Reviewerly/exHarmony huggingface dataset:
Personalized Watch-Based Fall Detection Using a Collaborative Edge-Cloud Framework

The majority of current smart health applications are deployed on a smartphone paired with a smartwatch. The phone is used as the computation platform or the gateway for connecting to the cloud while the watch is used mainly as the data sensing device. In the case of fall detection applications for older adults, this kind of setup is not very practical since it requires users to always keep their phones in proximity while doing the daily chores. When a person falls, in a moment of panic, it might be difficult to locate the phone in order to interact with the Fall Detection App for the purpose of indicating whether they are fine or need help. This paper demonstrates the feasibility of running a real-time personalized deep-learning-based fall detection system on a smartwatch device using a collaborative edge-cloud framework. In particular, we present the software architecture we used for the collaborative framework, demonstrate how we automate the fall detection pipeline, design an appropriate UI on the small screen of the watch, and implement strategies for the continuous data collection and automation of the personalization process with the limited computational and storage resources of a smartwatch. We also present the usability of such a system with nine real-world older adult participants.
Internal displacement measurements based on color fluorescent speckle pattern by multispectral stereo-DIC and refractive index correction

Fluorescent digital image correlation (DIC) is becoming popular for measuring 3D profiles and deformations in external surfaces. However, the simultaneous monitoring of interior layers is highly challenging due to the penetrability and refraction of light using monochromatic fluorescence. We propose a color fluorescent speckle pattern (CFSP) method for measuring the internal displacement of transparent objects based on multispectral stereo-DIC and refractive index correction. During sample fabrication, fluorescent speckle patterns exciting different colors are fabricated on both the surface and interior layers of objects. A virtual color stereo-DIC system is utilized to capture the CFSP on the surface and interior layers simultaneously from two different perspectives. Different color channels are practically equivalent to synchronized monochrome vision systems, having separate CFSP in external and internal measurements. In multispectral stereo-DIC calculation, the external surface is initially reconstructed through one channel of the system even if the surface is non-planar. Based on Snell’s law and the CFSP method, the internal layer is then reconstructed and corrected by establishing the geometry of the refractive stereo-DIC through another channel. The relative error of the thickness between two planar layers was proved to decrease from 33.4% to 0.7% after refractive index correction. Further experimental results validate the efficacy of this method for correcting the profile of the non-planar arc profile and determining the internal deformations of disc materials.
Learning Critically: Selective Self-Distillation in Federated Learning on Non-IID Data

Federated learning (FL) enables multiple clients to collaboratively train a global model while keeping local data decentralized. Data heterogeneity (non-IID) across clients has imposed significant challenges to FL, which makes local models re-optimize towards their own local optima and forget the global knowledge, resulting in performance degradation and convergence slowdown. Many existing works have attempted to address the non-IID issue by adding an extra global-model-based regularizing item to the local training but without an adaption scheme, which is not efficient enough to achieve high performance with deep learning models. In this paper, we propose a Selective Self-Distillation method for Federated learning (FedSSD), which imposes adaptive constraints on the local updates by self-distilling the global model&#x2019;s knowledge and selectively weighting it by evaluating the credibility at both the class and sample level. The convergence guarantee of FedSSD is theoretically analyzed and extensive experiments are conducted on three public benchmark datasets, which demonstrates that FedSSD achieves better generalization and robustness in fewer communication rounds, compared with other state-of-the-art FL methods.
Hierarchical Sketch Induction for Paraphrase Generation

We propose a generative model of paraphrase generation, that encourages syntactic diversity by conditioning on an explicit syntactic sketch.We introduce Hierarchical Refinement Quantized Variational Autoencoders (HRQ-VAE), a method for learning decompositions of dense encodings as a sequence of discrete latent variables that make iterative refinements of increasing granularity.This hierarchy of codes is learned through end-to-end training, and represents fine-to-coarse grained information about the input.We use HRQ-VAE to encode the syntactic form of an input sentence as a path through the hierarchy, allowing us to more easily predict syntactic sketches at test time.Extensive experiments, including a human evaluation, confirm that HRQ-VAE learns a hierarchical representation of the input space, and generates paraphrases of higher quality than previous systems.
TS-ANNO: An Annotation Tool to Build, Annotate and Evaluate Text Simplification Corpora

We introduce TS-ANNO, an open-source web application for manual creation and for evaluation of parallel corpora for text simplification.TS-ANNO can be used for i) sentencewise alignment, ii) rating alignment pairs (e.g., w.r.t.grammaticality, meaning preservation, ...), iii) annotating alignment pairs w.r.t.simplification transformations (e.g., lexical substitution, sentence splitting, ...), and iv) manual simplification of complex documents.For evaluation, TS-ANNO calculates inter-annotator agreement of alignments i) and annotations ii).
Quantified Reproducibility Assessment of NLP Results

This paper describes and tests a method for carrying out quantified reproducibility assessment (QRA) that is based on concepts and definitions from metrology.QRA produces a single score estimating the degree of reproducibility of a given system and evaluation measure, on the basis of the scores from, and differences between, different reproductions.We test QRA on 18 system and evaluation measure combinations (involving diverse NLP tasks and types of evaluation), for each of which we have the original results and one to seven reproduction results.The proposed QRA method produces degree-of-reproducibility scores that are comparable across multiple reproductions not only of the same, but of different original studies.We find that the proposed method facilitates insights into causes of variation between reproductions, and allows conclusions to be drawn about what changes to system and/or evaluation design might lead to improved reproducibility.
Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting

Word sense disambiguation (WSD) is a crucial problem in the natural language processing (NLP) community.Current methods achieve decent performance by utilizing supervised learning and large pre-trained language models.However, the imbalanced training dataset leads to poor performance on rare senses and zero-shot senses.There are more training instances and senses for words with top frequency ranks than those with low frequency ranks in the training dataset.We investigate the statistical relation between word frequency rank and word sense number distribution.Based on the relation, we propose a Z-reweighting method on the word level to adjust the training on the imbalanced dataset.The experiments show that the Z-reweighting strategy achieves performance gain on the standard English all words WSD benchmark.Moreover, the strategy can help models generalize better on rare and zero-shot senses.
Introduction to Clinical Prediction Models

Clinical prediction models include a diagnostic prediction model to estimate the probability of an individual currently having a disease (e.g., pulmonary embolism) and a prognostic prediction model to estimate the probability of an individual developing a specific health outcome over a specific time period (e.g., myocardial infarction and stroke in 10 years). Clinical prediction models can be developed by applying traditional regression models (e.g., logistic and Cox regression models) or emerging machine learning models to real-world data, such as electronic health records and administrative claims data. For derivation, researchers select candidate variables based on a literature review and clinical knowledge, and predictor variables in the final model based on pre-defined criteria (e.g., thresholds for the size of relative risk and p-values) or strategies such as the stepwise regression and the least absolute shrinkage and selection operator (LASSO) regression. For validation, the clinical prediction model’s performance is evaluated in terms of goodness of fit (e.g., R2), discrimination (e.g., area under the receiver operating characteristic curve or c-statistics), and calibration (e.g., calibration plot and Hosmer-Lemeshow test). Performance of a new variable added to an existing clinical prediction model is evaluated in terms of reclassification (e.g., net reclassification improvement and integrated discrimination improvement). The model should be validated using the original data to examine internal validity through methods such as resampling (e.g., cross-validation and bootstrapping) and using other participants’ data to examine external validity. For successful implementation of a clinical prediction model in actual clinical practice, presentation methods such as paper-based (nomogram) or web-based calculator and an easy-to-use risk score should be considered.
FIFA: Fast Inference Approximation for Action Segmentation

We introduce FIFA, a fast approximate inference method for action segmentation and alignment. Unlike previous approaches, FIFA does not rely on expensive dynamic programming for inference. Instead, it uses an approximate differentiable energy function that can be minimized using gradient-descent. FIFA is a general approach that can replace exact inference, improving its speed by more than 5 times while maintaining its performance. FIFA is an anytime inference algorithm that provides a better speed vs. accuracy trade-off compared to exact inference. We apply FIFA on top of state-of-the-art approaches for weakly supervised action segmentation and alignment as well as fully supervised action segmentation. FIFA achieves state-of-the-art results for most metrics on two action segmentation datasets.
Performance analysis of reconstructed image using colour demosaicing algorithm for digital images

Demosaicing algorithm is emerging as one of the most promising techniques in digital image processing because of providing high-quality image enhancement with low power and high speed. Edge detector, weighting block of anisotropic type, and a compensator with filters are the components of the proposed algorithm. Spatial filters and Laplacian methods are used in the filter-based compensation methodology. These improve edge detection, and the blurring effect is reduced. Hardware sharing reduces the cost of the system. Portable and fast systems are required to produce the test results in a short span. For the above-mentioned purpose, the system is designed in VLSI technology. MATLAB and Xilinx tools are used for the system design. A novel strategy to address shading contrasts in multiview video successions is proposed, and it utilises a thick coordinating-based worldwide enhancement structure. The proposed vitality work guarantees to safeguard nearby structures by controlling deviations from the first picture angles. When compared with the existing works 8% and 90.6% of power reduction is observed in the proposed work. It also improves the average colour signal-to-noise ratio quality by more than 1.6 dB.
Everything You Always Wanted to Know about the Metaverse* (*But Were Afraid to Ask)

In this perspective paper we discuss the metaverse, a sociotechnical system aimed at providing users with immersive, synchronous 3D experience of reality through interoperable platforms and sensor devices. We first examine the metaverse’s technology and its underlying processes and mechanisms, at the individual, organizational (providers companies) and institutional level. We then move to considering some mainstream theories built in the past decades that explain online behaviors (with social software etc.) and questioning their validity within the metaverse. In the concluding section of the paper, we propose meaningful research questions and lay out a research agenda, mainly addressed to IS scholarship but that may be considered by other neighbor fields such as communication and computer science. Throughout the paper we think of the metaverse as a phenomenon involving relevant business opportunities as well as ethical challenges and we thereby present our theoretical reflections along this overarching conceptual idea.
Phase-unwrapping-free 3D reconstruction in structured light field system based on varied auxiliary point

Three-dimensional (3D) reconstruction is an essential task in structured light field (SLF) related techniques and applications. This paper presents a new method to reconstruct a 3D object point by using many auxiliary points adjacent to it. The relationship between two points in a SLF system is derived. Different from conventional "direct" methods that reconstruct 3D coordinates of the object point by using phase, slope, disparity etc., the proposed method is an "indirect" method as the 3D coordinates of auxiliary points are not needed. Based on the auxiliary point theory, the wrapped phase obtained by 4-step phase-shifting method is sufficient for 3D reconstruction, without the need for phase unwrapping. To the best of our knowledge, this is the first strategy that combines the intrinsic characteristics of structured light and light field for phase-unwrapping-free 3D reconstruction. This paper also analyzes the constraints between system architecture parameters and phase rectification, phase to depth ratio, and presents a relatively simple criterion to guide the system design. Experimental results show that, with an appropriate system architecture, the proposed method can realize accurate, unambiguous, and reliable 3D reconstruction without phase unwrapping.
ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence

Analyzing conflicts and political violence around the world is a persistent challenge in the political science and policy communities due in large part to the vast volumes of specialized text needed to monitor conflict and violence on a global scale.To help advance research in political science, we introduce ConfliBERT, a domain-specific pre-trained language model for conflict and political violence.We first gather a large domain-specific text corpus for language modeling from various sources.We then build ConfliBERT using two approaches: pre-training from scratch and continual pretraining.To evaluate ConfliBERT, we collect 12 datasets and implement 18 tasks to assess the models' practical application in conflict research.Finally, we evaluate several versions of ConfliBERT in multiple experiments.Results consistently show that ConfliBERT outperforms BERT when analyzing political violence and conflict.Our code is publicly available. 1Expert Domain Corpora (EDC).We curated 2,293 MB of plain text from multiple professional sources relevant to conflict and diplomacy.
Co-Designing in Social VR - Process awareness and suitable representations to empower user participation

To allow non-designers' involvement in design projects new methods are needed. Co-design gives the same opportunity to all the multidisciplinary participants to co-create ideas simultaneously. Nevertheless, current co-design processes involving such users tend to limit their contribution to the proposal of basic design ideas only through brainstorming. The co-design approach needs to be enhanced by a properly suited representational ecosystem supporting active participation and by conscious use of structured verbal exchanges giving awareness of the creative process. In this respect, we developed two social virtual reality co-design systems, and a co-design verbal exchange methodology to favour participants' awareness of the co-creative process. By using such representations and verbal exchanges, participants could co-create with more ease by benefiting from being informed of the process and from the collective immersion, empowering their participation. This paper presents the rationale behind this approach of using Social VR in co-design and the feedback of three co-design workshops.
Social inferences from physical evidence via bayesian event reconstruction.

Humans can make remarkable social inferences by watching each other's behavior. In many cases, however, people can also make social inferences about agents whose behavior they cannot see, based only on the physical evidence left behind. We hypothesized that this capacity is supported by a form of mental event reconstruction. Under this account, observers derive social inferences by reconstructing the agent's behavior, based on the physical evidence that revealed their presence. We present a computational model of this idea, embedded in a Bayesian framework for action understanding, and show that its predictions match human inferences with high quantitative accuracy. Specifically, Experiment 1 shows that people can infer where an agent came from and which goal they pursued in a room, all from a small pile of cookie crumbs. Experiment 2 shows that people can explicitly reconstruct the actions that the agent took, and these reconstructed trajectories can predict the entry point and goal inferences from Experiment 1. Finally, Experiment 3 shows that people can also infer whether one or two agents were in a room based on the position of two piles of cookie crumbs. Our results shed light on how people extract social information from the physical world. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Equational Unification and Matching, and Symbolic Reachability Analysis in Maude 3.2 (System Description)

Abstract Equational unification and matching are fundamental mechanisms in many automated deduction applications. Supporting them efficiently for as wide as possible a class of equational theories, and in a typed manner supporting type hierarchies, benefits many applications; but this is both challenging and nontrivial. We present Maude 3.2’s efficient support of these features as well as of symbolic reachability analysis of infinite-state concurrent systems based on them.
Robot visual navigation estimation and target localization based on neural network

Abstract The high computational cost, complex external environment, and limited computing resources of embedded system are some major problems in traditional autonomous robot navigation methods. To overcome these problems, a mobile robot path planning navigation system based on panoramic vision was proposed. This method first describes the structure and functions of the navigation system. It explains how to use the environment to explore and map in order to create a panoramic vision sensor. Finally, it elaborates on the breadth-first search based on regression neural network (RNN) method, the Voronoi skeleton diagram method, the algorithm principle, and how to navigate by the planning path implementation of practical strategies. The simulation results illustrate that the breadth-first search method and the Voronoi skeleton graph method based on panoramic view have a high speed. The accessibility of RNN planning algorithm can effectively solve the difficult problems such as high computing overhead, complex navigation environment, and limited computing resources. In the actual robot navigation experiment, the difference in real-time performance and optimality performance that exists between the two algorithms is reflected in the length and duration of the course taken by the robot. When applied to a variety of site environments, the breadth-first search method requires between 23.2 and 45.3% more time to calculate the planned path than the Voronoi skeleton graph method, despite the fact that the planned path length is between 20.7 and 35.9% shorter using the breadth-first search method. It serves as a guide for choosing the appropriate algorithm to implement in practical applications.
A model-based smart contracts system via blockchain technology to reduce delays and conflicts in construction management processes

Purpose Smart contracts using blockchain technology (BCT) is a tool that decentralizes authority and makes it easier to upgrade the contract administration process by providing an efficient system. Current literature provides a good overview of contracts in the construction industry; however, the specific details of BCT's smart contracts applications in the three categories have not been addressed adequately: (1) information quality, (2) enhancing project schedule and progress payment time and (3) reducing conflicts among project stakeholders. Thus, this study aims to analyze smart contracts using BCT by creating a computerized contract model, specifically evaluating its impact on the three identified categories. Design/methodology/approach In this paper BCT-SmContract was developed through an automated program that utilizes blockchain to define the contractual agreements between different parties in a construction project. BCT-SmContract model provides a new technique to overcome the current challenges associated with factors identified in this study, i.e. (1) information quality, (2) enhancing project schedule and progress payment time and (3) reducing conflicts among project stakeholders. Afterward, the model was tested to ensure validity and reliability through a construction project. Findings The findings indicated that BCT-SmContract was approximately 90% faster to execute the contract and 100% accurate in reflecting the correct information about the project status, resulting in reduced conflicts. Originality/value This study has contributed in upgrading the traditional contracting method in construction by developing an automated smart contract model to enhance the processes and achieve higher accuracy.
Reducing Conversational Agents’ Overconfidence Through Linguistic Calibration

Abstract While improving neural dialogue agents’ factual accuracy is the object of much research, another important aspect of communication, less studied in the setting of neural dialogue, is transparency about ignorance. In this work, we analyze to what extent state-of-the-art chit-chat models are linguistically calibrated in the sense that their verbalized expression of doubt (or confidence) matches the likelihood that the model’s responses are factually incorrect (or correct). We find that these models are poorly calibrated, yet we show that likelihood of correctness can accurately be predicted. By incorporating such metacognitive features into the training of a controllable generation model, we obtain a dialogue agent with greatly improved linguistic calibration.