Reviewerly/exHarmony huggingface dataset:
Searching for Computing's Soul: Professional Practice and the Future of Computing Education

Computing has repeatedly proven its ability to change the world. Each new advancement of technology provides more power, speed, and efficiency, unleashing changes that would seem almost magical only a few years before. But even as each new thing becomes everyday, it also unleashes new challenges -- unexpected ethical dilemmas and often worsening cultural, environmental, and social injustices. As computing educators, we like to bask in the glow of our graduates' innovative abilities but we must also remain complicit in computing's capacity for harm. We might teach ideas of computing ethics or sustainability, but these mostly get relegated to soft skills ''thank goodness I've finished that ethics course, now can I get back to computing?''. I argue this problem can largely be traced back to computing's lack of purpose -- or an immature notion of a computing profession that lacks its own set of values and so accepts by default the neoliberal role as an enabler of efficiency. In this talk, I take a professional practice approach to consider how we might better integrate a hopeful and regenerative approach to computing education. I describe a professional framework of practice and its application in ethics, sustainability and decolonising computing and then provide examples of how these can be better integrated into computing education. I conclude with a manifesto for computing education.
Rethinking Single Sign-On: A Reliable and Privacy-Preserving Alternative with Verifiable Credentials

Single sign-on (SSO) has provided convenience to users in the web domain as it can authorize a user to access various resource providers (RPs) using the identity provider (IdP)'s unified authentication portal. However, SSO also faces security problems including IdP single-point failure and the privacy associated with identity linkage. In this paper, we present the initial design of an alternative SSO solution called VC-SSO to address the security and privacy problems while preserving SSO's usability. VC-SSO leverages the recently emerged decentralized identifier (DID) and verifiable credential (VC) framework in that a user only needs to authenticate with the IdP once to obtain a VC and then may generate multiple verifiable presentations (VPs) from the VC to access different RPs. This is based on the design that each RP has established a smart contract with the IdP specifying the service agreement and the VP schema for user authorization. We hope the proposed VC-SSO design marks the first step toward a future SSO system that provides strong reliability and privacy to users under adversarial conditions.
Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization

Summary A difficult problem in the service‐oriented computing paradigm is improving task scheduler policy or resource provisioning.In order to increase the performance of cloud applications, this article primarily focuses on tasks for resource mapping policy optimization. With the aim of reducing makespan and execution overhead and increasing the average resource utilization, we suggested an efficient independent task scheduler employing supervised neural networks in this paper. The suggested ANN‐based scheduler uses the status of the cloud environment and incoming tasks as inputs to determine the optimal computing resource for a given assignment as a result that assembles our goal. We proposed a novel algorithm in this paper that uses a hybrid methodology based on a swarm intelligence algorithm (PSO) in combination with a machine learning technique (ANN). PSO is used to prepare the train and test dataset for the neural network. Results clearly state that suggested work achieves significant improvement to considered algorithms in makespan (45%–55%), average VM utilization (15%–20%), and execution overhead(20%–30%).
New robust state‐estimation frameworks for angles‐only underwater target tracking problems

Summary Tracking a moving target using only angle measurements is a challenging and complex problem. This is because the measurements received from a sensor on a moving observer provide very little information about the target's movements. Additionally, there are uncertainties regarding the target's initial range and speed, which can further complicate the estimation process. The angle measurements themselves carry a lot of uncertainties as they can be affected by significant outliers, which do not fit the standard Gaussian distribution that estimation algorithms typically assume. Furthermore, the observer's position can also be uncertain due to various environmental factors. To address these challenges and to propose a robust, unified and accurate solution, two new estimation frameworks are proposed. They involve making use of a range and speed parametrization approach to compensate for the initial uncertainty in the target's range and speed. They also involve using a maximum correntropy (MC) criterion and a numerically stable centered error entropy (CEE) criterion to deal with measurement outliers and the observer motion uncertainties. The performance of the proposed frameworks is evaluated by comparing them to the traditional unscented Kalman filter (UKF) and its parametrized versions using different performance metrics. The simulations conducted showed that the developed algorithms perform better in terms of estimation accuracy than conventional methods. Specifically, the second framework based on the CEE criterion outperformed MC‐based estimators.
Investigating Factors Affecting English Language Engagement among the Law Department Learners in Papua: An Empirical Analysis”

The objective of the study is to investigate the factors affecting English Language Engagement among the Law Department Learners in Papua. This study is qualitative by implementing a cross-sectional design to capture a snapshot of English language engagement among the Law Department Learners in Papua. A diverse sample of the Law Department Learners in Papua from multiple schools had been selected. The sample included learners from different grades, socio-economic backgrounds, and language proficiency levels in the Law Department Learners in Papua. Qualitative data from open-ended questions were subjected to thematic analysis to identify recurring patterns and themes related to intrinsic and extrinsic factors. This study confirms that conventional monotony in instructional materials and insufficient reading practice are the main factors that make the Law Department Learners in Papua lazy to learn English in the classroom. It is recommended to delve deeper into the identified factors of conventional monotony in instructional materials and insufficient reading practice as contributors to students' lack of motivation in learning English in the classroom.&#x0D;
Systematic model bank determination approach for nonlinear systems using gap metric and stability margin

This paper introduces a novel systematic approach for designing multimodel controller tailored to nonlinear systems. Our methodology focuses on the precise selection of local controllers, aligning their performance with specific requirements while minimizing redundancy. We achieve this through a two-fold process: first, the creation of an initial multimodel bank using the Self-Organizing Map (SOM) algorithm. Then, we employ gap metrics to assess the similarity between linear subsystems and hierarchical agglomerative clustering to group local models. This process leads to the identification of model sets for aggregation. Finally, we use stability margins as a guidance to get the reduced bank to elaborate the multimodel controller, ensuring robust stability. The main advantages of our method lie in the elimination of redundancy, simplification of the controller structure and the guarantee of robust stability. Three highly nonlinear processes are studied to demonstrate the efficiency of the proposed multimodel control approach.
Autonomy, Power and the Special Case of Scarcity: Consumer Adoption of Highly Autonomous Artificial Intelligence

Abstract Unlike previous generations of artificial intelligence (AI), AI assistants today can autonomously perform actions without human input or intervention. Several studies have proposed but not tested the notion that increased levels of AI autonomy may ultimately conflict with consumers’ fundamental need for autonomy themselves. Across five experiments ( N = 1981), including representative samples and pre‐registered hypotheses, we investigate consumer responses to high (vs. low) AI autonomy in the context of online shopping. The results show a pronounced negative effect of high AI autonomy on consumers’ adoption intentions – an effect mediated by consumers’ relative state of powerlessness in the presence of high AI autonomy. However, when consumers face situations characterized by scarcity, such as when preferred options are being sold out rapidly (e.g. Black Friday), the aversive aspects of high (vs. low) AI autonomy are attenuated. Together, these findings offer novel insights regarding whether, when and why consumers are willing to adopt high (vs. low)‐autonomy AI assistants in online shopping settings.
Multi-Dimensional Cloud Model-Based Assessment and Its Application to the Risk of Supply Chain Financial Companies

The multi-dimensional cloud model is proposed as the expansion of the one-dimensional cloud model. The features of ambiguity and stochasticity in complex information situations are considered; thus, this optimized model can be utilized upon multiple value classifications and ordering via which the objects' attributes of physical and social can be reflected. Therefore, this promoted model is wildly used. This paper provides a knowledge graph by reviewing the theoretical research of the multi-dimensional cloud model and its related bibliographies, and Cite Space is applied here to give a visualization conclusion. In recent years, a multitude of theories and methods have emerged to address the challenges posed by fuzzy and stochastic uncertainty in various domains, such as image segmentation, data mining, prediction techniques, and comprehensive evaluation of multiple metrics and dimensions using uncertain linguistic variables.
Indicators for Organizational Digital Transformation in the Thai University Context

Organizational digital transformation comprises a number of significant factors for measuring success, each of which involves different evaluating criteria. This research was aimed at developing indicators and criteria behind Thai universities' organizational digital transformation by the use of the quantitative research method and a questionnaire for data collection. The informants included administrators of six Thai universities: 303 high-level administrators, 174 middle-level administrators, and 18 low-level administrators. From the total of 495 administrators, 262 completed and returned the questionnaire. Analyses were then performed based on the statistics showing frequencies and percentages. The findings showed nine key indicators, nine sub-indicators, and 10 evaluating criteria that are appropriate for evaluating the work outcomes in the dimension of digital transformation. The most appropriate indicators were the digital strategies and personnel. The results would benefit administrators, academics, researchers, and the officers involved in deploying the work of organizational digital transformation under the educational context. The benefits would be in planning and developing the policy in digital transformation of Thai universities that is in the right direction and more clarified. Other institutions would also be able to effectively and successfully apply the indicators in their digital transformation process.
Optimal compactness of fractional Fourier domain characterizes frequency modulated signals

The Fourier transform (FT) is a mathematical tool widely used in signal processing applications; however, it presents limitations when dealing with non-stationary time series. By considering the fractional powers of the FT operator, a generalized version is obtained known as the fractional FT. This transformation allows free rotations of the time–frequency plane that can be exploited for processing frequency modulated signals. This work addresses the problem of characterizing noisy, multicomponent, and non-linear frequency modulated signals through a proper order of the fractional FT, whose kernel consists of a chirp with linear frequency modulation. The estimation of the optimal fractional FT order obeys a strategy that includes the quantification of the compactness of fractional Fourier domains and the search for the order that leads to the most compact domain. For this purpose, five compactness measures are assessed in combination with four different optimization algorithms. Numerical experiments are performed on synthetic signals, generated under distinct frequency modulation conditions, and on real acoustic signals. The results reveal that the spectral second moment and the spectral entropy provide robust and reliable measures of the compactness of the fractional Fourier domain. These metrics enable an effective computation of the optimal fractional order that describes the frequency modulation content of the underlying signal. The optimization algorithms assessed in this study yield similar estimations of the optimal fractional order, yet the coarse-to-fine algorithm is more efficient in terms of computation time, followed by the particle swarm optimization algorithm. Moreover, it is verified that the strategy can be adopted for extracting dynamical information of the frequency modulation content from synthetic signals with multiple linear and non-linear components and from real acoustic data, e.g., bat and bird recordings. The extensive assessment of the signal processing strategy based on the fractional FT outlined in this work provides relevant information for exploring further applications with time series captured when studying complex and non-stationary processes, such as biological, medical, or economic systems.
Detecting Changes in Crowdsourced Social Media Images

We propose a novel service framework to detect changes in crowdsourced images. We use a service-oriented approach to model and represent crowdsourced images as image services. Non-functional attributes of an image service are leveraged to detect changes in an image. The changes are reported in form of a version tree. The version tree is constructed in a way that it reflects the extent of changes introduced in different versions. Afterwards, we find semantic differences in between different versions to determine the extent of changes introduced in a specific version. Preliminary experimental results demonstrate the effectiveness of the proposed approach.
Approximate symmetries and quantum error correction

Abstract Quantum error correction (QEC) is a key concept in quantum computation as well as many areas of physics. There are fundamental tensions between continuous symmetries and QEC. One vital situation is unfolded by the Eastin–Knill theorem, which forbids the existence of QEC codes that admit transversal continuous symmetry actions (transformations). Here, we systematically study the competition between continuous symmetries and QEC in a quantitative manner. We first define a series of meaningful measures of approximate symmetries motivated from different perspectives, and then establish a series of trade-off bounds between them and QEC accuracy utilizing multiple different methods. Remarkably, the results allow us to derive general quantitative limitations of transversally implementable logical gates, an important topic in fault-tolerant quantum computation. As concrete examples, we showcase two explicit types of quantum codes, obtained from quantum Reed–Muller codes and thermodynamic codes, respectively, that nearly saturate our bounds. Finally, we discuss several potential applications of our results in physics.
Analysis of Formant Trajectories of a Speech Signal for the Purpose of Forensic Identification of a Foreign Speaker

The solution of the fundamental tasks of modern forensics (identification and verification of a person by voice and speech, as well as profiling of a person by voice) is based on a comprehensive study of phonograms during phonoscopic examination. Currently, the scope of forensic phonetics and speech acoustics is increasingly in demand, while there is still a range of problems that require additional in-depth research from specialists. For a long time, the problem of studying foreign speech in a language unfamiliar to an expert has been relevant and extremely difficult for modern forensics. This task is especially complicated in the study of languages that have been little studied, taking into account the linguistic specifics of the analyzed languages, in particular, Romani. In this regard, one of the most promising is the language-independent automated method of formant analysis proposed in this paper, which makes it possible to study the segment parameters of a speech signal by an expert who does not speak the language spoken by a suspect.
Prosodic Interaction Models in a Conversation

Spoken dialogue systems are increasingly being developed to process the linguistic aspect of communication, yet they meet difficulties to capture the complex dynamics of social interaction. In particular, prosodic accommodation of conversational partners governed by the need for social approval has proved to enhance effectiveness of conversation. Providing automatic systems with the capacity to exhibit accommodation could improve their efficiency and make machines more likable and user-friendly. This article investigates the occurrence of mutual adaptation in human-human interaction, presenting the results on the dynamics of prosodic convergence and divergence in naturally occurring spoken dialogues by means of pitch and pitch range. The converging points are recurring lexemes – keywords produced by both speakers – showing evidence for thematic and prosodic unity of accommodation. Three prosodic interaction models are observed: convergence – divergence; convergence – intensified convergence; divergence – convergence. The three models of interaction show structural variance, convergence being their common feature. The results of the Mann-Whitney test show evidence that in the middle of a dyad the distinction in pitch between interlocutors is significantly less than at the end.
Emotional Speech Recognition of Holocaust Survivors with Deep Neural Network Models for Russian Language

Recognition of highly emotional speech remains a challenging case of automatic speech recognition task. The aim of this article is to carry out experiments on highly emotional speech recognition by investigating oral history archives provided by the Yad Vashem foundation. The material consists of elderly peoples’ emotional speech full of accents and common language. We analyze and preprocess 26 h of publicly available video interviews with Holocaust survivors. Our objective was to develop a system able to perform emotional speech recognition based on deep neural network models. We present and evaluate the obtained results that contribute to the research field of oral history archives.
Post-processing of Translated Speech by Pole Modification and Residual Enhancement to Improve Perceptual Quality

The perceptual quality of translated speech depends on the quantity of speech data used for training. The translation speech quality is poor when the system is trained with less data. The quality improves by gradually adding more speech data for training. This work demonstrates the significance of post-processing of translated speech by signal processing for improving perceptual quality. Initially, the target speech original residual is used to replace the translated speech residual. It is then replaced using the weighted residual obtained by speech enhancement. The pole modification of translated speech is also done. Finally, both weighted residual and pole modifications are combined. All the experiments show improvement in perceptual quality.
Sounds of &lt;sil&gt;ence: Acoustics of Inhalation in Read Speech

In spoken conversations, inhalations are associated with the initiation of speech. In multiparty conversations, inhalations are crucial as they signal turn-taking. However, in read speech, inhalations are not considered as holding any crucial information and hence are often omitted from the analysis, marked with a <sil> annotation. In this work, we demonstrate that the acoustics of inhalations may not be completely ‘silent’ and contain useful cues correlated with the contiguous linguistic sounds. Using a read speech database of Mizo, we show that the acoustics of inhalations show prominent anticipatory effects and are often correlated with the formants of the following vowel sounds.
Candidate Speech Extraction from Multi-speaker Single-Channel Audio Interviews

Video interviews are increasingly common, initially due to travel restrictions during the pandemic and now extended for economic reasons. However, in many developing nations, limited bandwidth makes video interviews impractical, leading to a preference for telephonic interviews. These interviews are often recorded for audit and analysis purposes. A candidate’s performance in an interview depends not only on their knowledge but also on how they respond to the interviewer. Both the content of their responses and their communication skills influence the overall selection process. For any downstream interview analysis or automation, a reliable method is needed to identify speech segments spoken by the candidate in a multi-speaker, single channel interview conversation. In this paper, we propose a pipeline to accurately identify candidate speech segments. This pre-processing step is crucial for analyzing various aspects of a candidate’s interview performance, such as answer analysis, confidence level, and emotional expression.