Reviewerly/exHarmony huggingface dataset:
Dzongkha Word Segmentation using Deep Learning

Natural Language Processing (NLP) has been applied to machine translation, chatbots, speech recognition, question and answer systems, document summarization and so on. The Dzongkha language of Bhutan, however, has not been considered in NLP systems, due, presumably, to the fact that the language is complex and written as a string of syllables without proper word boundaries. Thus, Dzongkha word segmentation is the essential first step in building the NLP applications. The novelty of our research is in applying Deep Learning to the task of Dzongkha word segmentation, avoiding the need for manual feature engineering. The segmentation problem is formulated as a syllable tagging task. We also incorporate the windows approach where the tag of a syllable depends on its surrounding syllables. Two sets of experiments were designed, with four models of varying context sizes in each set. We evaluated our models using the syllable-tagged-corpus prepared by Dzongkha Development Commission. The model with context size 2 achieved the highest F-score of 94.40% with 94.47% Precision and 94.35% Recall.
Bangla Voice Command Recognition in end-to-end System Using Topic Modeling based Contextual Rescoring

In this work, we perform contextual rescoring using multi-label topic modeling to improve the performance of an End-to-End Bangla voice command recognition system. We use a hybrid of Connectionist Temporal Classification (CTC) and Attention mechanism in our End-to-End architecture. We use Recurrent Neural Network (RNN) as language model and La-beled LDA (Latent Dirichlet allocation) for contextual rescoring. Our experiments show that our rescoring method reduces Word Error Rate (WER) from 16.7% to 12.8% in Bangla voice command recognition task when the relevant context is provided. The system does not lose any performance when irrelevant context is provided.
Industrial General Reinforcement Learning Control Framework System based on Intelligent Edge

This paper is about the intelligent edge-based reinforcement learning control framework technology for manufacturing field solution and features large-scale learning, scalable edge distribution technology that can be applied to various task. In this paper, two items are proposed as features. The first proposes the General Reinforcement Learning Framework(GRLF) in manufacturing, and the second proposes the edge solution technology based on the GRLF in manufacturing. We apply the industrial solution such as grid sorter system for example. As a result of the industrial GRLF based grid sorter system, it was confirmed that when a total of 100 deliveries are randomly received into the grid sorter system by any emitter, all shipments are 100% accurate. It also classifies approximately 0.5 deliveries per step. This shows the efficiency of classifying around 30 deliveries per minute, assuming a step is performed per second.
A new perspective for Minimal Learning Machines: A lightweight approach

This paper introduces a new procedure to train Minimal Learning Machines (MLM) for regression tasks. Besides that, we propose a new prediction process in MLM. A well-known drawback concerning the (original) MLM model formulation is the lack of sparseness.The most recent efforts on this problem strongly rely on the selection of reference points before training and prediction steps in MLM, all based on some supposition regarding the data. In the opposite direction, here, we explore another formulation of MLM in which we do not rely on any assumption regarding the data for prior selection. Instead, our proposal, named Lightweight Minimal Learning Machine (LW-MLM), builds a regularized system that imposes sparseness. We thrive in such a sparse criterion, not by selection but instead using a piece of weighted information into the model. We validate the contributions of this paper through four types of experiments to evaluate different aspects of our proposal: the prediction error performance, the goodness-of-fit of estimated vs. measured values, the norm values which are related to the sparsity, and finally, the prediction error in high dimensional settings. Based on the results, we show that LW-MLM is a valid alternative since achieved similar or higher accuracy rates against other variants being all seen as statistically equivalent.
CNN Optimization with a Genetic Algorithm

Hand tuning convolutional neural networks (CNN) for performance optimization can be tedious. A novel approach using a genetic algorithm to automate CNN hyper-parameter adjustment is proposed. This automated approach shows a 5% accuracy improvement over hand tuned methods and highly energy efficient networks on the Intel Movidius Compute Stick.
Online variational learning of finite inverted <scp>Beta‐Liouville</scp> mixture model for biomedical analysis

Abstract Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X‐ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta‐Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.
Self Supervised Super-Resolution PET Using A Generative Adversarial Network

Resolution limitations pose a continuing challenge for PET quantitation. While deep learning architectures based on convolutional neural networks (CNNs) have produced unprecedented accuracy at generating super-resolution (SR) PET images, most existing approaches are based on supervised learning. The latter requires training datasets with paired (low- and high-resolution) images, which are often unavailable for clinical applications. In this paper, we present a self-supervised SR (SSSR) technique for PET based on dual generative adversarial networks (GANs), which obviate the need for paired training data, ensuring wider applicability and adoptability. Our network receives as inputs a low-resolution PET image, a high-resolution anatomical MR image, and spatial information. An imperfect SR image generated by a separately-trained auxiliary CNN serves as an additional input to the network. This CNN is trained in a supervised manner using paired simulation datasets. The loss function for training the dual GANs consists of two adversarial loss terms, a cycle consistency term, and a total variation penalty on the SR image. The method was validated on clinical data by comparing the SSSR results with those generated from a supervised approach and from deconvolution stabilized by a total variation penalty. Our results show that SSSR, while weaker than its supervised counterpart, noticeably outperforms deconvolution as indicated by the peak signal-to-noise-ratio and structural similarity index measures.
Siamese Similarity Between Two Sentences Using Manhattan's Recurrent Neural Networks

Paraphrasing detection needs to understand the sentence as a whole, not just every word. Therefore, if a learning machine does it, it is necessary to process it as a whole word, the relationship between words in a sentence. Recurrent Neural Networks (RNN) are methods commonly used to describe the connection between inputs. This study proposed the detection of paraphrases between two sentences using Manhattan Recurrent Neural Networks. One sentence contains several words arranged in sequence using the Word2vec function. The connection of each word in the RNN network used Long Short-Term Memory. (LSTM). Obtained 800,000 pairs of training data provide accuracy of 82.54 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of new data. The experimental results showed that the amount of training data variation is dominant to the number of training data. These results are better than the accuracy of the new data, with half of the training data only yielding 64%. Besides, the result gave the number of epochs during the training collected increased data correctness result.
A Cryptographic Technique for Authentication and Validation of Forensic Account Audit Using SHA256

Fraudulent activities continue to pervade accounting records in many organizations. Though it is difficult task to curtail this crime, there are measures proposed to detect the perpetrators and in some cases prevent unauthorized activities in financial records. Our work proposes the use of cryptographic scheme to ensure that only authorized and valid records are added to the existing block of records which are tagged with timestamps. This has been achieved through the application of blockchain technology. Our approach uses sha256 algorithm for hashing accounting records thereby securing records the sample records which cannot not be altered easily. Moreover any attempt to alter any record would require all computing nodes to update the previously added records to the chain. Due to the structure of the block which disallows any data manipulation, our system proved efficient to maintaining the integrity of the forensic account records efficiently.
Trace Norm Generative Adversarial Networks for Sensor Generation and Feature Extraction

Generative Adversarial Networks (GANs) have been shown effective to generate realistic enough sensor data for industrial failure prediction. Compared to computer vision problems, where it is very common to have more than 1000 classes, the number of classes for industrial problems is very small. For instance, only two classes, failure or non-failure, are considered in failure prediction. When using GANs to generate industrial sensor data, many of the generated samples are from the same class and thus are highly correlated. However, this relationships among generated samples are not explicitly considered in existing GAN frameworks. This type of correlations can be captured by trace norm minimization. In this work, we propose Trace Norm GANs to enforce trace norm minimization on generated samples. Extensive experiments on real industrial data show that (1) Trace Norm GANs improve generated samples in terms of quality/diversity metrics, (2) Trace Norm GANs extract efficient features for supervised learning tasks.
Dynamic Ensemble Pruning Selection using Meta-Learning for Multi-Sensor Based Activity Recognition

With the ever-increasing sensor types and complexity in the field of activity recognition, proper multi-sensor configuration system is essential to balance the recognition performance improvement and increased computational complexity caused by the use of multiple homogeneous or heterogeneous sensors. The multi-sensor deployment problem is normally transformed to multiple ensemble classifier pruning problem, while the competence of various ensemble pruning approaches for a particular subject are generally different. In this paper, a dynamic ensemble pruning selection model using meta-learning (META-DEPS) is proposed to recommend the most competent ensemble pruning algorithm for a given test sample. We utilize statistics features of processed sensor dataset to form a representative meta-feature set. Then meta-features extracted from the training dataset are used to train the instance-based meta-learner with newly designed ranking rules. Finally, an ensemble pruning algorithm is selected based on the trained meta-learner and realize the prediction of test instance in the generalization phase. The system conducts empirical studies on real-world activity recognition dataset and the results show that the proposed meta-learning based framework improves the accuracy of activity recognition when compared against using conventional ensemble pruning.
Binary Recommender System with Artificial Intelligence Aids

An approach to the development of a binary recommender system based on intelligent methods and their evaluation based on quality metrics is proposed. The dynamics of the popularity of recommendation systems is analyzed. Binary classification models have been developed using neural networks, support vector machines and random forest. An assessment of the modeling stage is carried out using calculations of such quality metrics as accuracy, precision, recall, F score. The most effective model for building recommendations based on an experimental data set from the education sector has been determined. The prospects for the further development of the proposed approach in the context of incomplete data are identified.
Conditional BRUNO: A neural process for exchangeable labelled data

We present a neural process which models exchangeable sequences of high-dimensional complex observations conditionally on a set of labels or tags. Our model combines the expressiveness of deep neural networks with the data-efficiency of Gaussian processes, resulting in a probabilistic model for which the posterior distribution is easy to evaluate and sample from, and the computational complexity scales linearly with the number of observations. The advantages of the proposed architecture are demonstrated on a challenging few-shot view reconstruction task which requires generalization from short sequences of viewpoints, and a contextual bandits problem.
Anomaly Detection based on GS-OCSVM Classification

This research aims to apply one-class support vector machine classifier (OCSVM) for anomaly detection and estimate the hyperparameters of OCSVM using the grid search method. The proposed grid search one-class support vector machine algorithm (GS-OCSVM) is then applied to the fraud detection problem. Data used in this study consists of German credit card and European cardholder credit card transactions which treat the fraud transactions as anomalies. In this study, we estimated the values of the hyperparameters y and v of OCSVM by considering the maximum of area under the curve (AVC). The results show that the GS-OCSVM can detect fraud better than the isolation forest as true negative rate is higher than isolation forest for both datasets.
Game Theoretic Task Allocation to Reduce Energy Consumption in Containerized Cloud

Cloud computing provides information technology based solutions to the end-users as a utility. Virtual machine or the virtualization technology is the backbone of implementing cloud computing technologies. However, such implementation encounters the problem of tremendous energy consumption. One of the foremost issues in implementing cloud computing is high energy consumption. This can be reduced to some extent by proper allocation and efficient utilization of resources. At present, containerization is one of the broadly discussed techniques as an alternative to traditional virtualization solutions. In this paper, we propose a game-theoretic approach for resource allocation and a containerized cloud architecture which drastically reduces energy consumption than a virtual machine based cloud. We have used Google cluster traces data set for our experiment in the cloud with virtual machine and containerized cloud. Experimental results show that the energy consumption is minimized in the containerized cloud than a cloud with virtual machines.
Sampling Classes of Non-Bandlimited Signals Using Integrate-and-Fire Devices: Average Case Analysis

We investigate the use of integrate-and-fire systems to efficiently sample classes of non-bandlimited signals such as bursts of spikes. The sampling in this case is based on storing some timing information about the signal, and no information about its amplitude. We demonstrate that perfect reconstruction of these signals is possible when using proper prefiltering strategies before using a multi-channel integrate-and-fire system. We show that the probability of perfect reconstruction of the bursts of Diracs is high, even when the sufficient conditions for perfect reconstruction are not satisfied.
The Fundamentals of Digital Forensics

This chapter simply provides a brief introduction to the various fundamentals and concepts that are related to the digital forensics. The overview of the digital forensics comprises the life cycle of the digital forensics with different stages, i.e., the preparation, collection, analysis, and reporting. The evaluation of the digital forensics tools comprises the encase forensic, The FTK(forensics tool kit), and The Helix digital forensic tool with their benefits and limitations. The digital forensics tools and techniques examination comprises the digital examination techniques, along with the live forensics analysis and the recovery of window registry, and the comparison of the digital forensics tools, and also focuses on the ACPO guidelines for digital forensics analysis.
Creating Foundations for Secure Microarchitectures With Data-Oblivious ISA Extensions

It is not possible to write microarchitectural side channel-free code on commercial processors today. Even when we try, the resulting code is low performance. This article's goal is to lay an ISA-level foundation, called a Data-Oblivious ISA (OISA) extension, to address these problems. The key idea with an OISA is to explicitly but abstractly specify security policy, so that the policy can be decoupled from the microarchitecture and even the threat model. Analogous to a traditional ISA, this enables an OISA to serve as a portable security-centric abstraction for software while enabling security-aware implementation and optimization flexibility for hardware. The article starts by giving a deep-dive in OISA principles and formal definitions underpinning OISA security. We also provide a concrete OISA built on top of RISC-V, an implementation prototype on the RISC-V BOOM microarchitecture, a formal analysis and security argument, and finally extensive performance evaluation on a range of data-oblivious benchmarks.
ID-Based Outsourced Plaintext Checkable Encryption in Healthcare Database

A cryptographic primitive suggested by Carnard et al. in 2012 ensures that on input a plaintext, ciphertext with corresponding public key, it is possible to check if a ciphertext is encryption of a target plaintext with a corresponding public key. This tool enables public plaintext query on ciphertext. We introduce ID-based plaintext checkable encryption (ID-PCE) in outsourced healthcare database. ID-PCE uses receiver's ID as the public key to curtail problems associated with key certificate management in public key encryption. We adopts identity-based cryptography and it achieves a weak-IND-ID-CCA (W-IND-IDCCA). Finally, proof the security of our scheme using random oracle model.
Mining and Comparing User Reviews across Similar Mobile Apps

With the rapid development of the market for mobile apps, there are a number of apps with similar functions. To gain an advantage in such a competitive environment, developers need to understand not only the strengths and weaknesses of their app but also competitive apps. User reviews contain valuable information for comparing similar apps from user preference. In this paper, we propose UISAT (User-review mining via topic Identification, Sentiment Analysis and Topic matching across apps), an automated approach to compare user reviews from similar apps with the goal of mining user feedback from competitive apps by (i) extracting the hidden topics from large volumes of user reviews using topic modeling, (ii) combining a rule-based model, user rating and user-helpful for sentiment analysis of topics and (iii) matching relevant topics across apps. Empirical studies demonstrate that UISAT is effective and promising for developers to build and maintain a more competitive app.
Analysis of autopilot system, integrated with modelling and comparison of different controllers with the system

(2020). Analysis of autopilot system, integrated with modelling and comparison of different controllers with the system. Journal of Discrete Mathematical Sciences and Cryptography: Vol. 23, No. 5, pp. 1059-1068.
Augmented Reality-Based Multimedia in Early Reading Learning: Introduction of ICT to Children

Abstract The background of this research is the need to develop multimedia in early reading learning. Development of this Augmented Reality-Based Multimedia is also as an effort to introduce ICT to children. This study aims to produce a multimedia learning, which combines the use of flash cards, android applications, and 3-dimensional images. This research is based on Research and Development. The steps taken in this research process refer to Borg &amp; Gall (2003) research stages. The use of AR includes several stages, including: (1) downloading the “Let’s Learn to Read (AR)!” App in the play store on android phone; (2) install the application into phone; (3) open an application already installed on the phone; (4) scan AR reading card using application; (5) 3D images, letters, and words will appear on the phone screen; (6) Point your finger behind the phone in the word that appears on the screen to bring up the audio. This multimedia has been validated by experts and is tested on a limited and wide basis to elementary school students, parents, and teachers. The test considers functionality, efficiency, and usability of this media. The test results show that the user response to this media is good.
Speaker Recognition System based on Identity Vector using T-SNE Visualization and Mean-shift Algorithm

The process of manually labeling data is not affordable. Moreover, the lack of labeled data has led to a big performance gap between scoring baseline techniques in speaker recognition. This paper aims to propose two separate systems to fill this gap. The first system uses the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to represent the unlabeled development i-vectors into two space dimensions, then cluster them using the mean-shift algorithm. Finally, the Within-Class Covariance Normalization (WCCN) algorithm and test normalization technique are applied to remove unwanted variability from i-vectors. In the second system, zero normalization is also employed on the baseline system released by the NIST 2014 i-vector challenge dataset. The cosine similarity has been computed for scoring in the proposed methods. The evaluation results on the NIST 2014 i-vector challenge dataset show that the proposed methods achieve 23% and 8% relative improvement of the minimum detection cost function (minDCF) respectively. Moreover, we obtained 25% improvement by fusing these systems.