Reviewerly/exHarmony huggingface dataset:
Self-Supervised Acoustic Anomaly Detection Via Contrastive Learning

We propose an acoustic anomaly detection algorithm based on the framework of contrastive learning. Contrastive learning is a recently proposed self-supervised approach that has shown promising results in image classification and speech recognition. However, its application in anomaly detection is underexplored. Earlier studies have demonstrated that it can achieve state-of-the-art performance in image anomaly detection, but its capability in anomalous sound detection is yet to be investigated. For the first time, we propose a contrastive learning-based framework that is suitable for acoustic anomaly detection. Since most existing contrastive learning approaches are targeted toward images, the effect of other data transformations on the performance of the algorithm is unknown. Our framework learns a representation from unlabeled data by applying audio-specific data augmentations. We show that in the resulting latent space, normal and abnormal points are distinguishable. Experiments conducted on the MIMII dataset confirm that our approach can outperform competing methods in detecting anomalies.
Utilizing a Pretrained Language Model (BERT) to Classify Preservice Physics Teachers’ Written Reflections

Abstract Computer-based analysis of preservice teachers’ written reflections could enable educational scholars to design personalized and scalable intervention measures to support reflective writing. Algorithms and technologies in the domain of research related to artificial intelligence have been found to be useful in many tasks related to reflective writing analytics such as classification of text segments. However, mostly shallow learning algorithms have been employed so far. This study explores to what extent deep learning approaches can improve classification performance for segments of written reflections. To do so, a pretrained language model (BERT) was utilized to classify segments of preservice physics teachers’ written reflections according to elements in a reflection-supporting model. Since BERT has been found to advance performance in many tasks, it was hypothesized to enhance classification performance for written reflections as well. We also compared the performance of BERT with other deep learning architectures and examined conditions for best performance. We found that BERT outperformed the other deep learning architectures and previously reported performances with shallow learning algorithms for classification of segments of reflective writing. BERT starts to outperform the other models when trained on about 20 to 30% of the training data. Furthermore, attribution analyses for inputs yielded insights into important features for BERT’s classification decisions. Our study indicates that pretrained language models such as BERT can boost performance for language-related tasks in educational contexts such as classification.
Fraug: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals

In this paper, a data augmentation method is proposed for depression detection from speech signals. Samples for data augmentation were created by changing the frame-width and the frame-shift parameters during the feature extraction process. Unlike other data augmentation methods (such as VTLP, pitch perturbation, or speed perturbation), the proposed method does not explicitly change acoustic parameters but rather the time-frequency resolution of frame-level features. The proposed method was evaluated using two different datasets, models, and input acoustic features. For the DAIC-WOZ (English) dataset when using the DepAudioNet model and mel-Spectrograms as input, the proposed method resulted in an improvement of 5.97% (validation) and 25.13% (test) when compared to the baseline. The improvements for the CONVERGE (Mandarin) dataset when using the x-vector embeddings with CNN as the backend and MFCCs as input features were 9.32% (validation) and 12.99% (test). Baseline systems do not incorporate any data augmentation. Further, the proposed method outperformed commonly used data-augmentation methods such as noise augmentation, VTLP, Speed, and Pitch Perturbation. All improvements were statistically significant.
Mmlatch: Bottom-Up Top-Down Fusion For Multimodal Sentiment Analysis

Current deep learning approaches for multimodal fusion rely on bottom-up fusion of high and mid-level latent modality representations (late/mid fusion) or low level sensory inputs (early fusion). Models of human perception highlight the importance of top-down fusion, where high-level representations affect the way sensory inputs are perceived, i.e. cognition affects perception. These top-down interactions are not captured in current deep learning models. In this work we propose a neural architecture that captures top-down cross-modal interactions, using a feedback mechanism in the forward pass during network training. The proposed mechanism extracts high-level representations for each modality and uses these representations to mask the sensory inputs, allowing the model to perform top-down feature masking. We apply the proposed model for multimodal sentiment recognition on CMU-MOSEI. Our method shows consistent improvements over the well established MulT and over our strong late fusion baseline, achieving state-of-the-art results.
A Secure Design of the Healthcare IoT System using Blockchain Technology

Nowadays, the Internet of Things (IoT) is an emerging technology to interconnect the physical things for providing the best services in hands. IoT has different applications in many areas viz., Smart Health, Smart Transportation, and Smart Cities. Healthcare IoT is an information system to provide quick health services to patients. In medical service applications, IoT facilitates correspondence among specialists and remote patients who are equipped with wearable sensors. Patients&#x0027; data is critical and any security violation to such data may lead to severe problems. Some existing healthcare frameworks may prompt infringement on the security of the patients. Therefore, security must be implemented carefully in such systems. Blockchain is one of the secure techniques and has been applied to most IoT systems. Scarcely, the significant purposes behind using the Blockchain in medical care frameworks are its conspicuous provisions, i.e., Decentralization, Immutability, Transparency, Security, and Privacy. In this paper, we proposed a blockchain-based healthcare IoT system for the security of Electronic Health Records (EHRs). In this methodology, the health data of the patients are encrypted and stored at the hospital servers and related hash values are stored in the blockchain. Our primary intention is to give an appropriate, secure, and approved admittance to this critical clinical data of the patients using the Ethereum blockchain. This Ethereum based architecture provides an adaptable and secure framework for a smart healthcare system and outperforms the existing methods.
Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance

Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clinical workflow, and even fewer report performances in different hospitals. In this study, we provide detailed evaluations of clinical risk prediction models in live clinical workflows for three different use cases in three different hospitals.The main objective of this study was to evaluate clinical risk prediction models in live clinical workflows and compare their performance in these setting with their performance when using retrospective data. We also aimed at generalizing the results by applying our investigation to three different use cases in three different hospitals.We trained clinical risk prediction models for three use cases (ie, delirium, sepsis, and acute kidney injury) in three different hospitals with retrospective data. We used machine learning and, specifically, deep learning to train models that were based on the Transformer model. The models were trained using a calibration tool that is common for all hospitals and use cases. The models had a common design but were calibrated using each hospital's specific data. The models were deployed in these three hospitals and used in daily clinical practice. The predictions made by these models were logged and correlated with the diagnosis at discharge. We compared their performance with evaluations on retrospective data and conducted cross-hospital evaluations.The performance of the prediction models with data from live clinical workflows was similar to the performance with retrospective data. The average value of the area under the receiver operating characteristic curve (AUROC) decreased slightly by 0.6 percentage points (from 94.8% to 94.2% at discharge). The cross-hospital evaluations exhibited severely reduced performance: the average AUROC decreased by 8 percentage points (from 94.2% to 86.3% at discharge), which indicates the importance of model calibration with data from the deployment hospital.Calibrating the prediction model with data from different deployment hospitals led to good performance in live settings. The performance degradation in the cross-hospital evaluation identified limitations in developing a generic model for different hospitals. Designing a generic process for model development to generate specialized prediction models for each hospital guarantees model performance in different hospitals.
Dehumanization through humour and conspiracies in online hate towards Chinese people during the COVID‐19 pandemic

Since the beginning of the COVID-19 pandemic, there have been widespread conversations about the origins of the virus and who to blame for it. This article focuses on the online hate directed at Chinese and Asian people during the pandemic. Taking a critical discursive psychological approach, we analysed seven online threads related to COVID-19 and China from two Finnish websites (Suomi24 and Ylilauta) and one US (8kun) site. We identified three discursive trends associated with dehumanising Chinese populations: 'monstrous Chinese', 'immoral Chinese' and 'China as a threat', which created different forms of dehumanisation on a continuum from harsher dehumanisation to milder depersonalisation. The animalistic metaphors, coarse language, humorous frames and conspiracy beliefs worked to rhetorically justify the dehumanisation of Chinese individuals, making it more acceptable to portray them as a homogeneous and inhumane mass of people that deserves to be attacked. This study contributes to the field of discursive research on dehumanisation by deepening our knowledge of the specific features of Sinophobic hate speech.
Learning Under Concept Drift for Regression—A Systematic Literature Review

Context: The amount and diversity of data have increased drastically in recent years.However, in certain situations, the data to which a trained Machine Learning model is significantly different from testing data, a problem known as Concept Drift (CD).Because CD can be a serious issue, there has been a wealth of research on how to detect and work around it.However, most of the literature focuses on classification tasks.Objective: Making a Systematic Literature Review (SLR) for CD in the context of regression.Research questions: How to detect CD and how to build CD techniques for regression problems using machine learning?Method: We ran an automatic search process on reference databases, selecting papers from 2010 to August 2020, following the methodological process proposed by (Kitchenhame and Charters) (2007).Results:We selected 41 papers.Drift Detection Methods based on ensembles and neural networks with highlight OS-ELM were the most frequent in the selected papers with superior performance.However, only two papers confirm such superiority statistically.Furthermore, identify CD problems as the batch size, drift points, and where drift happens.Conclusions: SLR focuses on highlighting the existing literature on CD applied to regression.
An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series

As a substantial amount of multivariate time series data is being produced by the complex systems in smart manufacturing (SM), improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is challenging, as a sufficiently large amount of defective training data is often not available and frameworks are required to capture both the temporal and contextual dependencies across different time steps while being robust to noise. In this paper, we propose an unsupervised Attention-Based Convolutional Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding (ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time series. The framework starts by pre-processing and enriching the data, before constructing feature images to characterize the system statuses across different time steps by capturing the inter-correlations between pairs of time series. Afterwards, the constructed feature images are fed into an attention-based ConvLSTM autoencoder, which aims to encode the constructed feature images and capture the temporal behavior, followed by decoding the compressed knowledge representation to reconstruct the feature images’ input. The reconstruction errors are then computed and subjected to a statistical-based, dynamic thresholding mechanism to detect and diagnose the anomalies. Evaluation results conducted on real-life manufacturing data demonstrate the performance strengths of the proposed approach over state-of-the-art methods under different experimental settings.
Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization

Query-by-document (QBD) retrieval is an Information Retrieval task in which a seed document acts as the query and the goal is to retrieve related documents – it is particular common in professional search tasks. In this work we improve the retrieval effectiveness of the BERT re-ranker, proposing an extension to its fine-tuning step to better exploit the context of queries. To this end, we use an additional document-level representation learning objective besides the ranking objective when fine-tuning the BERT re-ranker. Our experiments on two QBD retrieval benchmarks show that the proposed multi-task optimization significantly improves the ranking effectiveness without changing the BERT re-ranker or using additional training samples. In future work, the generalizability of our approach to other retrieval tasks should be further investigated.
Modified Bat Algorithm for Optimal VM's in Cloud Computing

All task scheduling applications need to ensure that resources are optimally used, performance is enhanced, and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values (FCVs) to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components (most notably cloud infrastructure and cloud storage) (in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system's performance.This case study is one of the most important segments of cloud computing, i.e., Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines (VM's) of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm (BA).This proposed Modified Bat Algorithm (MBA) allows balancing the load among virtual machines.The proposed algorithm works in two variants: MBA with Overloaded Optimal Virtual Machine (MBA-OOVM) and Modified Bat Algorithm with Balanced Virtual Machine (MBA-BVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.
Blockchain based context-aware CP-ABE schema for Internet of Medical Things security

Nowadays, the number of corona patients is increasing significantly. The relationship between the Internet of Medical Things (IoMT) and the Internet is struggling to keep up with this number of patients. The transmission of Patient Health Records (PHR) to the care of a patient through Internet plays an important role in the remote monitoring and fast detection of new contaminated patient with coronavirus. Moreover, it has generated significant security and privacy concerns for the global health care system due to tampering of control messages. This paper focuses on the application of blockchain and smart contract mechanisms to solve the shortcomings of the current health application and propose a new security schema based on context-aware CP-ABE. The proposed schema includes context-aware policies to achieve a robust authentication of identity and confidentiality of patient's healthcare data. Therefore, the proposed schema shows promising results in enhancing security and minimizing encryption time in Fog cloud environments based on proxy-fog and reinforcement of security policies.
Deep Sentiment Learning for Measuring Similarity Recommendations in Twitter Data

The similarity recommendation of twitter data is evaluated by using sentiment analysis method.In this paper, the deep learning processes such as classification, clustering and prediction are used to measure the data.Convolutional neural network is applied for analyzing multimedia contents which is received from various sources.Recurrent neural network is used for handling the natural language data.The content based recommendation system is proposed for selecting similarity index in twitter data using deep sentiment learning method.In this paper, sentiment analysis technique is used for finding similar images, contents, texts, etc.The content is selected based on repetitive comments and trending information.Hash tag is also considered for data collection and prediction.The number tweets are accountable and each character is taken for evaluation.Deep belief network is generated using 512 × 512 × 3 layers system and 1056 trained data, 512 test data that are taken for convolution process.The deep belief network is generated using TensorFlow.TensorFlow is used to simulate the deep learning environments.Semantic analysis is applied for handling Twitter Data.The deep learning processes are classified into clustering, regression and prediction that are evaluated by step by setup approach.The experiments are carried out using similarity index calculation and measuring of accuracy.The results of similarity recommendation are compared with existing method and the results are recorded.Our proposed system gives better results comparing with existing experiments.
Attribute Prototype Network for Any-Shot Learning

Any-shot image classification allows to recognize novel classes with only a few or even zero samples. For the task of zero-shot learning, visual attributes have been shown to play an important role, while in the few-shot regime, the effect of attributes is under-explored. To better transfer attribute-based knowledge from seen to unseen classes, we argue that an image representation with integrated attribute localization ability would be beneficial for any-shot, i.e. zero-shot and few-shot, image classification tasks. To this end, we propose a novel representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. Furthermore, we introduce a zoom-in module that localizes and crops the informative regions to encourage the network to learn informative features explicitly. We show that our locality augmented image representations achieve a new state-of-the-art on challenging benchmarks, i.e. CUB, AWA2, and SUN. As an additional benefit, our model points to the visual evidence of the attributes in an image, confirming the improved attribute localization ability of our image representation. The attribute localization is evaluated quantitatively with ground truth part annotations, qualitatively with visualizations, and through well-designed user studies.
Appearance-Based Gaze Estimation for ASD Diagnosis

Biomarkers, such as magnetic resonance imaging (MRI) and electroencephalogram have been used to help diagnose autism spectrum disorder (ASD). However, the diagnosis needs the assist of specialized medical equipment in the hospital or laboratory. To diagnose ASD in a more effective and convenient way, in this article, we propose an appearance-based gaze estimation algorithm-AttentionGazeNet, to accurately estimate the subject's 3-D gaze from a raw video. The experimental results show its competitive performance on the MPIIGaze dataset and the improvement of 14.7% for static head pose and 46.7% for moving head pose on the EYEDIAP dataset compared with the state-of-the-art gaze estimation algorithms. After projecting the obtained gaze vector onto the screen coordinate, we apply accumulated histogram to taking into account both spatial and temporal information of estimated gaze-point and head-pose sequences. Finally, classification is conducted on our self-collected autistic children video dataset (ACVD), which contains 405 videos from 135 different ASD children, 135 typically developing (TD) children in a primary school, and 135 TD children in a kindergarten. The classification results on ACVD shows the effectiveness and efficiency of our proposed method, with the accuracy 94.8%, the sensitivity 91.1% and the specificity 96.7% for ASD.
SPDL: A Blockchain-Enabled Secure and Privacy-Preserving Decentralized Learning System

Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies have shown the feasibility of preserving privacy, enhancing training performance or introducing Byzantine resilience, but none of them simultaneously considers all of them. Therefore we face the following problem: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how can we efficiently coordinate the decentralized learning process while simultaneously maintaining learning security and data privacy for the entire system?</i> To address this issue, in this paper we propose SPDL, a blockchain-secured and privacy-preserving decentralized learning system. SPDL integrates blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR), and differential privacy seamlessly into one system, ensuring efficient machine learning while maintaining data privacy, Byzantine fault tolerance, transparency, and traceability. To validate our approach, we provide rigorous analysis on convergence and regret in the presence of Byzantine nodes. We also build a SPDL prototype and conduct extensive experiments to demonstrate that SPDL is effective and efficient with strong security and privacy guarantees.