Reviewerly/exHarmony huggingface dataset:
Job satisfaction in agile information systems development: A stakeholder perspective

Agile information systems development (ISD) claims to increase employees' job satisfaction. While previous research acknowledged increased job satisfaction among team members such as software developers, less attention has been paid to stakeholders in agile ISD. Furthermore, we lack evidence about the role of review meetings between team members and stakeholders. With the aim to tackle those current shortcomings, we set out to gain a deeper understanding on how agile ISD practices affect internal stakeholders' job satisfaction (SJS). Using a mixed methods approach, we identify predictors of SJS in an exploratory case study first. Second, we develop our theoretical model that was evaluated with a survey of agile ISD stakeholders. Findings of conditional process analysis show that agile practices positively affect SJS via perceived meaningfulness and interaction frequency. Our results provide evidence that collaboration between team members and stakeholders is crucial for enhancing SJS. We conclude that agile ISD practices have a positive effect on SJS. These findings have several implications for theory and offer a foundation for future research on stakeholders in agile ISD. Practical implications refer to the establishment of agile practices, meaningfulness at work and awareness for agile transformations.
Machine learning algorithm for cognitive engine

In the chapter, the machine learning algorithm is described for a cognitive engine and the relevant concepts of machine learning are discussed.
Model order reduction of a 3D biomechanical tongue model: a solution for real-time movement simulations to study speech motor control

Background and Objectives: This chapter presents results since our previous work on machine-learning based Model Order Reduction (MOR) method applied to a complex 3D Finite Element (FE) biomechanical model of the human tongue. This model is important to adress real-time simulation problems essential for future work on speech motor control. Methods: The proposed method is the same “a posteriori” MOR method as the one presented in our previous work but engaging within a more complex framework involving contacts and a reduced amount of training data. Results: The MOR method is evaluated for simulations involving only one muscle but with the complexity of contacts generating discontinuities in the boundary conditions. It is shown to be able to take into account, with sub-millimeter spatial accuracy, the behavior of the model with a reduced number of simulations for learning and extrapolating the prediction. Conclusion: Further evaluations of the MOR method will include tongue movements induced by multiple muscle activations and contact diversity. At this stage, our MOR method offers promising prospects for the use of the tongue model in a speech motor control context.
Metaheuristic algorithms

This chapter begins with a brief introduction to the basic ideas and historical development of metaheuristic algorithms. The aim in different periods was essentially the same: developing new metaheuristic algorithms. The main difference is that the focus is different between different algorithms, in the sense that, for example, in some algorithms, the focus is on using populations and mimicking social behavior; in other algorithms, the focus is on adjusting the parameter settings dynamically; and in others, the focus is on applying metaheuristic algorithms to parallel computing systems. A unified framework for metaheuristic algorithms composed of initialization, transition, evaluation, determination, and output operators is then presented to provide an integrated view of different metaheuristic algorithms. A comparison is made to emphasize the distinguishing features between metaheuristic algorithms, exhaustive search, and greedy algorithms.
Generative AI Art: Copyright Infringement and Fair Use

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Navigating Wanderland: Highlighting Off-Task Discussions in Classrooms

Off-task discussions during collaborative learning offer benefits such as alleviating boredom and strengthening social relationships, and are therefore of interest to learning scientists. However, identifying moments of off-task speech requires researchers to navigate massive amounts of conversational data, which can be laborious. We lay the groundwork for automatically identifying off-task segments in a conversation, which can then be qualitatively analyzed and coded. We focus on in-person, real-time dialog and introduce an annotation scheme that examines two facets of dialog typical to in-person classrooms: whether utterances are pertinent to the lesson, and whether utterances are pertinent to the classroom, more broadly. We then present two computational models for identifying off-task utterances.
Content Matters: A Computational Investigation into the Effectiveness of Retrieval Practice and Worked Examples

In this paper we argue that artificial intelligence models of learning can contribute precise theory to explain surprising student learning phenomena. In some past studies of student learning, practice produces better learning than studying examples, whereas other studies show the opposite result. We reconcile and explain this apparent contradiction by suggesting that retrieval practice and example study involve different learning cognitive processes, memorization and induction, respectively, and that each process is optimal for learning different types of knowledge. We implement and test this theoretical explanation by extending an AI model of human cognition — the Apprentice Learner Architecture (AL) — to include both memory and induction processes and comparing the behavior of the simulated learners with and without a forgetting mechanism to the behavior of human participants in a laboratory study. We show that, compared to simulated learners without forgetting, the behavior of simulated learners with forgetting matches that of human participants better. Simulated learners with forgetting learn best using retrieval practice in situations that emphasize memorization (such as learning facts or simple associations), whereas studying examples improves learning in situations where there are multiple pieces of information available and induction and generalization are necessary (such as when learning skills or procedures).
Towards Enriched Controllability for Educational Question Generation

Question Generation (QG) is a task within Natural Language Processing (NLP) that involves automatically generating questions given an input, typically composed of a text and a target answer. Recent work on QG aims to control the type of generated questions so that they meet educational needs. A remarkable example of controllability in educational QG is the generation of questions underlying certain narrative elements, e.g., causal relationship, outcome resolution, or prediction. This study aims to enrich controllability in QG by introducing a new guidance attribute: question explicitness. We propose to control the generation of explicit and implicit (wh)-questions from children-friendly stories. We show preliminary evidence of controlling QG via question explicitness alone and simultaneously with another target attribute: the question's narrative element. The code is publicly available at https://github.com/bernardoleite/question-generation-control .
Automatic Detection of Collaborative States in Small Groups Using Multimodal Features

Cultivating collaborative problem solving (CPS) skills in educational settings is critical in preparing students for the workforce. Monitoring and providing feedback to all groups is intractable for teachers in traditional classrooms but is potentially scalable with an AI agent who can observe and interact with groups. For this to be feasible, CPS moves need to first be detected, a difficult task even in constrained environments. In this paper, we detect CPS facets in relatively unconstrained contexts: an in-person group task where students freely move, interact, and manipulate physical objects. This is the first work to classify CPS in an unconstrained shared physical environment using multimodal features. Further, this lays the groundwork for employing such a solution in a classroom context, and establishes a foundation for integrating classroom agents into classrooms to assist with group work.
Moderating Role of Person’s Job Fit in the IT with Effectiveness of Agile Methodology on Project Success in Tanzania

Despite the importance of computing software, the process of developing software still faces various challenges.One of these is the proper methodology for developing software.This paper aims to analyze the multiple dimensions of software development and how Agile practices can affect them.To gather information about the various dimensions of software development, we surveyed software professionals from different backgrounds.The findings of this study will be used to analyze the effects of ASD on the software industry of Tanzania.The collected information is analyzed in two ways.The first involves summarizing the findings of the surveys.The results of the surveys are presented in a graphical manner.They show the opinions of the survey respondents on the effectiveness of Agile practices.We then used quantitative methods to analyze the data.The results of the surveys support the hypothesis that there is a strong relationship between the effectiveness of Agile practices and the various dimensions of software design.The importance of the job fit of a software development team and the use of agile practices is acknowledged by the findings of the survey.In terms of time, quality, cost, and stakeholder satisfaction, the fit of the team and ASD is very important for successful software development.The findings of this study can be generalized to other companies in the software industry of Tanzania.
Effect of Perspective and Visuo-Tactile Feedback in Virtual Reality-Based Posture Learning

Posture learning is required in rehabilitation and in sports such as yoga and martial arts. Virtual reality (VR) systems are being used to learn posture by superimposing the postures of the learner and instructor in cyberspace using avatars. In this study, we examined whether the presented perspective of the avatar (first-person vs. third-person perspective) and visuo-tactile feedback (tactile correct feedback + visual feedback vs. tactile incorrect feedback + visual feedback vs. visual only feedback) are effective for the posture learning. The results of an experiment (N = 24) suggested that use of the third-person perspective may result in accurate learning of the head position as compared with first-person perspective. Visuo-tactile feedback was found to improve the subjective rating on the ease of learning, while the presentation method in which tactile feedback is given when body position is correct was found to be more effective than tactile feedback given when body position is incorrect. The sense of agency was maintained at a high level under all conditions for perspective and visuo-tactile feedback and may have improved the learning accuracy of posture. The findings of this study are expected to contribute to the design of effective perspective and tactile presentation in VR-based motor learning.
Exploring the gamification of cybersecurity education in higher education institutions: An analytical study

Our world has become increasingly dependent on electronic technology. As most economic, cultural, and social activities are conducted in cyberspace, how to protect data from cyberattacks has arisen as a prominent challenge. Cybersecurity education and training that improves awareness among personnel is recognized as an effective approach. Higher education institutions (HEIs) have become prime cyberattack targets as they hold vast amounts of valuable research and personal data. This paper analyses the state of cybersecurity in HEIs and the problems of cybersecurity education, and proposes the solution of gamification of cybersecurity education. A detailed feasibility analysis and recommendations for developing cybersecurity education games are provided. This paper expands the theories of gamified cybersecurity education in China, and sheds light on enhancing the effectiveness of cybersecurity education in HEIs through games.
A Method to Plan the Path of a Robot Utilizing Deep Reinforcement Learning and Multi-Sensory Information Fusion

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Artificial Intelligence in Histologic Diagnosis of Ductal Carcinoma In Situ

A systematic review was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol to evaluate the diagnostic accuracy of artificial intelligence (AI) in ductal carcinoma in situ. Four databases were searched for articles up to December 2022: Embase, PubMed, Scopus, and Web of Science. 23 studies were included, and a search of grey literature was not performed. The following parameters were extracted: the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of each study. Statistical analysis of the included studies revealed that AI-assisted histopathological analysis is of high accuracy (83.78%), sensitivity (83.88%), and specificity (85.49%) and has a high positive predictive value (89.43%). Our results also reported that convolutional neural network (CNN) is the most commonly used mode of machine learning—21 models used only CNN, whereas 2 models used only support vector machines (SVM). On an average, CNN reported slightly higher accuracy and sensitivity (86.71% and 85.22%, respectively) than SVM (accuracy, 85.00%; sensitivity, 70.00%). When the 2 methods were combined, a mean accuracy of 82.52% and a mean sensitivity of 83.00% were achieved. The use of AI as a diagnostic adjunct can markedly improve the accuracy and efficiency of DCIS diagnosis and can, therefore, reduce pathologists’ workload.
Electronic learning among students at public universities in Ghana

This study was planned to establish the extent of the usage and benefits of mobile technologies to learn by distance learners at the University of Ghana and the University of Education, Winneba. The study adopted an explanatory sequential mixed-method design. Structured questionnaires were administered to 400 distance learners selected by multi-stage sampling technique and phone interviews involving 20 participants selected by random sampling technique to collect data. Using the Relative Important Index and Thematic analysis, the findings show that respondents not only agreed to all the mobile learning academic activities and benefits raised in this study as relevant but fundamental to their studies. The five most important mobile learning activities by students are communicating with colleagues for information, participating in online courses, doing coursework assignments, holding meetings among colleagues and instructors, and forming networks with peers on a social media platform. The highest of the first five highest-ranked benefits is finding relevant material for coursework with ease, studying at own pace in different environments, learning anywhere and anytime, receiving real-time information, and having quick access to coursework content. The findings of this study can encourage institutions offering distance education to build strategic electronic learning implementation plans and design guidelines by taking into consideration the highest-ranked important benefits and uses of mobile learning identified in this study.
Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis

Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. However, research has shown that the adversarial vulnerabilities of deep learning networks manifest themselves when DL is used for NLP tasks. Most mitigation techniques proposed to date are supervised—relying on adversarial retraining to improve the robustness—which is impractical. This work introduces a novel, unsupervised detection methodology for detecting adversarial inputs to NLP classifiers. In summary, we note that minimally perturbing an input to change a model’s output—a major strength of adversarial attacks—is a weakness that leaves unique statistical marks reflected in the cumulative contribution scores of the input. Particularly, we show that the cumulative contribution score, called CF-score, of adversarial inputs is generally greater than that of the clean inputs. We thus propose Con-Detect—a Contribution based Detection method—for detecting adversarial attacks against NLP classifiers. Con-Detect can be deployed with any classifier without having to retrain it. We experiment with multiple attackers—Text-bugger, Text-fooler, PWWS—on several architectures—MLP, CNN, LSTM, Hybrid CNN-RNN, BERT—trained for different classification tasks—IMDB sentiment classification, fake-news classification, AG news topic classification—under different threat models—Con-Detect-blind attacks, Con-Detect-aware attacks, and Con-Detect-adaptive attacks—and show that Con-Detect can reduce the attack success rate (ASR) of different attacks from 100% to as low as 0% for the best cases and ≈70% for the worst case. Even in the worst case, we note a 100% increase in the required number of queries and a 50% increase in the number of words perturbed, suggesting that Con-Detect is hard to evade.
Robust automatic modulation classification under noise mismatch

Abstract Automatic modulation classification plays a critical role in the intelligent reception of unknown wireless signals. In practice, the dynamic wireless environment brings a great challenge, and the actual test model is inconsistent with the training model. Therefore, aiming at the problem of noise mismatch, this paper proposes a new modulation classification method based on KD-GoogLeNet and Squeeze-Excitation (KD-GSENet). Using the k -dimensional tree, the complex wireless signals are converted into color images rather than normal constellations, which can enhance the classification features. Considering the attention block has the inherent advantage of assigning more weights to important features, this paper further uses it to improve the GoogLeNet. Finally, extensive experiments are presented including Gaussian noise, non-Gaussian noise, and the scenarios of noise mismatch. Numerical results verify the superior classification performance of the proposed KD-GSENet under different scenarios.
Phase congruency based on derivatives of circular symmetric Gaussian function: an efficient feature map for image quality assessment

Abstract Image quality assessment (IQA) has become a hot issue in the area of image processing, which aims to evaluate image quality automatically by a metric being consistent with subjective evaluation. The first stage of conventional IQA model design is the quality-aware feature selection. Taking advantages of early visual feature, Phase congruency (PC) operates in frequency domain to measure local structures such as edges, corners, lines, etc., by computing the local amplitudes and local energies in multiple scales. Conventional local PC features are calculated with log-Gabor-based filtrations in several orientations, and usually combined with other features for IQA model design. Generally, a directional filter is sensitive to the changes on specific direction, and insensitive to other directions. This leads to multi-directional calculation and much time consumption in practical applications. Recently, researchers suggested that spatially circular symmetric filters, such as gradient magnitude (GM) and Laplacian of Gaussian (LoG), are highly efficient quality features and hence have been widely used in various IQA model designs. With the odd-symmetric and even-symmetric properties of GM and LoG operators, the two features are a suitable pair for PC compositions and can be computed uniformly by a Gaussian function with one scale factor. In this regard, we propose to combine GM and LoG signals to construct a new PC model with non-directional property. With ability to catch different types of distortions, the proposed PC feature can be promoted to a full-reference IQA model simply with average pooling or standard deviation pooling, and shows state-of-the-art performance compared with existing methods. Furthermore, our proposed PC algorithm can take the place of conventional PC component in well-known FSIM metric, which achieves improved performance and spends less in computation cost. This study suggests that the proposed circular symmetric PC feature is a highly efficient quality feature and can be exclusively used in IQA model designs.
Ensemble reservoir computing for dynamical systems: prediction of phase-space stable region for hadron storage rings

Abstract We investigate the ability of an ensemble reservoir computing approach to predict the long-term behaviour of the phase-space region in which the motion of charged particles in hadron storage rings is bounded, the so-called dynamic aperture. Currently, the calculation of the phase-space stability region of hadron storage rings is performed through direct computer simulations, which are resource- and time-intensive processes. Echo State Networks (ESN) are a class of recurrent neural networks that are computationally effective, since they avoid backpropagation and require only cross-validation. Furthermore, they have been proven to be universal approximants of dynamical systems. In this paper, we present the performance reached by ESN based on an ensemble approach for the prediction of the phase-space stability region and compare it with analytical scaling laws based on the stability-time estimate of the Nekhoroshev theorem for Hamiltonian systems. We observe that the proposed ESN approach is capable of effectively predicting the time evolution of the extent of the dynamic aperture, improving the predictions by analytical scaling laws, thus providing an efficient surrogate model.
Enhancing privacy‐preserving mechanisms in Cloud storage: A novel conceptual framework

Summary Data privacy is critical for users who want to use Cloud storage services. There is a significant focus on Cloud service providers to address this need. However, in the evolving dynamic cyber‐space, privacy infractions are rising and pose threats to Cloud storage infrastructures. Several studies developed various models and techniques to ensure the privacy of Cloud storage contents. However, these models came with several shortages in the privacy‐preserving attributes they cover. Thus, this article identified a comprehensive set of Cloud data storage privacy‐preserving attributes to propose a flexible and efficient framework to handle the privacy problem. This framework uses a multi‐layer encryption storage structure and a one‐time password authentication technique. The findings of this article intend to help future communities to enhance existing techniques or develop new research‐based practical alternatives. Since Cloud computing is a rapidly growing technology, new privacy vulnerabilities emerge daily. Future research might confirm the findings of this article and test the suggested framework in different contexts.
Chemical design with GPU-based Ising machines

Ising machines are used to create molecules with desired properties. GPU-based Ising machines are shown to outperform qubit-based ones in terms of scalability.