Reviewerly/exHarmony huggingface dataset:
Analyzing the Data Completeness of Patients’ Records Using a Random Variable Approach to Predict the Incompleteness of Electronic Health Records

The purpose of this article is to illustrate an investigation of methods that can be effectively used to predict the data incompleteness of a dataset. Here, the investigators have conceptualized data incompleteness as a random variable, with the overall goal behind experimentation providing a 360-degree view of this concept conceptualizing incompleteness of a dataset both as a continuous, discrete random variable depending on the aspect of the required analysis. During the course of the experiments, the investigators have identified Kolomogorov–Smirnov goodness of fit, Mielke distribution, and beta distributions as key methods to analyze the incompleteness of a dataset for the datasets used for experimentation. A comparison of these methods with a mixture density network was also performed. Overall, the investigators have provided key insights into the use of methods and algorithms that can be used to predict data incompleteness and have provided a pathway for further explorations and prediction of data incompleteness.
Methods of Current Knowledge Teaching on the Cybersecurity Example

Teaching of modern cybersecurity specialists should be up to date and use the newest methods and methodologies in universities as the IT industry is rapidly growing and constantly changing. A good idea is to use methods of management in IT companies as methods for current knowledge teaching of university students. It is also worth engaging students not only in educational international projects but the research projects as well. This work analyzes the method for teaching students, and the Scrum methodology was selected and implemented for educational and research projects. Students participated in both projects, however, Scrum models should be different for them and this is illustrated in the paper. The visualization of collected statistical data of the performed educational project illustrated distributions of students by specialization and by marks. The distributions by marks showed that using the Scrum model for the teaching course significantly increases the marks compared with the average level marks of the students in their specializations.
Contrastive learning–guided multi-meta attention network for breast ultrasound video diagnosis

Breast cancer is the most common cause of cancer death in women. Early screening and treatment can effectively improve the success rate of treatment. Ultrasound imaging technology, as the preferred modality for breast cancer screening, provides an essential reference for early diagnosis. Existing computer-aided ultrasound imaging diagnostic techniques mainly rely on the selected key frames for breast cancer lesion diagnosis. In this paper, we first collected and annotated a dataset of ultrasound video sequences of 268 cases of breast lesions. Moreover, we propose a contrastive learning-guided multi-meta attention network (CLMAN) by combining a deformed feature extraction module and a multi-meta attention module to address breast lesion diagnosis in ultrasound sequence. The proposed feature extraction module can autonomously acquire key information of the feature map in the spatial dimension, whereas the designed multi-meta attention module is dedicated to effective information aggregation in the temporal dimension. In addition, we utilize a contrast learning strategy to alleviate the problem of high imaging variability within ultrasound lesion videos. The experimental results on our collected dataset show that our CLMAN significantly outperforms existing advanced methods for video classification.
Incremental causal connection for self-adaptive systems based on relational reference attribute grammars

Even though model-driven engineering reduces complexity during the development of self-adaptive systems and [email protected] enables using them during runtime, connecting models to different external systems still involves manual work. Those connections are essential to the complete system, as they enable external systems to react to changes in the internal model and vice versa. In our case, the model is based on Relational Reference Attribute Grammars, an extension of Attribute Grammars to enable conceptual models at runtime while retaining their benefits of modular specification and an incremental evaluation scheme. We present an approach to enable concise specification of the causal connection and needed transformations to match required formats or semantics. To show its applicability, a case study showing the coordination of multiple industrial robot arms using models is presented. We show that using our approach, connections can be specified more concisely while maintaining the same efficiency as hand-written code. The artefact comprising all source code and an executable version of the case studies is available at https://doi.org/10.5281/zenodo.7009758.
Man or machine? Comparing the difficulty of human translation versus neural machine translation post-editing

This study aims to compare neural machine translation (NMT) post-editing and human translation in terms of task difficulty while considering source text (ST) complexity and machine translation (MT) quality levels, two factors that have been rarely examined in previous comparison studies. Data were obtained from 60 trainee translators concerning the perceived and objective difficulties of post-editing and human translation tasks and the participants’ performance. It was found that (1) the difficulty of the NMT post-editing task, compared to human translation, was significantly influenced by both NMT quality and ST complexity; the difficulty of the post-editing task was significantly lower than that of the human translation task only in the case of high-quality NMT paired with complex ST, while the results were mixed for other interactions between NMT quality and ST complexity levels; (2) no strong correlations were found between the participants’ perceived difficulty and the measurements of objective difficulty and task performance for both post-editing and human translation tasks. Practical and research implications were discussed.
EASIER System. Evaluating a Spanish Lexical Simplification Proposal with People with Cognitive Impairments

Cognitive impairments in text reading and understanding not only affect individuals with intellectual disabilities but also deaf and deaf-blind ones, the illiterate, and immigrants whose native language is different, among others. Technology to support accessibility to texts is required to help people to understand information. This article addresses an evaluation of the EASIER tool conducted with intellectual disabilities and older people for the identification of complex and unusual words in Spanish texts and proposes them simpler synonyms for the complex word detected. Fifty participants were recruited in collaboration with an organization that works with people with intellectual disabilities. The participants were divided into two groups: Group 1 represented 25 older people, and Group 2 represented 25 people with intellectual disabilities. The evaluation showed that, for complex word identification, the selected texts were more complex in content for people with intellectual disabilities than for older people, which consequently suggests that the proposal had a greater benefit for the intellectually disabled group. Related to the proposal of substitutes for complex words, there is a high number of substitutions accepted in participants with a high school level of education for Group 1 and primary level of education for Group 2. Additional multivariate analysis shows that the system is adequate for people of advanced age.
On the Role of Speed in Technological and Biological Information Transfer for Computations

In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so in real-world implementations, the propagation speed of information cannot exceed the speed of its carrier. Because of this limitation, one must also consider the transfer time between computing units for any implementation. We need a different mathematical method to consider this limitation: classic mathematics can only describe infinitely fast and small computing system implementations. The difference between mathematical handling methods leads to different descriptions of the computing features of the systems. The proposed handling also explains why biological implementations can have lifelong learning and technological ones cannot. Our conclusion about learning matches published experimental evidence, both in biological and technological computing.
The research of the side-channel analysis method based on deep learning for trusted platform module

At present, the trusted platform module (TPM) has been widely used in electricity, finance, transportation, and other industries, and its security has attracted much attention. The research on the side-channel attack (SCA) can be conducive to improving the security design technology of trusted platform modules. The side channel attack on the trusted platform module is studied from the initial differential power analysis (DPA) to the machine learning method. This paper introduces the deep learning techniques in SCA and proposes a new SCA technique based on deep learning. Firstly, on basis of the characteristics of SCA on TPM, we improve the convolutional neural network (CNN) models, including ResNet, VGG, and Google Net. Then, for the SM4 cipher implemented with and without countermeasures, we implement some attack experiments by exploiting the SCA based on various deep learning models and compare the experimental results with the DPA attack. The experiment results show that under the same circumstance, the SCA based on the deep learning method is more effective. We compare the effects of different CNN models and hyper-parameters to provide the basis and data for further research.
Refined probability distribution module for fine-grained visual categorization

Fine-grained visual categorization is an important task in computer vision. Prior works on fine-grained visual categorization have paid much attention to addressing intra-class variation and inter-class similarity. However, they rarely study that task from the perspective of probability distribution. In this paper, we propose a novel refined probability distribution module based on deep convolutional neural network. Our module computes the probability of an image by fully utilizing the similarity information between images. Firstly, we use deep neural networks to obtain the initial probability distribution and extract features. Then, we build a network whose inputs are features for calculating image-to-image similarity scores. Finally, our module refines the initial probability distribution based on an effective batch random walk operation with similarity scores. Our module can be plugged into many deep convolutional neural networks. Experimental results show that our approach outperforms state-of-the-art methods on the CUB-200–2011, FGVC-Aircraft and Stanford Cars datasets respectively.
Development, validation, and feature extraction of a deep learning model predicting in-hospital mortality using Japan’s largest national ICU database: a validation framework for transparent clinical Artificial Intelligence (cAI) development

While clinical Artificial Intelligence (cAI) mortality prediction models and relevant studies have increased, limitations including the lack of external validation studies and inadequate model calibration leading to decreased overall accuracy have been observed. To combat this, we developed and evaluated a novel deep neural network (DNN) and a validation framework to promote transparent cAI development.Data from Japan's largest ICU database was used to develop the DNN model, predicting in-hospital mortality including ICU and post-ICU mortality by days since ICU discharge. The most important variables to the model were extracted with SHapley Additive exPlanations (SHAP) to examine the DNN's efficacy as well as develop models that were also externally validated.The area under the receiver operating characteristic curve (AUC) for predicting ICU mortality was 0.94 [0.93-0.95], and 0.91 [0.90-0.92] for in-hospital mortality, ranging between 0.91-0.95 throughout one year since ICU discharge. An external validation using only the top 20 variables resulted with higher AUCs than traditional severity scores.Our DNN model consistently generated AUCs between 0.91-0.95 regardless of days since ICU discharge. The 20 most important variables to our DNN, also generated higher AUCs than traditional severity scores regardless of days since ICU discharge. To our knowledge, this is the first study that predicts ICU and in-hospital mortality using cAI by post-ICU discharge days up to over a year. This finding could contribute to increased transparency on cAI applications.
The critical success factors’ investigation during knowledge management implementation within SME enterprises: a Participatory Design opportunity

Knowledge management (KM) is currently an important driver to develop dynamic capabilities for businesses, on behalf of competitiveness. Nonetheless, some critical success factors are hampering KM implementation, such as a lack of a KM strategy, cultural aspects, leadership, and technology. In this paper, we focused on the KM implementation within Small and Medium Enterprises (SMEs) by exploring Participatory Design potential to KM implementation in SMEs, by focusing on information technology as a critical success factor and exploring some organizational culture characteristics that can help in this context. To achieve this goal, we conducted an ethnographic study in the real environment of a consulting firm which is starting its own KM project. Our results show that Participatory Design might be recommended to SMEs' KM implementation, by taking advantage of already available, but underused technological tools.
Pricing Differentially Private Smart Meter Data in Distribution Networks

More electrification in the mobility and building sectors, as well as increased electricity generation from distributed renewable energy sources such as household photovoltaic systems, are two of the most promising paths toward a more environmentally sustainable energy system. Electric power distribution system operators (DSOs) must improve the network’s observability in this context to address a variety of technical and market-related concerns, including local network congestion, flexibility exchanges, and resource allocation. To achieve this goal, DSOs are installing smart meters at end-users’ locations, as well as measuring devices and monitoring systems on low and medium-voltage networks. Despite the fact that smart meters are an important part of this transformation, privacy laws prevent data from being used for anything other than normal operation and billing without the consent of end-users. We present a model for dealing with differentially private data from smart meters. After that, we present an optimization problem for pricing such differentially private smart meters data, taking into account the value generated for the DSO through state estimation. Using real-anonymous smart meter data from a DSO in Switzerland, we evaluate the effectiveness of our suggested mechanism for buying such differentially private data.
A homotopy-based wavelet method for extreme large bending analysis of heterogeneous anisotropic plate with variable thickness on orthotropic foundation

A novel homotopy-based decoupled wavelet numerical strategy is developed to investigate the large-deflection bending of variable-thickness anisotropic thin plate constituted by heterogeneous material resting on orthotropic foundation. The anisotropic plates are described undergoing sinusoidally initial imperfections with inhomogeneous thickness and exponentially varying elastic modules, while the subgrade reaction involves the nonlinear support effects by Winkler-type foundation along with Pasternak-type orthotropy. Geometric nonlinearity by uneven thickness and large deformation, material nonlinearity by heterogeneity and contact nonlinearity of orthotropic foundation are simultaneously considered with the highly coupled and strongly nonlinear variable-coefficient Von Kármán equations derived. The accuracy and convergence of the present formulation are validated in excellent agreement with published results, while the new results of extreme bending solutions are provided, which are few given by other methods and further verify the effectiveness and superiority of proposed wavelet procedures. Parametric investigations of influences of material orthotropy and inhomogeneity, unevenness of thickness, amplitude of initial imperfection along with orthotropy of nonlinear Winkler–Pasternak foundation have been studied, which are of great significance on the extremely large bending properties.
Investigating the use of UAUDG-VI in the mobile applications design for visually impaired considering accessibility, usability and UX

In the early stages of the APP development process, it is possible to consider quality aspects as Accessibility, Usability and User eXperience (UX) before the application is developed. This action reduces costs and avoids wasting time with rework. Moreover, part of the users of APPs are People who have some Visually Impairment (VIP). Therefore, to support designers to consider these quality aspects together during the design phase of APPs for VIP, we proposed the Usability, Accessibility and User eXperience Design Guidelines about Visual Impairment (UAUDG-VI) technique. To evaluate the 1st version of UAUDG-VI, a feasibility study was carried out with 24 students from a Requirements Engineering class. The study results identified suggestions for improvement of UAUDG-VI, generating a 2nd version of UAUDG-VI. After, an evaluation with 07 experts in HCI was carried out. Improvement suggestions were also identified, generating the 3rd version of UAUDG-VI. The main contribution of this paper is to encourage the scientific community to understand how the guidelines for new design technologies on quality have been proposed and evolved.
Longitudinal user experience studies in the IoT domain

User Experience (UX) is affected by users' emotions and motivations, among other subjective factors. Hence, changes in UX occur over time, according to the different contexts, and behavioral and internal user aspects. With the rising development of Internet of Things (IoT) systems to support daily activities and, at the same time, the increasing abandonment of these systems, the importance of ensuring good UX levels over time has become an even greater need. In this context, we conducted a snowballing procedure and systematic literature mapping to understand whether and how long-term UX evaluations have been conducted in IoT and the challenges and opportunities in conducting longitudinal studies in this field. We analyzed 55 papers that reported longitudinal evaluations in different IoT systems and 14 papers that reported UX evaluations in the IoT context. We also interviewed five software engineering professionals to learn about their IoT user-testing and evaluation perspectives. Our results provide a panorama of the conduction of longitudinal IoT studies and a glance at the directions the research on UX evaluation for IoT systems is taking. Hence, we indicate future research opportunities for evaluating long-term UX in IoT systems through the longitudinal approach, considering the complexity of these systems and their impact on users' everyday lives.
Embodiment in interactive installations

Ubiquitous and pervasive technologies, alongside the expanding role of the computer in our lives, present new dimensions to the concept of embodiment within the Human-Computer Interaction (HCI) field. Interactive installations, in particular, afford the technological and conceptual exploration of embodiment due to a high degree of creative freedom and more open objectives, making it a promising context to be investigated. In this paper, we present a systematic literature review on approaches to embodiment in the context of interactive installations. We searched four major digital libraries and screened a total of 3245 studies following a protocol described in the paper. The final selection consists of 67 entries. Our analysis shows a multitude of theoretical and practical approaches to embodiment in interactive installations, but also significant compatibility and combined use. We present detailed summaries of and discussion on how embodiment is conceptualized, put into practice, and what technologies are involved, revealing new perspectives and possibilities for investigating embodiment in HCI.
Hidden things and implicit interaction

Invisibility in Ubiquitous Computing (UbiComp) and Internet of Things (IoT) systems refers to the merge of technology in the environment or the interaction workload decrease so that users can focus on their daily activities. It has been a fundamental attribute for these systems since the UbiComp advent and has received different names and definitions over time. However, there is a lack of retrospective studies investigating how these perspectives contributed to the current concept of Invisibility. Therefore, this paper aims to understand how Invisibility evolved as a quality-in-use attribute. We applied the snowballing method to search for literature evidence about the attributes and interaction evaluation approaches related to Invisibility. We identified eight related attributes: Intelligibility, Accuracy, Control, Customization, Non-obtrusiveness, Diffusion, Transparency, and Understandability. Also, we observed that the existing evaluation approaches consider only part of these attributes. Our findings suggest opportunities to improve the approaches and for future research.
PTMOL

Online Social Networks (OSNs) have become one of the principal technological phenomena of the Web, gaining an eminent popularity among its users. With the growing worldwide expansion of OSN services, people have devoted time and effort to maintaining and manipulating their online identity on these systems. However, the processing of personal data through these networks has exposed users to various privacy threats. Consequently, new solutions need to be developed for addressing the threat scenarios to which a user is potentially exposed. In this sense, this paper proposes PTMOL (Privacy Threat MOdeling Language), an approach for modeling privacy threats in OSN domain. The proposed language aims to support the capture, organization and analysis of specific privacy threats that a user is exposed to when sharing assets in a social application, also enabling the definition of countermeasures to prevent or mitigate the effects of threat scenarios. The first language version has undergone a preliminary empirical study that identified its validity as a modeling language. The results indicate that the use of the language is potentially useful for identifying real privacy threats due to its exploratory and reflexive nature. We expect to contribute to support designers in making more preemptive decisions about user privacy risk, helping them to introduce privacy early in the development cycle of social applications.