Reviewerly/exHarmony huggingface dataset:
Generating Semantic Adversarial Examples via Feature Manipulation

The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wise perturbation to fool the classifier. An alternative approach is to have perturbations in the latent space. However, such perturbations are hard to control due to the lack of interpretability and disentanglement. In this paper, we propose a more practical adversarial attack by designing structured perturbation with semantic meanings. Our proposed technique manipulates the semantic attributes of images via the disentangled latent codes. The intuition behind our technique is that images in similar domains have some commonly shared but theme-independent semantic attributes, e.g. thickness of lines in handwritten digits, that can be bidirectionally mapped to disentangled latent codes. We generate adversarial perturbation by manipulating a single or a combination of these latent codes and propose two unsupervised semantic manipulation approaches: vector-based disentangled representation and feature map-based disentangled representation, in terms of the complexity of the latent codes and smoothness of the reconstructed images. We conduct extensive experimental evaluations on real-world image data to demonstrate the power of our attacks for black-box classifiers. We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.
Interpretation and Simplification of Deep Forest

&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;This paper proposes a new method for interpreting and simplifying a black box model of a deep random forest (RF) using a proposed rule elimination. In deep RF, a large number of decision trees are connected to multiple layers, thereby making an analysis difficult. It has a high performance similar to that of a deep neural network (DNN), but achieves a better generalizability. Therefore, in this study, we consider quantifying the feature contributions and frequency of the fully trained deep RF in the form of a decision rule set. The feature contributions provide a basis for determining how features affect the decision process in a rule set. Model simplification is achieved by eliminating unnecessary rules by measuring the feature contributions. Consequently, the simplified model has fewer parameters and rules than before. Experiment results have shown that a feature contribution analysis allows a black box model to be decomposed for quantitatively interpreting a rule set. The proposed method was successfully applied to various deep RF models and benchmark datasets while maintaining a robust performance despite the elimination of a large number of rules.&lt;br&gt;&lt;/div&gt;
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring

We formally prove the connection between k-means clustering and the predictions of neural networks based on the softmax activation layer. In existing work, this connection has been analyzed empirically, but it has never before been mathematically derived. The softmax function partitions the transformed input space into cones, each of which encompasses a class. This is equivalent to putting a number of centroids in this transformed space at equal distance from the origin, and k-means clustering the data points by proximity to these centroids. Softmax only cares in which cone a data point falls, and not how far from the centroid it is within that cone. We formally prove that networks with a small Lipschitz modulus (which corresponds to a low susceptibility to adversarial attacks) map data points closer to the cluster centroids, which results in a mapping to a k-means-friendly space. To leverage this knowledge, we propose Centroid Based Tailoring as an alternative to the softmax function in the last layer of a neural network. The resulting Gauss network has similar predictive accuracy as traditional networks, but is less susceptible to one-pixel attacks; while the main contribution of this paper is theoretical in nature, the Gauss network contributes empirical auxiliary benefits.
An Empirical Study on the Classification of Chinese News Articles by Machine Learning and Deep Learning Techniques

This study compares Chinese news classification results of machine learning (ML) and deep learning (DL). In processing ML, we chose Support Vector Machine (SVM) and Naive Bayes (NB) to form three models: Word2Vec-SVM, TFIDF-SVM, and TFIDF-NB. Since NB assumes that the words are independent, this is different from the concept of related word distribution in Word2Vec, so the combination with NB is excluded. In processing DL, we adopted Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), and used Word2Vec for word embedding. Experimental results showed that with proper word preprocessing, the difference of classification accuracy of ML and DL models is actually very small. Although the results show that Bi-LSTM performs the most accurate and has the lowest Loss compared to other DL techniques, its implementation process is the most time consuming. This study affirms the excellent results of CNN, while its Loss is the highest of the DL models. We also found that Word2Vec-SVM was superior to TFIDF-SVM in terms of efficiency, but its accuracy is not as good as expected. To summarize the classification accuracy in Bi-LSTM, LSTM, CNN, Word2vec-SVM, TFIDF-SVM, and NB are 89.3%, 88%, and 87.54%, 85.32%, 87.35%, 86.56%, respectively.
Improving the Open Stack Authentication system through federation with JASON Tokens

Many cloud computing systems are in place for providing different kinds of services, which include AWS, AZURE, Google Cloud, etc. which are proprietary.These clouds help the users implement their own IT requirements, but the users cannot configure or customize the cloud computing processes as per their needs, especially to handle the issue of security from the perspectives of authentication, access control, and data security.Open source cloud computing systems, which include Eucalyptus, Open Nebula, open stack, etc. allows the changes carried to the cloud computing systems primarily through configuration, the addition of API, the addition of processes, etc. Users can make changes to affect the system such that it works as per the user requirements, especially to improve the security system built into the cloud computing system, which sometimes found to be vulnerable for attack.Users are concerned about the security of their software data hosted on third-party IT infrastructure.Open Stack cloud computing platform is being used by many for implementing private clouds.Users can customize open stack as per their requirements.Open stack suffers from many security-related vulnerabilities that can be exploited by the users for attacking the user software and data.A review of the Open stack systems is required to find the gaps that are existing to plug the same.In this paper, a review of the Open stack presented, bringing out different kinds of vulnerabilities that exist in authenticating the users and a federation method using JSON tokens showed that help eliminating the Vulnerabilities existing in the open stack for enforcing security within the Keystone module of Open stack.
Arbitrary-order fractance approximation circuits with high order-stability characteristic and wider approximation frequency bandwidth

This paper discusses a novel rational approximation algorithm of arbitrary-order fractances, which has high order-stability characteristic and wider approximation frequency bandwidth. The fractor has been exploited extensively in various scientific domains. The well-known shortcoming of the existing fractance approximation circuits, such as the oscillation phenomena, is still in great need of special research attention. Motivated by this need, a novel algorithm with high order-stability characteristic and wider approximation frequency bandwidth is introduced. In order to better understand the iterating process, the approximation principle of this algorithm is investigated at first. Next, features of the iterating function and frequency-domain characteristics of the impedance function calculated by this algorithm are researched, respectively. Furthermore, approximation performance comparisons have been made between the corresponding circuit and other types of fractance approximation circuits. Finally, a fractance approximation circuit with the impedance function of negative 2 / 3-order is designed. The high order-stability characteristic and wider approximation frequency bandwidth are fundamental important advantages, which make our proposed algorithm competitive in practical applications.
Formation of a united information space of a research library

Over the past decade, the issue of uncontrolled growth of unsystematic information in Internet has remained acute for the scientific community. The problem of finding relevant information related to the distribution and autonomy of scientific information resources remains. A priority in the field of centralized access to the key scientifically significant sources of information is the creation of a united information space (UIS). The study aims to identify the main models to form systems integrating distributed information resources, and as a result to determine the structure of UIS formation in a research library. Two models were considered and analyzed in the study: a meta-aggregator and an integrated electronic library. During the analysis, elements, structure and a set of functions for users and employees of a research library are revealed for each model. The study allowed the drawing of the following conclusions: • The choice of a model for the UIS formation depends mostly on the formulation of tasks, the solution of which is the purpose of creating a system, as well as on the technological potential of the organizations involved in the process. •Multifunctionality of the system allows simultaneous use of the above-mentioned formation models. • Adding the element of interactivity to the structure of UIS of the research library will allow timely monitoring of changes in the information needs of scientists, reduction of time, labor and financial costs of both the library and a user. The article presents the criteria for choosing a model. For the first time the optimal effective structure of the UIS in the research library is described.
Self-balance Motion and Appearance Model for Multi-object Tracking in UAV

Under the tracking-by-detection framework, multi-object tracking methods try to connect object detections with target trajectories by reasonable policy. Most methods represent objects by the appearance and motion. The inference of the association is mostly judged by a fusion of appearance similarity and motion consistency. However, the fusion ratio between appearance and motion are often determined by subjective setting. In this paper, we propose a novel self-balance method fusing appearance similarity and motion consistency. Extensive experimental results on public benchmarks demonstrate the effectiveness of the proposed method with comparisons to several state-of-the-art trackers.
Hybrid Model of Seasonal ARIMA-ANN to Forecast Tourist Arrivals through Minangkabau International Airport

The number of tourist arrivals forecasting is required for the future development of tourism industry to improve the economic growth.The number tourist arrivals data can be analyzed by building a model so that it will help to find out the number of tourist arrivals in the next period which is through Minangkabau International Airport.The linear model that is used is Seasonal Autoregressive Integrated Moving Average (SARIMA) used and continued to build a nonlinear model of the residual SARIMA model using Artificial Neural Network (ANN).In this research, SARIMA model which obtained is SARIMA (1, 0, 1) (1, 1, 0) 12 .But, residual of SARIMA model has not been fulfilled an autocorrelation assumption so that it isproposed a new model of SARIMA-ANN.The residual model of SARIMA is built using the ANN model architecture with 2-2-2-1 network topology.The performance rate of time series model of tourist arrivals which is the data started on January 2012 until March 2019 is measured using Mean Absolute Percentage Error (MAPE).Based on the MAPE value of 17.1770% indicates that the model obtained having good performance to forecast the number of tourist arrivals through Minangkabau International Airport in the future.
Fast, robust, and accurate monocular peer-to-peer tracking for surgical navigation

Abstract Purpose This work presents a new monocular peer-to-peer tracking concept overcoming the distinction between tracking tools and tracked tools for optical navigation systems. A marker model concept based on marker triplets combined with a fast and robust algorithm for assigning image feature points to the corresponding markers of the tracker is introduced. Also included is a new and fast algorithm for pose estimation. Methods A peer-to-peer tracker consists of seven markers, which can be tracked by other peers, and one camera which is used to track the position and orientation of other peers. The special marker layout enables a fast and robust algorithm for assigning image feature points to the correct markers. The iterative pose estimation algorithm is based on point-to-line matching with Lagrange–Newton optimization and does not rely on initial guesses. Uniformly distributed quaternions in 4D (the vertices of a hexacosichora) are used as starting points and always provide the global minimum. Results Experiments have shown that the marker assignment algorithm robustly assigns image feature points to the correct markers even under challenging conditions. The pose estimation algorithm works fast, robustly and always finds the correct pose of the trackers. Image processing, marker assignment, and pose estimation for two trackers are handled in less than 18 ms on an Intel i7-6700 desktop computer at 3.4 GHz. Conclusion The new peer-to-peer tracking concept is a valuable approach to a decentralized navigation system that offers more freedom in the operating room while providing accurate, fast, and robust results.
Knowledge Management Technology Using Ontologies, Cognitive Models and Production Expert Systems

The article proposes a knowledge management technology based on the joint use of ontologies, cognitive models and production expert systems.Мethods and tools of semantic modeling (primarily ontological and cognitive), developed in the team of authors, were applied within the framework of a two-level research technology of energy security research.An important components of knowledge management technology are the methods of cognitive modeling and the transformation of cognitive models into production rules of the expert system.The proposed approach provides automation of the analysis and interpretation of cognitive maps using production expert systems, which, in turn, allows to reduce the human factor influence and improve the quality of preparation and substantiation of recommendations for decision making.The stages of the proposed knowledge management technology, the algorithm for transforming the cognitive model, the modification and development of tools to support the proposed technology based on the agent approach are considered.Examples of ontologies, cognitive maps and the results of cognitive maps converting into production rules of an expert system are given.The technology has been tested in studies of energy security problems, but may have wider application.
Genetic Algorithm Based Optimization of Deep Neural Network Ensemble for Personal Identification in Pedestrians Behaviors

Personal identification is a task of authenticating a person using individual biological features. Deep neural networks (DNNs) have demonstrated impressive performance in this field. It is well known, however, that no general algorithm is available for every application problem. For a new application task, it is very time-consuming for non-experts to design network structure, hyperparameters and an ensemble of base models adequately and effectively. In this paper, we present the genetic algorithm (GA) based approach in order to construct network structures automatically, tune their hyperparameters adequately and generate base models for the ensemble algorithm. Then the ensemble is constructed from the base models with different network structures. Our original personal identification dataset is employed as the numerical example in order to illustrate the performance of the proposed method. Convergence property, experiment results, as well as search performance are discussed in the numerical results. The results indicate that several single base models provided by the proposed algorithm have a strong classification ability by exploring the features of a pedestrian's behaviors and that an ensemble model which is constituted from these base models achieves better classification performance.
DIVERSITY AS AN ADVANTAGE: AN ANALYSIS OF CAREER COMPETENCIES FOR IT STUDENTS

Due to increasing digitization in all aspects of life, the demand for qualified software development professionals continues to increase.Students from underrepresented groups, such as first generation students from non-academic families, minorities, single parents and women represent an underutilized pool of untapped potential talent.The question arises as to which unique perspectives computer science graduates from underrepresented groups can bring to software development companies.In addition to programming skills, non-technical competencies, such as foreign language abilities, intercultural communication, creativity, conflict management, team-building and organizational skills are vital for success in diverse, international project teams.A large job market database for new graduates, developed for a consortium of universities in Bavaria, Germany, is analyzed using machine learning tools.Career competencies desired by recruiting companies are compared to potential advantages offered by computer science graduates from underrepresented groups.
Expert System in Detecting Children's Intelligence using Certainty Factor

Children are the most valuable asset for parents. Every child has a different intelligence. Some children have the ability to catch more; there are also those who have low intelligence. Children's intelligence can be known early to get a quick solution and help. Expert systems can be used to determine how smart a child is. Assessment using questions to the child with some predetermined criteria is one technique to determine the intelligence. Each criterion is a symptom that will be tested on a child. Certainty Factor Method is an excellent method in conducting tests on the intelligence of children. Some parameters are tested in determining intelligence. Applying the Certainty Factor method can help a psychologist determine how smart a child is.
Disambiguation for Arabic Question-Answering System

Because of the increasing amounts of Arabic content on the Internet and the increasing demand for information, Arabic question answering (QA) systems are gaining great importance. Nevertheless, automatic answering of questions in natural language is one of natural language processing’s most challenging tasks. In this paper, we address the issue of processing Arabic Question Answering in the medical domain where there are several specific challenges. The main challenge in dealing with medical field in Arabic language is the need to resolve ambiguity. This issue, though, was not thoroughly studied in related works. Therefore, our QA system requires disambiguation solution to select the correct meaning in order to return the correct answer. The goal of this work is to resolve Arabic-related ambiguities as well as medical-related ambiguities. To achieve this goal, we use dictionaries and transducers using NooJ platform to answer any factoid or complex medical question. Experimentations of the disambiguation task of our Arabic medical question answering system show interesting results.
Gowers U_2 norm as a measure of nonlinearity for Boolean functions and their generalizations

<p style='text-indent:20px;'>In this paper, we investigate the Gowers <inline-formula><tex-math id="M2">\begin{document}$ U_2 $\end{document}</tex-math></inline-formula> norm for generalized Boolean functions, and <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{Z} $\end{document}</tex-math></inline-formula>-bent functions. The Gowers <inline-formula><tex-math id="M4">\begin{document}$ U_2 $\end{document}</tex-math></inline-formula> norm of a function is a measure of its resistance to affine approximation. Although nonlinearity serves the same purpose for the classical Boolean functions, it does not extend easily to generalized Boolean functions. We first provide a framework for employing the Gowers <inline-formula><tex-math id="M5">\begin{document}$ U_2 $\end{document}</tex-math></inline-formula> norm in the context of generalized Boolean functions with cryptographic significance, in particular, we give a recurrence rule for the Gowers <inline-formula><tex-math id="M6">\begin{document}$ U_2 $\end{document}</tex-math></inline-formula> norms, and an evaluation of the Gowers <inline-formula><tex-math id="M7">\begin{document}$ U_2 $\end{document}</tex-math></inline-formula> norm of functions that are affine over spreads. We also give an introduction to <inline-formula><tex-math id="M8">\begin{document}$ \mathbb{Z} $\end{document}</tex-math></inline-formula>-bent functions, as proposed by Dobbertin and Leander [<xref ref-type="bibr" rid="b8">8</xref>], to provide a recursive framework to study bent functions. In the second part of the paper, we concentrate on <inline-formula><tex-math id="M9">\begin{document}$ \mathbb{Z} $\end{document}</tex-math></inline-formula>-bent functions and their <inline-formula><tex-math id="M10">\begin{document}$ U_2 $\end{document}</tex-math></inline-formula> norms. As a consequence of one of our results, we give an alternate proof to a known theorem of Dobbertin and Leander, and also find necessary and sufficient conditions for a function obtained by <i>gluing</i> <inline-formula><tex-math id="M11">\begin{document}$ \mathbb{Z} $\end{document}</tex-math></inline-formula>-bent functions to be bent, in terms of the Gowers <inline-formula><tex-math id="M12">\begin{document}$ U_2 $\end{document}</tex-math></inline-formula> norms of its components.
Breast Cancer Image Classification Using the Convolution Neural Network

Breast cancer is a type of tumour and the disease with the second-leading cause of death among women.Physicians diagnose the disease through tumour classification and decide whether it is malignant or benign.However, to accurately perform the classification is not an easy task, even by experts.Thus, diagnostic system automation is required to assist physicians in accurately diagnosing malignant tumours as cancer in the early stages.This paper attempts to improve the accuracy of breast cancer detection by utilising the deep learning convolution neural network (CNN).Experiments were conducted using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset.Compared to existing techniques, the use of CNN shows a better result and achieves 99.66% in terms of accuracy.
Visual Storytelling via Predicting Anchor Word Embeddings in the Stories

We propose a learning model for the task of visual storytelling. The main idea is to predict anchor word embeddings from the images and use the embeddings and the image features jointly to generate narrative sentences. We use the embeddings of randomly sampled nouns from the groundtruth stories as the target anchor word embeddings to learn the predictor. To narrate a sequence of images, we use the predicted anchor word embeddings and the image features as the joint input to a seq2seq model. As opposed to state-of-the-art methods, the proposed model is simple in design, easy to optimize, and attains the best results in most automatic evaluation metrics. In human evaluation, the method also outperforms competing methods.