Reviewerly/exHarmony huggingface dataset:
Design of Mission-Based Traversability Assessment (MeTA) Tool for Unmanned Vehicles

In this study, we develop an extensible framework, titled "Mission-Based Traversability Assessment (MeTA) Tool", that produces a more informative map of traversability based on specific situational contexts in a platform agnostic fashion. The aim of this map is to help make traversability-based decisions in complex outdoor environments. This is critical for making sound navigational decisions at the highest level, particularly when engaged in autonomous maneuver. The MeTA tool performs this by filtering out regions that should be avoided by the autonomous vehicle, utilizing a number of mechanisms that can be specified and customized based on the planning context.
MR imaging from CT scan data using generative adversarial network

Magnetic resonance (MR) imaging is an important computer aided diagnosis techniques with rich pathological information. Due to the factor of physical and physiological constraint, it affects the applicability of that technique seriously. However, computed tomography (CT)-based radiotherapy is more popular on account of its imaging rapidity and environmental simplicity. Therefore, it is of great theoretical and practical significance to design a method that can construct MR image from corresponding CT image. In this paper, we treat MR imaging as a machine vision problem and propose a multiconditional generative adversarial network (GAN) for MR imaging from CT scan data. Considering reversibility of GAN, both generator and reverse generator are designed for MR and CT imaging respectively, which can constrain each other and improve consistency between features of CT and MR images. In addition, we use VGG16 model to extract semantic features, perception error and voxel error fusing with original GAN loss is designed to enhance similarity of MR image structure and detail texture features. The experimental results with challenging public CT-MR imaging dataset show distinct performance improvement over other GANs utilized in medical imaging and demonstrate the effect of our method for medical image modal transformation.
Qrelation: an Agent Relation-Based Approach for Multi-Agent Reinforcement Learning Value Function Factorization

The Centralized Training with Decentralized Execution paradigm (CTDE), which trains policies centrally with additional information, is important for Multi-Agent Reinforcement Learning (MARL). For CTDE, value function factorization methods make use of state during training and factorize the value function into multiple local value functions for decentralized execution. These approaches do not fully consider the relational information among agents, resulting in sub-optimal models for complex tasks. To remedy this issue, we propose QRelation which is a graph neural network approach for value function factorization. It considers both the static relations (e.g., agent types) and dynamic relations (e.g., close-by). We show that QRelation can obtain better results than state-of-the-art methods on challenging StarCraft II benchmarks.
Kernel Estimation Network for Blind Super-Resolution

Existing super-resolution (SR) methods commonly assume that the degradation kernels are fixed and known (e.g., bicubic downsampling or single Gaussian blurring kernel). However, these methods suffer a severe performance drop when the real degradations deviate from this assumption. To address this issue, this paper proposes a novel kernel estimation network (KENet) for kernel prediction. Specifically, KENet predicts the degradation kernels by optimizing the kernel space loss in a supervised way, without extra iterations at the inference time. Moreover, we introduce an adaptive attention loss to constrain the kernel optimization space, which can bias the allocation of trainable model parameters towards the most informative components of the estimation kernels. Extensive experiments on synthetic and real images show that the proposed KENet not only encourages a more accurate way to predict degradation kernels but also outperforms existing state-of-the-art blind SR methods when combined with non-blind SR methods.
A Framework for an Automated Development Environment to Support the Data-driven Machine Learning Paradigm

In recent years a machine learning paradigm has emerged, focusing on a data-driven approach as opposed to traditional development. Advances in machine learning techniques have allowed researchers to make substantial gains in tackling complex problems in diverse fields such as medical diagnosis through image analysis, object detection and tracking, and natural language processing. However, often researchers only employ one or two machine learning algorithms with a static feature set while only testing a single hypothesis. This self-imposed bottleneck often produces suboptimal results because it arises from using machine learning within the classical, algorithmic context using existing development tools. Therefore, there is a need to create new development tools which reflect this change to the machine learning paradigm. In this research, we propose a development environment that allows researchers to leverage those capabilities more fully by shifting not only the tool they use but also their mindset. Our proposed environment serves as an intermediate tool, guiding the researcher and making full adoption of the machine learning paradigm throughout the software development process easier. To accomplish this, our framework is defined by a three-layer structure designed for subject domain assessment, data manipulation and feature set exploration. Supported by parallelism, data cleaning and feature engineering, this research provides a conceptual basis for future creation of development environments for the machine learning paradigm. Future development of such a conceptual design would allow for additional intelligent tools to aid the user in designing solutions and support reusability at the design level.
Balanced Ranking and Sorting For Class Incremental Object Detection

Class incremental learning has drawn much attention recently. Although many algorithms have been proposed for class incremental image classification, developing object detectors which can learn incrementally is still a challenge. Existing methods rely on knowledge distillation to achieve class incremental object detection (CIOD), which suffer from performance tradeoff between old and new classes. In this paper, we propose balanced ranking and sorting (BRS), to tackle the catastrophic forgetting and data imbalance problems for CIOD. Specifically, ranking & sorting with pseudo ground truths (RSP) and ranking & sorting transfer (RST) are developed to preserve the learned knowledge from the old model while learning new classes, in an unified framework. To mitigate the data imbalance problem, gradient rebalancing is performed with specific sample pairs. We demonstrate the effectiveness of our approach with extensive experiments on PASCAL VOC and COCO datasets, in which significant improvement over state-of-the-art methods is achieved.
Identification of Optimal Model for Multi-Class Classification of COVID Tweets

Covid-19 is the worst-hit pandemic that has affected humankind to date. It sent all major nations around the globe into lockdowns for at least half of 2020. The lockdown started to increase unrest in the population, and even some of them started sharing the emotions infused by the unrest and lockdown over social media platforms in the form of posts, stories, articles. The emotions that underlie those posts can be categorized into three categories positive, negative and neutral, and the individual posts can be classified into respective labels. We considered one of the social platforms&#x0027; Twitter and collected Twitter tweets. The dataset included the text from the tweet along with emotion. The dataset was pre-processed, including removing stop words from the dataset, stemming and lemmatizing the words from tweets text. Our work focused on various models that can be used to analyze sentiment and classification. The work includes implementing standard classification models like Naive-Bayes multinomial Classifier, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree Classifier, Logistic Regression, Deep Learning models - Long short-term memory (LSTM), Gated recurrent unit (GRU), Bidirectional long-short term memory (Bidirectional LSTM), Bidirectional Encoder Representations from Transformers (BERT). The results from all these models are compared and tried to establish the most efficient model based on accuracy. The BERT model outperformed all other methods when compared to other models developed using Machine Learning (ML) and Deep Learning (DL) techniques.
Self-Learned Video Super-Resolution with Augmented Spatial and Temporal Context

Video super-resolution methods typically rely on paired training data, in which the low-resolution frames are usually synthetically generated under predetermined degradation conditions (e.g., Bicubic downsampling). However, in real applications, it is labor-consuming and expensive to obtain this kind of training data, which limits the practical performance of these methods. To address the issue and get rid of the synthetic paired data, in this paper, we make exploration in utilizing the internal self-similarity redundancy within the video to build a Self-Learned Video Super-Resolution (SLVSR) method, which only needs to be trained on the input testing video itself. We employ a series of data augmentation strategies to make full use of the spatial and temporal context of the target video clips. The idea is applied to two branches of mainstream SR methods: frame fusion and frame recurrence methods. Since the former takes advantage of the short-term temporal consistency and the latter of the long-term one, our method can satisfy different practical situations. The experimental results show the superiority of our proposed method, especially in addressing the video super-resolution problems in real applications.
MRFSG-IG: MapReduce based Frequent Subgraph Mining using Integrated Graph Index

In a distributed environment, as the graph databases emerge from multiple sources, the graph database&#x0027;s size increases rapidly, resulting in a massive database. Mining frequent subgraphs is a fundamental problem for graph clustering, classification, and subgraph association rule mining. Nevertheless, extracting frequent subgraphs from an extensive graph database is complex work. Existing works for frequent mining subgraphs assume that the large graph database is stored on a single system. This assumption is not correct in all cases. This paper designs a novel algorithm in which frequent subgraph mining takes advantage of partitioning and graph indexing. We follow three necessary steps: graph partitioning, graph indexing, and frequent subgraph mining. We used structure-based graph partitioning for frequent subgraph mining. We use Integrated Graph Index as a vertical format of graphs in the case of frequent itemset mining. Integrated Graph Index has low maintenance costs, and it provides an easy way to add graphs in the massive graph database. We complete frequent mining subgraphs in a single Map-Reduce round. This approach is suitable for incremental mining of frequent subgraphs from the large graph database. We proved that this technique reduces running time to find frequent subgraphs over large graph databases.
Coastal Eco Explorer: A Mobile Application for Ecology Education

The Coastal Eco Explorer is an Apple and Android mobile application that delivers educational information for 64 ecological sites in Carolina Beach State Park. The app is an interdisciplinary collaboration between university faculty and state park rangers to educate park visitors. The Coastal Eco Explorer was launched in March 2021 and has several hundred users. This article describes the genesis of the Coastal Eco Explorer project, its technical architecture, and its current functionality. The app is the first official app published by the university. The app&#x2019;s development yielded several lessons learned of interest to educators interested in developing published end-user software.
Examination of the Financial Relationship Between Overall Stock Market Value and Corporate Investments in Privacy

To better understand why privacy breach incidents continue to occur, this research utilizes Event Study Methodology to examine an area of weakness linked to this phenomenon: corporate investments in privacy. It is argued in this study that a perceived lack of financial value exists to motivate proactive corporate investments in privacy. It is further argued that minimal concern exists for potential financial loss to overall corporate stock market value from privacy breach incidents to motivate reactive corporate investments in privacy. Quantitative analysis of the five-year data sample investigated in this research indicates minimal positive increase in overall corporate stock market value associated with corporate investments in privacy, in parallel with zero presence of long-term financial loss to overall corporate stock market value resulting from privacy breach incidents. This study presents the first known event study research quantifying the financial impact of corporate investments in privacy within the Information System, Assurance, or Cybersecurity domains and disciplines.
An Investigation of Decentralized Ledger Applications Using Ethereum in a Blockchain Network

The word blockchain is one of the most popular hotlists of today&#x0027;s emerging technologies. Because of decentralized secured applications, it offers many advantages and thus professionals from various domains are incorporating it in their fields. Though there are some minor security issues, given its advantages towards privacy in Peer-to-Peer (P2P) networks still it can be integrated into many other fields such as the Internet of Things (IoT), Cloud computing, Artificial intelligence, Banking, and Financial Sectors, Edge computing, etc. This research article focuses on studying and reviewing existing works in blockchain technology towards various applications by describing its architecture, and tools categories. Our contribution to this paper is to examine decentralized ledger applications using Ethereum in a blockchain network through Ganache-Command Line Interface (CLI), and determine its transaction behavior, to investigate the solidity program required for establishing smart contracts on Ethereum blockchain networks, and finally to give a comparative analysis of various blockchain tools suitable for running on multiple platforms.
A Multi-Task Learning Framework for Chinese Medical Procedure Entity Normalization

Medical entity normalization is a fundamental task in medical natural language processing and clinical applications. The task aims to map medical mentions to standard entities in a given knowledge base. In this paper, we focus on Chinese medical procedure entity normalization. This task brings an extra multi-implication challenge that a mention may link to multiple standard entities. To perform the task, we propose a novel deep neural multi-task learning framework to jointly model implication number prediction and entity normalization. Our model utilizes the multi-head attention mechanism to provide mutual benefits between the two tasks. Experimental results show that our method achieves comparable performance compared with the baseline methods.
Attributable Watermarking of Speech Generative Models

Generative models are now capable of synthesizing images, speeches, and videos that are hardly distinguishable from authentic contents. Such capabilities cause concerns such as malicious impersonation and IP theft. This paper investigates a solution for model attribution, i.e., the classification of synthetic contents by their source models via watermarks embedded in the contents. Building on past success of model attribution in the image domain, we discuss algorithmic improvements for generating user-end speech models that empirically achieve high attribution accuracy, while maintaining high generation quality. We show the tradeoff between attributability and generation quality under a variety of attacks on generated speech signals attempting to remove the watermarks, and the feasibility of learning robust watermarks against these attacks. Watermarked speech samples are available at https://attdemo.github.io/attdemofull.github.io.
A single image defogging algorithm for sky region recognition based on binary mask

Since entering the 21st century, technology has developed rapidly, the computer and image processing technology also ushered in a peak period of development.. However, as the natural environment became more hostile, in particular, smog is becoming more common, it will cause the sharpness degradation and color degree distortion of outdoor images collected. The use of these damaged images as input to the system must seriously affect the analysis and understanding of the system as a whole, greatly reduce the performance of the visual system. Therefore, to study how to effectively reconstruct degraded images into original images in foggy environment, it has important application value and practical significance to improve the performance and robustness of vision system. The minimum filtering process using local filtering in the original algorithm will inevitably lead to block effect. In this paper, a filter whose window size varies with the image size is used to solve the blocky phenomenon and halo effect easily caused by the original algorithm. At the same time, this paper uses the sky region recognition algorithm of binary mask graph to prevent the color distortion of the sky part in the image restoration. Then, a guide filter with fine transmittance effect and good speed is applied to further optimize the transmittance map.
Clustering Complex Subspaces in Large Dimensions

A methodology to cluster multiple sets of Gaussian multivariate complex observations based on the alignment of their column spaces is presented. These subspaces are identified with points in the Grassmann manifold and compared according to a similarity measure drawn from a chosen manifold distance, which is proportional to the squared projection–Frobenius norm. In order to guarantee that distances between subspaces of different dimensions are comparable, we proposed to normalise the corresponding decision statistics with respect to their asymptotic mean and variance, assuming that (i) the dimensions of both the observation and the involved subspaces are large but comparable in magnitude and (ii) both subspaces are generated by the same statistical law. A procedure is derived to estimate these normalisation parameters, leading to a new statistic that can be built exclusively from the observations. The method is applied to a MIMO wireless channel clustering problem, where is shown to outperform conventional similarity measures in terms of classification performance.
An Analysis of Website Phishing Awareness in Jamaica

Phishing, one of those not so difficult cyber-attacks to prevent, continues to plague society and hampers the performance of organizations. This is mainly because of the users&#x2019; high susceptibility to phishing attacks and reluctance to learn. In this paper, we highlight the degree to which Jamaicans are unaware of how to identify website phishing attacks and provide recommendations to reduce their unawareness. We believe through proper and consistent public education; website phishing attacks can be prevented.
Utilising Sampling Methods to Improve the Prediction on Customers’ Buying Intention

One of the e-commerce challenges is online shopping systems&#x2019; capability to predict the intention of customer purchasing, especially when building prediction models with unbalanced data sets. Data pre-processing methods, i.e., samplings, are commonly used to tackle the challenge for better prediction. We used a real-time online shopper purchasing intention data set from a published study in this project. The data set is unbalanced as it consists of only 15.5% of the Buy class and the remainder for the No-Buy class. In our experiments, six machine learning algorithms were applied and compared. They were K-Nearest Neighbour (KNN), Na&#x00EF;ve Bayes (NB), C4.5, Support Vector Machine (SVM), Sequential Minimal Optimization (SMO) and Multilayer Perceptron (MLP). Generally, these classification algorithms cannot predict the customer intention well, especially for the minority Buy class. We used sampling methods to improve the classification results. The methods used were over-sampling, under-sampling and hybrid sampling, which modified the data set class distribution to achieve better results. Our results showed that the hybrid sampling method gave a comparable classification result and required less computational cost than the previously published study.
Uniqueness Metric for Comparison of Dense Image Descriptors

Image features and their descriptors are broadly used in many computer vision applications ranging from image retrieval and camera calibration to object recognition and image registration. Desired qualities of image features and descriptors frequently include low sensitivity to noise and invariance to illumination, scale changes, and rotation, while preserving uniqueness. Having non-unique or very similar descriptors at different regions of the same image might affect negatively feature matching algorithms and limit their usefulness. Presently, the choice of the best feature detector-descriptor pair for a particular application is based on comparisons using binary evaluation metrics such as accuracy and precision. While these metrics in many cases predict well the overall performance, they cannot provide granular information about the local behavior. Moreover, if the descriptor is dense, i.e., every pixel produces a feature vector, then using binary global metrics is meaningless. We propose a uniqueness metric, scaled between [-1,1], for descriptors. It allows identifying how unique they are in a predefined local neighborhood. The proposed metric also allows an easier exploration of noise effects on image descriptors. Our results show the applicability of the proposed metric to any dense descriptor and demonstrate how uniquely each pixel can be identified within the neighborhood. The proposed metric also could serve for the development of new feature detectors that optimize uniqueness. Based on that metric, we provide a comparison of state-of-the-art descriptors with regard to noise robustness.
Secure Monitoring and Notification System for Cloud Infrastructures

As the number of cyber attacks on cloud infrastructures increases, cloud monitoring systems are needed to detect possible breaches, identify security gaps, and secure the cloud assets well before a possible cyber attack happens. In this paper, we present a monitoring system for cloud infrastructures that can collect statistics, detect several types of issues in the cloud and send notifications in a protected form to the owner. Our solution supports data confidentiality and integrity for cloud instance notifications and alerts, so that they can be sent via email or in popular messengers, such as Microsoft Teams or Slack. Furthermore, our solution provides fine-grained role-based and attribute-based access control for cloud instance notifications and alerts. We will also show how we integrated our cloud monitoring solution with the Amazon EC2 cloud. However, our approach is generic and can be used on other cloud platforms.