Reviewerly/exHarmony huggingface dataset:
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?

Predictive process monitoring (PPM) has been discussed as a use case of process mining for several years. PPM enables foreseeing the future of an ongoing business process by predicting, for example, relevant information on the way in which running processes terminate or on related process performance indicators. A large share of PPM approaches adopt Machine Learning (ML), taking advantage of the accuracy and precision of ML models. Consequently, PPM inherits the challenges of traditional ML approaches. One of these challenges concerns the need to gain user trust in the generated predictions. This issue is addressed by explainable artificial intelligence (XAI). However, in addition to ML characteristics, the choices made and the techniques applied in the context of PPM influence the resulting explanations. This necessitates the availability of a study on the effects of different choices made in the context of a PPM task on the explainability of the generated predictions. In order to address this gap, we systemically investigate the effects of different PPM settings on the data fed into an ML model and subsequently into the employed XAI method. We study how differences between the resulting explanations indicate several issues in the underlying data. Example of these issues include collinearity and high dimensionality of the input data. We construct a framework for performing a series of experiments to examine different choices of PPM dimensions (i.e., event logs, preprocessing configurations, and ML models), integrating XAI as a fundamental component. In addition to agreements, the experiments highlight several inconsistencies between data characteristics and important predictors used by the ML model on one hand, and explanations of predictions of the investigated ML model on the other.
The Revolution Has Arrived: AI Authorship and Copyright Law

Very recently, due largely to breakthroughs in deep learning technologies, AI has begun stepping into the shoes of human content generators and making valuable creative works at scale. Before the end of the decade, a significant amount of art, literature, music, software, and web content will likely be created by AI rather than traditional human authors. Yet the law, as it has so often historically, lags this technological evolution by prohibiting copyright protection for AI-generated works. The predominant narrative holds that even if AI can automate creativity, that this activity is not the right sort of thing to protect, and that protection could even harm human artists. AI-generated works challenge beliefs about human exceptionalism and the normative foundations of copyright law, which until now have offered something for everyone. Copyright can be about ethics and authors and protecting the sweat of a brow and personality rights. Copyright can also be about money and the public interest and offering incentives to create and disseminate content. But copyright cannot have it all with AI authors—there is valuable output being generated, but by authors with no interests to protect. This forces a reevaluation of copyright law’s history and purpose. This article argues that American copyright law is, and has been traditionally, primarily about benefiting the public interest rather than benefiting authors directly. As a result, AI-generated works are precisely the sort of thing the system was designed to protect. Protection will encourage people to develop and use creative AI and to produce and disseminate new works. Taken further, attributing authorship to AI when an AI has functionally done the work of a traditional author will promote transparency, efficient allocations of rights, and even counterintuitively protect human authors. Exploring the challenges associated with AI-generated works is an opportunity to rethink basic tenets of copyright law like originality, how the law should accommodate advances in technology, and even how to think more broadly about the rules that apply to people, machines, and other sorts of artificial authors.
Weakly supervised object localization with soft guidance and channel erasing for auto labelling in autonomous driving systems

Automated driving systems (ADSs) conceive an efficient and safe way of driving. The safety of ADSs depends on a precise object detector that needs to be upgraded continuously facing various environments. Massive annotations are required to utilize collected images of surroundings through vehicles and accommodate new environments. Auto labelling is one approach to alleviate such dilemma. To this end, we propose a novel Weakly Supervised Object Localization (WSOL) method which can localize objects precisely without detection annotations. This paper proposed Soft Guidance Module (SGM), Channel Erasing Module (CEM) and incorporate them into a multi-flow framework allowing the two mutually beneficial. Finally, experiments and visualizations are performed to evaluate our method on Stanford Cars, ILSVRC 2016 and CUB-200-2011 datasets.
MoEP-AE: Autoencoding Mixtures of Exponential Power Distributions for Open-Set Recognition

Open-set recognition aims to identify unknown classes while maintaining classification performance on known classes and has attracted increasing attention in the pattern recognition field. However, how to learn effective feature representations whose distributions are usually complex for classifying both known-class and unknown-class samples when only the known-class samples are available for training is an ongoing issue in open-set recognition. In contrast to methods implementing a single Gaussian, a mixture of Gaussians (MoG), or multiple MoGs, we propose a novel autoencoder that learns feature representations by modeling them as mixtures of exponential power distributions (MoEPs) in latent spaces called MoEP-AE. The proposed autoencoder considers that many real-world distributions are sub-Gaussian or super-Gaussian and can thus be represented by MoEPs rather than a single Gaussian or an MoG or multiple MoGs. We design a differentiable sampler that can sample from an MoEP to guarantee that the proposed autoencoder is trained effectively. Furthermore, we propose an MoEP-AE-based method for open-set recognition by introducing a discrimination strategy, where the MoEP-AE is used to model the distributions of the features extracted from the input known-class samples by minimizing a designed loss function at the training stage, called MoEP-AE-OSR. Extensive experimental results in both standard-dataset and cross-dataset settings demonstrate that the MoEP-AE-OSR method outperforms 14 existing open-set recognition methods in most cases in both open-set recognition and closed-set recognition tasks.
Construction and Evolution of Fault Diagnosis Knowledge Graph in Industrial Process

The steel industry production line is complicated and contains a substantial amount of equipment, leading to serious problems in fault diagnosis such as wrong inspection strategies, fault location, maintenance, and so on. To realize accurate and efficient equipment fault diagnosis, this paper proposes a steel production line equipment fault diagnosis knowledge graph (SPLEFD-KG) based on a novel relation-oriented model with global context information for jointly extracting overlapping relations and entities (ROMGCJE). A low-level self-learning SPLEFD-KG is first constructed using the triples extracted by ROMGCJE. However, this low-level SPLEFD-KG is incomplete, and only contains sparse paths for fault reasoning. To overcome this problem, a reinforcement learning framework is applied to mine hidden semantic knowledge to complete the missing relation. Besides, the graph neural networks are introduced to compute the embedding vector of new entities outside of the SPLEFD-KG for continuously completing missing entities. Finally, the low-level self-learning SPLEFD-KG evolves to one high-level SPLEFD-KG, which can provide information-rich and accurate fault-related knowledge. Extensive experiments conducted on the steel production line equipment failure dataset indicate that the novel SPLEFD-KG significantly improves fault diagnosis results and provides effective maintenance programs.
Computing Abductive Explanations

We study the computation of constrained explanations in the framework of abductive logic programming. A general characteristic of abductive reasoning is the existence of multiple abductive explanations. Therefore, identifying a subclass of “preferred explanations” is a relevant problem. A typical approach is to “prefer” explanations that are, in some sense, simple. Several concepts of simplicity were considered in the literature, most notably those based on minimality with respect to inclusion and cardinality. We adopt, as a measure of the quality of an explanation, its degree of arbitrariness that can be briefly described as the number of arbitrary assumptions that have been made to derive the explanation. The more arbitrary the explanation, the less appealing it is, with explanations having no arbitrariness, called constrained, being the preferred ones. In this article, we present a technique that, for a special class of theories, computes constrained explanations. It is based on a rewriting of the theory and the observation into a disjunctive logic program with negation so that the constrained explanations correspond to a subset of its stable models. The proposed technique lays the foundation for using ASP solvers to compute constrained explanations.
Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks

Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures. However, the lack of a sufficient number of medical images is the most common obstacle in conducting image-based analysis using deep learning. Due to the annotations scarcity, semi-supervised techniques in the automatic medical analysis are getting high attention. Artificial data augmentation and generation techniques such as generative adversarial networks (GANs) may help overcome this obstacle. In this work, we present an image generation approach that uses generative adversarial networks with a conditional discriminator where segmentation masks are used as conditions for image generation. We validate the feasibility of GAN-enhanced medical image generation on whole heart computed tomography (CT) images and its seven substructures, namely: left ventricle, right ventricle, left atrium, right atrium, myocardium, pulmonary arteries, and aorta. Obtained results demonstrate the suitability of the proposed adversarial approach for the accurate generation of high-quality CT images. The presented method shows great potential to facilitate further research in the domain of artificial medical image generation.
An algorithm for U–Pb geochronology by secondary ion mass spectrometry

Abstract. Secondary ion mass spectrometry (SIMS) is a widely used technique for in situ U–Pb geochronology of accessory minerals. Existing algorithms for SIMS data reduction and error propagation make a number of simplifying assumptions that degrade the precision and accuracy of the resulting U–Pb dates. This paper uses an entirely new approach to SIMS data processing that introduces the following improvements over previous algorithms. First, it treats SIMS measurements as compositional data using log-ratio statistics. This means that, unlike existing algorithms, (a) its isotopic ratio estimates are guaranteed to be strictly positive numbers, (b) identical results are obtained regardless of whether data are processed as normal ratios (e.g. 206Pb / 238U) or reciprocal ratios (e.g. 238U / 206Pb), and (c) its uncertainty estimates account for the positive skewness of measured isotopic ratio distributions. Second, the new algorithm accounts for the Poisson noise that characterises secondary electron multiplier (SEM) detectors. By fitting the SEM signals using the method of maximum likelihood, it naturally handles low-intensity ion beams, in which zero-count signals are common. Third, the new algorithm casts the data reduction process in a matrix format and thereby captures all sources of systematic uncertainty. These include significant inter-spot error correlations that arise from the Pb / U–UO(2) / U calibration curve. The new algorithm has been implemented in a new software package called simplex. The simplex package was written in R and can be used either online, offline, or from the command line. The programme can handle SIMS data from both Cameca and SHRIMP instruments.
UAV-based persistent full area coverage with dynamic priorities

This paper tackles the problem of performing persistent and complete coverage of a target space with a minimum number of Unmanned Aerial Vehicles (UAVs). The UAVs are required to preferentially visit subareas of interest with higher frequency than the rest of the surveillance area within a predefined time threshold. A Minimum Spanning Tree based recursive algorithm is firstly proposed to estimate the upper bound of minimum number of UAVs for area coverage with visiting frequency constraints. Then an autonomous path planning strategy is proposed for persistent coverage, where a combinatorial priority function is designed as the goal assignment strategy and a modified Dijkstra algorithm is applied as the goal planning to obtain the optimum path. Finally, computer simulations have been conducted for the evaluation of the proposed solution, and the obtained results show that these algorithms can compute valid and sound solutions under different setups for the UAVs.
Quantum SVR for Chlorophyll Concentration Estimation in Water With Remote Sensing

The increasing availability of quantum computers motivates researching their potential capabilities in enhancing the performance of data analysis algorithms. Similarly, as in other research communities, also in Remote Sensing (RS) it is not yet defined how its applications can benefit from the usage of quantum computing. This paper proposes a formulation of the Support Vector Regression (SVR) algorithm that can be executed by D-Wave quantum computers. Specifically, the SVR is mapped to a Quadratic Unconstrained Binary Optimization (QUBO) problem that is solved with Quantum Annealing (QA). The algorithm is tested on two different types of computing environments offered by D-Wave: The Advantage system, which directly embeds the problem into the Quantum Processing Unit (QPU), and a Hybrid solver that employs both classical and quantum computing resources. For the evaluation, we considered a biophysical variable estimation problem with RS data. The experimental results show that the proposed quantum SVR implementation can achieve comparable or in some cases better results than the classical implementation. This work is one of the first attempts to provide insight into how QA could be exploited and integrated in future RS workflows based on Machine Learning (ML) algorithms.
Moving Pedestrian Localization and Detection With Guided Filtering

Detecting a moving pedestrian is still a challenging task in a smart surveillance system due to dynamic scenes.Locating and detecting the moving pedestrian simultaneously influences the development of an integrated but low-resource smart surveillance system.This paper proposes a novel approach to locating and detecting moving pedestrians in a video.Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering.This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method.Subsequently, the pedestrians are detected using YOLOv2, YOLOv3, and YOLOv4 within the provided ROI.Our proposed method resulted in more processing frames per second compared with previous approaches.Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time.It costs around ~50 fps in CPU to classify moving pedestrians and maintain a highly accurate rate.Due to its fast processing, the proposed approach is suitable for IoT or smart surveillance device which has limited resource.
Human Fall Detection using YOLO: A Real-Time and AI-on-the-Edge Perspective

Researchers from all across the world are interested in human fall detection and activity recognition. Fall detection is an exciting topic that may be tackled in several ways. Several alternatives have been suggested in recent years. These applications determine whether a person is average, standing, or falling, among other activities. Elderly fall detection is vital among these activities. This is because it is a pretty typical and dangerous occurrence that affects people of all ages, with the elderly having a disproportionately negative impact. Sensors are typically used in these applications to detect rapid changes in a person's movement. They can be placed in smartphones, necklaces, and smart wristbands to turn them into “wearable” gadgets. These gadgets must be attached to the victim's bodies. This may be unsettling because we must constantly monitor this type of sensor. It is not always possible, and cannot be done in public settings with strangers. In this way, video camera image-based fall detection has several advantages over wearable sensor-based systems. A vision-based solution to fall detection is presented in this research. The suggested method's key component is that it can detect falls automatically on simple images from a typical video camera, eliminating the need for ambient sensors. It performs feature extraction based on the UR Fall self-annotated RGB dataset for fall detection. We used YOLO and its variants. YOLO allows for the detection of falls and a variety of actions for multiple people in the same scenario. As a result, this method using YOLOv1-v4 and tiny YOLOv4 can be employed in real-world situations using Raspberry-pi and OAK-D for edge solutions.
Image Feature-Based Watermarking

This paper carries out a comparative study of watermarking schemes that are based on the visual features of the cover media. More specifically, the watermark is least significant bit (LSB)-embedded into the cover media, utilizing one of the following: a) all the cover image pixels, b) pixels that depict a corner in the cover image, c) pixels that depict an edge in the cover image, d) pixels that depict a certain saliency in the cover image. The stego media is also subjected to a number of statistical and geometrical attacks and performance evaluation is carried out utilizing a number of metrics. The comparative study shows that LSB embedding in edge or corner detected pixels allows for the best robustness against Salt & Pepper attacks. While LSB embedding in edge detected or image saliency detected pixels allows for the best robustness against image shearing attacks.
A Novel Framework of Modelling, Control, and Simulation for Autonomous Quadrotor UAVs Utilizing Arduino Mega

In recent decades, there has been a constant increase in the use of unmanned aerial vehicles (UAVs). There has also been a huge growth in the number of control algorithms to support the many applications embodied by the vehicles, including challenges and open issues to develop. This paper focuses on three major classes of control methods applied to quadrotors in order to create an open-source model based on the Arduino Mega that allows for the derivation and design of quadrotor control strategies. We consider the perspective classes, including linear, nonlinear, and intelligent methods representing in details with applications in developing an open-source controller for the quadrotor using the Arduino Mega and the BNO055 9 DOF sensor. We propose Proportional Integral Derivative (PID), backstepping integrator, and model predictive control (MPC) to track a generated Lissajous curve for surveillance. Simulations in the Matlab–Simulink environment with 3D visualization of a developed quadrotor model using CAD software, with robustness and performance discussion, are provided. Our experimental work is developed with an extensive illustration of the hardware and algorithm design and by demonstrating the effectiveness of the proposed architectures. The results show promise in practical and in intelligent applications.
Pseudo-Chaotic Sequences Generated by the Discrete Arnold’s Map Over Z<sub>2<sup><i>m</i></sup></sub>: Period Analysis and FPGA Implementation

In this work, we propose a method to generate one-dimensional pseudo-chaotic sequences based on the discrete Arnold&#x2019;s map defined over the integer ring Z<sub>2<sup><i>m</i></sup></sub>. The period of the generated sequences is investigated using properties of the Fibonacci sequence over Z<sub>2<sup><i>m</i></sup></sub>. The pseudo-chaotic sequences are employed to design a pseudo-random number generator (PRNG), and the statistical properties of this PRNG are analyzed by the NIST statistical test suite. We implement the proposed PRNG in a field programmable gate array (FPGA) and investigate its hardware requirements.We show that for the same bit generation rate, the sequences defined over Z<sub>2<sup><i>m</i></sup></sub> require fewer hardware units than recently proposed sequences defined over Z<sub>3<sup><i>m</i></sup></sub>. The proposed PRNG has higher throughput and competitive hard-ware consumption when compared to other architectures in the literature.
Content-Based Movie Recommendation System Using MBO with DBN

The content-based filtering technique has been used effectively in a variety of Recommender Systems (RS).The user explicitly or implicitly provides data in the Content-Based Recommender System.The system collects this data and creates a profile for all the users, and the recommendation is generated by the user profile.The recommendation generated via content-based filtering is provided by observing just a single user's profile.The primary objective of this RS is to recommend a list of movies based on the user's preferences.A content-based movie recommendation model is proposed in this research, which recommends movies based on the user's profile from the Facebook platform.The recommendation system is built with a hybrid model that combines the Monarch Butterfly Optimization (MBO) with the Deep Belief Network (DBN).For feature selection, the MBO is utilized, while DBN is used for classification.The datasets used in the experiment are collected from Facebook and MovieLens.The dataset features are evaluated for performance evaluation to validate if data with various attributes can solve the matching recommendations.Each file is compared with features that prove the features will support movie recommendations.The proposed model's mean absolute error (MAE) and root-mean-square error (RMSE) values are 0.716 and 0.915, and its precision and recall are 97.35 and 96.60 percent, respectively.Extensive tests have demonstrated the advantages of the proposed method in terms of MAE, RMSE, Precision, and Recall compared to state-of-the-art algorithms such as Fuzzy C-means with Bat algorithm (FCM-BAT), Collaborative filtering with k-NN and the normalized discounted cumulative gain method (CF-kNN+NDCG), User profile correlation-based similarity (UPCSim), and Deep Autoencoder.