Reviewerly/exHarmony huggingface dataset:
Colored Petri Net Based Cache Side Channel Vulnerability Evaluation

The cache side channel leakage is a very serious security issue in the information security field. In order to solve this problem, a large number of security mechanisms have been applied to protect the cache. However, there are very limited methods we can choose to evaluate the cache side channel vulnerability, therefore, it is hard to know whether our system configuration or applied security mechanisms make caches more resistant to the cache side channel attacks. In this paper, we proposed a colored Petri net based method to model and score the cache side channel vulnerability. When given a side channel attack and related security mechanisms, our method utilized colored Petri net to model the requirements and the attack steps of cache attacks. Then we calculated the probability of success for each attack step according to the requirements and the computer environment, and the Common Vulnerability Scoring System (CVSS) was used to help us score the attack steps. Based on these probabilities and CVSS scores, we finally obtained a total risk score, which represented the threat level of the cache attacks in a specified computer environment with certain security mechanisms. This paper focused on the typical cache attacks and security mechanisms, and our experiments showed that we can conveniently evaluate and compare the threat level of cache attacks in the computer environment with different security mechanisms.
A semantically annotated JSON metadata structure for open linked cultural data in Neo4j

Digital culture is a mainstay of the emerging 6V era as both the digitization of existing cultural data and the creation of original native digital cultural content directly lead to the generation of large data volumes which may well be bursty, unstructured or semi-structured, and inherently multimodal. One way to address the increased complexity associated with 6V data is to create metadata structures which succinctly summarize a segment of the underlying cultural data, add semantic information, and serve as indexing and clustering points. This conference paper proposes a JSON description for cultural items which is tailored to the needs of the digital culture domain, generic enough to summarize the vast majority of cultural items, and flexible enough to be extended should the need arise. Moreover, it supports by construction multilevel clustering, semantic annotations, and links to and from relevant cultural items. As a concrete example, the proposed description has been imported to an instance of a Neo4j graph database. The latter takes full advantage of the capabilities offered by the proposed JSON metadata structure, especially the dynamic directed linking between similar cultural items. The abovementioned Neo4j is populated with cultural items from the Ionian Islands, a Greek region with rich cultural tradition.
Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL

Random vector functional ink (RVFL) networks belong to a class of single hidden layer neural networks in which some parameters are randomly selected. Their network structure in which contains the direct links between inputs and outputs is unique, and stability analysis and real-time performance are two difficulties of the control systems based on neural networks. In this paper, combining the advantages of RVFL and the ideas of online sequential extreme learning machine (OS-ELM) and initial-training-free online extreme learning machine (ITF-OELM), a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm (ITF-ORVFL) is investigated for training RVFL. The link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed, and the stability for nonlinear systems based on this learning algorithm is analyzed. The experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.
An experimental study for breast cancer prediction algorithms

Breast Cancer has turned into a typical disease around the globe and harvesting the life of young women and the main source of cancer death and caused 22.9% of a wide range of cancers in women. The development of massive breast cancer screening has led to earlier diagnosis and rapid management with a significant improvement in survival rate. The problem of automatically searching for information contained in medical images is urgently needed. In order to process this large volume of information, doctors are currently turning to the use of Machine Learning (ML) systems to assist in the analysis and interpretation of these images. In this paper, we have conducted an experimental study for three major ML algorithms such as K-Nearest Neighbor (KNN), Random Forest (RF) and Multilayer Perceptron (MLP) to classify Brest cancer. The experiments were conducted on WDBC dataset to obtain the best algorithms in term of accuracy. Finally, identify the most specific and relevant attributes of the malignant tumour classification through more than one feature selection algorithms.
Numerical techniques of nonlinear regression model estimation

The literature on numerical methods for fitting nonlinear regression model has grown enormously in the fast five decades. An important phase in nonlinear regression problems is the exploration of the relation between the independent and dependent variables. A largely unexplored area of research in nonlinear regression models concerns the finite sample properties of nonlinear parameters. The main object of this research study is to propose some nonlinear methods of estimation of nonlinear regression models, namely Newton-Raphson method, Gauss-Newton method, Method of scoring, Quadratic Hill-Climbing and Conjugate Gradient methods. In 2005, G.E. Hovland et al (see [5]). In his research article, presented a parameter estimation of physical time-varying parameters for combined-cycle power plant models. B. Mahaboob et al. (see [6]), in their research paper, proposed some computational methods based on numerical analysis to estimate the parameters of nonlinear regression model. S.J. Juliear et al. (see [7]), in their research paper, developed the method of unscented transformation (UT) to propagate mean and covariance information through nonlinear transformations.
Recovering Super-Resolution Generative Adversarial Network for Underwater Images

In this paper, we propose an end-to-end Recovering Super-Resolution Generative Adversarial Network (RSRGAN) to automatically learn super-resolution underwater images. RSRGAN mainly includes two parts. The first part is a Recovering GAN, aiming at color correction and removing noise in the images. The generator of Recovering GAN is based on an encoder-decoder network with self-attention on the global feature. The second part is a Super-Resolution GAN, which adopts the residual-in-residual dense block in its generator, to add details onto the results fed from the Recovering GAN. Both qualitative and quantitative experimental results show the advantage of RSRGAN over the state-of-the-art approaches for underwater image super-resolution.
The status of information security systems in banking sector from social engineering perspective

Social Engineering Attack has recently become a real threat affecting organizations, and 53.9% of such attacks target the banking sector. Successful attacks violate privacy by breaching sensitive data, and can cause huge financial loss for organizations and individuals, alongside reputational damage for firms. Although banks invest extensive resources in cyber security, with large budgets spent on securing their hardware and software, the human factor offers numerous weaknesses that can be easily exploited, and real and pertinent security challenges remain serious threats. This paper presents an information technology governance framework applied on a Jordanian bank to protect the system from social engineering attack. We worked on a case study that mainly focuses on phishing attack, which is considered one of the most common threats in banks, and the way staff will deal with it. The results show positive improvements in staff awareness and in avoiding such types of attacks, as well as a marked increase in reporting any suspicious activity noticed by employees.
Dynamic Correlation-Based Feature Selection for Feature Drifts in Data Streams

Learning from data streams requires efficient algorithms capable of constructing a model according to the arrival of new instances. These data stream learners need a quick and real-time response, but mainly, they must be tailored to adapt to possible changes in the data distribution, a condition known as concept drift. However, recent works have shown that changes of relevant feature subsets over time, called feature drift, may have significant impact in the learning process despite being commonly disregarded until now in the underlying concept of a data stream. To improve the performance of feature drifting data stream classification, in this work we present an algorithm called DCFS (Dynamic Correlation-based Feature Selection) that determines which features are the most important in each moment of a data stream. By implementing an adaptive strategy based on a drift monitor, in this algorithm, a correlation-based feature selection method is used to update the relevant feature subsets for data streams dynamically. The experimental results demonstrate that implementing our feature selection algorithm inside an incremental and online classifier leads the model to perform well on data stream datasets with feature drift, surpassing in some cases state-of-the-art data streams classifiers.
Domain Oriented Aspect Detection for Student Feedback System

Opinion Mining becomes popular and seeking the information on online review or feedback system. In conventional opinion mining techniques, it can examine how people feel about the given topic such as positive or negative feeling upon the feedback comments. In current trend, the goal of sentiment analysis is to dig the aspect word that is the fine grained sentiment information on various domains. So, the proposed system aims to analyze the aspect level sentiment analysis on student feedback system. The required feedback data are collected from the University of Computer Studies, Taungoo(UCST). This system uses OpenNLP parser for POS tagging and sentiWordNet lexical resources for defining the wordScore. The Domain Specific Ontology relating to UCST is created in the preprocessing stage of this system which supports the main process Aspect Detection. Finally, the accuracy of this system is measured by precision and recall by applying the Naïve Bayes Classification Approach on the dataset of feedbacks and their opinion. This system will assist the administrator of UCST to evaluate the performance of the University.
Audit Capability Level Using COBIT 5.0: A University Customer Care Center at University of Jember

This research aims to determine capability level of information system governance of UC3 in University of Jember through information system audit. UC3 (University Customer Care Center) is a complaint service system of academicians in the environment of University of Jember. The used audit framework in this research is COBIT 5 domain DSS (Deliver, Service and Support). DSS Domain consists of 6 process controls focusing on IT service process and its technical supports including system security, service sustainability, training and ongoing data management. Results of capability level audit is in level 1 for DSS01, DSS02 and DSS03 as well as level 0 for DSS03, DSS04 and DSS05, this indicates that UC3 is still limited to its implementation in order to achieve its business goals, without any management on planning, supervision and adjustment. Then, it leads to recommendation based on gap results of existing level and target level as well as from guidance of base practice PAM. The recommendation can be used as a guidance to improve effectiveness and efficiency of University of Jember to manage the UC3.
Intelligent Colocation of Workloads for Enhanced Server Efficiency

Many server applications achieve only a fraction of their theoretical peak performance due to bottlenecks in the shared caches, instruction execution units, I/O or memory bandwidth, even though the remaining resources may be underutilized. It is very hard for developers and runtime systems to ensure that all these critical resources are fully exploited by a single application. An attractive technique for increasing server system utilization is to colocate multiple applications on the same server. When applications share critical resources, however, these applications may adversely affect each other, due to contention on the shared resources. In this paper, we show that server efficiency can be improved by modeling the expected performance degradation of colocated applications from measured hardware performance counters, and exploiting such a model to determine an optimized mix of colocated applications. This paper presents a novel resource management approach and makes the following contributions: (1) a new machine learning model to predict the performance degradation of colocated applications from hardware counters and (2) an intelligent scheduling scheme deployed on an existing resource manager to enable application co-scheduling with minimum performance degradation. Our results show that our approach achieves performance improvements of 15 % (avg) and 26 % (max) compared to the standard policy commonly used by existing job managers.
An Exploration Study of the Kagenfels and Natzwiller Granites, Northern Vosges Mountains, France: A Combined Approach of Stream Sediment Geochemistry and Automated Mineralogy

Following a regional reconnaissance stream sediment survey that was carried out in the northern Vosges Mountains in 1983, a total of 20 stream sediment samples were collected with the aim of assessing the regional prospectivity for the granite-hosted base and rare metal mineralisation of the northern Vosges magmatic suite near Schirmeck. A particular focus of the investigation was the suspected presence of W, Nb and Ta geochemical occurrences in S-type (Kagenfels) and I-S-type (Natzwiller) granites outlined in public domain data. Multi-element geochemical assays revealed the presence of fault-controlled Sn, W, Nb mineralisation assemblages along the margins of the Natzwiller and Kagenfels granites. Characteristic geochemical fractionation and principal component analysis (PCA) trends along with mineralogical evidence in the form of cassiterite, wolframite, ilmenorutile and columbite phases and muscovite–chlorite–tourmaline hydrothermal alteration association assemblages in stream sediments demonstrate that, in the northern Vosges, S-type and fractionated hybrid I-S-type granites are enriched in incompatible, late-stage magmatic elements. This is attributed to magmatic fractionation and hydrothermal alteration trends and the presence of fluxing elements in late-stage granitic melts. This study shows that the fractionated granite suites in the northern Vosges Mountains contain rare metal mineralisation indicators and therefore represent possible targets for follow-up mineral exploration. The application of automated mineralogy (QEMSCAN®) in regional stream sediment sampling added significant value by linking geochemistry and mineralogy.
Price Forecasting by Back Propagation Neural Network Model

The process of predicting what will happen in the future by gathering and analyzing past and current data is referred to as forecasting. When trying to make a good forecasting, Back Propagation Neural Network (BPNN) is constructed with different aspects of viewpoints for the high accuracy of that forecasting. This paper introduces efficient and scalable BPNN model for forecasting, allowing different views on data to fuse the responses of the model in complex and exact forecasting. To exploit the application area of the model, Rice Price Data Set of Pyapon Town in Ayeyarwaddy Division, Republic of the Union of Myanmar was used as case study. Four main factors influenced on rice price and rice production are assumed as input neurons to visible layers of the model. BPNN model with four input factors proves that the accuracy is over 80 percentage.
Modelling and analysis of tagging networks in Stack Exchange communities

Abstract Large Question-and-Answer (Q&amp;A) platforms support diverse knowledge curation on the Web. While researchers have studied user behaviour on such platforms in a variety of contexts, there is relatively little insight into important by-products of user behaviour that also encode knowledge. Here, we analyse and model the macroscopic structure of tags applied by users to annotate and catalogue questions, using a collection of 168 Stack Exchange websites that span a diversity of sizes and topics. We study the distribution of tag frequencies and also the structure of ‘co-tagging’ networks where nodes are tags and links connect tags that have been applied to the same question. We find striking similarity in tagging structure across Stack Exchange communities, even though each community evolves independently (albeit under similar guidelines). Our findings thus provide evidence that social tagging behaviour is largely driven by the Stack Exchange platform itself and not by the individual Stack Exchange communities. We also develop a simple generative model that creates random bipartite graphs of tags and questions. Our model accounts for the tag frequency distribution but does not explicitly account for co-tagging correlations. Even under these constraints, we demonstrate empirically and theoretically that our model can reproduce a number of the statistical properties that characterize co-tagging networks.
Can NetGAN be Improved on Short Random Walks?

Graphs are useful structures that can model several important real-world problems. Recently, learning graphs has drawn considerable attention, leading to the proposal of new methods for learning these data structures. One of these studies produced NetGAN, a new approach for generating graphs via random walks. Although NetGAN has shown promising results in terms of accuracy in the tasks of generating graphs and link prediction, the choice of vertices from which it starts random walks can lead to inconsistent and highly variable results, specially when the length of walks is short. As an alternative to a random starting, this study aims to establish a new method for initializing random walks from a set of dense vertices. We purpose estimating the importance of a node based on the inverse of its influence over the whole vertices of its neighborhood through random walks of different sizes. The proposed method manages to achieve a significantly better accuracy, less variance and lesser outliers.
Szegedy quantum walks with memory on regular graphs

Quantum walks with memory (QWM) are types of modified quantum walks that record the walker's latest path. The general model of coined QWM is presented in Li et al. (Phys Rev A 93:042323, 2016). In this paper, we present the general Szegedy QWM model and we describe its relationship with the coined QWM model. A coined QWM can be transformed into a Szegedy QWM, while a Szegedy QWM can be transformed into a coined QWM with any partition. These results may help in the analysis of the coined QWM. By transforming a coined QWM into a Szegedy QWM, the essential structure of the coined QWM is revealed. We give an example and we prove that two known QWMs are equal when they have a proper position-dependent coin operator.
Low-Resolution Face Recognition in the Wild with Mixed-Domain Distillation

Low-resolution face recognition in the wild still is an open problem. In this paper, we propose to address this problem via a novel learning approach called Mixed-Domain Distillation (MDD). The approach applies a teacher-student framework to mix and distill knowledge from four different domain datasets, including private high-resolution, public high-resolution, public low-resolution web and target low-resolution wild face datasets. In this way, high-resolution knowledge from the well-trained complex teacher model is first adapted to public high-resolution faces and then transferred to a simply student model. The student model is designed to identify low-resolution faces, and could perform face recognition in the wild effectively and efficiently. Experimental results show that our proposed model outperforms several existing models for low-resolution face recognition in the wild.
Development process for intelligent user interfaces

The human-computer interaction (HCI) is a research field that supports the user interface development. Furthermore, HCI influences the intelligent user interface (IUI) area. IUI is a user interface that uses intelligent technology to reach the communication between humans and machines. The construction of IUI is not easy and requires the knowledge of different methods, techniques and technologies from many domains (such as, HCI, artificial intelligence, ergonomics, software engineering, etc.). In this way, the existence of a process is an important tool for IUI developers. The literature presents design processes and frameworks for user interface, but most of them do not offer any knowledge regarding the methods, techniques, and technologies that can be used to support the activities involved. Thus, this work proposes a development process for IUIs which offers recommendations about methods, techniques, and technologies. We hope that this process will help analysts and designers who work in IUI area.
Content Based Image Retrieval Approach using Deep Learning

In a world that seeks perfect results of any search query, an information retrieval system that produces an accurate and relevant output is desired. However, because of the famous semantic gab problem of image representation, a Content Based Image Retrieval (CBIR) system faces some difficulties, since it highly depends on the extracted image features as basis for a similarity check between the query image and database images. This purposed approach overcomes these difficulties with the aid of the most fast growing technology, namely Deep Learning. In addition, it explores the effects of merging the features extracted from the latter layers of the deep network to achieve better retrieval results. The experimental results demonstrate the effectiveness of the proposed scheme in terms of the number of relevant retrieved images of the query results, and the mean average precision, while keeping low computational complexity since it uses an already trained deep convolutional model called AlexNet. Thus in turn, a reduction in the complexity that combines training a deep model from the scratch has been achieved.
Teaching Software Maintenance

This paper outlines the content and techniques used to teach software maintenance to American university sophomores (second year students) who have had 3 semesters of programming. The course uses an introductory text that is geared to the maturity of the audience. By turning the project of the introductory course into a large software evolution exercise, the major topics of software engineering can still be easily introduced and examined. We present the course organization, evaluation rubrics, and student and instructor experiences from six offerings of the course to demonstrate that treating the project in an introductory course as a software evolution exercise on a large, mature system is a viable alternative to the usual (greenfield) approaches. As an added benefit, meaningful contributions to the open source community can be made.