Reviewerly/exHarmony huggingface dataset:
The Effect of Pre-Processing on the Classification of Twitter’s Flood Disaster Messages Using Support Vector Machine Algorithm

The eyewitness message on twitter as a social network sensor aims to determine the classification process's performance. In the classification of flood disaster messages, preprocessing data is required before the classification process is carried out. Preprocessing affects the resulting level of accuracy in the classification process. Stopword removal is part of preprocessing so that the effect of stopword removal on classification performance will be examined. Support Vector Machine (SVM) is used to classify by weighting words using Term Frequency-Inverse Document Frequency (TF-IDF). The data taken from Twitter is 3000 data with 1000 data each for each label. The effect of the stopword on accuracy performance can be seen in several experiments that have been carried out. We had already conducted three different experiments, and the highest level of accuracy was 76.6%, 76.87%, and 77.87%. Based on the experiments that have been carried out, stopword is very influential on the accuracy generated by the classification of flood disaster messages on Twitter.
Blockchain-Based Power Energy Trading Management

Distributed peer-to-peer power energy markets are emerging quickly. Due to central governance and lack of effective information aggregation mechanisms, energy trading cannot be efficiently scheduled and tracked. We devise a new distributed energy transaction system over the energy Industrial Internet of Things based on predictive analytics, blockchain, and smart contract technologies. We propose a solution for scheduling distributed energy sources based on the Minimum Cut Maximum Flow theory. Blockchain is used to record transactions and reach consensus. Payment clearing for the actual power consumption is executed via smart contracts. Experimental results on real data show that our solution is practical and achieves a lower total cost for power energy consumption.
Unsupervised Vehicle Re-Identification via Self-supervised Metric Learning using Feature Dictionary

The key challenge of unsupervised vehicle re-identification (Re-ID) is learning discriminative features from unlabelled vehicle images. Numerous methods using domain adaptation have achieved outstanding performance, but those methods still need a labelled dataset as a source domain. This paper addresses an unsupervised vehicle Re-ID method, which no need any types of a labelled dataset, through a Self-supervised Metric Learning (SSML) based on a feature dictionary. Our method initially extracts features from vehicle images and stores them in a dictionary. Thereafter, based on the dictionary, the proposed method conducts dictionary-based positive label mining (DPLM) to search for positive labels. Pair-wise similarity, relative-rank consistency, and adjacent feature distribution similarity are jointly considered to find images that may belong to the same vehicle of a given probe image. The results of DPLM are applied to dictionary-based triplet loss (DTL) to improve the discriminativeness of learnt features and to refine the quality of the results of DPLM progressively. The iterative process with DPLM and DTL boosts the performance of unsupervised vehicle Re-ID. Experimental results demonstrate the effectiveness of the proposed method by producing promising vehicle Re-ID performance without a pre-labelled dataset. The source code for this paper is publicly available on https://github.com/andreYoo/VeRI_SSML_FD.git.
Planning With Attitude

Planning trajectories for floating-base robotic systems that experience large attitude changes is challenging due to the nontrivial group structure of 3D rotations. This paper introduces a powerful and accessible approach for optimization-based planning on the space of rotations using only standard linear algebra and vector calculus. We demonstrate the effectiveness of the approach by adapting Newton's method to solve the canonical Wahba's problem, and modify the trajectory optimization solver ALTRO to plan directly on the space of unit quaternions, achieving superior convergence on problems involving significant changes in attitude.
Automatic Acetowhite Lesion Segmentation via Specular Reflection Removal and Deep Attention Network

Automatic acetowhite lesion segmentation in colposcopy images (cervigrams) is essential in assisting gynecologists for the diagnosis of cervical intraepithelial neoplasia grades and cervical cancer. It can also help gynecologists determine the correct lesion areas for further pathological examination. Existing computer-aided diagnosis algorithms show poor segmentation performance because of specular reflections, insufficient training data and the inability to focus on semantically meaningful lesion parts. In this paper, a novel computer-aided diagnosis algorithm is proposed to segment acetowhite lesions in cervigrams automatically. To reduce the interference of specularities on segmentation performance, a specular reflection removal mechanism is presented to detect and inpaint these areas with precision. Moreover, we design a cervigram image classification network to classify pathology results and generate lesion attention maps, which are subsequently leveraged to guide a more accurate lesion segmentation task by the proposed lesion-aware convolutional neural network. We conducted comprehensive experiments to evaluate the proposed approaches on 3045 clinical cervigrams. Our results show that our method outperforms state-of-the-art approaches and achieves better Dice similarity coefficient and Hausdorff Distance values in acetowhite legion segmentation.
A novel approach for learning ontology from relational database: from the construction to the evaluation

Abstract The aim of converting relational database into Ontology is to provide applications that are based on the semantic representation of the data. Whereas, representing the data using ontologies has shown to be a useful mechanism for managing and exchanging data. This is the reason why bridging the gap between relational databases and ontologies has attracted the interest of the ontology community from early years, and it is commonly referred to as the database-to-ontology mapping problem. In this paper, we: (1) propose a new life cycle for ontology learning from RDBs based on the software engineering requirements; (2) describe a new method for building ontology from Relational database based on the predefined life cycle; (3) add three new semantics that can be extracted from RDB; (4) we suggest an evaluation process based on two categories of metrics: (i) conceptual ontology (TBox) evaluation metrics; (ii) factual ontology (ABox) evaluation metrics.
Facial expression recognition on a quantum computer

Abstract We address the problem of facial expression recognition and show a possible solution using a quantum machine learning approach. In order to define an efficient classifier for a given dataset, our approach substantially exploits quantum interference. By representing face expressions via graphs, we define a classifier as a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states. We discuss the accuracy of the quantum classifier evaluated on the quantum simulator available on the IBM Quantum Experience cloud platform, and compare it with the accuracy of one of the best classical classifier.
Modified GAN-CAED to Minimize Risk of Unintentional Liver Major Vessels Cutting by Controlled Segmentation Using CTA/SPET-CT

This article substantially advances upon state-of-the-art to enhance liver vessels segmentation accuracy by leveraging advantages of synthetic PET-CT (SPET-CT) images in addition to computed tomography angiography (CTA) volumes. Our setup makes a hybrid solution of modified generative adversarial network-convolutional autoencoder (GAN-cAED) combining synthetic ability of GAN to deliver SPET-CT images with generative ability of cAED network in terms of latent learning to more refined segmentation of major liver vessels. We improve time complexity through a novel concept of controlled segmentation by introducing a threshold metric to stop segmentation up to a desired level. The innovative concept of controlled vessel segmentation with a stopping criterion via variant threshold levels will help surgeons to avoid unintentional major blood vessels cutting, reducing the risk of excessive blood loss. Clinically, such solutions offer computer-aided liver surgeries and drug treatment evaluation in a CTA-only environment, shorten the requirement of radioactive and expensive fused PET-CT images.
A Blockchain-Based Credibility Scoring Framework for Electronic Medical Records

The use of electronic medical records (EMRs) has simplified the processing of patient data because the patient does not need to carry documents and can simply show an identity card to retrieve a complete medical record history. This digitization has revolutionized the medical sector, but two major challenges exist with online records: security and trust. Data stored on the Internet is not secure and requires an additional security framework to safeguard and maintain complete trust with the data. In this paper, a blockchain-based security framework is proposed to maintain the confidentiality of EMRs. This solution is also used to securely access the patient's records in any hospital-using the patient's public key as a universal identifier-and to obtain the medical record history of a patient much more quickly even during emergencies. The proposed framework also uses a credit score approach to monitor the credibility of the entities authorized to input data into the blockchain. In this study, the Etherium platform is used to simulate the blockchain, and the InterPlanetary File System (IPFS) is used to store the EMRs. This approach improves trust with the uploaded data and reduces errors from the falsification of the data.
Iconographic Image Captioning for Artworks

Image captioning implies automatically generating textual descriptions of images based only on the visual input. Although this has been an extensively addressed research topic in recent years, not many contributions have been made in the domain of art historical data. In this particular context, the task of image captioning is confronted with various challenges such as the lack of large-scale datasets of image-text pairs, the complexity of meaning associated with describing artworks and the need for expert-level annotations. This work aims to address some of those challenges by utilizing a novel large-scale dataset of artwork images annotated with concepts from the Iconclass classification system designed for art and iconography. The annotations are processed into clean textual description to create a dataset suitable for training a deep neural network model on the image captioning task. Motivated by the state-of-the-art results achieved in generating captions for natural images, a transformer-based vision-language pre-trained model is fine-tuned using the artwork image dataset. Quantitative evaluation of the results is performed using standard image captioning metrics. The quality of the generated captions and the model’s capacity to generalize to new data is explored by employing the model on a new collection of paintings and performing an analysis of the relation between commonly generated captions and the artistic genre. The overall results suggest that the model can generate meaningful captions that exhibit a stronger relevance to the art historical context, particularly in comparison to captions obtained from models trained only on natural image datasets.
A joint learning model for click-through prediction in display advertising

Click-through rate (CTR) prediction is essential for targeted advertising and recommendation. At present, machine learning models are widely used to build CTR estimators, including logistic regression (LR), factorization machine (FM), and deep neural network (DNN). Unfortunately, these models adopt the single structure that only considers either low-order feature interactions (such as LR and FM) or high-order feature interactions (such as DNN), not sufficient for CTR prediction. Therefore, the joint learning models are proposed, such as Wide & Deep and DeepFM, which can exploit both high- and low-order feature interactions to predict CTR by combining two different models. In this paper, we first analyze the typical CTR prediction models’ structures and performance, and then summarize the general form and design rules of CTR estimators. Based on the general form, we further design a new joint learning model that combines two different residual networks to explore the feature interactions automatically. Compared with the widely adopted feed-forward neural network, the residual network is more capable of exploring complex feature interactions at different layers. Additionally, we introduce a neural attention network to learn the importance of each second-order interaction of features from various fields. Finally, we evaluate the prediction performance of the proposed model based on two real-world datasets (i.e., Criteo and Avazu) in terms of LogLoss and AUC metrics. The extensive experimental results demonstrate our model performs the best compared to the state-of-the-art baselines.
Leveraging the Bhattacharyya coefficient for uncertainty quantification in deep neural networks

Abstract Modern deep learning models achieve state-of-the-art results for many tasks in computer vision, such as image classification and segmentation. However, its adoption into high-risk applications, e.g. automated medical diagnosis systems, happens at a slow pace. One of the main reasons for this is that regular neural networks do not capture uncertainty. To assess uncertainty in classification, several techniques have been proposed casting neural network approaches in a Bayesian setting. Amongst these techniques, Monte Carlo dropout is by far the most popular. This particular technique estimates the moments of the output distribution through sampling with different dropout masks. The output uncertainty of a neural network is then approximated as the sample variance. In this paper, we highlight the limitations of such a variance-based uncertainty metric and propose an novel approach. Our approach is based on the overlap between output distributions of different classes. We show that our technique leads to a better approximation of the inter-class output confusion. We illustrate the advantages of our method using benchmark datasets. In addition, we apply our metric to skin lesion classification—a real-world use case—and show that this yields promising results.
Education Technology for Online Learning in Times of Crisis

Facing the difficulties of conducting face-to-face class and assessment, education institutions have to use online learning and assessment tools in times of crisis. In this paper, we explore the use of education technology for online learning and assessment in times of crisis. First, we examine the use of online learning and teaching tools by administrators, teachers, and students. Although there are security issues associated with online learning tools, the benefits of online learning include feasibility, flexibility, and accessibility. Second, we compare twelve proctoring systems for online assessment. Third, we present a basic evaluation framework for proctoring systems and propose some factors for consideration when choosing them. Last, we recommend a mixed set of options in using proctoring systems. The education technology promotes education institutions to take action effectively and maximizes students' learning outcomes.
Disease and Human Capital Accumulation: Evidence from the Roll Back Malaria Partnership in Africa

Abstract We estimate the effects of a large-scale antimalaria campaign on a rich set of human capital outcomes in 27 sub-Saharan African countries. Using pre-campaign malaria risk as treatment probability, we exploit quasi-experimental variation in period and cohort exposure to antimalaria campaigns. A conservative interpretation of our results shows a globally positive impact of antimalaria campaigns: they reduced the probability of infant mortality (1 p.p.) and birth (0.4 p.p.) and increased educational attainment (0.4 years) and the likelihood of adult paid employment (6 p.p.). These findings demonstrate how the effects of sweeping disease reduction efforts extend beyond health outcomes.
Asymmetric information and blockchains in soybean commodity markets

Abstract Asymmetric information is prevalent in the grain and oilseed markets. This paper demonstrates the benefits of blockchain technology to mitigate asymmetric information about the soybean's protein quality between sellers and buyers. We use decision trees to model information asymmetry under both conventional and blockchain scenarios. The results suggest that asymmetric information can be mitigated with a blockchain, resulting in substantial premiums (40–60 cents per bushel of soybeans). These results could have significant implications for the grain and oilseed industry in order to decrease transaction costs, to improve market efficiency, and to prioritize strategies for the procurement of soybeans. JEL CLASSIFICATION O33 Q13; Q17
Human-Guided Robot Behavior Learning: A GAN-Assisted Preference-Based Reinforcement Learning Approach

Human demonstrations can provide trustful samples to train reinforcement learning algorithms for robots to learn complex behaviors in real-world environments. However, obtaining sufficient demonstrations may be impractical because many behaviors are difficult for humans to demonstrate. A more practical approach is to replace human demonstrations by human queries, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> preference-based reinforcement learning. One key limitation of the existing algorithms is the need for a significant amount of human queries because a large number of labeled data is needed to train neural networks for the approximation of a continuous, high-dimensional reward function. To reduce and minimize the need for human queries, we propose a new GAN-assisted human preference-based reinforcement learning approach that uses a generative adversarial network (GAN) to learn human preferences and then replace the role of human in assigning preferences. The adversarial neural network is simple and only has a binary output, hence requiring much less human queries to train. Moreover, a maximum entropy based reinforcement learning algorithm is designed to shape the loss towards the desired regions or away from the undesired regions. To show the effectiveness of the proposed approach, we present some studies on complex robotic tasks without access to the environment reward in a typical MuJoCo robot locomotion environment. The obtained results show our method can achieve a reduction of about 99.8% human time without performance sacrifice.
Comparison of tissue segmentation performance between 2D U-Net and 3D U-Net on brain MR Images

In this paper, we compare the tissue segmentation performance of the 2D U-Net and 3D U-Net in brain MR images including Alzheimer's disease. The Open Access Series of Imaging Studies dataset used in this experiment consists of a cross-sectional collection of 416 subjects aged 18 to 96. The final segmentations were classified into 4 classes: background, cerebrospinal fluid, gray matter, and white matter. In the experiment, 3D U-Net showed highly stable segmentation results. Particularly, the evaluation of the 2D/3D U-Net segmentation has shown that the 3D U-Net provided higher dice similarity coefficients (94.4±4.5% ~ 95.6±3.0%) and lower Hausdorff distances ( 7.5±3.2mm ~ 12.3±3.9mm) than the 2D U-Net ( 89.6±4.9% ~ 96.22±3.1% and 9.1±5.5mm ~ 14.4±7.4mm). And the 3D U-Net provided a better performance despite having less training examples to learn from. The simple image processing pipeline was an added advantage. However, the number of parameters and the amount of memory required was a major limitation of using 3D U-Net.
Computationally Efficient Trajectory Planning for High Speed Obstacle Avoidance of a Quadrotor With Active Sensing

Quadrotor with active sensing was proposed recently to overcome the view field limitation and achieved an excellent perception ability in obstacle avoidance tasks. To realize high-speed flights of this quadrotor in unknown and cluttered environments, a computationally efficient trajectory planner is presented in this letter. Firstly, a non-taboo area gradient-based sampling (NAGS) algorithm is utilized to sample a multi-objective-directed collision-free local path. The algorithm runs in the state space with the consideration of active sensing direction and is able to find a feasible path in a few trials without falling into local minima. To further enhance real-time processing, a motion primitive generation algorithm concerning both time optimality and jerk optimality is raised, in which an offline and online integrated solver is utilized to achieve computational efficiency. This planner is tested and benchmarked against the state-of-the-art planners for high-speed flight. Real-world experiments show that our active sensing quadrotor with the proposed planner achieved a flight speed up to 5.2 m/s in an unknown and dense woods and managed to handle abrupt changes in goal direction at such a high speed.
FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators

In the past few years, Capsule Networks (CapsNets) have taken the spotlight compared to traditional convolutional neural networks (CNNs) for image classification. Unlike CNNs, CapsNets have the ability to learn the spatial relationship between features of the images. However, their complexity grows because of their heterogeneous capsule structure and the dynamic routing, which is an iterative algorithm to dynamically learn the coupling coefficients of two consecutive capsule layers. This necessitates specialized hardware accelerators for CapsNets. Moreover, a high-performance and energy-efficient design of CapsNet accelerators requires exploration of different design decisions (such as the size and configuration of the processing array and the structure of the processing elements). Toward this, we make the following key contributions: 1) FEECA, a novel methodology to explore the design space of the (micro)architectural parameters of a CapsNet hardware accelerator and 2) CapsAcc, the first specialized RTL-level hardware architecture to perform CapsNets inference with high performance and high energy efficiency. Our CapsAcc achieves significant performance improvement, compared to an optimized GPU implementation, due to its efficient implementation of key activation functions, such as squash and softmax, and an efficient data reuse for the dynamic routing. The FEECA methodology employs the Non-dominated Sorting Genetic Algorithm (NSGA-II) to explore the Pareto-optimal points with respect to area, performance, and energy consumption. This requires analytical modeling of the number of clock cycles required to perform each operation of the CapsNet inference and the memory accesses to enable a fast yet accurate design space exploration. We synthesized the complete accelerator architecture in a 45-nm CMOS technology using Synopsys design tools and evaluated it for the MNIST benchmark (as done by the original CapsNet paper from Google Brain's team) and for a more complex data set, the German Traffic Sign Recognition Benchmark (GTSRB).