Reviewerly/exHarmony huggingface dataset:
Data Augment in Imbalanced Learning Based on Generative Adversarial Networks

Imbalanced learning is a traditional problem in machine learning and widely occurs in many applications. Most of the methods apply simple geometric transformation for data augment to imbalanced datasets. Due to those methods learn from local information, they might generate noisy samples in the dataset with high dimension and special complexity. To solve the problem, we propose an improved Generative Adversarial Networks with modification function (GAN-MF) to approximate the true distribution of the minority class of the dataset. The model could generate data from an overall perspective to overcome the limitation of the simple geometric transformation. The performance of GAN-MF is compared against multiple standard oversampling algorithms on several imbalanced learning tasks. Experiments demonstrate that the model has an improvement in data augment for imbalanced learning.
Implementing Virtual Machine: A Performance Evaluation

A hypervisor is a hardware virtualization technique that allows multiple guest operating systems to run on a single host machine at the same time. Each Virtual Machine (VM) or known as guest operating system emulates all interfaces and resources of a real computer system. Virtualization is beneficial as one of the educational tools to facilitate students’ hands-on experiences and research activities. However, the performance of VM needs to be taken into consideration. We investigate the performance of a set of VMs using Oracle VirtualBox on several host machines, each of which has its own system specifications. We observe the resource utilization of each host machine in terms of its CPU utilization, CPU speed as well as memory usage. Experimental results show that the CPU utilization averages are 51.78%, 60.7% and 62.57% for cases before memory allocation, 1/2 of memory capacity and 2/3 of memory capacity, respectively. It is indicate that the utilization of a host processor is directly proportional to the memory capacity assigned for a virtual machine.
Deinterleaving Radar Pulse Train Using Neural Networks

Deinterleaving of pulse sequences of radar signals is an essential task for the identification of radars by modern digital receiver. A continuous wavelet transform is used, with an appropriate mother and a specific resolution factor, determining the PRI values of the radar pulse sequences using thresholding. This time-frequency analysis is used for separating the spectral patterns of interest.
Classification of Microarray Gene Expression Data using Weighted Grey Wolf Optimizer based Fuzzy Clustering

With the emergence of DNA microarray technology, scientists are continuously studying the expression levels of a large number of genes over different instances of time points. Analyzing the DNA microarray data confirm that the expression levels of two different genes vary simultaneously with the effect of external stimuli exhibiting different patterns. Therefore soft fuzzy clustering plays an important role in detecting patterns belonging to multiple clusters at the same time. Therefore this article proposes a novel fuzzy clustering technique utilizing weighted distance measure instead of the Euclidean distance using Grey Wolf Optimizer (GWO) as the global optimization techniques. Here clustering of microarray data is posed as a single objective optimization problem where the objective is to minimize the variability within the cluster and simultaneously maximizes the variability between the cluster. The newly proposed fuzzy-based weighted GWO clustering technique (Fuzzy-WDGWO) is then compared with some of the existing clustering techniques. Four different artificial datasets and three different real-life gene expression datasets have been considered to verify the efficiency of the proposed Fuzzy-WDGWO clustering technique both numerically as well as pictorially. Experimental analysis and performance evaluation of the proposed Fuzzy-WDGWO clustering technique show the superiority over the other existing clustering techniques such as PSO-FCM, FA-FCM, GWO-FCM, GWO-FCM, DE-FCM.
Unsupervised methods for Software Defect Prediction

Software Defect Prediction (SDP) aims to assess software quality by using machine learning techniques. Recently, by proposing the connectivity-based unsupervised learning method, Zhang et al. have been proven that unsupervised classification has great potential to apply to this problem. Inspiring by this idea, in our work we try to replicate the results of Zhang et al.'s experiment and attempt to improve the performance by examining different techniques at each step of the approach using unsupervised learning methods to solve the SDP problem. Specifically, we try to follow the steps of the experiment described in their work strictly and examine three other clustering methods with four other ways for feature selection besides using all. To the best of our knowledge, these methods are first applied in SDP to evaluate their predictive power. For replicating the results, generally results in our experiments are not as good as the previous work. It may be due to we do not know which features are used in their experiment exactly. Fluid clustering and spectral clustering give better results than Newman clustering and CNM clustering in our experiments. Additionally, the experiments also show that using Kernel Principal Component Analysis (KPCA) or Non-Negative Matrix Factorization (NMF) for feature selection step gives better performance than using all features in the case of unlabeled data. Lastly, to make replicating our work easy, a lightweight framework is created and released on Github.
Non-congruent non-degenerate curves with identical signatures

While the equality of differential signatures (Calabi et al, Int. J. Comput. Vis. 26: 107-135, 1998) is known to be a necessary condition for congruence, it is not sufficient (Musso and Nicolodi, J. Math Imaging Vis. 35: 68-85, 2009). Hickman (J. Math Imaging Vis. 43: 206-213, 2012, Theorem 2) claimed that for non-degenerate planar curves, equality of Euclidean signatures implies congruence. We prove that while Hickman's claim holds for simple, closed curves with simple signatures, it fails for curves with non-simple signatures. In the later case, we associate a directed graph with the signature and show how various paths along the graph give rise to a family of non-congruent, non-degenerate curves with identical signatures. Using this additional structure, we formulate congruence criteria for non-degenerate, closed, simple curves and show how the paths reflect the global and local symmetries of the corresponding curve.
Suggesting Questions that Match Each User’s Expertise in Community Question and Answering Services

Stack Overflow has been already recognized as an indispensable CQA (Community Question and Answering) service for developers. However developers as questioners sometimes cannot get any answers or they only can get incomplete answers to resolve their questions, since over several thousands of questions and answers are posted on a daily basis and expert developers are currently facing with a difficulty in finding questions which can be better or best answered by them. In order address the issue, our study aims at developing a bot that helps expert developers find questions matching each developer's expertise. The existing study [8] proposed a promising approach to the expert recommendation for CQA services, based on PMF (Probabilistic Matrix Factorization) algorithm. In order to improve the accuracy of the expert recommendation, in this paper we present our approach which combines the PMF (Probabilistic Matrix Factorization) approach and a term expansion techniques using word embeddings. As a result of an experiment comparing our approach with the existing approach only based on PMF, we show that our approach based on PMF and the term expansion with Word2vec and fastText slightly outperforms the existing approach only based on PMF.
Higher Education in Digital Age

The article presents a theoretical understanding of the content of the stages of university development and its transformations depending on the development of society. The paper describes four university models that were specific for historical stages (University 1.0, University 2.0, University 3.0, University 4.0.). Particular attention is paid to identifying features of the University 4.0., a promising model of universities that combines physical and virtual space, developing on digital platforms. The authors conclude that at the present stage of development of society there are obvious trends in the development of universities and higher education, including a change in the status of universities due to increased risks in science and experimentation, the transition from competition to partnership interaction, access to large databases (Big Data), the transition to multi-format open educational resources (Open Online Resources), a combination of new and traditional training formats, redevelopment of classrooms in the format of open space (Open Space Education). Obviously, such technologies as mobile training (Bring Your Own Devices), transition to the “inverted classes” (Flipped Classroom), the creation of “wearable” technologies like Google Glass, the development of adaptive learning through the introduction of digital platforms and the spread of the Internet of things will be widespread in the nearest future.
Semi-Supervised Eye Makeup Transfer by Swapping Learned Representation

This paper introduces an autoencoder structure to transfer the eye makeup from an arbitrary reference image to a source image realistically and faithfully using both synthetic paired data and unpaired data in a semi-supervised way. Different from the image domain transfer problem, our framework only needs one domain entity and follows an "encoding-swap-decoding" process. Makeup transfer is achieved by decoding the base representation from a source image and makeup representation from a reference image. Moreover, our method allows users to control the makeup degree by tuning makeup weight. To the best of our knowledge, there is no public large makeup dataset to evaluate data-driven approaches. We have collected a dataset of non-makeup images and with-makeup images of various eye makeup styles. Experiments demonstrate the effectiveness of our method with the state-of-the-art methods both qualitatively and quantitatively.
Portable solar panel efficiency measurement system

Solar panel plays a major role in the renewable energy sector. Unfortunately substandard and unspecified solar panels are seen now a days in the market of many countries. Furthermore, solar panel testing equipment is not available everywhere and is a costly device. So, to fulfill customer rights and to know the efficiency of the solar panels, a simple portable solar panel efficiency measurement system has been designed and developed. The system includes a single Ardunio Uno, a voltage sensor, a current sensor (ACS712), a multi meter and a lux meter. The measurement has been taken under real sun conditions. The system determines maximum voltage (Vmax), maximum current (Imax), maximum power (Pm), fill factor (FF) and efficiency (η) of the solar panel. The system reveals that the efficiency of a 10 W, 8 W and 0.65 W silicon solar panel are 9.65%, 8.59% and 8.4% respectively.
Effect of pulley primary angle variation and roller 11 grams on 110 cc Scoopy injection engine

Abstract Transmission automatic or known as (CVT) is a transmission that makes us feel comfortable because we only need to pull the gas without moving the transmission because the transmission is automatic. Automatic motors not only make driving easier but also facilitate the maintenance of the transmission. However, among racing motorbike lovers, this automatic transmission type motor is lacking in power. So, it needs to be modified to the CVT component to improve the performance of the machine. To improve this performance, we analyze pulley and standard roller manufacturers and primary pulleys with 3 kinds of pulley, namely standard angles of 14°, 13°, 12° and roller 11 gram variations to determine the comparison of Performa (horse power and torque). To find out the comparison of the test, we do the test using the Dyno test. From the test results, 13° pulley and 11 gram roller have increased significantly compared to standard 14° and 13 gram roller standard pulleys. This can be seen from the results of an increase in horse power and torque when using pulley 13° and 11 grams roller can be obtained on the highest horse power (Hp) at 3246 rpm, which is the highest 9.0 hp and torque (Nm) at the engine speed 2087 rpm which is 22.16 Nm. While the standard pulley of the factory is 14° and the standard 13 grams roller can be seen the highest horse power (Hp) at 3923 rpm round, which is 7.8 Hp and the highest Torque (Nm) at 2972 rpm engine speed which is 16.99 Nm. then from the results of this test analysis has increased horse power 1.2 hp and torque of 5.17 Nm.
Topology-Aware Hashing for Effective Control Flow Graph Similarity Analysis

Control Flow Graph (CFG) similarity analysis is an essential technique for a variety of security analysis tasks, including malware detection and malware clustering. Even though various algorithms have been developed, existing CFG similarity analysis methods still suffer from limited efficiency, accuracy, and usability. In this paper, we propose a novel fuzzy hashing scheme called topology-aware hashing (TAH) for effective and efficient CFG similarity analysis. Given the CFGs constructed from program binaries, we extract blended n-gram graphical features of the CFGs, encode the graphical features into numeric vectors (called graph signatures), and then measure the graph similarity by comparing the graph signatures. We further employ a fuzzy hashing technique to convert the numeric graph signatures into smaller fixed-size fuzzy hash signatures for efficient similarity calculation. Our comprehensive evaluation demonstrates that TAH is more effective and efficient compared to existing CFG comparison techniques. To demonstrate the applicability of TAH to real-world security analysis tasks, we develop a binary similarity analysis tool based on TAH, and show that it outperforms existing similarity analysis tools while conducting malware clustering.
The Impact of Pre-processing and Feature Selection on Text Classification

Nowadays text classification is dealing with unstructured and high-dimensionality text document. These textual data can be easily retrieved from social media platforms. However, this textual data is hard to manage and process for classification purposes. Pre-processing activities and feature selection are two methods to process the text documents. Therefore, this paper is presented to evaluate the effect of pre-processing and feature selection on the text classification performance. A tweet dataset is utilized and pre-processed using several combinations of pre-processing activities (tokenization, removing stop-words and stemming). Later, two feature selection techniques (Bag-of-Words and Term Frequency-Inverse Document Frequency) are applied on the pre-processed text. Finally, Support Vector Machine classifier is used to test the classification performances. The experimental results reveal that the combination of pre-processing technique and TF-IDF approach achieved greater classification performances compared to BoW approach. Better classification performances hit when the number of features is decreased. However, it is depending on the number of features obtained from the pre-processing activities and feature selection technique chosen.
Module generation without regret

Modules are an indispensable mechanism for providing abstraction to programming languages. To reduce the abstraction overhead in the usage of modules, Watanabe et al. proposed a language for generating and manipulating code of modules, and implemented it via a translation to plain MetaOCaml. Unfortunately, their solution has a serious problem of code explosion if functors are repeatedly applied to modules. Another problem in their solution is that it does not allow nested modules.
The lexical and grammatical sources of neg-raising inferences

We investigate neg(ation)-raising inferences, wherein negation on a predicate can be interpreted as though in that predicate's subordinate clause. To do this, we collect a large-scale dataset of neg-raising judgments for effectively all English clause-embedding verbs and develop a model to jointly induce the semantic types of verbs and their subordinate clauses and the relationship of these types to neg-raising inferences. We find that some neg-raising inferences are attributable to properties of particular predicates, while others are attributable to subordinate clause structure.
Graph-Based Fuzzy Logic for Extractive Text Summarization (GFLES)

High accuracy models for automatic text summarization are increasingly required due to the overwhelming growth of available text on the internet. In this paper, a new unsupervised approach for Automatic Text Summarization (ATS) employing Fuzzy Logic (FL) to optimize summary generation is proposed. The approach is extractive, graph-based and is applicable for both single and multiple documents. Our Graph-based Fuzzy Logic Extractive Text Summarization (GFLES) approach consists of five main stages: text pre-processing, text representation using graph model, features extraction during graph construction, sentence clustering using our previously developed Graph-based Growing Self-Organizing Map (G-GSOM) and lastly, sentences are ranked using FL and a summary is generated. GFLES is distinctive from other approaches by its four advantages: 1) employment of the graph model for features extraction, 2) a better substitute of Vector Space Model (VSM), 3) appreciating the importance of sub-topics of text by taking them into account before generating the summary by clustering sentences and 4) applicable to both single and multiple documents. Moreover, the use of FL on each cluster at the same time provides a foundation for further development of GFLES to be applied on big data. Using the DUC 2004 dataset, our experimental results demonstrate that GFLES is able to generate comprehensive text summaries. These results establish the potential of GFLES as a high accuracy model for automatic text summarization.
Face recognition with dense supervision

Recent advances in face recognition mostly concentrate on designing more discriminative loss functions or adding normalization on features/weights to make a single feature more accurate. In this work, inspired by the frequently used multi-patch ensemble method for face recognition and part-based models for person re-identification, we propose a novel training strategy to enhance the discriminability of deeply learned feature from another perspective, namely learning with dense supervision. The main idea is to apply multiple classification losses on top of multiple component features extracted from a single network. Ideally, each component feature is expected to be accurate and have low correlation with the others. To this end, we first design a metric called feature consistency to evaluate the correlation between one component feature and the others, which is defined as the sum of distances between one component feature and the others, where the distance here is measured with KL divergence between corresponding softmax probabilities. Then we use feature consistency to select which component features to sample for one learning pass by importance sampling. The dense supervision significantly outperforms the single supervision baseline and even performs on par with its multi-patch ensemble counterpart which has much more parameters (9×). Our experimental results match state-of-the-art performance on LFW, YTF, MegaFace and surpass the others on LFW BLUFR and VGGFace2 pose protocol, thereby achieving state-of-the-art. Specially, results on VGGFace2 also show the superiority of the dense supervision on cross-pose face matching.
A Multiple Layer Security Scheme Utilizing Information Matrices

This paper proposes a double-layer message security scheme that is implemented in two stages. First, the secret data is encrypted using the AES algorithm with a 256-bit key. Second, least significant bit (LSB) embedding is carried out, by hiding the secret message into an image of an information matrix. A number of performance evaluation metrics are discussed and computed for the proposed scheme. The obtained results are compared to other schemes in literature and show the superiority of the proposed scheme.
Ore shoots in folded and fractured rocks – Insights from 3D modelling of the Fosterville gold deposit (Victoria, Australia)

At the Fosterville gold deposit, high concentrations of visible and refractory gold are hosted in arrays of quartz and quartz-carbonate veins. They are distributed in elongate zones to form south plunging ore shoots within a folded and fractured turbidite sequence. Here we distinguish the influence of faults versus folds at Fosterville, by assessing the deposit-scale structural framework and hydrothermal fluid flow using a combination of 3D implicit modelling, structural analysis and 3D fault slip/dilation tendency analyses. Gold assay and bedding trend data, from a large drillhole dataset, were spatially interpolated and used to analyse and measure the orientation of ore bodies and folded host rocks. Field measurements of fold axis orientations were compared to bedding measurements in oriented drillcore, Bingham axial analysis of bedding trends, and orientation data from a 3D implicit model. Both field and drill core structural data show that ore shoots at Fosterville do not parallel the first order, gently (≤10°) south-plunging folds. Instead the orientation of the ore shoots is coincident with an intersection lineation between steeply west-dipping bedding-parallel faults and moderately dipping linkage faults that obliquely cut across the main structural grain, resulting in ore shoot plunges larger than 20°. Slip and dilation tendencies suggest that linkage faults had a higher likelihood of activity in the inferred far-field stress regimes at the time of mineralisation, relative to other structures at Fosterville. These linkage faults are therefore interpreted to have been preferential fluid flux conduits in the system despite appearing to be subordinate structures at the scale of the deposit. This new structural model for Fosterville highlights that fluid flow and ore shoot formation are strongly controlled by obliquely oriented structures that are unlike the main structural grain of first order folds and faults.
Knowledge in action: logico-philosophical approach to linguistic evidentiality

Abstract The present study focuses on a grammatical category called evidentiality. The primary meaning of evidentiality is concerned with information source. That is, it expresses whether something has been seen, heard or inferred. The aim here is to conduct a conceptual study of evidentiality in which use is made of formal tools. The fundamental intuition is that the distinction between ‘evidence’as ‘proof’and ‘evidentiality’as ‘to do with proof’is a crucial one. Evidentiality is a dynamic notion to be analysed through the use of knowledge by the agents, a knowledge in action, which involves an in-coming state and an out-coming state that is typical of the transmission of information. We propose our own approach in which the dynamics of knowledge in action is grasped in the context of a dynamic epistemic logic.