Reviewerly/exHarmony huggingface dataset:
Active Learning for Effectively Fine-Tuning Transfer Learning to Downstream Task

Language model (LM) has become a common method of transfer learning in Natural Language Processing (NLP) tasks when working with small labeled datasets. An LM is pretrained using an easily available large unlabelled text corpus and is fine-tuned with the labelled data to apply to the target (i.e., downstream) task. As an LM is designed to capture the linguistic aspects of semantics, it can be biased to linguistic features. We argue that exposing an LM model during fine-tuning to instances that capture diverse semantic aspects (e.g., topical, linguistic, semantic relations) present in the dataset will improve its performance on the underlying task. We propose a Mixed Aspect Sampling (MAS) framework to sample instances that capture different semantic aspects of the dataset and use the ensemble classifier to improve the classification performance. Experimental results show that MAS performs better than random sampling as well as the state-of-the-art active learning models to abuse detection tasks where it is hard to collect the labelled data for building an accurate classifier.
Diversity Sampling is an Implicit Regularization for Kernel Methods

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 19 February 2020Accepted: 01 December 2020Published online: 16 February 2021Keywordskernel, Nyström, determinantal point process, regularizationAMS Subject Headings68T01, 60G55Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied MathematicsCODEN: sjmdaq
A hybrid approach of neural networks for age and gender classification through decision fusion

Age and Gender are identified as very important attributes in human identification and these attributes are used in various fields of Human and Computer Interaction (HCI) such as security systems, video-surveillance systems, online purchasing systems, judicial systems, transport, medicine, and so many others. In recent years, age and gender estimation based on facial feature analysis have been articulated as a challenging research topic by many researchers in the HCI field. In this research, we aim to present a novel hybrid algorithm of Conventional Artificial Neural Networks (C-ANN) and Convolution Neural Networks (CNN) by using decision fusion techniques for age and gender classification. The novelty of our research is the fusion of the decisions obtained by the two neural networks to increase the accuracy of age and gender estimation. We used the probabilistic decision fusion techniques such as majority voting decision fusion, Naïve-Bayes combinationdecision fusion and sum rule decision fusion for better recognition accuracy rate. Among these techniques, the sum rule decision fusion provided the highest accuracy rate compared to the state of art because of reducing the adjacent classes’ likelihoods during decision classifications.
Unsupervised Federated Learning for Unbalanced Data

This work considers unsupervised learning tasks being implemented within the federated learning framework to satisfy stringent requirements for low-latency and privacy of the emerging applications. The proposed algorithm is based on Dual Averaging (DA), where the gradients of each agent are aggregated at a central node. While having its advantages in terms of distributed computation, the accuracy of federated learning training reduces significantly when the data is nonuniformly distributed across devices. Therefore, this work proposes two weight computation algorithms, with one using a fixed size bin and the other with self-organizing maps (SOM) that solves the underlying dimensionality problem inherent in the first method. Simulation results are also provided to show that the proposed algorithms' performance is comparable to the scenario in which all data is uploaded and processed in the centralized cloud.
Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce

Recommender systems (RSs) are essential for e-commerce platforms to help meet the enormous needs of users. How to capture user interests and make accurate recommendations for users in heterogeneous e-commerce scenarios is still a continuous research topic. However, most existing studies overlook the intrinsic association of the scenarios: the log data collected from platforms can be naturally divided into different scenarios (e.g., country, city, culture). We observed that the scenarios are heterogeneous because of the huge differences among them. Therefore, a unified model is difficult to effectively capture complex correlations (e.g., differences and similarities) between multiple scenarios thus seriously reducing the accuracy of recommendation results. In this paper, we target the problem of multi-scenario recommendation in e-commerce, and propose a novel recommendation model named Scenario-aware Mutual Learning (SAML) that leverages the differences and similarities between multiple scenarios. We first introduce scenario-aware feature representation, which transforms the embedding and attention modules to map the features into both global and scenario-specific subspace in parallel. Then we introduce an auxiliary network to model the shared knowledge across all scenarios, and use a multi-branch network to model differences among specific scenarios. Finally, we employ a novel mutual unit to adaptively learn the similarity between various scenarios and incorporate it into multi-branch network. We conduct extensive experiments on both public and industrial datasets, empirical results show that SAML consistently and significantly outperforms state-of-the-art methods.
Exploring Cryptocurrency Sentiments With Clustering Text Mining on Social Media

Social media has become a popular communication platform and aggregated mass information for sentimental analysis. As cryptocurrency has become a hot topic worldwide in recent years, this chapter explores individuals' behavior in sharing Bitcoin information. First, Python was used for extracting around one month's set of Tweet data to obtain a dataset of 11,674 comments during a month of a substantial increase in Bitcoin price. The dataset was cleansed and analyzed by the process documents operator of RapidMiner. A word-cloud visualization for the Tweet dataset was generated. Next, the clustering operator of RapidMiner was used to analyze the similarity of words and the underlying meaning of the comments in different clusters. The clustering results show 85% positive comments on investment and 15% negative ones to Bitcoin-related tweets concerning security. The results represent the generally bullish environment of the cryptocurrency market and general user satisfaction during the period concerned.
Blockchain enabled secure healthcare Systems

Blockchain technology is known for its application in fields such as finance, banking and cryptocurrency and these are the fields that have very sensitive data that has to be stored and transferred securely. An important advantage of blockchain based system is that it is decentralized. The data stored is managed by a P2P network which eliminates several disadvantages that occur in centralized systems. Blockchain uses ad-hoc message passing. One such risk in a blockchain is that it avoids the central point of failure. Due to this advantage blockchain can be used in Healthcare systems. There is also step increase in the number of IoT devices which in turn is a threat to the patient’s medical data. This paper is aimed at giving an overview of the use of blockchain in healthcare systems. Blockchain can be used not only to secure patient information in their EHR’s but also allows them to give permission to entities as and when deemed necessary. We can use Hyperledger here in order to provide data access control. In order to take advantage of its decentralized nature, the EHR can be distributed among the various entities of the network such as doctor, medical practitioner, labs and so on.
SAR image denoising based on multifractal feature analysis and TV regularisation

A new denoising technique is proposed in this study for synthetic aperture radar (SAR) images corrupted by speckle noise. The authors method extract informative features from a noisy speckled image, and then a denoised version of this image is estimated from the informative gradients, which are restricted to the features of this image. The technique of extracting features is designed on the framework of multifractal formalism followed by a reconstruction technique for the informative gradients based on the total variational (TV) regularisation framework. Experimental results demonstrate that the proposed approach is able to retain the finer details of the original image while removing noise. The superiority of the proposed approach is manifested qualitatively and quantitatively on comparing with state-of-the-art denoising techniques.
Learning to Detect Salient Object with Multi-source Weak Supervision

High-cost pixel-level annotations makes it appealing to train saliency detection models with weak supervision. However, a single weak supervision source hardly contain enough information to train a well-performing model. To this end, we introduce a unified two-stage framework to learn from category labels, captions, web images and unlabeled images. In the first stage, we design a classification network (CNet) and a caption generation network (PNet), which learn to predict object categories and generate captions, respectively, meanwhile highlights the potential foreground regions. We present an attention transfer loss to transmit supervisions between two tasks and an attention coherence loss to encourage the networks to detect generally salient regions instead of task-specific regions. In the second stage, we create two complementary training datasets using CNet and PNet, i.e., natural image dataset with noisy labels for adapting saliency prediction network (SNet) to natural image input, and synthesized image dataset by pasting objects on background images for providing SNet with accurate ground-truth. During the testing phases, we only need SNet to predict saliency maps. Experiments indicate the performance of our method compares favorably against unsupervised, weakly supervised methods and even some supervised methods.
A Novel Reliable Approach For Image Batik Classification That Invariant With Scale And Rotation Using MU2ECS-LBP Algorithm

Batik in Indonesia has various types of patterns, which are arranged repeatedly to illustrate the basic motifs of cloth as a whole. Types of batik motifs collected through several sources such as batik magazines, the internet or directly using a digital camera. Batik’s pattern automatic classification still requires some improvement especially in regards to invariant with scale and rotation. The batik‘s pattern of changing this dilemma at the same time needs a feature extraction algorithm that is reliable in supporting image classification. This algorithm is multiwindow and multiscale extended center symmetric local binary patterns (MU2ECS-LBP) which uses several windows such as the size of 6x6, 9x9, 12x12, and 15x15 or a combination between windows. To recognize the batik pattern automatically, we implement a batik classification method using kNN and ANN. After doing some experiments the results of the accuracy values with the kNN method, based on the effect of training images and image conditions, on multi-window 6-9-12-15, another multi-window 6-12-15 with overlapping images of 40 and 50 pixels and the number of image classes 5, 9 and 12 classes are 99.91% and 99.8%. With the ANN method, the accuracy classification value is based on the multi-window effect and image overlap, where the highest accuracy value on multiwindow 6-9-12-15 with ANN architecture 64-240-12 is 98.43%. A novel algorithm is a development of the local binary pattern algorithm, but by looking at the results of classification accuracy which is very good and reliable. So that the feature extraction algorithm is very feasible to be developed and continued for other research.
Application of Tensor Train Decomposition in S2VT Model for Sign Language Recognition

Sign language recognition is a conversion of sign language into text or speech, bridging the communication between the hearing and society.Recently, sequence-to-sequence video to text (S2VT) models has been employed in the field of sign language recognition as an effective method.However, more than 20 million parameters trained in S2VT models will result in a huge consumption in memory and computational resources, making it hard to be applied in mobile devices.In order to overcome this issue, we proposed to employ tensor-train decomposition in S2VT models to reduce the parameters.First, the impact of parameters of tensor-train factorization on the model performance was investigated systematically.After that, we applied tensor-train decomposition in different layers of a S2VT model to establish 6 tensor-train S2VT models for Chinese sign language recognition.The experimental results demonstrated that when the fully-connected layer and the first LSTM layer in S2VT was represented with tensor-train format, the model could obtain the best performance, remaining high accuracy and reducing parameters and memory significantly.The proposed tensor-train S2VT models can also be applied in other sequence-to-sequence problems to improve the performance.
Optimizing Deep Learning Recommender Systems Training on CPU Cluster Architectures

During the last two years, the goal of many researchers has been to squeeze the last bit of performance out of HPC system for AI tasks. Often this discussion is held in the context of how fast ResNet50 can be trained. Unfortunately, ResNet50 is no longer a representative workload in 2020. Thus, we focus on Recommender Systems which account for most of the AI cycles in cloud computing centers. More specifically, we focus on Facebook's DLRM benchmark. By enabling it to run on latest CPU hardware and software tailored for HPC, we are able to achieve up to two-orders of magnitude improvement in performance on a single socket compared to the reference CPU implementation, and high scaling efficiency up to 64 sockets, while fitting ultra-large datasets which cannot be held in single node's memory. Therefore, this paper discusses and analyzes novel optimization and parallelization techniques for the various operators in DLRM. Several optimizations (e.g. tensorcontraction accelerated MLPs, framework MPI progression, BFLOAT16 training with up to 1.8× speed-up) are general and transferable to many other deep learning topologies.
The human‐in‐the‐loop: an evaluation of pathologists’ interaction with artificial intelligence in clinical practice

One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and efficiency. Whereas stand-alone DIA has great potential benefit for research, little is known about the effect of DIA assistance in clinical use. The aim of this study was to investigate the clinical use characteristics of a DIA application for Ki67 proliferation assessment. Specifically, the human-in-the-loop interplay between DIA and pathologists was studied.We retrospectively investigated breast cancer Ki67 areas assessed with human-in-the-loop DIA and compared them with visual and automatic approaches. The results, expressed as standard deviation of the error in the Ki67 index, showed that visual estimation ('eyeballing') (14.9 percentage points) performed significantly worse (P < 0.05) than DIA alone (7.2 percentage points) and DIA with human-in-the-loop corrections (6.9 percentage points). At the overall level, no improvement resulting from the addition of human-in-the-loop corrections to the automatic DIA results could be seen. For individual cases, however, human-in-the-loop corrections could address major DIA errors in terms of poor thresholding of faint staining and incorrect tumour-stroma separation.The findings indicate that the primary value of human-in-the-loop corrections is to address major weaknesses of a DIA application, rather than fine-tuning the DIA quantifications.
Improved Acoustic Word Embeddings for Zero-Resource Languages Using Multilingual Transfer

Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. Such embeddings can form the basis for speech search, indexing and discovery systems when conventional speech recognition is not possible. In zero-resource settings where unlabelled speech is the only available resource, we need a method that gives robust embeddings on an arbitrary language. Here we explore multilingual transfer: we train a single supervised embedding model on labelled data from multiple well-resourced languages and then apply it to unseen zero-resource languages. We consider three multilingual recurrent neural network (RNN) models: a classifier trained on the joint vocabularies of all training languages; a Siamese RNN trained to discriminate between same and different words from multiple languages; and a correspondence autoencoder (CAE) RNN trained to reconstruct word pairs. In a word discrimination task on six target languages, all of these models outperform state-of-the-art unsupervised models trained on the zero-resource languages themselves, giving relative improvements of more than 30% in average precision. When using only a few training languages, the multilingual CAE-RNN performs better, but with more training languages the other multilingual models perform similarly. Using more training languages is generally beneficial, but improvements are marginal on some languages. We present probing experiments which show that the CAE-RNN encodes more phonetic, word duration, language identity and speaker information than the other multilingual models.
Contact tracing apps for self-quarantine in South Korea: rethinking datafication and dataveillance in the COVID-19 age

Purpose The first case of coronavirus disease 2019 (COVID-19) was documented in China, and the virus was soon to be introduced to its neighboring country – South Korea. South Korea, one of the earliest countries to initiate a national pandemic response to COVID-19 with fairly substantial measures at the individual, societal and governmental level, is an interesting example of a rapid response by the Global South. The current study examines contact tracing mobile applications (hereafter, contact tracing apps) for those who were subject to self-quarantine through the lenses of dataveillance and datafication. This paper analyzes online/digital data from those who were mandatorily self-quarantined by the Korean government largely due to returning from overseas travel. Design/methodology/approach This study uses an Internet ethnography approach to collect and analyze data. To extract data for this study, self-quarantined Korean individuals' blog entries were collected and verified with a combination of crawling and manual checking. Content analysis was performed with the codes and themes that emerged. In the COVID-19 pandemic era, this method is particularly useful to gain access to those who are affected by the situation. This approach advances the author’s understandings of COVID-19 contact tracing mobile apps and the experiences of self-quarantined people who use them. Findings The paper shows Korean citizens' understandings and views of using the COVID-19 self-tracing application in South Korea through examining their experiences. The research argues that the application functions as a datafication tool that collects the self-quarantined people's information and performs dataveillance on the self-quarantined people. This research further offers insights for various agreements/disagreements at different actors (i.e. the self-quarantined, their families, contact tracers/government officials) in the process of contact tracing for COVID-19. Originality/value This study also provides insights into the implications of information and technology as they affect datafication and dataveillance conducted on the public. This study investigates an ongoing debate of COVID-19's contact tracing method concerning privacy and builds upon an emerging body of literature on datafication, dataveillance, social control and digital sociology. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0377
Convexity preserving level set for left ventricle segmentation

In clinical applications of cardiac left ventricle (LV) segmentation, the segmented LV is desired to include the cavity, trabeculae, and papillary muscles, which form a convex shape. However, the intensities of trabeculae and papillary muscles are similar to myocardium. Consequently, segmentation algorithms may easily misclassify trabeculae and papillary muscles as myocardium. In this paper, we propose a level set method with a convexity preserving mechanism to ensure the convexity of the segmented LV. In the proposed level set method, the curvature of the level set contours is used to control their convexity, such that the level set contour is finally deformed as a convex shape. The experimental results and the comparison with other level set methods show the advantage of our method in terms of segmentation accuracy. Compared with the state-of-the-art methods using deep-learning, our method is able to achieve comparable segmentation accuracy without the need for training, while the deep-learning based method requires a large set of training data and high-quality manual segmentation. Therefore, our method can be conveniently used in situation where training data and their manual segmentation are not available.
A Novel Risk Matrix Approach Based on Cloud Model for Risk Assessment Under Uncertainty

The fuzziness and randomness are significant epistemic uncertainty within the qualitative categorization of the two critical factors, the frequency and severity, which are not fully considered in the traditional risk matrix.This paper mainly proposes a cloud risk matrix method for the risk assessment of process safety considering the epistemic uncertainty in expert elicitation.The cloud model is employed to provide a mathematical expression for the fuzziness and randomness in the linguistic variables by its two numerical characteristics entropy En and hyperentropy He.An adjusted Mamdani inference algorithm is constructed for the determination of an integrated risk cloud given the value of input variables.And the centroid method is improved to be adapted to the calculation of risk index from the enormous cloud droplets in the integrated risk cloud.A case study of risk assessment for distillation column unit is performed to illustrate the process of cloud risk matrix in detail, and the validity and rationality are verified by contrast to the results from the fuzzy risk matrix.Results indicate the proposed method can handle the randomness of qualitative concepts, which is more suitable to the practical condition.Moreover, the effect of the hyperentropy He on the randomness of risk index is also investigated and discussed for the reference to parameter selection.The proposed cloud risk matrix can provide an effective risk inference tool in a wide range of engineering fields.