Reviewerly/exHarmony huggingface dataset:
Augmented Classical Self-organizing Map for Visualization of Discrete Data with Density Scaling

In a previous publication, we introduced a method called hSOM to improve the comprehension of Kohonen’s self-organizing map. Self-organizing maps are an unsupervised analogue of the artificial neural network which preserves the topology of its input space. It efficiently summaries multidimensional data, but is difficult to visualize in a manner that is accessible to those trying to interpret it. The hSOM method improves upon the classical visualization depicting a SOM by allowing for the proportion of each output node’s instances of a discrete variable to be visualized, allowing distribution to be ascertained. This chapter extends that research by addressing visual noise that can arise out of dense hSOM visualizations and by adding an additional case study to evaluate hSOM’s performance.
Acquisition of Relevant Hand-Wrist Features Using Leap Motion Controller: A Case of Study

All the gestures and movements we make are influenced by our psychomotor abilities. This mobility deteriorates over the years. It is logical to think that an older individual has worse mobility than a younger one if they do not suffer from other pathologies. On this premise, the main aim of this research work is based on the detection of semantic biomechanical features, using a hand-tracking controller (Leap Motion) through three calibration tests. The data collected by this hand-tracker has allowed to measure hand-wrist movements. Likewise, this paper aims to highlight different tasks based on those used by neurologists customarily based on the Hoehn Yahr [11] and UPDRS scales [9]. Indeed, this study intends to visualize the differences between healthy participants. The manuscript provides some promising findings that will help tailoring biometric indicators for non-normative participants in future research using this technology.
A Fuzzy Rule Based Directional Approach for Salt and Pepper Noise Removal with Edge Preservation

Existing impulse noise reduction schemes generally deals with fat-tailed noise densities, using different non-linear filtering approaches while maintaining the texture information and edges along with handling the random impulses still is a challenging assignment in the field of image processing. Fuzzy filters are one of the approaches that can be appropriate for impulse noise elimination. This approach, not only can deal with the randomness of impulsive noisy pixels, as well as do handle the edge factors. This proposed system represents a restructured technique, in which thin-tailed and intermediate thin-tailed noises are removed by a fuzzy filter. At first, the scheme calculates a fuzzy derivative, so that it turns out to be less susceptible to local variations because of image structure; such as edges. After calculating fuzzy derivation, then fuzzy smoothing is performed and the membership functions are regulated based on the noise echelons. In first stage, fuzzy derivative is calculated for eight different directions and in second stage, fuzzy smoothing is performed by those fuzzy derivatives on the assistance of neighbouring pixel values. Those two phases are depended on fuzzy regulations which are created using membership functions. The proposed method is vastly effective on removing high density noises.
Menu Text Recognition of Few-shot Learning

Recent advances in OCR show that end-to-end (E2E) training pipelines including detection and identification can achieve the best results. However, many existing methods usually focus on case insensitive English characters. In this paper, we apply an E2E approach, the multiplex multilingual mask TextSpotter, which performs script recognition at the word level and uses different recognition headers to process different scripts while maintaining uniform loss, thus optimizing script recognition and multiple recognition headers simultaneously. Experiments show that this method is superior to the single-head model with similar number of parameters in end-to-end identification tasks.
Reading Comprehension Based Question Answering System in Bangla Language with Transformer-Based Learning

Question answering (QA) system in any language is an assortment of mechanisms for obtaining answers to user questions with various data compositions. Reading comprehension (RC) is one type of composition, and the popularity of this type is increasing day by day in Natural Language Processing (NLP) research area. Some works have been done in several languages, mainly in English. In the Bangla language, neither any dataset available for RC nor any work has been done in the past. In this research work, we develop a question-answering system from RC. For doing this, we construct a dataset containing 3636 reading comprehensions along with questions and answers. We apply a transformer-based deep neural network model to obtain convenient answers to questions based on reading comprehensions precisely and swiftly. We exploit some deep neural network architectures such as LSTM (Long Short-Term Memory), Bi-LSTM (Bidirectional LSTM) with attention, RNN (Recurrent Neural Network), ELECTRA, and BERT (Bidirectional Encoder Representations from Transformers) to our dataset for training. The transformer-based pre-training language architectures BERT and ELECTRA perform more prominently than others from those architectures. Finally, the trained model of BERT performs a satisfactory outcome with 87.78% of testing accuracy and 99% training accuracy, and ELECTRA provides training and testing accuracy of 82.5% and 93%, respectively.
Regularized Learning in RKHS

This chapter is devoted to the application of regularization tools presented in Chap. 3 .
Content-Aware Image Smoothing Based on Fuzzy Clustering

Literature contains a large variety of content-aware smoothing methods. As opposed to classical smoothing methods, content-aware ones intend to regularize the image while avoiding the loss of relevant visual information. In this work, we propose a novel approach to content-aware image smoothing based on fuzzy clustering, specifically the Spatial Fuzzy c-Means (SFCM) algorithm. We develop the proposal and put it to the test in the context of automatic analysis of immunohistochemistry imagery for neural tissue analysis.
In-Air Handwriting Recognition Using Acoustic Impulse Signals

Abstract This paper presents AcousticPAD, a contactless and robust handwriting recognition system that extends the input and interactions beyond the touchscreen using acoustic signals, thus very useful under the impact of the COVID-19 epidemic. To achieve this, we carefully exploit acoustic pulse signals with high accuracy of time of fight (ToF) measurements. Then we employ trilateration localization method to capture the trajectory of handwriting in air. After that, we incorporate a data augmentation module to enhance the handwriting recognition performance. Finally, we customize a back propagation neural network that leverages augmented image dataset to train a model and recognize the acoustic system generated handwriting characters. We implement AcousticPAD prototype using cheap commodity acoustic sensors, and conduct extensive real environment experiments to evaluate its performance. The results validate the robustness of AcousticPAD, and show that it supports 10 digits and 26 English letters recognition at high accuracies.
Generalized Framework for Image and Video Object Segmentation Using Affinity Learning and Message Passing Gnns

Despite significant amount of work reported in the computer vision literature, segmenting images or videos based on multiple cues such as objectness, texture and motion, is still a challenge. This is particularly true when the number of objects to be segmented is not known or there are objects that are not classified in the training data (unknown objects). A possible remedy to this problem is to utiize graph-based clustering techniques such as Correlation Clustering. It is known that using long range affinities (Lifted multicut), makes correlation clustering more accurate than using only adjacent affinities (Multicut). However, the former is computationally expensive and hard to use. In this paper, we introduce a new framework to perform image/motion segmentation using an affinity learning module and a Message Passing Graph Neural Network (MPGNN). The affinity learning module uses a permutation invariant affinity representation to overcome the multi-object problem. The paper shows, both theoretically and empirically, that the proposed MPGNN aggregates higher order information and thereby converts the Lifted Multicut Problem (LMP) to a Multicut Problem (MP), which is easier and faster to solve. Importantly, the proposed method can be generalized to deal with different clustering problems with the same MPGNN architecture. For instance, our method produces competitive results for single image segmentation (on BSDS dataset) as well as unsupervised video object segmentation (on DAVIS17 dataset), by only changing the feature extraction part. In addition, using an ablation study on the proposed MPGNN architecture, we show that the way we update the parameterized affinities directly contributes to the accuracy of the results.
A Webextension Based Framework for the Assessment of Main Content Extraction Methods from Web Pages

Main content extraction removes ancillary areas and isolates primary content from a web page. According to its purpose and approaches, there are numerous datasets, methods, and performance measures. Researchers and developers has difficulty in a comparative assessment including their own method, datasets, and performance measures. Therefore, we have developed a WebExtension (web browser extension) based framework for assessing main content extraction methods. This software has functionalities to create a dataset, curate data, execute Javscript/Typescript extraction methods, and evaluate the methods with multiple measures for a comparative assessment. We aim to help researchers and developers create a new dataset, label data with various user groups, develop and test their methods, and design specialized assessment procedures for their extraction method.
Counterpart Semantics at Work: Independence and Incompleteness Results in Quantified Modal Logic

In this paper we make a targeted contribution to the model theory of quantified modal logic (QML). Specifically, we investigate the independence of two first-order modal principles thoroughly discussed in the literature on QML: the Barcan formula BF and the necessity of fictionality N $$\lnot $$ E. We make use of counterpart semantics to prove the mutual independence of these two principles in various QML calculi on normal modal bases as strong as S5. Further, these independence results are in turn used to prove the incompleteness of a number of QML calculi based on free logic and containing BF with respect to Kripke semantics.
Data to Value: An ‘Evaluation-First’ Methodology for Natural Language Projects

While for data mining projects (for example in the context of e-commerce) some methodologies have already been developed (e.g. CRISP-DM, SEMMA, KDD), these do not account for (1) early evaluation in order to de-risk a project (2) dealing with text corpora (“unstructured” data) and associated natural language processing processes, and (3) non-technical considerations (e.g. legal, ethical, project management aspects). To address these three shortcomings, a new methodology, called “Data to Value”, is introduced, which is guided by a detailed catalog of questions in order to avoid a disconnect of large-scale NLP project teams with the topic when facing rather abstract box-and-arrow diagrams commonly associated with methodologies.
Triple Assessment for the Diagnosis of Carcinoma Breast in a Tertiary Care Hospital of Tripura: A Cross-sectional Study

Introduction: A combination of invasive and non-invasive procedures, clinical examination, radiological imaging (mammography/ ultrasonography/ Magnetic Resonance Imaging (MRI)) and fine needle aspiration cytology called the triple assessment test is being increasingly used in place of the more invasive core needle biopsy and histopathology. Aim: To evaluate accuracy of triple assessment in the preoperative diagnosis of patients with breast carcinoma and to determine sensitivity and specificity with regards to histopathology in the diagnosis of the disease. Materials and Methods: A cross-sectional study was conducted among 61 women of more than 25 years of age having palpable breast lump, attending the surgery Outpatient Department (OPD) and breast clinic of a tertiary care hospital from January 2017 to February 2019. Data on socio-demographic status, menstrual and obstetric information, clinical examination performed, mammography, Fine Needle Aspiration Cytology (FNAC), High resolution sonography breast and histopathology were recorded into predesigned and pretested proformas and analyzed using Statistical Package for Social Sciences (SPSS) version 25.0. Results: Out of total 61 patients participated in the present study, most patients were of 41-50 years of age, with a mean age of 44.23±7.37 years, married, non-vegetarian and without any past history of alcohol consumption. Sensitivity and specificity of triple assessment was 98.3% and 100% respectively. The positive predictive value of Triple Assessment was 100% while the negative predictive value was 66.7%. All values were significantly better than both clinical breast examination and FNAC in detecting malignancies. Conclusion: The triple test was also found to be as accurate in diagnosing breast carcinoma in this geographical region. A patient with a negative triple test report can be safely followed up without the need for biopsy.
(Commit-and-Prove) Predictable Arguments with Privacy

Predictable arguments introduced by Faonio, Nielsen and Venturi [14] are private-coin argument systems where the answer of the prover can be predicted in advance by the verifier. In this work, we study predictable arguments with additional privacy properties. While the authors in [14] showed compilers for transforming PAs into PAs with zero-knowledge property, they left the construction of witness indistinguishable predictable arguments (WI-PA) in the plain model as an open problem. In this work, we first propose more efficient constructions of zero-knowledge predictable arguments (ZK-PA) based on trapdoor smooth projective hash functions (TSPHFs). Next, we consider the problem of WI-PA construction in the plain model and show how to transform PA into WI-PA using non-interactive witness-indistinguishable proofs. As a relaxation of predictable arguments, we additionally put forth a new notion of predictability called Commit-and-Prove Predictable Argument (CPPA), where except the first (reusable) message of the prover, all the prover's responses can be predicted. We construct an efficient zero-knowledge CPPA in the non-programmable random oracle model for the class of all polynomial-size circuits. Finally, following the connection between predictable arguments and witness encryption, we show an application of CPPAs with privacy properties to the design of witness encryption schemes, where in addition to standard properties, we also require some level of privacy for the decryptors who own a valid witness for the statement used during the encryption process.
A Novel Internet-of-Drones System with Edge Computing and Blockchain for Search and Rescue

The utilization of technologies such as unmanned aerial vehicles (UAV), Internet-of-Things (IoT), computer vision, artificial intelligence, and wireless communication helps increase the possibilities to find missing people in search and rescue (SAR) missions. For instance, large areas can be searched in a minimal time, including locations that a rescue team cannot reach easily. In most UAV-based systems currently used in SAR missions, drones and UAVs are deployed by a rescue team to search for a specific area where an accident or a natural disaster occurs. These drones collect and transmit images and videos to a ground control station (GCS), where a computer can process the data to detect missing people automatically, or a rescue team’s eyes can visualize the data. Nonetheless, these systems have several limitations. For instance, the quality of service may be reduced significantly when a connection between a drone and a GCS is interrupted. In addition, security-related aspects such as data integrity, data privacy, transparency, confidentiality, and traceability are not properly considered. In this paper, we propose a novel Internet-of-Drones (IoD) system utilizing edge computing together with blockchain technology to overcome some of the existing limitations and improve the quality of service of SAR missions. For instance, the proposed system architecture enables computation offloading to enhance the energy efficiency of drones and minimize system latency. In addition, the system also enables artificial intelligence at the edge of a network to detect a human from captured images and videos in real-time. The presented system is implemented with different drones, edge servers, and a blockchain network. The proof-of-concept design shows that the proposed IoD system can be a suitable candidate for improving the quality of service of SAR.
Multiple Views and Categories Condition GAN for High Resolution Image

GAN network has been become a novel augmented data with many variants. Our proposed method is inspired by the CGAN model, which could generate images of various categories from latent representation and class controlling vector. However, the CGAN model could not generate images with different viewpoints. In addition, the synthetic images are of low-resolution ones. In this paper, a novel GAN architecture, named High Resolution Categories Condition GAN ( $$HC\_GAN$$ ), is proposed to improve the quality of synthetic images. Nevertheless, we also propose a new method to generate images on different classes and viewpoints at a higher resolution than input images, which is named High resolution Multiple Views and Categories condition GAN ( $$HMVC\_GAN$$ ). In this model, viewpoint controlling vector and classes controlling vector are contemporaneously integrated into the high resolution GAN model. Experiments are conducted on various hand gesture datasets, in which hand images are complex hand shapes, appearances are highly diversified on different viewpoints. Results show that our proposed method would obtain competent quality not only in different categories but also in different directions.
Thai Named Entity Corpus Annotation Scheme and Self Verification by BiLSTM-CNN-CRF

Corpus is one of the essential parts of language research, especially for the low resource language. To ensure the researching result to be most effective, the corpus that has been used also requires effectiveness and accuracy. The Thai language has some special characteristics that cause difficulty in building the corpus and affect the error of those corpora. Therefore, this paper proposes an effective and efficient approach to clean up the existing Named Entity corpus before using it in any language research. The THAI-NEST corpus is adopted to verify the consistency and integrity of the data and re-design with our proposed model. The revised corpus is verified by the BiLSTM-CNN-CRF model that combined the features among word, POS, and Thai character clusters (TCCs). Experimental results show the effectiveness of the verification, which increased the accuracy by up to 12%, and the model can effectively detect and handle errors of word segmentation and NE tag consistency.
Deep Active Recognition Through On-Line Cognitive Learning

Cognitive learning mechanism can actively raise the deep models’ proficiency with a few training labels gradually. Inspired by human-being’s acquire new knowledge steps, we develop a deep active recognition framework based on the analysis of models’ cognitive error to fine tune the deep models on-line. The transformation of the cognitive errors is defined, and the corresponding knowledge is obtained to identify the models’cognitive information. Based on the cognitive knowledge, the sensitive samples are selected to finely tune the models on-line. To avoid forgetting the previous learned knowledge, the selected prior training samples are used as the refreshening samples. The experiments demonstrate that the sensitive samples can benefit the target recognition and the cognitive learning mechanism can boost the deep models performance efficiently. The characterization of cognitive information can restrain the other samples’ disturbance to the models’ cognition effectively and the on-line training method can save mass of the time evidently.
Convolutional neural networks for image synthesis

Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. Traditional machine learning based methods (such as random forest and sparse learning) have to firstly design features and then learn the non-linear mapping from the input space to the output space. Compared to these conventional methods, convolutional neural networks (CNNs) can capture this complex non-linear mapping in an end-to-end manner. CNNs not only save the labor for hand-crafted feature engineering, they can also better optimize the non-linear mapping by combing feature representation learning and non-linear mapping together. Successful cross-modality synthesis has been conducted between certain medical imaging modalities. In this chapter, we will introduce different CNN architectures for medical image synthesis, elements of model training, some practical aspects, and commonly known networks.
Dark Chain Detection Based on the IP Address

In recent years, the attack methods of black industry are changing day by day, among which “hidden hyperlink” is a more serious problem, because itself is not much different from ordinary hyperlinks, so the hidden hyperlink in the program is more hidden and difficult to detect. This thesis is mainly concerned with the field of network security, and aims to provide a technique for detecting hidden hyperlinks based on IP addresses. The technique firstly identifies the domain name that needs to be judged, then scans the domain name information of all the links under the website, converts the domain name information into the corresponding IP address, then hands over the IP address information to a third-party threat intelligence agency and the existing malicious IP address database for comparison, and at the same time combines the manual review to identify it as a hidden hyperlink with the title and keywords as the main detection target, and to The data identified as hidden hyperlinks will be extracted and the relevant features will be machine learned, and the data set will be continuously grown to improve the accuracy of machine learning, thus forming a system for automatic detection of hidden hyperlinks in real-world operations.
Smart Technologies and Aging Society

This study describes the application of smart technologies in an aging society, which typically has the percentage of lder adults exceeding 7% of total population. Two types of smart technologies are focused on in this chapter: smart home technologies and smart mobility technologies. Smart home technologies directly help older adults age in place through the purchase and application of different smart home technological products, which are not very expensive but take time to become familiar with their utilizations. Unlike smart home technologies, which are bought and sold through the private markets, the deployment and operation of smart mobility technologies are typically handled by public entities such as transit operators. Smart mobility technologies would not only improve mobility and independence for older adults but also raise economic efficiency and reduce operating costs for public entities as well.