Reviewerly/exHarmony huggingface dataset:
User Studies, Mediation of Information and Information Literacy in the Contexts of Social Vulnerability: Possible Dialogues

This article seeks to trace the convergences between the sub-areas/disciplines of user studies, mediation of information and information literacy aimed at the contexts of social vulnerability, seeking to present more holistic views that contribute to the discussions of the area and for the development of social, digital and cultural protagonism of unfortunate groups. In this sense, the methodological procedures were established through the ends as a qualitative, exploratory and descriptive research, seeking to discuss the theoretical-conceptual assumptions and the possible dialogues among the themes brought. Also, the study is classified as bibliographic, since it used reading, analysis and interpretation of concepts and theories about the themes brought up, seeking to present such assumptions and dialogues around the bias of the social perspective of Information Science. As a result of the discussion, it is understood that user studies, mediation of information and information literacy can be considered interference actions, as they are concerned with the understanding and training of critical and reflective citizens in the face of socio-informational realities. The set of these actions consolidates an investigative posture of both the one who teaches as well the apprentice, as it allows empowerment and emancipation in the tangle of available information.
Evaluating Audio-to-Text utilizing Dragon in the Context of Just-in-Time Requirements

The research in Just-in-time (JIT) requirements has evidently advanced in the past few years. Many papers have been published in this area, however, less attention has been paid to the area of capturing verbal JIT requirements and evaluating the audio-to-text applications. In many situations, these requirements are obtained informally with ongoing communication between stakeholders. It is of paramount concern to provide the best quality record of these verbal interactions and meetings, and lack of detail would inevitably create problems later in the development cycle. Speech transcription is considered to be an essential component in capturing verbal JIT requirements. Software that transcribes audio-to-text and provides a complete and accurate set of requirements specifications is needed. To that end, we conducted a preliminary evaluation of Nuance Dragon Naturally Speaking (DNS) software and the results show that DNS achieved 88.83% accuracy in transcribing the verbal requirements.
An Efficient Federated Genetic Programming Framework for Symbolic Regression

Symbolic regression is an important method of data-driven modeling, which can provide explicit mathematical expressions for data analysis. However, the existing genetic programming algorithms for symbolic regression require centralized storage of all data, which is unrealistic in many practical applications that involve data privacy. If the data comes from different sources, such as hospitals and banks, it is prone to privacy breaches and security issues. To this end, we propose an efficient federated genetic programming framework that can train a global model without integrated data. Each client can process decentralized data locally in parallel, without sending the original data to the server. This method not only protects the privacy of the data but also reduces the time required for data collection. Moreover, a mean shift aggregation mechanism is developed for aggregating local fitness. Considering the samples <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\prime }$</tex-math></inline-formula> relative importance, the mechanism improves the imbalance of symbolic regression data on real-life by incorporating weights into fitness function. Furthermore, based on this framework and self-learning gene expression programming (SL-GEP), a federated self-learning gene expression programming algorithm is developed. The experimental results show that, compared with standard SL-GEP which is a training model based on decentralized data only, our proposed federated genetic programming method is effective to protect data privacy and can have consistently better generalization performance.
The role of coherence theory in attractor quantum neural networks

We investigate attractor quantum neural networks (aQNNs) within the framework of coherence theory. We show that: i) aQNNs are associated to non-coherence-generating quantum channels; ii) the depth of the network is given by the decohering power of the corresponding quantum map; and iii) the attractor associated to an arbitrary input state is the one minimizing their relative entropy. Further, we examine faulty aQNNs described by noisy quantum channels, derive their physical implementation and analyze under which conditions their performance can be enhanced by using entanglement or coherence as external resources.
ReasonFuse: Reason Path Driven and Global–Local Fusion Network for Numerical Table-Text Question Answering

Numerical Table-Text Question Answering aims to predict the program over heterogeneous tabular and textual information, which has recently attracted strong attention in the NLP research community. Current state-of-the-art methods typically use a Seq2Seq-based model to predict programs, which may suffer from the inherent limitations of error propagation. Also, existing models just combine table-text context information and previously predicted operation when reasoning. This approach may lose information over long distances especially when the program is long, and fail to exploit the inter-dependencies between operations in the program. In this paper, we propose a joint Reason Path Driven and Global–Local Fusion Network (ReasonFuse) to alleviate the above problems. First of all, inspired by the intuition that operator prediction is a classification task while operand prediction is an extraction task, we propose a reason path driven architecture that predicts operators first and then guides the operands prediction jointly. After that, the global–local information fusion mechanism is designed to aggregate the context information and global–local program information to mitigate the loss of information over long distances. Last but not least, the soft-dropout mechanism is applied when training to improve the ability and generalization of the model. We conduct extensive experiments on the FinQA and MathQA datasets, our model achieves significant improvements as compared to all published results. The ablation study and case study verify the effectiveness, generalization, and interpretability of our model.
Implementation of Remote-Sensing Data Processing Platform Based on Computable Storage

Previous researches on accelerating remote sensing data processing are based on traditional von Neumann architecture, which separates storage and computation. Under the architecture, data must be obtained from the storage device first and then transmitted to Field Programmable Gate Array (FPGA) through the system bus. The power consumption caused by the data handling is huge, even exceeding the energy consumption required for data processing. In order to reduce the migration of remote sensing data and alleviate the problems of storage wall and power wall under von Neumann architecture, we design a remote sensing data processing platform based on the system architecture of computable storage, which uses Solid-State Disk (SSD) with computing capability to process the remote sensing data and realize accelerated remote sensing data processing. Based on this platform, applications related to remote sensing data processing such as compression, target detection, and image classification are deployed in SSD to improve the information acquisition rate in remote sensing data. Experimental results show that after compression being offloaded to SSD computing performance is improved by 2.27 times compared with the host CPU. Compared with the host GPU, the target detection speed is improved by 6.25% and the power consumption is reduced by 66.7%. Compared with the host, the detection speed of remote sensing image classification is improved by 78.8%, the power consumption is reduced by 70%, achieving the expected classification effect. The Remote Sensing data Processing Platform based on Computable Storage (CSRSPP) distributes various computing tasks to the SSD for execution, which not only improves the processing speed of computing tasks, but also greatly reduces the power consumption of the platform.
Path-Wise Attention Memory Network for Visual Question Answering

Visual question answering (VQA) is regarded as a multi-modal fine-grained feature fusion task, which requires the construction of multi-level and omnidirectional relations between nodes. One main solution is the composite attention model which is composed of co-attention (CA) and self-attention(SA). However, the existing composite models only consider the stack of single attention blocks, lack of path-wise historical memory, and overall adjustments. We propose a path attention memory network (PAM) to construct a more robust composite attention model. After each single-hop attention block (SA or CA), the importance of the cumulative nodes is used to calibrate the signal strength of nodes’ features. Four memoried single-hop attention matrices are used to obtain the path-wise co-attention matrix of path-wise attention (PA); therefore, the PA block is capable of synthesizing and strengthening the learning effect on the whole path. Moreover, we use guard gates of the target modal to check the source modal values in CA and conditioning gates of another modal to guide the query and key of the current modal in SA. The proposed PAM is beneficial to construct a robust multi-hop neighborhood relationship between visual and language and achieves excellent performance on both VQA2.0 and VQA-CP V2 datasets.
A Comprehensive Study on Namecoin

Abstract: Financial transaction networks are among the world's biggest networks. The digital (crypto) currency network, such as Bitcoin, is a relatively new sort of financial network. Namecoin is a cryptocurrency that is based on Bitcoin and includes functionalities such as DNS. The Namecoin network has nearly 17 million edges and over 2 million nodes. The analysis of such a crypto currency network can aid in the modelling or prediction of future transaction network growth. We evaluated the Namecoin blockchain data in 7 six-month periods in order to analyse the transaction network graph over time. In comparison to Bitcoin, our data imply that user behaviour and development patterns are different [1]. "An experimental open-source technology that improves the decentralisation, security, censorship resistance, privacy, and speed of certain components of the Internet infrastructure, such as DNS and identities," according to Namecoin's definition.
The Impact of Human Resource Flexibility on Organizational Success

The Impact Of Green Supply Chain Management Practices On Competitive Advantage: Applied Study On Industrial Companies In Jordan. The study aims to show the impact of green supply chain management practices and their impact on achieving competitive advantage in industrial organizations in Jordan. Descriptive and analytical approach was applied. The study sample of (200) employees in the supervision level(top management) in industrial organizations in the Hashemite Kingdom of Jordan. The researcher used a questionnaire to collect data that included the green supply chain management measure and the measure of competitive advantage, and verified indicators of its validity and reliability. The results of the study showed that the practices of green supply chain management in industrial organizations in Jordan came with a medium degree for all domains (green procurement, green manufacturing, green marketing) and the scale as a whole, and the competitive advantage in industrial organizations in Jordan came with a high degree for the scale as a whole and for the dimensions (creativity, quality, and Flexibility), while it was rated medium for the two dimensions (cost, and reputation). The results indicated that there is a statistically significant effect at the level of significance (α≤0.05) between green supply chain management prctices and all dimensions of competitive advantage (cost, quality, creativity, reputation, flexibility) and competitive advantage as a whole in industrial organizations. Keywords: Green supply chain management practices, competitive advantage, industrial organizations, Jordan.
Forensic cloud environment: a solution for big data forensics

Big data forensics is a new and interesting research field because of the enormous amount of data and a variety of digital sources that are available today. Many of the proposed techniques in the literature use a workstation environment to carry out digital forensics investigations involving big data. The challenge of these techniques includes lack of evidence correlation, intelligence and knowledge sharing, and security lapses. In this paper, we propose a method to carry out digital forensic involving big data in the cloud. The key ingredient of our solution is a novel cloud environment called forensic cloud environment (FCE). We outline the key components of FCE, for each component and we discuss the role it plays in FCE's ability to handle big data. Finally, we evaluate the efficacy of FCE against forensic toolkit (FTK). The performance evaluation indicates that the FCE performs much better than FTK when dealing with big data forensic.
Vehicle Detection In Remote Sensing Images

Traffic monitoring and management is one of the most crucial tasks of governing bodies in modern big cities. With each passing day the traffic problem grows in complexity due to the continuous increase of participating vehicles and the hard expansion of the road network and parking places. In this article we introduce a new method for vehicle detection and localization in parking lots using high resolution UAV images. In order to end up with practical and yet effective approach, which could be implemented on low computing hardware resources and integrated with the camera in the UAV, we considered simple steps in the proposed algorithm for optimization. It follows the machine learning pipeline such as preprocessing, sensing, feature extraction, training and classification. In preprocessing the images are thresholded iteratively in multiple color spaces to extract the candidate regions of interest (ROI). The algorithm relies on point and shape features using fast techniques in the feature extraction. The features are then clustered by the K-means algorithm and represented by the resulted clusters’ centers. Region based linear classification is finally applied using SVM to classify if the object is a vehicle or else. The proposed approach proved high detection and classification accuracy more than 86% and still running under the low complexity constraint..
Data Availability Statements in <i>The Journal of Organic Chemistry</i>, <i>Organic Letters</i>, and <i>ACS Organic &amp; Inorganic Au</i>

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTData Availability Statements in The Journal of Organic Chemistry, Organic Letters, and ACS Organic & Inorganic AuGéraldine Masson*Géraldine MassonInstitut de Chimie des Substances Naturelles, Université Paris Saclay, CNRS, UPR2301, 1 Avenue de la Terrasse, Gif-sur-Yvette, 91198 Cedex, France*Email: [email protected]More by Géraldine Massonhttps://orcid.org/0000-0003-2333-7047, Marisa C. Kozlowski*Marisa C. KozlowskiDepartment of Chemistry, Roy and Diana Vagelos Laboratories, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States*Email: [email protected]More by Marisa C. Kozlowskihttps://orcid.org/0000-0002-4225-7125, and Scott J. Miller*Scott J. MillerDepartment of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States*Email: [email protected]More by Scott J. Millerhttps://orcid.org/0000-0001-7817-1318Cite this: ACS Org. Inorg. Au 2022, 2, 5, 370–372Publication Date (Web):September 12, 2022Publication History Received30 August 2022Published online12 September 2022Published inissue 5 October 2022https://doi.org/10.1021/acsorginorgau.2c00049Copyright © 2022 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceCC: Creative CommonsBY: Credit must be given to the creatorNC: Only noncommercial uses of the work are permittedND: No derivatives or adaptations of the work are permittedArticle Views606Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (3 MB) Get e-AlertsSUBJECTS:Elements,Gold,Optical properties,Organic chemistry,Quality management Get e-Alerts
Numerical investigation of effects of tongue articulation and velopharyngeal closure on the production of sibilant [s]

A numerical simulation of sibilant /s/ production with the realistically moving vocal tract was conducted to investigate the flow and acoustic characteristics during the articulation process of velopharyngeal closure and tongue movement. The articulation process was simulated from the end of /u/ to the middle of /s/ in the Japanese word /usui/, including the tongue elevation and the velopharyngeal valve closure. The time-dependent vocal tract geometry was reconstructed from the computed tomography scan. The moving immersed boundary method with the hierarchical structure grid was adopted to approach the complex geometry of the human speech organs. The acoustic characteristics during the co-articulation process were observed and consistent with the acoustic measurement for the subject of the scan. The further simulations with the different closing speeds of the velopharyngeal closure showed that the far-field sound during the co-articulation process was amplified with the slower closing case, and the velum closure speed was inverse proportional to the sound amplitude with the slope value of - 35.3 dB s/m. This indicates possible phonation of indistinguishable aeroacoustics sound between /u/ and /s/ with slower velopharyngeal closure.
Human Activity Recognition Using Single Frame CNN

Human Activity Recognition (HAR) is one of the most ongoing research fields in computer vision for different contexts like surveillance, military, healthcare. So Human Activity Recognition is a category of time series classification problem that needs to be solved. Here the data from a series of timestamps to accurately detect the action that is present in the video is considered. This study uses single- frame Convolutional Neural Networks as a learning method for Human Activity Recognition (HAR). Unlike the other conventional machine learning methods, which require expertise on domain-specific knowledge, Convolutional neural networks can extract the features automatically. This study comprises reading and pre-processing the dataset, constructing the Convolutional Neural Networks model as per need, Compiling and training the model and using the single-frame CNN method. This approach is implemented using various technologies like Tensor flow, Open cv.
Center point to pose: Multiple views 3D human pose estimation for multi-person

3D human pose estimation has always been an important task in computer vision, especially in crowded scenes where multiple people interact with each other. There are many state-of-the-arts for object detection based on single view. However, recovering the location of people is complicated in crowded and occluded scenes due to the lack of depth information for single view, which is the lack of robustness. Multi-view Human Pose Estimation for Multi-Person became an effective approach. The previous multi-view 3D human pose estimation method can be attributed to a strategy to associate the joints of the same person from 2D pose estimation. However, the incompleteness and noise of the 2D pose are inevitable. In addition, how to associate the joints itself is challenging. To solve this issue, we propose a CTP (Center Point to Pose) network based on multi-view which directly operates in the 3D space. The 2D joint features in all cameras are projected into 3D voxel space. Our CTP network regresses the center of one person as the location, and the 3D bounding box as the activity area of one person. Then our CTP network estimates detailed 3D pose for each bounding box. Besides, our CTP network is Non-Maximum Suppression free at the stage of regressing the center of one person, which makes it more efficient and simpler. Our method outperforms competitively on several public datasets which shows the efficacy of our center point to pose network representation.
Fast Solar Image Retrieval and Classification by Fuzzy Rules

The paper proposes a method for classifying and fast retrieving full-disk images of the Sun chromosphere and corona collected by the Solar Dynamics Observatory spacecraft. The method uses a convolutional autoencoder to encode the solar images in the form of a concise semantic hash. The speed of the retrieval comes from the boosting meta-learning to construct a set of fuzzy rules describing the hash parameters. The rules constitute a set of weak classifiers voting for the final image class. This allows for fast retrieving similar images in vast collections of solar images.
Text-Guided Sketch-to-Photo Image Synthesis

We propose a text-guided sketch-to-image synthesis model that semantically mixes style and content features from the latent space of an inverted Generative Adversarial Network (GAN). Our goal is to synthesize plausible images from human facial sketches and their respective text descriptions. In our approach, we adapted a generative model termed Contextual GAN (CT-GAN) that efficiently encodes visual-linguistic semantic features pre-trained on over 400 million text-image pairs at different resolutions along the model. Also, we introduced an intermediate mapping network called c-Map that combines textual and visual-based features to a disentangled latent space <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</i> + for better feature matching. Furthermore to maximise the computational performance of our model, we implemented a linear-based attention scheme along the pipeline of our model to eliminate the drawbacks of inefficient attention modules that are quadratic in complexity. Finally, the hierarchical setting of our model ensures that textual, style and content features are synthesised based on their unique fine grained details, which result in visually appealing images.