Reviewerly/exHarmony huggingface dataset:
Validation of a feature-based likelihood ratio method for the SAILR software. Part II: Elemental compositional data for comparison of glass samples

SAILR is open-source software designed to calculate forensic likelihood ratios (LR) from probability distributions of reference data. The purpose of this study was to demonstrate validation of a multivariate feature-based LR method for SAILR using compositional data on glass fragments. Validation was performed using designated performance characteristics, e.g., accuracy, discrimination, and calibration. These characteristics were measured using performance metrics such as cost of the log likelihood ratio and equal error rate. The LR method was developed simultaneously to a baseline method having features less discriminating, but being better aligned with the normality assumption for within-source variation. The baseline method served as the floor of acceptable performance. The results showed that the available data supported LR methods using three elemental features or less. Best performance was obtained using calcium, magnesium, and silicon. The within-source variation in elemental features was slightly leptokurtic (heavy-tailed), violating the assumption of normality. The data were therefore normalized using Lambert W transformation and the performance of the LR method using normalized data was compared with that using non-normalized data. Although performance improved with normalization, the difference was small. Limits of LR output were set to 1/512 ≤ LR ≤ 158 using the empirical lower and upper boundaries (ELUB) LR method. This limited range was primarily a consequence of notable within-source variation. By passing the tests of normality and outperforming the baseline method, the method was considered valid for use in SAILR for data relevant to the background data set, using the defined range of LRs.
Estimation of Human Biomechanics during Registration with a Wearable Device

Abstract In article research of human biomechanics is carried out in the case of registering his movements using a single accelerometer, which is located in a wearable device. Mobile phones, smart watches or fitness bracelets can act as a wearable device. It should be noted that the research is oriented at evaluating biomechanics using a mobile phone, but the results obtained in the form of algorithms and a general approach can be transferred to cases of using other wearable devices.
A Multi-Message Multi-Receiver Signcryption Scheme with Edge Computing for Secure and Reliable Wireless Internet of Medical Things Communications

Thanks to recent advancements in biomedical sensors, wireless networking technologies, and information networks, traditional healthcare methods are evolving into a new healthcare infrastructure known as the Internet of Medical Things (IoMT). It enables patients in remote areas to obtain preventative or proactive healthcare services at a cheaper cost through the ease of time-independent interaction. Despite the many benefits of IoMT, the ubiquitously linked devices offer significant security and privacy concerns for patient data. In the literature, several multi-message and multi-receiver signcryption schemes have been proposed that use traditional public-key cryptography, identity-based cryptography, or certificateless cryptography methods to securely transfer patient health-related data from a variety of biomedical sensors to healthcare professionals. However, certificate management, key escrow, and key distribution are all complications with these methods. Furthermore, in terms of IoMT performance and privacy requirements, they are impractical. This article aims to include edge computing into an IoMT with secure deployment employing a multi-message and multi-receiver signcryption scheme to address these issues. In the proposed method, certificate-based signcryption and hyperelliptic curve cryptography (HECC) have been coupled for excellent performance and security. The cost study confirms that the proposed scheme is better than the existing schemes in terms of computational and communication costs.
QL-IQA: Learning distance distribution from quality levels for blind image quality assessment

Recently, blind image quality assessment (BIQA) has been intensively studied with deep learning. However, the limited quality-annotated datasets restrict its further development. Although patch-based methods have been leveraged to generate more training data, they usually assign the image quality score to all patches in an image. Consequently, much noise would be introduced. To avoid this, we propose a method learning distance distribution from quality levels to expand the training data, which is pseudo Siamese network-based no-reference image quality assessment (NR-IQA). Specifically, we firstly use the K-means clustering algorithm to classify the quality scores of distorted images into five levels. Subsequently, the pseudo Siamese network is adopted to learn the distance distribution from these quality levels. We mainly decrease distances between the quality scores of images from the same quality level and increase distances between the quality scores of images from different quality levels, in which the distorted images can be compared across different distortion types. Finally, we introduce a fusion layer to average the quality scores learnt by the two branches of the trained pseudo Siamese network, and take fine-tuning on this basis to learn image quality score from one image. To demonstrate the effectiveness of the proposed approach, we evaluate it on benchmark databases (LIVE, TID2013 and LIVE MD). The result shows superior performance to some widely used NR-IQA methods and full-reference IQA (FR-IQA) methods. Cross-database evaluation verifies high generalization ability and high effectiveness of our model.
A Shadow Detection Algorithm Based on Multiscale Spatial Attention Mechanism for Aerial Remote Sensing Images

Automated shadow detection is an important research problem in the field of remote sensing image processing. The shadow regions seriously affect the interpretation of the remote sensing images. However, the existing methods have poor detection effect for small shadow regions, and the ability to distinguish between weakly illuminated regions and shadow regions is insufficient. For this reason, we propose a shadow detection network based on multiscale spatial attention mechanism for aerial remote sensing images, called MSASDNet. First, the backbone based on residual block is employed to extract the preliminary features of the input image. Then, we design a multiscale feature extraction module based on the spatial attention mechanism to extract multiscale features with spatial attention information, which can suppress the influence of complex nonshadow regions on the detection and improve the detection ability of small shadow regions. Finally, the decoder structure based on deconvolution is used to predict the shadow mask from the combined feature. Experiments performed on the Aerial Imagery dataset for Shadow Detection (AISD) dataset demonstrate the superiority of MSASDNet in terms of quantitative and qualitative comparison with several state-of-the-art methods. The code will release soon in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HITLDY/MSASDNet</uri> .
Lightweight Long and Short-Range Spatial-Temporal Graph Convolutional Network for Skeleton-Based Action Recognition

In skeleton-based human action recognition domain, the methods based on graph convolution networks have great success recently.However, most graphical neural networks consider the skeleton as a spatiotemporally uncorrelated graph and rely on a predetermined adjacency matrix, ignoring the spatiotemporal relevance of human actions and taking up significant computational costs.Meanwhile, the methods use graph convolution to focus too much on the neighboring nodes of the joints and ignore the totality of the action.In this work, we propose a lightweight but efficient neural network called NLB-ACSE based on the Graph Convolutional Network (GCN).Our model consists of two large branches: nonlocal block branch that focuses on the long distance features and adaptive cross-spacetime edge branch that focuses on the short distance features.Both branches extract information across time and space, and focus on long and short information.Some simple but effective strategies also are applied to our model, such as semantics, maxpooling, and fusion inputs, which have small parameter burden but obtain a higher accuracy on ablation study.The proposed method with an order of magnitude smaller size than most previous papers is evaluated on three large datasets, NTU60, NTU120, and Northwesten-UCLA.The experimental results show that our method achieves the state-of-the-art performance.
Key Assistance, Key Agreement, and Layered Secrecy for Bosonic Broadcast Channels

Secret-sharing building blocks based on quantum broadcast communication are studied. The confidential capacity region of the pure-loss bosonic broadcast channel is determined with key assistance, under the assumption of the long-standing minimum output-entropy conjecture. If the main receiver has a transmissivity of $\eta\lt\frac{1}{2}$, then confidentiality solely relies on the key-assisted encryption of the one-time pad. We also address conference key agreement for the distillation of two keys, a public key and a secret key. A regularized formula is derived for the key-agreement capacity region. In the pure-loss bosonic case, the key-agreement region is included within the capacity region of the corresponding broadcast channel with confidential messages. We then consider a network with layered secrecy, where three users with different security ranks communicate over the same broadcast network. We derive an achievable layered-secrecy region for a pure-loss bosonic channel that is formed by the concatenation of two beam splitters.
Towards a Simulation-based Conversational Assistant for the Operation and Engineering of Production Plants

Conversational Assistants like Alexa, Siri, and Google Assistant have become part of our everyday lives and faced wide adoption in industry. They support users in various tasks, such as playing songs or booking flights, making users' lives easier. Nevertheless, conversational assistants have not yet found their way into production, even though they would offer advantages, such as intuitive use through natural language and easy access to information. Simulation-based skills can also add a new layer in production by making simulation power accessible in an automated way. In this paper, we aim to discuss the concept of a simulation-based Conversational Assistant by presenting an architecture and the workflow within the architecture.
Lookup Tables for Power Generation Performance of Photovoltaic Systems Covering 40 Geographic Locations (Wilayats) in the Sultanate of Oman, with and without Solar Tracking, and General Perspectives about Solar Irradiation

An energy modeler for solar photovoltaic (PV) systems may be limited to climatic data of certain major cities, not covering the one for which the PV system is intended. Additionally, a person not skilled in solar PV modeling may still desire a quick estimate of PV system electricity generation to help decide the level of investment in PV systems. This work addresses these points by establishing lookup tables to summarize predicted electricity generation, solar irradiation, and optimum orientation at various locations in the Sultanate of Oman. The results are produced by processing simulation data using the online open-access tool PVGIS (Photovoltaic Geographical Information System) of the European Commission’s Joint Research Centre (EC-JRC). The tables cover 40 out of the country’s 61 s-level administrative divisions (wilayats) and cover fixed and movable PV panels. The results show that the yearly electricity generation can change up to 11.86% due to the change of location. Two-axis PV tracking offers a small improvement (about 4% on average) over single-vertical-axis tracking but offers noticeable improvement (about 34% on average) over optimally oriented fixed PV panels. Monthly profiles of expected PV electricity generation, as well as the generation drop due to changing the PV mounting from free standing to building integrated, were examined for three locations. As general perspectives that may be of interest to global readers, this work provides quantitative evidence of the overall accuracy of the PVGIS-SARAH database through comparison with ground-measured global horizontal irradiation (GHI). In addition, a full example is presented considering 12 different countries in the northern and southern hemispheres that brings the attention of solar energy modelers to the level of errors they may encounter when the impact of longitude (thus, the exact location) is ignored for simplicity, while focus is given to the latitude.
Spatial-wise and channel-wise feature uncertainty for occluded person re-identification

Occluded person re-identification is a challenging task since the available data often suffers from information incompleteness and spatial misalignment. Most state-of-the-art occluded models rely on the external model to provide additional semantic information. However, for the time being, external models, such as the human parsing model and the pose estimation model cannot provide accurate semantic information under a complex occlusion environment and may introduce errors to the Re-ID model instead. In this paper, we propose an occluded person Re-ID model that mines the latent recognizable information of the person image itself, without the help of external models. Feature/Data uncertainty can reduce the influence of noisy samples in datasets and has been discussed in person Re-ID and face recognition, we extend the uncertainty to the micro feature level, and propose the spatial-wise and channel-wise feature uncertainty to constantly refine the features in the spatial domain and the channel domain respectively during feature construction by weakening the influence of noise features. Extensive experiments on the occluded datasets and holistic datasets have proved the effectiveness of our proposed methods.
TransFGU: A Top-Down Approach to Fine-Grained Unsupervised Semantic Segmentation

Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up approaches that try to group pixels into regions based on their visual cues or certain predefined rules. As a result, it is difficult for these bottom-up approaches to generate fine-grained semantic segmentation when coming to complicated scenes with multiple objects and some objects sharing similar visual appearance. In contrast, we propose the first top-down unsupervised semantic segmentation framework for fine-grained segmentation in extremely complicated scenarios. Specifically, we first obtain rich high-level structured semantic concept information from large-scale vision data in a self-supervised learning manner, and use such information as a prior to discover potential semantic categories presented in target datasets. Secondly, the discovered high-level semantic categories are mapped to low-level pixel features by calculating the class activate map (CAM) with respect to certain discovered semantic representation. Lastly, the obtained CAMs serve as pseudo labels to train the segmentation module and produce the final semantic segmentation. Experimental results on multiple semantic segmentation benchmarks show that our top-down unsupervised segmentation is robust to both object-centric and scene-centric datasets under different semantic granularity levels, and outperforms all the current state-of-the-art bottom-up methods. Our code is available at https://github.com/damo-cv/TransFGU .
Characterization of Multilevel Dynamics and Decoherence in a High-Anharmonicity Capacitively Shunted Flux Circuit

We present the design and characterization of a three-Josephson-junction superconducting loop circuit with three large shunt capacitors. Used as a qubit, the circuit shows long energy-relaxation times, of the order of 40 ${ \unicode[Times]{x3BC}}$s and a spin-echo dephasing time of 9.4 ${ \unicode[Times]{x3BC}}$s. The circuit has high anharmonicity, of 2$\pi$$\times$3.69 GHz. We extract the multilevel relaxation and dephasing rates of the circuit used as a qutrit and discuss the possible sources for the decoherence. The high anharmonicity allows for fast qubit control with nanosecond-range gate durations and a measured average gate fidelity of 99.92%, characterized by randomized benchmarking. These results demonstrate interesting potential use for fast nanosecond-time-scale two-qubit gates and multilevel quantum logic.
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part III—Model Evaluation and Other Points of Significance

Various available metrics to describe model performance in terms of discrimination (area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 Score) and calibration (slope, intercept, Brier score, expected/observed ratio, Estimated Calibration Index, Hosmer-Lemeshow goodness-of-fit) are presented. Recalibration is introduced, with Platt scaling and Isotonic regression as proposed methods. We also discuss considerations regarding the sample size required for optimal training of clinical prediction models-explaining why low sample sizes lead to unstable models, and offering the common rule of thumb of at least ten patients per class per input feature, as well as some more nuanced approaches. Missing data treatment and model-based imputation instead of mean, mode, or median imputation is also discussed. We explain how data standardization is important in pre-processing, and how it can be achieved using, e.g. centering and scaling. One-hot encoding is discussed-categorical features with more than two levels must be encoded as multiple features to avoid wrong assumptions. Regarding binary classification models, we discuss how to select a sensible predicted probability cutoff for binary classification using the closest-to-(0,1)-criterion based on AUC or based on the clinical question (rule-in or rule-out). Extrapolation is also discussed.
Analysis of Legal Case Document Automated Summarizer

The Legal case documents available after judgements are lengthy in size and need to be summarized for future reference by the legal experts, professionals and clients who need help for reference to similar cases. In the Common Law system of India, the legal case documents summary is done manually by specially appointed legal attorneys and is referred to as Gold Summaries. This process is time consuming as the legal case documents are very voluminous in size. We endeavor the survey of different automated legal domain based automated summarization techniques using artificial intelligence. We try to compare some extraction based legal case documents summarizers. The paper initiates with general introduction to legal domain text summarization techniques, different metrics to measure the performance of automated summaries generated by various legal domain-based text summarization methodologies. The conclusion finally suggests some directions for future research.
Task assignment to counter the effect of developer turnover in software maintenance: A knowledge diffusion model

Developer churn is the overall turnover in a software organization’s staff. Existing developers leave and new ones join the project. Retaining the knowledge of the software source code among the development team in such scenarios is an essential factor to keep the software maintenance cost as low as possible. Knowledge diffusion is an activity that could mitigate the negative impact of developer churn, while a task assignment strategy could pay an important role to attain good knowledge diffusion among the team members and effectively lower the likelihood of knowledge loss. In this work, a self-adaptive task assignment (SATA) approach is proposed that adaptively switches between cost-oriented and diffusion-oriented strategies over subsequent rounds of task assignments. An entropy-based model is applied to estimate the current conditions of the development team from the knowledge concentration perspective. This model is assisted by a learning automata and evolutionary algorithms to offer smart assignments. The experimental results show that, particularly in teams with medium churn rates, applying an entropy-aware task assignment model can reduce the total maintenance cost up to slightly over 50%, provided that the knowledge demands in the team over successive rounds of task assignment remain stationary. There are also improvements in terms of the projects’ bus factor which prevent the project to lose its key knowledge. Even for projects where there is no saving in maintenance costs, SATA results in knowledge being more distributed among the developers, resulting in a more resilient project. SATA improves the long-term sustainability of development teams with developer turnover. Projects and their managers can hence rely on it when there is the risk of knowledge loss due to developer turnover.
Cooperative and Decentralized Decision-Making for Loyal Wingman UAVs

With the application of decision-making techniques, it is possible to automate many of the intelligent decisions of UAVs, leaving human commanders to focus on higher-level decisions in modern warfare scenarios, providing a crucial tactical advantage. This paper applies key methods to enable decentralized decision-making for autonomous combat UAVs, also known as loyal wingman (LW) drones. A simulator was developed to simulate to implement and analyze the behaviors of the so-called LW. We used as the main decision-making technique the construction of a behavior tree (BT) capable of providing decisions in a decentralized manner, and for parameters optimization, the PSO optimization algorithm was used. Our approach shows a promising result of threat elimination efficiency of approximately 93%.
Identifying human intention during assembly operations using wearable motion capturing systems including eye focus

Simulating human motion behavior in assembly operations helps to create efficient collaboration plans for humans and robots. However, identifying human intention may require high quality human motion capture data in order to discriminate micro-actions and human attention. In this regard, a human motion capture setup that combines various systems such as joint body, finger, and eye trackers is proposed in combination with a methodology of identifying the intention of human operators as well as for predicting sequences of activities. The approach may lead to safer human-robot collaboration.
Depth induces scale-averaging in overparameterized linear Bayesian neural networks

Inference in deep Bayesian neural networks is only fully understood in the infinite-width limit, where the posterior flexibility afforded by increased depth washes out and the posterior predictive collapses to a shallow Gaussian process. Here, we interpret finite deep linear Bayesian neural networks as data-dependent scale mixtures of Gaussian process predictors across output channels. We leverage this observation to study representation learning in these networks, allowing us to connect limiting results obtained in previous studies within a unified framework. In total, these results advance our analytical understanding of how depth affects inference in a simple class of Bayesian neural networks.