Reviewerly/exHarmony huggingface dataset:
Spectral warping and data augmentation for low resource language ASR system under mismatched conditions

The performance of an Automatic Speech Recognition System (ASR) system deteriorates while using it on children speech, due to large variations and mismatch of acoustic and linguistic variables between spoken utterances of adults and children. Another important reason for the low efficiency of ASR models is the data scarcity of children speech data for low resource-language like Punjabi. The proposed work in this paper tries to address the both challenges i.e. acoustic and linguistic variations challenge, and data scarcity problem, thereby improves performance of a children speech ASR system for Punjabi language. To handle the first issue of acoustic and linguistic variations, the proposed work uses formant modification as a spectral warping technique to reduce the variation between children speech and adult speech. Further, to address the second issue of data scarcity, this paper discusses training of ASR models on augmented children speech data. Also, the work combines well established Mel-Frequency Cepstral Coefficients (MFCC) features extraction technique with Frequency Domain Linear Prediction (FDLP) to propose MFCC-FDLP hybrid approach for front end feature extraction. For implementing the data augmentation, Tacotron 2, an end-to-end Text to Speech (TTS) generative model has been used. The proposed work uses MFCC, FDLP and hybrid MFCC + FDLP techniques for front end feature extraction, Time Delay Neural Network (TDNN) for backend acoustic modeling, and trigram language model to implement continuous Punjabi language ASR systems. To increase robustness of the proposed ASR system, we have included a batch of lexically diverse words in our pronunciation model which achieved a relative improvement of 29.44%.
Biometric-Based Human Recognition Systems: An Overview

With the proliferation of automated systems for reliable and highly secure human authentication and identification, the importance of technological solutions in biometrics is growing along with security awareness. Indeed, conventional authentication methodologies, consisting of knowledge-based systems that make use of something you know (e.g., username and password) and token-based systems that make use of something you have (e.g., identification card), are not able to meet the strict requirements of reliable security applications. Conversely, biometric systems make use of behavioral (extrinsic) and/or physiological (intrinsic) human characteristics, overcoming the security issues affecting the conventional methods for personal authentication. This book chapter provides an overview of the most commonly used biometric traits along with their properties, the various biometric system operating modalities as well as various security aspects related to these systems. In particular, it will be discussed the different stages involved in a biometric recognition process and further discuss various threats that can be exploited to compromise the security of a biometric system. Finally, in order to evaluate the systems’ performance, metrics must be adopted. The most widely used metrics are, therefore, discussed in relation to the provided system accuracy and security, and applicability in real-world deployments.
FedEqual: Defending Model Poisoning Attacks in Heterogeneous Federated Learning

With the upcoming edge AI, federated learning (FL) is a privacy-preserving framework to meet the General Data Protection Regulation (GDPR). Unfortunately, FL is vulnerable to an up-to-date security threat, model poisoning attacks. By successfully replacing the global model with the targeted poisoned model, malicious end devices can trigger backdoor attacks and manipulate the whole learning process. The traditional researches under a homogeneous environment can ideally exclude the outliers with scarce side-effects on model performance. However, in privacy-preserving FL, each end device possibly owns a few data classes and different amounts of data, forming into a substantial heterogeneous environment where outliers could be malicious or benign. To achieve the system performance and robustness of FL's framework, we should not assertively remove any local model from the global model updating procedure. Therefore, in this paper, we propose a defending strategy called FedEqual to mitigate model poisoning attacks while preserving the learning task's performance without excluding any benign models. The results show that FedEqual outperforms other state-of-the-art baselines under different heterogeneous environments based on reproduced up-to-date model poisoning attacks.
Break-Glass Conceptual Model for Distributed EHR management system based on Blockchain, IPFS and ABAC

The recently proposed Blockchain-based healthcare system proposes an interesting vision for the level of data integrity and security. This research aims to propose a conceptual model of a break-glass conceptual for Blockchain-based healthcare systems. In case of emergency, it provides access to the whole patient’s medical records for healthcare professionals as quickly as possible regarding patients’ privacy and data security. The proposed conceptual model was designed based on blockchain technology, IPFS (InterPlanetary File System), and ABAC (Attribute-Based Access control) as a novel design in this domain. In current healthcare systems, regulatory and non-integrated offline data sources make it near impossible for timely access to patients’ EHRs and EMRs, even in case of emergencies for healthcare professionals. Our conceptual model could be a satisfactory alternative not only for patients but also for governing organizations to handle this situation clearly by regarding patients’ privacy. Additionally, it can work in an untrusted environment, and it doesn’t require bypassing the access control system to make the patients’ data available. In case of emergencies, healthcare professionals receive medical records access near just in time with regard to all the rights of security and privacy based on the attribute which were set by the patients in the past. This novel conceptual model has been designed by coupling Blockchain technology with IPFS, and the attribute base control system (ABAC).
Is this URL Safe: Detection of Malicious URLs Using Global Vector for Word Representation

Users are frequently exposed to many unknown links through advertisements and emails. These links may contain URLs to mount targeted attacks like spamming, phishing, and malware installation. Using blacklist of URLs is the most widely used defense mechanism to detect a malicious URLs. However, automatically generating such a list for fresh malicious URLs is challenging. Detecting a URL as malicious using the lexicographical approach is an important research problem. This paper proposes a malicious URL detection mechanism using natural language processing. We use features including word vector representation obtained through GloVe along with statistical cues and n-gram on blacklist words. The proposed approach is efficient, and it does not require inputs from external servers to identify malicious URLs. Experiments are performed on 227,909 size database containing 80,128 benign and 147,781 malicious URLs. Proposed system has achieved an accuracy of 89% for ANN model with GloVe based features.
Integrative data reuse at scientifically significant sites: Case studies at Yellowstone National Park and the La Brea Tar Pits

Abstract Scientifically significant sites are the source of, and long‐term repository for, considerable amounts of data—particularly in the natural sciences. However, the unique data practices of the researchers and resource managers at these sites have been relatively understudied. Through case studies of two scientifically significant sites (the hot springs at Yellowstone National Park and the fossil deposits at the La Brea Tar Pits), I developed rich descriptions of site‐based research and data curation, and high‐level data models of information classes needed to support integrative data reuse. Each framework treats the geospatial site and its changing natural characteristics as a distinct class of information; more commonly considered information classes such as observational and sampling data, and project metadata, are defined in relation to the site itself. This work contributes (a) case studies of the values and data needs for researchers and resource managers at scientifically significant sites, (b) an information framework to support integrative reuse at these sites, and (c) a discussion of data practices at scientifically significant sites.
Video super-resolution via mixed spatial-temporal convolution and selective fusion

• A Mixed Spatial-Temporal Convolution block combines spatial-temporal information. • Selective feature fusion strategy ensures the effectiveness of feature merging. • Motion compensation alleviates the influence of misalignments. • Our approach can achieve better performance on various datasets. Video super-resolution aims to recover the high-resolution (HR) contents from the low-resolution (LR) observations relying on compositing the spatial-temporal information in the LR frames. It is crucial to model the spatial-temporal information jointly since the video sequences are three-dimensional spatial-temporal signals. Compared with explicitly estimating motions between the 2D frames, 3D convolutional neural networks (CNNs) have been shown its efficiency and effectiveness for video super-resolution (SR), as a natural way of spatial-temporal data modelling. Though promising, the performance of 3D CNNs is still far from satisfactory. The high computational and memory requirements limit the development of more advanced designs to extract and fuse the information from a larger spatial and temporal scale. We thus propose a Mixed Spatial-Temporal Convolution (MSTC) block that simultaneously extracts the spatial information and the supplemented temporal dependency among frames by jointly applying 2D and 3D convolution. To further fuse the learned features corresponding to different frames, we propose a novel similarity-based selective features strategy, unlike precious methods directly stacking the learned features. Additionally, an attention-based motion compensation module is applied to alleviate the influence of misalignment between frames. Experiments on three widely used benchmark datasets and real-world dataset show that, relying on superior feature extraction and fusion ability, the proposed network can outperform previous state-of-the-art methods, especially for recovering the confusing details.
A high‐performance insulators location scheme based on YOLOv4 deep learning network with GDIoU loss function

This paper proposes a Gaussian Distance Intersection over Union (GDIoU) loss function-based YOLOv4 deep learning network to solve the problem of slow speed and low accuracy insulator location in power facilities health inspection. In the scheme, A GDIoU loss function is designed to accelerate the convergence speed of the YOLOv4 deep learning network; at the same time, the GDIoU loss is added as one part of the network propagation loss, and the insulator's location accuracy is accordingly improved. Moreover, a re-location scheme for tilt insulators correction is proposed to enhance the location accuracy of the insulators in different spatial angle states. Large amounts of field insulator images were gathered as training and testing samples to evaluate the performance of the proposed scheme. The experimental results have demonstrated that the GDIoU-based YOLOv4 deep learning network combined with the tilt correction scheme can improve the insulator location speed by three times compared with the peer schemes, and the average precision is increased by 7.37% compared with the naive YOLOv4 network. The performance of the proposed scheme meets the requirement of online insulator location adequately.
Automated Security Assessment for the Internet of Things

Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inefficient. To address this problem, we propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions for predicting vulnerability metrics. The predicted metrics are then input into a two-layered graphical security model, which consists of an attack graph at the upper layer to present the network connectivity and an attack tree for each node in the network at the bottom layer to depict the vulnerability information. This security model automatically assesses the security of the IoT network by capturing potential attack paths. We evaluate the viability of our approach using a proof-of-concept smart building system model which contains a variety of real-world IoT devices and poten-tial vulnerabilities. Our evaluation of the proposed framework demonstrates its effectiveness in terms of automatically predicting the vulnerability metrics of new vulnerabilities with more than 90% accuracy, on average, and identifying the most vulnerable attack paths within an IoT network. The produced assessment results can serve as a guideline for cybersecurity professionals to take further actions and mitigate risks in a timely manner.
RAMT-GAN: Realistic and accurate makeup transfer with generative adversarial network

Automatic makeup transfer aims to precisely transfer the makeup style from a given reference makeup face image to a source image while preserving face identity and background information. Current works have achieved promising results in makeup transfer by using deep learning techniques. However, existing methods are focused on only one or two requirements, thus cannot simultaneously achieve face identity preservation, background retention, and accurate makeup transfer. Besides, it is also difficult to acquire a pair of well-aligned face images with different makeup styles. Aimed at these problems, we propose RAMT-GAN (Realistic and Accurate Makeup Transfer Generative Adversarial Network), a GAN-based image transformation framework for achieving realistic and accurate makeup style transfer. Specifically, we utilize a dual input/output network that builds on the BeautyGAN architecture to achieve cross-domain image transformation. Then, identity preservation loss and background invariant loss are introduced in RAMT-GAN to help synthesize realistic and accurate face makeup images. Extensive experiments demonstrate that the proposed makeup transfer model can synthesize makeup faces with accurate reference style as well as maintaining the identity information and the background information.
5G-Empowered Drone Networks in Federated and Deep Reinforcement Learning Environments

Discovering important knowledge that may be available from databases while preserving the privacy of sensitive information is a hot research subject in data mining recently. With the establishment of 5G, several data-intensive applications will be developed. Privacy of information over the network is increasingly relevant, and as edge computing has grown more critical, applications running over drone networks require protection. The privacy of information while utilizing data is a trade-off that needs to be addressed. In this article, a deep-reinforcement-learning-based technique is applied to hide the sensitive information from a given database while keeping the balance between privacy protection and knowledge discovery during the sanitization process. Furthermore, minimizing known side effects that can be caused in the sanitization process is also considered. A particular set of recommendations, along with potential applications, are discussed with use cases.
A Mocktail of Source Code Representations

Efficient representation of source code is essential for various software engineering tasks such as code classification and code clone detection. Most recent approaches for representing source code still use AST and do not leverage semantic graphs such as CFG and PDG. One effective technique for representing source code involves extracting paths from the AST and using a learning model to capture program properties. Code2vec is one such path-based approach that uses an attention-based neural network to learn code embeddings which can then be used for various downstream tasks. However, this approach uses only AST and does not leverage CFG and PDG. Even though an integrated graph approach (Code Property Graph) exists for representing source code, it has only been explored in the domain of software security. Moreover, it does not leverage the paths from the individual graphs. Our idea is to extend the path-based approach code2vec to include the semantic graphs CFG and PDG with AST, which is largely unexplored in software engineering. We evaluate our approach on the task of METHODNAMING using a C dataset of 730K methods collected from GitHub. In comparison to code2vec, our approach improves the F1 score by 11% on the full dataset and up to 100% with individual projects. We show that semantic features from the CFG and PDG paths drastically improve the performance of the software engineering tasks. We envision that looking at a mocktail of source code representations for various software engineering tasks can lay the foundation for a new line of research and a re-haul of existing research.
A network of sky imagers for spatial solar irradiance assessment

A network of seven low-cost hemispheric sky-imaging cameras has been installed in the Los Angeles basin. This network of cameras provides wide sky coverage to perform spatial solar irradiance assessments. An Image to Irradiance algorithm (I2I) is proposed to simultaneously derive high-resolution diffuse, direct and global solar irradiance from sky images. Spatial interpolation using the Kriging method is used to derive the irradiance field for the whole basin area. The relatively inexpensive network of cameras can provide spatially resolved GHI that is more accurate than GHI derived from GOES-west satellite images provided by the Cooperative Institute for Meteorological Satellite Studies (CIMSS) when the distance to the nearest site is less than 40 km. This work successfully demonstrates that, with minor trade-off in accuracy, solar irradiance monitoring can be achieved using off-the-shelf cameras in the absence of radiometers.
Motif-based memory networks for complex-factoid question answering

Knowledge-based question answering (KBQA) is an interesting but challenging task in the field of natural language processing. And in recent years, there is increasing interest in introducing deep learning models for answering complex-factoid questions, which associate with multiple facts and require multi-hop inference. However, the complex-factoid question answering mainly faces two challenges: (1) multiple entities are involved in these questions, which bring multiple initial states for knowledge reasoning; (2) a number of complex-factoid questions require the intersection of multiple related sub-paths in knowledge bases, which demands repeated explorations of path reasoning, matching and assembling. To address the above challenges, we propose a motif-based Memory Network for answering complex-factoid questions, which introduces motifs as the basic constituents for semantic representation, and meanwhile includes a specific-designed memory network for knowledge reasoning and matching. Extensive experiments on real datasets demonstrate that our model significantly outperforms the state-of-the-art methods.
Energy Aware Resource Optimization using Unified Metaheuristic Optimization Algorithm Allocation for Cloud Computing Environment

In recent times, cloud computing (CC) has rapidly emerged as an effective framework for offering IT infrastructure, resources, and services on a pay-per-use basis. An extensive utilization of CC and virtualization technologies has resulted in the development of large-scale data centers which spend massive quantity of energy and have significant carbon footprints. Since 3% of global electricity is being consumed by the data centers in the present world, energy efficiency becomes a major issue in data centres and cloud computing. At the same time, resource allocation finds useful in CC to effectively utilize the available computing resources in the network for facilitating the processing of complex task which necessitate large-scale processing. In this view, this paper presents new hybrid metaheuristics for energy efficiency resource allocation (HMEERA) for the CCC environment. The proposed model initially performs the feature extraction process based on the task demands from many clients and feature reduction process takes place using principal component analysis (PCA). Then, the integrated features are used by the HMEERA technique for optimal resource allocation. The HMEERA model involves the hybridization of the Group Teaching Optimization Algorithm (GTOA) with rat swarm optimizer (RSO) algorithm, called GTOA-RSO for optimal resource allocation. The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in cloud datacenter. For experimental validation, a comprehensive set of simulations were performed using CloudSim tool. The experimental results showcased the superior performance of the HMEERA model interms of different aspects.
LSP: Lightweight Smart-Contract-Based Transaction Prioritization Scheme for Smart Healthcare

In recent years, several blockchain-based models have emerged to provide a secure way to store and access sensitive electronic medical records (EMRs) across the healthcare sector. These records are of different priorities and business requirements. From our comprehensive literature review, we observe that the existing models have no provision of prioritizing the EMR transactions. This critically affects the quick and streamline sharing of emergency EMRs in a smart healthcare environment. Furthermore, the lack of prioritization significantly restricts the optimal usage of the blockchain network. Motivated by this, we first propose a lightweight and deterministic method to prioritize the flow of emergency healthcare transactions through the smart contract. We also propose logical stateless transaction models for different entities involved in the system with varying levels of trust. Finally, the performance of the model based on the private Ethereum is verified and it outperforms the existing benchmark model in terms of usefulness in the healthcare setting and computation overhead with the use of a simple prioritizing algorithm. The obtained results demonstrate the feasibility of the proposed scheme in the real-time smart healthcare system.
An artificially intelligent approach for automatic speech processing based on triune ontology and adaptive tribonacci deep neural networks

Automatic Speech Recognition systems have become essential for an independent automation during the present-day era. A hybrid approach for Automatic Speech Recognition, the TriNNOnto has been proposed in this paper which, integrates different approaches like Language Model integrated with dynamic Triune Ontology generation scheme, Acoustic Model and Feature modelling are hybridised based on the Tribonacci based Deep Neural Network, which decides upon the number of layers depending on the size of the samples and their count. The dynamic generation of Ontologies based on the language models and triune ontology for automatic speech recognition is quite novel. The strategies for feature extraction as and the Tribonacci based deep neural network, based the dynamic adjustment of the number of layers using Tribonacci series contributes towards novelty as well as enhances the performance of speech recognition. The proposed strategy has been evaluated for two datasets and an accuracy of 98.15% and 95.18%, have been achieved for the CMUKids and the TIMIT datasets, respectively with low word error rates.