Reviewerly/exHarmony huggingface dataset:
Technical note: A mobile collaborative workspace to assist forensic experts in disaster victim identification scenarios

Integrated approaches to disaster victim identification (DVI) management have led to a need for technologies to improve interaction among parties involved in post-mortem (PM) and ante-mortem (AM) data collection through better communication and coordination. Mobile Forensic Workspace© (MFW) is a collaborative mobile system that not only facilitates the systematic collection of high-quality data, but also allows DVI professionals to coordinate activities and exchange data through secure real-time communication at major disaster scenarios in accordance with security, privacy and legal protocols. MFW is adaptable to any communication format (text, voice calls, photographs, etc.) and is dynamically self-reconfigurable when connectivity problems arise. It also allows data integration and backup through secure communication channels between local and remote servers. The feasibility of the system has been demonstrated through implementation of MFW on the iOS platform for iPhone, iPod Touch and iPad terminals. A further strength of MFW is that it provides out-of-the-box support for INTERPOL DVI forms. The application of information and communication technologies for DVI was shown to be useful in improving DVI management by enhancing the quality of data collection and enabling non-Internet dependent real-time data sharing and communication.
Complex amplitude domain filtering for phase measurement in speckle interferometric optics

Phase map filtering is considered an indispensable process in the measurement delivered by speckle interferometric optics. For recent measurements, quite a lot of scenarios require an optimized time consumption. A notable feature of the raw phase map is the 2π modulo of the phase distribution, as retrieved by the inverse tangent operation. Instead of employing the filtering method directly on the phase fringe pattern, the wrapped phase is converted into the sine–cosine domain with its sine and cosine components, and then the filtering method is applied to them. However, it is the nature of the sine–cosine filtering framework that consumes approximately 70% calculation resources for the recalculation of the intermediate phase map and the sine/cosine components at every loop, which limits the temporal property of related measurement. To address the issues of efficiency and quality at the same time, a new filtering domain is proposed, namely the complex amplitude domain filtering framework. The filtering is conducted at the stage of recovering the complex amplitude without calculating the intermediate phase map at every loop. The merit of complex amplitude domain filtering is that it has a good filtering efficiency to remove the speckle noise, preserves the details of the sharp edge in the phase-wrapped area at the same time, and no additional phase shift is generated during the filtering. Most of the existing filters and strategies used in the sine–cosine filtering domain can be directly applied in the proposed complex amplitude domain. Preliminary experiments show that, compared to the sine–cosine domain filtering, the proposed complex amplitude domain filtering accelerates the filtering process by at least 40% at no cost of filtering quality, and particularly shows a better filtering performance with the partial differential equations (PDE) noise reduction method.
Situation-Aware eXplainability for Business Processes Enabled by Complex Events

Traditionally, the organizational IT landscape is split between Business Process engines that are developed to handle process execution workloads and Complex Event Processing engines designed to search for event correlations in real time to derive actionable insights. For the benefit of process trustworthiness, this work focuses on combining the two engines, resulting in an enriched form of a process log that serves as an input to recently developed eXplainable Artificial Intelligence frameworks, yielding more adequate explanations for process execution outcomes. A designated methodology and a test scheme were created to systematically implement and evaluate our overall approach and its effectiveness in gaining situation-aware explainability. Specifically, we demonstrate our approach using a dataset populated for an illustrative process example, replaying its trace log against the PROTON CEP engine and feeding the result as an input for the SHAP explainer.
SvTPM: SGX-based Virtual Trusted Platform Modules for Cloud Computing

Virtual Trusted Platform Modules (vTPMs) are widely used in commercial cloud platforms (e.g., VMware Cloud, Google Cloud, and Microsoft Azure) to provide virtual root-of-trust and security services for virtual machines. Unfortunately, current state-of-the-art vTPM implementations for cloud computing cannot provide strong protection for vTPMs at run-time and suffer from poor performance under binding vTPMs to a physical TPM. In this paper, we propose SvTPM, an SGX-based virtual trusted platform module, which provides complete life cycle protection of vTPMs in the cloud and does not rely on the physical TPM. SvTPM provides strong isolation protection so malicious cloud tenants or even cloud administrators cannot access vTPM's private keys or any other sensitive data. In this paper, we implement a prototype of SvTPM, which identifies and solves a couple of critical security challenges for vTPM protection with SGX, such as NVRAM rollback attacks, NVRAM binding attacks, and vTPM rollback attacks. SvTPM also shows how to establish trust between vTPM and SGX Platform. Our performance evaluation shows that the NVRAM launch time of SvTPM is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1700\times$</tex-math></inline-formula> faster than vTPM built upon hardware TPM. In TPM standard command evaluation, we find that SvTPM incurs negligible performance overhead while providing strong isolation and protection. To our knowledge, SvTPM is the first practical work to solve the critical security challenges of securing vTPM using SGX.
Datasets for the Quality Assessment of Light Field Imaging: Comparison and Future Directions

With the increasing research focus on light field imaging in recent years, it has become essential for researchers in this field to either benefit from access to equipped laboratories with light field acquisition devices and displays or to have access to publicly available light field imaging datasets. Some datasets are indeed available, each with a different nature. For instance, some contain real world images or sources while others are based on purely synthetic images or sources generated by computer graphic tools; others are a combination of both. Datasets for the quality assessment of light field content include pristine light field content as well as sources affected by different levels of impairments. The latter are tested subjectively by a panel of viewers and often objective metrics are also calculated. This paper presents a comprehensive comparative review of 33 publicly available datasets that span from content-only datasets to specific task based datasets and quality assessment datasets. While our aim is to review and investigate what each dataset has to offer and which tests had been considered by their proposers, we also take the opportunity to leverage the results of previous studies to identify and discuss the challenges ahead and identify the areas with potential for improvement.
Extraction Method of Secret Message Based on Optimal Hypothesis Test

As the ultimate goal of steganalysis, secret message extraction plays a decisive role in obtaining secret communication evidence and cracking down on criminal activities. For STC (Syndrome-Trellis Codes)-based adaptive steganography, existing pioneering work on secret message extraction: the method based on run test under plaintext embedding may misjudge incorrect stego key as correct stego key, resulting in the failure of extraction. To avoid such a situation, this manuscript proposed a secret message extraction method based on optimal hypothesis test with 100% accuracy under plaintext embedding. First, it is proved that there is a probability distribution difference between the sub-sequence extracted by correct and incorrect stego key. Then, based on the difference, an optimal hypothesis test model is designed to recover the correct stego key. Finally, given the probability of type I and II errors, the sample size and threshold in the hypothesis test are derived. Classic adaptive steganography such as HUGO (Highly Undetectable Steganography) and J-UNIWARD (JPEG Universal Wavelet Relative Distortion) have been conducted experiment, showing that the proposed method can extract message with 100% accuracy and 44 bits sample size, which verifies the correctness of the theorem and the effectiveness of the method.
Detecting Systematic Deviations in Data and Models

Trustworthy artificial intelligence researchers should seek to better detect and characterize systematic deviations in data and models (that is, bias). This article provides data scientists with motivation, theory, code, and examples on how to perform disciplined discovery of systematic deviations in data and models at the subset level.
A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning

The Ghana Digital Seismic Network (GHDSN) data, with six broadband sensors, operating in southern Ghana for two years (2012-2014). The recorded dataset is processed for simultaneous event detection and phase picking by a Deep Learning (DL) model, the EQTransformer tool. Here, the detected earthquakes consisting of supporting data, waveforms (including P and S arrival phases), and earthquake bulletin are presented. The bulletin includes the 559 arrival times (292 P and 267 S phases) and waveforms of the 73 local earthquakes in SEISAN format. The supporting data encompasses the preliminary crustal velocity models obtained from the joint inversion analysis of the detected hypocentral parameters. These parameters comprised of a 6- layer model of the crustal velocity (Vp and Vp/Vs ratio), incident time sequence, and statistical analysis of the detected earthquakes and hypocentral parameters analyzed and relocated by the updated crustal velocity and graphic representation of them a 3D live figure enlighting the seismogenic depth of the region. This dataset has a unique appeal for earth science specialists to analyze and reprocess the detected waveforms and characterize the seismogenic sources and active faults in Ghana. The metadata and waveforms have been deposited at the Mendeley Data repository [1].
A Novel Method for Generating the M-Tri-Basis of an Ordered Γ-Semigroup

In this paper, we discuss the hypothesis that an ordered Γ-semigroup can be constructed on the M-left(right)-tri-basis. In order to generalize the left(right)-tri-basis using Γ-semigroups and ordered semigroups, we examined M-tri-ideals from a purely algebraic standpoint. We also present the form of the M-tri-ideal generator. We investigated the M-left(right)-tri-ideal using the ordered Γ-semigroup. In order to obtain their properties, we used M-left(right)-tri-basis. It was possible to generate a M-left(right)-tri-basis from elements and their subsets. Throughout this paper, we will present an interesting example of order ⪯mlt(⪯mrt), which is not a partial order of S. Additionally, we introduce the notion of quasi-order. As an example, we demonstrate the relationship between M-left(right)-tri-basis and partial order.
Assessing Heuristic Evaluation in Immersive Virtual Reality—A Case Study on Future Guidance Systems

A variety of evaluation methods for user interfaces (UI) exist such as usability testing, cognitive walkthrough, and heuristic evaluation. However, UIs such as guidance systems at transit hubs must be evaluated in their intended application field to allow the effective and valid identification of usability flaws. However, what if evaluations are not feasible in real environments, or laboratorial conditions cannot be ensured? Based on adapted heuristics, in the present study, the method of heuristic evaluation is combined with immersive Virtual Reality (VR) for the identification of usability flaws of dynamic guidance systems (DGS) at transit hubs. The study involved usability evaluations of nine DGS concepts using the newly proposed method. The results show that compared to computer-based heuristic evaluations, the use of immersive VR led to the identification of an increased amount of “severe” usability flaws as well as overall usability flaws. Within a qualitative assessment, immersive VR is validated as a suitable tool for conducting heuristic evaluations involving significant advantages such as the creation of realistic experiences in laboratorial conditions. Future work seeks to further prove the suitability of using immersive VR for heuristic evaluations and compare the proposed method to other evaluative methods.
How to stop undesired propagations by using bi-level genetic algorithms

In this work we introduce a general model to analyse any type of propagation system, whether it represents the spread of a fire, fake news, a virus, etc. We study the computational complexity of the problem of minimising the impact of a propagation by cutting some of the connections it can spread through. Even limiting the scope of our cutting strategy to be short-term, we show that the problem is Σ2P-complete, that is, it is one level above NP-complete in the complexity hierarchy. Intuitively, in Σ2P-complete problems a hard search for the value fulfilling some property is tackled, but just evaluating that property for each candidate value requires performing another hard, independent search. This complexity suggests that a good method to deal with the problem under consideration is a two-level genetic algorithm, that is, a genetic algorithm that uses another genetic algorithm as fitness function: the former algorithm searches for good candidates for solving the target optimisation, and for each candidate within its population, its fitness is calculated by running the latter algorithm. We apply this implementation to two case studies, compare its results with those of greedy and minimax algorithms, and report experimental results. Our results indicate that bi-level genetic algorithms are good candidates to deal with Σ2P-complete problems.
Federated probability memory recall for federated continual learning

Federated Continual Learning (FCL) approaches exist two major problems of the probability bias and the imbalance in parameter variations. These two problems lead to catastrophic forgetting of the network in the FCL process. Therefore, this paper proposes a novel FCL framework, Federated Probability Memory Recall (FedPMR), to mitigate the probability bias problem and the imbalance in parameter variations. Firstly, for the probability bias problem, this paper designs the Probability Distribution Alignment (PDA) module, which consolidates the memory of old probability experience. Specifically, PDA maintains a replay buffer and uses the probability memory stored in the buffer to correct the offset probabilities of the previous tasks during the two-stage training. Secondly, to alleviate the imbalance in parameter variations, this paper designs the Parameter Consistency Constraint (PCC) module, which constrains the magnitude of neural weight changes for previous tasks. Concretely, PCC applies a set of adaptive weights to subsets of the regularization term that constrains parameter changes, forcing the current model to be sufficiently close to the past model in parameter space distance. Experiments with various levels of task similitude across clients demonstrate that our technique establishes the new state-of-the-art performance when compared to previous FCL approaches.
Fuzzy model-based sparse clustering with multivariate t-mixtures

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Consumer Satisfaction with the Online Dispute Resolution on a Second-Hand Goods-Trading Platform

With the development of internet-related technology, more and more consumers are trading on second-hand platforms. However, due to the uncertainty of second-hand products, there are a lot of transaction disputes on the platform. It is important to efficiently solve the transaction disputes on the platform. Based on the comment data on social media, this paper systematically studies the public opinion towards the online dispute resolution of Xianyu, China’s largest second-hand goods-trading platform, using word-frequency analysis, theme analysis and sentiment analysis. The study found that online disputes mainly focus on the return and refund issues between buyers and sellers. Crowdsourcing online dispute resolution cannot completely solve all problems, and the platform is still needed to help solve disputes. Consumers are concerned about the convenience of the process operation and the fairness of the results of the crowdsourcing online dispute resolution, as well as the contact channels of customer service. The platform should prevent these situations in advance and avoid disputes from the source. Moreover, the platform should perfect the design of public review system and mechanism, broaden the appeal channels of platform customer service, and improve the platform dispute resolution mechanism. It is of great significance for the development of a second-hand goods-trading platform and the application of an online dispute resolution mechanism to mine the service evaluation and satisfaction factors of consumers on the online dispute resolution mechanism.
An Empirical Study of Class Rebalancing Methods for Actionable Warning Identification

Actionable warning identification (AWI) is crucial for improving the usability of static analysis tools. Currently, machine learning (ML)-based AWI approaches are notably common, which mainly focus on seeking high performance by improving the warning feature extraction and advancing the AWI model training. However, these approaches ignore an important fact that the number of actionable warnings is much smaller than that of unactionable warnings in the warning dataset used for the AWI model training (i.e., the class imbalance). Learning from such an imbalanced dataset may limit the performance of ML-based AWI approaches. To bridge the above gap, we are the first to conduct a comprehensive empirical study to investigate the impact of class imbalance on the ML-based AWI performance, whether class rebalancing methods can improve the ML-based AWI performance, and the differences of class rebalancing methods in the ML-based AWI model. Our empirical study is performed on 9 real-world and large-scale warning datasets, 25 typical class rebalancing methods, and 7 commonly used ML models. The experimental results show that 1) the class imbalance has a negative impact on the ML-based AWI performance; 2) 85% class rebalancing methods can significantly improve the ML-based AWI performance, but 8% ones do not work in the imbalanced warning datasets; 3) RandomOverSampler combined with AdaBoost/Random Forest can make the ML-based AWI model achieve optimal performance on nine warning datasets. Finally, we provide three practical guidelines that could help refine ML-based AWI approaches.
Example-based explainable AI and its application for remote sensing image classification

We present a method of explainable artificial intelligence (XAI), “What I Know (WIK)”, to provide additional information to verify the reliability of a deep learning model by showing an example of an instance in a training dataset that is similar to the input data to be inferred and demonstrate it in a remote sensing image classification task. One of the expected roles of XAI methods is verifying whether inferences of a trained machine learning model are valid for an application, and it is an important factor that what datasets are used for training the model as well as the model architecture. Our data-centric approach can help determine whether the training dataset is sufficient for each inference by checking the selected example data. If the selected example looks similar to the input data, we can confirm that the model was not trained on a dataset with a feature distribution far from the feature of the input data. With this method, the criteria for selecting an example are not merely data similarity with the input data but also data similarity in the context of the model task. Using a remote sensing image dataset from the Sentinel-2 satellite, the concept was successfully demonstrated with reasonably selected examples. This method can be applied to various machine-learning tasks, including classification and regression.
A Case Study in Formal Analysis of System Requirements

One of the goals of the 4SECURail project has been to demonstrate the benefits, limits, and costs of introducing formal methods in the system requirements definition process. This has been done, on an experimental basis, by applying a specific set of tools and methodologies to a case study from the railway sector. The paper describes the approach adopted in the project and some considerations resulting from the experience.
Abrupt moving target tracking based on quantum enhanced particle filter

Abrupt-motion tracking is challenging due to the target's unpredictable action. Although particle filter (PF) is suitable for target tracking of nonlinear non-Gaussian systems, it suffers from the problems of particle impoverishment and sample-size dependency. This paper proposed a quantum-inspired particle filter for abrupt-motion tracking. We apply the concept of quantum superposition to transform classical particles into quantum particles. Quantum representation and corresponding quantum operations are addressed to utilize quantum particles. The superposition property of quantum particles avoids the concerns of particle impoverishment and sample-size dependency. The proposed diversity-preserving quantum-enhanced particle filter (DQPF) obtains better accuracy and stability with fewer particles. A smaller sample size also helps to reduce computational complexity. Moreover, it has significant advantages for abrupt-motion tracking. The quantum particles are propagated at the prediction stage. They will exist at possible places when abrupt motion occurs, which reduces the tracking delay and enhances the tracking accuracy. This paper conducted experiments compared to state-of-the-art particle filter algorithms. The numerical results demonstrate that the DQPF is not susceptible to motion mode and particle number. Meanwhile, DQPF maintains excellent accuracy and stability.