Reviewerly/exHarmony huggingface dataset:
Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines

The massive nature of modern university programming courses increases the burden on academic workers. The Digital Teaching Assistant (DTA) system addresses this issue by automating unique programming exercise generation and checking, and provides means for analyzing programs received from students by the end of semester. In this paper, we propose a machine learning-based approach to the classification of student programs represented as Markov chains. The proposed approach enables real-time student submissions analysis in the DTA system. We compare the performance of different multi-class classification algorithms, such as support vector machine (SVM), the k nearest neighbors (KNN) algorithm, random forest (RF), and extreme learning machine (ELM). ELM is a single-hidden layer feedforward network (SLFN) learning scheme that drastically speeds up the SLFN training process. This is achieved by randomly initializing weights of connections among input and hidden neurons, and explicitly computing weights of connections among hidden and output neurons. The experimental results show that ELM is the most computationally efficient algorithm among the considered ones. In addition, we apply biology-inspired algorithms to ELM input weights fine-tuning in order to further improve the generalization capabilities of this algorithm. The obtained results show that ELMs fine-tuned with biology-inspired algorithms achieve the best accuracy on test data in most of the considered problems.
Coded Over-the-Air Computation for Model Aggregation in Federated Learning

This letter introduces a new coded transmission design for model aggregation in federated learning (FL) over Gaussian multiple access channels (MAC), named coded over-the-air computation (codedAirComp). It enjoys the optimality of analog AirComp-based uncoded transmission for fast model aggregation, but also leverages the traditional source-channel separation principle for more practical uses. Specifically, the proposed codedAirComp employs stochastic uniform quantization for local gradient compression and nested lattice coding for channel transmission. Compared with the traditional coding scheme, the proposed scheme significantly reduces the model aggregation distortion and improves the overall learning accuracy.
A federated cloud architecture for processing of cancer images on a distributed storage

The increased accuracy and exhaustivity of modern Artificial Intelligence techniques in supporting the analysis of complex data, such as medical images, have exponentially increased real-world data collection for research purposes. This fact has led to the development of international repositories and high-performance computing solutions to deal with the computational demand for training models. However, other stages in the development of medical imaging biomarkers do not require such intensive computing resources, which has led to the convenience of integrating different computing backends tailored for the processing demands of the various stages of processing workflows. We present in this article a distributed and federated repository architecture for the development and application of medical image biomarkers that combines multiple cloud storages with cloud and HPC processing backends. The architecture has been deployed to serve the PRIMAGE (H2020 826494) project, aiming to collect and manage data from paediatric cancer. The repository seamlessly integrates distributed storage backends, an elastic Kubernetes cluster on a cloud on-premises and a supercomputer. Processing jobs are handled through a single control platform, synchronising data on demand. The article shows the specification of the different types of applications and a validation through a use case that make use of most of the features of the platform.
Graphical Inference in Linear-Gaussian State-Space Models

State-space models (SSM) are central to describe time-varying complex systems in countless signal processing applications such as remote sensing, networks, biomedicine, and finance to name a few. Inference and prediction in SSMs are possible when the model parameters are known, which is rarely the case. The estimation of these parameters is crucial, not only for performing statistical analysis, but also for uncovering the underlying structure of complex phenomena. In this paper, we focus on the linear-Gaussian model, arguably the most celebrated SSM, and particularly in the challenging task of estimating the transition matrix that encodes the Markovian dependencies in the evolution of the multi-variate state. We introduce a novel perspective by relating this matrix to the adjacency matrix of a directed graph, also interpreted as the causal relationship among state dimensions in the Granger-causality sense. Under this perspective, we propose a new method called GraphEM based on the well sounded expectation-maximization (EM) methodology for inferring the transition matrix jointly with the smoothing/filtering of the observed data. We propose an advanced convex optimization solver relying on a consensus-based implementation of a proximal splitting strategy for solving the M-step. This approach enables an efficient and versatile processing of various sophisticated priors on the graph structure, such as parsimony constraints, while benefiting from convergence guarantees. We demonstrate the good performance and the interpretable results of GraphEM by means of two sets of numerical examples.
Real-time risk prediction of colorectal surgery-related post-surgical complications using GRU-D model

Post-surgical complications (PSCs) have been an increasing concern for hospitals in light of Medicare penalties for 30-day readmissions. PSCs have become a target for quality improvement and benchmark for the healthcare system. Over half (60 %) of the deep or organ space surgical site infections are discovered after discharge, leading to a readmission. There has thus been a push to develop risk prediction models for targeted preventive interventions for PSCs. We experiment several Gated Recurrent Unit with Decay (GRU-D) based deep learning architectures with various feature sampling schemes in predicting the risk of colorectal PSCs and compare with atemporal logistic regression models (logit). We used electronic health record (EHR) data of 3,535 colorectal surgical patients involved in the national surgical quality improvement program (NSQIP) between 2006 and 2018. Single layer, stacked layer, and multimodal GRU-D models with sigmoid activation were used to develop risk prediction models. Area Under the Receiver Operating Characteristic curve (AUROC) was calculated by comparing predicted probability of developing complications versus truly observed PSCs (NSQIP adjudicated) within 30 days after surgery. We set up cross-validation and an independent held-out dataset for testing model performance consistency. Results and conclusion: The primary contribution of our study is the formulation of a novel real-time PSC risk prediction task using GRU-D with demonstrated clinical utility. GRU-D outperforms the logit model in predicting wound and organ space infection and shows improved performance as additional data points become available. Logit model outperforms GRU-D before surgery for superficial infection and bleeding. For the same sampling scheme, there is no obvious advantage of complex architectures (stacked, multimodal) over single layer GRU-D. Obtaining more data points closer to the occurrence of PSCs is more important than using a more frequent sampling scheme in training GRU-D models. The fourth predicted risk quartile by single layer GRU-D contains 63 %, 59 %, and 66 % organ space infection cases, at 4 h before, 72 h after, and 168 h after surgery, respectively, suggesting its potential application as a bedside risk assessment tool.
Rule-Based Arabic Sentiment Analysis using Binary Equilibrium Optimization Algorithm

With the development of websites and social networks, Internet users generate a massive amount of comments and information on the Web. Sentiment analysis, also called opinion mining, offers an opportunity to mine the people's sentiments and emotions from the textual comments. In the last decade, sentiment analysis has been applied in research areas such as recommendation and support systems and has become an area of interest for many researchers. Therefore, many studies have been carried out on English, while other languages, such as Arabic, received less attention. Increasingly, sentiment analysis researchers use machine learning due to its excellent performance. However, the generated models are black boxes and non-interpretable by the users. The rule-based classification is a promising approach for generating interpretable models. This work proposes a classification rule-based Arabic sentiment analysis approach together with a new binary equilibrium optimization metaheuristic algorithm as an optimization method for classification rule generation from Arabic documents. The proposed approach has been experimented on the Opinion Corpus for Arabic (OCA) and generates a classification model of thirteen rules. The comparison results with state-of-the-art methods show that the proposed approach outperforms all other white-box models regarding classification accuracy.
LTC-SUM: Lightweight Client-Driven Personalized Video Summarization Framework Using 2D CNN

This paper proposes a novel lightweight thumbnail container-based summarization (LTC-SUM) framework for full feature-length videos. This framework generates a personalized keyshot summary for concurrent users by using the computational resource of the end-user device. State-of-the-art methods that acquire and process entire video data to generate video summaries are highly computationally intensive. In this regard, the proposed LTC-SUM method uses lightweight thumbnails to handle the complex process of detecting events. This significantly reduces computational complexity and improves communication and storage efficiency by resolving computational and privacy bottlenecks in resource-constrained end-user devices. These improvements were achieved by designing a lightweight 2D CNN model to extract features from thumbnails, which helped select and retrieve only a handful of specific segments. Extensive quantitative experiments on a set of full 18 feature-length videos (approximately 32.9 h in duration) showed that the proposed method is significantly computationally efficient than state-of-the-art methods on the same end-user device configurations. Joint qualitative assessments of the results of 56 participants showed that participants gave higher ratings to the summaries generated using the proposed method. To the best of our knowledge, this is the first attempt in designing a fully client-driven personalized keyshot video summarization framework using thumbnail containers for feature-length videos.
Dual-branch self-attention network for pedestrian attribute recognition

Pedestrian attribute recognition (PAR) is still a challenging task in real surveillance scenes, where the difficulties, such as occlusion, complex background, and varying views, degrade the recognition accuracy. To fully exploit attribute correlation and regional context, we propose a dual-branch self-attention network for PAR: (1) For the attribute branch, the second-order self-attention module (SO-SAM) is first introduced to derive the second-order feature maps; they are then fused with the first-order information to learn unique features for each attribute using the constrained loss function. (2) For the context branch, multiple adaptive visual tokens and a group of multi-head context self-attention modules (C-SAM) are exploited to describe the image and explore the relationships between different regions. The experimental results on three main public benchmarks, RAP, PA100K, and PETA datasets, demonstrate the effectiveness of the proposed method.
A Novel and Efficient Influence-Seeking Exploration in Deep Multiagent Reinforcement Learning

Although recent years witnessed notable success for a cooperative setting in multi-agent reinforcement learning (MARL), efficient explorations are still challenging primarily due to the complex dynamics of inter-agent interactions constituting the high dimension of action spaces. For an efficient exploration, it is necessary to quantify influences that can represent interactions among agents and use them to obtain more information about the complexity of multi-agent systems. In this paper, we propose a novel influence-seeking exploration (ISE) scheme, which encourages agents to preferably explore action spaces significantly influenced by others and thus helps in speeding up the learning curve. To measure the influence of other agents in action selection, we use the variance of joint action-values with different action sets of agents that obtained by an estimation technique to lessen computation overhead. To this end, we first present an analytical approach inspired by the concept of approximated variance propagation and then apply it to an exploration scheme. We evaluate the proposed exploration method on a set of StarCraft II micromanagement as well as modified predator-prey tasks. Compared to state-of-the-art methods, the proposed method achieved performance improvements of 10&#x0025; in StarCraft II micromanagement and 50&#x0025; in modified predator-prey tasks approximately.
ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation

Infrared cameras are often utilized to enhance the night vision since the visible light cameras exhibit inferior efficacy without sufficient illumination. However, infrared data possesses inadequate color contrast and representation ability attributed to its intrinsic heat-related imaging principle, which hinders its application. Although, the domain gaps between unpaired nighttime infrared and daytime visible videos are even huger than paired ones that captured at the same time, establishing an effective translation mapping will greatly contribute to various fields. In this case, the structural knowledge within nighttime infrared videos and semantic information contained in the translated daytime visible pairs could be utilized simultaneously. To this end, we propose a tailored framework ROMA that couples with our introduced cRoss-domain regiOn siMilarity mAtching technique for bridging the huge gaps. To be specific, ROMA could efficiently translate the unpaired nighttime infrared videos into fine-grained daytime visible ones, meanwhile maintain the spatiotemporal consistency via matching the cross-domain region similarity. Furthermore, we design a multiscale region-wise discriminator to distinguish the details from synthesized visible results and real references. Moreover, we provide a new and challenging dataset encouraging further research for unpaired nighttime infrared and daytime visible video translation, named InfraredCity, which is $20$ times larger than the recently released infrared-related dataset IRVI. Codes and datasets are available https://github.com/BIT-DA/ROMA here.
Impact of extracurricular factors on the academic performance of university students during the COVID-19 pandemic

This article aims to study the incidence of extracurricular factors relating to (a) personal work situation and place of residence; (b) family finances; and (c) access to the virtual environment on the academic results of university students during the COVID-19 pandemic. Regression models were used to determine the impact of the different factors on academic performance in a sample of 138 students of the Primary Education Teaching Degree at a Spanish Public University. The results show that students who devote themselves wholly to studying without having to work obtain better academic results than those who have to combine study and work. Furthermore, internet access affects academic results, with students having ADSL and Wi-Fi via smartphones reporting the highest grades.
Determining Maximal Entropy Functions for Objective Bayesian Inductive Logic

Abstract According to the objective Bayesian approach to inductive logic, premisses inductively entail a conclusion just when every probability function with maximal entropy, from all those that satisfy the premisses, satisfies the conclusion. When premisses and conclusion are constraints on probabilities of sentences of a first-order predicate language, however, it is by no means obvious how to determine these maximal entropy functions. This paper makes progress on the problem in the following ways. Firstly, we introduce the concept of a limit in entropy and show that, if the set of probability functions satisfying the premisses contains a limit in entropy, then this limit point is unique and is the maximal entropy probability function. Next, we turn to the special case in which the premisses are categorical sentences of the logical language. We show that if the uniform probability function gives the premisses positive probability, then the maximal entropy function can be found by simply conditionalising this uniform prior on the premisses. We generalise our results to demonstrate agreement between the maximal entropy approach and Jeffrey conditionalisation in the case in which there is a single premiss that specifies the probability of a sentence of the language. We show that, after learning such a premiss, certain inferences are preserved, namely inferences to inductive tautologies. Finally, we consider potential pathologies of the approach: we explore the extent to which the maximal entropy approach is invariant under permutations of the constants of the language, and we discuss some cases in which there is no maximal entropy probability function.
Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)

There is a strong trend in the development of control systems for multi-rotor unmanned aerial vehicles (UAVs), where minimization of a control signal effort is conducted to extend the flight time. The aim of this article is to shed light on the problem of shaping control signals in terms of energy-optimal flights. The synthesis of a UAV autonomous control system with a brain emotional learning based intelligent controller (BELBIC) is presented. The BELBIC, based on information from the feedback loop of the reference signal tracking system, shows a high learning ability to develop an appropriate control action with low computational complexity. This extends the capabilities of commonly used fixed-value proportional–integral–derivative controllers in a simple but efficient manner. The problem of controller tuning is treated here as a problem of optimization of the cost function expressing control signal effort and maximum precision flight. The article introduces several techniques (bio-inspired metaheuristics) that allow for quick self-tuning of the controller parameters. The performance of the system is comprehensively analyzed based on results of the experiments conducted for the quadrotor model.
Experimental quantum state discrimination using the optimal fixed rate of inconclusive outcomes strategy

The problem of non-orthogonal state discrimination underlies crucial quantum information tasks, such as cryptography and computing protocols. Therefore, it is decisive to find optimal scenarios for discrimination among quantum states. We experimentally investigate the strategy for the optimal discrimination of two non-orthogonal states considering a fixed rate of inconclusive outcomes (FRIO). The main advantage of the FRIO strategy is to interpolate between unambiguous and minimum error discrimination by solely adjusting the rate of inconclusive outcomes. We present a versatile experimental scheme that performs the optimal FRIO measurement for any pair of generated non-orthogonal states with arbitrary a priori probabilities and any fixed rate of inconclusive outcomes. Considering different values of the free parameters in the FRIO protocol, we implement it upon qubit states encoded in the polarization mode of single photons generated in the spontaneous parametric down-conversion process. Moreover, we resort to a newfangled double-path Sagnac interferometer to perform a three-outcome non-projective measurement required for the discrimination task, showing excellent agreement with the theoretical prediction. This experiment provides a practical toolbox for a wide range of quantum state discrimination strategies using the FRIO scheme, which can significantly benefit quantum information applications and fundamental studies in quantum theory.
GDPR Compliant Data Storage and Sharing in Smart Healthcare System: A Blockchain-Based Solution

Smart healthcare systems provide user-centric medical services to patients based on collected information of patients inducing personal health information (PHI) and personal identifiable information (PII). The information (PII and PHI) flows into the smart healthcare system with or without any regulation and patient concern with the help of new information and communication technologies (ICT). The use of ICT comes with the security and privacy issues of collected PII and PHI data. The Europe Union has published the General Data Protection Regulation (GDPR) to regulate the flow of personal information. Towards this end, this paper proposes a blockchain-based data storage and sharing framework for a smart healthcare system that complies with the “Privacy by Design” rule of the GDPR. The personal information collected from patients is stored on off-chain storage (IPFS), and other information is stored on the blockchain ledger, which is visible to all participants. The smart contracts are designed to share the PII data with another participant based on prior permission of the data owner. The proposed framework also includes the deletion of PII and PHI in the system as per the “Right to be Forgotten” GDPR rule. Security and privacy analyses are performed for the framework to demonstrate the security and privacy of data while sharing and at rest. The comparative performance analysis demonstrates the benefit of the proposed GDPR-compliant data storage and sharing framework using blockchain. It is evident from the reported results that the proposed framework outperforms the state-of-the-art techniques in terms of performance metrics in a smart healthcare system.
ArCo: Attention-reinforced transformer with contrastive learning for image captioning

Image captioning is a significant step toward achieving automatic interactions between humans and computers, in which a textual sequence of the content of an image is generated. Recently, the transformer-based encoder–decoder paradigm has made great achievements in image captioning. This method is usually trained with a cross-entropy loss function. However, for various captions of images with the same meaning, the computed losses may be different. The result is that the descriptions of images tend to be consistent, which limits the diversity of image captioning. In this paper, we present an attention-reinforced transformer, a transformer-based architecture for image captioning. The architecture improves the image encoding stage, which exploits the relationships between image regions by integrating a feature attention block (FAB). During the training phase, we trained the model with a combination of cross-entropy loss and contrastive loss. We experimentally explored the performance of ArCo and other fully attentive models. We also validated the baseline of the transformer for image captioning with different pre-trained models. Our proposed approach was demonstrated to achieve a new state-of-the-art performance on the offline ‘Karpathy’ test split and online test server.