Reviewerly/exHarmony huggingface dataset:
Universal Densities Exist for Every Finite Reference Measure

As it is known, universal codes, which estimate the entropy rate consistently, exist for stationary ergodic sources over finite alphabets but not over countably infinite ones. We generalize universal coding as the problem of universal densities with respect to a fixed reference measure on a countably generated measurable space. We show that universal densities, which estimate the differential entropy rate consistently, exist for finite reference measures. Thus finite alphabets are not necessary in some sense. To exhibit a universal density, we adapt the nonparametric differential (NPD) entropy rate estimator by Feutrill and Roughan. Our modification is analogous to Ryabko’s modification of prediction by partial matching (PPM) by Cleary and Witten. Whereas Ryabko considered a mixture over Markov orders, we consider a mixture over quantization levels. Moreover, we demonstrate that any universal density induces a strongly consistent Cesàro mean estimator of conditional density given an infinite past. This yields a universal predictor with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0-1$ </tex-math></inline-formula> loss for a countable alphabet. Finally, we specialize universal densities to processes over natural numbers and on the real line. We derive sufficient conditions for consistent estimation of the entropy rate with respect to infinite reference measures in these domains.
Reversible extended secret image sharing with ability to correct errors based on Chinese remainder theorem

The reversible extended secret image sharing (RESIS) scheme can safely segment the secret image into a shadow image and embed it into the cover image, while ensuring that both the secret image and the cover image are completely restored. The existing schemes do not consider the attack on the information transmission channel, and often cannot correctly recover the secret image when attacked. In view of this, this paper fully considers the active attack on the information channel, and then proposes a RESIS scheme with error correction capability. In this paper, the Reed-Solomon code is used to detect modification attacks and correct errors to a certain extent. Additionally, the lossless recovery effect of both the secret image and the cover image is accomplished in conjunction with secret sharing scheme based on the Chinese remainder theorem. According to experimental findings, this method can resist certain active attacks.
Automatic evaluation of spontaneous oral cancer speech using ratings from naive listeners

In this paper, we build and compare multiple speech systems for the automatic evaluation of the severity of a speech impairment due to oral cancer, based on spontaneous speech. To be able to build and evaluate such systems, we collected a new spontaneous oral cancer speech corpus from YouTube consisting of 124 utterances rated by 100 non-expert listeners and one trained speech-language pathologist, which we made publicly available. We evaluated the systems in two scenarios: a scenario where transcriptions were available (reference-based) and a scenario where transcriptions might not be available (reference-free). The results of extensive experiments showed that (1) when transcriptions were available, the highest correlation with the human severity ratings was obtained using an automatic speech recognition (ASR) retrained with oral cancer speech. (2) When transcriptions were not available, the best results were achieved by a LASSO model using modulation spectrum features. (3) We found that naive listeners' ratings are highly similar to the speech pathologist's ratings for speech severity evaluation. (4) The use of binary labels led to lower correlations of the automatic methods with the human ratings than using severity scores.
Penalized maximum likelihood inference under the mixture cure model in sparse data

When a study sample includes a large proportion of long-term survivors, mixture cure (MC) models that separately assess biomarker associations with long-term recurrence-free survival and time to disease recurrence are preferred to proportional-hazards models. However, in samples with few recurrences, standard maximum likelihood can be biased.We extend Firth-type penalized likelihood (FT-PL) developed for bias reduction in the exponential family to the Weibull-logistic MC, using the Jeffreys invariant prior. Via simulation studies based on a motivating cohort study, we compare parameter estimates of the FT-PL method to those by ML, as well as type 1 error (T1E) and power obtained using likelihood ratio statistics.In samples with relatively few events, the Firth-type penalized likelihood estimates (FT-PLEs) have mean bias closer to zero and smaller mean squared error than maximum likelihood estimates (MLEs), and can be obtained in samples where the MLEs are infinite. Under similar T1E rates, FT-PL consistently exhibits higher statistical power than ML in samples with few events. In addition, we compare FT-PL estimation with two other penalization methods (a log-F prior method and a modified Firth-type method) based on the same simulations.Consistent with findings for logistic and Cox regressions, FT-PL under MC regression yields finite estimates under stringent conditions, and better bias-and-variance balance than the other two penalizations. The practicality and strength of FT-PL for MC analysis is illustrated in a cohort study of breast cancer prognosis with long-term follow-up for recurrence-free survival.
Integration of the latent variable knowledge into deep image captioning with Bayesian modeling

Abstract Automatic image captioning systems assign one or more sentences to images to describe their visual content. Most of these systems use attention‐based deep convolutional neural networks and recurrent neural networks (CNN‐RNN‐Att). However, they must optimally use latent variables and side information within the image concepts. This study aims to integrate a latent variable into image captioning using CNN‐RNN‐Att. A Bayesian modeling framework is used for this work. As an instance of a latent variable, High‐Level Semantic Concepts (HLSCs) of tags are used to implement the proposed model. The Bayesian model output interpretation is to localize the entire image description process and breaks it down into sub‐problems. Thus, a baseline descriptor subnet is trained independently for each sub‐problem, and it is the only expert in captioning for a given HLSC. The final output is the caption derived from the subnet; its HLSC is closest to the image content. The results indicate that CNN‐RNN‐Att applied to data localized using HLSCs improves the captioning accuracy of the proposed method, which can be compared to the latest state‐of‐the‐art and most accurate captioning systems.
Simultaneous Preparation of Two Optical Cat States Based on a Nondegenerate Optical Parametric Amplifier

The optical cat state, known as the superposition of coherent states, has broad applications in quantum computation and quantum metrology. Increasing the number of optical cat states is crucial to implement complex quantum information tasks based on them. Here, we prepare two optical cat states simultaneously based on a nondegenerate optical parametric amplifier. By subtracting one photon from each of two squeezed vacuum states, two odd cat states with orthogonal superposition direction in phase space are prepared simultaneously, which have similar fidelity of 60% and amplitude of 1.2. Compared with the traditional method to generate two odd optical cat states based on two degenerate optical parametric amplifiers, only one nondegenerate optical parametric amplifier is applied in our experiment, which saves half of the quantum resource of nonlinear cavities. The presented results make a step toward preparing the four-component cat state, which has potential applications in fault-tolerant quantum computation.
Time-aware deep reinforcement learning with multi-temporal abstraction

Deep reinforcement learning (DRL) is advantageous, but it rarely performs well when tested on real-world decision-making tasks, particularly those involving irregular time series with sparse actions. Although irregular time series with sparse actions can be handled using temporal abstractions for the agent to grasp high-level states, they aggravate temporal irregularities by increasing the range of time intervals essential to represent a state and estimate expected returns. In this work, we propose a general Time-aware DRL framework with Multi-Temporal Abstraction (T-MTA) that incorporates the awareness of time intervals from two aspects: temporal discounting and temporal abstraction. For the former, we propose a Time-aware DRL method, whereas for the latter we propose a Multi-Temporal Abstraction mechanism. T-MTA was tested in three standard RL testbeds and two real-life tasks (control of nuclear reactors and prevention of septic shock), which represent four common contexts of learning environments, online and offline, as well as fully and partially observable. As T-MTA is a general framework, it can be combined with any model-free DRL method. In this work, we examined two in particular: the Deep Q-Network approach and its variants, and Truly Proximal Policy Optimization. Our results show that T-MTA significantly outperforms competing baseline frameworks, including a standalone Time-aware DRL framework, MTAs, and the original DRL methods without considering either type of temporal aspect, especially when partially observable environments are involved and the range of time intervals is large.
Remote Learning in Times of COVID-19 in Colombia

Remote learning replaced for almost a year the traditional face-to-face education to maintain and ensure the continuity of the teaching-learning process during the Covid-19 pandemic. This article reports the results of an exploratory and descriptive-transversal study that exposes primary and secondary school students’ perceptions about their experience with remote learning during 2020. Though the research study was also aimed at finding out potential teaching and learning issues inherent to remote education, we only focus on reporting the main findings on the determining factors that hindered or enhanced students' learning during their remote education experience. Data were collected through two different surveys addressed to 101 secondary and primary school students from Neiva and Pitalito, the two most populated towns in the state of Huila, Colombia. The findings showed that the students’ learning was both positively and negatively influenced by several factors affecting interaction inside and outside of the classroom setting.
Phishing uniform resource locator detection using machine learning: A step towards secure system

Abstract The advancement in technology has led to increase in cyber‐attacks. Hackers have become more skilled at finding the loopholes in the system and can penetrate easily on to host network. The rate of cybercrimes is increasing exponentially with the growth of digital era. Phishing is considered as one of the top cybercrimes that has impacted the society at large. As per Kaspersky reports 2021, around 22% attacks were phishing attacks. This paper explores methods for detecting phishing uniform resource locator (URLs) by analyzing various features using Machine Learning techniques. Various data mining algorithms are used to learn data patterns that can identify and differentiate between benign and phishing websites using phishing website data set. The best results are shown by an XGBoost Model that provides more than 90% accuracy on the balanced class dataset.
Dual-aware Domain Mining and Cross-aware Supervision for Weakly-supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation with image-level annotation uses localization maps from the classifier to generate pseudo labels. However, such localization maps focus only on sparse salient object regions, it is difficult to generate high-quality segmentation labels, which deviates from the requirement of semantic segmentation. To address this issue, we propose a dual-aware domain mining and cross-aware supervision (DDMCAS) method for weakly-supervised semantic segmentation. Specifically, we propose a dual-aware domain mining (DDM) module consisting of graph-based global reasoning unit and salient-region extension controller, which produces dense localization maps by exploring object features in salient regions and adjacent non-salient regions simultaneously. In order to further bridge the gap between salient regions and adjacent non-salient regions to generate more refined localization maps, we propose a cross-aware supervision (CAS) strategy to recover missing parts of the target objects and enhance weak attention in adjacent non-salient regions, leading to pseudo labels of higher quality for training the segmentation network. Based on the generated pseudo-labels, extensive experiments on PASCAL VOC 2012 dataset demonstrate that our method outperforms state-of-the-art methods using image-level labels for weakly supervised semantic segmentation.
<scp><i>MyEasyHealthcare</i></scp>: An efficient and secure three‐tier blockchain‐based healthcare system

Abstract Blockchain systems have seen vast growth due to the immense potential in developing secure applications for education, healthcare, and so forth. The healthcare system is extensively researched to provide convenience to human life. With the exponential growth in healthcare systems and devices, patient data security and privacy issues are becoming primary concerns. Blockchain is emerging as a solution to secure healthcare records, but it faces certain shortcomings like transaction time, execution time, gas cost consumption, bandwidth utilization, and so forth. The current article designed an extensive blockchain‐based healthcare system ( MyEasyHealthcare ) with reduced gas consumption, transaction cost, execution cost, and bandwidth utilization, along with enhanced security at three levels. At the first level, the professionals and patients get registered, which provides identity access management. Secondly, authorization is required for each registered entity by the owners. Lastly, the third level includes a doctor‐patient relationship where the hospital's owner assigns a patient to a particular doctor. The data is protected from the outer world and is preserved only between the doctor and the patient. Moreover, to include the majority of tasks for hospital management, the developed system incorporates a smart contract to record seven different parameters for patient diagnosis by a physician and 15 different parameters by a pathologist. The designed system is evaluated for the amount of gas consumed, transaction cost, execution cost, and bandwidth utilization by simulating/executing the written smart contract on InterPlanetary File System (IPFS) and Remix to check the feasibility of the developed system ( MyEasyHealthcare ) for the real world; the results testify the proposed system is useful in the real world.
Artificial intelligence assisted diagnoses of fine-needle aspiration of breast diseases: a single-center experience

Abstract: Purpose: Since 2010, physicians from Afghanistan have been uploading images of histological and cytological specimens to a telemedicine internet platform (iPath network) for expert evaluation. From this collective work, all cases with fine-needle aspirations (FNA) of mammary gland diseases were extracted and analyzed. The aim of the present retrospective feasibility study is to investigate the utility of artificial intelligence assisted diagnoses in fine-needle aspiration (FNA) of breast diseases.Material and Methods: A total of 3304 microphotographic images from 438 patients of smears from FNA of the mammary gland were available for this study. Telemedical expert diagnoses from 4 experienced cytopathologists were available in all 438 cases. Their diagnosis (malignant tumor of the mammary gland or benign mammary gland disease) was set as the gold standard. AI analysis was performed using i) clinical context data and ii) two different image recognition methods to determine the probability values for the presence of malignant breast tumor. Youden index and AUC (area under the curve) were used to evaluate test performance. Results: A score for invasive breast cancer (IBC) calculated from contextual variables agreed with the expert diagnosis (accuracy) in 85.2% and with the two image recognition systems in 78.4% and 65.2%. This simplifies health healthcare management of breast diseases in low income countries as in many patients the less expensive and less time-consuming technique of FNA may replace a histological examination.Conclusion: Image classification and analysis of context variables can be used to test the validity and plausibility of cytologic diagnoses, especially when cytologic interpretation has to be performed by people who are inexperienced in cytopathology.
Introduction

This special issue represents some of the recent developments within argumentation studies. The following overview provides some historical context for the five papers which constitute this special issue. The Aristotelian roots of modern argumentation studies are briefly presented, as well as some further developments within Greek and Roman rhetoric. Furthermore, the most important developments in argumentation studies during the last few decades are sketched. Several modern approaches to argumentation theory are introduced, among them Perelman/Olbrechts-Tyteca’s New Rhetoric, Toulmin’s model of argumentation, formal dialectics, fallacy theory, informal logic, the Argumentum Model and Pragma-Dialectics. Finally, the thematic focus of the five papers is described.
Online Voting System

The project "Online Voting System" aims to simplify voting in any type of election. In India, presently voting is done either using ballot paper or with EVM machines. In the traditional Voting approach, Individuals are required to visit voting stations and cast their votes. People from various locations who don’t have their voting cards are unable to cast their votes. Also, the user's validation is poor and inappropriate. Further, the conventional voting system involves a lot of manual work which is a time-consuming process. Therefore, to overcome the drawbacks in the existing system this particular system was proposed to mark our work much easier and to reduce wastage of time. This work deals with design, building and testing a online voting system that facilitates user (the person who is eligible for voting), candidate (Candidate are the users who are going to stand in elections for their respective party), Election Commission Officer (Election Commission Officer who will verify whether registered user and candidates are authentic or not) to participate in online voting. This voting system is highly secured and has a very simple, user-friendly, and reliable design. The proposed application is developed and tested to work on Ethernet and allow online voting. The user can register by giving his personal details and the image of his face which gets stored in the database presented at the central side. After the voting date is fixed and the user gets pop up notifications on his\her android phone via GCM (Google Cloud Messaging). After that the users open the application then the face authentication is done at server side using the OTP. If the user is a valid user, then OTP is sent to the user's mail address the user accesses the voting form using their OTP, casts their vote, clicks the submit button, and then logs out.
A multi‐feature‐based intelligent redundancy elimination scheme for cloud‐assisted health systems

Abstract Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud‐assisted health systems. Deduplication eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple duplicates. Delta compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks, but our observations indicate that this is disobedient when data have sparse duplicate blocks. In addition, there are many overlapped deltas in the resemblance detection process of post‐deduplication delta compression, which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non‐similar blocks, resulting in inefficient system throughput. Therefore, a multi‐feature‐based redundancy elimination scheme, called MFRE, is proposed to solve these problems. The similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate attribute. Then, similarity‐based dynamic post‐deduplication delta compression and temporal locality‐based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ratios. Moreover, the clustering method based on block‐relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system throughput. Experiments demonstrate that the proposed method, compared to the state‐of‐the‐art method, improves the compression ratio and system throughput by 9.68% and 50%, respectively.
Learning Management Systems Evaluation Using Frizzy DEMATEL

E-learning is the process of learning through digital instruments. One of the most common tools is distance learning, followed by learning management systems. However, hybrid interactions and partnerships are reshaping the world of online learning. The emergence of social networking and online learning groups, the widespread use of smartphones, and a growing understanding of the potential for computer games to revolutionize learning are recent developments. Other significant breakthroughs include improvements in Intelligent Tutoring Systems, the free Open Educational Resources movement, and the development of immersive environments that let users interact with virtual worlds and enhance their perception of the physical world through technology.
Dynamic logic-based attack graph for risk assessment in complex computer systems

Nowadays, Information and Communication Technologies (ICT) play a significant role in our modern daily life. Computer networks breakdown can strongly impact everything in our life such as personal data, industrials, banks, oil pipelines, hospitals, nuclear reactors, military platforms, etc. Assessing their security is a necessity to reduce the risk of compromise by an attacker. Nevertheless, the actual solutions are rarely adapted to the high complexity of modern computer systems. They often rely on too much human work and the used algorithms do not scale well. Furthermore, the evolution of the system over time is rarely modeled and is therefore not considered in the evaluation of its security. This paper proposes a dynamic attack graph generation method allowing to model attack paths by considering the evolution of the system over time. We compute the probabilities of compromise of the system components by simulating several cyberattacks from the previously constructed dynamic attack graph. We tested our solution on a use case of several thousand of machines. The measured results demonstrate its ability to assess the threat in complex systems caused by combining exploitation of successive vulnerabilities.