Reviewerly/exHarmony huggingface dataset:
Fuzzy Cognitive Maps: A Business Intelligence Discussion

Modeling complex systems by means of computational models has enabled experts to understand the problem domain without the need of waiting for the real events to happen. In that regard, fuzzy cognitive maps (FCMs) have become an important tool in the neural computing field because of their flexibility and transparency. However, obtaining a model able to align its dynamical behavior with the problem domain is not always trivial. In this paper, we discuss some aspects to be considered when designing FCM-based simulation models by relying on a business intelligence case study. In a nutshell, when the fixed point is unique, we recommend to focus on the number of iterations to converge instead of focusing on the reached attractor and stress the importance of the transfer function chosen in the model.
Establishing and Maintaining Root of Trust on Commodity Computer Systems

Suppose that a trustworthy program must be booted on a commodity system that may contain persistent malware. Establishing root of trust (RoT) ensures the system has all and only the content chosen by a trusted verifier or the verifier discovers unaccounted content, with high probability. Obtaining such an assurance is challenging because malware can survive in system states across repeated secure- and trusted-boot operations and act on behalf of a powerful remote adversary. I this presentation, I illustrate both the theoretical and practical challenges of RoT establishment unconditionally; i.e., without secrets, trusted hardware modules (e.g., TPMs, HSMs) or adversary computation bounds. I also illustrate the only unconditional solution to this problem known to date. Establishing root of trust forces the adversary to repeat the malware-insertion attack, perhaps at some added cost. However, the inherent size and complexity of commodity OS components (aka., the "giants") render them vulnerable to such successful attacks. In contrast, small and simple software components with rather limited function and high-assurance security properties (aka., the "wimps") can, in principle, be resistant to attack. Maintaining root of trust assures a user that a commodity computer's wimps are isolated from, and safely co-exist with, adversary-controlled giants. However, regardless how secure program isolation may be, I/O separation must also be achieved despite the pitfalls of commodity architectures that encourage I/O hardware sharing, not isolation. In this presentation, I also illustrate the challenges of I/O separation and present and approach that enables the co-existence secure wimps with insecure giants, via an example of a system implemented at CMU.
Patients Stratification in Imbalanced Datasets: A Roadmap

Learning in an imbalanced context is characterized by high disproportion ratios of data instances number belonging to each class of the dataset. Attributing the correct class for each instance is well studied using supervised learning techniques. However, the examination of effects of the same phenomenon in unsupervised learning environments lags behind. Some of the main issues hindering the application of unsupervised learning techniques (clustering techniques) in an imbalanced data setting are highlighted. It also presents a solution to deal with the showcased issues. This solution evades the noticed drawbacks by employing another set of clustering algorithms while including them in an aggregated learning framework. This set of algorithms would be assessed by measures tailored to the nature of these techniques and to the unique constraints that the imbalanced learning environment imposes. The suggested framework is intended to be applied to the patients stratification problem.
An Optimized Component Selection Algorithm for Self-Adaptive Software Architecture Using the Component Repository

Component-based software engineering focuses on the development and reuse of components. The component reuse depends on the storage and retrieval processes. This article presents the component repository model for the developers to achieve good productivity. The component selection from the component repository according to the functionality and requirements is a crucial process. This article proposed an algorithm for optimizing component selection with functionality constraints like customer size, reliability, and performance. The experimental result evaluates the performance of the algorithm.
Research and Application of Question Bank Information System Based on Internet Plus

With the popularity of the Internet and smart phones, more and more people choose to use mobile phones to learn, which can make full use of trivial time and improve the efficiency of learning. Similarly, using mobile phones to learn, you can get the latest learning information at any time, so you can learn more about the times. With the rapid development of Internet technology, more and more industries use advanced network information technology to manage information. With the in-depth promotion of network technology in the field of education, the traditional education model can not fully meet the needs in the field of education. The concept of a new form of education that combines Internet technology and education, namely "Internet + education" is proposed.. This paper proposes the research and design of the information system of the "Signal and System" course based on the Android platform. The system realizes the basic functions of the answering software, enabling users to use the software to learn related content, and to classify and collect the topics. And the review and study of the wrong questions can make the user grasp the key points of knowledge and attract the learners. The research and development of this system mainly includes system feasibility and demand analysis, system interface design, design and implementation of various functions of the system, and debugging and testing of software. Finally, through the system's multiple test runs, it performed well.
Unveiling Systematic Biases in Decisional Processes

Decisional processes are at the basis of several security and privacy applications. However, they are often not transparent and can be affected by human or algorithmic biases that may lead to systematically misleading or unfair outcomes. To unveil these biases, one has to identify which information was used to make the decision and to quantify to what extent such information has influenced the process outcome. Two classes of techniques are widely used to determine possible correlation between variables within decisional processes from observational data: (i) econometric techniques, in particular regression analysis, and (ii) knowledge discovery techniques, in particular association rules mining. However, these techniques, taken individually, have intrinsic drawbacks that limit their applicability. In this work, we propose an approach for unveiling biases in decisional processes, which leverages association rule mining for systematic hypothesis generation and regression analysis for model selection and recommendation extraction. We demonstrate the proposed approach in the context of discrimination detection, showing that not only it provides 'statistically significant' evidence of discrimination but it also allows for a more efficient operationalization of the recommendations extracted, upon which the decision maker can operate.
Noise Convolutional Neural Networks and FPGA Implementation

Convolutional neural networks (CNNs) are primarily a cascaded set of pattern recognition filters, which are trained by big data. It enables us to solve complex problems of computer vision applications. A conventional CNN requires numerous parameters (weights) and computations. In this study, we propose a noise CNN (NCNN), which consists of conventional convolutional operation in the former layer and a noise convolutional operation in the latter layers. Noise convolution can be realized by pointwise convolution with the addition of noise to retain recognition accuracy for a large kernel size convolution layer. Using data obtained from theoretical analysis, we apply various convolution layers including a conventional convolution in the former layer and a point-wise convolution with noise in the latter one. Further, we propose an architecture for a noise convolution operation with a pseudo-random circuit as the noise generator. We implement the proposed NCNN in the Xilinx Inc. ZCU104 FPGA evaluation board. The experimental results show that the proposed NCNN can preserve recognition accuracy and achieve high performance.
Quantification of emotional values for attributes from sentiment analysis based on mobile phone reviews

Abstract E-commerce online reviews are commonly short in length, lack of contextual semantics, and are mixed with explicit emotions and implicit emotions. The subjects of implicit emotions are not easy to be extracted and be defined. Now product-oriented attributes are used to define implicit and fine-grained emotions with the help of the domain emotion dictionary that is constructed by extracted explicit attribute-emotion pairs through using a variety of part-of-speech rules. And implicit emotion and attribute pairs for product “mobile phone” are mined with the help of explicit attribute-emotion pairs and probability calculation method. At last, explicit and implicit emotion values are calculated by considering degree and negative modifiers. The experimental results show that proposed method is effective and has certain commercial value. And the process method is easy to extend to other product fields.
Meta-genetic programming for static quantum circuits

Quantum programs are difficult for humans to develop due to their complex semantics that are rooted in quantum physics. It is therefore preferable to write specifications and then use techniques such as genetic programming (GP) to generate quantum programs instead. We present a new genetic programming system for quantum circuits which can evolve solutions to the full-adder and quantum Fourier transform problems in fewer generations than previous work, despite using a general set of gates. This means that it is no longer required to have any previous knowledge of the solution and choose a specialised gate set based on it.
Specific features of clay minerals in ancient crusts of weathering of various rocks in diamondiferous regions

Results of complex research of different age ancient weathering crusts in various rocks (terrigenous-carbonate rocks of Lower Paleozoic, dolerites, tuffs and tufogene formations, kimberlites) within the main diamondiferous regions of the Siberian platform indicated that complicated multicomponent composition of initial formations (with the exception of terrigenous-carbonate rocks), containing di- and trioctahedral minerals, the structure of which has tri- and bivalent rock-forming elements, stipulated decelerated transformation of the initial material. Development of incomplete weathering profiles is first of all caused by weak ejection of bivalent cations from primary minerals. That is why newly emerging phases will be dioctahedral and often preserve mixed composition of cations. The most important typomorphic indications of clay formations in the studied weathering crusts are as follows: a) omnipresent dioctahedral hydromica (2М1) in the weathering crust of terrigenouscarbonate rocks and its association in the most mature profiles with kaolinite of relatively ordered structure, than of kaolinite, having been formed at the expense of other rocks; b) constant availability of trappean formation (tuffs, tufogene rocks, dolerites) in sections of crusts of weathering together with di- and trioctahedral montmorillonite, as well as disordered vermiculite-montmorillonite mixed-layered formation, to this or that degree disordered kaolinite, associated in the weathering crust of tufogene rocks with halloysite (at complete absence of micaceous minerals in the products of weathering); c) the content in the crust of weathering of kimberlites together with polycationic montmorillonite of a significant quantity of trioctahedral chlorite (packets δ and δ'), serpentine (structural types A and B) and altered to various degree phlogopite, including related with it hydromica 1M.
Power Network System Identification and Recovery Based on the Matrix Completion

Abstract Health detection of power networks is a complex and important issue. In order to reduce the detection complexity of the power network, a matrix recovery method based on the matrix completion has been proposed in this paper. Matrix completion has attracted considerable attentions in computer vision, system identification, and machine learning in recent years. This paper presents a survey on algorithms about matrix completion including Singular Value Threshold (SVT), Alternating Direction Method (ADM) and so on. In order to reproduce algorithms about matrix completion and evaluate the performance of these algorithms, this paper is present. The numerical experiments are conducted to evaluate the performance of algorithms. An application about image denoising is also carried out.
Mobile Image Retrieval System for Cloud Service Based on Convolutional Neural Network and Hadoop

These years, global digital images are increasing at an unprecedented speed. Using smart mobile is a convenient way to share what users experienced anytime and anywhere through social networks. Retrieving these distributed images has become an important research issue. In order to speed up the image retrieval in mobile cloud service, an efficient content-based image retrieval (CBIR) system based on Hadoop is designed and proposed. Firstly, the features of image are extracted by a convolutional neural network, and the features of the image library are divided into several sub feature databases. Secondly, image retrieval results are obtained according to the matching results by Hadoop distributed platform. The simulation experiments verify the efficiency of the proposed system.
A Classification Model for Drug Addicts Based on Improved Random Forests Algorithm

To accomplish the rapid screening of drug addicts and to meet the requirements of modern police work, this research employs a data-mining technology that utilizes real samples of drug addicts as well as non-drug addicts. The aim is to construct a classification model based on pulse wave data. After the pre-processing of pulse wave data, the original random forest classification model is initially established with high accuracy, but with a relatively low recall rate and F1 score. To resolve this issue, an improved classification model is henceforth proposed. The improved model mainly involves three improvement strategies: firstly, perform cross-validation by dividing multiple training sets and test sets to obtain generalization errors; secondly, balance the sample distribution using down-sampling techniques; and finally, select model parameters based on multi-criteria analysis. According to the evaluation results of accuracy, precision, recall rates, and F1 scores, the performance of the improved random forest classification model has demonstrated its superiority and robustness via experiments using different datasets.
A Modified Cultural Algorithm for Feature Selection of Biomedical Data

An important step in developing predictive models is determining the best features to be used for building the models. Feature selection algorithms are frequently adopted to remove non-informative and redundant features from the dataset before building the predictive models. However, it can be a significant challenge to determine the features which would make the best predictors, particularly in large datasets. Importantly, building a model with the best subset of features can make it more interpretable and efficient. This paper proposes a novel feature selection algorithm which is based on the cultural meta-heuristic optimization algorithm. The modified Cultural Algorithm was developed and optimized for achieving high accuracy and Area Under the Curve. The quality of the selected features was assessed using the performance of a Support Vector Machine classifier. The proposed algorithm was tested on five benchmark datasets and achieved an average accuracy of 0.923 and an Area Under the Curve of 0.898 across all datasets. Although the performance of the proposed modified Cultural Algorithm was comparable with that of the standard Genetic Algorithm, the modified Cultural Algorithm required a smaller number of features and shorter execution time than the Genetic Algorithm.
The Atmospheric Light Estimation by Divergence Operator

In the field of optics and computer vision, image defogging has attracted much attention due to its wide application. On the premise of fully analyzing the characteristics of foggy images, this paper proposes a new method to improve the estimation of atmospheric light by improving the divergence information. Experimental results show that compared with the traditional defogging algorithm, this algorithm has a faster processing speed, and the recovered image details are prominent, which can effectively remove the haze in the image and has a better visual effect.
Analysis of Review Text on a Golf Course Reservation Site

On EC site, customer’s reviews (product review) have a gat influence on other customer’s purchase. Rating points for products and evaluation on review itself are expected to be utilized for marketing. In this study, we analyze reviews and rating points of a golf courses reservation site. Moreover, we evaluate positive points for golf courses and we clarified the characteristics of their reviews. Concretely, we performed logistic regression to discriminate evaluation for golf course by using the genre of review sentences.
Subjective Perception Space of Musical Passages

An experimental construction of the subjective (emotional) space of perception of musical sound fragments used in the GarageBand app for iOS to help the composer has been carried out. A representative set of 20 sound fragments was selected on the basis of an experiment on the classification of all samples from this database, which were further evaluated in 3 types of experiments: on the basis of multidimensional scaling of differences between them, on the basis of direct scaling of emotional qualities (Semantic Differential Method for 25 adjective pairs), based on direct scaling on three SAM scales (in the PXLab variant). As a result, the individual features (points of view) of these sound samples were investigated and a correlation comparison of the obtained results was carried out. As a result, the interrelation of all used scales is shown and a way of their integration into a four- dimensional spherical model of emotions is presented.
Measuring Critical Reception in Kids Through Consumption of Risky Challenges Videos in YouTube

This project researches if critical reception facing YouTube content can be promoted in children between nine and eleven years old, focusing in analysis, evaluation and taking a stand facing the videos of risky challenges. This is an interdisciplinary project that used a mixed methodology, mostly quantitative which consists in four phases, two of them will be exposed in this paper. One phase let us know what the kids like and consume. The other phase allow us to measure the level of three abilities of critical thinking. We found that even if the kids do not consume a lot of channels with risky challenges, it is important to enhance the ability to take a stand in order to avoid that children hurt themselves.
Generation of Individual Activity Classifiers for the Use in Mobile Context-Aware Applications

In recent years, various methods for the automated recognition of human activities based on the analysis of sensor data using machine learning approaches have been researched. The continuously increasing hardware performance of mobile devices such as smartphones and wearables and the ongoing development of existing algorithms have resulted in steadily higher recognition rates. These latest advances resulted in an growing demand for intelligent and context sensitive mobile applications. However, the generation of valid ground truth information with a suitable quality remains a major challenge. In addition, there is currently no standardized procedure for generating an activity classifier for the use in custom application areas. The holistic workflow introduced in this paper focuses on the recognition of activities using mobile devices. For this purpose, the generation of a ground truth information by recording and annotating sensor data is described. The generated data set is used for transfer learning in a machine learning framework and the resulting model is the basis for a mobile real-time classification application. The data source is a current study of the University of Applied Sciences Mittweida.
Novel Algorithm on Human Body Fall Detection

This research work provides a novel algorithm in computer vision for detecting human fall by the help of the trigonometric equation without any sort of machine learning or deep neural networks. Manual monitoring for fall detection can be very expensive as well as time consuming. There are many kinds of research on fall detection recently, but most of them either use wearable sensor technology or machine learning. Very few kinds of research have used image processing technique, where the end result is not much promising. Wearing additional sensors for detecting fall can be uncomfortable for senior citizens. Additionally, machine learning techniques, which requires heavy computational power of computers, might not be financially feasible for massive use, especially residential places. In this research, we have developed an algorithm, that depends on traditional computer vision and trigonometric logic, which requires very less computational power. This is ideal for massive use either for residential use or industrial purposes.
Runtime analysis of evolutionary algorithms: basic introduction

tutorial Share on Runtime analysis of evolutionary algorithms: basic introduction: introductory tutorial at GECCO 2019 Authors: Per Kristian Lehre University of Birmingham, Birmingham, UK University of Birmingham, Birmingham, UKView Profile , Pietro S. Oliveto University of Sheffield, Sheffield, UK University of Sheffield, Sheffield, UKView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 662–693https://doi.org/10.1145/3319619.3323394Published:13 July 2019Publication History 0citation108DownloadsMetricsTotal Citations0Total Downloads108Last 12 Months12Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Study on Data Security Sharing Model Based on Attribute Encryption

In view of the problems of key management and access control in traditional encryption technology, this paper proposes a data security sharing model based on attribute encryption. Firstly, we studied the property encryption and data sharing model. Secondly, proposed an attribute encryption algorithm. The user's private key is generated by the addition homomorphic encryption algorithm, which solves the key hosting problem. This paper proposed the data security sharing algorithm supporting partial decryption, which can reduce the calculation cost of user decryption. An efficient scheme of real-time attribute key cancellation is designed, which not only reduces the communication key, but also protects the forward and backward security of the data. Compared with the other two methods, the proposed method has lower computational and decryption costs.