Reviewerly/exHarmony huggingface dataset:
Geological significance of the spatial clustering and alignment of vents in zones of distributed volcanism: A critical review

The spatial distribution of volcanic activity has been the subject of scientific enquiry for more than 350 years. Nevertheless, it has been only until recently that modern techniques of analysis started to be used to characterize such distribution. As a result, in the past 40 years many methods were devised, or adopted from other fields of study, aiming to achieve that purpose. Ironically, the plethora of now available methodologies has made problematic the selection of one method of study to characterize the spatial distribution of volcanic vents. In addition, given the diversity of methods, and their underlying assumptions, there are issues concerning the form in which the results of any specific method should be interpreted in a volcanic context. In this work I make emphasis on the relationship between the embedded assumptions of several common methods of study with the concept of a “natural partition”. Throughout this work it is remarked that 1) it is extremely important to gain conscience about the several assumptions implicit on each method of analysis, 2) never loose sight that not all volcanic systems are equal, and 3) that it is a myth that any quantitative method can provide reliable information about any system. The convenience of assessing more than one conceptual model to explain the formation of a cluster-subcluster structure is also mentioned. Overall, it is shown that the complexity of volcanic phenomena cannot be encapsulated by using a single method of study, or an automatized selection of parameters. Thus, given the complexity and diversity of volcanic phenomena, there is no particular method, nor set of methods, that can be recommended to be used on every situation of interest. Yet, as a golden rule, it is suggested that more than one method of analysis is used at every location of study, looking for congruency of results within a range of spatial scales.
Pre-training Code Representation with Semantic Flow Graph for Effective Bug Localization

Enlightened by the big success of pre-training in natural language processing, pre-trained models for programming languages have been widely used to promote code intelligence in recent years. In particular, BERT has been used for bug localization tasks and impressive results have been obtained. However, these BERT-based bug localization techniques suffer from two issues. First, the pre-trained BERT model on source code does not adequately capture the deep semantics of program code. Second, the overall bug localization models neglect the necessity of large-scale negative samples in contrastive learning for representations of changesets and ignore the lexical similarity between bug reports and changesets during similarity estimation. We address these two issues by 1) proposing a novel directed, multiple-label code graph representation named Semantic Flow Graph (SFG), which compactly and adequately captures code semantics, 2) designing and training SemanticCodeBERT based on SFG, and 3) designing a novel Hierarchical Momentum Contrastive Bug Localization technique (HMCBL). Evaluation results show that our method achieves state-of-the-art performance in bug localization.
Quantum Kernels for Difficult Visual Discrimination

Quantum machine learning has attracted much attention in recent years as one of the applications of quantum computing. In particular, classification using quantum kernels has attracted attention as a means of efficient classification by mapping to a feature space. We aim to use quantum machine learning to identify good and defective products in images including factory products, building, and farm products. Though a fruit apple looks delicious, it has a vine crack inside when it is split in two, it loses its commercial value. It is very difficult to distinguish normal apples from apples with internal vine cracks which are not visible on the exterior, using photographs of the exterior. In this study, an attempt was made to classify internal defects with classical and quantum kernels using binarized images of apples, which are difficult to distinguish with the naked eye. As a result, the accuracy of the classical kernel was less than 0.75. However, with the quantum kernel, an accuracy of over 0.94 could be obtained. The performance of the quantum kernel varied significantly depending on its type. We found that quantum kernel circuits having Hadamard, and control Ry gate have affected the construction of the learning model. We have demonstrated that high accuracy can be achieved by using quantum kernels for the classification of images that are difficult to discriminate visually.
Approximation- and Quantization-Aware Training for Graph Neural Networks

Graph Neural Networks (GNNs) are one of the best-performing models for processing graph data. They are known to have considerable computational complexity, despite the smaller number of parameters compared to traditional Deep Neural Networks (DNNs). Operations-to-parameters ratio for GNNs can be tens and hundreds of times higher than for DNNs, depending on the input graph size. This complexity indicates the importance of arithmetic operation optimization within GNNs through model quantization and approximation. In this work, for the first time, we combine both approaches and implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quantization</i> - and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">approximation-aware</i> training for GNNs to sustain their accuracy under the errors induced by inexact multiplications. We employ matrix multiplication CUDA kernel to speed up the simulation of approximate multiplication within GNNs. Further, we demonstrate the execution speed, accuracy, and energy efficiency of GNNs with approximate multipliers in comparison with quantized low-bit GNNs. We evaluate the performance of state-of-the-art GNN architectures (i.e., GIN, SAGE, GCN, and GAT) on various datasets and tasks (i.e., Reddit-Binary, Collab for graph classification, Cora and PubMed for node classification) with a wide range of approximate multipliers. Our framework is available online: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TUM-AIPro/AxC-GNN</uri>
Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data

Objective This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data. Methods The structured data was used to train fourteen classical Machine Learning models including advanced ensemble trees, neural networks and k -nearest neighbors. The unstructured data was used to fine-tune a pre-trained Bio Clinical BERT Transformer Deep Learning model. The structured and unstructured data were then merged into a tabular dataset after vectorization of the clinical text and a dimensional reduction through Latent Dirichlet Allocation. The study used the free and publicly available Medical Information Mart for Intensive Care (MIMIC) III database, on the open AutoML Library AutoGluon. Performance is evaluated with respect to two types of random classifiers, used as baselines. Results The best model from structured data demonstrates high performance (ROC AUC = 0.944, PRC AUC = 0.655) with limited interpretability, where the most important predictors of prolonged LOS are the level of blood urea nitrogen and of platelets. The Transformer model displays a good but lower performance (ROC AUC = 0.842, PRC AUC = 0.375) with a richer array of interpretability by providing more specific in-hospital factors including procedures, conditions, and medical history. The best model trained on mixed data satisfies both a high level of performance (ROC AUC = 0.963, PRC AUC = 0.746) and a much larger scope in interpretability including pathologies of the intestine, the colon, and the blood; infectious diseases, respiratory problems, procedures involving sedation and intubation, and vascular surgery. Conclusions Our results outperform most of the state-of-the-art models in LOS prediction both in terms of performance and of interpretability. Data fusion between structured and unstructured text data may significantly improve performance and interpretability.
Petrographical and Geochemical Features of Sulfide Mineralization in the Walash Group, Gallala Area, Kurdistan Region of Iraq

In this article, a Fe-rich sulfide mineralization hosted within the Walash Group in the Gallala area, on the northwestern side of the Zagros Orogen is investigated for the first time. The mineralization occurs as massive sulfide veins and veinlets, as well as disseminated within volcanic lithologies. The objective of the study is to characterize the sulfide-rich mineralization using petrographical and geochemical methods, applied in both the ores and the corresponding host formations, including the characterization of volcanic rock alteration. Petrographic data indicates the presence of Fe-rich sulfide mineralization in altered basaltic andesite to andesite rocks, which are affected by hydrothermal alteration such as actinolization, chloritization, sericitization, and epidotization. The geochemical analysis of the fresh rock samples reveals enrichment of iron in the suture zones, but low copper and zinc concentrations. The mineralogical and textural signatures of sulfide minerals lead to inferring that the ore mineralization origin is volcanogenic to the hydrothermal type of deposits.
Getting Outside the Bug Boxes (Keynote)

Sometimes, we humans find ourselves a bit slow to abandon the comfort of sitting “inside the box”, and this can detract from our ability to innovate. In this talk, I’ll share some outside-the-box perspectives, gleaned from decades of software engineering work, on boxes I’ve seen when thinking about bugs — from failures to faults, from finding to fixing, and from traditional to very non-traditional notions of “what counts” as a bug. I’ll consider the intellectually freeing perspectives that can come from moving outside the “mechanisms” box to policies; the enhancement to applicability from moving outside sub-sub-area boxes to the whole software lifecycle; the differences revealed when moving outside the “typical developer” box to diverse humans; and the plethora of possibilities arising from moving outside the “buggy code” box to a wide range of bug types.
Low-Depth Flag-Style Syndrome Extraction for Small Quantum Error-Correction Codes

Flag-style fault-tolerance has become a linchpin in the realization of small fault-tolerant quantum-error correction experiments. The flag protocol's utility hinges on low qubit over-head, which is typically much smaller than in other approaches. However, as in most fault-tolerance protocols, the advantages of flag-style error correction come with a tradeoff: fault tolerance can be guaranteed, but such protocols involve high-depth circuits, due to the need for repeated stabilizer measurements. Here, we demonstrate that a dynamic choice of stabilizer measurements, based on past syndromes, and the utilization of elements from the full stabilizer group, leads to flag protocols with lower-depth syndrome-extraction circuits for the [[5], [1], [3]] code, as well as for the Steane code when compared to the standard methods in flag fault tolerance. We methodically prove that our new protocols yield fault-tolerant lookup tables, and demonstrate them with a pseudothreshold simulation, showcasing large improvements for all protocols when compared to previously-established methods. This work opens the dialogue on exploiting the properties of the full stabilizer group for reducing circuit overhead in fault-tolerant quantum-error correction.
QPLEX: Realizing the Integration of Quantum Computing into Combinatorial Optimization Software

Quantum computing has the potential to surpass the capabilities of current classical computers when solving complex problems. Combinatorial optimization has emerged as one of the key target areas for quantum computers as problems found in this field play a critical role in many different industrial application sectors (e.g., enhancing manufacturing operations or improving decision processes). Currently, there are different types of high-performance optimization software (e.g., ILOG CPLEX and Gurobi) that support engineers and scientists in solving optimization problems using classical computers. In order to utilize quantum resources, users require domain-specific knowledge of quantum algorithms, SDKs and libraries, which can be a limiting factor for any practitioner who wants to integrate this technology into their workflows. Our goal is to add software infrastructure to a classical optimization package so that application developers can interface with quantum platforms readily when setting up their workflows. This paper presents a tool for the seamless utilization of quantum resources through a classical interface. Our approach consists of a Python library extension that provides a backend to facilitate access to multiple quantum providers. Our pipeline enables optimization software developers to experiment with quantum resources selectively and assess performance improvements of hybrid quantum-classical optimization solutions.
Evaluating Robust Entanglement on a Trapped Ion Platform

We present proof of principle results for evaluating an entanglement volumetric benchmark [1] on trapped ion platforms. The benchmark quantifies the robustness of multipartite and bipartite entanglement using stabilizer measurements and witness functions. Each n-qubit graph state is prepared and used to evaluate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> state-specific stabilizer strings. These stabilizer measurements are used to evaluate entanglement witness functions. The entanglement benchmark defines families of graph states associated with an initial sub-graph of the hardware qubit connectivity - with all-to-all connectivity, trapped ion systems provide a flexibility in the choice of this initial graph, and this will affect the associated family. In this work, we present results targeting several classes of entangled states: 1) 1-D cluster states, 2) n-qubit GHZ states, and 3) cycle graph states. These states have been found in the literature as standard hardware benchmarks, and have connections to many near-term applications.
Genetic-based two granularity ordering methods for multiple workflow scheduling

In cloud computing, multiple workflow scheduling is important to optimize resource allocation and utilization for concurrent executions of diverse workflows across different applications. While previous research has focused on clustering-based resource allocation to reduce communication overheads by grouping tasks, it often overlooks the significance of task execution ordering, limiting overall performance optimization. To address the limitation, we propose two genetic-based approaches, considering task and cluster-level characteristics, to introduce novel ordering techniques for multi-workflow scheduling under cluster-based resource allocation. By comparing two granularity ordering methods, we offer valuable insights for efficient task management in multi-workflow environments. Our experiments demonstrate that the proposed approaches, especially the task granularity-based ordering method, outperform existing primary clustering methods, particularly for scenarios involving a large number of workflows or highly parallel workflows.
Human Activity Recognition Using CNN and Lstm Deep Learning Algorithms

Human Activity Recognition recognizes and classifies the activities performed by the users or people based on the data collected from the sensors of special devices such as smart-watches, smartphones etc. It has become easy to collect a huge amount of data from inertial sensors that are embedded in wearable devices. An accelerometer and gyroscope sensors are most commonly used inertial sensors. There are various already available datasets, in our paper, we are using the Wireless Sensor Data Mining dataset which contains 1,098,207 data of 6 physical activities performed. In this paper, the activities we aim to classify are walking, jogging, going up and downstairs, standing, and sitting. There are various algorithms applied on the various datasets. In our paper, we use Convolutional Neural Network and Long Short-Term Memory deep learning algorithm on the data set, we split the data into training data [80%] and testing data [20%]. By using a confusion matrix, we recognize and classify the activities performed using maximum accuracy.
Research on vocal teaching model based on heuristic teaching method in China’s universities

The article discusses the application significance and specifics of heuristic teaching method in China's university vocal teaching. The heuristic teaching method can effectively strengthen students' mastery of vocal knowledge and promote the development of divergent thinking among students. It aims to mobilize students' creativity through guidance and inspiration around the teaching objectives of vocal.
Multiscale Data-Driven Energy Estimation and Generation

We develop a multiscale approach to estimate high-dimensional probability distributions. Our approach applies to cases in which the energy function (or Hamiltonian) is not known from the start. Using data acquired from experiments or simulations we can estimate the energy function. We obtain a representation of the approximate probability distribution based on a multiscale cascade of conditional probabilities. This representation allows for fast sampling of many-body systems in various domains, from statistical physics to cosmology. Our method—the wavelet-conditional renormalization group (WCRG)—proceeds scale by scale, estimating models for the conditional probabilities of “fast degrees of freedom” conditioned by coarse-grained fields. These probability distributions are modeled by energy functions associated with scale interactions, and are represented in an orthogonal wavelet basis. The WCRG decomposes the microscopic energy function as a sum of interaction energies at all scales and can efficiently generate new samples by going from coarse to fine scales. Near phase transitions, this representation of the approximate probability distribution completely avoids the “critical slowing-down” of direct estimation and sampling algorithms. This is explained theoretically by combining results from RG and wavelet theories, and verified numerically for the Gaussian and φ4-field theories. We show that multiscale WCRG energy-based models are more general than local potential models and can capture the physics of complex many-body interacting systems at all length scales. This is demonstrated for weak-gravitational-lensing fields reflecting dark-matter distributions in cosmology, which include long-range interactions with long-tail probability distributions. The WCRG has a large number of potential applications in nonequilibrium systems, where the underlying distribution is not known a priori. Finally, we discuss the connection between the WCRG and deep network architectures.17 MoreReceived 18 July 2022Revised 2 October 2023Accepted 28 August 2023DOI:https://doi.org/10.1103/PhysRevX.13.041038Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasPhase transitionsStochastic inferenceTechniquesEntropic sampling methodsRenormalization groupStatistical Physics & Thermodynamics
A Deep Learning Based Offline Optical Character Recognition Model for Printed Ottoman Turkish

Developing efficient optical character recognition (OCR) systems for printed Ottoman text is a problem since current OCR models created for Arabic have restrictions that make it difficult to be performed. The performance of these models has been shown to be low when used for the recognition of Ottoman text. It has also been shown that these models that have been subjected to specialized training on Ottoman text have produced results that are not sufficient. In this study, an analysis of printed Ottoman Turkish documents in the Matbu font is conducted using a deep learning model that is proposed. Through the use of an end-to-end trainable architecture that integrates convolutional neural networks (CNNs) with bidirectional long short-term memory (BiLSTM) units, this study proposes an efficient solution to the Ottoman optical character recognition (OCR) issue. Experimental results show that the proposed model achieved overall scores for accuracy, sensitivity, and precision of 99.6%, 87.1%, and 93.3% on the test dataset respectively.
The Shifting Landscape of Cybersecurity: The Impact of Remote Work and COVID-19 on Data Breach Trends

Abstract This study examines the impact of the COVID-19 pandemic on cybersecurity and data breaches, with a specific focus on the shift toward remote work. The study identifies trends and offers insights into cybersecurity incidents by analyzing data breaches two years before and two years after the start of remote work. Data was collected from the Montana Department of Justice Data Breach database and consisted of data breaches that occurred between April 2018 and April 2022. The findings inform best practices for cybersecurity preparedness in remote work environments, aiding organizations to enhance their defenses. Although the study's data is limited to Montana, it offers valuable insights for cybersecurity professionals worldwide. As remote work continues to evolve, organizations must remain adaptable and vigilant in their cybersecurity strategies.
Discovering the synergies among information literacy and mobile learning in higher education

This research aims at exploring the synergies and relationships between information literacy and mobile learning domains in Higher Education environments. The method is based on a narrative literature review of available articles in English (2006-2022) and one selection of the most relevant items using qualitative content analysis, to discover the main topics and their relationships. Results confirm that information literacy and m-learning are close and interrelated domains. Mobile technologies and academic libraries perform an intermediating role in those relationships. With the support of mobile technologies, the progressive integration of information literacy and mobile learning may lead the academic library to experience and channel an enhanced service. The greatest challenge consists in adapting the concepts and behaviors informational and pedagogical to a technology that evolves relentlessly but, in any event, ought to point to individual and community needs. The academic library is a pivotal entity in channeling the progress of IL and mobile learning.