Reviewerly/exHarmony huggingface dataset:
RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

Abstract All financial sectors are facing the most common frauds, which are digital transactional frauds. Fraudsters have always engaged in illegal activities such as stealing personal information and logging in with unauthorised credentials. Many machine learning algorithms predict whether the transaction is factual or nonfactual but fail to decrease the processing time. Hybrid models are used in this case to identify the fraud in a quick and efficient manner. This article demarcates to construct a novel model, RDQN, i.e., deep reinforcement learning, that combines with the rough set theory. This article has three steps, including data pre-processing to determine the quality of the data, which affects the learning ability of the model, determining the structural relationship and gaining useful features from the data set using rough set theory, and doing a hybridization of DNN (deep neural network) and Q learning, which is called DQN. It uses the MISH activation function and the ReLU activation function in different layers for training dynamics in the neural network. The proposed model classifies and predicts that the transaction belongs to the category implemented by the agents by activating the reward function. The reinforcement-learning agent’s performance improves based on reward assessment. This reward function gives a more precise value for each transaction, and no fraudster can escape from the agent’s sight. This novel approach improves accuracy and reduces processing time by considering the best feature selection during the process.
MVM-LBP : mean−variance−median based LBP for face recognition

This paper proposes a novel descriptor called Mean-Variance-Median based Local binary pattern (MVM-LBP). The Median binary pattern (MBP) calculates the difference between median of all the pixels in a 3 $$\times$$ 3 pixel window and respective eight neighbours. The main drawback of MBP is that two different pixel windows can have same MBP code which is not appropriate in face recognition systems. To improve the performance of the system, the proposed descriptor (MVM-LBP) involves two more statistics mean and variance and uses the mean, variance, and median values of all the pixels in a 3 $$\times$$ 3 pixel window to get a more robust feature vector. To check the excellence of the proposed descriptor, the proposed method is applied on two publicly available facial data sets AT&T, and faces94. The outcomes show that the proposed descriptor gives encouraging results.
Provocative Insinuations as Hate Speech: Argumentative Functions of Mentioning Ethnicity in Headlines

Abstract We explore a particular type of propagandistic message, which we call “provocative insinuation”. For example: ‘Iraqi refugee is convicted in Germany of raping and murdering teenage girl’. Although this sentence seems to merely report a fact, it also conveys a potentially hateful message about Iraqi refugees. We look at the argumentative roles that these utterances play in public discourse. Specifically, we argue that they implicitly address the question of the integration of refugees and migrants, and in fact aim to tilt the audience against these groups by strongly inviting hearers to make generalisations based on “striking” properties. We examine different strategies to counteract the conveyed hateful message.
Reconceiving Argument Schemes as Descriptive and Practically Normative

Abstract We propose a revised definition of “argument scheme” that focuses on describing argumentative performances and normative assessments that occur within an argumentative context, the social context in which the scheme arises. Our premise-and-conclusion structure identifies the typical instantiation of an argument in the argumentative context, and our critical framework describes a set of normative assessments available to participants in the context, what we call practically normative assessments. We distinguish this practical normativity from the rationally or universally normative assessment that might be imposed from outside the argumentative context. Thus, the practical norms represented in an argument scheme may still be subject to rational critique, and the scheme avoids the is/ought fallacy. We ground our theoretical discussion and observations in an empirical study of US district court opinions resolving legal questions about copyright fair use and the lawyers’ briefs that led to them, instantiating our definition of argument scheme in the “argument for classification by precedent.” Our definition addresses some criticisms the argument-scheme construct has received. For example, using our data, we show that a minimally well formed instance of this type of argument does not shift any conventional burden from the proponent of the argument to its skeptics. We also argue that these argument schemes need not be seen as dialogical.
Students feedback analysis model using deep learning-based method and linguistic knowledge for intelligent educational systems

Abstract Student feedback analysis is time-consuming and laborious work if it is handled manually. This study explores the use of a new deep learning-based method to design a more accurate automated system for analysing students’ feedback ( called DTLP: deep learning and teaching process ). The DTLP employs convolutional neural networks (CNNs), bidirectional LSTM (BiLSTM), and attention mechanism. To the best of our knowledge, a deep learning-based method using a unified feature set, which is representative of word embedding, sentiment knowledge, sentiment shifter rules, linguistic and statistical knowledge, has not been thoroughly studied with regard to sentiment analysis of student feedback. Furthermore, DTLP uses multiple strategies to overcome the following drawbacks: contextual polarity; sentence types; words with similar semantic context but opposite sentiment polarity; word coverage limit of an individual lexicon; and word sense variations . To evaluate the DTLP, we conducted an experiment on a large volume of students’ feedback. The results showed ( i ) DTLP outperforms the existing systems in the field, ( ii ) DTLP that learns from this unified feature set can acquire significantly higher performance than one that learns from a feature subset, ( iii ) the ensemble of sentiment shifter rules, word embedding, statistical, linguistic, and sentiment knowledge allows DTLP to obtain significant performance, and ( iv ) an attention mechanism into CNN-BiLSTM improves the performance of DTLP. In addition, the deployed method looks for potential causes behind student feedback.
Bicomplex Neural Networks with Hypergeometric Activation Functions

Abstract Bicomplex convolutional neural networks (BCCNN) are a natural extension of the quaternion convolutional neural networks for the bicomplex case. As it happens with the quaternionic case, BCCNN has the capability of learning and modelling external dependencies that exist between neighbour features of an input vector and internal latent dependencies within the feature. This property arises from the fact that, under certain circumstances, it is possible to deal with the bicomplex number in a component-wise way. In this paper, we present a BCCNN, and we apply it to a classification task involving the colourized version of the well-known dataset MNIST. Besides the novelty of considering bicomplex numbers, our CNN considers an activation function a Bessel-type function. As we see, our results present better results compared with the one where the classical ReLU activation function is considered.
Architecting decentralized control in large-scale self-adaptive systems

Abstract Architecting a self-adaptive system with decentralized control is challenging. Indeed, architects shall consider several different and interdependent design dimensions and devise multiple control loops to coordinate and timely perform the correct adaptations. To support this task, we propose Decor , a reasoning framework for architecting and evaluating decentralized control. Decor provides (i) multi-paradigm modeling support, (ii) a modeling environment for MAPE-K style decentralized control, and (iii) a co-simulation environment for simulating the decentralized control together with the managed system and estimating the quality attributes of interest. We apply the Decor in three case studies: an intelligent transportation system, a smart power grid, and a cloud computing application. The studies demonstrate the framework’s capabilities to support informed architectural decisions on decentralized control and adaptation strategies.
Diluted binary neural network

Binary neural networks (BNNs) are promising on resource-constrained devices because they reduce memory consumption and accelerate inference effectively. However, they are still potential on performance improvement. Prior studies attribute performance degradation of BNNs to limited representation ability and gradient mismatch. In this paper, we find that it also results from the mandatory representation of small full-precision auxiliary weights to large values. To tackle with this issue, we propose an approach dubbed as Diluted Binary Neural Network (DBNN). Besides avoiding mandatory representation effectively, the proposed DBNN also alleviates sign flip problem to a large extent. For activations, we jointly minimize quantization error and maximize information entropy to develop the binarization scheme. Compared with existing sparsity-binarization approaches, DBNN trains network from scratch without other procedures and achieves larger sparsity. Experiments on several datasets with various networks demonstrate the superiority of our approach.
JEMMA: An extensible Java dataset for ML4Code applications

Abstract Machine Learning for Source Code () is an active research field in which extensive experimentation is needed to discover how to best use source code’s richly structured information. With this in mind, we introduce : An Extensible Java Dataset for Applications, which is a large-scale, diverse, and high-quality dataset targeted at . Our goal with is to lower the barrier to entry in by providing the building blocks to experiment with source code models and tasks. comes with a considerable amount of pre-processed information such as metadata, representations (e.g., code tokens, ASTs, graphs), and several properties (e.g., metrics, static analysis results) for 50,000 Java projects from the dataset, with over 1.2 million classes and over 8 million methods. is also extensible allowing users to add new properties and representations to the dataset, and evaluate tasks on them. Thus, becomes a workbench that researchers can use to experiment with novel representations and tasks operating on source code. To demonstrate the utility of the dataset, we also report results from two empirical studies on our data, ultimately showing that significant work lies ahead in the design of context-aware source code models that can reason over a broader network of source code entities in a software project—the very task that is designed to help with.
A Water Cycle Algorithm for Optimal Design of IIR Filters

In this work, a water cycle algorithm (WCA) is applied for the optimal design of infinite impulse response (IIR) low pass and high pass filter. The WCA is based on evaporation and raining processes, which help to sustain equilibrium among diversification and intensification ability of the algorithm. In order to ensure the stability of the IIR filter, a lattice equivalent approach has been used. For experimentation, 8th order low pass and high pass IIR filters have been undertaken. For low pass IIR filters, the objective function value obtained by WCA is 0.9966, and passband error, stopband error, and squared error values are very less and in the order of E-05. Further, it is found that WCA is able to achieve maximum stopband attenuation, i.e., 28.4681 dB. The obtained results are compared with that of the other state-of-the-art techniques and conclude that WCA is capable of finding the best solution.
A Study of Machine Translation Models for Kannada-Tulu

Over the past ten years, neural machine translation (NMT) has seen tremendous growth and is now entering a phase of maturity. Despite being the most popular solution for machine translation (MT), it performs sub-optimally on under-resourced language pairs due to lack of parallel corpora as compared to high-resourced language pairs. The implementation of NMT techniques for under-resourced language pairs is receiving the attention of researchers and has resulted in a significant amount of research for many under-resourced language pairs. In view of the growth of MT, this paper describes a set of practical approaches for investigating MT between Kannada and Tulu. These two languages belong to the family of Dravidian languages and are under-resourced due to lack of tools and resources particularly the parallel corpus for MT. Since there are no parallel corpora for the Kannada-Tulu language pair for MT, this work aims to construct a parallel corpus for this language pair. As manual construction of parallel corpus is laborious, data augmentation is introduced to enhance the size of the parallel corpus along with suitable preprocessing techniques. Different NMT schemes such as recurrent neural network (RNN) baseline, bidirectional recurrent neural network (BiRNN), transformer-based NMT with and without subword tokenization, and statistical machine translation (SMT) models are implemented for MT of Kannada-Tulu and Tulu-Kannada language pairs. Empirical results reveal that the impact of data augmentation increases the bilingual evaluation understudy (BLEU) score of the proposed models. Transformer-based models with subword tokenization outperformed the other models with BLEU scores 41.82 and 40.91 for Kannada-Tulu and Tulu-Kannada MT, respectively.
Improving Amharic Handwritten Word Recognition Using Auxiliary Task

Amharic is one of the official languages in the Federal Democratic Republic of Ethiopia. It uses an Ethiopic script derived from Ge’ez which is an ancient and currently a liturgical language. Amharic is also one of the most widely used literature-rich languages in Ethiopia. There are very limited innovative and customized research works in Amharic optical character recognition (OCR) in general and Amharic handwritten text recognition in particular. In this study, Amharic handwritten word recognition was investigated. State-of-the-art deep learning techniques including convolutional neural networks together with recurrent neural networks and connectionist temporal classification (CTC) loss were used to make the recognition in an end-to-end fashion. More importantly, an innovative way of complementing the loss function using the auxiliary task from the row-wise similarities of the Amharic alphabet was tested to show a significant recognition improvement over a baseline method. The findings of this study promote innovative problem-specific solutions as well as open insights into generalized solutions that emerge from problem-specific domains.
Sentiment Analysis Through Fourier Transform Techniques in NLP

FNET encoder architecture can be compared with traditional transformer models, with limited accuracy loss by replacing self-attention sublayers that mix input information. It have shown for text classification tasks, replacing multihead attention in transformer encoder with standard, un-parameterized Fourier transform is able to achieve 92–97% of accuracy of transformer counterpart on GLUE benchmark, but also train faster with 60% lesser time in our case at input length. Moving forward, we merged both of un-parameterized Fourier transform and multihead attention in transformer encoder which help to improve the accuracy further from transformer base model by 5–10% with increase of 30–40% training time over GPU. Hybrid models tend to outperform transformers base counterparts. They can take advantage of Fourier sublayers mixing better than BERT model. In this paper, we study hybrid transformers model with self-attention, Fourier transformations, and hybrid models to study long-range dependencies in context of classification tasks in natural language processing.
Cubism: Co-balanced Mixup for Unsupervised Volcano-Seismic Knowledge Transfer

Volcanic eruptions are severe global threats. Forecasting these unrests via monitoring precursory earthquakes is vital for managing the consequent economic and social risks. Due to various contextual factors, volcano-seismic patterns are not spatiotemporal invariant. Training a robust model for any novel volcano-seismic situation relies on a costly, time-consuming and subjective process of manually labeling data; using a model trained on data from another volcano-seismic setting is typically not a viable option. Unsupervised domain adaptation (UDA) techniques address this issue by transferring knowledge extracted from a labeled domain to an unlabeled one. A challenging problem is the inherent imbalance in volcano-seismic data that degrades the efficiency of an adopted UDA technique. Here, we propose a co-balanced UDA approach, called Cubism, to bypass the manual annotation process for any newly monitored volcano by utilizing the patterns recognized in a different volcano-seismic dataset with labels. Employing an invertible latent space, Cubism alternates between a co-balanced generation of semantically meaningful inter-volcano samples and UDA. Inter-volcano samples are generated via the mixup data augmentation technique. Due to the sensitivity of mixup to data imbalance, Cubism introduces a novel co-balanced ratio that regulates the generation of inter-volcano samples considering the conditional distributions of both volcanoes. To the best of our knowledge, Cubism is the first UDA-based approach that transfers volcano-seismic knowledge without any supervision of an unseen volcano-seismic situation. Our extensive experiments show that Cubism significantly outperforms baseline methods and effectively provides a robust cross-volcano classifier.
Summarizing Data Structures with Gaussian Process and Robust Neighborhood Preservation

Latent variable models summarize high-dimensional data while preserving its many complex properties. This paper proposes a locality-aware and low-rank approximated Gaussian process latent variable model (LolaGP) that can preserve the global relationship and local geometry in the derivation of the latent variables. We realize the global relationship by imitating the sample similarity non-linearly and the local geometry based on our newly constructed neighborhood graph. Formally, we derive LolaGP from GP-LVM and implement a locality-aware regularization to reflect its adjacency relationship. The neighborhood graph is constructed based on the latent variables, making the local preservation more resistant to noise disruption and the curse of dimensionality than the previous methods that directly construct it from the high-dimensional data. Furthermore, we introduce a new lower bound of a log-posterior distribution based on low-rank matrix approximation, which allows LolaGP to handle larger datasets than the conventional GP-LVM extensions. Our contribution is to preserve both the global and local structures in the derivation of the latent variables using the robust neighborhood graph and introduce the scalable lower bound of the log-posterior distribution. We conducted an experimental analysis using synthetic as well as images with and without highly noise disrupted datasets. From both qualitative and quantitative standpoint, our method produced successful results in all experimental settings.
Blockchain Based B-Health Prototype for Secure Healthcare Transactions

The rapid advances in crypto wallet are re-defining privacy around transactions. A Crypto wallet comprises of software containing private and public keys and uses Blockchain to send and receive currency. Since the wallet will be based on the Blockchain, every transaction will be recorded, and every transaction will be stored. The privacy concerns have emerged out interaction between the normal transactions being made and the transactions made using blockchain in many fields like healthcare, defence etc. For the check on transactional details even the currency is not stored at one location; instead, they all exist as a transaction records on the Blockchain. In creating a Crypto Digital Wallet, we will solve traditional wallet problems. As these wallets store private and public keys, a user is facilitated with various operations such as sending or receiving coins, Portfolio balance, and crypto currency trading. This also ensures the user's privacy by using a hexadecimal address of the wallet. However, the currency's address to be exchanged differs from one service provider to another. Therefore, a Blockchain-based wallet provides all the necessary features for safe and secure transfers and exchange of funds between different healthcare consumers. It will also allow easy exchange of currency between different countries when patients are treated in different countries the issue of currency exchange will also be conquered. The renowned papers from various journals are studied and then it is discovered that there is no crypto wallet that can provide personalized point of service to the patients. In this paper, we have tried creating a "Crypto wallet based on Blockchain," to manage healthcare data the issues of security and data breach will be eradicated.
Analysis of MAPE-K Loop in Self-adaptive Systems for Cloud, IoT and CPS

Self-adaptive approaches aim to address the complexity of modern computing generated by the runtime variabilities and uncertainties. In this context, MAPE-K loop is considered today a major approach for the design and implementation of self-adaptive solutions because it captures in a systematic way the main steps of the adaptation process: (1) Monitor the execution context, (2) Analyze the monitored context, (3) Plan the appropriate adaptation strategy, (4) Execute the adaptation strategy, all these steps using a common Knowledge about the context. Implementations of MAPE-K loops may be particularly complex, domain specific, as well as case study dependent. In this paper, we provide a preliminary analysis of MAPE-K loops in various artifacts in different application domains (i.e., cloud - Hogna and TMA, cyber-physical systems - TRAPP and AMELIA, Internet of Things - DeltaIoT). Our main objective is to outline the similarities and differences among the available implementations of MAPE-K control feedback loops in self-adaptive systems. Additionally, the application domains of the considered examples are highly related, so that solutions in one domain may trigger developments in others. We also provide an insight into MAPE-K loops to enable researchers and practitioners to use, re-use, improve the available solutions.
Learning Optimal Decision Trees Under Memory Constraints

Existing algorithms for learning optimal decision trees can be put into two categories: algorithms based on the use of Mixed Integer Programming (MIP) solvers and algorithms based on dynamic programming (DP) on itemsets. While the algorithms based on DP are the fastest, their main disadvantage compared to MIP-based approaches is that the amount of memory these algorithms may require to find an optimal solution is not bounded. Consequently, for some datasets these algorithms can only be executed on machines with large amounts of memory. In this paper, we propose the first DP-based algorithm for learning optimal decision trees that operates under memory constraints. Core contributions of this work include: (1) strategies for freeing memory when too much memory is used by the algorithm; (2) an effective approach for recovering the optimal decision tree when parts of the memory are freed. Our experiments demonstrate a favorable trade-off between memory constraints and the run times of our algorithm.