Reviewerly/exHarmony huggingface dataset:
Design and Evaluation of a Smart Environment with a Conversational Agent for Mindful Eating

The field of Human-Food Interaction focuses on creating and evaluating HCI interactions around food. Increasingly, the use of smartphones is part of people’s eating experience which leads to distractions and unawareness on the amount and quality of food that is eaten. In contrast, mindful eating promotes people to focus completely on their food, reducing distractions. We present an experiment in which we evaluate a smart environment for mindful eating/drinking aimed at enhancing the hedonic experience. A conversational agent acts as a hub for the smart environment and guides the user in the preparation and appreciation of tea and its pairing with a snack. We compared how independent participants assessed the system under two conditions: mindful (n = 10) and music (n = 12). Participants who interacted with the mindful conversational agent rated the system more likable in comparison with the agent that played music. Similarly, those in the mindful condition reported increased appreciation of the colors and smells of their tea. Additionally, our results show that all participants (n = 22) perceived the system as accurate, likable, quick to react, and promotes awareness. Conversely, participants rated the system low in annoyance, cognitive demand, and distraction.
Improving User Intent Detection in Urdu Web Queries with Capsule Net Architectures

Detecting the communicative intent behind user queries is critically required by search engines to understand a user’s search goal and retrieve the desired results. Due to increased web searching in local languages, there is an emerging need to support the language understanding for languages other than English. This article presents a distinctive, capsule neural network architecture for intent detection from search queries in Urdu, a widely spoken South Asian language. The proposed two-tiered capsule network utilizes LSTM cells and an iterative routing mechanism between the capsules to effectively discriminate diversely expressed search intents. Since no Urdu queries dataset is available, a benchmark intent-annotated dataset of 11,751 queries was developed, incorporating 11 query domains and annotated with Broder’s intent taxonomy (i.e., navigational, transactional and informational intents). Through rigorous experimentation, the proposed model attained the state of the art accuracy of 91.12%, significantly improving upon several alternate classification techniques and strong baselines. An error analysis revealed systematic error patterns owing to a class imbalance and large lexical variability in Urdu web queries.
Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspective

The healthcare sector is very interested in machine learning (ML) and artificial intelligence (AI). Nevertheless, applying AI applications in scientific contexts is difficult due to explainability issues. Explainable AI (XAI) has been studied as a potential remedy for the problems with current AI methods. The usage of ML with XAI may be capable of both explaining models and making judgments, in contrast to AI techniques like deep learning. Computer applications called medical decision support systems (MDSS) affect the decisions doctors make regarding certain patients at a specific moment. MDSS has played a crucial role in systems&apos; attempts to improve patient safety and the standard of care, particularly for non-communicable illnesses. They have moreover been a crucial prerequisite for effectively utilizing electronic healthcare (EHRs) data. This chapter offers a broad overview of the application of XAI in MDSS toward various infectious diseases, summarizes recent research on the use and effects of MDSS in healthcare with regard to non-communicable diseases, and offers suggestions for users to keep in mind as these systems are incorporated into healthcare systems and utilized outside of contexts for research and development.
MagInfoNet: Magnitude Estimation Using Seismic Information Augmentation and Graph Transformer

Abstract In this study, we propose a reliable data‐driven tool, MagInfoNet, to enhance the accuracy of magnitude estimation. Its architecture was assembled using the Pre‐Inform and Mag‐Pred modules to replace and update the key functions of traditional seismic analysis workflows. The Pre‐Inform module with the residual network was used for data pretreatment by combining the intrinsic characteristics of seismic signals with the potential features of the arrival and travel times. Meanwhile, using a graph transformer with an improved cyclic graph, the Mag‐Pred module was used to calculate magnitudes by the preprocessed information and the autocorrelation of seismic time series. Training and testing data were randomly selected from the Stanford Earthquake Data Set. The results show that the estimation accuracy, generalization, and robustness of the proposed MagInfoNet are better than those of three machine learning models. Besides, MagInfoNet can perform better for those samples with larger epicentral distances, enhancing the monitoring capacity of existing system for earthquake events in remote areas. Finally, we discuss the interpretability of the explainable MagInfoNet to verify the role of advanced neural network modules.
Phase retrieval of two random phase-shifting interferograms using Zernike coefficient extraction network

This paper proposes a deep learning method for phase retrieval from two interferograms. The proposed method converts phase retrieval into the Zernike coefficient extraction problem, which can achieve Zernike coefficient extraction from two interferograms with random phase shifts. After knowing Zernike coefficients, the phase distribution can be retrieved using Zernike polynomials. The pre-filtering and phase unwrapping process are not required using the proposed method. The simulated data are analyzed, and the root mean square (RMS) of phase error reaches 0.01 λ. The effectiveness of the method is verified by the measured data.
PUPVOTE: Blockchain-Based Voting System Using NEAR Protocol

University student elections are usually held using paper ballots [2] or online survey forms [4]–[7] where security is a concern [8]. This paper presents the creation of a blockchain-based voting system as a solution for the security issues of university student council elections. Blockchain is a type of distributed ledger technology where security is improved by storing transactions in several independent storages [10] [11]. In this study, NEAR Protocol was used in creating the voting system. It was simulated in the NEAR Testnet and as-pect was used to check the smart contract operation. Finally, ISO 25010 was used to check the overall performance of the voting system. As-pect testing showed that the smart contract works as intended and the voting system passed the ISO 25010 product quality evaluation.
Quantization Aware Training with Order Strategy for CNN

In this paper, we present a novel quantization-aware training method proceeding in a specific order in deep learning network. Conventional quantization techniques generally perform all layers at the same time. However, quantization sensitivity is different for each layer. For example, a quantization error increases as the quantizing bit-widths decrease, therefore the quantized model is hard to converge in the training process. To handle the issue, we propose the layer selection module which controls the order of layers to be quantized. Specifically, each layer is sequentially quantized in the order of selection so that the quantized model can be converged effectively. In our experiments, we show the quantization results on the CIFAR-10 image dataset. We also analyze the results between the different selection methods and verify the effectiveness of our proposed method.
CODE: A Moving-Window-Based Framework for Detecting Concept Drift in Software Defect Prediction

Concept drift (CD) refers to data distributions that may vary after a minimum stable period. CD negatively influences models’ performance of software defect prediction (SDP) trained on past datasets when applied to the new datasets. Based on previous studies of SDP, it is confirmed that the accuracy of prediction models is negatively affected due to changes in data distributions. Moreover, cross-version (CV) defect data are naturally asymmetric due to the nature of their class imbalance. In this paper, a moving window-based concept-drift detection (CODE) framework is proposed to detect CD in chronologically asymmetric defective datasets and to investigate the feasibility of alleviating CD from the data. The proposed CODE framework consists of four steps, in which the first pre-processes the defect datasets and forms CV chronological data, the second constructs the CV defect models, the third calculates the test statistics, and the fourth provides a hypothesis-test-based CD detection method. In prior studies of SDP, it is observed that in an effort to make the data more symmetric, class-rebalancing techniques are utilized, and this improves the prediction performance of the models. The ability of the CODE framework is demonstrated by conducting experiments on 36 versions of 10 software projects. Some of the key findings are: (1) Up to 50% of the chronological-defect datasets are drift-prone while applying the most popular classifiers used from the SDP literature. (2) The class-rebalancing techniques had a positive impact on the prediction performance for CVDP by correctly classifying the CV defective modules and detected CD by up to 31% on the resampled datasets.
Narrative Visualization with Augmented Reality

The following study addresses, from a design perspective, narrative visualization using augmented reality (AR) in real physical spaces, and specifically in spaces with no semantic relation with the represented data. We intend to identify the aspects augmented reality adds, as narrative possibilities, to data visualization. Particularly, we seek to identify the aspects augmented reality introduces regarding the three dimensions of narrative visualization—view, focus and sequence. For this purpose, we adopted a comparative analysis of a set of fifty case studies, specifically, narrative visualizations using augmented reality from a journalistic scope, where narrative is a key feature. Despite the strong explanatory character that characterizes the set of analyzed cases, which sometimes limits the user’s agency, there is a strong interactive factor. It was found that augmented reality can expand the narrative possibilities in the three dimensions mentioned—view, focus and sequence—but especially regarding visual strategies where simulation plays an essential role. As a visual strategy, simulation can provide the context for communication or be the object of communication itself, as a replica.
Estimation of Risk Factors Related to Heart Attack with Xgboost That Machine Learning Model

Objective: The objective of this work is to classify heart attack cases using the open-access heart attack dataset and one of the machine learning techniques called XGBoost. Another aim is to reveal the risk factors associated with having a heart attack as a result of the modeling and to associate these factors with heart attack.Methods: In the study, modeling was done with the XGBoost method using an open access data set including the factors associated with heart attack. Model results were evaluated with accuracy, balanced accuracy, specificity, positive predictive value, negative predictive value, and F1-score performance metrics. In addition, 10-fold cross-validation method was used in the modeling phase. Finally, variable importance values were obtained by modeling. Results: Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score from by XGBoost modeling were 89.4%, 89.4%, 88.4%, 90.3%, 88.4%, 90.3%, and 88.4%, respectively. According to the variable importance values obtained for the input variables in the data set examined in this study, thal2, oldpeak, thal3, ca1, and exang1 were obtained as the most important variables associated with heart attack.Conclusions: With the machine learning model used, the heart attack dataset was classified quite successfully, and the associated risk factors were revealed. Machine learning models can be used as clinical decision support systems for early diagnosis and treatment.
Integrity of virtual testing for crash protection

The interest in virtual testing is globally rapidly increasing because of several advantages compared to physical tests in laboratories. In the area of passive car safety, finite element simulations can be used to get further insights, use more biofidelic human models and make the overall assessment more robust by incorporating more variety in the virtual testing load cases. For a successful implementation of virtual testing in regulations or consumer information, the integrity of the procedure has to be ensured. As car simulation models used within the virtual testing are usually not shared with the evaluation institutions due to intellectual property (IP) issues, this is a challenging task. Stringent validation and certification procedures are needed and it has to be ensured that the models used in these steps are the same as the ones used for the virtual testing. In this paper, we developed a secure procedure for model version control. Through analysis of possible threats for both sides, car manufacturer and evaluation institution, we defined requirements, which the new procedure should satisfy. These requirements state that the integrity and authenticity of all shared documents should be protected, as well as the confidentiality of the simulation model. By considering all prerequisites, we developed an architecture for a new procedure. This architecture uses cryptographic algorithms such as hash functions and digital signatures to ensure integrity and authenticity, as well as secure computation mechanisms such as Intel Software Guard Extensions (SGX). In our proof-of-concept implementation, we demonstrated how a secure wrapper around LS-DYNA can produce a signed report to authenticate the input model files based on a hash tree and link them to the simulation results. The evaluation institution can use a matching verification tool to verify that the models were not manipulated compared to other simulation runs or the qualification process. The developed procedure can be used for trustworthy implementation of virtual testing into consumer information or regulation for the assessment of car safety with strengthened integrity. Further research is needed to develop comparable procedures for other simulation software packages or ideally integrate it directly into the simulation software.
Western Experience of Risk Management in Higher Education

The Western experience of risk management at the university is analyzed. The difficulties and problems of implementing this methodology at the university are considered. The main risks of the university and the causes of their occurrence are studied.
Towards Automated Updates of Software Dependencies

Modern software applications use existing software components to fast forward the development process, resulting in software dependencies. These components will evolve for various reasons and could also be updated to address critical issues existing in the component, like vulnerabilities that could impact the software that depend on them. Therefore, an application’s dependencies should be updated to their latest version to avoid these issues. However, changes in a software component can cause critical failures in the dependent software if the new version includes incompatibilities compared to its previous version. These incompatibilities between two library versions are known as breaking changes. Each software system that depends on the component will need to update its code to accommodate these breaking changes, making it difficult to automate the update process. This research analyzes a set of dependency updates and code changes made in response to fixing breaking changes in Java projects. The breaking changes will be used to create a comprehensive taxonomy of the types of breaking changes that occur in Java projects. The data gathered on the dependency updates will be analyzed to identify patterns that can be applied across different projects to automate the dependency update process.
Graphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust Model

The Cluster Weighted Robust Model (CWRM) is a recently introduced methodology to robustly estimate mixtures of regressions with random covariates. The CWRM allows users to flexibly perform regression clustering, safeguarding it against data contamination and spurious solutions. Nonetheless, the resulting solution depends on the chosen number of components in the mixture, the percentage of impartial trimming, the degree of heteroscedasticity of the errors around the regression lines and of the clusters in the explanatory variables. Therefore, an appropriate model selection is crucially required. Such a complex modeling task may generate several “legitimate” solutions: each one derived from a distinct hyperparameters specification. The present article introduces a two step-monitoring procedure to help users effectively explore such a vast model space. The first phase uncovers the most appropriate percentages of trimming, whilst the second phase explores the whole set of solutions, conditioning on the outcome derived from the previous step. The final output singles out a set of “top” solutions, whose optimality, stability and validity is assessed. Novel graphical and computational tools—specifically tailored for the CWRM framework—will help the user make an educated choice among the optimal solutions. Three examples on real datasets showcase our proposal in action. Supplementary files for this article are available online.
The Influence of Competency-Based VR Learning Materials on Students’ Problem-Solving Behavioral Intentions—Taking Environmental Issues in Junior High Schools as an Example

With the increasing price–performance ratio of virtual reality equipment, there has been an increase in research on the use of VR in education. The learning content of biology courses for junior high schools often introduces the interaction between organisms and their surroundings. Through the interactive design of VR learning materials, students can enhance their understandings of the impact of human behaviors on the environment. They can become immersed in the virtual world using a virtual camera to explore environmental pollution and record pollution sources, which exist in their surrounding environment. In this study, one-group pre- and post-test designs were adopted to collect and analyze data on students’ reactions before and after implementation of the VR learning course for environmental issues in a junior high school. The questionnaires were analyzed to evaluate the impact on course identity and behavioral intentions of students regarding problem-solving. The results reveal the VR interactive learning course has positive effects on both the course identity and the students’ behavioral intentions for problem-solving.
Power of the Sine Hamiltonian Operator for Estimating the Eigenstate Energies on Quantum Computers

Quantum computers have been shown to have tremendous potential in solving difficult problems in quantum chemistry. In this paper, we propose a new classical-quantum hybrid method, named as power of sine Hamiltonian operator (PSHO), to evaluate the eigenvalues of a given Hamiltonian (Ĥ). In PSHO, for any reference state |φ0⟩, the normalized energy of the sinn(H^τ)|φ0⟩ state can be determined. With the increase of the power, the initial reference state can converge to the eigenstate with the largest |sin(Eiτ)| value in the coefficients of the expansion of |φ0⟩, and the normalized energy of the sinn(H^τ)|φ0⟩ state converges to Ei. The ground- and excited-state energies of a Hamiltonian can be determined by taking different τ values. The performance of the PSHO method is demonstrated by numerical calculations of the H4 and LiH molecules. Compared with the current popular variational quantum eigensolver method, PSHO does not need to design the ansatz circuits and avoids the complex nonlinear optimization problems. PSHO has great application potential in near-term quantum devices.
Modern Chinese Diachronic Corpus Construction and the Language Development Based on a 77-year Corpus

Based on a diachronic corpus spanning 77 years from 1872 to 1949, this paper investigates the characters and words used in Chinese newspapers. The Zipfian distribution in word use can be seen in the corpus, and the top frequency of words varies dramatically throughout epochs. The frequency of word types and tokens exhibits an inverted V-shaped trend. In terms of char/word entropy, a similar tendency has been discovered. Words that existed only once in history do well in representing linguistic life in the period. At the same time, the proportion of new words in the entire corpus is decreasing, reflecting the steady stabilizing of word growth.
Scaling Up Face Masks Classification Using a Deep Neural Network and Classical Method Inspired Hybrid Technique

Classification of persons wearing and not wearing face masks in images has emerged as a new computer vision problem during the COVID-19 pandemic.In order to address this problem and scale up the research in this domain, in this paper a hybrid technique by employing ResNet-101 and multi-layer perceptron (MLP) classifier has been proposed.The proposed technique is tested and validated on a self-created face masks classification dataset and a standard dataset.On self-created dataset, the proposed technique achieved a classification accuracy of 97.3%.To embrace the proposed technique, six other state-of-the-art CNN feature extractors with six other classical machine learning classifiers have been tested and compared with the proposed technique.The proposed technique achieved better classification accuracy and 1-6% higher precision, recall, and F1 score as compared to other tested deep feature extractors and machine learning classifiers.
Analyzing syntactic constructs of Java programs with machine learning

The massive number of open-source projects in public repositories has notably increased in the last years. Such repositories represent valuable information to be mined for different purposes, such as documenting recurrent syntactic constructs, analyzing the particular constructs used by experts and beginners, using them to teach programming and to detect bad programming practices, and building programming tools such as decompilers, Integrated Development Environments or Intelligent Tutoring Systems. An inherent problem of source code is that its syntactic information is represented with tree structures, while traditional machine learning algorithms use n-dimensional datasets. Therefore, we present a feature engineering process to translate tree structures into homogeneous and heterogeneous n-dimensional datasets to be mined. Then, we run different interpretable (supervised and unsupervised) machine learning algorithms to mine the syntactic information of more than 17 million syntactic constructs in Java code. The results reveal interesting information such as the Java constructs that are barely (and widely) used (e.g., bitwise operators, union types and static blocks), different language features and patterns mostly (and barely) used by beginners (and experts), the discovery of particular types of source code (e.g., helper or utility classes, data transfer objects and too complex abstractions), and how complexity is an inherent characteristic in some clusters of syntactic constructs.