories towards Engineering a Novel Instrument and Framework of Web-Based Systems Success: Results of the Pilot Test

Abstract Due to globalization and speedy of ICT and systems development, more emphasis needs to be on quality of systems and it requires an accurate, fast, effective system so that the success measure of system is essential. Currently systems rate of fall is still a high one of the reasons behind that is the weakness of the available instruments that being used for systems success measurement. This study aims to come out with a successful instrument for successfully system quality measuring. In this article, the results of the pilot test have been illustrated.
Designing an IMS-LD Meta-Model of a New Smart Learning Management System

The context of this work is that of designing an IMS-LD metamodel of an LMS. The approach is to first think about the conditions for creating an LMS between learners and designing the IT environment that supports this system. In this chapter, the authors try to adapt the IMS-LD model with a metamodel for LMS based on the hybridization between learning theories. This adaptation will go through three stages: first, the development of the meta-model of LMS; second, the study of the correspondence between the developed meta-model and IMS-LD model; and finally, their transformation to IMS-LD meta-model.
Discussion on the Value of Literature Network Technology Based on Internet Data Analysis

Gesar's epic and Homer's epic are both influential literary masterpieces in the world, but the intersection or comparative study between them is relatively weak. Today we use network technology to do further research and discussion on it. From the perspective of literary creation mode; From the perspective of composite multilayer comparison of three aspects such as for this, the article made a comb and made plain to comment on this basis, the comparative study on the next step of the two great epics, puts forward the prospect, especially points out that the ecological aesthetics is a clue of the research on two epic and new trend, this is in line with the current information age. As a result, it is not difficult to understand the meaning and value of contrast research two epic, finally, posing as a comparative study of two epic, with sprang out, the purpose of further research.
Real-time Activation Pattern Monitoring and Uncertainty Characterisation in Image Classification

Deep neural networks (DNNs) have become very popular recently and have proven their potential especially for image classification. However, their performance depends significantly on the network structure and data quality. This paper investigates the performance of DNNs and especially of faster region based convolutional neural networks (R-CNNs), called faster R-CNN when the network testing data differ significantly from the training data. This paper proposes a framework for monitoring the neuron patterns within a faster R-CNN by representing distributions of neuron activation patterns and by calculating corresponding distances between them, with the Kullback-Leibler divergence. The patterns of the activation states of ‘neurons’ within the network can therefore be observed if the faster R-CNN is ‘outside the comfort zone’, mostly when it works with noisy data and data that are significantly different from those used in the training stage. The validation is performed on publicly available datasets: MNIST [1] and PASCAL [2] and demonstrates that the proposed framework can be used for real-time monitoring of supervised classifiers.
Spatial music

Abstract Everyone agrees that musical works are individuated by essential elements such as tone, harmony, and rhythm. Some argue that timbre or instrumentation is an essential element of some musical works, too. I argue here that there is a further essential element of some musical works: spatial location. Some works of music are partly constituted by the location and motion of their sound sources. I begin by describing works of spatial music and arguing that they exist. I then consider the implications for the ontology of music. Hardcore formalists cannot allow for spatial music. I argue that two other views, which allow a close relationship between sounds and musical works, can allow for works of spatial music. However, their ability to do so turns on issues in the philosophy of sound. I appeal to work in the philosophy of sound to show that music is an art not just of hearing, but of sounds. Musical elements can be located just like sounds are located.
Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing

Privacy attack on individual records has great concern in privacy preserving data publishing. When an intruder who is interested to know the private information of particular person of his interest, will acquire background knowledge about the person. This background knowledge may be gained though publicly available information such as Voter's id or through social networks. Combining this background information with published data; intruder may get the private information causing a privacy attack of that person. There are many privacy attack models. Most popular attack models are discussed in this chapter. The study of these attack models plays a significant role towards the invention of robust Privacy preserving models.
End-to-end Visual Navigation with Intrinsic Motivation in 3D Maze-like Environments

Learning to navigate in visually rich environment with sparse reward is a long-standing problem in developing AI agent. In this paper, inspired by spontaneous exploration behaviour in animals, we proposed an end-to-end visual navigation method which use intrinsic reward to motivate explore-exploit strategy. Firstly, in order to directly obtain control policy from pixel-level input and implement end-to-end training, we take deep reinforcement learning as basic navigation framework. Secondly, we allow the agent to create reward for itself, and the intrinsic reward is related to episode memory and include two parts: (1) the frequency of agent reach a state is recorded, and these counts are then used to compute bonus according to the classic count-based method; (2) we also take temporal distance as a basis for distributing bonus, and the magnitude of it is determined by environment steps between current observation and those in memory. Finally, such intrinsic reward is summed up with the real task reward, thus making agent is able to use the combined reward to learn control policy. We test our approach in 3D maze-like environment in DMlab, and validate its navigation performance through different reward scenarios. The experiment results show our approach can get effective goal-driven behaviour from raw sensory input and outperform the baselines across all tasks. Furthermore, the agent equipped with our intrinsic motivation appear to explore faster in no reward environment.
Model-Driven Software Modernization

Modernization of legacy systems is a new research area in the software industry intended to provide support for transforming an existing software system to a new one that satisfies new demands. This chapter analyzes software modernization based on the architecture-driven modernization (ADM). In this context, software modernization is supported by metamodels to describe existing systems, discoverers to automatically create models of these systems, and tools to understand and transform complex models created out of existing systems. This chapter provides an overview of the-state-of-the-art in ADM-based software modernization techniques. Taxonomy of different techniques is described. A description of how traditional techniques such as static and dynamic analysis can be integrated with ADM standards is presented. This chapter also analyzes current challenges and strategic directions in software modernization.
Modeling a New Smart Learning Management System Based on the EML

The context of this work is that of designing an IMS-LD model for a new smart LMS. The work is specifically in the field or seeking to promote using information technology from a distance. The approach is to first think about the conditions for creating a real new smart LMS between learners and designing the IT environment that supports this LMS. In this chapter, the authors try to adapt the IMS-LD model with a new smart LMS based on traditional pedagogy, behaviorism, cognitivism, and social constructivism. This adaptation will go through three stages: first, the development of an LMS model; second, the study of the correspondence between the developed model and the IMS-LD model; finally, their transformation to the IMS-LD model.
Responsive Library Website Design and Adoption of Federated Search Tools for Library Services in Developing Countries

Library attention has been shifted away from only physical services, since much of its operations are now going virtual, and almost everything about library activities now compete for online attention. Librarians have no choice than to conform to the trend since the major concern of library is to meet the need of its clienteles at all cost. Moreover, the paradigm shift in information ecosystem is now e-discovery tools. Responsive website design and federated search tools are part of the tools adopted to meet and satisfy information needs of library users in this era of information exploration. This chapter looks at the concept of responsive website design and federated search, components of responsive websites design, need for adoption of responsive website design for libraries in developing countries, federated search products and vendors, benefits of federated search technology, dichotomy between open search and federated search, adoption of federated search tools in developing countries, issues to consider with the federated search tool.
Designing a Socially Open Narrative Generation System

The authors propose the design of a Socially Open Narrative Generation System (SONGS) that co-creates a collection of diverse narratives from a narrative generation program and people. This is a challenge of the social application of narrative generation technology used for vitalizing the social activity of producing and sharing narratives. The key idea is to connect and unify individual narrative productions by many agents, including a computer program and many humans, via a collection of narratives produced and accumulated by these agents. At the same time, SONGS is the practice of a computational approach to narratology as a model for the social process of narrative production. This chapter describes the key concepts and mechanism design of SONGS with several experimental programs.
Introduction

The content of this chapter is about some basic knowledge related to Natural Language Processing and Thai language principles, which are the essential background that helps readers to understand the followed contents in the next chapters.
Implementing Web-Based Learning and Teaching Using Mobile Technology

Mobile technologies have increasingly become pervasive in everyday life over the past few decades. As a result of their affordability and wide availability, mobile devices have become commonly used by several people. Efforts are being made by researchers and institutions to provide infrastructure, content, and resources related to the integration of mobile devices into learning environments are worthy of note. These have led to the provision of more learner motivation, better interaction between learners and teachers, thus leading to an improved educational experience. This book chapter emphasizes on the benefits of mobile learning and also presents a case study of the development of a mobile learning system that facilitates interactive learning in a University environment. The system provides for the student, mobile access to various functions that can improve the process of learning including assessment and provision of feedback to learners. The system was tested and evaluated with satisfactory results.
Fault Tolerant Cloud Systems

Cloud computing refers to a model for accessing computing resource like networks, servers, storage, applications, and services remotely. Cloud computing offers these resources as a service, namely infrastructure-as-a-service, platform-as-a-service, and software-as-a-service. To use these services, two roles involved: the cloud provider offers the service and the cloud customer consumes the service. These resources are efficiently shared and utilized by customers and it is called workload. The requirement of workload depends on customer demands that vary from higher to lower. Based on the customer demand, cloud provider makes the resource available efficiently. In the context of cloud, the workload is based on web-based service or jobs processed in batch mode. The arrival process of jobs in the cloud is not often deterministic. The irregular increase or decrease in workload has a vital impact on resource provision. Monitoring the resources helps in measuring the performance of the cloud so that the resource can be provisioned to customers efficiently.
On Developing Fair and Orderly Cloud Markets

While cloud markets promise virtually unlimited resource supplies, standardized commodities and proper services, some providers may not be able to offer effectual physical capacity to serve large customers. A solution is cloud collaborations, in which multiple providers unite forces in order to conjointly offer capacities in the cloud markets. Supposably, both the Quality of Service and security properties of such collaborations will be determined by “the weakest link in the chain”, therefore resulting in a trade-off between the monetary aggregates, cumulative capacity and the non-functional attributes of a cloud collaboration. Based on previous research, this paper examines efficient composition of cloud collaborations from the broker's perspective, considering Quality of Service and information security requirements of multiple cloud providers and users and presents an exact approach CCCP-EXA.KOM for building cloud collaborations. Furthermore, it proposes a Mixed Integer Programming-based heuristic optimization approach CCCP-PRIOSORT.KOM and provides its quantitative evaluation in comparison with prior optimal approach.
Improving ADA-boost as a Popular Ensemble in Classification Problems

In Data Mining, several classification algorithms are used to perform classification based on single learner but classification accuracy is not in an effective manner. To increase the accuracy of the classification then multiple learners are combined to get better results. The multiple learners are trained and combined into an ensemble. The ensemble can increase generalization ability and robustness [3]. Based on the advantage of ensemble, the ensemble classification is a major concern in research directions of machine learning. Another importance of ensemble is that it is much stronger than single base learner to produce accurate hypothesis. The ensembles are divided into homogeneous or heterogeneous, dependent or independent ensembles. The dependent ensemble methods like boosting and AdaBoost algorithms are promisingly provide an accurate hypothesis. Finally, AdaBoost can be a better classifier ensemble to generate accurate results.
Use VR Technology to Construct Physical Education Teaching Auxiliary System

VR technology is a technology that many research teams have focused on in recent years. VR technology combines human-machine interface technology, sensor technology, artificial intelligence and other technologies. It can create a lifelike artificial simulation environment for users through wearable sensing devices, simulating various perceptions of people in the natural environment, and bringing reality to users Feelings. At present, physical education in our country has the problems of insufficient student enthusiasm and insufficient professionalism of teachers. In order to solve these problems, we can try to bring VR technology into the physical education classroom. The virtual teaching environment platform designed in this paper uses VRML-Java synchronization and coordination technology and 3-layer B/S structure, takes the virtual unit as the main part, sets different parameters, completes virtual services, builds a real scene for students, and helps students immerse themselves in In sports. This platform has also built a complete system service to meet the teaching needs of teachers and students, and fully mobilize students’ enthusiasm for sports.
TDNN:A Tensor Decomposition Adversarial Defense Method Based on Neural Network

In recent years, neural networks have shown strong performance on various tasks. However, neural networks show the vulnerability to carefully designed noise of adversarial examples. Through research, it is found that the neural networks usually have good robustness to common noise, but almost no resistance to carefully designed imperceptible perturbations noise of adversarial examples. To solve this problem, related works have proposed to transform the noise of the adversarial sample into random ordinary noise, which greatly protects the model from adversarial attack. To solve this problem, we propose an adversarial defense method based on tensor decomposition, which use tensor decomposition technology to decompose and reconstruct the image, and retain the main features of the image and remove the perturbation of adversarial examples. Based on traditional tensor decomposition method, we further propose the tensor decomposition of neural networks method (TDNN). Compared with traditional tensor decomposition, TDNN has better defense effect and lower running time. Beside TDNN can be combined with existing defense methods and does not require extra changes for model. Through Rigorous experiments show that TDNN can remove carefully added perturbation and greatly improve the robustness of the model.
An Efficient, Secure, and Queryable Encryption for NoSQL-Based Databases Hosted on Untrusted Cloud Environments

NoSQL-based databases are attractive to store and manage big data mainly due to high scalability and data modeling flexibility. However, security in NoSQL-based databases is weak which raises concerns for users. Specifically, security of data at rest is a high concern for the users deployed their NoSQL-based solutions on the cloud because unauthorized access to the servers will expose the data easily. There have been some efforts to enable encryption for data at rest for NoSQL databases. However, existing solutions do not support secure query processing, and data communication over the Internet and performance of the proposed solutions are also not good. In this article, the authors address NoSQL data at rest security concern by introducing a system which is capable to dynamically encrypt/decrypt data, support secure query processing, and seamlessly integrate with any NoSQL- based database. The proposed solution is based on a combination of chaotic encryption and Order Preserving Encryption (OPE). The experimental evaluation showed excellent results when integrated the solution with MongoDB and compared with the state-of-the-art existing work.
Research on designing and making of a mobile training facility for intervention and rescue personnel in toxic / flammable / explosive environments

The decisive factor in ensuring success for an intervention and rescue operation in toxic or chemical aggressive environments lies in optimal and efficient design of the training process for rescue personnel, including intervention in confined spaces. The current paper shows the designing and making of a modern infrastructure - mobile training facility with different high difficulty degree training routes, allowing the simulation of intervention activities in confined areas, horizontally and vertically, spaces with low visibility, above normal limits temperature and humidity, etc. as well as the measurement of several physiological parameters (pulse, blood oxygen level, calories consumed, etc.) that allow permanent monitoring of rescuers’ health condition during the training process. The mobile training facility consists of a space for physical training (endless ladder, impact device, treadmill, elliptical bike, stepper), an indoor training circuit and a control room that allows the coordination of all activities carried out within an exercise. The platform on which the training facility is placed can be moved to the business quarters where intervention and rescue activities take place, so that personnel engaged in this activity go through a complex training program, as close as possible to real-life interventions.