Reviewerly/exHarmony huggingface dataset:
Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process

Recent research efforts in lifelong learning propose to grow a mixture of models to adapt to an increasing number of tasks. The proposed methodology shows promising results in overcoming catastrophic forgetting. However, the theory behind these successful models is still not well understood. In this paper, we perform the theoretical analysis for lifelong learning models by deriving the risk bounds based on the discrepancy distance between the probabilistic representation of data generated by the model and that corresponding to the target dataset. Inspired by the theoretical analysis, we introduce a new lifelong learning approach, namely the Lifelong Infinite Mixture (LIMix) model, which can automatically expand its network architectures or choose an appropriate component to adapt its parameters for learning a new task, while preserving its previously learnt information. We propose to incorporate the knowledge by means of Dirichlet processes by using a gating mechanism which computes the dependence between the knowledge learnt previously and stored in each component, and a new set of data. Besides, we train a compact Student model which can accumulate cross-domain representations over time and make quick inferences. The code is available at https://github.com/dtuzi123/Lifelong-infinite-mixture-model.
Smart ledger: The blockchain‐based accounting information recording protocol

Abstract Blockchain technology has the potential to reduce transaction errors and enhance the quality of reporting significantly. This article proposes a conceptual blockchain‐based protocol, labelled Smart Ledger, as a replacement for the traditional accounting information recording system. A smart ledger is a computerized algorithm that utilizes blockchain technology to perform accounting ledger functions. Its validity is based on two mechanisms within the blockchain architecture: Fractional Accounting Transactions (FAT) and Hierarchical Accounting Transaction Execution (HATE). As such, the smart ledger is a hybrid protocol that combines accounting information recording principles with the immutability of blockchain technology. If widely implemented, it could have a significant impact on accounting practices and the accounting profession.
Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists' experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN's basic architecture to reduce overfitting. Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96% & 81% with a balanced dataset, respectively. The regularized CNN model training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset.
Toward Large-Scale Evolutionary Multitasking: A GPU-Based Paradigm

Evolutionary multitasking (EMT), which shares knowledge across multiple tasks while the optimization progresses online, has demonstrated superior performance in terms of both optimization quality and convergence speed over its single-task counterpart in solving complex optimization problems. However, most of the existing EMT algorithms only consider handling two tasks simultaneously. As the computational cost incurred in the evolutionary search and knowledge transfer increased rapidly with the number of optimization tasks, these EMT algorithms cannot meet today’s requirements of optimization service on the cloud for many real-world applications, where hundreds or thousands of optimization requests (labeled as large-scale EMT) are often received simultaneously and require to be optimized in a short time. Recently, graphics processing unit (GPU) computing has attracted extensive attention to accelerate the applications possessing large-scale data volume that are traditionally handled by the central processing unit (CPU). Taking this cue, toward large-scale EMT, in this article, we propose a new EMT paradigm based on the island model with the compute unified device architecture (CUDA), which is able to handle a large number of continuous optimization tasks efficiently and effectively. Moreover, under the proposed paradigm, we develop the GPU-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implicit</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicit</i> knowledge transfer mechanisms for EMT. To evaluate the performance of the proposed paradigm, comprehensive empirical studies have been conducted against its CPU-based counterpart in large-scale EMT.
Data sharing practices of health researchers in Africa: a scoping review protocol

Objective: The aim of the review is to map the existing evidence regarding the data-sharing practices of health researchers in African countries. This review will also identify perceptions; barriers; facilitators; ethical-, legal-, and author-reported recommendations; as well as institutional- and funding-related aspects that are being considered by African health researchers on data sharing in Africa and, as a result, identify areas for development and improvement in health care on the continent. Introduction: The sharing of health-related data has been widely discussed in the literature. However, sharing health-related data has yet to become a common practice among health researchers in Africa, which bears a large burden of health diseases globally. The sharing of health research data could lead to greater development and improvement in health care in Africa. Inclusion criteria: This review will incorporate studies that report on data sharing among health researchers in Africa. All primary, secondary, and gray literature will be considered for inclusion. Studies on data sharing on topics other than health-related data will be excluded. No language restrictions will be applied. Methods: The JBI scoping review methodological framework will be adopted. An initial search of databases such as MEDLINE, Scopus, LILACS, and Web of Science will be conducted. All search results will be screened and relevant data extracted by two independent reviewers. The findings will be presented in the final scoping review report and illustrated in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews flow diagram.
Subsea field layout optimization (Part I) – directional well trajectory planning based on 3D Dubins Curve

Directional well trajectory planning, which includes the optimization of the drilling site location and the trajectory between the drilling site to the completion interval, plays an important role in reducing subsea field development cost. The traditional well trajectory planning methods are based on the projected 2D profile of the wellbore trajectory with empirical knowledge or trial-and-error method to select a proper drilling site. In this study, we propose a new efficient optimization method based on the 3D Dubins curve, which has been widely used in autopilot for path planning but has never been mentioned in drilling industry. In short, we use gradient descent method to find the best drilling site location while adopting the 3D Dubins curve as the optimal wellbore trajectory to reach each completion interval so that the "1-site-n-wells" problem can be easily solved. Abundant case studies including both mathematically representative cases and the real practical field cases are conducted to demonstrate the feasibility and efficiency of our method. Wider application of our method for more complex situations are also discussed. This work is the first of a series of papers which systematically introduce an efficient method for subsea field layout optimization to minimize the development cost.
Comparative Study of Covid-19 Tweets Sentiment Classification Methods

Covid-19 is a disease caused by a virus and has become a pandemic in many countries around the world. The disease not only affects public health, but also affects other aspects of life. People tend to write comments about things happening during the pandemic on social media, one of which is Twitter. Sentiment analysis on Twitter data is not an easy task due to the characteristics of the tweeter text which is user generated content. Therefore, in this paper, a sentiment analysis study is carried out on Twitter data using three schemes, namely the vector space model (Bag of Words and TF-IDF) with Support Vector Machine, word embedding (word2vec and Glove) with Long Short-Term Memory, and BERT (Bidirectional Encoder Representations from Transformers). Based on the conducted experiments, BERT achieved the best performance compared to the other two schemes, reaching 0.85 (weighted F1-score) and 0.83 (macro F1-score) for the classification of three sentiment classes on Kaggle competition data (Coronavirus tweets NLP – Text Classification).
Searching from the Prediction of Visual and Language Model for Handwritten Chinese Text Recognition

In this paper, we build the deep neural networks for offline handwritten Chinese text recognition (HCTR) with only convolutional layers and one of the mainstream learning methods Connectionist Temporal Classification (CTC). Iteratively, different configurations of network architectures with residual and squeeze-and-excitation structures are explored. We ease the serious overfitting issue by applying high dropout rate 0.9 at the input of the last classification layer, and synthesize new text samples with isolated characters by reusing the character-level bounding boxes from the CASIA-HWDB train set. These empirical and intuitive tricks help us achieve the character error rate (CER) at 6.38% on the ICDAR2013 competition set. To further improve the performance, at each step of the CTC decoding, we propose a novel context beam search (CBS) algorithm, which conducts decoding from both the prediction of the basic visual model and another customized transformer-based language model simultaneously. The final CER is reduced to 2.49%. Code will be available online at https://github.com/intel/handwritten-chinese-ocr.
Modulation format recognition based on constellation diagrams and the Hough transform

High-speed wireless communication is necessary in our personal lives, in both working and living spaces. This paper presents a scheme for wireless optical modulation format recognition (MFR) based on the Hough transform (HT). The HT is used to project constellation diagrams onto another space for efficient feature extraction. Constellation diagrams are obtained at optical signal-to-noise ratios (OSNR) ranging from 5 to 30 dB for eight different modulation formats (2/4/8/16 phase-shift keying and 8/16/32/64 QAM). Different classifiers are used for the task of MFR: AlexNet, VGG16, and VGG19. A study of the effect of varying the number of samples on the accuracy of the classifiers is provided for each modulation format. To evaluate the proposed scheme, the efficiency of the three classifiers is studied at different values of OSNR. The obtained results reveal that the proposed scheme succeeds in identifying the wireless optical modulation format blindly with a classification accuracy up to 100%, even at low OSNR values less than 10 dB.
A Survey on Few-Shot Learning in Natural Language Processing

The annotated dataset is the foundation for Supervised Natural Language Processing. However, the cost of obtaining dataset is high. In recent years, the Few-Shot Learning has gradually attracted the attention of researchers. From the definition, in this paper, we conclude the difference in Few-Shot Learning between Natural Language Processing and Computer Vision. On that basis, the current Few-Shot Learning on Natural Language Processing is summarized, including Transfer Learning, Meta Learning and Knowledge Distillation. Furthermore, we conclude the solutions to Few-Shot Learning in Natural Language Processing, such as the method based on Distant Supervision, Meta Learning and Knowledge Distillation. Finally, we present the challenges facing Few-Shot Learning in Natural Language Processing.
Integration of UVL in FeatureIDE

Variability models are prevalent for specifying the commonalities and variabilities of configurable systems. A large variety of tools support using, editing, and analyzing variability models. However, the different tools often depend on distinct textual notations to store and read variability models which induces a large effort for researchers and practitioners. This additional effort could be reduced if the community adopted a single format. Following the goal of the MODEVAR initiative to develop a widely adopted variability language, we provided a first proposal with the Universal Variability language (UVL) in previous work. For a textual format to be adopted, an important aspect is an as small as possible effort when integrating the format in other tools. In this work, we discuss the integration of UVL in FeatureIDE. We use the integration to examine the applicability of UVL and our parser library to existing tools and gather further requirements for the language design. Furthermore, we provide a thorough documentation on the implementation to be used as reference and guidance for integration in other tools.
“COVID has made Everyone Digital and Digitally Independent”: Understanding Working Women's DFS and Technology Adoption during COVID Pandemic in Bangladesh

Women in developing nations lag behind from self-empowerment because of some cultural barriers where the integration of technology and digital financial services helps them to be empowered. On the other hand, to avail such technology-based services as Digital Financial Services (DFS), there are also additional challenges for women in developing countries. During the COVID pandemic, the situation has changed all over where both genders had to rely on and adopt technology for working and financial purposes. Our research is an ongoing research and currently we explored n=20 urban formally working women in Bangladesh to understand how COVID impacts them to adopt technology and technology-based services. These women had comparatively less exploration in digital services before the pandemic. COVID instantly increased their technology adoption significantly. However, COVID grows some negative concerns regarding excessive technology impact on their work-life, kids, and finance. Based on findings, we mention a few design recommendations regarding ways of technology usage that will help women to reduce their excessive technology-adoption concerns and help them to use the technology in better ways.
FedACS: Federated Skewness Analytics in Heterogeneous Decentralized Data Environments

The emerging federated optimization paradigm performs data mining or artificial intelligence techniques locally on the edge devices, enabling scientists and engineers to utilize the blooming edge data with privacy protection. In such a paradigm, since data cannot be shared or gathered, data heterogeneity naturally emerges, which significantly degrades the performance of federated optimization, ultimately leading to poor quality of federated services. In this paper, we present the first work on characterizing the data heterogeneity in the framework of federated analytics, i.e., to collectively carry out analytics tasks without raw data sharing, and use the information to create a desirable data environment via intelligent client selection. Our proposed Analytics-driven Client Selection framework, named FedACS, tackles the data heterogeneity problem in three steps. First, clients are in charge of generating insights about local data without disclosure of sensitive information. Then, the server uses these insights to infer the situation of clients’ data heterogeneity based on the Hoeffding’s inequality. Finally, a dueling bandit is formulated to intelligently select clients with slighter data heterogeneity to form a desirable client pool. FedACS can be universally applied to all kinds of federated optimization tasks, and gains benefits including privacy protection, infrastructure reuse, and client load reduction. To test its efficiency, we further customize it to assist federated learning, a popular scenario of federated optimization. According to experiment results, FedACS reduces the accuracy degrading by up to 65.6%, and speeds up the convergence for up to 2.4 times.
An Efficient Feature Fusion of Graph Convolutional Networks and Its Application for Real-Time Traffic Control Gestures Recognition

Recently, skeleton-based gesture and action recognition have emerged thanks to the progress in human pose estimation.Gesture representation using skeletal data is robust since skeletal data are invariant to the individual's appearance.Among different approaches proposed for skeleton-based action/gesture recognition, Graph Convolutional Network (GCN) and its variations have obtained great attention thanks to its ability to capture the graph essence of the skeletal data.In this paper, we aim to design an efficient scheme using relative joints of skeleton sequences adapted in a GCN framework.Both spatial features (i.e., joint positions) and temporal ones (i.e., the velocity of joints) are combined to form the input of Attention-enhanced Adaptive GCN (AAGCN).The proposed framework deals with limitations of the original AAGCN when it works on challenging datasets with incomplete and noisy skeletal data.Extensive experiments are carried out on three datasets CMDFALL, MICA-Action3D, NTU-RGBD.Experimental results show that the proposed method achieves superior performance compared with existing methods.Moreover, to illustrate the application of the proposed method in real-time traffic control gesture recognition for autonomous vehicles, we have evaluated the proposed method on the TCG dataset.The obtained results show that the proposed method offers real-time computation capability and good recognition results.These results suggest a promising solution to deploy a real-time and robust recognition technique for gesture-based traffic control in autonomous vehicles.
DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning

The rapid advancement in deep learning makes the differentiation of authentic and manipulated facial images and video clips unprecedentedly harder. The underlying technology of manipulating facial appearances through deep generative approaches, enunciated as DeepFake that have emerged recently by promoting a vast number of malicious face manipulation applications. Subsequently, the need of other sort of techniques that can assess the integrity of digital visual content is indisputable to reduce the impact of the creations of DeepFake. A large body of research that are performed on DeepFake creation and detection create a scope of pushing each other beyond the current status. This study presents challenges, research trends, and directions related to DeepFake creation and detection techniques by reviewing the notable research in the DeepFake domain to facilitate the development of more robust approaches that could deal with the more advance DeepFake in future.
An Oblivious Approach to Machine Translation Quality Estimation

Machine translation (MT) is being used by millions of people daily, and therefore evaluating the quality of such systems is an important task. While human expert evaluation of MT output remains the most accurate method, it is not scalable by any means. Automatic procedures that perform the task of Machine Translation Quality Estimation (MT-QE) are typically trained on a large corpus of source–target sentence pairs, which are labeled with human judgment scores. Furthermore, the test set is typically drawn from the same distribution as the train. However, recently, interest in low-resource and unsupervised MT-QE has gained momentum. In this paper, we define and study a further restriction of the unsupervised MT-QE setting that we call oblivious MT-QE. Besides having no access no human judgment scores, the algorithm has no access to the test text’s distribution. We propose an oblivious MT-QE system based on a new notion of sentence cohesiveness that we introduce. We tested our system on standard competition datasets for various language pairs. In all cases, the performance of our system was comparable to the performance of the non-oblivious baseline system provided by the competition organizers. Our results suggest that reasonable MT-QE can be carried out even in the restrictive oblivious setting.
Revisiting Context-Tree Weighting for Bayesian Inference

We revisit the statistical foundation of the celebrated context tree weighting (CTW) algorithm, and we develop a Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, along with an associated collection of methodological tools for exact inference for discrete time series. In addition to deterministic algorithms that learn the a posteriori most likely models and compute their posterior probabilities, we introduce a family of variable-dimension Markov chain Monte Carlo samplers, facilitating further exploration of the posterior. The performance of the proposed methods in model selection, Markov order estimation and prediction is illustrated through simulation experiments and real-world applications.