Reviewerly/exHarmony huggingface dataset:
Planning and distributed control for cooperative transportation of a non-uniform slung-load by multiple quadrotors

This paper presents a novel control and planning algorithm for cooperative slung-load transportation using multiple quadrotors. Each aerial robot modeled as a quadrotor is connected to a non-uniform payload by the cables. Udwadia-Kalaba equations are used for calculating the forces on the cables acting on each robot. A novel leaderless distributed adaptive control algorithm is presented which not only ensures the safe transport of payload in the generated path in the presence of external disturbances but also manages to solve the problems related to the interaction of payload with the aerial robots. Moreover, the payload dynamics is not considered in the design of the controller which facilitates the load transportation in any size, shape, or weight by the robotic team without compromising performance. Then, an improved rapidly exploring random tree star (RRT*) algorithm is developed to generate desired path for each aerial robot in order to avoid known obstacles for cooperative payload transportation problem. The proposed path planning algorithm is capable of finding a free collision path in any situation, specially where high maneuverability is crucial to avoid collision of the payload with obstacles. Three numerical simulations are conducted to verify the proposed planning and control scheme. Additionally, in the distributed control algorithm simulation, two indexes including ISE and IADU demonstrate the improved performance of the distributed control over independent control of robots. The superior performance of the proposed controller in terms of reduced tracking errors in presence of external disturbances is also shown in comparison with a distributed nonlinear H∞ design.
A fuzzy association rule-based classifier for imbalanced classification problems

Imbalanced classification problems are attracting the attention of the research community because they are prevalent in real-world problems and they impose extra difficulties for learning methods. Fuzzy rule-based classification systems have been applied to cope with these problems, mostly together with sampling techniques. In this paper, we define a new fuzzy association rule-based classifier, named FARCI, to tackle directly imbalanced classification problems. Our new proposal belongs to the algorithm modification category, since it is constructed on the basis of the state-of-the-art fuzzy classifier FARC–HD. Specifically, we modify its three learning stages, aiming at boosting the number of fuzzy rules of the minority class as well as simplifying them and, for the sake of handling unequal fuzzy rule lengths, we also change the matching degree computation, which is a key step of the inference process and it is also involved in the learning process. In the experimental study, we analyze the effectiveness of each one of the new components in terms of performance, F-score, and rule base size. Moreover, we also show the superiority of the new method when compared versus FARC–HD alongside sampling techniques, another algorithm modification approach, two cost-sensitive methods and an ensemble.
Augmented reality mobile apps for cultural heritage reactivation

The use of augmented reality and GPS-guided applications has proven successful for reviving cultural heritage. However, the impact of using these technologies on the learning process of cultural heritage has not been studied in depth. This article reports the findings of how using GPS and augmented reality with mobile apps impacted learning during a project to reactivate cultural heritage around Medellin's Cisnero Square and its surroundings (also called the "Park of Lights"). An experimental test comparing two groups was performed. The experimental design goal was to determine the differences in learning and user perception when the proposed Mobile App was and was not used in guiding the cultural heritage tour as part of Cisnero Square's reactivation. The results showed that the experimental group achieved better learning and cultural heritage learning outcomes. This study offers valuable insights into technological applications for reactivating cultural heritage, which can be applied almost anywhere.
Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

Abstract This paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F 1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F 1 score of 75.2% and accuracy of 90.7% compared to F 1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.
Computational Image Encryption Techniques: A Comprehensive Review

Images contain very sensitive and confidential information. Because images play a significant role in many applications such as military communication, remote-sensing, and medical-imaging, therefore, it is necessary to protect sensitive and confidential information from unauthorized use and modification. To achieve this objective, encryption is one of the best methods among the information hiding methods. In recent years, many image encryption approaches are designed by the researchers. They use different concepts for image encryption to increase security. The main aim of this paper is to present a comprehensive review of the existing image encryption approaches. These approaches are categorized based on different concepts such as chaotic maps, DNA, compressive sensing, and optical image encryption. Comparisons are made among the existing approaches to access the various security parameters. Key performance metrics are also presented. The future scope of image encryption is also presented to encourage the research community.
Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction

This paper considers a new problem of adapting a pretrained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the parametric SMPL model [36] underperform in new domains with unexpected, domain-specific attributes, such as camera parameters, lengths of bones, backgrounds, and occlusions. Our general idea is to dynamically fine-tune the source model on test video streams with additional temporal constraints, such that it can mitigate the domain gaps without over-fitting the 2D information of individual test frames. A subsequent challenge is how to avoid conflicts between the 2D and temporal constraints. We propose to tackle this problem using a new training algorithm named Bilevel Online Adaptation (BOA), which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training iteration. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images

Tissue/region segmentation of pathology images is essential for quantitative analysis in digital pathology. Previous studies usually require full supervision (e.g., pixel-level annotation) which is challenging to acquire. In this paper, we propose a weakly-supervised model using joint Fully convolutional and Graph convolutional Networks (FGNet) for automated segmentation of pathology images. Instead of using pixel-wise annotations as supervision, we employ an image-level label (i.e., foreground proportion) as weakly-supervised information for training a unified convolutional model. Our FGNet consists of a feature extraction module (with a fully convolutional network) and a classification module (with a graph convolutional network). These two modules are connected via a dynamic superpixel operation, making the joint training possible. To achieve robust segmentation performance, we propose to use mutable numbers of superpixels for both training and inference. Besides, to achieve strict supervision, we employ an uncertainty range constraint in FGNet to reduce the negative effect of inaccurate image-level annotations. Compared with fully-supervised methods, the proposed FGNet achieves competitive segmentation results on three pathology image datasets (i.e., HER2, KI67, and H&E) for cancer region segmentation, suggesting the effectiveness of our method. The code is made publicly available at https://github.com/zhangjun001/FGNet.
Opportunities and challenges of using biometrics for business: Developing a research agenda

Recently, biometric data generated by fingerprints, hand geometry, heart rate, voice patterns, facial characteristics and expressions, brain activity and body movement has increased in both volume and prominence. Surprisingly, academic business literature has remained relatively silent on the immense potential of biometric data, as well as on the various dangers that come with its collection and usage. This article sets out to (1) detail what biometric data entails and how it may be used, (2) describe opportunities associated with using biometric data in various business applications, (3) discuss challenges related to biometric data collection and usage, privacy and security, storage and safety, and potential for reduced inclusiveness and enhanced biases, and (4) outline related directions for future research.
Scale-aware Automatic Augmentation for Object Detection

We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scaleaware search space, where both image- and box-level augmentations are designed for maintaining scale invariance. Upon this search space, we propose a new search metric, termed Pareto Scale Balance, to facilitate search with high efficiency. In experiments, Scale-aware AutoAug yields significant and consistent improvement on various object detectors (e.g., RetinaNet, Faster R-CNN, Mask R-CNN, and FCOS), even compared with strong multi-scale training baselines. Our searched augmentation policies are transferable to other datasets and box-level tasks beyond object detection (e.g., instance segmentation and keypoint estimation) to improve performance. The search cost is much less than previous automated augmentation approaches for object detection. It is notable that our searched policies have meaningful patterns, which intuitively provide valuable insight for human data augmentation design. Code and models are available at https://github.com/Jia-ResearchLab/SA-AutoAug.
Securing communication between things using blockchain technology based on authentication and SHA-256 to improving scalability in large-scale IoT

The IoT ecosystem allows communication between billions of devices worldwide that are collecting data autonomously. The vast amount of data generated by these devices must be controlled totally securely. The centralized solutions are not capable of responding to these concerns due to security challenges and scalability problems. Thus, blockchain technology is an effective solution, and the “distributed” method has been employed to overcome these concerns to allow for entirely secure communication between devices. The present paper proposes a technique consisting of two steps. The first step deals with the authentication of each node using the identity-based signature to secure communication between devices on a blockchain platform. In the second step, blocks are sent by hashing. For this purpose, the identities of the devices have been used as public keys. The theory analysis and simulation results indicated the superiority of the proposed method in terms of average detection rate, throughput, scalability, time spent on block authentication, and consumption energy compared to S-LoRaWAN and DLBA-IoT. Also, the simulation results show how the proposed approach can significantly increase the security of each thing and network security.
Semi-Supervised Active Learning with Temporal Output Discrepancy

While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset. Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. The core of our approach is a measurement Temporal Output Discrepancy (TOD) that estimates the sample loss by evaluating the discrepancy of outputs given by models at different optimization steps. Our theoretical investigation shows that TOD lower-bounds the accumulated sample loss thus it can be used to select informative unlabeled samples. On basis of TOD, we further develop an effective unlabeled data sampling strategy as well as an unsupervised learning criterion that enhances model performance by incorporating the unlabeled data. Due to the simplicity of TOD, our active learning approach is efficient, flexible, and task-agnostic. Extensive experimental results demonstrate that our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
Identifying Bug Patterns in Quantum Programs

Bug patterns are erroneous code idioms or bad coding practices that have been proved to fail time and time again, which are usually caused by the misunderstanding of a programming language's features, the use of erroneous design patterns, or simple mistakes sharing common behaviors. This paper identifies and categorizes some bug patterns in the quantum programming language Qiskit and briefly discusses how to eliminate or prevent those bug patterns. We take this research as the first step to provide an underlying basis for debugging and testing quantum programs.
Understanding Failures of Deep Networks via Robust Feature Extraction

Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances. We introduce and study a method aimed at characterizing and explaining failures by identifying visual attributes whose presence or absence results in poor performance. In distinction to previous work that relies upon crowdsourced labels for visual attributes, we leverage the representation of a separate robust model to extract interpretable features and then harness these features to identify failure modes. We further propose a visualization method aimed at enabling humans to understand the meaning encoded in such features and we test the comprehensibility of the features. An evaluation of the methods on the ImageNet dataset demonstrates that: (i) the proposed workflow is effective for discovering important failure modes, (ii) the visualization techniques help humans to understand the extracted features, and (iii) the extracted insights can assist engineers with error analysis and debugging.
Machine learning methods for imbalanced data set for prediction of faecal contamination in beach waters

Predicting water contamination by statistical models is a useful tool to manage health risk in recreational beaches. Extreme contamination events, i.e. those exceeding normative are generally rare with respect to bathing conditions and thus the data is said to be imbalanced. Modeling and predicting those rare events present unique challenges. Here we introduce and evaluate several machine learning techniques and metrics to model imbalanced data and evaluate model performance. We do so by using a) simulated data-sets and b) a real data base with records of faecal coliform abundance monitored for 10 years in 21 recreational beaches in Uruguay (N ≈ 19000) using in situ and meteorological variables. We discuss advantages and disadvantages of the methods and provide a simple guide to perform models for a general audience. We also provide R codes to reproduce model fitting and testing. We found that most Machine Learning techniques are sensitive to imbalance and require specific data pre-treatment (e.g. upsampling) to improve performance. Accuracy (i.e. correctly classified cases over total cases) is not adequate to evaluate model performance on imbalanced data set. Instead, true positive rates (TPR) and false positive rates (FPR) are recommended. Among the 52 possible candidate algorithms tested, the stratified Random forest presented the better performance improving TPR in 50% with respect to baseline (0.4) and outperformed baseline in the evaluated metrics. Support vector machines combined with upsampling method or synthetic minority oversampling technique (SMOTE) performed well, similar to Adaboost with SMOTE. These results suggests that combining modeling strategies is necessary to improve our capacity to anticipate water contamination and avoid health risk.
An improvement or a gimmick? The importance of user perceived values, previous experience, and industry context in human–robot service interaction

To tackle the debate over whether using service robots is an improvement or merely a gimmick, this research examined actual users' attitudes and long-term usage behavior in human–robot service interactions from the perspective of perceived values. Study 1 revealed that the effect of perceived hedonic value on users' attitudes is contingent upon perceived utilitarian value. When utilitarian value is low, hedonic value negatively affects users' attitudes, leading service robots to be perceived as a gimmick. When utilitarian value is high, hedonic value positively contributes to users' attitudes, causing users to see service robots as a service improvement. Study 2 found that perceived utilitarian and hedonic values evoke users' future behavioral intention. The strengths of these impacts depend upon users' previous experiences. When users have less (vs. more) previous experience, the effect of perceived hedonic (vs. utilitarian) value is more influential on users' future behavior. The effects of perceived values also depend on the industry context: when the setting is utilitarian (vs. hedonic), perceived utilitarian (vs. hedonic) value plays a more prominent role in determining users’ future behavioral intention. These findings provide theoretically and practically meaningful implications regarding human–robot service interaction across settings in tourism, hospitality and destination marketing and management. • Utilitarian value is essential in forming users' attitudes towards service robots. • The effect of hedonic value on users' attitudes depends on utilitarian value. • Utilitarian and hedonic values inspire users' future behavioral intentions. • The relative importance of each value varies upon users' prior experience. • The relative importance of each value varies by industry context.
An efficient feature selection framework based on information theory for high dimensional data

Feature selection plays a vital role in many fields, particularly in pattern recognition and bioinformatics, for selecting informative and relevant features from high dimensional datasets. The increase in dimensionality of data along with the existence of redundant and irrelevant features leads to challenging performance issues when processing and analysing the data. In this paper, an effective feature selection technique called mutual information and Monte Carlo based feature selection (MIMCFS) is proposed. It comprises of two stages. The first stage aims to select predominant features from the high dimensional data. The second stage involves elimination of redundant features that were selected in the first stage. For the purpose of implementing the first stage, a new feature selection strategy based on the approximate Markov blanket and the concept of mutual information is proposed to find out irrelevant and redundant features. In second stage, to avoid misjudgement of redundant features as relevant features, a new strategy based on Monte Carlo tree search technique is proposed in order to completely eradicate redundant features and to improve feature interaction. For experimental evaluation, eight benchmark microarray datasets including imbalanced ones pertaining to cancer analysis are used. Further, in order to compare and justify the performance of the proposed feature selection method, seven state-of-art feature selection techniques namely CFS, Relief, DISR, JMI, CMIM and CMI are employed. The outputs from these feature selection techniques are provided to three standard classifiers namely Naive Bayes, SVM and C4.5 in order to assess the significance of the selected features in building classification models. 10-fold cross validation is adopted to evaluate the classifiers. Accuracy, precision, recall, f-measure, standard deviation, statistical significance metrics are measured to quantify the classifier performance. Experimental results demonstrate the outstanding performance of the proposed algorithm when compared to that of the standard existing methods.
Blockchain Transaction Processing

A blockchain is an append-only linked-list of blocks, which is maintained at each participating node. Each block records a set of transactions and their associated metadata. Blockchain transactions act on the identical ledger data stored at each node. Blockchain was first perceived by Satoshi Nakamoto as a peer-to-peer digital-commodity (also known as crypto-currency) exchange system. Blockchains received traction due to their inherent property of immutability-once a block is accepted, it cannot be reverted.