Reviewerly/exHarmony huggingface dataset:
Prospective and external validation of stroke discharge planning machine learning models

Machine learning may be able to help with predicting factors that aid in discharge planning for stroke patients. This study aims to validate previously derived models, on external and prospective datasets, for the prediction of discharge modified Rankin scale (mRS), discharge destination, survival to discharge and length of stay. Data were collected from consecutive patients admitted with ischaemic or haemorrhagic stroke at the Royal Adelaide Hospital from September 2019 to January 2020, and at the Lyell McEwin Hospital from January 2017 to January 2020. The previously derived models were then applied to these datasets with three pre-defined cut-off scores (high-sensitivity, Youden's index, and high-specificity) to return indicators of performance including area under the receiver operator curve (AUC), sensitivity and specificity. The number of individuals included in the prospective and external datasets were 334 and 824 respectively. The models performed well on both the prospective and external datasets in the prediction of discharge mRS ≤ 2 (AUC 0.85 and 0.87), discharge destination to home (AUC 0.76 and 0.78) and survival to discharge (AUC 0.91 and 0.92). Accurate prediction of length of stay with only admission data remains difficult (AUC 0.62 and 0.66). This study demonstrates successful prospective and external validation of machine learning models using six variables to predict information relevant to discharge planning for stroke patients. Further research is required to demonstrate patient or system benefits following implementation of these models.
Automated Question and Answer Generating System for Educational Platforms

Learning through the web gets to be well known which encourages learners to learn any kind of stuff at any time from the internet assets. In exam preparing questions and answering is have moved into the technology world. In Many industries, more activities have begun to shift as a result of the increased changes brought about by the Covid-19 virus to people&#x0027;s usual livelihoods, and one significant component whose technologization has created concerns is education. This paper presents a novel system that has been introduced to improve the standards of instructing via virtual and non-virtual platforms by ensuring that both the educational staff and the students are provided with the same level of understanding of their education. The support system ensures that the students and educational staff are provided with an automatic question and answer generation mechanism which will thereby improve the quality of education by presenting a standardized method of preparing questions to the educational staff, while similarly providing a better opportunity to improve study methods for the students.
A User Study on Hybrid Graph Visualizations

Hybrid visualizations mix different metaphors in a single layout of a network. In particular, the popular NodeTrix model, introduced by Henry, Fekete, and McGuffin in 2007, combines node-link diagrams and matrix-based representations to support the analysis of real-world networks that are globally sparse but locally dense. That idea inspired a series of works, proposing variants or alternatives to NodeTrix. We present a user study that compares the classical node-link model and three hybrid visualization models designed to work on the same types of networks. The results of our study provide interesting indications about advantages/drawbacks of the considered models on performing classical tasks of analysis. At the same time, our experiment has some limitations and opens up to further research on the subject.
ACL-RoBERTa-CNN Text Classification Model Combined with Contrastive Learning

Aiming at the problem of sparse Chinese text features and mixing of long and short texts, which lead to the difficulty of extracting word vector features and the single convolution kernel of traditional neural network and redundant parameters, the ACL-RoBERTa-CNN text classification model uses a contrast learning method to learn a uniformly distributed vector representation in order to achieve the effect of regular expression of space. Input the same sentence into dropout twice as "positive pairs", replacing the traditional data enhancement method. Use the contrast learned RoBERTa pre-training model to train the word vector, send the word vector to the CNN layer, use different size convolution kernels to capture the information of different length words in each piece of data, finally combine with Softmax the classifier classifies the extracted features. The experimental results on two public data sets show that ACL-RoBERTa-CNN classification performance is better than TextCNN, TextRNN, LSTM-ATT, RoBERTa-LSTM, RoBERTa-CNN and other deep learning text classification models.
Fairness, Accountability, Sustainability, Transparency (FAST) of Artificial Intelligence in Terms of Hospitality Industry

Artificial intelligence (AI) is playing very significant role in every domain of life and spreading widely all across the globe. It covers all major fields like automation, hospitality industries, medical sectors, technology industry, and defense services. AI assists the painstaking development of data compilation which is a fundamental element of AI expansion. Since, all are using AI without knowing much about the ethics and fairness of AI and using it only as per their requirements. AI ethics is a set of values, principles, and techniques that utilize generally established values of accurate and inaccurate to direct ethical behavior in the expansion and use of AI technologies. The Organization for Economic Co-operation and Development (OECD) principles on AI introduced to encourage artificial intelligence that is pioneering and reliable and respects human rights and ethical values. Here, in this paper, it is tried to demonstrate FAST track principles, i.e., fairness, accountability, sustainability, and transparency along with OECD. These principles will also can be utilized when we are using this technology in hospitality industry. During the international pandemic situation (COVID-19) when everyone used to avoid stay and visits to hotels and restaurants, AI and some other technologies make it possible to provide safe and hygienic stay to the customers. Here, in this paper, we also cover the benefits and advantages of AI in this domain.
A composite framework of deep multiple view human joints feature extraction and selection strategy with hybrid adaptive sunflower optimization‐whale optimization algorithm for human action recognition in video sequences

Abstract In computer vision and pattern recognition field, video‐based human action recognition (HAR) is the most predominant research area. Object recognition is needed to recognize the subjects regarding video contents, which allows reactive enquiry in a large number of camera contents, mainly in security based platforms where there is a prevalent growth of closed circuit television cameras. Generally, object detectors that have high performance are trained on a large collection of public benchmarks. Identifying human activities from unconstrained videos is the primary challenging task. Further, the feature extraction and feature selection from these unconstrained videos is also considered as a challenging issue. For that, in this article a new composite framework of HAR model is constructed by introducing an efficient feature extraction and selection strategy. The proposed feature extraction model extracts multiple view features, human joints features based on the domain knowledge of the action and fuses them with deep high level features extracted by an improved fully resolution convolutional neural networks. Also, it optimizes the feature selection strategy using the hybrid whale optimization algorithm and adaptive sun flower optimization that maximizes the feature entropy, correlation. It minimizes the error rate for improving the recognition accuracy of the proposed composite framework. The proposed model is validated on four different datasets, namely, Olympics sports, Virat Release 2.0, HMDB51, and UCF 50 sports action dataset to prove its effectiveness. The simulation results show that the proposed composite framework outperforms all the existing human recognition model in terms of classification accuracy and detection rate.
An Adaptive Clustering Algorithm Based on Local-Density Peaks for Imbalanced Data Without Parameters

Imbalanced data clustering is a challenging problem in machine learning. The main difficulty is caused by the imbalance in both cluster size and data density distribution. To address this problem, we propose a novel clustering algorithm called LDPI based on local-density peaks in this study. First, an initial sub-cluster construction scheme is designed based on a 3-dimensional (3-D) decision graph that can easily detect the initial sub-cluster centers and identify the noise points. Second, a sub-cluster updating strategy is designed, which can automatically identify the false sub-cluster centers and update the initial sub-clusters. Third, a sub-cluster merging scheme is designed, which merges the updated initial sub-clusters into final clusters. Consequently, the proposed algorithm has three advantages: 1) It does not require any input parameters; 2) It can automatically determine the cluster centers and number of clusters; 3) It is suitable for imbalanced datasets and datasets with arbitrary shapes and distributions. The effectiveness of LDPI is demonstrated experimentally and the superiority of LDPI is identified by comparison with 5 state-of-the-art algorithms.
Message-Restriction-Free Commitment Scheme Based on Lattice Assumption

A commitment scheme is a fundamental protocol and an essential component of basic cryptographic tasks, such as zero-knowledge identification. In recent years, lattice-based cryptography has been intensively studied owing to its potential to be promising post-quantum cryptography. Therefore, the commitment schemes based on lattice assumption have been studied for practical applications. Typically, many applications require to commit arbitrary vectors rather than only short ones. In order to send such large messages, one of a crucial challenges of commitment schemes is to increase the size of a message string. Various existing studies have been done to enlarge the size of a message string so far. Baum et al. constructed the commitment scheme, which can allow sending large message size in 2018. However, the domain available for message string is still being used for non-message purposes.
Orthographic Comparison Revealed by Ambient Sentiment Classification

Several deep neural network versions have been successfully used to create parametric models that project variable-duration spoken word segments onto fixed-size vector representations, also known as acoustic word embeddings (AWEs). However; it is uncertain to what extent the distance in the growing AWE space can evaluate word-form similarity. This study investigates whether acoustic embedding space distance is related to phonological dissimilarity. The performance of supervised techniques for AWEs with different neural architectures and learning aims to solve this question. AWE models are trained in controlled settings for two languages (German and Czech) and evaluate the embeddings on two tasks: word discrimination and phonological similarity. The findings reveal that (1) the embedding space distance weakly corresponds with phonological distance in the best circumstances, and (2) enhancing word discrimination performance does not always result in models that better reflect word phonological similarity. The results emphasize the need to reconsider existing intrinsic AWE evaluations.
Fair termination of binary sessions

A binary session is a private communication channel that connects two processes, each adhering to a protocol description called session type . In this work, we study the first type system that ensures the fair termination of binary sessions. A session fairly terminates if all of the infinite executions admitted by its protocol are deemed unrealistic because they violate certain fairness assumptions . Fair termination entails the eventual completion of all pending input/output actions, including those that depend on the completion of an unbounded number of other actions in possibly different sessions. This form of lock freedom allows us to address a large family of natural communication patterns that fall outside the scope of existing type systems. Our type system is also the first to adopt fair subtyping , a liveness-preserving refinement of the standard subtyping relation for session types that so far has only been studied theoretically. Fair subtyping is surprisingly subtle not only to characterize concisely but also to use appropriately, to the point that the type system must carefully account for all usages of fair subtyping to avoid compromising its liveness-preserving properties.
The need for a numeric measure of explainability

The attempt to concretely define the concept of explainability in terms of other vaguely described notions, is doomed to fail. We argue that a rigorous numeric measure is needed to take a significant step forward. Such a measure would allow the objective comparison of machine learning models that are explainable to varying degrees. In the longer term, a numeric measure might make it possible to compare humans and machines in terms of explanatory capacities, thereby establishing a very specific kind of Turing test.We introduce some notions and rigorous definitions related to explainability, in particular completeness and consistency. The objective of this paper is that the introduced terms can act as a concrete starting point for the construction of a mathematically expressed measure of explainability. We also show how the idea of model selection, where a model is chosen from a candidate set by optimizing the trade-off between complexity and goodness of fit, can be applied to select explainable models.
Machine learning: Factorization Machines and Normalized Discounted Cumulative Gain for Tourism Recommender System Optimisation

The online tourism industry has significantly developed in recent years. One prominent feature of a tourism recommender system is to assist the users in finding out potential attractions based on their preferences. A recommender system is a specific type of intelligent system, which exploits historical user ratings on items or additional information. In this research, we developed and implemented a tourism recommender system that consists of two Machine Learning algorithms. The first proposed model is the Factorisation Machines (FM), and the second model is the Normalized Discounted Cumulative Gain (NDCG) with Deep Neural Network (DNN) system, utilised to improve the search results. The evaluation shows that the FM method is effective and highly aggregated with low-frequency user rating data and could produce high-quality outputs. Finally, we expand on current trends and provide new perspectives of this new exciting development in the field of recommender systems.
MAP-Elites to Generate a Team of Agents that Elicits Diverse Automated Gameplay

The objective of this work is to provide a procedure to generate a team of players for a game so they are available to the developer to choose from and that can be used for automated gameplay.Our solution applies the MAP-Elites algorithm to generate agents with distinct behaviours.The resulting agents are distributed in the space of features based on the result of their actions when playing a game: wins, score, % explored, interactions, kills, items collected, etc.The criteria used as the performance of the elites does not come from how well an agent plays a game, but by the time it takes it to play it and determine the cell in the map it falls into.We present and implement the solution and include details about the agent used, as well as the list of heuristics created to elicit differentiated behaviours within the game, representing distinct types of players.We executed the algorithm implemented for three different games and a total of 33 configurations.The size and diversity of the pool of agents generated allow running automated gameplays in each of the games to elicit different expected behaviours.The options are limited by the distribution of the team within the space, given by the pair of features or the characteristics of the game.The methodology gives the flexibility to extend the features or modify the range of existing ones to have control over the behavioural space and, therefore, the characteristics of the generated team.
The Use of Persona Towards Human-Centered Design in Health Field: Review of Types and Technologies

Persona is a user modeling technique used to provide insight into the audience and communicate their needs and behavior more convincingly and memorably. Persona has been adopted in several fields, including healthcare. This paper aims to review the use of Persona in the health field by providing an overview of the concept of Persona and examining and comparing the types of Persona in terms of the technologies used in the data collection and segmentation phases. The paper found that the data-driven Persona could overcome the limitations of the qualitative Persona, as it could cover a large sample size and take advantage of advanced analytical techniques such as machine learning. Furthermore, the use of social media as a data source recently gained researchers’ interest.
Tensor Network Circuit Simulation at Exascale

Tensor network methods are incredibly effective for simulating quantum circuits. This is due to their ability to efficiently represent and manipulate the wave-functions of large interacting quantum systems. We describe the challenges faced when scaling tensor network simulation approaches to Exascale compute platforms and introduce QuantEx, a framework for tensor network circuit simulation at Exascale.
Deep Learning Methods for Device Authentication Using RF Fingerprinting

Radio Frequency (RF) fingerprinting is an emerging technology for advanced device authentication. In this work, we investigate the feasibility of utilizing three different types of fundamental waveform for the purpose of RF fingerprinting. The Short-time Fourier Transform (STFT) is adopted to exploit potential RF fingerprints, which results are then converted into spectrograms and are used to train a convolutional neural network (CNN) for device authentication. An experimental setup constructed using five software-defined radios (SDRs) is tested against an additive white Gaussian noise (AWGN) channel to verify its performance under varying signal-to-noise ratios (SNRs). The experimental results show a promising classification accuracy ranging between 94.1&#x0025; and 99.2&#x0025; depending on the utilized waveform.
Evaluating Differentially Private Generative Adversarial Networks Over Membership Inference Attack

As communication technology advances with 5G, the amount of data accumulated online is explosively increasing. From these data, valuable results are being created through data analysis technologies. Among them, artificial intelligence (AI) has shown remarkable performances in various fields and is emerging as an innovative technology. In particular, machine learning and deep learning models are evolving rapidly and are being widely deployed in practical applications. Meanwhile, behind the widespread use of these models, privacy concerns have been continuously raised. In addition, as substantial privacy invasion attacks against machine learning and deep learning models have been proposed, the importance of research on privacy-preserving AI is being emphasized. Accordingly, in the field of differential privacy, which has become a de facto standard for preserving privacy, various mechanisms have been proposed to preserve the privacy of AI models. However, it is unclear how to calibrate appropriate privacy parameters, taking into account the trade-off between a model’s utility and data privacy. Moreover, there is a lack of research that analyzes the relationship between the degree of differential privacy guarantee and privacy invasion attacks. In this paper, we investigate the resistance of differentially private AI models to substantial privacy invasion attacks according to the degree of privacy guarantee, and analyze how privacy parameters should be set to prevent the attacks while preserving the utility of the models. Specifically, we focus on generative adversarial networks (GAN), which is one of the most sophisticated AI models, and on the membership inference attack, which is the most fundamental privacy invasion attack. In the experimental evaluation, by quantifying the effectiveness of the attack based on the degree of privacy guarantee, we show that differential privacy can simultaneously preserve data privacy and the utility of models with moderate privacy budgets.
DB-BlendMask: Decomposed Attention and Balanced BlendMask for Instance Segmentation of High-Resolution Remote Sensing Images

Instance segmentation is an important method for high-resolution remote sensing images (HRRSIs) analysis. Traditional instance segmentation algorithms are not suitable to analyze complex HRRSIs that exhibit: 1) various shapes and sizes of targets; 2) a large number of small targets; and 3) data with long tail distribution. Here we introduce DB-BlendMask, an efficient and accurate instance segmentation method that can accommodate complex HRRSIs. It is composed of size balance coefficient (SBC), class balance module (CBM), and decomposed attention blender module (DA-Blender module). SBC consists of a fair weight allocation strategy for positive samples in object detection. CBM combines classification obtained in object detection stage to guide the semantic feature extraction. Complementary to a traditional convolutional neural network (CNN) architecture, DA-Blender module has the ability to considerably compress space complexity of attention and merge attention with semantic feature to generate the instance mask. We compare the performance of DB-BlendMask with a benchmark Mask R-CNN on two typical datasets, iSAID, and ISPRS Postdam. We obtain an average detection precision of 39.2% on iSAID and 63.6% on ISPRS Postdam, which corresponds to an improvement of 2.5% and 2.7%, respectively, compared to the benchmark in a real-time scenario.
Convolutional Neural Network Optimization for Phishing Email Classification

Phishing emails are one of the most common and effective tools that cybercriminals use to gain access to an organization’s network or personal information. To detect these attacks, email service providers use a variety of tools and indicators, such as the URLs that attackers include in their email messages. However, cybercriminals are able to bypass these detection techniques by omitting URLs in their messages and instead engaging victims in a conversation to advance their attacks. In this paper, we investigate the performance of convolutional neural network (CNN) models that identify phishing attacks by analyzing only the text in the email messages. The models take as input an embedding of the text in the email’s body and output a probability indicating the likelihood that the message is malicious. We evaluate several CNN architectures using real-world phishing emails and find that the best performing one can identify phishing attacks with an accuracy of 98.139%, recall of 98.125%, and precision of 98.269%.