Reviewerly/exHarmony huggingface dataset:
One Class Support Vector Machine Active Learning Method for Unbalanced Data

Active learning is an important method to efficiently reduce the labeled sample size in large-scale machine learning. As the large amount of data presents the nature of imbalance, the study of active learning methods for corresponding unbalanced datasets highlights important practical significance. There is a problem with the current algorithm. The initialization of active learning becomes difficult with the increase of the unbalanced degree, which causes more labels waste. This paper proposes a one-class support vector machine active learning (OCSVM-AL) method for this problem. Firstly, we obtain the structure information of the sample set by k-means clustering. Then, the OCSVM-AL is used on each cluster. For each cluster, OCSVM-AL iteratively marks the sample from the cluster and adds the marked sample to the marked sample pool. A one-class support vector machine model is trained by using majority class samples in the marked sample pool. The trained model is used to predict the unlabeled samples in this cluster, and the labels of unlabeled samples is obtained. By the results of each cluster, all sample labels are obtained. Simulation experiments proved that this method is an effective active learning method and can significantly reduce the labeled sample size.
Problems of transition from traditional forms of educational process to the distance, digital format in the context of the COVID-19 pandemic: a regional aspect

Abstract This article outlines the results of a study of transition of educational organizations training specialists at the higher education level from traditional contact methods of direct interaction with students (classroom lectures, practical (laboratory) classes, etc.) to distance, contactless forms of educational process in the context of the COVID-19 pandemic. The study considers specific problems that higher education institutions faced during the spread of the COVID-19 virus and proposes ways to address emerging ones. In addition, the article reflects the results of the educational process of one of the higher education institutions in the Krasnoyarsk region (the Institute of Land Surveying, Cadaster and Nature Management of the Krasnoyarsk State Agrarian University), on the basis of which the impact of the identified problems on the educational process is assessed. Positive aspects of "digitalization" of educational institutions, which allowed ensuring the continuity and accessibility of the educational process, are also indicated. The benefits of the distance mode of knowledge transfer and the prospects for the development of distance learning considering the threats of the COVID-19 virus have been identified and noted. Establishing problems of transition to a distance form of conducting the educational process allowed to determine the factors and conditions that should be taken into account to ensure that both teachers and students are as effective and efficient as possible.
Data Center Network Design for Internet-Related Services and Cloud Computing

We study an integrated data center network design problem for Internet-related services and cloud computing, of which the objective is to minimize total operating cost and service delay penalty by optimizing data center location, footprint allocation, and resource provisioning decisions, while incorporating essential features such as latency, power, multiple resources, configuration limits, and interdependent footprints. We formulate the optimal design problem as a nonlinear mathematical program. We analyze a queueing model to approximate the service latency and provide tractable reformulations. We further develop Lagrangian relaxation methods and generate strengthening cuts by exploiting the structural properties of the problem to enhance computational efficiency for large-scale problems. Our numerical studies demonstrate that the proposed integrated model, which jointly optimizes location, allocation, and resource provisioning, can significantly reduce cost and improve quality-of-service when compared with a hierarchical approach that optimizes these decisions sequentially. Moreover, our proposed solution methods outperform state-of-the-art commercial software in computational performance. Based on real-world data sets, the proposed model selects data centers that have been chosen by major cloud computing infrastructure providers. We also draw managerial insights that can be used as design guidelines in practice.
A Fast Implementation of the Rijndael Substitution Box for Cryptographic AES

Today's current standard in Cryptography is the symmetric Advanced Encryption Standard (AES) as selected by the US National Institute of Standards and Technology (NIST). It is also known as Rijndael Encryption Algorithm. With the emergence of high-performance cloud computing, it is important for the security encryption schemes to perform at higher speeds to provide fast, efficient, and secure data transmissions. The Rijndael S-box (substitution box) is the only non-linear component of the cryptosystem and significantly affects the overall performance of the AES encryption scheme. In this paper, we investigate various implementations for improving the hardware performance of the Rijndael S-box component of the AES algorithm in terms of delay and number of logic elements on the Altera Cyclone IV FPGA (Field programmable gate arrays) using the Intel Quartus II software and Verilog Hardware Description Language (Verilog HDL).
Learning Gaussian Hierarchy Embedding for Relation Prediction in Knowledge Graph

Abstract Knowledge graph embedding methods, which transform high-dimensional and complex graph contents into low-dimensional vectors (or matrices, tensors) representations. They are useful for a wide range of graph analysis tasks including link prediction, node classification, recommendation and visualization. Most existing approaches represent graph nodes as vectors in a low-dimensional embedding space, ignoring the semantic hierarchy and the uncertainty present in the real-world graphs. To address this challenge, in this paper, we propose GHAKE, Gaussian Hierarchy Knowledge Graph Embedding, a novel graph embedding method that preserves both semantic hierarchy and node uncertain effectively and efficiently in an end-to-end manner. GHAKE effectively models uncertainty through Gaussian embeddings, and models semantic hierarchy via mapping entities into the polar coordinate system. In our comprehensive experiments, we evaluate GHAKE on real-world graphs, and the results demonstrate that can effectively model the semantic hierarchies and the uncertain of entities in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task.
Global Software Piracy, Technology and Property Rights Institutions

This study extends the literature on fighting software piracy by investigating how Intellectual Property Rights (IPRs) regimes interact with technology to mitigate software piracy when existing levels of piracy are considered. Two technology metrics (internet penetration rate and number of PC users) and six IPR mechanisms (constitution, IPR law, main IP laws, WIPO Treaties, bilateral treaties and multilateral treaties) are used in the empirical analysis. The statistical evidence is based on (i) a panel of 99 countries for the period 1994–2010 and (ii) interactive contemporary and non-contemporary quantile regressions. The findings show that the relevance of IPR channels in the fight against software piracy is noticeably contingent on the existing levels of technology embodied in the pirated software. There is a twofold policy interest for involving modern estimation techniques such as interactive quantile regressions. First, it uncovers that the impact of IPR systems on software piracy may differ depending on the nature of technologies used. Second, the success of initiatives to combat software piracy is contingent on existing levels of the piracy problem. Therefore, policies should be designed differently across nations with high, intermediate and low levels of software piracy.
Wisdom of the contexts: active ensemble learning for contextual anomaly detection

In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods for CAD use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets, with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several "useful" contexts to unveil them. In this work, we leverage active learning and ensembles to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. We propose a novel approach, called WisCon (Wisdom of the Contexts), that automatically creates contexts from the feature set. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active classifiers, unsupervised contextual and non-contextual anomaly detectors, and supervised classifiers) on seven datasets. Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the "wisdom" of multiple contexts is necessary.
PPDM-TAN: A Privacy-Preserving Multi-Party Classifier

Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, process, and manage enormous amounts of data while ensuring the preservation of private information. Cryptography, as shown by previous research, is the most accurate approach to acquiring knowledge while maintaining privacy. In this paper, we present an extension of a privacy-preserving data mining algorithm, thoroughly designed and developed for both horizontally and vertically partitioned databases, which contain either nominal or numeric attribute values. The proposed algorithm exploits the multi-candidate election schema to construct a privacy-preserving tree-augmented naive Bayesian classifier, a more robust variation of the classical naive Bayes classifier. The exploitation of the Paillier cryptosystem and the distinctive homomorphic primitive shows in the security analysis that privacy is ensured and the proposed algorithm provides strong defences against common attacks. Experiments deriving the benefits of real world databases demonstrate the preservation of private data while mining processes occur and the efficient handling of both database partition types.
Improving Sign Language Recognition by Combining Hardware and Software Techniques

People with hearing and speaking disabilities often have trouble communicating with the general population. They can often communicate amongst themselves, and, perhaps, with close family members and friends, if they have knowledge of a sign language. That being said, there has yet to be a widespread adoption of sign language by the general population. To remedy this problem, there is an increasing interest in developing hardware and software techniques for sign language gesture recognition, but there is a serious lack of technologies that fuse hardware and software. This paper proposes a techniques that aims to improve the accuracy of a software-based gesture recognition system by incorporating the Myo Armband, developed by Thalmic Labs. In our prototype, we train the software-based module using the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) methods, and train the Myo-based module using the Support Vector Machine method. A dataset of nine gestures was used, with each repeated five times for a total of 45 trials for both the software-based and hardware-based modules. The Myo-based module achieved an accuracy of 49% while the software-based module had an accuracy of 89%; the results were then combined.
Assessing the Impact of Text Preprocessing in Sentiment Analysis of Short Social Network Messages in the Russian Language

Presently, social networks provide individuals with a unique ability to share personal opinions with others across the world. On one of such mediums, Twitter, personal opinions and general information are presented in the form of short texts (with a limit of 280 characters). Machine learning algorithms could be leveraged in detecting sentiments and opinions within such textual data. For more efficient results, text is usually transformed into a better state through the process of pre-processing. To detect the impact of this step, we used a corpus of Twitter posts in Russian language labelled either as positive or negative sentiments and applied various combinations of preprocessing techniques. Furthermore, we classified the transformed texts with a Naive Bayes Classifier and Logistic Regression and furthermore evaluated the accuracy. Results for each combination of preprocessing type and model were compiled into tables and the conclusions on impact of preprocessing with respect to the Russian language were highlighted.
AdaBoost-based Refused Bequest Code Smell Detection with Synthetic Instances

Software requirements are constantly changing. Consequently, the development process is frequently under time pressure, which results in technical debt. To illustrate the symptoms of technical debt, 22 code smells have been introduced to indicate the poor design in code fragment, among which refused bequest is one of the most harmful smells and with high diffuseness. However, refused bequest is rarely taken into account because there is a lack of dataset. Moreover, it is difficult to design the detection rules for refused bequest compared with other popular smells. In this paper, we propose a machine-learning-based refused bequest smell detection framework SEADART, which features the utilization of a set of synthetic smelly instances. Specifically, SEADART comprises three components: (1) a smell generation approach, and (2) a model training strategy, and (3) an AdaBoost-based detection model. We evaluate the performance of the proposed framework. The evaluation results suggest that the generated smelly instances are reliable, and the trained AdaBoost model significantly outperforms the state-of-the-art over a real-world dataset.
The Quantitative Relationship between Adversarial Training and Robustness of CNN Model

With the increasing application of deep neural networks in security-critical systems, robustness becomes an important property for deep learning. However, deep neural networks are very vulnerable to perturbations, especially adversarial attacks. The adversarial training by adding adversarial examples to the training set has become a common method to improve the performance of deep neural networks. In this paper, based on the existing robustness metrics and indicators, we study the quantitative influence of adversarial training on the robustness of network-in-network and residual network, and the differences of indicators are compared. The experimental results show that adversarial training can affect the accuracy and robustness of CNN models, and the variation trends of testing accuracy and robustness are opposite.
Anaphora Resolution from Social Media Text in Indian Languages (SocAnaRes-IL)- Overview

Resolution of anaphors is required for any application which require Natural Language Understanding (NLU) such as Information Extraction, Conversation Analysis, Opinion Mining, Machine Translation etc. The growth of social media platforms such as twitter, facebook for communication between people has led to the creation of huge user generated data different from the normal text data. This is leading to the development of a new challenge and perspective in language technology research. Thus there is great need to develop applications such as Anaphora resolution, co-reference resolution which can be used for the development of NLU systems. This shared task is on Anaphora resolution from the microblog text from Twitter for languages such as Hindi, Tamil and Malayalam (Indian Languages). Also we gave data from English which can be used as resource rich language, if one wants to take Indian languages as resources poor language. There were six registered groups who took data for development and testing but only one group submitted the run. They have used Deep learning for analysis.
Comparative Analysis of Urdu Parts Of Speech Taggers using Machine Learning Techniques

In modern age technology-based solution can help to maximize consistency of process in bounded discretion-sentencing regimes. Part of Speech Tagging is an exceptionally natural in applications. For example, speech processing, text mining, data extraction and data mining techniques. The language handling of Urdu is a difficult task due to its morphosynthetic uncertainty. The individuals portray, the improvement of a novel tagging approach, utilizing a Conditional Random Field (CRF). In our work we have introduced the Conditional Random Field (CRFs) approach for Urdu Parts of Speech Tagging Model shows a valid accuracy and adjusted language free as a reliant list of capabilities. Our methodology was compared against Recurrent Neural Network Techniques, KNN, Support Vector Machine, Naive Bayes and CART to show that Conditional Random Fields (CRFs) has better accuracy than the other machine learning classifiers.
Software Defect Classification Approach Based on the Modified Latent Dirichlet Allocation Topic Model Considering the Domain Characters

The existing defect classification approaches do not consider the domain characters of software defects, such as aeronautics domain, astronautics domain and so on. Therefore, the precision rate and the recall rate of these defect classification approaches are not very accurate in many conditions. To resolve this problem we present a new defect classification approach based on the modified Latent Dirichlet Allocation (LDA for short) topic model combining with domain characters to improve the performance of the defect classification. First, we propose the defect segmentation approach based on the special domain characters. Then, we propose a modified LDA topic model combining with software requirements. Based on the proposed modified LDA model, we obtain a new defect classification approach. Finally, the experiment result shows that the precision rate and the recall rate of the defect classification are au improved up to 15%~20% compared with the existing classification models. Thus, we consider that this new classification approach is very suitable for classifying the defects with obvious domain characters.
Performance evaluation of sensor information fusion system based on cloud theory and fuzzy pattern recognition

The sensor information fusion system was composed of complex factors, and there was no unified quantitative standard for each factor. During the system evaluation, the existing evaluation method cannot be applied to all influencing factors, and the result of the evaluation cannot represent the true performance of the system. This paper proposed an evaluation method combining cloud theory and fuzzy pattern recognition model. Based on the cloud theory, the reasonable conversion of qualitative indicators was realized, and the uncertainty of attribute factors was evaluated indefinitely. Combined with the fuzzy pattern recognition model, an evaluation model was established for quantitative indicators, and it was comprehensively processed by fuzzy evaluation method. Evaluation examples show that the method can more accurately and objectively evaluate the performance of the system.
Relationships between emoticon usage and recipient groups in students' text messages

Emoticons are pictographic representations of facial expressions that are used to convey emotions in text messages and other similar methods of communication. Most research on emoticons has examined how they are used in public forums rather than in private messaging. Using a sample of undergraduate students (n=106; male 52.83%; mean age 20.26 years, SD 1.93), this study examines the use of emoticons in private text communication. Results reveal that emoticon usage is highest amongst friends, followed by siblings, then parents, other family members and more distant connections. Emoticons representing positive emotions are more commonly used than those representing strong negative emotions. Emoticons representing relief were found to be used particularly within peer group communication, whereas emoticons representing contentment were used more with family members and other, more distant, connections. The use of the "Relieved" emoticon with peers may reflect overcoming the stressors associated with shared educational challenges, whereas using the "Content" emoticon outside peers and family may represent emotional modulation and presentation.
Visual Style Extraction from Chart Images for Chart Restyling

Creating a good looking chart for better visualization is time consuming. There are plenty of well-designed charts on the Web, which are ideal references for imitation of chart style. However, stored as bitmap images, reference charts have hinder machine interpretation of style settings and thus difficult to be directly applied. In this paper, we extract visual properties from reference chart images as style templates to restyle charts. We first construct a large-scale dataset of 187,059 chart images from real world data, labeled with predefined visual property values. Then we introduce an end-to-end learning network to extract the properties based on two image-encoding approaches. Furthermore, in order to capture spatial relationships of chart objects, which are crucial in solving the task, we propose a novel positional encoding method to integrate clues of relative positions between objects. Experimental results show that our model significantly outperforms baseline models. By adding positional features, our model achieves better performance. Finally, we present the application for chart restyling based on our model.
Image encryption algorithm based on Logistic-Sine compound chaos

Aiming at the problems of uneven distribution of chaotic behavior, insufficient key space, and small amount of key change in a single chaotic system, a compound chaotic system (Logistic-Sine) composed of a single Logistic map and a Sine map is used to perform confusion and diffusion processing on the plain image. The confusion operation and the diffusion operation are respectively realized by the combination of the add modulus operation, XOR operation and the add modulus operation, so as to improve the security of encryption. The simulation results show that the chaotic behavior of the encryption algorithm of the compound chaotic system is more densely distributed, and the key space and the key change are larger. This method has the advantages of high security, strong resistance to attack, and fast encryption speed.