Reviewerly/exHarmony huggingface dataset:
Combination of Trigram and Rule-based Model for Singlish to Sinhala Transliteration by Focusing Social Media Text

Language transliteration has become an important area of language processing. Most Sri Lankans use “Singlish”, in which Sinhala (Sinhalese) words are written in English letters in social media. This study evaluates several methods of Singlish to Sinhala transliteration and addresses the problem of transliteration by using a trigram method. A trigram model is combined with the rule-based method which is a system of the UCSC implemented early. The trigram model was trained with Singlish YouTube comments and with their corresponding Sinhala transliteration. The trigram model uses the implementation of the trigram tagger, and the training data set was also formatted as in POS tag dataset. Together the Trigram and Rule base model gives better results for transliteration with the letter-wise accuracy of 77%. This approach is enriched with a natural UX approach to capture the Singlish text content.
Second-Order Guarantees in Federated Learning

Federated learning is a useful framework for centralized learning from distributed data under practical considerations of heterogeneity, asynchrony, and privacy. Federated architectures are frequently deployed in deep learning settings, which generally give rise to non-convex optimization problems. Nevertheless, most existing analysis are either limited to convex loss functions, or only establish first-order stationarity, despite the fact that saddle-points, which are first-order stationary, are known to pose bottlenecks in deep learning. We draw on recent results on the second-order optimality of stochastic gradient algorithms in centralized and decentralized settings, and establish second-order guarantees for a class of federated learning algorithms.
Stablecoins: Survivorship, Transactions Costs and Exchange Microstructure

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Algorithm for reasoning with words based on linguistic fuzzy cognitive map

Summary In this paper, we introduce a method for reasoning with words based on hedge algebra using linguistic cognitive map ( ). Our model consists of a set of vertices and edges whose values are linguistic variables that are constrained by a linguistic lattice. The algorithm for the system studied consists of fuzzification, reasoning, and defuzzification. Further, the total effect between any two vertices in the is computed.
Temporal Label Aggregation for Unintentional Action Localization

Humans can easily understand whether a person’s action is intentional or not. However, it is very challenging to teach a machine to recognize this due to the lack of referable comparisons and reliable annotations. Given a video with unintentional action, the annotations are usually unreliable due to the intrinsic ambiguity from multiple annotators and the subjective appraisals. To address this problem, we propose a new framework which online aggregates multiple probabilistic labels for unintentional action localization. Specifically, we first model the uncertainty of annotations with a temporal probability distribution, and then develop a label attention model to aggregate the reliable annotations in an online manner. We evaluate our method on the public OOPS dataset where each video contains multiple annotations of unintentional action and our experimental results show that mining reliable supervision information from multiple unreliable annotations achieves significant improvements over the baseline methods.
A Study on Ensemble Methods for Classification

Classification is the most common task in machine learning which aims in categorizing the input to set of known labels. Numerous techniques have evolved over time to improve the performance of classification. Ensemble learning is one such technique which focuses on improving the performance by combining diverse set of learners which work together to provide better stability and accuracy. Ensemble learning is used in various fields including medical data analysis, sentiment analysis and banking data analysis. The proposed work focuses on surveying the techniques used in ensemble learning which covers stacking, boosting and bagging techniques, improvements in the field and challenges addressed in ensemble learning for classification. The motivation is to understand the role of ensemble methods in classification across various fields.
MRT: Tracing the Evolution of Scientific Publications

The fast development of science and technology is accompanied by the booming of cutting edge research. Researchers need to digest more and more recently published publications in order to keep themselves up to date. This becomes tough in particular with the prevalence of preprint publishing such as arXiv, where inspiring works could come out without being peer-reviewed. Is that possible to design an automatic system to help researchers quickly gain a glimpse of a piece of work or gain useful background knowledge for deeply understanding it To this end, we proposed a practical framework called Master Reading Tree (MRT) to trace the evolution of scientific publications. In this framework, we can build annotated evolution roadmaps for publications and identify important previous works or evolution tracks by generating expressive embeddings and clustering them into various groups. With comprehensive evaluations, our proposed framework demonstrates its superior capability in capturing underlying relations behind publications over several baseline algorithms. Finally, we integrated the proposed MRT framework on AMiner, an online academic platform, where users can generate roadmaps using MRT for free and their interactions are further used to refine the model.
Financial Innovation: The Impact of Mobile Money on Innovative Economic Growth

This paper examines the effect of mobile money development on economic growth and development in sub-Saharan Africa (SSA) for 2011–2018 period using the partial least squares (PLS) regression. The causality within and between mobile money development, which include the continuous surge in registered mobile money agents, rapid growth of annual transactions as well as the overall yearly capitalization of mobile money transactions on the sub-Saharan African financial sector development and economy. It was established that mobile money development has a significant impact on economic growth, where GDP per capita was employed as the dependent variable, but the model results may largely depend on the variable used to proxy for economic growth. Also, a significant positive Pearson correlations were found between mobile money activities and financial development as well as GDP, and hence proving that the rise of mobile money activities like number of agents and volume of transactions has an effect on economic development in sub-Saharan African economies per our study model. Mobile money is an alternative mode of banking for the unbanked population; thus, it is not surprising that its expansion and easy access positively affects financial sector growth in the region. Hence, the adoption and development of electronic banking and payment affect the economy through various ways like trade, household consumption and remittances. Although, any policy initiative implemented to encourage and boost this type of payment and banking method may not immediately affect the economy, however, the mobile money system facilitates the pooling of capital and its onward effective allocation to productive sectors, thus promoting and enhancing innovative development in the region.
Accelerating Large-Scale Nearest Neighbor Search with Computational Storage Device

K-nearest neighbor algorithm that searches the K closest samples in a high dimensional feature space is one of the most fundamental tasks in machine learning and image retrieval applications. Computational storage device that combines computing unit and storage module on a single board becomes popular to address the data bandwidth bottleneck of the conventional computing system. In this paper, we propose a nearest neighbor search acceleration platform based on computational storage device, which can process a large-scale image dataset efficiently in terms of speed, energy, and cost. We believe that the proposed acceleration platform is promising to be deployed in cloud datacenters for data-intensive applications.
Cluster analysis via random partition distributions

Abstract Hierarchical and k‐medoids clustering are deterministic clustering algorithms defined on pairwise distances. We use these same pairwise distances in a novel stochastic clustering procedure based on a probability distribution. We call our proposed method CaviarPD, a portmanteau from cluster analysis via random partition distributions. CaviarPD first samples clusterings from a distribution on partitions and then finds the best cluster estimate based on these samples using algorithms to minimize an expected loss. Using eight case studies, we show that our approach produces results as close to the truth as hierarchical and k‐medoids methods, and has the additional advantage of allowing for a probabilistic framework to assess clustering uncertainty. The method provides an intuitive graphical representation of clustering uncertainty through pairwise probabilities from partition samples. A software implementation of the method is available in the CaviarPD package for R.
Approaches to measure class importance in Knowledge Graphs

The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the same principles and technologies. Such a scenario requires to develop techniques of Linked Data Summarization. The concept of a class is one of the core elements used to define the ontologies which sustain most of the existing KGs. Moreover, classes are an excellent tool to refer to an abstract idea which groups many individuals (or instances) in the context of a given KG, which is handy to use when producing summaries of its content. Rankings of class importance are a powerful summarization tool that can be used both to obtain a superficial view of the content of a given KG and to prioritize many different actions over the data (data quality checking, visualization, relevance for search engines…). In this paper, we analyze existing techniques to measure class importance and propose a novel approach called ClassRank. We compare the class usage in SPARQL logs of different KGs with the importance ranking produced by the approaches evaluated. Then, we discuss the strengths and weaknesses of the evaluated techniques. Our experimentation suggests that ClassRank outperforms state-of-the-art approaches measuring class importance.
A Novel Algorithm for mobile robot path planning

Robot path planning is a methodology that guides a robot to accomplish a mission through an uncertain environment optimally and safely. The primary responsibility involved in path planning is environment modeling and path searching. Path planning is generally specified as given a robot and a summary of an environment to find a collision path between certain predefined points. This paper presented a novel algorithm called generalized Laser Simulator (GLS) for path finding in a global environment. The methodology of this research consists of global path planning. The proposed algorithm is designed and presented. Simulation results show the algorithm's effectiveness in finding an optimal path to the set goal point from a defined start point. Lastly, the proposed algorithm is compared with the Laser simulator (LS) and A*method to verify its effectiveness. The Generalized laser simulator has advantages in the total time and path length compared to A* and laser simulator algorithm.
Multi-Class Micro-CT Image Segmentation Using Sparse Regularized Deep Networks

It is common in anthropology and paleontology to address questions about extant and extinct species through the quantification of osteological features observable in micro-computed tomographic (mCT) scans. In cases where remains were buried, the grey values present in these scans may be classified as belonging to air, dirt, or bone. While various intensity-based methods have been proposed to segment scans into these classes, it is often the case that intensity values for dirt and bone are nearly indistinguishable. In these instances, scientists resort to laborious manual segmentation, which does not scale well in practice when a large number of scans are to be analyzed. Here we present a new domain-enriched network for three-class image segmentation, which utilizes the domain knowledge of experts familiar with manually segmenting bone and dirt structures. More precisely, our novel structure consists of two components: 1) a representation network trained on special samples based on newly designed custom loss terms, which extracts discriminative bone and dirt features, 2) and a segmentation network that leverages these extracted discriminative features. These two parts are jointly trained in order to optimize the segmentation performance. A comparison of our network to that of the current state-of-the-art U-NETs demonstrates the benefits of our proposal, particularly when the number of labeled training images are limited, which is invariably the case for mCT segmentation.
SFAC:A Smart Contract-Based Fine-Grained Access Control for Internet of Things

With the development of Internet of things, more and more objects are connected to the Internet. It will lead to data leakage caused by arbitrary access easily. Thus, an effective method to prevent data leakage is needed. Traditional access control schemes include role-based access control (RBAC), attribute-based access control (ABAC), and capability-based access control (CapBAC) are all using a central or a third trusted party to manage user information, which is prone to single failure. In order to solve the problems, in this paper, a fine-grained access control scheme based on blockchain technology named SFAC is proposed. In recent years, blockchain technology has been widely studied and applied in various fields. It has the advantages of decentralization, imitation tampering, and traceability, and is a distributed public ledger. Therefore, combined with blockchain, SFAC can not only solve the problem of centralization, but also allow users to request resources in batches, and uses token mechanism to facilitate users’ requests. After the user gets a token, it can directly use the token to revoke resources. Besides, we use a smart contract to implement our proposed scheme and carrying out a series of experiments on the test network of Ethereum. The experimental results show that our SAFC is feasible.
Hybrid Model for Virtual Machine Optimization in Cloud Data Center

Resource sharing and optimization plays a vital role in the cloud computing paradigm. Virtual Machine (VM) optimization is a key technology to improve resource utilization in cloud data centers by enhancing performance. The utilization of resources in cloud data centers can be improved by virtualization and by selecting the most suitable host for each Virtual Machine (VM). A novel VM optimization technique is proposed by using a First-Fit Decreasing (FFD) algorithm along with Particle Swarm Optimization (PSO) algorithm. The proposed solution evaluates overloaded and under loaded Physical Machine (PM), based on the threshold sorts the VM, and provides the best VM-PM Mapping. The proposed model results show better performance when compared with the existing standard algorithms.
DCGAN based Pre-trained model for Image Reconstruction using ImageNet

Despite recent achievements in generative image modeling, generating better quality image samples from complex datasets such as ImageNet remains an illusory goal. The objective of this paper is to train Deep Convolutional Generative Adversarial Network at the well-known CIFAR10 dataset and study the instabilities specific to such scale and then test the large-scale ImageNet dataset for establishment of the proposed DCGAN. We find that applying a pre-trained DCGAN can remove the complexity and also can learn prior details of images and improve the quality of generated image. Our modifications on DCGAN lead to models which set the new cutting edge in class-contingent image reconstruction on pre-trained GAN's. When tested on ImageNet at 128 × 128 resolution, our model (DCGAN) lowers the loss between the generated and real image samples which shows that the proposed DCGAN model works well with both the datasets.
User-Centered Design of a Library Tutorials Page: A Solution to Digital Hoarding

Library tutorials offer on demand and as-needed instruction, a necessity as higher education continues to extend the reach of online learning. Our institution has increased its online course offerings alongside a university-wide shift in pedagogical and student-centered philosophy. To align our library tutorials webpage with these advances, we achieved a comprehensive redesign through a survey, student usability study, and librarian focus group. Guided by user-experience design, web accessibility, and Universal Design for Learning principles, we organized the new web page to better support our learning community.
Tree-Based Ensemble Methods for Complexity Reduction of VVC Intra Coding

The rise of new applications and emerging services required ultra-high definition (UHD) led to the development of a new video compression standard, name Versatile Video Coding (VVC). In comparison with High Efficiency Video Coding, the new standard increases the coding efficiency, at the expense of greatly increasing the computational complexity of the encoding process, especially in UHD and 360°video. To overcome this issue, this paper presents a novel method to reduce the computational complexity of intra-coded 360°video in Equirectangular Projection format, based on 3 tree-based ensemble models to predict the maximum partition depth that should be used for intra-coding of the complex nested data structures (Quad Tree, Binary Tree and Ternary Tree) used in the VVC standard. Two different tree-based ensemble methods are studied, namely Random Forest and Extremely Randomised Trees. The experimental results achieve, on average, complexity reductions of 56.25% and 57.45% for Random Forests and Extremely Randomised Trees methods, with small loss of coding efficiency of 1.37 % and 1.46 %, respectively.
Prediction of Length of Stay Using Vital Signs at the Admission Time in Emergency Departments

Length of Stay (LOS) prediction at the time of admission can give clinicians insight into the illness severity of patients and enable them to prevent complications and adverse events. It can also help hospitals to manage their facilities and manpower more efficiently. This paper first applies Borderline-SMOTE and multivariate Gaussian process imputer techniques to overcome data skewness and handle missing values which have been ignored by most studies. Then, based on our conversation with clinicians, patients are stratified into five classes according to their LOS. Finally, five machine learning algorithms, including support vector machine, deep neural networks, random forest, extreme gradient boosting, and decision tree are developed to predict LOS of unselected patients admitted to the emergency department at Odense University Hospital. These models utilize information of patients at the time of admission, including age, gender, heart rate, respiratory rate, oxygen saturation, and systolic blood pressure. Performance of predictive models on the data before and after imputation and class balancing are investigated using the area under the curve metric and the results show that our proposed solutions for data skewness and missing values challenges improve the performance of predictive models by an average of 13%.
Building a Prototype for Easy to Use Collaborative Immersive Analytics

The increase in the size and complexity of today's datasets creates a need to develop and experiment with novel data visualization methods. One of these innovations is immersive analytics, in which extended reality technologies such as virtual reality headsets are used to present and study data in virtual worlds. But while the use of immersive analytics dates back to the end of the 20th century, it wasn't until recently that collaboration in these data visualization environments was taken in consideration. One of the problems currently surrounding this field is the lack of availability of easy to use cooperative data visualization tools that take advantage of the modern, easily attainable head mounted display virtual reality solutions. This work proposes to create an accessible collaborative immersive analytics framework that users with low virtual reality background can master, and share, regardless of platform. With this in mind, a prototype of a visualization platform was developed in Unity3D that allows users to create their own visualizations and collaborate with other users from around the world. Additional features such as avatars, resizable visualizations and data highlighters were implemented to increase immersion and collaborative thinking. The end result shows promising qualities, as it is platform versatile, simple to setup and use and is capable of rapidly enabling groups to meet and analyse data in an immersive environment, even across the world.
Robust subspace clustering network with dual-domain regularization

The field of deep subspace clustering has advanced rapidly in recent years. Ideas such as self-expression and self-supervision have led to innovative network design and improved clustering performance. However, it is observed that the nonlinear low-dimensional manifold constraint is valid in not only the ambient but also the latent feature spaces. Meanwhile, the issue of robustness has been largely overlooked in the literature of deep subspace clustering despite previous studies on robust model-based clustering. Based on these two observations, we present a robust subspace clustering network (RSCN) based on a novel hybrid loss function with dual-domain regularization. On the one hand, we propose to replace the existing L2 loss by a robust hybrid function inspired by half-quadratic minimization; on the other hand, we come up with a novel strategy of sparsity regularization in the dual domain (both ambient and feature space). To the best of our knowledge, this is the first attempt to incorporate dual manifold constraints into deep subspace clustering. Experimental results show that our new network outperforms the existing state-of-the-art on several widely-studied datasets such as Extended Yale B, COIL20, and COIL100. The performance gain of our RSCN over several other competing approaches improves dramatically in the presence of noise contamination.
Holography and the Luxury Industry

The luxury goods industry and holography have a lengthy history together. In this article, we review the applications of holography to the industry and the relevant technical requirements, in particular when the hologram itself is the luxury item, when the hologram is used to promote luxury items, and when the hologram is used for authentication of luxury items. We then explore some possible scenarios for the evolution of this relationship.