Reviewerly/exHarmony huggingface dataset:
Qubit propagation through lab simulated atmospheric turbulence

The Air Force Institute of Technology (AFIT) is building a quantum optics and quantum information laboratory to study single photon phenomena and its applications. We propose an experiment to characterize the effects of atmospheric turbulence on single photons and entangled pair of photons as a function of statistical quantities that define a turbulent atmosphere (such as the Fried parameter, Greenwood frequency, Rytov variance, ect).This presentation details the initial experiment studying single photon propagation using an atmospheric turbulence simulator that statistically represents ground-to-space quantum communications.
How do translators select among competing (near-)synonyms in translation?

Abstract This article investigates how translators choose between multiple competing onomasiological variants to express (verbal) inchoativity in English-to-Dutch translations. Using a corpus-based multifactorial research design, we measure the impact of three well-known socio-cognitive mechanisms on the actual choice, namely the complexity principle, risk aversion, and cognate exposure. We apply the behavioural profile method, which allows us to operationalise these three explanatory mechanisms via ID-tags, and we then use conditional random forest modelling to determine the impact of each mechanism on the choice between four competing verbs of inchoativity. The results of our analyses show that the complexity principle plays a clear role in translated texts, as there is a significant preference for the active construction and for prototypical verbs in passive constructions. Genre-specific risk-averse behaviour as well as cognate avoidance were not observed.
INDONESIA DIGITAL GOVERNMENT AUDITING MODEL USING RULE BASED AND CLOUD CASE-BASED REASONING

This paper explores the possibility of combining process model of Digital Government auditing, Rule Based and Cloud Case-Based Reasoning. Digital Government and Digital Government Auditing definition retrieve from Indonesia Presidential Regulation number 95-year 2018. consist two goals that the regulation aims, the first was to realize good governance clean, effective, transparent, and accountable as well as quality and reliable public services an digital government system is required; and second was to improve cohesiveness and efficiency of the electronic-based government system governance and management required national digital government; The existence variation of regulations and auditing standard creating complex reference database and hard to find solution for assessment and auditing Indonesia Digital Government. Rule-based and Case-based reasoning could help auditor in searching and documenting unsolved and solved auditing problem. The result shown that there is possibility to enhance audit process to create better audit result and more efficient in audit working time.
Using Algorithmic Transformations and Sensitivity Analysis to Unleash Approximations in CNNs at the Edge

Previous studies have demonstrated that, up to a certain degree, Convolutional Neural Networks (CNNs) can tolerate arithmetic approximations. Nonetheless, perturbations must be applied judiciously, to constrain their impact on accuracy. This is a challenging task, since the implementation of inexact operators is often decided at design time, when the application and its robustness profile are unknown, posing the risk of over-constraining or over-provisioning the hardware. Bridging this gap, we propose a two-phase strategy. Our framework first optimizes the target CNN model, reducing the bitwidth of weights and activations and enhancing error resiliency, so that inexact operations can be performed as frequently as possible. Then, it selectively assigns CNN layers to exact or inexact hardware based on a sensitivity metric. Our results show that, within a 5% accuracy degradation, our methodology, including a highly inexact multiplier design, can reduce the cost of MAC operations in CNN inference up to 83.6% compared to state-of-the-art optimized exact implementations.
Multimodality: Reshaping Anthropology

Multimodality offers anthropologists an inflection on the way we do research, produce scholarship, teach students, and relate to diverse publics. Advancing an expanding array of tools, practices, and concepts, multimodality signals a change in the way we pay attention and attend to the diverse possibilities for understanding the human experience. Multimodality recognizes the way smartphones, social media, and digital software transform research dynamics in unprecedented ways, while also drawing upon long-standing practices of recording and presenting research through images, sounds, objects, and text. Rather than flatten out ethnographic participant observation into logocentric practices of people-writing, multimodal ethnographies diversify their modes of inquiry to produce more-than-textual mediations of sensorial research experiences. By emphasizing kaleidoscopic qualities that give shape to an emergent, multidimensional, and diversifying anthropology, multimodality proposes alternatives to enduring and delimiting dichotomies, particularly text/image. These new configurations invite unrealized disciplinary constellations and research collaborations to emerge, but also require overhauling the infrastructures that support training, dissemination, and assessment.
An argumentative approach for handling inconsistency in prioritized Datalog ± ontologies

Prioritized Datalog ± is a well-studied formalism for modelling ontological knowledge and data, and has a success story in many applications in the (Semantic) Web and in other domains. Since the information content on the Web is both inherently context-dependent and frequently updated, the occurrence of a logical inconsistency is often inevitable. This phenomenon has led the research community to develop various types of inconsistency-tolerant semantics over the last few decades. Although the study of query answering under inconsistency-tolerant semantics is well-understood, the problem of explaining query answering under such semantics took considerably less attention, especially in the scenario where the facts are prioritized. In this paper, we aim to fill this gap. More specifically, we use Dung’s abstract argumentation framework to address the problem of explaining inconsistency-tolerant query answering in Datalog ± KB where facts are prioritized, or preordered. We clarify the relationship between preferred repair semantics and various notions of extensions for argumentation frameworks. The strength of such argumentation-based approach is the explainability; users can more easily understand why different points of views are conflicting and why the query answer is entailed (or not) under different semantics. To this end we introduce the formal notion of a dialogical explanation, and show how it can be used to both explain showing why query results hold and not hold according to the known semantics in inconsistent Datalog ± knowledge bases.
Design and Application of Legally Valid Payment Templates Based on Linking Contracts

Smart contracts are widely employed in many industries as a result of the high-quality development of science and economic technology, as well as the introduction of blockchain, which can automatically conduct retrieval, verification, and payment tasks. Smart contracts as an emerging topic, particularly the study of smart legal contracts, must remain forward-looking, and the smart contract sector cannot wait for the legal status of smart contracts to be resolved before advancing. The relative lag of the law becomes unavoidable due to the unassembled and unpredictable character of the law and thus its legislation. In this paper, we explore the incorporation of smart contracts into the scope of legal regulation, the construction of a series of systems for smart contracts, and the prognosis of smart contracts in terms of contract logic, arbitration process, and formal verification from the current law. Furthermore, a smart contract payment template based on semantic-aware graph neural networks is proposed to address the traditional smart contract vulnerability detection payment template method's low detection accuracy and high false alarm rate, as well as the neural network-based method's insufficient mining of bytecode-level smart contract features. Experiments comparing the method described in this research to comparable methods reveal that the strategy proposed in this study improves all types of indicators significantly.
Coordinating Lifesaving Product Development Projects with no Preestablished Organizational Governance Structure

We employed a longitudinal, grounded theory approach to investigate the management of an innovative product developed in the context of a life-or-death global emergency. This project involved about 40 multidisciplinary researchers, multisector companies, and open-source collaboration. Inductively, we identified the emergence and evolution of project coordination approaches, roles, and the project network along with the project's life cycle. The present study shows how configurations of these elements coevolve as the project moves forward with no predefined structures or procedures. Therefore, it provides insights for coordinating innovative projects in the face of extreme time constraints and ill-defined institutional frameworks.
Prediction Model of Piano Collective Class Teaching and Learning Effect Based on Deep Learning

This paper proposes a prediction model of piano collective class teaching and learning effect based on DL network in order to realise the precise prediction and evaluation of piano collective class instructional effect and promote the improvement of piano collective class instructional quality. The idea of an instructional assessment index is quantified in this paper using specific data as its input and educational impact as its output. In parallel, several training networks are established to correspond to the first-level evaluation indexes, and the input samples are normalised. Finally, MATLAB performs the empirical research. According to the findings, this method's prediction accuracy can reach 94.41 percent, which is about 10.22 percent higher than that of conventional methods. This prediction model is somewhat realistic and feasible. When used to predict and assess instructional quality, this method not only eliminates the subjectivity of experts in the evaluation process but also yields satisfactory evaluation outcomes and has a broad range of applications. According to the model's predictions and evaluation findings in this paper, appropriate teachers can better understand the drawbacks of the collective class model, focus on some important aspects of teaching activities, and then enhance instructional methods and effects.
TN-GTN: fault diagnosis of aircraft wiring network over edge computing

Abstract Fault diagnosis of the aircraft wiring network plays an important role in the intelligent manufacture of the aircraft. Many studies focus on the feature-based machine learning methods. However, these methods are improper in handling the data on heterogeneous graphs. Due to the scatter of the valid feature information, the relevant information between the test nodes is ignored by these methods, which leads to the low accuracy fault diagnosis. Taking the advantage of the 5G technology that can remotely process large-scale graph data, this work proposes a fault diagnosis method named “topological network-graph transformer network (TN-GTN).” TN-GTN can improve the fault diagnosis accuracy through feature enhancement and classification, which is based on the topological information of heterogeneous graphs. The graph network is able to learn new graph structures by identifying useful meta-paths and multi-hop connections between unconnected nodes on original graphs. Feature-enhanced test nodes are used to classify the final labels by the artificial neural network. Results of the performed experiment showed that TN-GTN reduced the dependence on domain knowledge and achieved an accurate classification of the fault diagnosis on aircraft wiring network.
Deep image inpainting via contextual modelling in ADCT domain

Pixel-based generative image inpainting has been widely researched over recent years and certain level of success via deep learning of feature representations and hallucinations of missing pixel values from surrounding backgrounds have also been reported in the literature. However, existing approaches rely on context-based attentions and progressive inferences to capture the pixel correlations yet such pixel-based approaches often fail to adapt to the constantly varying ranges and distances among surrounding background pixels. On the other hand, the modelling cost is also increasingly expensive whenever correlations of those pixels at longer distance away are to be exploited. To resolve the problem, we implement the principle of learning and hallucinating frequency components rather than pixel values. Therefore, we can avoid the dilemma that, on one hand the wish is to exploit all correlated pixels inside the image no matter how far away they are spatially located, but on the other, the price of increasing the modelling cost incurred by those pixels far away from the missing regions has to be paid. Extensive experiments carried out verify the effectiveness of the proposed method, which outperforms the representative existing state of the arts in terms of all assessment metrics.
GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture Model

Recent studies on unsupervised object detection based on spatial attention have achieved promising results. Models, such as AIR and SPAIR, output "what" and "where" latent variables that represent the attributes and locations of objects in a scene, respectively. Most of the previous studies concentrate on the "where" localization performance. However, we claim that acquiring "what" object attributes is also essential for representation learning. This study presents a framework, GMAIR, for unsupervised object detection. It incorporates spatial attention and a Gaussian mixture in a unified deep generative model. GMAIR can locate objects in a scene and simultaneously cluster them without supervision. Furthermore, we analyze the "what" latent variables and clustering process. Finally, we evaluate our model on MultiMNIST and Fruit2D datasets. We show that GMAIR achieves competitive results on localization and clustering compared with state-of-the-art methods.
Sentiment Analysis of Tourist Scenic Spots Internet Comments Based on LSTM

With the win-win development of tourism and the Internet, word-of-mouth ranking of tourist attractions is a valuable reference factor. We try to find a correlation between tourist reviews and taste ranking of tourist attractions. We study the sentiment features of tourist online reviews from the technical perspective of natural language processing, so we propose an improved long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We abandon traditional dictionaries and machine learning methods. A deep neural network approach was chosen to decompose multisentiment travel reviews into different morpheme levels for classification. Then, through preprocessing, text sentiment topic detection, and sentiment classification network, an accurate grasp of the sentiment features of reviews is finally achieved. To test the performance of our method, we built a web review database by crawler for experimental validation. Experimental results show that our method maintains more than 90% accuracy in comment sentiment detection, significantly outperforming dictionary methods and machine learning methods.
Babel Fish Democracy? Prospects for Addressing Democratic Language Barriers through Machine Translation and Interpretation

Abstract This article works to show that machine translation and interpretation technologies can play significant roles in addressing language barriers in democratic polities, including beyond the state. Numerous shared‐language or interpretation models have been proposed to address such barriers, while possible machine models have largely been dismissed in the literature. Yet, online translation is now ubiquitous globally, and governments and international organizations increasingly use machine translation and some interpretation applications. Such technologies, it is shown, can greatly expand information uptake and participation by ordinary citizens in linguistically diverse polities. They also can avoid key fairness and cost concerns faced by other models, or help address them in hybrid configurations, especially in online settings. While speech‐to‐speech applications may never achieve the seamless vernacular “Babel fish” interpretations implied as necessary for some deliberative modes, machine models can be seen as valuable for addressing language barriers within a range of approaches to democracy.
Reform and Practice of College Japanese Test Mode Using Big Data Analysis

With the acceleration of economic globalization, the demand for Japanese professionals is also increasing. In view of this situation, this paper puts forward a set of college Japanese test systems. A college Japanese teaching model based on big data is established, and an improved clustering algorithm is proposed by comparing correlation analysis and data mining techniques. Aiming at the phenomenon that ACA (ant colony algorithm) cannot guarantee that all data objects are acquired and collected by ants and the same data object can be accessed repeatedly, this paper puts forward some corresponding improvements. The clustering results show that the clustering accuracy of the algorithm is 80%. This proves the rationality and feasibility of dynamically evaluating students’ achievements based on clustering algorithm, and it is of great significance to introduce data mining into the teaching environment.
Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network

The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme.
High-performance cartesian genetic programming on GPU for the inference of gene regulatory networks using scRNA-seq time-series data

The inference of Gene Regulatory Networks (GRNs) is an important topic with biotechnological and health applications, as comprehending patterns of gene interactions can lead to findings regarding living organisms. Evolutionary computation techniques, such as genetic programming, have been successfully applied to infer GRNs. However, evaluating candidate GRNs during the evolutionary search is computationally expensive and the adoption of such a type of method becomes an impediment when a large number of genes are involved. In this paper, we propose a strategy to infer GRN models from gene expression data using Cartesian Genetic Programming (CGP) and high-performance computing for reducing its processing time. In this regard, Graphics Processing Units (GPUs) are used through OpenCL to parallelize and accelerate the evaluation of the models. The results obtained in the computational experiments show that the high-performance CGP on GPU is suitable for inferring GRNs due to its significant reduction in the processing time when compared to the standard sequential CGP. Also, the proposal obtained better or competitive results when compared to state-of-art algorithms. Furthermore, the proposal outputs an interpretable solution that can help the domain experts in the field of Systems Biology.
Genetic programming, standardisation, and stochastic gradient descent revisited

The use of Z-score standardisation to improve the performance of genetic programming is well known within symbolic regression. Additionally, Z-score standardisation is known to be a key element in the effective application of stochastic gradient descent. However, a thorough treatment of genetic programming with stochastic gradient descent (GPZGD) does not exist in the literature. This paper introduces a recalibrated variant of GPZGD and tests its performance within the recently-proposed SRBench framework: the resulting variant of GPZGD demonstrates excellent performance relative to existing symbolic regression methods. Additionally, this paper provides some exploration of SRBench itself and suggests areas of potential improvement to increase the utility of the SRBench framework.
Genetic algorithm cleaning in sequential data mining

Sequence mining has been receiving increasing attention from the data science community as it helps reveal the underlying patterns in a given phenomenon. For instance, clustering sequences of "actions" taken by users provide understandable trajectories of the events. However, such sequences often include inessential actions recorded during data collection. It is generally challenging to distinguish between noise and information during clustering. This paper proposes using a Genetic Algorithm (GA) to denoise the sequences properly before they undergo clustering. The intent is to improve the clustering quality, as measured by Silhouette Score. We present preliminary results obtained by applying this approach to the data collected by a well-established Intelligent Tutoring System (Epplets.org) used by novice programmers. Our findings reveal that the GA can discover sequence cleanings that improve the quality of the clustering process. These results motivate further research into applying Evolutionary Techniques to data cleaning in sequence mining.