Provenance in scientific workflows is essential for understand- ing and reproducing processes, while in business processes, it can ensure compliance and correctness and facilitates process mining. However, the provenance of process adaptations, especially modifications during execu- tion, remains insufficiently addressed. A review of the literature reveals a lack of systematic approaches for capturing provenance information about adaptive workflows/processes. To fill this gap, we propose the AdProv method for collecting, storing, retrieving, and visualizing prove- nance of runtime workflow adaptations. In addition to the definition of the AdProv method in terms of steps and concepts like change events, we also present an architecture for a Provenance Holder service that is essential for implementing the method. To ensure semantic consistency and interoperability we define a mapping to the ontology PROV Ontol- ogy (PROV-O). Additionally, we extend the XES standard with elements for adaptation logging. Our main contributions are the AdProv method and a comprehensive framework and its tool support for managing adap- tive workflow provenance, facilitating advanced provenance tracking and analysis for different application domains.

Privacy-preserving computation (PPC) solutions, such as secure multiparty computation (MPC) and homomorphic encryption (HE), are deployed increasingly often to guarantee data confidentiality for computations over private, distributed data. Similarly, we observe a steep increase in the adoption of zeroknowledge proofs (ZKPs) to guarantee (public) verifiability of locally executed computations. We project that applications that are data intensive and require strong privacy guarantees, are also likely to require verifiable correctness guarantees, especially when they are outsourced. Combining methods for verifiability and privacy protection has clear benefits, however certain challenges stand before their widespread practical adoption. In this work, we analyze existing solutions that combine verifiability with privacy-preserving computations over distributed data, in order to preserve confidentiality and guarantee correctness at the same time. We classify and compare 41 different schemes, regarding solution approach, security, efficiency, and practicality. Lastly, we discuss some of the most promising solutions in this regard, and present various open challenges and directions for future research.

Context: Change mining enables organizations to understand the changes that occurred in their business processes. This allows them to enhance their business processes and adapt to dynamic environments. Therefore, change mining is becoming a topic of interest for researchers, scholars, and practitioners. Objective: Motivated by the goal of establishing the state of the art in this area, this paper aims to investigate the literature in change logging and mining in process-aware information systems, provide an overview of the methods that are used in the existing publications, and identify gaps in the research on the topic of logging and mining process changes. Method: A literature review is conducted with the objective to identify and define methods to mine, store, and record changes in business processes. From 1136 publications, we selected 6 papers related to changes in business process and extended the list to 9 papers by including the relevant articles referenced by the papers that we selected originally. Results: In answer of our research questions, we have identified two classes of change mining methods, two ways of recording the changes into change logs, five formats for change log representation, and four objectives to be learned from changes. Conclusion: The literature review provides a summary of existing change mining and logging methods in process-aware information systems and identifies a number of research gaps in the area.

Cloud computing has emerged as a crucial solution for managing data- and compute-intensive workflows, offering scalability to address dynamic demands. However, security concerns persist, especially for workflows involving sensitive data and tasks. One of the main gaps in the literature is the lack of robust and flexible measures for reacting to these security violations. To address this, we propose an innovative approach leveraging Reinforcement Learning (RL) to formulate adaptation chains, responding effectively to security violations within cloud-based workflows. These chains consist of sequences of adaptation actions tailored to attack characteristics, workflow dependencies, and user-defined requirements. Unlike conventional single-task adaptations, adaptation chains provide a comprehensive mitigation strategy by taking into account both control and data dependencies between tasks, thereby accommodating conflicting objectives effectively. Moreover, our RL-based approach uses insights from past responses to mitigate uncertainties associated with adaptation costs. We evaluate the method using our jBPM and Cloudsim Plus based implementation and compare the impact of selected adaptation chains on workflows with the single adaptation approach. Results demonstrate that the adaptation chain approach outperforms in terms of total adaptation cost, offering resilience and adaptability against security threats.

In the world of science new technology have opened up the possibility to rely on advanced computational methods and models to conduct and produce scientific research. An important aspect of scientific and business workflows is provenance - which refers to the information describing the production, history or lineage of an end product, which can also be data, digitalized processes and other not tangible artifacts. While there are already systems, tools and standards to capture provenance of data and workflows the provenance of adaptations/changes in workflows has not been addressed yet. In this paper we carry out a literature review to establish the state of the art on this topic and present our methodology and findings. Our findings confirm that provenance of adaptation has not been addressed adequately in the fields of business and scientific workflows. The two fields also have different motivation for recording the lineage of data or processes. While scientific workflows are interested in reproducibility and visualization, business workflows solutions are indirectly connected to compliance, exception handling and analysis. The adaptive nature of workflows in both fields is not reflected in the research on process provenance yet, as our results show. The use of standard provenance standards is also not wide spread.

Business Process Management (BPM) provides organisations with a systematic approach to model, execute, analyse, and continuously improve their business processes. Traditionally, BPM practices focus on the economic performance of business processes. With the advent of Green BPM, environmental sustainability emerged as an additional performance dimension, allowing it to support organisations in achieving their sustainability goals. However, most existing Green BPM initiatives focus on specific phases of the BPM life cycle or particular environmental performance indicators. As a result, organisations only have a limited understanding of the sustainability of their business processes and lack the means to control their environmental impact during process execution. This paper proposes a Green BPM life cycle that integrates environmental sustainability into every one of its phases without being restricted to specific indicators. An extension of BPMN, one of the standard notations for modelling business processes, is defined to allow process models to capture the environmental performance indicators that should be monitored during process execution, together with target values that can optionally be specified for each indicator to control the environmental impact of the process. An architecture is further presented for integrating these indicators into existing BPM systems by connecting calculator services to the process life cycle events. As a first step towards achieving full life cycle coverage, a prototype modelling tool for the BPMN extension and a prototype calculator service for carbon emissions have been developed.

In this paper we present a tool for adaptive process log generation and analysis of the correlation between KPI (Key Performance Indicator) values and changes in adaptive processes. The tool features a component called Next(Log) helping users to generate initial business process logs using any preferred method and subsequently allows them to adapt these logs based on their own defined rules while ensuring an intuitive and coherent user interface. The adapted logs are then used for log analysis with the ML.Log component, which employs machine learning techniques to find patterns of matching KPI values and adaptation injections in the logs. The tool therefore supports the research on the challenges imposed by the lack of sufficient amount of data from adaptive process logs and the open issues in identifying at what KPIs values changes are required and what kind of changes would have the best impact on the process performance at run time.

ABSTRACT Radio galaxies exhibit a rich diversity of morphological characteristics, which make their classification into distinct types a complex challenge. To address this challenge effectively, we introduce an innovative approach for radio galaxy classification using COSFIRE filters. These filters possess the ability to adapt to both the shape and orientation of prototype patterns within images. The COSFIRE approach is explainable, learning-free, rotation-tolerant, efficient, and does not require a large training set. To assess the efficacy of our method, we conducted experiments on a benchmark radio galaxy data set comprising of 1180 training samples and 404 test samples. Notably, our approach achieved an average accuracy rate of 93.36 per cent. This achievement outperforms contemporary deep learning models, and it is the best result ever achieved on this data set. Additionally, COSFIRE filters offer better computational performance, ∼20 × fewer operations than the DenseNet-based competing method (when comparing at the same accuracy). Our findings underscore the effectiveness of the COSFIRE filter-based approach in addressing the complexities associated with radio galaxy classification. This research contributes to advancing the field by offering a robust solution that transcends the orientation challenges intrinsic to radio galaxy observations. Our method is versatile in that it is applicable to various image classification approaches.

Aggregate statistics play an important role in extracting meaningful insights from distributed data while preserving privacy. A growing number of application domains, such as healthcare, utilize these statistics in advancing research and improving patient care. In this work, we explore the challenge of input validation and public verifiability within privacy-preserving aggregation protocols. We address the scenario in which a party receives data from multiple sources and must verify the validity of the input and correctness of the computations over this data to third parties, such as auditors, while ensuring input data privacy. To achieve this, we propose the "VPAS" protocol, which satisfies these requirements. Our protocol utilizes homomorphic encryption for data privacy, and employs Zero-Knowledge Proofs (ZKP) and a blockchain system for input validation and public verifiability. We constructed VPAS by extending existing verifiable encryption schemes into secure protocols that enable N clients to encrypt, aggregate, and subsequently release the final result to a collector in a verifiable manner. We implemented and experimentally evaluated VPAS with regard to encryption costs, proof generation, and verification. The findings indicate that the overhead associated with verifiability in our protocol is 10x lower than that incurred by simply using conventional zkSNARKs. This enhanced efficiency makes it feasible to apply input validation with public verifiability across a wider range of applications or use cases that can tolerate moderate computational overhead associated with proof generation.

The serverless cloud computing execution model presents an opportunity for implementing data processing pipelines in a cost efficient manner with minimum or no consideration about managing their operational aspects. In doing so, however, a major concern that emerges is the adoption of provider-specific languages required for orchestrating serverless functions and supporting services into the higher order logic of pipelines. As a result, the vendor lock-in problem inherent to cloud computing becomes further intensified. In this work we propose an approach to mitigate this issue through the adoption and adaptation of well-known patterns from the literature as the means for modeling the pipeline logic. By providing mappings from these patterns to vendor-specific orchestration language constructs we allow for efficient realization of provider-agnostic pipeline models as provider-specific executable workflows. An industrial case study provides evidence towards the suitability of our proposal for practical purposes.

The rise of social media has transformed the landscape of news dissemination, presenting new challenges in combating the spread of fake news. This study addresses the automated detection of misinformation within written content, a task that has prompted extensive research efforts across various methodologies. We evaluate existing benchmarks, introduce a novel hybrid word embedding model, and implement a web framework for text classification. Our approach integrates traditional frequency–inverse document frequency (TF–IDF) methods with sophisticated feature extraction techniques, considering linguistic, psychological, morphological, and grammatical aspects of the text. Through a series of experiments on diverse datasets, applying transfer and incremental learning techniques, we demonstrate the effectiveness of our hybrid model in surpassing benchmarks and outperforming alternative experimental setups. Furthermore, our findings emphasize the importance of dataset alignment and balance in transfer learning, as well as the utility of incremental learning in maintaining high detection performance while reducing runtime. This research offers promising avenues for further advancements in fake news detection methodologies, with implications for future research and development in this critical domain.

Federated social networks have become an appealing choice as alternatives to mainstream centralized platforms. In the current global context, where the user’s activity on various social networks is monitored, influenced and manipulated, alternative platforms that offer the possibility of owning and controlling one’s own data are of great importance. Mastodon stands out among decentralized alternatives in the fediverse. In this study, we conduct a time-based dynamics analysis of various Mastodon instances, from popular ones to country-specific servers. Moreover, we conducted an analysis of registration account dynamics based on certain topics, such as academic, political and activism in general. Throughout the paper, we reveal the user adoption of Mastodon from multiple instances and metrics. Our results show a growth pattern of instances in terms of accounts in certain periods of time, and due to social events, reinforcing our assumption of it being already trusted as a decentralized platform. Our work holds significance in the wider context of studying and understanding the adoption rates of decentralized networks as ethical alternatives to centrally controlled ones.

Federated social networks have become an appealing choice as alternatives to mainstream centralized platforms. In the current global context, where the user’s activity on various social networks is monitored, influenced and manipulated, alternative platforms that offer the possibility of owning and controlling one’s own data are of great importance. Mastodon stands out among decentralized alternatives in the fediverse. In this study, we conduct a time-based dynamics analysis of Mastodon instances within a specific period. Our results show a growth pattern of instances in terms of accounts in certain periods of time, and due to social events, reinforcing our assumption of it being already trusted as a decentralized platform. Our work holds significance in the wider context of studying and understanding the adoption and evolution of decentralized platforms as ethical alternatives to Big Tech platforms.

The high dimensionality of time-series data presents challenges for direct mining, including time and computational resource costs. In this study, a novel data representation method for time series is proposed and validated in a hierarchical clustering task. First, the bidirectional segmentation algorithm, called BPLR, is introduced for piecewise linear representation (PLR). Through this method, the original time series is transformed into a set of linear fitting (LF) functions, thereby producing a concise, lower-dimensional LF time series that encapsulates the original data. Next, based on dynamic time warping (DTW) distance, a new similarity measure is proposed to compute the distance between any two LF time series, which is called LF-DTW distance. The proposed LF-DTW distance exhibits good performance in handling time-scale distortions between time series. Finally, hierarchical clustering is realized based on the proposed LF-DTW distance. The efficiency and advantages of the proposed approach are validated through experimental results using real-world data. Owing to its ability to capture the inherent structure of time series, the proposed approach consistently outperforms methods based on classic distance metrics and other existing clustering algorithms.

Today, the number of data-intensive and compute-intensive applications like business and scientific workflows has dramatically increased, which made cloud computing more popular in the matter of delivering a large amount of computing resources on demand. On the other hand, security is a critical issue affecting the wide adoption of cloud technologies, especially for workflows that are mostly dealing with sensitive data and tasks. In this paper, we carry out a review of the state-of-the-art on how security and privacy concerns in scientific and business workflows in cloud environments are being addressed and identify the limitations and gaps in the current body of knowledge in this area. In this extensive literature review, we first present a classification of the state-of-the-art security solutions organized according to the phases of the workflow life cycle they target. Based on our findings, we provide a detailed review and classification of the most relevant available literature focusing on the execution, monitoring, and adaptation phases of workflows. Finally, we present a list of open research issues related to the security of cloud-based workflows and discuss them.

The shear number of sources that will be detected by next-generation radio surveys will be astronomical, which will result in serendipitous discoveries. Data-dependent deep hashing algorithms have been shown to be efficient at image retrieval tasks in the fields of computer vision and multimedia. However, there are limited applications of these methodologies in the field of astronomy. In this work, we utilize deep hashing to rapidly search for similar images in a large database. The experiment uses a balanced dataset of 2708 samples consisting of four classes: Compact, FRI, FRII, and Bent. The performance of the method was evaluated using the mean average precision (mAP) metric where a precision of $88.5\%$ was achieved. The experimental results demonstrate the capability to search and retrieve similar radio images efficiently and at scale. The retrieval is based on the Hamming distance between the binary hash of the query image and those of the reference images in the database.

Privacy-preserving computation (PPC) methods, such as secure multiparty computation (MPC) and homomorphic encryption (HE), are deployed increasingly often to guarantee data confidentiality in computations over private, distributed data. Similarly, we observe a steep increase in the adoption of zero-knowledge proofs (ZKPs) to guarantee (public) verifiability of locally executed computations. We project that applications that are data intensive and require strong privacy guarantees, are also likely to require correctness guarantees, especially when outsourced. While the combination of methods for verifiability and privacy protection has clear benefits, certain challenges stand before their widespread practical adoption. In this work, we analyze existing solutions that combine verifiability with privacy-preserving computations over distributed data, in order to preserve confidentiality and guarantee correctness at the same time.We classify and compare 32 different schemes, regarding solution approach, security, efficiency, and practicality. Lastly, we discuss some of the most promising solutions in this regard, and present various open challenges and directions for future research.

Modern radio telescopes will generate, on a daily basis, data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of machine learning to complement human-aided and traditional statistical techniques. Recently, there has been a surge in scientific publications focusing on the use of machine/deep learning in radio astronomy, addressing challenges such as source extraction, morphological classification, and anomaly detection. This study provides a comprehensive and concise overview of the use of machine learning techniques for the morphological classification of radio galaxies. It summarizes the recent literature on this topic, highlighting the main challenges, achievements, state-of-the-art methods, and the future research directions in the field. The application of machine learning in radio astronomy has led to a new paradigm shift and a revolution in the automation of complex data processes. However, the optimal exploitation of machine/deep learning in radio astronomy, calls for continued collaborative efforts in the creation of high-resolution annotated data sets. This is especially true in the case of modern telescopes like MeerKAT and the LOw-Frequency ARray (LOFAR). Additionally, it is important to consider the potential benefits of utilizing multi-channel data cubes and algorithms that can leverage massive datasets without relying solely on annotated datasets for radio galaxy classification.

Efficient anomaly detection is at the forefront of ensuring optimized operations and system safety, especially in the field of engineering. It is deemed indispensable for the enhancement of work efficiency, maximization of resource utilization, and proactive address of potential risks. From this practical perspective, a new method for granular representation of time-series data is proposed in this study and is validated through a collective anomaly detection task. The density peak clustering (DPC) algorithm is employed to evaluate clusters in 2-D data, which are created from the original dataset and its variations. Using the principle of justifiable granularity, each cluster is depicted, allowing the time series to be converted into rectangular information granules. A distinctive similarity measure, designed to assess the similarity among subsequences in this format, is then introduced, with subsequences having reduced similarity being pinpointed as anomalies. Extensive testing on various datasets confirms that the effectiveness and applicability of the proposed method are evident in real-world scenarios. This approach not only enhances the understanding of time-series patterns, but is also recognized for its prominence in engineering applications where precise anomaly detection is demanded. Due to its ability to capture the inherent structure of time series and analyze the correlation among data, superior effectiveness is consistently exhibited by the proposed approach when compared to other existing methods.