Reviewerly/exHarmony huggingface dataset:
A Fast Image Dehazing Algorithm Using Morphological Reconstruction

Outdoor images are used in a vast number of applications, such as surveillance, remote sensing, and autonomous navigation. The greatest issue with these types of images is the effect of environmental pollution: haze, smog, and fog originating from suspended particles in the air, such as dust, carbon and water drops, which cause degradation to the image. The elimination of this type of degradation is essential for the input of computer vision systems. Most of the state-of-the-art research in dehazing algorithms is focused on improving the estimation of transmission maps, which are also known as depth maps. The transmission maps are relevant because they have a direct relation to the quality of the image restoration. In this paper, a novel restoration algorithm is proposed using a single image to reduce the environmental pollution effects, and it is based on the dark channel prior and the use of morphological reconstruction for the fast computing of transmission maps. The obtained experimental results are evaluated and compared qualitatively and quantitatively with other dehazing algorithms using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index; based on these metrics, it is found that the proposed algorithm has improved performance compared to recently introduced approaches.
Gradient based sample selection for online continual learning

A continual learning agent learns online with a non-stationary and never-ending stream of data. The key to such learning process is to overcome the catastrophic forgetting of previously seen data, which is a well known problem of neural networks. To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal. Previous work often depend on task boundary and i.i.d. assumptions to properly select samples for the replay buffer. In this work, we formulate sample selection as a constraint reduction problem based on the constrained optimization view of continual learning. The goal is to select a fixed subset of constraints that best approximate the feasible region defined by the original constraints. We show that it is equivalent to maximizing the diversity of samples in the replay buffer with parameter gradient as the feature. We further develop a greedy alternative that is cheap and efficient. The advantage of the proposed method is demonstrated by comparing to other alternatives under the continual learning setting. Further comparisons are made against state of the art methods that rely on task boundaries which show comparable or even better results for our method.
A Systematic Review of Challenges and Opportunities of Blockchain for E-Voting

A blockchain is a distributed, digitized and consensus-based secure information storage mechanism. The present article provides an overview of blockchain based e-voting systems. The primary purpose of this review is to study the up-to-date state of blockchain-based voting research along with associated possible challenges while aiming to forecast future directions. The methodology applied in the review is a systematic review approach. Following an introduction to the basic structure and features of the blockchain in relation to e-voting, we provide a conceptual description of the desired blockchain-based e-voting application. Symmetrical and asymmetrical cryptography improvements play a key role in developing blockchain systems. We have extracted and reviewed 63 research papers from scientific databases that have advised the adoption of the blockchain framework to voting systems. These articles indicate that blockchain-supported voting systems may provide different solutions than traditional e-voting. We classified the main prevailing issues into the five following categories: general, integrity, coin-based, privacy and consensus. As a result of this research, it was determined that blockchain systems can provide solutions to certain problems that prevail in current election systems. On the other hand, privacy protection and transaction speed are most frequently emphasized problems in blockchain applications. Security of remote participation and scalability should be improved for sustainable blockchain based e-voting. It was concluded that frameworks needed enhancements in order to be used in voting systems due to these reservations.
A systematic review of smartphone-based human activity recognition methods for health research

Abstract Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.
Unsupervised Commonsense Question Answering with Self-Talk

Natural language understanding involves reading between the lines with implicit background knowledge.Current systems either rely on pretrained language models as the sole implicit source of world knowledge, or resort to external knowledge bases (KBs) to incorporate additional relevant knowledge.We propose an unsupervised framework based on self-talk as a novel alternative to multiple-choice commonsense tasks.Inspired by inquiry-based discovery learning (Bruner, 1961), our approach inquires language models with a number of information seeking questions such as "what is the definition of ..." to discover additional background knowledge.Empirical results demonstrate that the self-talk procedure substantially improves the performance of zeroshot language model baselines on four out of six commonsense benchmarks, and competes with models that obtain knowledge from external KBs.While our approach improves performance on several benchmarks, the selftalk induced knowledge even when leading to correct answers is not always seen as helpful by human judges, raising interesting questions about the inner-workings of pre-trained language models for commonsense reasoning.
Research progress in optical neural networks: theory, applications and developments

Abstract With the advent of the era of big data, artificial intelligence has attracted continuous attention from all walks of life, and has been widely used in medical image analysis, molecular and material science, language recognition and other fields. As the basis of artificial intelligence, the research results of neural network are remarkable. However, due to the inherent defect that electrical signal is easily interfered and the processing speed is proportional to the energy loss, researchers have turned their attention to light, trying to build neural networks in the field of optics, making full use of the parallel processing ability of light to solve the problems of electronic neural networks. After continuous research and development, optical neural network has become the forefront of the world. Here, we mainly introduce the development of this field, summarize and compare some classical researches and algorithm theories, and look forward to the future of optical neural network.
An automatic procedure to create fuzzy ontologies from users’ opinions using sentiment analysis procedures and multi-granular fuzzy linguistic modelling methods

The high amount of information that users continually provides to the Internet is unorganized and difficult to interpret. Unluckily, there is no point in having high amounts of information that we cannot work with. Therefore, there is a need of methods that sort this information and stores it in a way that can be easily accessed and processed. In this paper, a novel method that uses sentiment analysis procedures in order to automatically create fuzzy ontologies from free texts provided by users in social networks is presented. Moreover, multi-granular fuzzy linguistic modelling methods are used in order to select the best representation mean to store the information in the fuzzy ontology. Thanks to the presented method, information is transformed and presented in an organized way making it possible to properly work with it.
Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks.
Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding

We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset.RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and instructions) than other VLN datasets.It emphasizes the role of language in VLN by addressing known biases in paths and eliciting more references to visible entities.Furthermore, each word in an instruction is time-aligned to the virtual poses of instruction creators and validators.We establish baseline scores for monolingual and multilingual settings and multitask learning when including Room-to-Room annotations (Anderson et al., 2018b).We also provide results for a model that learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations.The size, scope and detail of RxR dramatically expands the frontier for research on embodied language agents in simulated, photo-realistic environments.
MetaPred

In recent years, large amounts of health data, such as patient Electronic Health Records (EHR), are becoming readily available. This provides an unprecedented opportunity for knowledge discovery and data mining algorithms to dig insights from them, which can, later on, be helpful to the improvement of the quality of care delivery. Predictive modeling of clinical risks, including in-hospital mortality, hospital readmission, chronic disease onset, condition exacerbation, etc., from patient EHR, is one of the health data analytic problems that attract lots of the interests. The reason is not only because the problem is important in clinical settings, but also is challenging when working with EHR such as sparsity, irregularity, temporality, etc. Different from applications in other domains such as computer vision and natural language processing, the data samples in medicine (patients) are relatively limited, which creates lots of troubles for building effective predictive models, especially for complicated ones such as deep learning. In this paper, we propose MetaPred, a meta-learning framework for clinical risk prediction from longitudinal patient EHR. In particular, in order to predict the target risk with limited data samples, we train a meta-learner from a set of related risk prediction tasks which learns how a good predictor is trained. The meta-learned can then be directly used in target risk prediction, and the limited available samples in the target domain can be used for further fine-tuning the model performance. The effectiveness of MetaPred is tested on a real patient EHR repository from Oregon Health & Science University. We are able to demonstrate that with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) as base predictors, MetaPred can achieve much better performance for predicting target risk with low resources comparing with the predictor trained on the limited samples available for this risk alone.
User-Independent American Sign Language Alphabet Recognition Based on Depth Image and PCANet Features

Sign language is the most natural and effective way for communications among deaf and normal people.American Sign Language (ASL) alphabet recognition (i.e.fingerspelling) using marker-less vision sensor is a challenging task due to the difficulties in hand segmentation and appearance variations among signers.Existing color-based sign language recognition systems suffer from many challenges such as complex background, hand segmentation, large inter-class and intra-class variations.In this paper, we propose a new user independent recognition system for American sign language alphabet using depth images captured from the low-cost Microsoft Kinect depth sensor.Exploiting depth information instead of color images overcomes many problems due to their robustness against illumination and background variations.Hand region can be segmented by applying a simple preprocessing algorithm over depth image.Feature learning using convolutional neural network architectures is applied instead of the classical handcrafted feature extraction methods.Local features extracted from the segmented hand are effectively learned using a simple unsupervised Principal Component Analysis Network (PCANet) deep learning architecture.Two strategies of learning the PCANet model are proposed, namely to train a single PCANet model from samples of all users and to train a separate PCANet model for each user, respectively.The extracted features are then recognized using linear Support Vector Machine (SVM) classifier.The performance of the proposed method is evaluated using public dataset of real depth images captured from various users.Experimental results show that the performance of the proposed method outperforms state-of-the-art recognition accuracy using leave-one-out evaluation strategy.
A Survey of User Profiling: State-of-the-Art, Challenges, and Solutions

Advancements in information and communication technology, and online web users have given attention to the virtual representation of each user, which is crucial for effective service personalization.Meeting users need and preferences is an ongoing challenge in service personalization.This issue can be addressed through the building of a comprehensive user profile.A user profile is the summary of the user's interests, characteristics, behaviours, and preferences, while user profiling is the system of collecting, organizing and inferring the user profile information.Many reviews on user profiling have been conducted but none focused on the effective profile modeling process.Hence, this article aims to provide a review of the recent state-of-the-art approach to user profiling.These include methods, description, characteristics, and taxonomy of the user profile.The study of the existing user profiling modeling in the aspect of data acquisition, feature extraction, profiling techniques, and profiling approaches (with the identification of their strengths and weaknesses) and the performance measures are also provided.In addition, the research challenges were also discussed with a focus on privacy, datasets, cold start issues, trust issues, and computational complexity.Moreover, the article identified an open research direction that serves as solutions to the identified challenges and motivation for further researchers in advancing user profiling.The findings showed that an effective modeling process enhances the construction of accurate user profile for service personalization.
BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques

In software fault prediction systems, there are many hindrances for detecting faulty modules, such as missing values or samples, data redundancy, irrelevance features, and correlation. Many researchers have built a software bug prediction (SBP) model, which classify faulty and non-faulty module which are associated with software metrics. Till now very few works has been done which addresses the class imbalance problem in SBP. The main objective of this paper is to reveal the favorable result by feature selection and machine learning methods to detect defective and non-defective software modules. We propose a rudimentary classification based framework Bug Prediction using Deep representation and Ensemble learning (BPDET) techniques for SBP. It combinedly applies by ensemble learning (EL) and deep representation(DR). The software metrics which are used for SBP are mostly conventional. Staked denoising auto-encoder (SDA) is used for the deep representation of software metrics, which is a robust feature learning method. Propose model is mainly divided into two stages: deep learning stage and two layers of EL stage (TEL). The extraction of the feature from SDA in the very first step of the model then applied TEL in the second stage. TEL is also dealing with the class imbalance problem. The experiment mainly performed NASA (12) datasets, to reveal the efficiency of DR, SDA, and TEL. The performance is analyzed in terms of Mathew co-relation coefficient (MCC), the area under the curve (AUC), precision-recall area (PRC), F-measure and Time. Out of 12 dataset MCC values over 11 datasets, ROC values over 6 datasets, PRC values overall 12 datasets and F-measure over 8 datasets surpass the existing state of the art bug prediction methods. We have tested BPDET using Wilcoxon rank sum test which rejects the null hypothesis at α = 0.025. We have also tested the stability of the model over 5, 8, 10, 12, and 15 fold cross-validation and got similar results. Finally, we conclude that BPDET is a stable and outperformed on most of the datasets compared with EL and another state of the art techniques.
Select, Answer and Explain: Interpretable Multi-Hop Reading Comprehension over Multiple Documents

Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences. In this paper, we propose an effective and interpretable Select, Answer and Explain (SAE) system to solve the multi-document RC problem. Our system first filters out answer-unrelated documents and thus reduce the amount of distraction information. This is achieved by a document classifier trained with a novel pairwise learning-to-rank loss. The selected answer-related documents are then input to a model to jointly predict the answer and supporting sentences. The model is optimized with a multi-task learning objective on both token level for answer prediction and sentence level for supporting sentences prediction, together with an attention-based interaction between these two tasks. Evaluated on HotpotQA, a challenging multi-hop RC data set, the proposed SAE system achieves top competitive performance in distractor setting compared to other existing systems on the leaderboard.
Window of multistability and its control in a simple 3D Hopfield neural network: application to biomedical image encryption

In this contribution, the problem of multistability control in a simple model of 3D HNNs as well as its application to biomedical image encryption is addressed. The space magnetization is justified by the coexistence of up to six disconnected attractors including both chaotic and periodic. The linear augmentation method is successfully applied to control the multistable HNNs into a monostable network. The control of the coexisting four attractors including a pair of chaotic attractors and a pair of periodic attractors is made through three crises that enable the chaotic attractors to be metamorphosed in a monostable periodic attractor. Also, the control of six coexisting attractors (with two pairs of chaotic attractors and a pair of periodic one) is made through five crises enabling all the chaotic attractors to be metamorphosed in a monostable periodic attractor. Note that this controlled HNN is obtained for higher values of the coupling strength. These interesting results are obtained using nonlinear analysis tools such as the phase portraits, bifurcations diagrams, graph of maximum Lyapunov exponent, and basins of attraction. The obtained results have been perfectly supported using the PSPICE simulation environment. Finally, a simple encryption scheme is designed jointly using the sequences of the proposed HNNs and the sequences of real/imaginary values of the Julia fractals set. The obtained cryptosystem is validated using some well-known metrics. The proposed method achieved entropy of 7.9992, NPCR of 99.6299, and encryption time of 0.21 for the 256*256 sample 1 image.
Learning a Few-shot Embedding Model with Contrastive Learning

Few-shot learning (FSL) aims to recognize target classes by adapting the prior knowledge learned from source classes. Such knowledge usually resides in a deep embedding model for a general matching purpose of the support and query image pairs. The objective of this paper is to repurpose the contrastive learning for such matching to learn a few-shot embedding model. We make the following contributions: (i) We investigate the contrastive learning with Noise Contrastive Estimation (NCE) in a supervised manner for training a few-shot embedding model; (ii) We propose a novel contrastive training scheme dubbed infoPatch, exploiting the patch-wise relationship to substantially improve the popular infoNCE; (iii) We show that the embedding learned by the proposed infoPatch is more effective; (iv) Our model is thoroughly evaluated on few-shot recognition task; and demonstrates state-of-the-art results on miniImageNet and appealing performance on tieredImageNet, Fewshot-CIFAR100 (FC-100).