Reviewerly/exHarmony huggingface dataset:
Stopword Identification and Removal Techniques on TC and IR applications: A Survey

The concept of “Stopword” was first introduced by H.P. Luhn in 1958. In Natural Language Processing (NLP), Stop word is a common word that is neither indexed nor searchable in a computer search engine. Example of stop words are `a', `the', `is' etc. Removing stopword is Pre-processing step in majority of NLP applications, including IR (Information Retrieval) and TC (Text Classification). Some of the benefits of removing stop word are - decrease in size of corpus by 35-45%, improvement of efficiency and accuracy of the text mining applications thus helping in reduction of time and space complexity of overall application. In this paper, we discuss the various major stopword identification techniques used by the researchers in last few decades, for Indian Language and Non-Indian Languages. Also, we present a survey of methods used for stopword list generation with their characteristics. We have also mentioned the effect of various stopword removal techniques applied on TC and IR application domains. A comprehensive list of resources publicly available for static stop words in various languages is also given for quick reference.
Theory of adaptive SVD regularization for deep neural networks

Deep networks can learn complex problems, however, they suffer from overfitting. To solve this problem, regularization methods have been proposed that are not adaptable to the dynamic changes in the training process. With a different approach, this paper presents a regularization method based on the Singular Value Decomposition (SVD) that adjusts the learning model adaptively. To this end, the overfitting can be evaluated by condition numbers of the synaptic matrices. When the overfitting is high, the matrices are substituted with their SVD approximations. Some theoretical results are derived to show the performance of this regularization method. It is proved that SVD approximation cannot solve overfitting after several iterations. Thus, a new Tikhonov term is added to the loss function to converge the synaptic weights to the SVD approximation of the best-found results. Following this approach, an Adaptive SVD Regularization (ASR) is proposed to adjust the learning model with respect to the dynamic training characteristics. ASR results are visualized to show how ASR overcomes overfitting. The different configurations of Convolutional Neural Networks (CNN) are implemented with different augmentation schemes to compare ASR with state-of-the-art regularization methods. The results show that on MNIST, F-MNIST, SVHN, CIFAR-10 and CIFAR-100, the accuracies of ASR are 99.4%, 95.7%, 97.1%, 93.2% and 55.6%, respectively. Although ASR improves the overfitting and validation loss, its elapsed time is not significantly greater than the learning without regularization.
On the application of Augmented Reality in a learning factory working environment

The manufacturing industry is currently facing numerous challenges such as shortening innovation cycles, the demographic challenge and cost pressure. Augmented Reality (AR) offers tremendous potential for enhancing manufacturing processes in terms of productivity, education and safety and is therefore being intensively researched by scientists at present. One of the advantages is that it facilitates support for employees and optimizes the working environment. Thus, operators can exploit their innovation potential and creativity. The aim of this paper is to discuss the potentials of AR to face current challenges in production, show state-of-the art applications and present an own developed AR application to support the employee with their work tasks. With the application introduced, employees are provided with real-time data from their machinery, which is displayed in their field of vision. In addition, the application enables the processing of more complex and non-repetitive tasks that would otherwise require additional know-how or personnel. By using the application, instructions for maintenance work are embedded in the environment, allowing the operator to focus on his or her tasks. In the event of a problem, the operator can share the environment so an expert can perform remote assistance in real-time. In order to increase the acceptance of AR technologies, the application has been integrated into the LEAD factory at Graz University of Technology and is actively used in training courses. In this paper this AR application is tested and the test persons are asked with a questionnaire regarding their experience.
Evaluating Web and Mobile User Interfaces With Semiotics: An Empirical Study

Computer interface signs, such as navigational links, thumbnails, small images, command buttons, symbols, icons, etc., which serve as communication artifacts between designers/systems and users, constitute an under-researched area. To design and evaluate intuitive interface signs, the Semiotic Interface Sign Design and Evaluation (SIDE) framework was developed. The aim of this study is to test the framework's applicability to evaluate web and mobile user interfaces. To that end, two empirical user studies were conducted among a total of 86 practitioners (n1 = 58, n2 = 28). The results show that the SIDE framework helps identify unique usability problems, such as the intuitiveness of interface signs in terms of their referential meaning, which would not have been detected with traditional heuristic evaluation methods. The paper increases our understanding of the intuitive nature of interface signs of web and mobile interfaces, and of the practical use of intuitive signs.
Performance Evaluation of Caps-Net Based Multitask Learning Architecture for Text Classification

The classification of the text involving the process of identification and categorization of text is a tedious and a challenging task too. The Capsules Network (Caps-Net) which is a unique architecture with the capability to confiscate the basic attributes comprising the insights of the particular field that could help in bridging the knowledge gap existing between the source and the destination tasks and capability learn more robust representation than the CNN-Convolutional neural networks in the image classification domain is utilized in the paper to classify the text. As the multi –task learning capability enables to part insights between the tasks that are related and enhances data used in training indirectly, the Caps-Net based multi task learning frame work is proposed in the paper. The proposed architecture including the Caps-Net effectively classifies the text and minimizes the interference experienced among the multiple tasks in the multi –task learning. The architecture put forward is evaluated using various text classification dataset ensuring the efficacy of the proffered frame work
Quaternion-Based Bilinear Factor Matrix Norm Minimization for Color Image Inpainting

As a new color image representation tool, quaternion has achieved excellent results in the color image processing, because it treats the color image as a whole rather than as a separate color space component, thus it can make full use of the high correlation among RGB channels. Recently, low-rank quaternion matrix completion (LRQMC) methods have proven very useful for color image inpainting. In this paper, we propose three novel LRQMC methods based on three quaternion-based bilinear factor (QBF) matrix norm minimization models. Specifically, we define quaternion double Frobenius norm (Q-DFN), quaternion double nuclear norm (Q-DNN) and quaternion Frobenius/nuclear norm (Q-FNN), and then show their relationship with quaternion-based matrix Schatten-p (Q- Schatten-p ) norm for certain p values. The proposed methods can avoid computing quaternion singular value decompositions (QSVD) for large quaternion matrices, and thus can effectively reduce the calculation time compared with existing (LRQMC) methods. The experimental results demonstrate the superior performance of the proposed methods over some state-of-the-art low-rank (quaternion) matrix completion methods.
Adversarial Learning for Personalized Tag Recommendation

We have recently seen great progress in image classification due to the success of deep convolutional neural networks and the availability of large-scale datasets. Most of the existing work focuses on single-label image classification. However, there are usually multiple tags associated with an image. The existing works on multi-label classification are mainly based on lab curated labels. Humans assign tags to their images differently, which is mainly based on their interests and personal tagging behavior. In this paper, we address the problem of personalized tag recommendation and propose an end-to-end deep network which can be trained on large-scale datasets. The user-preference is learned within the network in an unsupervised way where the network performs joint optimization for user-preference and visual encoding. A joint training of user-preference and visual encoding allows the network to efficiently integrate the visual preference with tagging behavior for a better user recommendation. In addition, we propose the use of adversarial learning, which enforces the network to predict tags resembling user-generated tags. We demonstrate the effectiveness of the proposed model on two different large-scale and publicly available datasets, YFCC100 M and NUS-WIDE. The proposed method achieves significantly better performance on both the datasets when compared to the baselines and other state-of-the-art methods. The code is publicly available at https://github.com/vyzuer/ALTReco.
Friet: An Authenticated Encryption Scheme with Built-in Fault Detection

In this work we present a duplex-based authenticated encryption scheme $$\textsc {Friet}$$ based on a new permutation called $$\textsc {Friet-P}$$. We designed $$\textsc {Friet-P}$$ with a novel approach for cryptographic permutations and block ciphers that takes fault-attack resistance into account and that we introduce in this paper. In this method, we build a permutation $${f_C}$$ to be embedded in a larger one, $$f$$. First, we define $$f$$ as a sequence of steps that all abide a chosen error-correcting code $$C$$, i.e., that map $$C$$-codewords to $$C$$-codewords. Then, we embed $${f_C}$$ in $$f$$ by first encoding its input to an element of C, applying $$f$$ and then decoding back from C. This last step detects a fault when the output of $$f$$ is not in $$C$$. We motivate the design of the permutation we use in $$\textsc {Friet}$$ and report on performance in soft- and hardware. We evaluate the fault-detection capabilities of the software and simulated hardware implementations with attacks. Finally, we perform a leakage evaluation. Our code is available at https://github.com/thisimon/Friet.git .
Attacking the Quantum Internet

The main service provided by the coming Quantum Internet will be creating entanglement between any two quantum nodes. We discuss and classify attacks on quantum repeaters, which will serve roles similar to those of classical Internet routers. We have modeled the components for and structure of quantum repeater network nodes. With this model, we point out attack vectors, then analyze attacks in terms of confidentiality, integrity and availability. While we are reassured about the promises of quantum networks from the confidentiality point of view, integrity and availability present new vulnerabilities not present in classical networks and require care to handle properly. We observe that the requirements on the classical computing/networking elements affect the systems' overall security risks. This component-based analysis establishes a framework for further investigation of network-wide vulnerabilities.
Understanding GANs in the LQG Setting: Formulation, Generalization and Stability

Generative Adversarial Networks (GANs) have become a popular method to learn a probability model from data. In this paper, we provide an understanding of basic issues surrounding GANs including their formulation, generalization and stability on a simple LQG benchmark where the generator is Linear, the discriminator is Quadratic and the data has a high-dimensional Gaussian distribution. Even in this simple benchmark, the GAN problem has not been well-understood as we observe that existing state-of-the-art GAN architectures may fail to learn a proper generative distribution owing to (1) stability issues (i.e., convergence to bad local solutions or not converging at all), (2) approximation issues (i.e., having improper global GAN optimizers caused by inappropriate GAN's loss functions), and (3) generalizability issues (i.e., requiring large number of samples for training). In this setup, we propose a GAN architecture which recovers the maximum-likelihood solution and demonstrates fast generalization. Moreover, we analyze global stability of different computational approaches for the proposed GAN and highlight their pros and cons. Finally, through experiments on MNIST and CIFAR-10 datasets, we outline extensions of our model-based approach to design GANs in more complex setups than the considered Gaussian benchmark.
On an optimal analogy-based software effort estimation

Context: An analogy-based software effort estimation technique estimates the required effort for a new software project based on the total effort used in completing past similar projects. In practice, offering high accuracy can be difficult for the technique when the new software project is not similar to any completed projects. In this case, the accuracy will rely heavily on a process called effort adaptation, where the level of difference between the new project and its most similar past projects is quantified and transformed to the difference in the effort. In the past, attempts to adapt to the effort used machine learning algorithms; however, no algorithm was able to offer a significantly higher performance. On the contrary, only a simple heuristic such as scaling the effort by consulting the difference in software size was adopted. Objective:More recently, million-dollar prize data-science competitions have fostered the rapid development of more powerful machine learning algorithms, such as the Gradient boosting machine and Deep learning algorithm. Therefore, this study revisits the comparison of software effort adaptors that are based on heuristics and machine learning algorithms. Method:A systematic comparison of software effort estimators, which they all were fully optimized by Bayesian optimization technique, was carried out on 13 standard benchmark datasets. The comparison was supported by robust performance metrics and robust statistical test methods. Conclusion:The results suggest a novel strategy to construct a more accurate analogy-based estimator by adopting a combined effort adaptor. In particular, the analogy-based model that adapts to the effort by integrating the Gradient boosting machine algorithm and a traditional adaptation technique based on productivity adjustment has performed the best in the study. Particularly, this model significantly outperformed various state-of-the-art effort estimation techniques, including a current standard benchmark algorithmic-based technique, analogy-based techniques, and machine learning-based techniques.
Multi-PRF and multi-frame track-before-detect algorithm in multiple PRF radar system

This paper considers the detection and tracking of dim targets for multiple pulse repetition frequency (PRF) pulsed Doppler radar systems through the use of multi-frame track-before-detect (TBD) methods. The main challenge is that each measurement frame is a mixture with measurements recorded, periodically, at different PRF settings, thus with different range and Doppler ambiguities. To achieve this goal, we propose variable step size multi-PRF and multi-frame (or scan) TBD (MP-MF-TBD) algorithm. Specifically, by incorporating the ambiguity factors related to both the range and velocity ambiguities, MP-MF-TBD first accomplishes the target SNR integration directly using multiple ambiguous measurement frames. After analyzing the properties of the over-threshold tracks, we further propose an efficient detection procedure, which can effectively eliminate the false tracks caused by the measurement ambiguities and accomplish the target kinematic state estimation and multi-PRF ambiguity resolution simultaneously. The proposed MP-MF-TBD does not require certain limitations on the PRF type (i.e., low, medium and high PRF) or the number of PRFs in pulsed Doppler radar systems, and appears to be robust to the target loss when target fall into a blind frequency band of certain PRFs. Extensive numerical simulations demonstrated the efficacy of the proposed MP-MF-TBD by comparing with classical techniques.
LIGHTER-R: Optimized Reversible Circuit Implementation For SBoxes

This article presents the `LIGHTER-R' tool that aims at implementing a given 4-bit SBox using reversible logic libraries. An SBox is a basic building block in the symmetric key cryptography. In order to analyze the security of the symmetric key algorithms in the futuristic reversible computing paradigm, one needs to implement those algorithms in the reversible manner. Our tool offers an end-to-end flow for implementing a given 4-bit SBox (which is the common choice in recent designs) in reversible circuits while optimizing the cost for a given cost metric. It extends the tool LIGHTER, which is developed for classical computing. LIGHTER-R enjoys the advantages of LIGHTER; such as, easy to use and free.
MAG-GAN: Massive attack generator via GAN

Adversarial attacks reveal the vulnerability of deep neural networks (DNNs). These attacks fool DNNs by adding small perturbations to normal examples. Currently, most attacks involve generating a single example or target a single deep model. To gain insight into adversarial attacks and develop a robust defense, this study focuses on a generic attack model applicable to most adversarial attacks. A novel mass-generator of adversarial examples with a strong attack ability and involving small perturbations is presented herein. The main contributions of this work include proposing a generic framework for adversarial attacks, designing comprehensive evaluation metrics for adversarial examples, and developing a novel method for mass-generating adversarial examples via a generative adversarial network (MAG-GAN). Finally, experiments were conducted to demonstrate the good performance of MAG-GAN compared with state-of-the-art attack methods. Once the model was trained, adversarial examples were mass-generated with a small perturbation and a strong attack ability. Furthermore, it was found that MAG-GAN model can be adopted as an efficient tool to reveal the vulnerability and improve the defense ability of existing DNNs. A promising result is that the target model mounted in MAG-GAN exhibited a good defense performance after game training, which is equivalent to adversarial training.
Query Focused Abstractive Summarization via Incorporating Query Relevance and Transfer Learning with Transformer Models

In the Query Focused Abstractive Summarization (QFAS) task, the goal is to generate abstractive summaries from the source document that are relevant to the given query. In this paper, we propose a new transfer learning technique by utilizing the pre-trained transformer architecture for the QFAS task in the Debatepedia dataset. We find that the Diversity Driven Attention model (DDA), which was the first model applied on this dataset, only performs well when the dataset is augmented by creating more training instances. In contrast, without requiring any in-domain data augmentation, our proposed approach outperforms the DDA model as well as sets a new state-of-the-art result.
Credit Card Fraud Detection Techniques – A Survey

The rapid growth of e-commerce and internet lead to a wide increase in the credit card use. Due to increase in the usage of credit cards there is vast increase in the credit card frauds in the recent years. Artificial neural network considers effectiveness of neural networks in the detecting credit card frauds. This system trains the neural network based on the data of the customer from the past. Training artificial neural network is difficult because every time the activity is different in each of the transaction. So, we can conFigure an artificial neural network with a genetic algorithm for this task. We create a combination of simulated annealing and genetic algorithm to get better results. Genetic algorithm uses a biological way to find the fittest solution from various solutions. Simulated annealing has effective algorithm which on combining with Genetic algorithm proves efficient in the detecting credit card frauds. Simulated annealing after processing credit card details of owner passes through ANN network for detection.
Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?

In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during the period 1 January 2012 to 20 August 2019. Our findings suggest that, overall, trading on daily data is more profitable than going intraday. Furthermore, we concluded that the Buy and Hold strategy outperforms the examined alternatives on an intraday basis, while Simple Moving Averages yield the best performances when dealing with daily data.
Implementing Relevance Feedback for Content-Based Medical Image Retrieval

Content-based image medical retrieval (CBMIR) is a technique for retrieving medical images on the basis of automatically derived image features such as colour, texture and shape.There are many applications of CBMIR, such as teaching, research, diagnosis and electronic patient records.The retrieval performance of a CBMIR system depends mainly on the representation of image features, which researchers have studied extensively for decades.Although a number of methods and approaches have been suggested, it remains one of the most challenging problems in current (CBMIR) studies, largely due to the well-known ''semantic gap'' issue that exists between machine-captured low-level image features and human-perceived high-level semantic concepts.There have been many techniques proposed to bridge this gap.This study proposes a novel relevance feedback retrieval method (RFRM) for CBMIR.The feedback implemented here is based on voting values performed by each class in the image repository.Here, eighteen using colour moments and GLCM texture features were extracted to represent each image and eight common similarity coefficients were used as similarity measures.After briefly researching using a single random image query, the top images retrieved from each class are used as voters to select the most effective similarity coefficient that will be used for the final searching process.Our proposed method is implemented on the Kvasir dataset, which has 4,000 images divided into eight classes and was recently widely used for gastrointestinal disease detection.Intensive statistical analysis of the results shows that our proposed RFRM method has the best performance for enhancing both recall and precision when it uses any group of similarity coefficients.