Reviewerly/exHarmony huggingface dataset:
Efficacy of Data Fusion Using Convolved Multi-Output Gaussian Processes

This paper evaluates the efficacy of a machine learning approach to data fusion using convolved multi-output Gaussian processes in the context of geological resource modeling.It empirically demonstrates that information integration across multiple information sources leads to superior estimates of all the quantities being modeled, compared to modeling them individually.Convolved multi-output Gaussian processes provide a powerful approach for simultaneous modeling of multiple quantities of interest while taking correlations between these quantities into consideration.Experiments are performed on large scale data taken from a mining context.
Fuzzy and Mathematical Effort Estimation Models for Web Applications

This paper proposed an Effort Estimation Model for optimizing the deployment of Web Applications Based Fuzzy and Practical Models. Fuzzy logic approach is applied for estimating the development effort, which is compared with practical efforts model in the development process with interpreting the historical data available for the existing functionalities. This paper presented effort estimation model that involves two levels development and requirements for web applications built on three-tier architecture using Microsoft technologies. The first level estimates published by Project Managers and the second level estimates presented by Project Leaders or Developers for any new requirement or enhancements. The model considers the classification of each task as either Low or Medium or High complexity. These tasks pertain to the lowest level parts in bottom-up estimation. Efforts are estimated for designing, coding and unit testing of these tasks and the efforts are summed up to get the effort estimation for the higher level which is a feature to be implemented. The paper also discusses about the application of the effort estimation model by taking a new requirement as a case study. The first level estimates calculated using the effort estimation model has a variance of about 25% when compared with the actual effort. This variance is very much acceptable considering the fact that the first level estimates can be tolerable up to 35%. The proposed effort estimation model would help the project managers to efficiently control the project, manage the resources effectively, and improve the software development process and also trade off analyses among schedule, performance, quality and functionality. Fuzzy logic is used to verify the claims made in efforts estimation. It is proposed a new relation between the number of data and efforts value membership for actual data.
A Hierarchical Blockchain of Things Network For Unified Carbon Emission Trading (HBUETS):A Conceptual Framework

Climate change is a major issue that has serious disastrous implications on the environment, which if continues on the same pattern, will cause severe consequences. One of the leading causes of climate change is the emissions of harmful gases, especially CO2. In 1997 the Kyoto protocol was signed where 192 countries created a system that monetizes CO2 emissions with an aim to control them. A carbon trading scheme was signed by most countries, but the scheme fell short of its goals due to corruption, manipulation, and lack of integrity. This work presents an improved blockchain-based approach to achieve the objective of reducing carbon emissions. Blockchain’s distinct features such as security, immutability, transparency, traceability, and trust, make it a robust and reliable solution for the in-hand carbon trading market. Previous Blockchain-based proposals were not comprehensive, practical, applicable, or efficient enough to form an effective solution. This research addresses the current gaps and proposes a comprehensive framework that employs smart contracts in Blockchain of Things (BoT) to ensure integrity in the system and reach fair trade status that favors the environment over companies’ cost reductions and profit-making. The result is an optimized carbon emission trading framework, fully transparent with automated trading and control mechanisms.
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters

Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters. However, on public cloud clusters, due to the moderate inter-connection bandwidth between instances, traditional state-of-the-art distributed training systems cannot scale well in training large-scale models. In this paper, we propose a new computing and communication efficient top-k sparsification communication library for distributed training. To further improve the system scalability, we optimize I/O by proposing a simple yet efficient multi-level data caching mechanism and optimize the update operation by introducing a novel parallel tensor operator. Experimental results on a 16-node Tencent Cloud cluster (each node with 8 Nvidia Tesla V100 GPUs) show that our system achieves 25%-40% faster than existing state-of-the-art systems on CNNs and Transformer. We finally break the record on DAWNBench on training ResNet-50 to 93% top-5 accuracy on ImageNet.
Hyperspectral Image Stripe Detection and Correction Using Gabor Filters and Subspace Representation

Hyperspectral images (HSIs) exist in directional stripes commonly due to the failure of pushbroom acquisition. These stripes are not only vertically and horizontally oriented but also tend to be oblique. Furthermore, they can also be aperiodic and heavy. To address this problem, we propose a hyperspectral destriping algorithm, namely, GF-destriping. Taking advantage of the high sparsity and strong directionality of stripes in HSIs, Gabor filters are used to detect the stripes band by band first, and then, an advanced inpainting method, FastHyIn, is used to recover to the striped image. The numerical experiments on simulated data and real data sets show that our proposed algorithm is efficient and superior to state-of-the-art HSI destriping algorithms.
Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

Background:The Load Balancing (LB) schemes in cloud computing consider both current and future utilization of resources to decide the most suitable Virtual Machines (VMs) to be migrated to the most appropriate Physical Machines (PMs).But, the possibility of network congestion occurrence was high when increasing the bandwidth use between VMs within the cloud data centers.Also, a less-than-optimal migration of VMs can lead to high network traffic since it causes inter-VM traffic for traversing the bottleneck network routes.Objective: To enhance the efficiency of LB and reduce the possibility of congestion occurrence during VM migration in cloud data centers.Methods: Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction with Multipath Traffic Routing (OH-BAC-FUP-MTR) mechanism are presented in this article to achieve the above objective.Initially, the OH-BAC-FUP mechanism is performed to decide the most suitable VMs to be migrated to the most suitable PMs.During VM migration, if any congestion exists due to high bandwidth use or traffic flows, then the MTR algorithm is applied to partition the flows and route them through multiple link-disjoint routes.Based on this, the congestion is avoided while ensuring the bandwidth and security grade demands.Also, the highest traffic on the path is applied as a congestion factor.Moreover, the current and future network states are taken into account for MTR to select the most optimal route from multiple routes with no consideration of the past use of the paths.Findings: The simulation outcomes demonstrate the OH-BAC-FUP-MTR mechanism consumes 8.74% of overall energy, 6.8% of Service Level Agreement violation Time per Active Host (SLATAH), 27.63% of Performance Degradation due to Migration (PDM), 22.98% of SLA Violation (SLAV), 27.27% of VM migrations and 30.77% of hosts shutdown compared to the OH-BAC-FUP using Linear Regression (LR) and Optimal Piecewise LR (OPLR).
Speaker Recognition using Random Forest

Speaker identification has become a mainstream technology in the field of machine learning that involves determining the identity of a speaker from his/her speech sample. A person’s speech note contains many features that can be used to discriminate his/her identity. A model that can identify a speaker has wide applications such as biometric authentication, security, forensics and human-machine interaction. This paper implements a speaker identification system based on Random Forest as a classifier to identify the various speakers using MFCC and RPS as feature extraction techniques. The output obtained from the Random Forest classifier shows promising result. It is observed that the accuracy level is significantly higher in MFCC as compared to the RPS technique on the data taken from the well-known TIMIT corpus dataset.
Entity Linking as a Population Mechanism for Skill Ontologies: Evaluating the Use of ESCO and Wikidata

Ontologies or databases describing occupations in terms of competences or skills are an important resource for a number of applications. Exploiting large knowledge graphs thus becomes a promising direction to update those ontologies with entities of the latter, which may be updated faster, especially in the case of crowd-sourced resources. Here we report a first assessment of the potential of that strategy matching knowledge elements in ESCO to Wikidata using NER and document similarity models available at the Spacy NLP libraries. Results show that the approach may be effective, but the use of pre-trained language models and the short texts included with entities (labels and descriptions) does not result in sufficient quality for a fully automated process.
Implementation Of Text Mining For Emotion Detection Using The Lexicon Method (Case Study: Tweets About Covid-19)

Information and news about Covid-19 received various responses from social media users, including Twitter users. Changes in netizen opinion from time to time are interesting to analyze, especially about the patterns of public sentiment and emotions contained in these opinions. Sentiment and emotional conditions can illustrate the public's response to the Covid-19 pandemic in Indonesia. This research has two objectives, first to reveal the types of public emotions that emerged during the Covid-19 pandemic in Indonesia. Second, reveal the topics or words that appear most frequently in each emotion class. There are seven types of emotions to be detected, namely anger, fear, disgust, sadness, surprise, joy, and trust. The dataset used is Indonesian-language tweets, which were downloaded from April to August 2020. The method used for the extraction of emotional features is the lexicon-based method using the EmoLex dictionary. The result obtained is a monthly graph of public emotional conditions related to the Covid-19 pandemic in the dataset.
Abstractive Review Summarization based on Improved Attention Mechanism with Pointer Generator Network Model

Nowadays online reviews play an important role by giving an helping hand to the customers to know about other customer’s opinions about the product they are going to purchase. This also guides the organizations as well as government sectors to increase their quality of product and services. So automatic review summarization becomes more important rather than summarizing it manually as it saves time. The aim of this work is to produce a comprehensive summary which includes all key content from the source text. The Proposed Automatic Review Summarization model with improved attention mechanism increases the semantic knowledge and thus improves the summary’s eminence. This encoder-decoder model aims to generate summary in an abstractive way. The Pointer generator mechanism solves the problem of rare words which are out-of-vocabulary and the repetition issues are overcome by coverage mechanism. Experiments were conducted on Amazon’s mobile reviews dataset reveals that the proposed methodology generated more accurate abstractive review summarization when compared with existing techniques. The performance of the summary report is measured using the evaluation metric ROUGE.
Multi goals and multi scenes visual mapless navigation in indoor using meta-learning and scene priors

The goal of visual mapless navigation is to navigate from a random starting point in a scene to a specified target in an unknown environment. A fundamental challenge in visual mapless navigation is generalizing to a novel environment, where the layout of the scenes and appearance of targets are unfamiliar. Furthermore, traditional navigation models are frozen during inference resulting in poor adaptability. To address these issues, we propose a multi goals and multi scenes visual mapless navigation model, which integrates meta learning with spatial relationships between different object categories. In this way, our method not only improves the generalization on multi goals in multi scenes but also encourages effective navigation. Experimental results on AI2-THOR dataset show that our approach significantly outperforms the state-of-the-art model SAVN by >27.05% for the average success rate and by >31.7% for the average SPL. Our source code and data of this paper are available at: https://github.com/zhiyu-tech/WHU-VMN.
Subjective analysis of social distance monitoring using YOLO v3 architecture and crowd tracking system

The lethal infection, World Health Organization (WHO) reported coronavirus (COVID-19) as a pandemic.Lack of proper vaccine, low levels of immunity against COVID-19 has led to vulnerability of the human beings. Due tolack of efficient vaccine treatment, the only options left to fight against this pandemic are lockdown and social distance.This work offers an autonomous monitoring system on social distancing using deep learning techniques. The proposedarchitecture tracks the humans on roads and calculates their distance between each other. This surveillance detects thefurore violation of social distance utilizing CCTV cameras. The proposed framework uses YOLO v3 object-detectionmodel built on COCO dataset and used to classify human class among 79 classes. The bounding box's dimensions andcentroid coordinates are computed in the two-dimensional feature space from the pairwise vectorized L2 norm and athreshold is fixed for computing the distance maintained between each other. We illustrate the superior performance ofour framework checked against other state of the art methods regarding inference speed, mean average precision and lossdefined from the localization.
Detection and Classification of Leukocytes in Blood Smear Images

Manual analysis of microscopic blood smears by highly expert pathologists is labor-intensive, time-consuming, and is subject to inter-observer variations. Recent innovations in image processing and computer vision techniques have improvised digital pathology in terms of objectivity and reproducibility. Traditional computer vision-based methods of recognition of white blood cell (WBC) from a pathological blood smear image includes the process of detection, segmentation, and classification. This paper presents a review of state-of-the-art detection, segmentation, and classification techniques for white blood cell analysis. The goal of this work is to present an introduction to the field, provide enough information about the analysis methods developed so far, and to be an appropriate reference for the researchers looking forward in this field. The methods under review are classified into intensity and feature based. The crucial steps involved in these techniques, mathematical foresights, performance evaluation techniques, issues, and future directions are discussed.
Text Augmentation in a Multi-Task View

Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training.In this paper, we propose an alternative perspective-a multi-task view (MTV) of data augmentation-in which the primary task trains on original examples and the auxiliary task trains on augmented examples.In MTV data augmentation, both original and augmented samples are weighted substantively during training, relaxing the constraint that augmented examples must resemble original data and thereby allowing us to apply stronger levels of augmentation.In empirical experiments using four common data augmentation techniques on three benchmark text classification datasets, we find that the MTV leads to higher and more robust performance improvements than traditional augmentation.
Understanding Pre-Editing for Black-Box Neural Machine Translation

Pre-editing is the process of modifying the source text (ST) so that it can be translated by machine translation (MT) in a better quality.Despite the unpredictability of black-box neural MT (NMT), pre-editing has been deployed in various practical MT use cases.Although many studies have demonstrated the effectiveness of pre-editing methods for particular settings, thus far, a deep understanding of what pre-editing is and how it works for black-box NMT is lacking.To elicit such understanding, we extensively investigated human pre-editing practices.We first implemented a protocol to incrementally record the minimum edits for each ST and collected 6,652 instances of preediting across three translation directions, two MT systems, and four text domains.We then analysed the instances from three perspectives: the characteristics of the pre-edited ST, the diversity of pre-editing operations, and the impact of the pre-editing operations on NMT outputs.Our findings include the following: (1) enhancing the explicitness of the meaning of an ST and its syntactic structure is more important for obtaining better translations than making the ST shorter and simpler, and (2) although the impact of pre-editing on NMT is generally unpredictable, there are some tendencies of changes in the NMT outputs depending on the editing operation types.
A novel multi-target multi-camera tracking approach based on feature grouping

Multi-target multi-camera tracking systems track many pedestrians through videos taken from multiple cameras. Generally, multi-target multi-camera tracking comprises three steps, namely, detection, feature extraction, and data association. It also involves a number of marginal post-processing procedures, such as pruning and interpolating. The task is a complicated and challenging problem. In this work, we mainly focus on the process of data association. When correlation clustering-based algorithms are adopted in data association, serious information loss may be observed, especially when pedestrians in a video run into occlusion. Thus, we propose a method called feature group which mitigates the decline in accuracy under occlusions. The proposed method is intuitional but easy to implement without changing the original framework. After comprehensive experiments, the proposed method is proved effective and is able to make substantial improvements on the DukeMTMC dataset. The feature group method is also competitive relative to other state-of-the-art methods.
To Block or not to Block: Experiments with Machine Learning for News Comment Moderation.

Today, news media organizations regularly engage with readers by enabling them to comment on news articles. This creates the need for comment moderation and removal of disallowed comments – a time-consuming task often performed by human moderators. In this paper we approach the problem of automatic news comment moderation as classification of comments into blocked and not blocked categories. We construct a novel dataset of annotated English comments, experiment with cross-lingual transfer of comment labels and evaluate several machine learning models on datasets of Croatian and Estonian news comments. Team name: SuperAdmin; Challenge: Detection of blocked comments; Tools/models: CroSloEn BERT, FinEst BERT, 24Sata comment dataset, Ekspress comment dataset.
Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability

Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction.However, we find in experiments that many paths given by these models are actually unreasonable, while little work has been done on interpretability evaluation for them.In this paper, we propose a unified framework to quantitatively evaluate the interpretability of multi-hop reasoning models so as to advance their development.In specific, we define three metrics, including path recall, local interpretability, and global interpretability for evaluation, and design an approximate strategy to calculate these metrics using the interpretability scores of rules.Furthermore, we manually annotate all possible rules and establish a Benchmark to detect the Interpretability of Multi-hop Reasoning (BIMR).In experiments, we verify the effectiveness of our benchmark.Besides, we run nine representative baselines on our benchmark, and the experimental results show that the interpretability of current multi-hop reasoning models is less satisfactory and is 51.7% lower than the upper bound given by our benchmark.Moreover, the rule-based models outperform the multi-hop reasoning models in terms of performance and interpretability, which points to a direction for future research, i.e., how to better incorporate rule information into the multihop reasoning model.
Recommendations of the European Advisory Committee of Cytotechnology and European Federation of Cytology Societies for Training and Education of Cytotechnologists in Europe

Background: Faced with changes in cytodiagnostics, cervical cancer screening programs, the introduction and application of new methods, the cytotechnological educational program requires the necessary changes and additions. Insufficient, uneven as well as inaccessible education of cytotechnologists in European countries was the basis for making these recommendations. Summary: The results of previous research and publications related to the currently available education of cytotechnologists in Europe, the needs and suggestions were given by the European Advisory Committee of Cytotechnology (EACC) and European Federation of Cytology Societies (EFCS) for optimal education of future generations of cytotechnologists were used in the preparation of these recommendations. The EACC and EFCS propose a 1-year education and training program divided into 3 modules: gynecological, nongynecological exfoliative, and fine-needle aspiration cytology. Training programs should be organized by an accredited university, preferably a combination of internal education in a cytology laboratory and theoretical education at the university. Cytopathologists and cytotechnologists with at least 5 years of work experience in cytodiagnostics should participate in education. Upon completion of the training program, the EACC and EFCS propose an official name: EFCS certified cytotechnologist. Key Messages: The EACC and EFCS believe that it is extremely important that these recommendations are recognized and implemented by institutions that provide education for cytotechnologists so that they can meet the growing requirements of the profession with their acquired knowledge and competencies.
Bootstrapping Multilingual AMR with Contextual Word Alignments

We develop high performance multilingual Abstract Meaning Representation (AMR) systems by projecting English AMR annotations to other languages with weak supervision.We achieve this goal by bootstrapping transformerbased multilingual word embeddings, in particular those from cross-lingual RoBERTa (XLM-R large).We develop a novel technique for foreign-text-to-English AMR alignment, using the contextual word alignment between English and foreign language tokens.This word alignment is weakly supervised and relies on the contextualized XLM-R word embeddings.We achieve a highly competitive performance that surpasses the best published results for German, Italian, Spanish and Chinese.