Reviewerly/exHarmony huggingface dataset:
A convolutional neural network-based classification of local earthquakes and tectonic tremors in Sanriku-oki, Japan, using S-net data

Abstract Low-frequency tremors have been widely detected in many tectonic zones, and are often located adjacent to megathrust zones, indicating that their spatiotemporal evolution provides important insights into megathrust events. The envelope correlation method (ECM) is commonly used to detect tremors. However, the ECM also detects regular earthquakes, which requires the separation of these two signals after the initial detection. In addition, signals of tremors are weak, so classifying tremors from noises is also an essential problem. We develop a convolutional neural network (CNN)-based method using a single S-net station located off Sanriku region, Northeast Japan, to classify local earthquakes, tremors, and noise. Along the Japan Trench, especially in a region focused in this study, local earthquakes and tremors occurred in coexistence within a small region, so detection, location, and discrimination of these events are the key to understand the relationship between slow and regular earthquakes. The spectrograms of the three-component velocity waveforms that were recorded during 16 August 2016 to 14 August 2018 are used as the training and test datasets for the CNN. The CNN successfully classified 100%, 96%, and 98% of the earthquakes, tremors, and noise, respectively. We also showed a successful application of our method to continuous waveform data including a tremor to explore the feasibility of the proposed method in classifying tremors and noise in continuous streaming data. The output probabilities for the true classifications decrease with increasing epicentral distance and/or decreasing event magnitude. This highlights the need to train the CNN using tremors proximal to the seismic stations for detecting tremors using multiple stations.
KinVoices: Using Voices of Friends and Family in Voice Interfaces

With voice user interfaces (VUIs) becoming ubiquitous and speech synthesis technology maturing, it is possible to synthesise voices to resemble our friends and relatives (which we will collectively call 'kin') and use them on VUIs. However, designing such interfaces and investigating how the familiarity of kin voices affect user perceptions remain under-explored. Our surveys and interviews with 25 users revealed that VUIs using kin voices were perceived as more engaging, persuasive and safer yet eerier than VUIs using common virtual assistant voices. We then developed a technology probe, KinVoice, an Alexa-based VUI that was deployed in three households over two weeks. Users set reminders using KinVoice, which in turn, gave the reminders in synthesised kin voices. This was to explore users' needs, uncover challenges involved and inspire new applications. We discuss design guidelines for integrating familiar kin voices into VUIs, applications that benefit from its usage, and implications for balancing voice realism and usability with security and diversification.
The Effect of Artificial Intelligence on the AIS Excellence in Jordanian Banks

Artificial intelligence technologies have revolutionized the field of information technology thru the creation of smart devices and programs that work and interact like humans.It is related to a field of science that aims to provide machines with the ability to perform functions such as logic, planning, learning, and perception.In this sense, the accounting discipline was affected by artificial intelligence in the restructuring of business models and business processes.This study aims to debate how (AI) Artificial Intelligence affects the Accounting information system (AIS) excellence in Jordanian banks.This research is a survey study, and a questionnaire was used for data collection.The sample of the study encompasses all the 13 commercial banks in Jordan, the questionnaire was designed based on the Likert scale was forwarded to the accountant, managers, and internal auditors at the 13 targeted banks.The number of respondents was 278.A reliability test, a correlation matrix, and linear regression used by using SPSS 25.In light of those results, the study reached several recommendations, including the need to enhance the use of artificial intelligence in the banks to raise the excellence of the banks, and that the banks' administration should assist expert Jordanian systems in acquiring knowledge from the reality of knowledge bases systems stored in many areas that support senior management.In this research, closing discussion is provided for academic contribution might help the professionals and academics to carry out and improve artificial intelligence steps and AIS in financial sector.
CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle-rich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the concept of a coverage tree (CT) with a TSP-inspired tree traversal strategy. The CT-CPP method is validated on a high-fidelity underwater simulator and the results are compared to an existing terrain following CPP method. The results show that CT-CPP yields significant reduction in trajectory length, energy consumption, and reconstruction error.
Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors

Evaluation metrics are a key ingredient for progress of text generation systems.In recent years, several BERT-based evaluation metrics have been proposed (including BERTScore, MoverScore, BLEURT, etc.) which correlate much better with human assessment of text generation quality than BLEU or ROUGE, invented two decades ago.However, little is known what these metrics, which are based on black-box language model representations, actually capture (it is typically assumed they model semantic similarity).In this work, we use a simple regression based global explainability technique to disentangle metric scores along linguistic factors, including semantics, syntax, morphology, and lexical overlap.We show that the different metrics capture all aspects to some degree, but that they are all substantially sensitive to lexical overlap, just like BLEU and ROUGE.This exposes limitations of these novelly proposed metrics, which we also highlight in an adversarial test scenario.
The Research of Convolutional Neural Network Based on Integrated Classification in Question Classification

As a new generation of search engine, automatic question answering system (QAS) is becoming more and more important and has become one of the hotspots of computer application research and natural language processing (NLP). However, as an indispensable part of the QAS, the role of question classification is an understood thing in the system. In view of this, to further make the performance of question classification much better, both the feature extraction and the classification model were explored. On the study of existing CNN research, an improved CNN model based on Bagging integrated classification (“W2V + B-CNN” for short) is proposed and applied to question classification. Firstly, we combine the characteristics of short texts, use the Word2Vec tool to map the features of the words to a certain dimension, and organize the question sentences into the form of a two-dimensional matrix similar to the image. Then, the trained word vectors are used as the input of the CNN for feature extraction. Finally, the Bagging integrated classification algorithm is used to replace the Softmax classification of the traditional CNN for classification. In other words, the good of W2V + B-CNN model is that it can make use of the advantages of CNN and Bagging integrated classification at the same time. Overall, the new model can not only use the powerful feature extraction capabilities of CNN to extract the potential features of natural language questions but also use the good data classification capabilities of the integrated classification algorithm for feature classification at the same time, which can help improve the accuracy of the W2V + B-CNN in the application of question classification. The comparative experiment results prove that the effect of the W2V + B-CNN is significantly better than that of the CNN and other classification algorithms in question classification.
Formalizing Ordinal Partition Relations Using Isabelle/HOL

This is an overview of a formalisation project in the proof assistant Isabelle/HOL of a number of research results in infinitary combinatorics and set theory (more specifically in ordinal partition relations) by Erd\H{o}s--Milner, Specker, Larson and Nash-Williams, leading to Larson's proof of the unpublished result by E.C. Milner asserting that for all $m \in \mathbb{N}$, $\omega^\omega\arrows(\omega^\omega, m)$. This material has been recently formalised by Paulson and is available on the Archive of Formal Proofs; here we discuss some of the most challenging aspects of the formalisation process. This project is also a demonstration of working with Zermelo-Fraenkel set theory in higher-order logic.
Trolls, Pressure and Agenda: The discursive fight on Twitter in Turkey

Censorship, banning, and imprisonment are different methods used to suppress dissenting voices in traditional media and have now evolved into a new form with bot and troll accounts in the digital media age in Turkey. Is it possible to construct a bloc with counter-trolls against the escalating political pressure on the media in the post-truth era? Are counter-trolls capable of setting the agenda? This article discusses the possibility of constructing a bloc against the escalating political pressure in Turkey on the media through counter-trolls in the context of communicative rationality. First, it observes the ruling party’s troll politics strategy on Twitter, then examines the counter-discourses against political pressure today; thereafter it analyzes the discourse in hashtags on the agenda of the Boğaziçi University protests. Firstly, 18,000 tweets are examined to understand the suppress-communication strategy of the AK Party trolls. Secondly, the agenda-setting capacity of counter-trolls is observed between January 1, 2020, and February 5, 2021, and 18,000 tweets regarding Boğaziçi protests are examined to analyze the communication strategy of the counter-trolls. The study shows that the populist government instrumentalizes communication in social media, and Twitter does not have enough potential for the Gramscian counter-hegemony, but the organized actions and discourses have the potential to create public opinion.
pyNeVer: A Framework for Learning and Verification of Neural Networks

Automated verification of neural networks (NNs) was first proposed
Quantum phase estimations with spin coherent states superposition

The quantum metrological performance of spin coherent states superposition is considered, and conditions for measurements with the Heisenberg-limit (HL) precision are identified. It is demonstrated that the choice of the parameter-generating operator can lead to physically different estimation outcomes. In particular, closed-form analytical descriptions for the performance of spin cat states are derived. These findings show the routes to careful control of parameters necessary for achieving HL precision and provide insightful information on the geometry of the specific coherent state superposition and its relevance to the performance of the states for parameter estimations.
A Correlation Based Recommendation System for Large Data Sets

Abstract Correlation determination brings out relationships in data that had not been seen before and it is imperative to successfully use the power of correlations for data mining. In this paper, we have used the concepts of correlations to cluster data, and merged it with recommendation algorithms. We have proposed two correlation clustering algorithms (RBACC and LGBACC), that are based on finding Spearman’s rank correlation coefficient among data points, and using dimensionality reduction approach (PCA) along with graph theory respectively, to produce high quality hierarchical clusters. Both these algorithms have been tested on real life data (New York yellow cabs dataset taken from http://www.nyc.gov ), using distributed and parallel computing (Spark and R). They are found to be scalable and perform better than the existing hierarchical clustering algorithms. These two approaches have been used to replace similarity measures in recommendation algorithms and generate a correlation clustering based recommendation system model. We have combined the power of correlation analysis with that of prediction analysis to propose a better recommendation system. It is found that this model makes better quality recommendations as compared to the random recommendation model. This model has been validated using a real time, large data set (MovieLens dataset, taken from http://grouplens.org/datasets/movielens/latest ). The results show that combining correlated points with the predictive power of recommendation algorithms, produce better quality recommendations which are faster to compute. LGBACC has approximately 25% better prediction capability but at the same time takes significantly more prediction time compared to RBACC.
Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines

Problem-driven visualization work is rooted in deeply understanding the data, actors, processes, and workflows of a target domain. However, an individual's personality traits and cognitive abilities may also influence visualization use. Diverse user needs and abilities raise natural questions for specificity in visualization design: Could individuals from different domains exhibit performance differences when using visualizations? Are any systematic variations related to their cognitive abilities? This study bridges domain-specific perspectives on visualization design with those provided by cognition and perception. We measure variations in visualization task performance across chemistry, computer science, and education, and relate these differences to variations in spatial ability. We conducted an online study with over 60 domain experts consisting of tasks related to pie charts, isocontour plots, and 3D scatterplots, and grounded by a well-documented spatial ability test. Task performance (correctness) varied with profession across more complex visualizations (isocontour plots and scatterplots), but not pie charts, a comparatively common visualization. We found that correctness correlates with spatial ability, and the professions differ in terms of spatial ability. These results indicate that domains differ not only in the specifics of their data and tasks, but also in terms of how effectively their constituent members engage with visualizations and their cognitive traits. Analyzing participants' confidence and strategy comments suggests that focusing on performance neglects important nuances, such as differing approaches to engage with even common visualizations and potential skill transference. Our findings offer a fresh perspective on discipline-specific visualization with specific recommendations to help guide visualization design that celebrates the uniqueness of the disciplines and individuals we seek to serve.
Application of Optical Flow Technique and Photogrammetry for Rockfall Dynamics: A Case Study on a Field Test

Optical flow is a vision-based approach that is used for tracking the movement of objects. This robust technique can be an effective tool for determining the source of failures on slope surfaces, including the dynamic behavior of rockfall. However, optical flow-based measurement still remains an issue as the data from optical flow algorithms can be affected by the varied photographing environment, such as weather and illuminations. To address such problems, this paper presents an optical flow-based tracking algorithm that can be employed to extract motion data from video records for slope monitoring. Additionally, a workflow combined with photogrammetry and the optical flow technique has been proposed for producing highly accurate estimations of rockfall motion. The effectiveness of the proposed approach has been evaluated with the dataset obtained from a photogrammetry survey of field rockfall tests performed by the authors in 2015. The results show that the workflow adopted in this study can be suitable to identify rockfall events overtime in a slope monitoring system. The limitations of the current approach are also discussed.
Current Issues in the Conceptualization and Measurement of Executive Function Skills

Executive functions (EF) are domain general cognitive processes that contribute to individual learning, problem solving, and planning across the lifespan. Multidisciplinary interest in EF skills stems from their broad relevance to functionally important outcomes. However, this multidisciplinary interest in EF has also resulted in a large literature that is not internally consistent. In this chapter, we characterize historical shifts in the conceptualization and definition of EF skills. We describe quantitative and qualitative aspects of developmental change in EF skills. Finally, we highlight discrepancies that exist between current measurement approaches. The inconsistencies and discrepancies that we highlight are not surprising given the maturity of the EF literature. By drawing attention to these issues, we aim to provide readers a broad vantage from which they can critically evaluate the other chapters in this volume.
Charge dynamics in quantum-circuit refrigeration: Thermalization and microwave gain

Previous studies of photon-assisted tunneling through normal-metal–insulator–superconductor junctions have exhibited potential for providing a convenient tool to control the dissipation of quantum-electric circuits in situ. However, the current literature on such a quantum-circuit refrigerator (QCR) does not present a detailed description for the charge dynamics of the tunneling processes or the phase coherence of the open quantum system. Here, we derive a master equation describing both quantum-electric and charge degrees of freedom, and discover that typical experimental parameters of low temperature and yet lower charging energy yield a separation of time scales for the charge and quantum dynamics. Consequently, the minor effect of the different charge states can be taken into account by averaging over the charge distribution. We also consider applying an ac voltage to the tunnel junction, which enables control of the decay rate of a superconducting qubit over four orders of magnitude by changing the drive amplitude; we find an order-of-magnitude drop in the qubit excitation in 40 ns and a residual reset infidelity below 10−4. Furthermore, for the normal island, we consider the case of charging energy and single-particle level spacing large compared to the superconducting gap, i.e., a quantum dot. Although the decay rates arising from such a dot QCR appear low for use in qubit reset, the device can provide effective negative damping (gain) to the coupled microwave resonator. The Fano factor of such a millikelvin microwave source may be smaller than unity, with the latter value being reached close to the maximum attainable power.
Pre-service Teachers’ perceptions on instant messaging and orthographic competence

&#x0D; &#x0D; &#x0D; This work examines the perception held by pre-service teachers of Spanish in Chile and Argentina of the digital written norm in IM, together with their opinion regarding the influence of textisms on secondary students’ acquisition of orthographic competence. The study uses a transactional approach based on surveys and uses and applies a descriptive non-experimental design. Results, when compared with studies for the European Spanish variant, showed that pre-service teachers in Chile and Argentina considered use of textisms as harmful to secondary students’ development of orthographic competence, despite their own frequent use of textisms. However, one of the main findings is a certain degree of tolerance of specific types of textisms in digital writing, as was a more integrative approach to those written variables on the part of participants in Argentina.&#x0D; &#x0D; &#x0D;
Compiling with continuations, correctly

In this paper we present a novel simulation relation for proving correctness of program transformations that combines syntactic simulations and logical relations. In particular, we establish a new kind of simulation diagram that uses a small-step or big-step semantics in the source language and an untyped, step-indexed logical relation in the target language. Our technique provides a practical solution for proving semantics preservation for transformations that do not preserve reductions in the source language. This is common when transformations generate new binder names, and hence α-conversion must be explicitly accounted for, or when transformations introduce administrative redexes. Our technique does not require reductions in the source language to correspond directly to reductions in the target language. Instead, we enforce a weaker notion of semantic preorder, which suffices to show that semantics are preserved for both whole-program and separate compilation. Because our logical relation is transitive, we can transition between intermediate program states in a small-step fashion and hence the shape of the proof resembles that of a simple small-step simulation. We use this technique to revisit the semantic correctness of a continuation-passing style (CPS) transformation and we demonstrate how it allows us to overcome well-known complications of this proof related to α-conversion and administrative reductions. In addition, by using a logical relation that is indexed by invariants that relate the resource consumption of two programs, we are able show that the transformation preserves diverging behaviors and that our CPS transformation asymptotically preserves the running time of the source program. Our results are formalized in the Coq proof assistant. Our continuation-passing style transformation is part of the CertiCoq compiler for Gallina, the specification language of Coq.
Modeling Visual Containment for Web Page Layout Optimization

Abstract Web pages have become fundamental in conveying information for companies and individuals, yet designing web page layouts remains a challenging task for inexperienced individuals despite web builders and templates. Visual containment, in which elements are grouped together and placed inside container elements, is an efficient design strategy for organizing elements in a limited display, and is widely implemented in most web page designs. Yet, visual containment has not been explicitly addressed in the research on generating layouts from scratch, which may be due to the lack of hierarchical structure. In this work, we represent such visual containment as a layout tree, and formulate the layout design task as a hierarchical optimization problem. We first estimate the layout tree from a given a set of elements, which is then used to compute tree‐aware energies corresponding to various desirable design properties such as alignment or spacing. Using an optimization approach also allows our method to naturally incorporate user intentions and create an interactive web design application. We obtain a dataset of diverse and popular real‐world web designs to optimize and evaluate various aspects of our method. Experimental results show that our method generates better quality layouts compared to the baseline method.