Reviewerly/exHarmony huggingface dataset:
Comparison of SVM and Naïve Bayes for Sentiment Classification using BERT data

Expressing views related to any common interest of area is becoming very general due to the excessive use of internet on the handheld devices like mobiles. Everyone is free to share his opinions within his social network. Having some useful knowledge from those opinions is the challenge for text classification techniques. In this work, the movies related opinions in the form of long and more than one sentence are analyzed to categorize them as positive or negative sentiments towards the movie. In this paper, the machine learning approach using the supervised learning methods is used for the implementation of the model. The data is prepared using the BERT model where plain text is directly converted into a numerical data form. The experiments are conducted using the Support Vector Machine and Naïve Bayes algorithms. Accuracy, F1 score, and other metrics have been evaluated for the comparison of the results of both algorithms. The results show that the predictions by SVM have high accuracy than Naïve Bayes.
A Short Text Classification Model for Electrical Equipment Defects Based on Contextual Features

The defective information of substation equipment is usually recorded in the form of text. Due to the irregular spoken expressions of equipment inspectors, the defect information lacks sufficient contextual information and becomes more ambiguous. To solve the problem of sparse data deficient of semantic features in classification process, a short text classification model for defects in electrical equipment that fuses contextual features is proposed. The model uses bi-directional long-short term memory in short text classification to obtain the contextual semantics of short text data. Also, the attention mechanism is introduced to assign weights to different information in the context. Meanwhile, this model optimizes the convolutional neural network parameters with the help of the genetic algorithm for extracting salient features. According to the experimental results, the model can effectively realize the classification of power equipment defect text. In addition, the model was tested on an automotive parts repair dataset provided by the project partners, thus enabling the effective application of the method in specific industrial scenarios.
An enhanced text classification model by the inverted attention orthogonal projection module

The orthogonal projection method has made significant progress in text classification, especially in generating discriminative features. This method obtains more pure and suitable for classification features by projecting text features onto the orthogonal direction of common features (which are not helpful for classification and actually confuse performance). However, this approach requires an additional branch network to generate these common features, which reduces the flexibility of this method compared to representation optimisation methods such as self-attention mechanisms, as it requires significant modification of the base network structure to use. To address this issue, this paper proposes the Inversed Attention Orthogonal Projection Module (IAOPM). IAOPM uses inversed attention (IA) to iteratively reverse the attention map on text features, encouraging the network to remove discriminating features from the text features and obtain potential common features. Unlike the original orthogonal projection method, IAOPM can extract common features within a single network without any branch networks, increasing the flexibility of the orthogonal projection method. We also use an orthogonal loss to ensure the quality of the common features during training, so IAOPM also has better purity performance than the original method. Experiments show that text classification models based on IAOPM outperform the baseline models, self-attention mechanisms, and the original orthogonal projection method on multiple text classification datasets with an average accuracy increase of 1.02%, 0.44%, and 0.52%, respectively.
Active Learning for Imbalanced Civil Infrastructure Data

Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is costly and time-consuming, we are working towards fully automating the visual inspections to support the prioritization of maintenance activities. To that end we combine recent advances in drone technology and deep learning. Unfortunately, annotation costs are incredibly high as our proprietary civil engineering dataset must be annotated by highly trained engineers. Active learning is, therefore, a valuable tool to optimize the trade-off between model performance and annotation costs. Our use-case differs from the classical active learning setting as our dataset suffers from heavy class imbalance and consists of a much larger already labeled data pool than other active learning research. We present a novel method capable of operating in this challenging setting by replacing the traditional active learning acquisition function with an auxiliary binary discriminator. We experimentally show that our novel method outperforms the best-performing traditional active learning method (BALD) by 5% and 38% accuracy on CIFAR-10 and our proprietary dataset respectively.
CAT: Controllable Attribute Translation for Fair Facial Attribute Classification

As the social impact of visual recognition has been under scrutiny, several protected-attribute balanced datasets emerged to address dataset bias in imbalanced datasets. However, in facial attribute classification, dataset bias stems from both protected attribute level and facial attribute level, which makes it challenging to construct a multi-attribute-level balanced real dataset. To bridge the gap, we propose an effective pipeline to generate high-quality and sufficient facial images with desired facial attributes and supplement the original dataset to be a balanced dataset at both levels, which theoretically satisfies several fairness criteria. The effectiveness of our method is verified on sex classification and facial attribute classification by yielding comparable task performance as the original dataset and further improving fairness in a comprehensive fairness evaluation with a wide range of metrics. Furthermore, our method outperforms both resampling and balanced dataset construction to address dataset bias, and debiasing models to address task bias.
QFT: Post-training Quantization via Fast Joint Finetuning of All Degrees of Freedom

The post-training quantization (PTQ) challenge of bringing quantized neural net accuracy close to original has drawn much attention driven by industry demand. Many of the methods emphasize optimization of a specific per-layer degree of freedom (DoF), such as grid step size, preconditioning factors, nudges to weights and biases, often chained to others in multi-step solutions. Here we rethink quantized network parameterization in HW-aware fashion, towards a unified analysis of all quantization DoF, permitting for the first time their joint end-to-end finetuning. Our single-step simple and extendable method, dubbed quantization-aware finetuning (QFT), achieves 4b-weights quantization results on-par with SoTA within PTQ constraints of speed and resource.
Unleashing the Potential of Adaptation Models via Go-getting Domain Labels

In this paper, we propose an embarrassingly simple yet highly effective adversarial domain adaptation (ADA) method. We view ADA problem primarily from an optimization perspective and point out a fundamental dilemma, in that the real-world data often exhibits an imbalanced distribution where the large data clusters typically dominate and bias the adaptation process. Unlike prior works that either attempt loss re-weighting or data re-sampling for alleviating this defect, we introduce a new concept of go-getting domain labels (Go-labels) to replace the original immutable domain labels on the fly. The reason why call it as "Go-labels" is because "go-getting" means able to deal with new or difficult situations easily, like here Go-labels adaptively transfer the model attention from over-studied aligned data to those overlooked samples, which allows each sample to be well studied (i.e., alleviating data imbalance influence) and fully unleashes the potential of adaption model. Albeit simple, this dynamic adversarial domain adaptation framework with Go-labels effectively addresses data imbalance issue and promotes adaptation. We demonstrate through theoretical insights, empirical results on real data as well as toy games that our method leads to efficient training without bells and whistles, while being robust to different backbones.
Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism

In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional neural network (DCNN) image classifiers, called L-CAM-Fm and L-CAM-Img, are proposed. Both methods use an attention mechanism that is inserted in the original (frozen) DCNN and is trained to derive class activation maps (CAMs) from the last convolutional layer’s feature maps. During training, CAMs are applied to the feature maps (L-CAM-Fm) or the input image (L-CAM-Img) forcing the attention mechanism to learn the image regions explaining the DCNN’s outcome. Experimental evaluation on ImageNet shows that the proposed methods achieve competitive results while requiring a single forward pass at the inference stage. Moreover, based on the derived explanations a comprehensive qualitative analysis is performed providing valuable insight for understanding the reasons behind classification errors, including possible dataset biases affecting the trained classifier (Source code is made publicly available at: https://github.com/bmezaris/L-CAM ).
Variability Matters: Evaluating Inter-Rater Variability in Histopathology for Robust Cell Detection

Large annotated datasets have been a key component in the success of deep learning. However, annotating medical images is challenging as it requires expertise and a large budget. In particular, annotating different types of cells in histopathology suffer from high inter- and intra-rater variability due to the ambiguity of the task. Under this setting, the relation between annotators' variability and model performance has received little attention. We present a large-scale study on the variability of cell annotations among 120 board-certified pathologists and how it affects the performance of a deep learning model. We propose a method to measure such variability, and by excluding those annotators with low variability, we verify the trade-off between the amount of data and its quality. We found that naively increasing the data size at the expense of inter-rater variability does not necessarily lead to better-performing models in cell detection. Instead, decreasing the inter-rater variability with the expense of decreasing dataset size increased the model performance. Furthermore, models trained from data annotated with lower inter-labeler variability outperform those from higher inter-labeler variability. These findings suggest that the evaluation of the annotators may help tackle the fundamental budget issues in the histopathology domain.
ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization

Modality selection is an important step when designing multimodal systems, especially in the case of cross-domain activity recognition as certain modalities are more robust to domain shift than others. However, selecting only the modalities which have a positive contribution requires a systematic approach. We tackle this problem by proposing an unsupervised modality selection method (ModSelect), which does not require any ground-truth labels. We determine the correlation between the predictions of multiple unimodal classifiers and the domain discrepancy between their embeddings. Then, we systematically compute modality selection thresholds, which select only modalities with a high correlation and low domain discrepancy. We show in our experiments that our method ModSelect chooses only modalities with positive contributions and consistently improves the performance on a $$\textsc {Synthetic}{\rightarrow }\textsc {Real}$$ domain adaptation benchmark, narrowing the domain gap.
Multi-scale Attention-Based Multiple Instance Learning for Classification of Multi-gigapixel Histology Images

Histology images with multi-gigapixel of resolution yield rich information for cancer diagnosis and prognosis. Most of the time, only slide-level label is available because pixel-wise annotation is labour intensive task. In this paper, we propose a deep learning pipeline for classification in histology images. Using multiple instance learning, we attempt to predict the latent membrane protein 1 (LMP1) status of nasopharyngeal carcinoma (NPC) based on haematoxylin and eosin-stain (H &E) histology images. We utilised attention mechanism with residual connection for our aggregation layers. In our 3-fold cross-validation experiment, we achieved average accuracy, AUC and F1-score 0.936, 0.995 and 0.862, respectively. This method also allows us to examine the model interpretability by visualising attention scores. To the best of our knowledge, this is the first attempt to predict LMP1 status on NPC using deep learning.
ECCV 2022 Sign Spotting Challenge: Dataset, Design and Results

The ECCV 2022 Sign Spotting Challenge focused on the problem of fine-grain sign spotting for continuous sign language recognition. We have released and made publicly available a new dataset of Spanish sign language of around 10 h of video data in the health domain performed by 7 deaf people and 3 interpreters. The added value of this dataset over existing ones is the frame-level precise annotation of 100 signs with their corresponding glosses and variants made by sign language experts. This paper summarizes the design and results of the challenge, which attracted 79 participants, contextualizing the problem and defining the dataset, protocols and baseline models, as well as discussing top-winning solutions and future directions on the topic.
Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch

We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs) in PyTorch, a class of Neural Networks designed specifically for efficient inference in both online and batch processing scenarios. We offer a comprehensive introduction and guide to CINs and their implementation in practice, and provide best-practices and code examples for composing complex modules for modern Deep Learning. Continual Inference is readily downloadable via the Python Package Index and at \url{www.github.com/lukashedegaard/continual-inference}.
Hierarchical I3D for Sign Spotting

Most of the vision-based sign language research to date has focused on Isolated Sign Language Recognition (ISLR), where the objective is to predict a single sign class given a short video clip. Although there has been significant progress in ISLR, its real-life applications are limited. In this paper, we focus on the challenging task of Sign Spotting instead, where the goal is to simultaneously identify and localise signs in continuous co-articulated sign videos. To address the limitations of current ISLR-based models, we propose a hierarchical sign spotting approach which learns coarse-to-fine spatio-temporal sign features to take advantage of representations at various temporal levels and provide more precise sign localisation. Specifically, we develop Hierarchical Sign I3D model (HS-I3D) which consists of a hierarchical network head that is attached to the existing spatio-temporal I3D model to exploit features at different layers of the network. We evaluate HS-I3D on the ChaLearn 2022 Sign Spotting Challenge - MSSL track and achieve a state-of-the-art 0.607 F1 score, which was the top-1 winning solution of the competition.
Consistency Regularization for Domain Adaptation

Collection of real world annotations for training semantic segmentation models is an expensive process. Unsupervised domain adaptation (UDA) tries to solve this problem by studying how more accessible data such as synthetic data can be used to train and adapt models to real world images without requiring their annotations. Recent UDA methods applies self-learning by training on pixel-wise classification loss using a student and teacher network. In this paper, we propose the addition of a consistency regularization term to semi-supervised UDA by modelling the inter-pixel relationship between elements in networks' output. We demonstrate the effectiveness of the proposed consistency regularization term by applying it to the state-of-the-art DAFormer framework and improving mIoU19 performance on the GTA5 to Cityscapes benchmark by 0.8 and mIou16 performance on the SYNTHIA to Cityscapes benchmark by 1.2.
An Introduction to Blockchain Technology: Recent Trends

Blockchain is a buzzword in this knowledge era. It is a burgeoning concept emerged as a cutting-edge technology with lots of features being applied in varied domains. Features are inevitable in that it has evolved as a major player of the future digital economy. The Corporates are trying to understand the concept and exploring to their optimum level in formulating policies for the implementation and execution of the blockchain technology in their work system. This makes ease not only at the work system but also makes hassle free from many unwanted tasks. The purpose of this article is to illustrate the key characteristics and benefits of blockchain technology when used in conjunction with supply chain, healthcare, tourism, education, human resource, space, and finance industry. The study attempts to make propositions about how technology will evolve and impact the industry. The goal of this article is to identify and discuss blockchain technology as it applies to the many domains stated. To address the aforementioned, the author begins with defining blockchain, including its characteristics, benefits, types, recent and future trends, in order to improve grasp of this new technology.
Blockchain: A Study of New Business Model

One of the most amazing technological advancements of the twenty-first century is blockchain. Blockchain technology is most famously used in the creation and management of cryptocurrencies like bitcoin and Ethereum. Blockchain is being studied outside of the financial services sector in areas like taxation, supply chain management, company operations, and governance. However, not all pertinent fields of research have investigated blockchain in depth. Current research in this area mostly focuses on designing business processes for the blockchain or on execution engines that can implement processes using smart contracts. On the other hand, research into whether and how blockchains can help with business process monitoring has received less attention. With the use of blockchain technology, collaborative business operations involving untrustworthy parties can be carried out in a decentralized setting. Business Process Management (BPM) provides a good context in which these technologies can be used since blockchain and distributed ledger technologies are particularly suitable to build trusted settings when players do not trust one another. Several research proposals have shown that it is possible to create collaborative business processes for blockchains using high-level notations like the Business Process Model and Notation (BPMN) and then have the platform automatically generate the code artifacts needed to run those processes. The study shows how businesses and regulators may use Blockchain to uplevel corporate operations, improve efficiency, and cut costs. The main blockchain disadvantages that users should be aware of before implementing the technology are also emphasized. The chapter also considers how businesses may use blockchain to fully capitalize on the fourth industrial revolution. This work attempts to close this gap by outlining a collection of pertinent research difficulties that result from the implementation of blockchain technologies in business process monitoring solutions, describing the current methodologies to address these challenges, and presenting a reference architecture for doing so.
Bitcoin: Beginning of the Cryptocurrency Era

This case discusses how the evolution of Cryptocurrency and related technology changing the Business Models in different sectors. In spite of receiving backslash in its earlier days, it is observed that the impacts and consequences of cryptocurrencies were having a growing effect on more on industries and business models. And, there has been a huge funding on digital wallets and payment systems in the past couple of years. Facebook launching its own cryptocurrency is the best example to witness this fact. The technology behind Cryptocurrencies is Blockchain, a distributed network connected through peer-to-peer (P2P) nodes. The distinct features of the cryptocurrency like improved security, transparency and liability makes them more popular and a major reason for replacing the traditional methods. Cryptocurrencies, otherwise termed as digital currencies are cryptographically protected and are extremely difficult to forge. Being digital in nature cryptocurrencies are well suited for online transactions and the business process can be automated through smart contracts, a significant feature of cryptocurrencies. This virtual currency basically is used either as a payment token or as an investment (similar to shares). FinTech and Banking are the pioneers in adapting cryptocurrencies in their business models. However, those are not the only one domain where cryptocurrencies are revolutionizing the business models. This case illuminates the distinction between blockchain and bitcoin, working model of Bitcoin, business benefits, and challenges of Bitcoin, how Bitcoin taking over the mainstream process and reformation of ecommerce using Bitcoin. Most of the time it is interpreted that Bitcoin and Blockchain are the same. Actually, it is NOT. Bitcoin is the most popular digital currency and Blockchain was bundled up in the same solution when Bitcoin was released and Bitcoin was the very first application of blockchain, it is often misinterpreted as the same. It was introduced with the aim of evading government currency controls by eliminating third-party payment processing mediators and streamlining online transactions. Paypal, the pioneer in online transactions is efficient in international fund transfers but they have a very high transaction fee. Apart from this P2P transaction has certain constraints like caps on transfer amount, restrictions on locations etc.
Energy-Efficient Locking Protocol in Virtual Machine Environments

A huge number of transactions has to be concurrently performed to design and implement current distributed applications. However, each server in a system consumes the large amount of electric energy to perform methods issued by transactions. In this paper, the EE2PL-VM (Energy-Efficient Two-Phase Locking in Virtual Machine environment) protocol is newly proposed to not only serialize conflicting transactions but also reduce the total processing electric energy consumption (PEE) of servers. The evaluation results show the EE2PL-VM protocol can reduce the total PEE of servers compared with the 2PL (Two-Phase Locking) protocol.
Terminology Extraction of New Energy Vehicle Patent Texts Based on BERT-BILSTM-CRF

Automatic extraction of domain terminology plays an important role in constructing domain knowledge graphs, translating domain documents and learning domain core knowledge. To improve the accuracy of terminology extraction in the field of New Energy Vehicles (NEV) and learn the core content of the NEV patent texts. Combining the BERT pre-training model with the BILSTM-CRF deep learning model, we propose a BERT-BILSTM-CRF based terminology extraction method for NEV patents. Experimenting on a self-built text dataset of 885 NEV patent abstracts, the proposed model achieves an accuracy of 88.62%, which is better than other deep learning terminology extraction models.