Reviewerly/exHarmony huggingface dataset:
SurvNAM: The machine learning survival model explanation

An extension of the Neural Additive Model (NAM) called SurvNAM and its modifications are proposed to explain predictions of a black-box machine learning survival model. The method is based on applying the original NAM to solving the explanation problem in the framework of survival analysis. The basic idea behind SurvNAM is to train the network by means of a specific expected loss function which takes into account peculiarities of the survival model predictions. Moreover, the loss function approximates the black-box model by the extension of the Cox proportional hazards model, which uses the well-known Generalized Additive Model (GAM) in place of the simple linear relationship of covariates. The proposed method SurvNAM allows performing local and global explanations. The global explanation uses the whole training dataset. In contrast to the global explanation, a set of synthetic examples around the explained example are randomly generated for the local explanation. The proposed modifications of SurvNAM are based on using the Lasso-based regularization for functions from GAM and for a special representation of the GAM functions using their weighted linear and non-linear parts, which is implemented as a shortcut connection. Many numerical experiments illustrate efficiency of SurvNAM.
Usable and secure? User perception of four authentication methods for mobile banking

Smartphone authentication is becoming a cornerstone security component, so it is necessary to have methods that are usable and secure to ensure adequate protection, especially for mobile banking. Though biometric authentication seems to be perceived as very usable by users, there is a lack of research to compare smartphone-based fingerprint verification to other authentication methods for mobile banking in terms of usability and perceived security. Using two independent samples, we conducted a study for a younger group aged 26–54 (N = 229) and an older group aged 55+ (N = 239) about their perceptions of the usability and security of four authentication methods: fingerprint, PIN, token, and card reader. All four methods were evaluated positively for both usability and security, with fingerprint verification evaluated as the most usable and the most secure method for mobile banking. Interestingly, none of our hypothesized predictors (i.e., age, gender, education, smartphone self-efficacy, smartphone security behaviour, knowledge of secure smartphone behaviour) was consistently related to how users perceive the usability and security of the examined methods. This suggests that smartphone users would be able to successfully adopt, and be quite satisfied with, any of the tested methods, regardless of demography or smartphone skills.
Multimodal Event Processing: A Neural-Symbolic Paradigm for the Internet of Multimedia Things

With the Internet of Multimedia Things (IoMT) becoming a reality, new approaches are needed to process realtime multimodal event streams.Existing approaches to event processing have limited consideration for the challenges of multimodal events, including the need for complex content extraction, and increased computational and memory costs.This article explores event processing as a basis for processing real-time IoMT data.This article introduces the multimodal event processing (MEP) paradigm, which provides a formal basis for native approaches to neural multimodal content analysis (i.e., computer vision, linguistics, and audio) with symbolic event processing rules to support real-time queries over multimodal data streams using the multimodal event processing language to express single, primitive multimodal, and complex multimodal event patterns.The content of multimodal streams is represented using multimodal event knowledge graphs to capture the semantic, spatial, and temporal content of the multimodal streams.The approach is implemented and evaluated within a MEP engine using single and multimodal queries achieving near real-time performance with a throughput of ∼30 frames processed per second (fps) and subsecond latency of 0.075-0.30s for video streams of 30 fps input rate.Support for high input stream rates (45 fps) is achieved through content-aware load-shedding techniques with a ∼127X latency improvement resulting in only a minor decrease in accuracy.
An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963-0.972), heart failure AUC of 0.838 (CI: 0.825-0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821-0.842), pulmonary embolism AUC of 0.802 (CI: 0.764-0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499-2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.
Dual-Channel Multi-Task CNN for No-Reference Screen Content Image Quality Assessment

Nowadays the problem of image quality assessment (IQA) for screen content images (SCIs) has become a research hotspot as they are ubiquitous in multimedia applications. Although the quality assessment of natural images (NIs) has been continuously developed in the past few decades, few NI-oriented IQA methods can be directly applied on SCIs due to different visual characteristics between them. In this paper, we present a no-reference quality prediction approach considering the content information of SCIs, which is based on dual-channel multi-task convolutional neural network. First, we segment a SCI into small patches and classify them as the textual patches and the pictorial patches. Then, we devise a novel dual-channel convolutional neural network (CNN) to predict the quality of textual patches and pictorial patches. Finally, we propose an effective adaptive weighting strategy for quality score aggregation. The proposed CNN is built on an end-to-end multi-task learning framework, which assists the SCI quality prediction task through the histogram of oriented gradient (HOG) feature prediction task to learn a better mapping between the input patch and its quality score. The adaptive weighting strategy further improves the representation ability of each SCI patch. Experimental results on two largest SCI-oriented databases demonstrate that the proposed method outperforms most of the state-of-the-art no-reference IQA methods and the full-reference IQA methods.
3-D Path Planning Using Improved RRT* Algorithm for Robot-Assisted Flexible Needle Insertion in Multilayer Tissues

In the field of minimally invasive surgery, flexible needles can avoid blood vessels and organs more flexibly compared to rigid needles. One of the main challenges when using flexible needles to reach lesions is planning a suitable path. Due to the non-holonomic characteristic of the flexible needle dynamics and the tissue deformation caused by the needle tip during the insertion, the accessibility and safety of the needle’s states need to be considered in the path planning stage. In this article, we propose an adaptable algorithm by improving the canonical rapidly exploring random trees* (RRT*) algorithm to compute a path for the flexible needle to reach targets in a layered tissue environment. The improved RRT* algorithm that addresses the motion constraints of the flexible needle renders the computed path comparatively smoother and optimal in some approximation sense. In the proposed algorithm, a strategy of adapting some of its parameters for different tissues during the insertion is developed, which improves the safety of surgeries. Moreover, the path cost used in the algorithm takes the potential fields of surrounding obstacles into account, which is used to deal with the influence of the local movement of tissues during the needle puncture process. Simulations are conducted to verify the effectiveness of the proposed algorithm. The results show that the improved RRT* algorithm generates a smooth and safe path which satisfies the motion constraints of the flexible needle in layered tissue environment.
Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

Even though several advances have been made in recent years, handwritten script recognition is still a challenging task in the pattern recognition domain. This field has gained much interest lately due to its diverse application potentials. Nowadays, different methods are available for automatic script recognition. Among most of the reported script recognition techniques, deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms. However, the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error, which renders them unfeasible. This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources. To alleviate this shortcoming, this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization (QPSO), which is capable of automatically evolving the meaningful convolutional neural network (CNN) topologies. The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts, namely Bangla, Devanagari, and Dogri, consisting of handwritten characters and digits. Empirically, the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.
Witnessing objectivity on a quantum computer

Abstract Understanding the emergence of objectivity from the quantum realm has been a long standing issue strongly related to the quantum to classical crossover. Quantum Darwinism (QD) provides an answer, interpreting objectivity as consensus between independent observers. Quantum computers provide an interesting platform for such experimental investigation of QD, fulfiling their initial intended purpose as quantum simulators. Here we assess to what degree current Noisy intermediate-scale quantum devices can be used as experimental platforms in the field of QD. We do this by simulating an exactly solvable stochastic collision model, taking advantage of the analytical solution to benchmark the experimental results.
Implicit and Explicit Feature Purification for Age-Invariant Facial Representation Learning

This paper presents a new method, named implicit and explicit feature purification (IEFP), for age-invariant face recognition. Facial features extracted from a face image contain the information about the identity, age, and other attributes. For age-invariant face recognition, it is important to remove the irrelevant information, and retain the identity information only, in the facial features. Through the two proposed feature purification mechanisms, our framework can produce facial-feature embeddings that preserve identity information as much as possible and are insensitive to age variations. Specifically, on the one hand, a special network module is devised to implicitly purify the original facial features obtained from a face encoder. On the other hand, to obtain purer facial feature representations for age-invariant face recognition, irrelevant information within the implicitly purified features, such as the age, is further removed. This is realized by using a regularizer, based on information theory, to explicitly minimize the correlation between identity-related features and age-related features. Comprehensive ablation studies show that these two feature purification schemes can work independently, as well as collaboratively, to achieve better performance. Extensive evaluations on several benchmark data sets show that the IEFP method is on par with those competitors learned on far more favorable training samples, and it achieves the best performance in a fair comparison. Furthermore, we provide mathematical interpretation to explain the effectiveness of our approach, and find that it tends to generate low-rank, yet high-dimensional, representations for age-invariant face recognition.
Security bounds for decoy-state quantum key distribution with arbitrary photon-number statistics

Typically, descriptions of decoy-state quantum key distribution involve the assumption of Poissonian photon-number statistics. Here, the authors extend this to an arbitrary distribution in order to better model implementations that use non--laser light source technology, like LEDs and quantum dots.
A relational theory of effects and coeffects

Graded modal types systems and coeffects are becoming a standard formalism to deal with context-dependent, usage-sensitive computations, especially when combined with computational effects. From a semantic perspective, effectful and coeffectful languages have been studied mostly by means of denotational semantics and almost nothing has been done from the point of view of relational reasoning. This gap in the literature is quite surprising, since many cornerstone results — such as non-interference , metric preservation , and proof irrelevance — on concrete coeffects are inherently relational. In this paper, we fill this gap by developing a general theory and calculus of program relations for higher-order languages with combined effects and coeffects. The relational calculus builds upon the novel notion of a corelator (or comonadic lax extension ) to handle coeffects relationally. Inside such a calculus, we define three notions of effectful and coeffectful program refinements: contextual approximation , logical preorder , and applicative similarity . These are the first operationally-based notions of program refinement (and, consequently, equivalence) for languages with combined effects and coeffects appearing in the literature. We show that the axiomatics of a corelator (together with the one of a relator) is precisely what is needed to prove all the aforementioned program refinements to be precongruences, this way obtaining compositional relational techniques for reasoning about combined effects and coeffects.
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning

Federated learning (FL) is an emerging collaborative machine learning (ML) framework that enables training of predictive models in a distributed fashion where the communication among the participating nodes are facilitated by a central server. To deal with the communication bottleneck at the server, decentralized FL (DFL) methods advocate rely on local communication of nodes with their neighbors according to a specific communication network. In DFL, it is common algorithmic practice to have nodes interleave (local) gradient descent iterations with gossip (i.e., averaging over the network) steps. As the size of the ML models grows, the limited communication bandwidth among the nodes does not permit communication of full-precision messages; hence, it is becoming increasingly common to require that messages be <i>lossy, compressed</i> versions of the local parameters. The requirement of communicating compressed messages gives rise to the important question: <i>given a fixed communication budget, what should be our communication strategy to minimize the (training) loss as much as possible?</i> In this article, we explore this direction, and show that in such compressed DFL settings, there are benefits to having <i>multiple</i> gossip steps between subsequent gradient iterations, even when the cost of doing so is appropriately accounted for, e.g., by means of reducing the precision of compressed information. In particular, we show that having <inline-formula><tex-math notation="LaTeX">${\mathcal O}(\log \frac{1}{\epsilon })$</tex-math></inline-formula> gradient iterations with constant step size - and <inline-formula><tex-math notation="LaTeX">${\mathcal O}(\log \frac{1}{\epsilon })$</tex-math></inline-formula> gossip steps between every pair of these iterations - enables convergence to within <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> of the optimal value for a class of non-convex problems that arise in the training of deep learning models, namely, smooth non-convex objectives satisfying Polyak-Łojasiewicz condition. Empirically, we show that our proposed scheme bridges the gap between centralized gradient descent and DFL on various machine learning tasks across different network topologies and compression operators.
Explainable AI for Fighting COVID-19 Pandemic: Opportunities, Challenges, and Future Prospects

Novel coronavirus illness 2019 (COVID-19) continues to spread exponentially and has incurred over 7,000,000 infections and 400,000 deaths worldwide. To build an efficient and easy diagnostic framework for COVID-19, artificial intelligence has revealed significant success in several activities, from image processing to natural language processing, particularly with the advent of deep learning (DL). The medical profession requires a high degree of precision and accuracy. Interpretations for system assumptions and forecasts are also required to explain their consistency. This needs better interpretability, which also implies the need to grasp the process behind the algorithms. Unfortunately, the black box existence of the DL remains unresolved, and certain system decisions are still badly known. The model's relevance to the black-box challenge comes under the eXplainable AI (XAI) area, there is explanation usage is generally accepted as a crucial aspect for the realistic application of AI models, especially during the COVID-19 outbreak. Therefore, this Chapter presents the applicability and prospect of AI during the current pandemic. Also, the chapter discusses the challenges facing using eXplainable AI in the healthcare system. A framework on which COVID-19 diagnosis can be based on eXplainable AI is also proposed in this chapter.
Machine Learning Applied to Datasets of Human Activity Recognition: Data Analysis in Health Care

In order to remain active and productive, older adults with poor health require a combination of advanced methods of visual monitoring, optimization, pattern recognition, and learning, which provide safe and comfortable environments and serve as a tool to facilitate the work of family members and workers, both at home and in geriatric homes. Therefore, there is a need to develop technologies to provide these adults autonomy in indoor environments.This study aimed to generate a prediction model of daily living activities through classification techniques and selection of characteristics in order to contribute to the development in this area of knowledge, especially in the field of health. Moreover, the study aimed to accurately monitor the activities of the elderly or people with disabilities. Technological developments allow predictive analysis of daily life activities, contributing to the identification of patterns in advance in order to improve the quality of life of the elderly.The vanKasteren, CASAS Kyoto, and CASAS Aruba datasets were used to validate a predictive model capable of supporting the identification of activities in indoor environments. These datasets have some variation in terms of occupation and the number of daily living activities to be identified.Twelve classifiers were implemented, among which the following stand out: Classification via Regression, OneR, Attribute Selected, J48, Random SubSpace, RandomForest, RandomCommittee, Bagging, Random Tree, JRip, LMT, and REP Tree. The classifiers that show better results when identifying daily life activities are analyzed in the light of precision and recall quality metrics. For this specific experimentation, the Classification via Regression and OneR classifiers obtain the best results.The efficiency of the predictive model based on classification is concluded, showing the results of the two classifiers, i.e., Classification via Regression and OneR, with quality metrics higher than 90% even when the datasets vary in occupation and number of activities.
An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence

In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS).
Human Augmentation Technologies for Employee Well-Being: A Research and Development Agenda

The COVID-19 pandemic has changed the style of work. In adapting to the changing work environment, human augmentation technologies (HAT) can provide employees with new options to support their work. However, the agenda for research and development of HAT for the new normal is still unclear. In this study, we set two research questions: (i) what type of technology demand has emerged among employees due to the COVID-19 pandemic; and (ii) what is the nature of job satisfaction experienced by employees during the COVID-19 pandemic? This study aims to clarify the technology demand and job satisfaction of employees during the COVID-19 pandemic. We analyzed data from in-depth interviews with employees based on a grounded theory approach to answer the research questions and proposed an agenda for the research and development of HAT to enhance employees' well-being in this new normal based on the crosspoint of technology demand and job satisfaction. The theoretical contribution of this study is the development of models of technology demand and job satisfaction of employees during the COVID-19 pandemic. The practical contribution is the development of a crosspoint framework to enable the development of HAT to support work while considering their impact on employees' well-being.