Reviewerly/exHarmony huggingface dataset:
AI in education risk assessment mechanism analysis

Artificial intelligence (AI) has had a significant impact on several sectors and fields. Failure Modes and Effects Analysis (FMEA) is a powerful risk management and prevention tool that can help companies identify and address any weaknesses in their practices. The Risk Priority Number (RPN) approach has been criticized for its flaws since it calculates the product of severity, occurrence, and detection ratings of threats. By considering a variety of criteria and aspects, and by thoughtfully and carefully combining expert perspectives, the proposed FMEA model may more accurately evaluate the risks associated with AI in Education. Picture Fuzzy Sets (PFSs) and Grey Relational Analysis-TOPSIS (GRA-TOPSIS) are combined to achieve this. The goal of this research is to use an updated FMEA model to assess and rank the risk hierarchy of the seven identified hazards. According to the data, algorithmic risk is the most significant issue that requires urgent attention. Relevant constraints and suggestions are also provided to encourage research on the dangers of AI in Education. Suggestions include creating a national or international regulatory authority to control the use of artificial intelligence in education, promoting the use of the FMEA technique as a common framework for assessing the risks connected to AI in Education.
Integrating QuickBooks® in financial accounting classrooms: evidence from the UAE

The surge in technology adoption has transformed students into ‘digital natives’ who require innovative teaching methods to enhance their engagement and learning. This paper investigates the impact of integrating QuickBooks®, an accounting mobile app, on students’ learning, engagement, and interest in the course. It utilizes a survey questionnaire and focus group design to collect data from a sample of 160 undergraduate financial accounting students at a leading public university in the UAE. The findings reveal that students positively viewed the use of QuickBooks® on their learning experience. Students reported enhanced class engagement and interest in the course, which was otherwise viewed as generally dull. Using QuickBooks® in solving assessments was perceived more effective relative to pen-and-paper assessments and during uncertain times. This paper contributes to the literature on the impact of mobile technology in accounting education and suggests innovative tools to enhance the graduate attributes and employability for educators’ consideration.
Functional quantum abstract detecting systems

Abstract Quantum abstract detecting systems (QADS) provide a common framework to address detection problems in quantum computers. A particular QADS family, that of combinatorial QADS, has been proved to be useful for decision problems on eigenvalues or phase estimation methods. In this paper, we consider functional QADS, which not only have interesting theoretical properties (intrinsic detection ability, relation to the QFT), but also yield improved decision and phase estimation methods, as compared to combinatorial QADS. A first insight into the comparison with other phase estimation methods also shows promising results.
A multimodal deep learning architecture for smoking detection with a small data approach

Covert tobacco advertisements often raise regulatory measures. This paper presents that artificial intelligence, particularly deep learning, has great potential for detecting hidden advertising and allows unbiased, reproducible, and fair quantification of tobacco-related media content. We propose an integrated text and image processing model based on deep learning, generative methods, and human reinforcement, which can detect smoking cases in both textual and visual formats, even with little available training data. Our model can achieve 74% accuracy for images and 98% for text. Furthermore, our system integrates the possibility of expert intervention in the form of human reinforcement. Using the pre-trained multimodal, image, and text processing models available through deep learning makes it possible to detect smoking in different media even with few training data.
The Potential of Diffusion-Based Near-Infrared Image Colorization

Camera traps, an invaluable tool for biodiversity monitoring, capture wildlife activities day and night. In low-light conditions, near-infrared (NIR) imaging is commonly employed to capture images without disturbing animals. However, the reflection properties of NIR light differ from those of visible light in terms of chrominance and luminance, creating a notable gap in human perception. Thus, the objective is to enrich near-infrared images with colors, thereby bridging this domain gap. Conventional colorization techniques are ineffective due to the difference between NIR and visible light. Moreover, regular supervised learning methods cannot be applied because paired training data are rare. Solutions to such unpaired image-to-image translation problems currently commonly involve generative adversarial networks (GANs), but recently, diffusion models gained attention for their superior performance in various tasks. In response to this, we present a novel framework utilizing diffusion models for the colorization of NIR images. This framework allows efficient implementation of various methods for colorizing NIR images. We show NIR colorization is primarily controlled by the translation of the near-infrared intensities to those of visible light. The experimental evaluation of three implementations with increasing complexity shows that even a simple implementation inspired by visible-near-infrared (VIS-NIR) fusion rivals GANs. Moreover, we show that the third implementation is capable of outperforming GANs. With our study, we introduce an intersection field joining the research areas of diffusion models, NIR colorization, and VIS-NIR fusion.
Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys

Abstract Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern surveys are typically designed to permit quantification of data uncertainty through data quality metrics by using quality assurance and quality control (QA/QC) methods. However, these metrics, such as data accuracy and precision, are obtained through the data generation phase. Consequently, it is unclear how residual uncertainty in geochemical data can be minimized (denoised). This is a limitation to propagating uncertainty through downstream activities, particularly through complex models, which can result from the usage of artificial intelligence-based methods. This study aims to develop a deep learning-based method to examine and quantify uncertainty contained in geochemical survey data. Specifically, we demonstrate that: (1) autoencoders can reduce or modulate geochemical data uncertainty; (2) a reduction in uncertainty is observable in the spatial domain as a decrease of the nugget; and (3) a clear data reconstruction regime of the autoencoder can be identified that is strongly associated with data denoising, as opposed to the removal of useful events in data, such as meaningful geochemical anomalies. Our method to post-hoc denoising of geochemical data using deep learning is simple, clear and consistent, with the amount of denoising guided by highly interpretable metrics and existing frameworks of scientific data quality. Consequently, variably denoised data, as well as the original data, could be fed into a single downstream workflow (e.g., mapping, general data analysis or mineral prospectivity mapping), and the differences in the outcome can be subsequently quantified to propagate data uncertainty.
Resource Scheduling in URLLC and eMBB Coexistence Based on Dynamic Selection Numerology

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication.
Passive dynamical decoupling of trapped-ion qubits and qudits

We propose a method to dynamically decouple every magnetically sensitive hyperfine sublevel of a trapped ion from magnetic field noise, simultaneously using integrated circuits to adiabatically rotate its local quantization field. These integrated circuits allow passive adjustment of the effective polarization of any external (control or noise) field. By rotating the ion's quantization direction relative to this field's polarization, we can perform ``passive'' dynamical decoupling (PDD), inverting the linear Zeeman sensitivity of every hyperfine sublevel. This dynamically decouples the entire ion, rather than just a qubit subspace. Fundamentally, PDD drives the transition ${m}_{F}\ensuremath{\rightarrow}\ensuremath{-}{m}_{F}$ for every magnetic quantum number ${m}_{F}$ in the system---with only one operation---indicating it applies to qudits with constant overhead in the dimensionality of the qudit. We show how to perform pulsed and continuous PDD, weighing each technique's insensitivity to external magnetic fields versus their sensitivity to diabaticity and control errors. Finally, we show that we can tune the sinusoidal oscillation of the quantization axis to a motional mode of the crystal in order to perform a laser-free two-qubit gate that is insensitive to magnetic field noise.
Robust gates with spin-locked superconducting qubits

Dynamical decoupling is effective in reducing gate errors in most quantum computation platforms and is therefore projected to play an essential role in future fault-tolerant constructions. In superconducting circuits, however, it has proven difficult to utilize the benefits of dynamical decoupling. In this work, we present a theoretical proposal that incorporates a continuous version of dynamical decoupling, namely, spin locking, with a coupler-based CZ gate for transmons and provide analytical and numerical results that demonstrate its effectiveness.
What Does 'Failure' Mean in Civic Tech? We Need Continued Conversations About Discontinuation

research-article Open Access Share on What Does 'Failure' Mean in Civic Tech? We Need Continued Conversations About Discontinuation Authors: Andrea Hamm Weizenbaum Institute for the Networked Society Weizenbaum Institute for the Networked SocietySearch about this author , Yuya Shibuya University of Tokyo University of TokyoSearch about this author , Teresa Cerratto Pargman Stockholm University Stockholm UniversitySearch about this author , Roy Bendor Delft University of Technology Delft University of TechnologySearch about this author , Christoph Raetzsch Aarhus University Aarhus UniversitySearch about this author , Mennatullah Hendawy Ain Shams University Ain Shams UniversitySearch about this author , Rainer Rehak Weizenbaum Institute for the Networked Society Weizenbaum Institute for the Networked SocietySearch about this author , Gwen Klerks Eindhoven University of Technology Eindhoven University of TechnologySearch about this author , Ben Schouten Eindhoven University of Technology Eindhoven University of TechnologySearch about this author , Nicolai Brodersen Hansen Aalborg University Aalborg UniversitySearch about this author Authors Info & Claims InteractionsVolume 31Issue 2March - April 2024pp 34–38https://doi.org/10.1145/3641815Published:28 February 2024Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Nonlinear dimensionality reduction with q-Gaussian distribution

Abstract In recent years, the dimensionality reduction has become more important as the number of dimensions of data used in various tasks such as regression and classification has increased. As popular nonlinear dimensionality reduction methods, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) have been proposed. However, the former outputs only one low-dimensional space determined by the t-distribution and the latter is difficult to control the distribution of distance between each pair of samples in low-dimensional space. To tackle these issues, we propose novel t-SNE and UMAP extended by q-Gaussian distribution, called q-Gaussian-distributed stochastic neighbor embedding (q-SNE) and q-Gaussian-distributed uniform manifold approximation and projection (q-UMAP). The q-Gaussian distribution is a probability distribution derived by maximizing the tsallis entropy by escort distribution with mean and variance, and a generalized version of Gaussian distribution with a hyperparameter q. Since the shape of the q-Gaussian distribution can be tuned smoothly by the hyperparameter q, q-SNE and q-UMAP can in- tuitively derive different embedding spaces. To show the quality of the proposed method, we compared the visualization of the low-dimensional embedding space and the classification accuracy by k-NN in the low-dimensional space. Empirical results on MNIST, COIL-20, OliverttiFaces and FashionMNIST demonstrate that the q-SNE and q-UMAP can derive better embedding spaces than t-SNE and UMAP.
Photonic Which-Path Entangler Based on Longitudinal Cavity-Qubit Coupling

We show that a modulated longitudinal cavity-qubit coupling can be used to control the path taken by a multiphoton coherent-state wave packet conditioned on the state of a qubit, resulting in a qubit--which-path (QWP) entangled state. QWP states can generate long-range multipartite entanglement using strategies for interfacing discrete- and continuous-variable degrees of freedom. Using the approach presented here, entanglement can be distributed in a quantum network without the need for single-photon sources or detectors.
GrassCaré: Visualizing the Grassmannian on the Poincaré Disk

Abstract This paper introduces a novel method for visualizing high-dimensional Grassmannians through 2D embeddings on the Poincaré disk. The proposed approach involves the construction of affinity matrices on each manifold, followed by the minimization of KL-divergence between the geodesics affinity. This process enables the identification of an optimal projection that effectively preserves the geometry of the original high-dimensional Grassmannian. Our main theoretical contribution lies in bounding the embedding loss with respect to two factors: the logarithm of the number of subspaces and a term dependent on the distribution of subspaces within the Grassmannian. Notably, this term is smaller when subspaces exhibit well-defined clusters, and larger in the absence of any discernible structure. We complement our theoretical analysis with comprehensive experiments on both synthetic and real datasets. The experimental results showcase the superiority of our embedding in accurately visualizing Grassmannians compared to existing representations.
Tensions between the political, institutional, and project levels when developing professional digital competence in teacher education. A cultural historical activity theory analysis of inhibiting and facilitating factors

This study is a Cultural Historical Activity Theory analysis of the tensions between object and motive at the political, institutional and project management levels for five digitalisation projects aimed at integrating professional digital competence (PDC) in Norwegian teacher education (TE). The study is inspired by ethnography and builds on a variety of qualitative data (including qualitative interviews (N = 14)) gathered through the author’s participation in different roles (2017–2021). Identifying objects and motives across levels allows for the identification of factors inhibiting and facilitating factors the development of PDC in TE. An analytical model was developed to identify the objects and motives at each level, showing a political object of preparing for the future, an institutional object of creating high quality TE, and a project management object of delivering a lasting impact on TE. These differing objects and motives guide the activities at each level and reveal tension, including between the political push for change, project management’s attempts to deliver impact, and a culture built on academic freedom which seems counterproductive by slowing things down. The study also reveals facilitating factors, such as funding and relevance in collaboration with schools, and inhibiting factors like academic freedom and the expected speed of change.
Dickson polynomial-based secure group authentication scheme for Internet of Things

Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.
Activity scenarios simulation by discovering knowledge through activities of daily living datasets

Efficiently recognizing Activities of Daily Living (ADLs) requires overcoming challenges in collecting datasets through innovative approaches. Simultaneously, it involves adapting to the demand for interpreting human activities amidst temporal sequences of actions and interactions with objects, considering real-life scenarios and resource constraints. This study investigates the potential of generating synthetic training data for ADLs recognition using the VirtualHome2KG framework. Furthermore, we investigate the transformative potential of simulating activities in virtual spaces, as evidenced by our survey of real-world activity datasets and exploration of synthetic datasets in virtual environments. Our work explicitly simulates activities in the 3D Unity platform, affording seamless transitions between environments and camera perspectives. Furthermore, we meticulously construct scenarios not only for regular daily activities but also for abnormal activities to detect risky situations for independent living, ensuring the incorporation of critical criteria. We incorporate one contemporary method for abnormal activity detection to demonstrate the efficacy of our simulated activity data. Our findings suggest that our activity scenario preparation accomplishes the intended research objective while paving the way for an interesting research avenue.
A heterogeneous 3-D stacked PIM accelerator for GCN-based recommender systems

Abstract Modern recommendation systems integrate graph convolution neural networks (GCN) for enhancing embedding representation. Compared with widely deployed neural network-based models, the extra message propagation layer of GCN-based recommendation is featured with extensive computations and irregular memory access. However, architecture designs for prevailing deep neural network recommendation models assume simple pooling in the embedding layer. ReRAM-based GCN accelerators are specialized for graph-related operations. However, they are designed for general graphs, while GCN-based recommendation models mainly operate on the user-item graph. In this paper, we proposed a resistive random accessed memory (ReRAM) based processing-in-memory (PIM) accelerator, ReGCNR, for GCN-based recommendation. ReGCNR is featured with three key innovations. First, we exploit the 3-dimensional (3-D) stacked heterogeneous ReRAM to fit with the large-size embedding table and user-item graph. Then, we propose a joint degree mapping schema that maximizes the efficiency of the execution pipeline. After that, ReGCNR assembles a well-coordinated pipeline and hardware scheduling design to boost overall system performance. Results show that ReGCNR outperforms GPU by 69.83 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> and 56.67 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> in terms of average speedup and energy saving, respectively. In addition, ReGCNR outperforms state-of-the-art ReRAM-based solutions by 11.13 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> speedups and 7.22 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> energy savings on average.