Reviewerly/exHarmony huggingface dataset:
Research of Automatic Speech Recognition of Asante-Twi Dialect For Translation

This paper presents a new way of building low-resourced dialect Automatic Speech Recognition (ASR) systems using a small database using the Asante-Twi dialect. Three different ASR systems with different features and methods have been tested and tried using the Kaldi toolkit. For the first and second Asante-Twi ASR systems, Mel Frequency Cepstral Coefficients (MFCC) feature extraction method was used with different context dependent parameters for each and the Perceptual Linear Prediction(PLP) feature extraction method was used for the third ASR system. To enhance the performance of the ASR systems, all feature extraction methods of the systems are improved using Cepstral Mean and Variance Normalization (CMVN) and Delta(Δ) dynamic features. In addition, the acoustic model unit of each ASR system using the GMM-HMM pattern classifier algorithm has been improved by training two context dependent (triphone) models, one on top of the other, and both on top of context independent (monophone) models to deliver better performance. Word Error Rate(WER) is used as metrics for measuring the accuracy performance of the systems. As the correct parameter setting for triphone models were used, the second ASR system saw about 50% reduction in %WER for the first triphone model and about 10% reduction in %WER values for the second triphone model as compared to the first ASR system. Decoding results show that the second ASR system was the most efficient system of all the ASR systems in percent WER because it produced the lowest value of 5.15% WER obtained from context dependent triphone models. The third ASR system, using the same triphone parameters as the second ASR, was the worst performing of all three. Thus, MFFCs are found to be the most suitable feature extraction technique when using noise-free data with context-dependent acoustic models being the best method for GMM-HMM acoustic modeling on a limited amount of data.
Enhancing Cloud Data Privacy Using Pre-Internet Data Encryption

Cloud computing is one of the greatest and authoritative paradigms in computing as it provides access and use of various third-party services at a lower cost. However, there exist various security challenges facing cloud computing especially in the aspect of data privacy and this is more critical when dealing with sensitive personal or organization's data. Cloud service providers encrypt data during transfer from the local hard drive to the cloud server and at the server-side, the only problem is that the encryption key is stored by the service provider meaning they can decrypt your data. This paper discusses how cloud security can be enhanced by using client-side data encryption (pre-internet encryption), this will allow the clients to encrypt data before uploading to the cloud and store the key themselves. This means that data will be rendered to the cloud in an unreadable and secure format that cannot be accessed by unauthorized persons.
BOhance: Bayesian Optimization for Content Enhancement

We present BOhance, an efficient solution for optimizing digital content like images. Our approach enhances the standard and widely-used method for optimizing content, A/B testing, by using Bayesian Optimization. Our work effectively extends A/B testing in the continuous domain where A/B testing cannot efficiently test infinitely many variants. We test our approach on an image enhancement task where we use iterative human feedback on different variants of an image to arrive at the optimal variant. BOhance auto-generates candidate content variants to be tested based on the human feedback on prior variants. We demonstrate with user-studies conducted on Amazon Mechanical Turk that BOhance can be both time and cost-efficient; and a superior alternative to existing solutions. Furthermore, we conduct a Visual Turing Test to obtain human impressions on the optimum variants generated by BOhance. Our experiments show that given a human-enhanced image and an image generated by BOhance, 53% users think that the BOhance image was generated by a human expert.
Constructing Partial MDS Codes from Reducible Algebraic Curves

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 30 July 2020Accepted: 26 July 2021Published online: 13 December 2021Keywordsalgebraic geometric codes, reducible curves, locally repairable codes, PMDS codesAMS Subject Headings94B27, 14H45, 05D40, 11T71Publication DataISSN (print): 0895-4801ISSN (online): 1095-7146Publisher: Society for Industrial and Applied MathematicsCODEN: sjdmec
One-Stage Open Set Object Detection with Prototype Learning

Convolutional Neural Network (CNN) based object detection has achieved remarkable progress. However, most existing methods work on closed set assumption and can detect only objects of known classes. In real-world scenes, an image may contain unknown-class foreground objects that are unseen in training set but of potential interest, and open set object detection aims at detecting them as foreground, rather than rejecting them as background. A few methods have been proposed for this task, but they suffer from either low speed or unsatisfactory ability of unknown identification. In this paper, we propose a one-stage open set object detection method based on prototype learning. Benefiting from the compact distributions of known classes yielded by prototype learning, our method shows superior performance on identifying objects of both known and unknown classes from images in the open set scenario. It also inherits all advantages of YOLO v3 such as the high inference speed and the ability of multi-scale detection. To evaluate the performance of our method, we conduct experiments with both closed & open set settings, and especially assess the performance of unknown identification using recall and precision of the unknown class. The experimental results show that our method identifies unknown objects better while keeping the accuracy on known classes.
Feature Optimization on Dual Leap Motion Controller for Indonesian Sign Language

Sign language is a language formed by a combination of finger, hand, body movements and facial expressions used by persons with disabilities such as deaf and speech impaired. One of these sign language recognitions is recognition using Leap Motion Controller (LMC) sensor technology. In addition to the sign language that is formed has diversity such as folded fingers, hidden fingers, indonesian sign forms also have characteristics and shapes that are almost similar to one another. The LMC sensor is not always able to recognize all forms of signs properly. In this study, optimization is proposed at the feature level where optimization aims to provide more detailed features and characteristics of each sign language formed. The stages of the process are designing the layout of the sensors, adding features and combining feature data from each sensor. The test of the feature optimization on this dual LMC sensor can provide an increase in the recognition accuracy of the given Indonesian sign language. The Indonesian sign language can be recognized well with an average accuracy of 87.24% and the optimization carried out is able to produce an increase in accuracy of up to 2.88%.
Towards Improving Human Activity Recognition Using Artificial Neural Network

In medical research and human survey systems, human activity recognition has widespread application. This system leverages the power of smartphone sensors and has proven to be dynamic in various sectors. However, human activity recognition is subject to swift changes in user behavior. This has led to difficulty in prediction of human activities. Previously, Long Short Term Memories (LSTMs) have been used to predict the next instance of activity based upon recent activities. Although LSTM has demonstrated its efficacy in human activity prediction with an accuracy of 92.1% on the HAR UCI Machine Learning Repository, there is a scope for increase in accuracy. This work proposes a robust human activity recognition system by creating a sequential deep neural network. The HAR repository was considered as the dataset to be fed to the proposed model. On inference, the model yields an accuracy of 96.44% on the test set. The results explicate that the model could potentially serve as an aid in the pressing problem of human activity prediction.
Comparison of Semantic Web Data Performance Using Virtual and Cloud Services

The term “big data” refers to the rapid development and availability of incredibly massive data collections that can be computationally processed to reveal patterns, trends, and relationships. The term “big data” refers to a compilation of vast amounts of data produced by various sources. Data collection and retrieval are becoming more complicated as the volume of data grows. Big Data Techniques refers to a modern wave of technologies and architectures that are designed to derive information from massive amounts of data in a number of formats. Hadoop uses a massively parallel computing strategy to process data that is scattered through a commodity cluster instead of being processed sequentially. The Hadoop MapReduce Model's mapper and reducer phases aid in the processing of vast volumes of data in a distributed environment, which has an impact on efficiency. Each portion of the data can be separated into smaller bits and stored on a different node within the cluster. With various data loading sizes, nodes were organised using the Amazon web service and the virtual environment. When compared to both frameworks, Hadoop designed in the cloud plays a critical role in data warehousing and clustering to achieve the highest results. As a result, the semantic web data are divided into pre- processing and clustering in this analysis to boost accuracy.
Document-Level Biomedical Relation Extraction with Generative Adversarial Network and Dual-Attention Multi-Instance Learning

Document-level relation extraction (RE) aims to extract relations among entities within a document, which is more complex than its sentence-level counterpart, especially in biomedical text mining. Chemical-disease relation (CDR) extraction aims to extract complex semantic relationships between chemicals and diseases entities in documents. In order to identify the relations within and across multiple sentences at the same time, existing methods try to build different document-level heterogeneous graph. However, the entity relation representations captured by these models do not make full use of the document information and disregard the noise introduced in the process of integrating various information. In this paper, we propose a novel model DAM-GAN to document-level biomedical RE, which can extract entity-level and mention-level representations of relation instances with R-GCN and Dual-Attention Multi-Instance Learning (DAM) respectively, and eliminate the noise with Generative Adversarial Network (GAN). Entity-level representations of relation instances model the semantic information of all entity pairs from the perspective of the whole document, while the mention-level representations from the perspective of mention pairs related to these entity pairs in different sentences. Therefore, entity- and mention-level representations can be better integrated to represent relation instances. Experimental results demonstrate that our model achieves superior performance on public document-level biomedical RE dataset BioCreative V Chemical Disease Relation(CDR).
An Efficient Framework for Multi-Label Learning in Non-stationary Data Stream

Mining multi-label data stream is a huge challenge due to its properties of multiple labels and dynamic streaming characteristics, where each label may experience different con-cept drifts simultaneously or distinctly and class imbalance, etc. Existing works either concentrate on only one type of drift for all labels or ignore the dynamic class imbalance, which usually re-sults in a great degeneration in classification performance. To this end, we propose an efficient ensemble based on the Self-Adjusting Memory (SAM) and Enhanced Punishment mechanism, named as SAMEP, where the Self Adjusting Memory is used to adapt to the heterogeneous concept drift for different labels in multi-label data and the Enhanced Punishment mechanism is utilised to deal with the mining difficulties brought by multiple labels, such as class imbalance, etc. Extensive experiments conducted on multi-label datasets for six common metrics demonstrate the effectiveness of our proposed algorithm as compared to 7 latest well-known multi-label data stream classification methods demonstrated the effectiveness of our proposed method. In addition, non-parametric statistical (the Friedman test with Nemenyi post-hoc) analysis validate the effectiveness of our proposed framework.
Multi-Scale, Super-Resolution Remote Imaging via Deep Conditional Normalizing Flows

Many onboard vision tasks for spacecraft navigation require high-quality remote sensing images with clearly decipherable features. However, design constraints and the operational and environmental conditions limit their quality. Enhancing images through post-processing is a cost-efficient solution. Current deep-learning methods that enhance low-resolution images through super-resolution do not quantify network uncertainty of predictions and are trained at a single scale, which hinders practical integration in image-acquisition pipelines. This work proposes performing multi-scale super-resolution using a deep normalizing flow network for uncertainty-quantified and Monte Carlo-based estimates so that image enhancement for spacecraft vision tasks may be more robust and predictable. The proposed network architecture outperforms state-of-the-art super-resolution models on in-orbit lunar imagery data. Simulations demonstrate its viability on task-based evaluations for landmark identification.
Active Learning for Extracting Technical Terms Covering Multiword Phrases

Automatic extraction of technical terms is an important task for organizing a set of documents. While the sequence labeling formulation is the major approach, we explore a method that takes examples of terms as input and outputs phrases in the same category as the given terms to avoid the heavy cost for building a training dataset. The existing methods in this direction are template-based, which the user cannot give any feedback to the system even if some of the extracted terms are not intended. This paper proposes a framework for extracting technical terms that considers the user’s feedback by adopting active learning approach. The proposed method can extract terms consisting of multiple words by dynamically accessing an inverted index created in advance. We empirically show the effectiveness of the proposed method in comparison to the straightforward application of active learning to an existing method.
Trust-aware Crowdsourcing for Radio Environment Mapping with Privacy Preservation

Database-driven Dynamic Spectrum Sharing (DSS) enhances active monitoring of spectrum usage, such that secondary users are allowed to opportunistically access licenced spectrum with the no-harmful-interference-to-primary-user constraint. A geo-location database administrator (DBA) maintains spectrum availability over its service region by periodically collecting and processing spatio-temporal spectrum data to construct Radio Environment Maps (REMs). To attract mobile users in the outsourced sensing tasks of spectrum measurements, crowdsourced spectrum sensing has great potential in REM construction. However, false measurements submitted by faulty/malicious users cannot be neglected. Constructing better REMs based on crowdsourced spectrum sensing remains an open challenge, especially considering privacy protection of mobile users. In this work, we introduce TAC-REM (Trust-aware Crowdsourced spectrum sensing for the resulting REM optimization), a novel differentially-private reverse auction mechanism. TAC-REM allows the DBA to iteratively select trustworthy spectrum measurements in the presence of false measurements. During each iteration, the DBA employs a spatio-temporal scheme for trustworthiness evaluation, which acts as a guide for user recruitment in crowdsourced spectrum sensing. The proposed adaptive greedy algorithm gradually incorporates the most trustworthy spectrum measurements. The process of user recruitment can be modeled as a stochastic submodular maximization problem, while guaranteeing budget feasibility, approximate REM accuracy maximization, differential bid privacy. Extensive simulations confirm the efficacy and efficiency of the proposed mechanism.
Application of Deep Convolution Neural Network in Breast Cancer Prediction using Digital Mammograms

Cancer, a diagnosis so dreaded and scary, that its fear alone can strike even the strongest of souls. The disease is often thought of as untreatable and unbearably painful, with usually, no cure available. Among all the cancers, breast cancer is the second most deadliest, especially among women. What decides the patients' fate is the early diagnosis of the cancer, facilitating subsequent clinical management. Mammography plays a vital role in the screening of breast cancers as it can detect any breast masses or calcifications early. However, the extremely dense breast tissues pose difficulty in the detection of cancer mass, thus, encouraging the use of machine learning (ML) techniques and artificial neural networks (ANN) to assist radiologists in faster cancer diagnosis. This paper explores the MIAS database, containing 332 digital mammograms from women, which were augmented and preprocessed, and fed into a custom and different pre-trained convolutional neural network (CNN) models, with the aim of differentiating healthy tissues from cancerous ones with high accuracy. Although the pre-trained CNN models produced splendid results, the custom CNN model came out on top, achieving test accuracy, AUC, precision, recall and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F}_{1}$</tex> scores of 0.9362, 0.9407, 0.9200, 0.8025 and 0.8572 respectively while having minimal to no overfitting. The paper, along with proposing a new custom CNN model for better breast cancer classification using raw mammograms, focuses on the significance of computer-aided detection (CAD) models overall in the early diagnosis of breast cancer. While a diagnosis of breast cancer may still leave patients dreaded, we believe our research can be a symbol of hope for all.
Construction Safety Early Warning System of Power Transmission and Transformation Project Based on GIM

In the digital era, the construction of smart power grid puts forward higher requirements for engineering construction. In order to improve the level of security control and work efficiency of power transmission and transformation construction site, an intelligent early warning system based on video recognition and personnel positioning is developed, verified and applicated in this paper. The functions of timely collection of safety risks, accurate judgement of safety rules and real-time feedback of early warning results are realized. At the same time, through the positioning subsystem server and the on-site intelligent security behavior analysis server, the warning notice is converted into digital signal and linked with the on-site voice broadcast equipment to realize the on-site warning, informing and reminding function.
A Novel One-vs-Rest Classification Framework for Mutually Supported Decisions by Independent Parallel Classifiers

We put forward a generic classification architecture of independent parallel CNNs that explicitly exploits a “mutual exclusivity” or “classifiers’ mutually supported decisions” property underlying many dataset domains of interest, namely that in many cases an image in a given dataset might almost unquestionably belong to one class only. Our framework incorporates several designed-to-purpose opinion aggregation decision rules that are triggered when the mutual exclusivity property is or is not satisfied; and makes use of “weights” which intuitively mirror the confidence each CNN has in identifying its corresponding class. Our framework can thus (a) take advantage of clear class boundaries when these exist, and (b) effectively assign items to classes with increased confidence, even when clear class boundaries do not exist. We confirm the effectiveness of our approach via experiments conducted on a well-known dataset from the waste classification domain.
Monocular Endoscope Video-based Augmented Reality for Transoral Laryngeal Tumor Resection Surgery

Locating the tumor during the transoral laryngeal tumor resection surgery is challenging yet important for the surgeons. Hence an augmented reality-based computer-assisted surgery system is proposed to register the pre-operative CT model to intra-operative monocular endoscopy video. The epiglottis is treated as an anatomical marker that is rigid to the tumor. The proposed system consists of initial target map reconstruction, the registration between CT model and intra-operative initial map, and the target’s motion tracking during the operation. A deep learning tracking method is used to estimate the region of the target during the process of initial map reconstruction. The size scale between the map reconstructed from monocular video images and the CT model is solved in the registration procedure. During the surgery, the registered CT model is guided by the motion estimated from the intra-operative video to provide the augmented reality effect. Qualitative experiments are conducted on the clinical dataset, which shows the potential of its further clinical application.
Evaluation Paradigms in Question Answering

Question answering (QA) primarily descends from two branches of research: (1) Alan Turing's investigation of machine intelligence at Manchester University and (2) Cyril Cleverdon's comparison of library card catalog indices at Cranfield University.This position paper names and distinguishes these paradigms.Despite substantial overlap, subtle but significant distinctions exert an outsize influence on research.While one evaluation paradigm values creating more intelligent QA systems, the other paradigm values building QA systems that appeal to users.By better understanding the epistemic heritage of QA, researchers, academia, and industry can more effectively accelerate QA research.
An efficient virtual machine allocation using single stage weapon target assignment model in cloud software‐defined network environment

Summary With the rapid growth of the Internet, technological advancements, and the increasing number of cloud users, a paradigm transformation towards separate data and control planes with a centralized control point is taking place. As a result of this separation, software‐defined networks (SDNs) have distinct software controllers for routers and the capacity to manage network behavior. Virtual machine placement (VMP) and virtual machine migration (VMM) are two examples of SDN usage in data centers. The issue of VMP in cloud environments is a significant challenge that has been widely researched but not fully resolved. In this research, we used the single stage weapon target assignment (SSWTA) to model the virtual machine allocation (VMA) for jobs in order to maximize the resource utilization of the physical machines (PMs) by reducing idle time. Furthermore, this research proposes three algorithms: job assignment algorithms, PM allocation (PMA) using the least recently used (LRU) policy, and optimal VMA. The proposed VMA's performance is compared to other related VMA models such as first fit decreasing, and it is determined to be efficient in terms of resource utilization.