Reviewerly/exHarmony huggingface dataset:
Construction of College Student Data Information Management System Based on Association Rule Algorithm

In view of the insufficiency that the construction of the college student data management system cannot be processed based on the evidence theory, a kind of method for the construction of the college student data management system that is dependent on the association rule algorithm is put forward in this paper, which aims to solve the problems in the construction of the college student data management system and so on. Based on the association rule algorithm, the pre-processing of the college student data management system is established. In addition, dependent on the characteristics of the association rule algorithm, the related centroid is calculated, and the centroid is taken as the representative information of the cluster. The basic probability assignment is carried out in accordance with the generalized triangular fuzzy membership function, and the evidence is formed after the assignment, thus completing the construction of the college student data management system. The simulation results show that the method put forward in this paper is effective and can be used to carry out reasonable evaluation on the college student data management system.
Reconstructing Colored Strip-Shredded Documents based on the Hungarians Algorithm

One of the common problem in forensic science and investigation science is reconstructing destroyed documents that have been strip-shredded. This work intend to design a strip matching algorithm to resemble edges of the strips, in order to reconstruct the original document. The proposed algorithm is divided into three phases. First is image-based similarity evaluation to produce a score function (which includes building “distance matrix”). The second phase is assignment phase that matches a shred border pixels of the right side to a left side of another shred (using Hungarians algorithm). The third phase is defining the sequence according to the matched strips in order to merge the shreds and reconstruct the document. The proposed work is compared with a nearest neighbor search algorithm in term of accuracy and speed. The Hungarians reassembling algorithm scores better accuracy and run time than nearest neighbor reassembling. The proposed approach scored (96.2 percent) as an average accuracy for reassembling an available online benchmark.
An overview of Digital Forensics Education

This paper follows an initial review conducted as part of an EU-funded Erasmus+ project under the programme of Capacity Building in Higher Education. The original paper focused on providing the state of the art in undergraduate computer forensic programmes [22]. This work is part of a study towards the EU funded Pathway in Forensic Computing (FORC) project. FORC aims to address the challenges in information society development concerned Cyber Security and privacy in a world oriented towards e-technologies. The project meets the regional needs of the Middle East area by responding to the current and emerging cyber security threats by educating the IT and Legal professionals in the field of e-crime, thus supporting development of e-based economics, life and society in partner countries. The work is funded under project reference number 574063-EPP-1-2016-1-IT-EPPKA2-CBHE-JP, Grant Agreement 2016 - 2556 / 001 - 001. In this paper we focus on the second work package of the project (WP-2) aiming to `establish a forensic computing pathway' and the first task for this work package aiming at `defining pathway objectives, learning outcomes, and career perspective'. In this second paper we focus more on postgraduate programmes, online provision, and career pathways. We also consider the availability of programmes in the US, while emphasis is given more on digital forensic education.
Intuitionistic fuzzy recursive enumerable languages and recursive languages

In this paper a new class of Fuzzy Turing Machine is introduced which is called Intuitionistic Fuzzy Turing Machine. This Intuitionistic Fuzzy Turing Machine uses t – norm for membership degree and its dual t – conorm for non-membership degree. We prove a theorem that guaranteed us sufficient conditions for Intuitionistic Fuzzy recursive enumerable languages are closed under Intuitionistic Fuzzy Intersection. We show that Deterministic Intuitionistic Fuzzy Turing Machines and Nondeterministic Intuitionistic Fuzzy Turing Machines are not equivalent as far as the power of recognizing Intuitionistic Fuzzy languages is concerned.
Deep hashing based on triplet labels and quantitative regularization term with exponential convergence

Summary Due to the outstanding performance of the deep network architecture–based hash for data storage and retrieval in recent years, it has been widely applied in massive image retrieval. Most previous approaches have not paid attention to the significant effect on the hash model of quantization error during the learning process. Furthermore, the saturated loss function may result in similar hash codes being generated by large‐difference images. The underuse of classification information in the training process also brings about poor performance by hash codes in retrieval assignments. In this paper, we propose a novel quantitative regularization term with an exponential convergence rate to minimize the impact of quantization error on the model and accelerate the convergence speed of the network. In the training process, to resolve the dilemma caused by saturation loss functions, a new sigmoid function with a slope parameter that can be changed automatically according to the number of iterations is proposed. For the sufficient application of classification information, triplet labels and image labels are used in parallel under the same framework by integrating image labels into the output layer. The experiment results indicate that our algorithm is superior to several advanced hash methods on two standard datasets.
Reliable Component Instance for Multi-tenant Software as a Service Application

Multitenant Software as a Service (SaaS) applications provides high level of application customization, enhanced process flow and access control, using a shared instance of the application. Cloud reliability is complex due to its resource intricacy and massiveness. The proposed solution considers reliability of the application during design and runtime. A reliable application needs to acclimate dynamically to the conditions in the cloud to enhance the reliability of its service. Component Instance (CI) is dynamically created based on the application's load and placed at different availability zones to enhance the reliability of the application. The objective of the proposed solution is to minimize multi-tenant SaaS application reliability risk considering reliability score of datacenter server energy, server and network capacity. Our evaluation and discussions shows that the proposed reliable framework enhances the reliability of the application and reduces the application risk factor.
Sulfate Mineral Mapping With Hyperspectral Imagery, A Case Study Of The Rodalquilar Area, Se Spain

Sulfate minerals are important indicators in prospecting for hydrothermal metal deposits. Ore deposits in the Rodalquilar area are closely related to sulfate minerals. The hyperspectral HyMap data in the region were obtained, and rock samples were collected in the field. Three types of pure sulfate minerals, namely, gypsum, jarosite and alunite, were found by analysis using an analytical spectroscopy device (ASD). Through the spectral analysis of sulfate minerals, the mapping of sulfate minerals was carried out by the method of multi-range spectral feature fitting (MRSFF). The results obtained in the field verified the validity of the mineral mapping.
Logic-Based Online Complex Event Rule Learning with Weight Optimization

Complex event recognition system process continuous data stream in real time and use the defined event rules to infer the occurrence of the event in time. The rules used by complex event recognition systems are generally defined by experts manually. However, the manual definition of rules is often complicated and error-prone. Logic-based event recognition system has been studied in recent years. Event calculus is a kind of temporal logic that has become a logic-based expression of event rules. It combines with statistical relationship learning to conduct reasoning and learning of events, effectively solving the complicated and error-prone problems of artificial construction rules. Currently, logic-based event rule learning is mostly combined with ILP for top-down search rules, and the training time is often long. The existing logic-based online rule learning algorithm has a long training time and a fast learning rate. We proposed a method based on root mean square prop (RMSprop), a weight learning method to improve the original algorithm. Moreover, we optimize the weight of the rule structure, and combine the existing rule structure learning method and weight learning method to learn rules. The optimized method has been obtained good results. Finally, we design a set of experiments to verify the feasibility and effectiveness of our method.
C2Ont: An OWL Ontology Learning Approach from Apache Cassandra

Big Data is a massive volume of both unstructured and structured data. It is crucial to efficiently represent big data as knowledge for data management. Ontologies provide knowledge as a formal description of a domain of interest. Therefore, the ontology learning approach is proposed for Apache Cassandra. It is composed of six mapping rules and converts OWL ontology from data in Cassandra by applying these mapping rules. NorthWind dataset is applied for demonstrating how to learn ontology from data in Cassandra. The evaluation result indicates that our approach can learn ontology in covering terminologically the modeled domain since the adequacy of extracted ontology is greater than 15%.
MON: Multiple Output Neurons

Existing basic artificial neurons merge multiple weighted inputs and generate a single activated output. This paper explores the applicability of a new structure of a neuron, which merges multiple weighted inputs like existing neurons, but instead of generating single output, it generates multiple outputs. The proposed "Multiple Output Neuron" (MON) can reduce computation in a basic XOR network. Furthermore, a MON based convolutional neural network layer (MONL) is described. Proposed MONL can backpropagate errors, thus can be used along with other CNN layers. MONL reduces the network computations, by reducing the number of filters. Reduced number of filters limits the network performance, thus MON based neuroevolution (MON-EVO) technique is also proposed. MON-EVO evolves the MONs into single output neurons for further improvement in training. Existing neuroevolution techniques do not utilize backpropagation but MONs can utilize backpropagation. Experimental networks trained using the CIFAR-10 classification dataset show that proposed MONL and MON-EVO provide a solution for reduced training computation and neuroevolution using backpropagation.
e-Learning Web Services and Their Composition Strategy in SOA

With the adventKeserwani Pankaj Kumar of advanced communication technologies, the concept of e-learning came into practice. Most of the e-learning systems are operating in client server and distributed system environment. The activity of e-learning remained confined to predefined need due to the lack of suitableSamaddar Shefalika Ghosh dynamic software architecture of newer technology. With the advent of service-oriented architecture (SOA), a suitable and better solution framework has been proposed for e-learning. Different functions of e-learning system areKumar Praveen implemented as stand-alone web services. The strength of the approach followed in this paper is reusability and interoperability. To achieve the new functionality in e-learning system, the composition of relevant stand-alone web services are required to make the system fully capable of interactive services making electronic teaching learning feasible. Web service composition is a SOA-based model to make a composite web service by existing stand-alone or other composite services. In this paper, we present a full-fledged e-learning system with the help of web services and their composition as per SOA standard.
Elementary structural data composition patterns

Data composition is used in programming languages and data transfer representation languages (such as JSON, XML and network protocols) to build complex objects, but the particular language constructs may differ. The paper presents common primitives underlying structural data composition techniques in both groups of languages: sets, multisets, sequences, maps, multimaps and variant types. The primitives are described using the patterns methodology. Application of the patterns is considered with respect to the declarative parts of the languages (compile-time types in programming languages, XML Schema for XML) and usage parts (run-time types, JSON objects, XML documents). The patterns are intended for programming language designers, developers of high-level data transfer protocols, as well as students studying common approaches in different languages. The patterns may be used to facilitate conversion between different forms of data (e.g., arrays in memory and sequences of values in a data transfer channel).
Multi-class Human Body Parsing with Edge-Enhancement Network

Single human parsing aims at partitioning an image into semantically consistent regions belonging to the body parts or clothing items, which has gained remarkable improvement owing to a wide range of proposed methodologies. From the perspective of the loss design, besides the parsing loss of the final output, most existing studies target on exploiting multiple other losses to enhance parsing results, which is hard to make the model reach balanced condition by adjusting their ratios and may weaken the potential of some losses. In this work, we propose an edge enhancement module to emphasize the potential of edge loss and boundary information. At the same time, local and global information will be explored for complex multi-class human body parsing problem by densely connected atrous spatial pyramid pooling. This scheme results in a simple yet powerful Edge-Enhancement Network (EEN). Extensive experiments demonstrate that EEN achieves 56.55% mIoU on LIP dataset and 62.60% mIoU on CIHP dataset, which outperform the state-of-the-arts by 3.45% and 4.02%, respectively. The code of EEN is available at https://github.com/huangxi6/EEN .
The Security Risks of Cloud Computing

Cloud computing is rapidly increasing for achieving comfortable computing. Cloud computing has essentially security vulnerability of software and hardware. For achieving secure cloud computing, the vulnerabilities of cloud computing could be analyzed in a various and systematic approach from perspective of the service designer, service operator, the designer of cloud security and certifiers of cloud systems. The paper investigates the vulnerabilities and security controls in terms of secure cryptography. For achieving the research aims, this paper analyzes technological security vulnerability, operational weakness and suggests secure ways for cloud systems.
Two Improving Methods of EOG-based Eye Movement Detection for HCI

In this paper, we proposed two methods to reduce the errors that happens during Electrooculogram(EOG)-based eye movement detection, especially in the case of EOG-based input system or Human-Computer-Interface (HCI). The first one is about taking measures in the detection algorithm, and the second one is by employing the Posner paradigm. Their validity was experimentally evaluated and the results show that our approaches can improve the accuracy of EOG-based eye movement detection by about 10%.
Construction of Situation Assessment Indicator System Based on Latitude and Longitude Lines of Information Security

In order to fully understand the overall security situation of the network system and control the security risks, it is necessary to shift the network security situation assessment from qualitative to qualitative and quantitative. The existing situation assessment work has problems such as incomplete considerations, inconsistent measurement standards, and unspecified implementation plans. The fundamental reason is the lack of a comprehensive situation assessment indicator system. Based on the information security latitude and longitude line theory of Academician Fang Binxing, this paper proposes a two-dimensional framework of situation assessment indicator system, mines and subdivides the content of the framework, forms a measurable and atomized indicator sub-item, and gives examples of data collection and quantification, establishes a multi-dimensional mapping relationship from indicator system to actual environment, and provides theoretical reference for quantitative assessment of security situation.
An Approach on Performance Monitoring in Cloud Application

Cloud computing is now becoming most clearly the interesting concept and priority for every individual to access over the internet. The advantageous features of cloud computing in scaling up and down, flexibility in resource monitoring. The main aspect of Auto scaling is to provide the facility to every users to scale up and down the resources as and when their requirements demand, provisioning only the resource which are needed in Dynamic and billed for which they have used i.e. Pay-as-you-go model. Since the Usage are in automatic nature it involve less human intervention, less prone to individual involvement by which we incur less errors and thus we reduce manpower and reduction in cost. Since the cloud computing user are elastic in nature, we have choice to go for the most promising technique called auto scaling, which has the provision to balances the workload for incoming request and reduction in total cost to meet deadline. In our work we investigated to Identify the key challenge in Load balancing for Auto scaling in AWS for various work load in computing application. Proposed Algorithm for Virtual Machine optimal load balancing Algorithm, it is found perform better in term of response time comparing with the existing algorithm.
NEOKNN: A Network Embedding Method Only Knowing Neighbor Nodes

Recently, network embedding has attracted increasing research attention, due to its effectiveness in a variety of applications. Most of the existing work aims to embed varied kinds of networks, such as plain networks, dynamic networks, and attributed networks. However, all of them require sufficient information such as network structure or node attributes. In practice, we can hardly obtain the entire network structure and abundant attributes. The network information provided is often limited. For example, we may only know a few neighbors of each node. In this paper, we address an important embedding problem (i.e., embedding nodes in a limited situation), and propose a novel deep network embedding framework in a soft coupling way to infer node embedding in such a situation. Extensive experimental results demonstrate that, given limited information, our representation significantly outperforms the representations learned by state-of-the-art methods.
A reversible image watermarking scheme based on a new modulation mode of DCT coefficients

Abstract Watermarking reversibility is one of the main requirements for medical and some other applications. In these fields even a slight change in the original image can lead to significant mistakes in the decision making process. In addition, the watermarking scheme should allow to embed a large amount of hidden data into the original image. This paper proposes a blind, fully reversible, high capacity scheme that utilizes an Even-Odd Modulation technique to modify the cosine coefficients while embedding the watermark bits. In multi-layer mode the scheme is capable to embed 3145728 watermark bits into the image of size 512x512 with the following indicators: the values of Peak Signal Noise Ratio of the watermarked images are about 70 dB, the Mean Structural Similarity Index Measure between the embedded and extracted watermarks in all cases is equal to one and the scheme allows to restore the original image with 100% accuracy. The validity and superiority of the proposed scheme is verified through extensive simulations.
Reading for pleasure

Reading for pleasure, a term more commonly used in England than elsewhere, is essentially volitional, choice-led reading of any kind of text. This chapter, examines the tensions and challenges that exist for teachers seeking to redress the balance, argues that in order to build readers for life, not simply readers for the system, opportunities need to be seized to create interactive and reciprocal reading communities. It explores the benefits of reading for pleasure are discussed, then the evidence with regard to children’s dis/engagement as readers is presented, before the nature of reading communities and the challenges and possibilities offered by their construction. Considerable international evidence reveals that reading for pleasure and reading engagement are strong predictors of reading attainment. Multiple studies from across the world and over time indicate that reading enjoyment has a strong bi-directional link with reading attainment.
Efficient Medical Video Streaming by Pre-Processing and Network Traffic Prioritization in Real-Time

Developing advanced healthcare applications to cater to the requirements of an ever-growing population has become one of the key areas of research in engineering. One major application in this area is medical video streaming, which is often used for remote monitoring of patients. Medical video streaming helps to overcome geographical barriers and offers medical services at the convenience of the patient. However, as medical videos carry critical and time-sensitive information, retaining the quality and reducing latency during transmission is paramount for accurate medical diagnosis. This paper presents the concept of effective medical video streaming, which incorporates novel methods in video pre-processing, video compression, and transmission of medical data over optical networks.
A High-Level Interactive Query Language for Big Data Analytics Based on a Functional Model

Information technologies such as the internet, and social networks, produce vast amounts of data exponentially (known as Big Data) and use conventional information systems. Big Data is characterized by volume, a high rate of generation, and variety. Systems integration and data querying systems must be adapted to cope with the emergence of Big Data. The authors' interest is with the impact Big Data has on the decision-making environment, most particularly, the data querying phase. Their contribution is the development of a parallel and distributed platform, named high level query language for big data analytics (HLQL-BDA), created to query vast amounts of data in a computer cluster based on the MapReduce paradigm. The query language in HLQL-BDA is implemented by means of interactive query language based on a functional model. The researchers' experiment shows the scalability of HLQL-BDA when they increase the number of nodes and the size of data.
Multi-screen lock

A critical issue for a mobile device user is the protection of stored sensitive data and their access, via the device. A widely used technique for protecting the mobile device with a display is the lock screen. Many security methods have been proposed but they suffer from technical vulnerabilities, algorithm weakness, security issues due to using, or high complexity that prevents large user groups from adopting it for everyday use. In this paper, an easy and secure technique for screen locking is proposed. Comparison to existing methods is offered, highlighting the strong points of the proposed solution, and the mathematical background is offered proving the strong security level achieved.