Reviewerly/exHarmony huggingface dataset:
A survey on security challenges and solutions in the IOTA

Wide-scale adoption of the Internet of Everything requires decentralized security, responsibility, and trust among the stakeholders. All these can be achieved by a Distributed Ledger Technology (DLT) backbone. As a mathematical model for enabling this DLT backbone, IOTA’s Tangle is gaining popularity due to its scalability and freedom from transaction fees. Unlike blockchain, the Tangle uses a Directed Acyclic Graph (DAG) structure, and its design does not cover essential blockchain pitfalls, including expensive Proof of Work (PoW), limited throughput, high transaction costs, and significant confirmation delays. The original IOTA is evolving into a Coordinator-less environment, the Coordicide. It requires additional modules, such as auto-peering and a reputation system, to fully exploit Tangle’s scalability and complete decentralization potential. Nevertheless, each new evolutionary update adds complexity and may introduce security threats. Therefore, the present survey’s motivation is a detailed security analysis of the IOTA. To spur developers and researchers’ interest and summarize the security status in IOTA, we have drawn the current review. Our survey outlines security vulnerabilities on IOTA and their mitigation strategies and explores several important open directions to be researched further. The vulnerabilities are discussed on both the original IOTA and its upcoming Coordicide version. In summary, this survey is first in the field for (i) understanding the basic functionalities of the IOTA, (ii) listing the security solutions provided in the literature against the reported and unreported attacks, and (iii) presenting open research questions (RQ) for directing the future investigations on IOTA.
Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks

Blink detection is an important technique in a variety of settings, including facial movement analysis and signal processing. However, automatic blink detection is very challenging because of the blink rate. This research work proposed a real-time method for detecting eye blinks in a video series. Automatic facial landmarks detectors are trained on a real-world dataset and demonstrate exceptional resilience to a wide range of environmental factors, including lighting conditions, face emotions, and head position. For each video frame, the proposed method calculates the facial landmark locations and extracts the vertical distance between the eyelids using the facial landmark positions. Our results show that the recognizable landmarks are sufficiently accurate to determine the degree of eye-opening and closing consistently. The proposed algorithm estimates the facial landmark positions, extracts a single scalar quantity by using Modified Eye Aspect Ratio (Modified EAR) and characterizing the eye closeness in each frame. Finally, blinks are detected by the Modified EAR threshold value and detecting eye blinks as a pattern of EAR values in a short temporal window. According to the results from a typical data set, it is seen that the suggested approach is more efficient than the state-of-the-art technique.
Meta-learning approaches for learning-to-learn in deep learning: A survey

Compared to traditional machine learning, deep learning can learn deeper abstract data representation and understand scattered data properties. It has gained considerable attention for its extraordinary performances. However, existing deep learning algorithms perform poorly on new tasks. Meta-learning, known as learning to learn, is one of the effective techniques to overcome this issue. Meta-learning’s generalization ability to unknown tasks is improved by employing prior knowledge to assist the learning of new tasks. There are mainly three types of meta-learning methods: metric-based, model-based, and optimization-based meta-learning. We investigate classical algorithms and recent meta-learning advances. Second, we survey meta-learning application in real world scenarios. Finally, we discuss present challenges and future research directions of meta-learning.
Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care

Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.
BIM integrated smart contract for construction project progress payment administration

In recent years few blockchain based progress payment methods have been proposed to improve the traditional payment process for construction projects. The blockchain based methods require the off-chain real world information to be connected to the blockchain which could be challenging for construction projects. This paper presents a novel building information modeling (BIM) integrated smart contract progress payment administration system, in which as-constructed BIM is used to link the real world with the blockchain. Results of a survey of construction professionals revealed that the proposed system has a significant potential for improving the traditional payment procedure by accelerating the process and by making the progress payments less prone to disputes, especially for lump sum projects. The results also indicate the potential of future research on semi-autonomous BIM integrated smart contract systems to accelerate the adoption of the BIM and smart contract technologies for progress payment administration within the construction sector.
Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect

• Comprehensive review of health monitoring based sensors. • Categorize the sensors into homogeneous, dual and heterogeneous. • Discuss their strengths, drawbacks and application domains. • Outline major issues and significant solutions. • Highlights future prospect of health monitoring sensors. Mobile and wearable devices embedded with multiple sensors for health monitoring and disease diagnosis are growing fields with the potential to provide efficient means for remote health management. A sensor-based health monitoring system offers an essential mechanism for real-time diagnosis and management to detect/predict, recommend treatment and prevent the onset of diseases. This paper aims to synthesize the research efforts on mobile and wearable sensors for health monitoring. It will investigate sensors, components of health monitoring systems, major application areas, challenges, and solutions faced during the implementation of health monitoring systems by researchers and practitioners. It was observed that sensors embedded in mobile and wearable devices for health monitoring are broadly categorized into homogeneous, dual, and heterogeneous sensors. In health monitoring, heterogeneous sensor-based is widely implemented and the most effective due to its ability to combine multiple sensors from various domains. The fusion of multiple sensors provides reliability, credibility, and better accuracy for monitoring multiple health parameters. We observed that researchers follow established procedures such as data collection, data transmission, preprocessing, feature extraction and development, data analysis, and evaluation of different algorithms for implementation of the health monitoring system. Supervised machine learning algorithms such as support vector machine, decision tree, k-nearest neighbors, and deep learning methods were the most implemented methods, while accuracy was the favored evaluation measure for health monitoring. Generally, we found that a health monitoring system is implemented to resolve health issues in the areas of human activity and posture monitoring, sleep disorder, sleep stage detection, fall monitoring in the elderly, depression, and mood swing detection. Other important areas include Parkinson’s disease management, cardiac diseases monitoring, disease diagnosis, and well-being, and Corona virus detection and contact tracing to minimize infection rate. Furthermore, the review succinctly highlights various challenges impeding the development of sensor-based health monitoring systems with significant solutions that were recommended in the literature to ameliorate these challenges discussed. From the review, it can be acknowledged that various research efforts have been conducted to develop effective health monitoring systems, and many new systems have been implemented. However, there is still much work to be done which we have also discussed under future prospects.
Hesitant Fuzzy Decision Tree Approach for Highly Imbalanced Data Classification

Imbalanced data classification is a problem of great interest in both machine learning and bioinformatics. A classification problem contains imbalanced data when the distribution of data samples is not the same in different classes. That is, there is usually a large difference among the number of instances in different classes. If this is the case, learning algorithms, with their goal of maximizing the accuracy of the inferred model, may ignore the samples of the smaller class. In this chapter we consider the problem of constructing a hesitant fuzzy decision tree for the classification of imbalanced datasets.
SST: Spatial and Semantic Transformers for Multi-Label Image Recognition

Multi-label image recognition has attracted considerable research attention and achieved great success in recent years. Capturing label correlations is an effective manner to advance the performance of multi-label image recognition. Two types of label correlations were principally studied, i.e., the spatial and semantic correlations. However, in the literature, previous methods considered only either of them. In this work, inspired by the great success of Transformer, we propose a plug-and-play module, named the Spatial and Semantic Transformers (SST), to simultaneously capture spatial and semantic correlations in multi-label images. Our proposal is mainly comprised of two independent transformers, aiming to capture the spatial and semantic correlations respectively. Specifically, our Spatial Transformer is designed to model the correlations between features from different spatial positions, while the Semantic Transformer is leveraged to capture the co-existence of labels without manually defined rules. Other than methodological contributions, we also prove that spatial and semantic correlations complement each other and deserve to be leveraged simultaneously in multi-label image recognition. Benefitting from the Transformer's ability to capture long-range correlations, our method remarkably outperforms state-of-the-art methods on four popular multi-label benchmark datasets. In addition, extensive ablation studies and visualizations are provided to validate the essential components of our method.
Progressive Meta-Learning With Curriculum

Meta-learning offers an effective solution to learn new concepts under scarce supervision through an episodic-training scheme: a series of target-like tasks sampled from base classes are sequentially fed into a meta-learner to extract cross-task knowledge, which can facilitate the quick acquisition of task-specific knowledge of the target task with few samples. Despite its noticeable improvements, the episodic-training strategy samples tasks randomly and uniformly, without considering their hardness and quality, which may not progressively improve the meta-leaner’s generalization. In this paper, we propose Progressive Meta-learning using tasks from easy to hard. First, based on a predefined curriculum, we develop a Curriculum-Based Meta-learning (CubMeta) method. CubMeta is in a stepwise manner, and in each step, we design a BrotherNet module to establish harder tasks and an effective learning scheme for obtaining an ensemble of stronger meta-learners. Then we move a step further to propose an end-to-end Self-Paced Meta-learning (SepMeta) method. The curriculum in SepMeta is effectively integrated as a regularization term into the objective so that the meta-learner can measure the hardness of tasks adaptively, according to what the model has already learned. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed methods. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nobody-777</uri> .
Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model

The cryptocurrency market has been developed at an unprecedented speed over the past few years. Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Although cryptocurrency ensures legitimate and unique transactions by utilizing cryptographic methods, this industry is still in its inception and serious concerns have been raised about its use. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples’ perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the cryptocurrency which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications including long short term memory (LSTM) and gated recurrent unit (GRU). LSTM and GRU are stacked where the GRU is trained on the features extracted by LSTM. Utilizing term frequency-inverse document frequency, word2vec, and bag of words (BoW) features, several machine learning and deep learning approaches and a proposed ensemble model are investigated. Furthermore, TextBlob and Text2Emotion are studied for emotion analysis with the selected models. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. Results suggest that the performance of machine learning models is comparatively better when BoW features are used. The proposed LSTM-GRU ensemble shows an accuracy of 0.99 for sentiment analysis, and 0.92 for emotion prediction and outperforms both machine learning and state-of-the-art models.
Illumination Unification for Person Re-Identification

The performance of person re-identification (re-ID) is easily affected by illumination variations caused by different shooting times, places and cameras. Existing illumination-adaptive methods usually require annotating cross-camera pedestrians on each illumination scale, which is unaffordable for a long-term person retrieval system. The cross-illumination person retrieval problem presents a great challenge for accurate person matching. In this paper, we propose a novel method to tackle this task, which only needs to annotate pedestrians on one illumination scale. Specifically, (i) we propose a novel Illumination Estimation and Restoring framework (IER) to estimate the illumination scale of testing images taken at different illumination conditions and restore them to the illumination scale of training images, such that the disparities between training images with uniform illumination and testing images with varying illuminations are reduced. IER achieves promising results on illumination-adaptive dataset and proving itself a proper baseline for cross-illumination person re-ID. (ii) we propose a Mixed Training strategy using both Original and Reconstructed images (MTOR) to further improve model performance. We generate reconstructed images that are consistent with the original training images in content but more similar to the restored images in style. The reconstructed images are combined with the original training images for supervised training to further reduce the domain gap between original training images and restored testing images. To verify the effectiveness of our method, some simulated illumination-adaptive datasets are constructed with various illumination conditions. Extensive experimental results on the simulated datasets validate the effectiveness of the proposed method. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/FadeOrigin/IUReId</uri> .
Background-Click Supervision for Temporal Action Localization

Weakly supervised temporal action localization aims at learning the instance-level action pattern from the video-level labels, where a significant challenge is action-context confusion. To overcome this challenge, one recent work builds an action-click supervision framework. It requires similar annotation costs but can steadily improve the localization performance when compared to the conventional weakly supervised methods. In this paper, by revealing that the performance bottleneck of the existing approaches mainly comes from the background errors, we find that a stronger action localizer can be trained with labels on the background video frames rather than those on the action frames. To this end, we convert the action-click supervision to the background-click supervision and develop a novel method, called BackTAL. Specifically, BackTAL implements two-fold modeling on the background video frames, i.e., the position modeling and the feature modeling. In position modeling, we not only conduct supervised learning on the annotated video frames but also design a score separation module to enlarge the score differences between the potential action frames and backgrounds. In feature modeling, we propose an affinity module to measure frame-specific similarities among neighboring frames and dynamically attend to informative neighbors when calculating temporal convolution. Extensive experiments on three benchmarks are conducted, which demonstrate the high performance of the established BackTAL and the rationality of the proposed background-click supervision.
Group-Invariant Quantum Machine Learning

Quantum Machine Learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., with no assumptions about the problem embedded in the model) are likely to have trainability and generalization issues, especially for large problem sizes. As such, it is fundamental to develop schemes that encode as much information as available about the problem at hand. In this work we present a simple, yet powerful, framework where the underlying invariances in the data are used to build QML models that, by construction, respect those symmetries. These so-called group-invariant models produce outputs that remain invariant under the action of any element of the symmetry group $\mathfrak{G}$ associated to the dataset. We present theoretical results underpinning the design of $\mathfrak{G}$-invariant models, and exemplify their application through several paradigmatic QML classification tasks including cases when $\mathfrak{G}$ is a continuous Lie group and also when it is a discrete symmetry group. Notably, our framework allows us to recover, in an elegant way, several well known algorithms for the literature, as well as to discover new ones. Taken together, we expect that our results will help pave the way towards a more geometric and group-theoretic approach to QML model design.
Trustworthy AI

Modern AI systems have become of widespread use in almost all sectors with a strong impact on our society. However, the very methods on which they rely, based on Machine Learning techniques for processing data to predict outcomes and to make decisions, are opaque, prone to bias and may produce wrong answers. Objective functions optimized in learning systems are not guaranteed to align with the values that motivated their definition. Properties such as transparency, verifiability, explainability, security, technical robustness and safety, are key to build operational governance frameworks, so that to make AI systems justifiably trustworthy and to align their development and use with human rights and values.
ROS Navigation Tuning Guide

The ROS navigation stack has become a crucial component for mobile robots using the ROS framework. It is powerful, yet requires careful fine tuning of parameters to optimize its performance on a given robot, a task that is not as simple as it looks and potentially time-consuming. This tutorial chapter presents a ROS navigation tuning guide for beginners that aims to serve as a reference for the “how” and “why” when setting the value of key parameters. The guide covers parameter settings for velocity and acceleration, global and local planners, costmaps, and the localization package amcl. It also discusses recovery behaviors as well as the dynamic reconfiguration feature of ROS. We assume that the reader has already set up the navigation stack and ready to optimize it. The material in this chapter originally appeared on ROS Tutorials since 2017 with experiments conducted on ROS indigo. Nevertheless, the ROS navigation stack has been mostly stable since indigo, while the ROS 2 navigation stack shares significant overlap in terms of packages and key parameters, with several innovations. Hence, we present this material in the context of the most recent release (ROS Noetic) at the time of writing. We discuss notable changes made in the ROS 2 navigation stack with respect to ROS Noetic. A video demonstration of the outcome following this guide is available at https://y2u.be/1-7GNtR6gVk .
Empirical investigation of active learning strategies

Many predictive tasks require labeled data to induce classification models. The data labeling process may have a high cost. Several strategies have been proposed to optimize the selection of the most relevant examples, a process referred to as active learning. However, a lack of empirical studies comparing different active learning approaches across multiple datasets makes it difficult identifying the most promising strategies, or even assessing the relative gain of active learning over the trivial random selection of instances. In this study, a comprehensive comparison of active learning strategies is presented, with various instance selection criteria, different classification algorithms and a large number of datasets. The experimental results confirm the effectiveness of active learning and provide insights about the relationship between classification algorithms and active learning strategies. Additionally, ranking curves with bands are introduced as a means to summarize in a single chart the performance of each active learning strategy for different classification algorithms and datasets.