Reviewerly/exHarmony huggingface dataset:
Multi-agent modeling and simulation in the AI age

With the rapid development of artificial intelligence (AI) technology and its successful application in various fields, modeling and simulation technology, especially multi-agent modeling and simulation (MAMS), of complex systems has rapidly advanced. In this study, we first describe the concept, technical advantages, research steps, and research status of MAMS. Then we review the development status of the hybrid modeling and simulation combining multi-agent and system dynamics, the modeling and simulation of multi-agent reinforcement learning, and the modeling and simulation of large-scale multi-agent. Lastly, we introduce existing MAMS platforms and their comparative studies. This work summarizes the current research situation of MAMS, thus helping scholars understand the systematic technology development of MAMS in the AI era. It also paves the way for further research on MAMS technology.
Predicting weathering indices in soils using FTIR spectra and random forest models

• MIA, CIA, CIW and V were suitable indices for evaluation of weathering rate in aridisols. • FTIR was a suitable approach for prediction of weathering indices in a short time. • RF model was able to identify the important bands for different weathering indices. Weathering indices based on the relative proportions of different chemical elements are a useful tool to investigate the degree of weathering of soils. This characterization is missing in West Azerbaijan, northern Iran, and thus the main goals of this work were to assess the suitability of different indices to determine soil weathering, and to predict weathering indices using Fourier Transform Mid-infrared (FTIR) spectroscopy and Random Forest (RF) models. Soil major elemental oxides were determined in 12 profiles by X-ray Fluorescence (XRF), and the degree of chemical weathering was characterized using various indices: Chemical Index of Alteration (CIA), Chemical Index of Weathering (CIW), Mineralogical Index of Alteration (MIA), Weathering Index of Parker (WIP), SiO 2 /R 2 O 3 , Vogt Index (V) and Chemical Proxy of Alteration (CPA). For each index, a RF model based on the FTIR spectra of the samples was optimized and calibrated. The soils showed a weak to moderate degree of weathering, and CIA, CIW, MIA and V were the most appropriate for this characterization. The constructed models showed R 2 values of 0.79, 0.75 and 0.71 for MIA, CIA, and CIW, respectively. The important spectral bands for prediction were those related to the presence of smectitic clays, which indicates that weathering is related to the alteration of primary minerals and the neoformation of smectites. The predicted spatial distribution of the weathering indices showed that Typic Haplocalcids and Typic Haploxerepts are at an earlier stage of weathering compared to Haplargids and Haplocambids in the area. The combination of FTIR spectroscopy and RF models is a rapid, efficient, and cost-effective technique to predict weathering indices in large datasets when XRF data are limited.
Quantum Monte Carlo Integration: The Full Advantage in Minimal Circuit Depth

This paper proposes a method of quantum Monte Carlo integration that retains the full quadratic quantum advantage, without requiring any arithmetic or quantum phase estimation to be performed on the quantum computer. No previous proposal for quantum Monte Carlo integration has achieved all of these at once. The heart of the proposed method is a Fourier series decomposition of the sum that approximates the expectation in Monte Carlo integration, with each component then estimated individually using quantum amplitude estimation. The main result is presented as theoretical statement of asymptotic advantage, and numerical results are also included to illustrate the practical benefits of the proposed method. The method presented in this paper is the subject of a patent application [Quantum Computing System and Method: Patent application GB2102902.0 and SE2130060-3].
Convergence rate of Artificial Neural Networks for estimation in software development projects

Nowadays, companies are investing in brand new software, given that fact they always need help with estimating software development, effort, costs, and the period of time needed for completing the software itself. In this paper, four different architectures of Artificial Neural Networks (ANN), as one of the most desired tools for predicting and estimating effort in software development, were used. This paper aims to determine the convergence rate of each of the proposed ANNs, when obtaining the minimum relative error, first depending on the cost effect function, then on the nature of the data on which the training, testing, and validation is performed. Magnitude relative error (MRE) is calculated based on Taguchi’s orthogonal plans for each of these four proposed ANN architectures. The fuzzification method, five different datasets, the clustering method for input values of each dataset, and prediction were used to achieve the best model for estimation. Based on performed parts of the experiment, it can be concluded that the convergence rate of each proposed architecture depends on the cost effect function and the nature of projects in different datasets. By following the prediction throughout all experimental parts, it can be further confirmed that ANN-L36 gave the best results in this proposed approach. The main advantages of this model are as follows: the number of iterations is less than 10, shortened effort estimation time thanks to convergence rate, simple architecture of each proposed ANN, large coverage of different values of actual project efficiency, and minimal MMRE. This model can also serve as an idea for the construction of a tool that would be able to reliably, efficiently and accurately estimate the effort when developing various software projects.
The Epistemology of No Platforming: Defending the Defense of Stupid Ideas on University Campuses

No platforming is the practice of preventing or prohibiting someone from contributing to public discussion because that person advances what are—or are thought to be—objectionable views. Some of the most newsworthy cases of no platforming occur on university campuses. Despite what others have claimed, there are no good epistemic reasons for no platforming in that context.
Implement Image Encryption on Chaotic and Discrete Transform Domain Encryption

The standard uproarious image encryption estimation has a couple of issues, for instance, low security, picture scrambling, and scattering, low efficiency and tranquil affectability, and high relationship. Accordingly, improved picture encryption figuring subject to disrupted mapping and discrete wavelet change space is proposed in this paper. As an issue of first significance, the image scrambling just changes the circumstance of each pixel, aside from can't change the pixel regard. Its quantifiable features don't change. Thusly, picture encryption estimation, by and large, grasps the blend of scrambling and dim scale spread, through various rounds of encryption, to improve the image chaos and scattering characteristics. By then, the image is taken care of by wavelet change. Finally, the image is mapped by chaos. Exploratory results show that the computation has certain central focuses on true execution, generous execution, and key affectability and meets the necessities of picture security and consistent execution.
Implementation of a Secure Ride-Sharing DApp Using Smart Contracts on Ethereum Blockchain

The idea of sharing economy gives rise to unique ideas and develops innovative businesses. This article aims to relate the smart city concept by introducing the smart transport system and explores the opportunities of adopting blockchain technology in ride-sharing services. Blockchain technology is a distributed, decentralized public ledger that allows peer-to-peer transactions in a secured way without any third party. This paper proposes a blockchain-based framework from the existing centralized framework for a ride-sharing service and implements the same as a decentralized application (DApp) based on smart contracts on Ethereum Blockchain. Using smart contracts facilitate the users with automated transactions, removes the intermediaries, and enables various activities to be carried out safely and securely. Implementation of smart contracts is done using the Solidity programming language. This DApp uses the min matching algorithm to match riders requesting rideshare to save total travel distance. With the overwhelming growth in the usage of cryptocurrencies, smart contracts usage in applications as proposed in this paper can transform the sharing economy.
Evaluating Saliency Methods for Neural Language Models

Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model.In these cases, how do we identify when are these interpretations trustworthy enough to be used in analyses?To address this question, we conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models.We evaluate the quality of prediction interpretations from two perspectives that each represents a desirable property of these interpretations: plausibility and faithfulness.Our evaluation is conducted on four different datasets constructed from the existing human annotation of syntactic and semantic agreements, on both sentencelevel and document-level.Through our evaluation, we identified various ways saliency methods could yield interpretations of low quality.We recommend that future work deploying such methods to neural language models should carefully validate their interpretations before drawing insights.
StEER: A Community-Centered Approach to Assessing the Performance of the Built Environment after Natural Hazard Events

Since its founding in 2018, the Structural Extreme Events Reconnaissance (StEER) Network has worked to deepen the capacity of the Natural Hazards Engineering (NHE) community for coordinated and standardized assessments of the performance of the built environment following natural hazard events. This paper positions StEER within the field of engineering reconnaissance and the Natural Hazards Engineering Research Infrastructure (NHERI), outlining its organizational model for coordinated community-led responses to wind, seismic, and coastal hazard events. The paper’s examination of StEER’s event response workflow, engaging a range of hardware and delivering a suite of products, demonstrates StEER’s contributions in the areas of: workflow and data standardization, data reliability to enable field-observation-driven research &amp;amp; development, efficiency in data collection and dissemination to speed knowledge sharing, near-real- time open data access for enhanced coordination and transparency, and flexibility in collaboration modes to reduce the “overhead” associated with reconnaissance and foster broad NHE community engagement in event responses as part of field and virtual assessment structural teams (FAST/VAST). StEER’s creation of efficient systems to deliver well-documented, reliable data suitable for diverse re-uses as well as rapidly disseminated synopses of the impact of natural hazard events on the built environment provide a distinctive complement to existing post-event reconnaissance initiatives. The implementation of these policies, protocols and workflows is then demonstrated with case studies from five events illustrating StEER’s different field response strategies: the Nashville, Tennessee Tornadoes (2020) – a Hazard Gradient Survey; the Palu Earthquake and Tsunami in Indonesia (2018) – a Representative Performance Study; the Puerto Rico Earthquakes (2019/2020) – using Targeted Case Studies; Hurricane Laura (2020) – leveraging Rapid Surveys to enable virtual assessments; and Hurricane Dorian (2019) in the Bahamas – a Phased Multi-Hazard Investigation. The use of these strategies has enabled StEER to respond to 36 natural hazard events, involving over 150 different individuals to produce 45 published reports/briefings, over 5000 publicly available app-based structural assessments, and over 1600 km (1000 mi) of street-level panoramic imagery in its first 2years of operation.
The Drawing and Perception of Architectural Spaces through Immersive Virtual Reality

The technologies that have sought to intervene in the architectural drawing process have focused on the sense of sight, leaving aside the use of the hands and the entire body that together achieve more sensory designs. Nowadays, to the benefit of the draftsman, that ideal scenery in which sight, hands and body work holistically is returning thanks to Immersive Virtual Reality (IVR). The purpose of this research is to analyze the perception of two-dimensionally drawn spaces, the drawing of such spaces through three-dimensional sketches in IVR, and both the perception of 3D sketched spaces and those which are also modeled realistically in IVR. First and fifth year architecture students went through the four phases of the experiment: (a) the perception of a space based on 2D sketches, (b) real-scale 3D space drawing in IVR, (c) the perception of a space drawn in 3D in IVR, and (d) the perception of the same space realistically modeled in 3D in IVR. Through three questionnaires and a grading sheet, the data was obtained. The perception of two-dimensionally drawn spaces was high (70.8%), while the precision of a space drawn in an IVR was even higher (83.9%). The real or natural scale in which the spaces can be experienced in an IVR is the characteristic that was most recognized by the students; however, this and the other qualities did not allow for a reliable conclusion for a homogeneous perception of sensations within the virtual spaces.
Off-The-Shelf Stylus: Using XR Devices for Handwriting and Sketching on Physically Aligned Virtual Surfaces

This article introduces the Off-The-Shelf Stylus (OTSS), a framework for 2D interaction (in 3D) as well as for handwriting and sketching with digital pen, ink, and paper on physically aligned virtual surfaces in Virtual, Augmented, and Mixed Reality (VR, AR, MR: XR for short). OTSS supports self-made XR styluses based on consumer-grade six-degrees-of-freedom XR controllers and commercially available styluses. The framework provides separate modules for three basic but vital features: 1) The stylus module provides stylus construction and calibration features. 2) The surface module provides surface calibration and visual feedback features for virtual-physical 2D surface alignment using our so-called 3ViSuAl procedure, and surface interaction features. 3) The evaluation suite provides a comprehensive test bed combining technical measurements for precision, accuracy, and latency with extensive usability evaluations including handwriting and sketching tasks based on established visuomotor, graphomotor, and handwriting research. The framework’s development is accompanied by an extensive open source reference implementation targeting the Unity game engine using an Oculus Rift S headset and Oculus Touch controllers. The development compares three low-cost and low-tech options to equip controllers with a tip and includes a web browser-based surface providing support for interacting, handwriting, and sketching. The evaluation of the reference implementation based on the OTSS framework identified an average stylus precision of 0.98 mm ( SD = 0.54 mm) and an average surface accuracy of 0.60 mm ( SD = 0.32 mm) in a seated VR environment. The time for displaying the stylus movement as digital ink on the web browser surface in VR was 79.40 ms on average ( SD = 23.26 ms), including the physical controller’s motion-to-photon latency visualized by its virtual representation ( M = 42.57 ms, SD = 15.70 ms). The usability evaluation ( N = 10) revealed a low task load, high usability, and high user experience. Participants successfully reproduced given shapes and created legible handwriting, indicating that the OTSS and it’s reference implementation is ready for everyday use. We provide source code access to our implementation, including stylus and surface calibration and surface interaction features, making it easy to reuse, extend, adapt and/or replicate previous results ( https://go.uniwue.de/hci-otss ).
Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter

Speech Act Classification determining the communicative intent of an utterance has been investigated widely over the years as a standalone task.This holds true for discussion in any fora including social media platform such as Twitter.But the emotional state of the tweeter which has a considerable effect on the communication has not received the attention it deserves.Closely related to emotion is sentiment, and understanding of one helps understand the other.In this work, we firstly create a new multi-modal, emotion-TA ('TA' means tweet act, i.e., speech act in Twitter) dataset called EmoTA collected from open-source Twitter dataset.We propose a Dyadic Attention Mechanism (DAM) based multi-modal, adversarial multi-tasking framework.DAM incorporates intra-modal and inter-modal attention to fuse multiple modalities and learns generalized features across all the tasks.Experimental results indicate that the proposed framework boosts the performance of the primary task, i.e., TA classification (TAC) by benefitting from the two secondary tasks, i.e., Sentiment and Emotion Analysis compared to its uni-modal and single task TAC (tweet act classification) variants.
A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications

With the rapid development of the Internet of Things (IoT), wearable devices have become ubiquitous and interconnected in daily lives. Because wearable devices collect, transmit, and monitor humans’ physiological signals, data privacy should be a concern, as well as fully protected, throughout the whole process. However, the existing privacy protection methods are insufficient. In this paper, we propose a practical privacy-preserving mechanism for physiological signals collected by intelligent wearable devices. In the data acquisition and transmission stage, we employed existing asymmetry encryption-based methods. In the data publishing stage, we proposed a new model based on the combination and optimization of k-anonymity and differential privacy. An entropy-based personalized k-anonymity algorithm is proposed to improve the performance on processing the static and long-term data. Moreover, we use the symmetry of differential privacy and propose the temporal differential privacy mechanism for real-time data to suppress the privacy leakage while updating data. It is proved theoretically that the combination of the two algorithms is reasonable. Finally, we use smart bracelets as an example to verify the performance of our mechanism. The experiment results show that personalized k-anonymity improves up to 6.25% in terms of security index compared with traditional k-anonymity, and the grouping results are more centralized. Moreover, temporal differential privacy effectively reduces the amount of information exposed, which protects the privacy of IoT-based users.
EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain

Transformer-based neural language models have led to breakthroughs for a variety of natural language processing (NLP) tasks.However, most models are pretrained on general domain data.We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representation of biomedical terminology (PubMed-BERT) on a clinical corpus along with a novel entity-centric masking strategy to infuse domain knowledge in the learning process.We show that such a model achieves superior results on clinical extraction tasks by comparing our entity-centric masking strategy with classic random masking on three clinical NLP tasks: cross-domain negation detection (Wu et al., 2014), document time relation (Doc-TimeRel) classification (Lin et al., 2020b), and temporal relation extraction (Wright-Bettner et al., 2020).We also evaluate our models on the PubMedQA (Jin et al., 2019) dataset to measure the models' performance on a nonentity-centric task in the biomedical domain.The language addressed in this work is English.
Meta-Learned Spatial-Temporal POI Auto-Completion for the Search Engine at Baidu Maps

Point Of Interest Auto-Completion (abbr. as POI-AC) is one of the featured functions for the search engine at Baidu Maps. It can dynamically suggest a list of POI candidates within milliseconds as a user enters each character (e.g., English, Chinese, or Pinyin character) into the search box. Ideally, a user may need to provide only one character and immediately obtain the desired POI at the top of the POI list suggested by POI-AC. In this way, the user's keystrokes can be dramatically saved, which significantly reduces the time and effort of typing, especially on mobile devices that have limited space for display and user interfaces. Despite using a user's profile and input prefixes for personalized POI suggestions, however, the state-of-the-art approach, i.e., P^3AC, still has a long way to go so as to generate not only personalized but, more importantly, time- and geography-aware suggestions. In this paper, we find that 17.9% of users tend to look for diverse POIs at different times or locations using the same prefix. This insight drives us to establish an end-to-end spatial-temporal POI-AC (abbr. as ST-PAC) module to replace P3AC at Baidu Maps. To alleviate the problem of the long-tail distribution of time- and location-specific data on POI-AC, we further propose a meta-learned ST-PAC (abbr. as MST-PAC) updated by an efficient MapReduce algorithm. MST-PAC can significantly overcome the "long-tail" issue and rapidly adapt to the cold-start POI-AC tasks with fewer examples. We sample several benchmark datasets from the large-scale search logs at Baidu Maps to assess the offline performance of MST-PAC in line with multiple metrics, including Mean Reciprocal Rank (MRR), Success Rate (SR) and normalized Discounted Cumulative Gain (nDCG). The consistent improvements on these metrics give us more confidence to launch this meta-learned POI-AC module online. As a result, the critical indicator on user satisfaction online, i.e., the average number of keystrokes in a POI-AC session, significantly decreases as well. For now, MST-PAC has already been deployed in production at Baidu Maps, handling billions of POI-AC requests every day. It confirms that MST-PAC is a practical and robust industrial solution for large-scale POI Search.
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints

Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text.While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of taskspecific examples.We propose NEUROLOGIC DECODING, a simple yet effective algorithm that enables neural language models -supervised or not -to generate fluent text while satisfying complex lexical constraints.Our approach is powerful yet efficient.It handles any set of lexical constraints that is expressible under predicate logic, while its asymptotic runtime is equivalent to conventional beam search.Empirical results on four benchmarks show that NEUROLOGIC DECODING outperforms previous approaches, including algorithms that handle a subset of our constraints.Moreover, we find that unsupervised models with NEUROLOGIC DECODING often outperform supervised models with conventional decoding, even when the latter is based on considerably larger networks.Our results suggest the limit of large-scale neural networks for fine-grained controllable generation and the promise of inference-time algorithms.