Reviewerly/exHarmony huggingface dataset:
Implementation of Automated Bengali Parts of Speech Tagger: An Approach Using Deep Learning Algorithm

Parts-of-Speech(POS) tagging is the technique to assign each word in a sentence as an individual part of speech. POS tagging is the first important step in Natural Language Processing applications (NLP). In some languages, POS tagging works well with higher accuracy, but in the Bengali language, it is still an unsolved problem. The Bengali language is much ambiguous and inflectional, where every word has many more variants based on their suffixes and prefixes. Although developing POS tagging is not new for the Bengali language, we aim to make a highly accurate model with a minimal dataset. Here we developed a deep learning model, and it is mainly based on suffixes, which are parts of Bengali grammar. Moreover, we did experiment with a Bengali corpus that contains 2927 words with their corresponding parts of speech tags. The accuracy of our proposed POS tagging deep learning model is 93.90%. We also included this model as a python package to our open-source Bengali Natural language processing toolkit (BNLTK), which is now live on pipy.org.
Optimal testing of discrete distributions with high probability

We study the problem of testing discrete distributions with a focus on the high probability regime. Specifically, given samples from one or more discrete distributions, a property P, and parameters 0< є, δ <1, we want to distinguish with probability at least 1−δ whether these distributions satisfy P or are є-far from P in total variation distance. Most prior work in distribution testing studied the constant confidence case (corresponding to δ = Ω(1)), and provided sample-optimal testers for a range of properties. While one can always boost the confidence probability of any such tester by black-box amplification, this generic boosting method typically leads to sub-optimal sample bounds.
CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing

In contrast to great success of memory-consuming face editing methods at a low resolution, to manipulate high-resolution (HR) facial images, i.e., typically larger than $$768^2$$ pixels, with very limited memory is still challenging. This is due to the reasons of 1) intractable huge demand of memory; 2) inefficient multi-scale features fusion. To address these issues, we propose a NOVEL pixel translation framework called Cooperative GAN(CooGAN) for HR facial image editing. This framework features a local path for fine-grained local facial patch generation (i.e., patch-level HR, LOW memory) and a global path for global low-resolution (LR) facial structure monitoring (i.e., image-level LR, LOW memory), which largely reduce memory requirements. Both paths work in a cooperative manner under a local-to-global consistency objective (i.e., for smooth stitching). In addition, we propose a lighter selective transfer unit for more efficient multi-scale features fusion, yielding higher fidelity facial attributes manipulation. Extensive experiments on CelebA-HQ well demonstrate the memory efficiency as well as the high image generation quality of the proposed framework.
Modeling Difficulties in Data Modeling

We study modeling difficulties encountered by experienced modelers while performing a data modeling task and compare our observations with findings we obtained from studying modeling processes of non-experienced modelers. Using the concept of cognitive breakdowns, we analyze audio-visual protocols of the modelers' modeling processes, recordings of modelers' interactions with the employed modeling software tool and survey data of modelers about their own perceptions of modeling difficulties. Based on a mixed methods research design, we identify typical modeling difficulties modelers face when performing data modeling. The present findings suggest nine types of modeling difficulties related to modeling entity types, generalization hierarchies, relationship types, attributes, and cardinalities. Contrasting the identified modeling difficulties with difficulties encountered by non-experienced modelers contributes to a better and more complete understanding of modeling processes performed by modeling experts and novices—and to inform design science research on specific targeted tool support for overcoming these difficulties at different stages of modelers' mastering of data modeling.
Extracting Inherent Model Structures and Identifying Parameters of Time-Varying Systems Using Local Linear Neuro-Fuzzy Networks

The main challenges in identifying time-varying systems are to extract the inherent model structure and formulate a generic approach for various time-varying processes. In this article, a local linear neuro-fuzzy networks based approach is proposed for the first time to tackle this issue. The basic idea is to utilize the superior approximation capability of the fuzzy neural networks to expand the unknown time-varying parameters so that various nonlinear and discontinuous processes can be captured. In addition, the local linear model tree optimization algorithm is incorporated in our approach to optimize the network architecture. On one hand, the least number of neurons corresponding to the time-varying parameters can be automatically determined, thus avoiding the superfluous bases and the troublesome overfitting problem; on the other hand, the optimized distribution of the neurons could significantly increase the interpretability of the results, thus contributing to locating the domains where the parameters exhibit strong nonlinearity or change sharply. Moreover, we demonstrate that our approach could simultaneously extract the parsimonious model structure while identifying the time-varying parameters, without adding additional procedures or increasing computation burden. Considering the generality and the unique merits, our proposed approach could significantly advance the applications of artificial neural network techniques in system identification.
Template attacks on ECC implementations using performance counters in CPU

We demonstrate a new set of template attacks on ECC implementations using the performance counters in CPU. Template attacks are powerful mechanisms that can combine statistical intelligence for modelling side channel leakages and can thus compromise complex crypto implementations. Automated attack phases add to the efficiency of this approach. We introduce a new approach of using CPU counter values for constructing templates to carry out attacks on crypto implementations, which also opens up the possibility of many other template attacks that have been demonstrated earlier using power analysis, to be feasible on the modern day architectures. The values obtained from multiple CPU counters are used to generate templates, which are further matched with the traces obtained on the target implementation for guessing the bit values of the secret scalar used during the scalar multiplication function of the ECC implementation. We demonstrate the feasibility of the attack approach on virtualized environments such as the resources of the cloud computing infrastructure. We further model the attack, define the evaluation metrics and demonstrate the effectiveness of the attack approach.
Graph-Based Semi-Supervised Learning for Activity Labeling in Health Smart Home

Health Smart Home (HSH) is an important part of smart city. This technology provides a new kind of remote medical treatment, and can effectively alleviate the shortage of medical resources caused by aging population and help elderly people live at home more safely and independently. Activity recognition is the core of Health Smart Home. However, constructing activity recognition models usually requires a large amount of labeled data, which imposes a heavy burden on manual labeling. In this article, the authors propose an activity labeling approach based on a graph-based semi-supervised learning algorithm. This approach can divide the raw sensor event sequence without any label information into appropriate segments. Consecutive sensor events that occurred in a same activity are grouped into a same segment. In addition, this approach requires only a small number of manually labeled segments to complete the labeling of the remaining large number of unlabeled segments, thereby greatly reducing the burden of manual labeling. After that, all the labeled data can be further used for activity recognition in smart homes. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed activity labeling approach.
Automatic Lung Segmentation in Computed Tomography Images Using Active Shape Model

Lung segmentation in Computed Tomography (CT) images plays a vital role in the diagnosis, detection and three-dimensional visualization of lung nodules. In addition, the stability, accuracy and efficiency of lung segmentation in CT images have a significant impact on the performance of Computer-Aided Detection (CAD) systems. Lung segmentation is usually the first step in lung CT images analysis. In this paper, a fully automated algorithm for recognition and segmentation the lung in 3D X-ray images using the Active Shape Model (ASM) is presented. Proposed algorithms not only split the left and right lungs automatically, but also include the juxta-pleural nodules as a result of segmentation. This method is based on the ASM algorithm, which automatically detects nodules attached to the lung wall. This algorithm applied to 7 CT images of the lungs that include juxta-pleural nodules and calculate the division dice of segmentation.
Deep Learning Methods for Animal Recognition and Tracking to Detect Intrusions

Over the last few years, there has been a steady rise in number of reported human–animal conflicts. While there are several reasons for increase in such conflicts, foremost among them is the reduction in forest cover. Animals stray close to human settlements in search of food, and often end up raiding crops or preying on cattle. There are at times human causalities as well. Proficient, reliable, and autonomous monitoring of human settlements bordering forest areas can help reduce such animal–human conflicts. A broad range of techniques in computer vision and deep-learning has shown enormous potential to solve such problems. In this paper, a novel, efficient, and reliable system is presented which automatically detects wild-animals using computer vision. The proposed method uses the YOLO object detection model to ascertain presence of wild animals in images. The model is fine-tuned for identifying six different entities—humans, and five different types of animals (elephant, zebra, giraffe, lion, and cheetah). Once detected, the animal is tracked using CSRT to determine its intentions, and based on the perceived information, notifications are sent to alert the concerned authorities. The design of a prototype for the proposed solution is also described, which uses Raspberry Pi devices equipped with cameras. The proposed method achieves an accuracy of 98.8% and 99.8% to detect animals and humans, respectively.
LST-Net: Learning a Convolutional Neural Network with a Learnable Sparse Transform

The 2D convolutional (Conv2d) layer is the fundamental element to a deep convolutional neural network (CNN). Despite the great success of CNN, the conventional Conv2d is still limited in effectively reducing the spatial and channel-wise redundancy of features. In this paper, we propose to mitigate this issue by learning a CNN with a learnable sparse transform (LST), which converts the input features into a more compact and sparser domain so that the spatial and channel-wise redundancy can be more effectively reduced. The proposed LST can be efficiently implemented with existing CNN modules, such as point-wise and depth-wise separable convolutions, and it is portable to existing CNN architectures for seamless training and inference. We further present a hybrid soft thresholding and ReLU (ST-ReLU) activation scheme, making the trained network, namely LST-Net, more robust to image corruptions at the inference stage. Extensive experiments on CIFAR-10/100, ImageNet, ImageNet-C and Places365-Standard datasets validated that the proposed LST-Net can obtain even higher accuracy than its counterpart networks with fewer parameters and less overhead.
Multi-scale Multi-attention Network for Moiré Document Image Binarization

In this paper, we propose a Multi-scale Multi-attention Network (MsMa-Net) to binarize document images contaminated by moiré patterns from camera-captured screens. Given a polluted image, MsMa-Net first learns to distinguish clean features from contaminated ones at different spatial scales via a Multi-scale feature extraction submodule (Ms-sub). In this way, detailed text information could be preserved as much as possible. Meanwhile, moiré patterns could be purified preliminarily. Then, obtained multi-scale features are adaptively interweaved through a proposed Multi-attention submodule (Ma-sub) at the channel level, the spatial level, and the correlation level, respectively. By modelling such relationships among multi-scale features, Ma-sub can further highlight text contents and suppress moiré patterns for yielding clean demoiré document images. All the demoiré images flow to a proposed Binarization submodule (Bi-sub) to produce final high-quality binarized document images. Besides, considering the scarce data support for the moiré document image binarization task, we create a new Moiré Document Image (MoDI) dataset for training and evaluating the proposed model. Extensive experiments demonstrate that MsMa-Net achieves state-of-the-art performance over several available datasets and MoDI dataset.
FEAT: A Fairness-Enhancing and Concept-Adapting Decision Tree Classifier

Fairness-aware learning is increasingly important in socially-sensitive applications for the sake of achieving optimal and non-discriminative decision-making. Most of the proposed fairness-aware learning algorithms process the data in offline settings and assume that the data is generated by a single concept without drift. Unfortunately, in many real-world applications, data is generated in a streaming fashion and can only be scanned once. In addition, the underlying generation process might also change over time. In this paper, we propose and illustrate an efficient algorithm for mining fair decision trees from discriminatory and continuously evolving data streams. This algorithm, called FEAT (Fairness-Enhancing and concept-Adapting Tree), is based on using the change detector to learn adaptively from non-stationary data streams, that also accounts for fairness. We study FEAT's properties and demonstrate its utility through experiments on a set of discriminated and time-changing data streams.
A Brief Overview of Swarm Intelligence-Based Algorithms for Numerical Association Rule Mining

Numerical Association Rule Mining is a popular variant of Association Rule Mining, where numerical attributes are handled without discretization. This means that the algorithms for dealing with this problem can operate directly, not only with categorical, but also with numerical attributes. Until recently, a big portion of these algorithms were based on a stochastic nature-inspired population-based paradigm. As a result, evolutionary and swarm intelligence-based algorithms showed big efficiency for dealing with the problem. In line with this, the main mission of this chapter is to make a historical overview of swarm intelligence-based algorithms for Numerical Association Rule Mining, as well as to present the main features of these algorithms for the observed problem. A taxonomy of the algorithms was proposed on the basis of the applied features found in this overview. Challenges, waiting in the future, finish this paper.
A Comparison Based Breast Cancer High Microscopy Image Classification Using Pre-trained Models

Breast Cancer is most common but difficult to diagnose at an early stage, commonly found in women but there are some cases of men also. The early tedious way of treating breast cancer was quite time-taking as well as causes a dispute between the pathologists based on their analysis. Due to this, pathologists were not able to find out the cancerous tissues on time. Ordinarily, the malignancy shapes in either of the breast. Deep learning has shown much improvement in the diagnosis of Breast cancer, we propose a new technique to diagnose breast cancer tissue by the processing of the biopsy or histopathology images. These images are of large dimensions. Passing these images will require a high computational GPU and it is not possible to use. So we break it into different smaller sizes. Here, we are using H&E staining to make the images more clear to increase the chances of detecting the cancerous tissue. The image extracted from histopathology images utilizing the pre-trained CNN models VGG 19, Inception V3 and ResNet 152 with various sizes of crops can precisely recognize typical and malignant growth tests. The proposed model is state of the heart in terms of accuracy than all previously reported work, using CNN and LightGBM.
Qualitative analysis of virtual reality adoption by tourism operators in low-density regions

Virtual reality technologies have grown in complexity and functionality, increasing their relevance in various sectors of activity. Applying these innovations to tourism is advantageous for the organisations, the tourists and even the territories. They can provide multisensory immersivity, simulate actions and movements in real-time, enriching reality using interactive virtual objects. Thus, captivating visitors and showing territories from richer and livelier perspectives. This study describes a review of the adoption of virtual reality technology at the organisational level. In order to adapt the literature review to the tourism topic, the process of analysis of the literature was carried out with a constant concern to analyse research that studied the topic. Assuming the need to further develop the work done and to obtain a confirmation on the contribution resulting from the bibliographic review, a focus group was developed, which allowed several specialists to discuss collaboratively and interactively the research project topics without having to be in face-to-face contact. The results of this study allow not only to acknowledge the relevance of virtual reality for tourism but also to understand how the existing literature and experts are aligned with the identification of opportunities for disruptive development of solutions that trigger tourism destinations and organisational development.
Network Steganography Using Extension Headers in IPv6

With the advent of technology, data security over the networks has become a crucial responsibility of organizations these days. One of the techniques to secure data over the networks is through information hiding. The art of hiding data in media files to make it secure from interception is termed as Steganography. Image, Audio, Video, Document, Network packets are different types of medium that are used to hide data within them. On the other hand, where Internet Protocol Version 4 (IPv4) is expected to become obsolete soon due to limited addressing space, Internet Protocol version 6 (IPv6) the next generation protocol, is soon going to take over the internet. In this paper, we propose a network steganography technique which uses Internet Protocol Version 6 packets having zero or more (up to four) extension headers to hide secret data and carry it from one end to other end over a network. Our technique is robust and at present can transfer five bits of covert data per IPv6 packet. This bandwidth can further be increased to few more bits, by increasing the number of extension headers attached to an IPv6 packet up to the permissible limit.
A Machine Learning Model to Detect Fake Voice

Nowadays, there are different digital tools that permit the editing of digital content as audio files and they are easily accessed in mobile devices and personal computers. Audio forgery detection has been one of the main topics in the forensics field, as it is necessary to have reliable evidence in court. These audio recordings that are used as digital evidence may be forged and methods that are able to detect if they have been forged are required as new ways of generation of fake content continue growing. One method to generate fake content is imitation, in which a speaker can imitate another, using signal processing techniques. In this work, a passive forgery detection approach is proposed by manually extracting the entropy features of original and forged audios created using an imitation method and then using a machine learning model with logistic regression to classify the audio recordings. The results showed an accuracy of 0.98 where all forged audios were successfully detected.
MI$$^2$$GAN: Generative Adversarial Network for Medical Image Domain Adaptation Using Mutual Information Constraint

Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models. Generative adversarial network (GAN), which synthesize plausible images, is one of the potential solutions to address the problem. However, the existing GAN-based approaches are prone to fail at preserving image-objects in image-to-image (I2I) translation, which reduces their practicality on domain adaptation tasks. In this paper, we propose a novel GAN (namely MI\(^2\)GAN) to maintain image-contents during cross-domain I2I translation. Particularly, we disentangle the content features from domain information for both the source and translated images, and then maximize the mutual information between the disentangled content features to preserve the image-objects. The proposed MI\(^2\)GAN is evaluated on two tasks—polyp segmentation using colonoscopic images and the segmentation of optic disc and cup in fundus images. The experimental results demonstrate that the proposed MI\(^2\)GAN can not only generate elegant translated images, but also significantly improve the generalization performance of widely used deep learning networks (e.g., U-Net).
Enhanced Redundant Measurement-Based Kalman Filter for Measurement Noise Covariance Estimation in INS/GNSS Integration

Adaptive Kalman filters (AKF) have been widely applied to the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system. However, the traditional AKF methods suffer from the problems of filtering instability or covariance underestimation, especially when the GNSS measurement disturbances occur. In this paper, an enhanced redundant measurement-based AKF is developed to improve the filtering performance. The scheme is based on the mutual difference sequence derived from the redundant measurement of INS. By using the mutual difference sequence, the measurement noise covariance can be estimated without being affected by the inaccuracy estimates, hence avoiding the risk of filtering divergence. In addition, the kernel density estimation is used to estimate the GNSS measurement noise’s probability density to detect whether the Gaussian properties of the measurement noise are maintained. When the noise statistics are far from Gaussian distribution, the difference sequence will be modeled as an autoregressive process using the Burg’s method. The real variance of the difference sequence can then be updated relying on the autoregressive model in order to avoid the covariance underestimation. A field experiment was carried out to evaluate the performance of the proposed method. The test results demonstrate that the proposed method can effectively mitigate the GNSS measurement disturbances and improve the accuracy of the navigation solution.