Reviewerly/exHarmony huggingface dataset:
Empirical Exploration of Open-Source Issues for Predicting Privacy Compliance

In the last decade, privacy has gained a significant interest in software and information systems engineering mainly due to the emergence of privacy regulations, including the General Data Protection Regulation (GDPR). However, checking privacy compliance is challenging and depends on many factors, such as the programming language and the software architecture, as well as the underlying regulation. In this exploratory research, we aim to study whether positive discussions on privacy-related issues in Open-Source Software (OSS) environments can predict privacy compliance of the software. Such predictions are beneficial in different scenarios, including in software reuse. Our main contribution will lie in conceptually modeling and understanding the relations between privacy compliance and positive discussions of privacy-related OSS issues. The research comprises three parts: (1) identifying privacy-related issues using supervised machine learning techniques; (2) improving the identification of privacy-related issues utilizing ontologies; and (3) identifying the sentiment of privacy-related issues and analyzing relations to privacy compliance. This paper describes the design and results of part 1, as well as the design of parts 2 and 3.
A Zero-trust Access Control Scheme Based on CPABE

Abstract The introduction of new power terminals has improved the efficiency of the new power system, but it has also brought new risks. It is important to research ways to prevent cloud resources from unauthorized access by these terminals. To address this issue, an access control scheme was proposed based on Ciphertext-Policy Attribute-Based Encryption (CPABE) technology, which can encrypt cloud resource information and make access control fine-grained. We also combine the SM9 algorithm to reduce the key management burden of traditional CPABE technology and introduce a zero-trust mechanism to provide a higher level of security protection for the subsequent access control process of the terminal. Additionally, we prove the correctness and analysis of the attack, which can be resisted by the proposed scheme, demonstrating its feasibility and security. Finally, a comparison was made between our scheme and other CPABE schemes. Our scheme is proven to perform better in performance.
Bitcoin Blocksize, Custodial Security, and Price

Blockchain characteristics have been an under investigated potential driver of the valuation of cryptocurrencies. In this paper, we investigate one of the characteristics of the Bitcoin blockchain, the blocks size relative to the limit (i.e. fullness). We predict that as the block size approaches this limit, the fullness of the block functions as a deterrent to nefarious actors that are attempting a cryptoexchange security breach or cryptoexchange fraud. The Fullness of a block is a sign of congestion. This congestion makes it more difficult to launder stolen funds through the network and successfully exit. We hypothesize that this congestion effect increases the security of custodial wallets. We find that block fullness is negatively associated with the incidents of successful cryptoexchange breaches and frauds, that involve bitcoin. We also find that this rise in security is a positively associated determinant of bitcoin’s value. To address endogeneity, we exploit the segregated witness software upgrade to the Bitcoin protocol as a natural experiment for identification. We interpret the results of this natural experiment as demonstration of a causal relationship. The findings are robust to a variety of alternative specifications including but not limited to alternative proxies for network effects and measures of cybercrime involving cryptoexchanges and bitcoin. This line of research has important implications for policymakers and users in identifying where and when the Bitcoin ecosystem is most vulnerable to attack.
Towards Making XR 100% Accessible: A Discussion Panel

Given the increased usage of extended reality (XR), it is important to consider what difficulties users may have using these systems. This discussion panel will provide an open dialog about what accessibility means for XR systems and for whom. Each panelist will provide a brief overview of the following topics within their bailiwick followed by Q &amp; A. Aaron Gluck will discuss broadening access to XR by users with disabilities. Jessyca Derby will discuss heuristics for making AR/MR experiences more accessible and what resources are available to assist designers and practitioners, Thom Gable will discuss inclusive design in the XR space more broadly and discuss the approach within Microsoft, Nicholas Kelling will discuss the role behavioral and sensory accessibility plays in opening XR to a broader audience, and Carmen Van Ommen will discuss how best practices from other domains can be applied to XR
Semantic segmentation network stacking with genetic programming

Abstract Semantic segmentation consists of classifying each pixel of an image and constitutes an essential step towards scene recognition and understanding. Deep convolutional encoder–decoder neural networks now constitute state-of-the-art methods in the field of semantic segmentation. The problem of street scenes’ segmentation for automotive applications constitutes an important application field of such networks and introduces a set of imperative exigencies. Since the models need to be executed on self-driving vehicles to make fast decisions in response to a constantly changing environment, they are not only expected to operate reliably but also to process the input images rapidly. In this paper, we explore genetic programming (GP) as a meta-model that combines four different efficiency-oriented networks for the analysis of urban scenes. Notably, we present and examine two approaches. In the first approach, we represent solutions as GP trees that combine networks’ outputs such that each output class’s prediction is obtained through the same meta-model. In the second approach, we propose representing solutions as lists of GP trees, each designed to provide a unique meta-model for a given target class. The main objective is to develop efficient and accurate combination models that could be easily interpreted, therefore allowing gathering some hints on how to improve the existing networks. The experiments performed on the Cityscapes dataset of urban scene images with semantic pixel-wise annotations confirm the effectiveness of the proposed approach. Specifically, our best-performing models improve systems’ generalization ability by approximately 5% compared to traditional ensembles, 30% for the less performing state-of-the-art CNN and show competitive results with respect to state-of-the-art ensembles. Additionally, they are small in size, allow interpretability, and use fewer features due to GP’s automatic feature selection.
Employing Machine Learning and Internet of Things for Malaria Outbreak Prediction in Rwanda

Malaria is a threatening disease which is caused by a bite of female mosquitoes called anopheles and when it is not discovered at its earlier stage, it can put the life of many people at risk and even reduce the workforce of the country. However, its rate of transmission can be decreased if the information regarding the development of these mosquitoes are made available in due time. However, there is a lack of real time information about Malaria spreading to help the Ministry of Health to know the development of malaria mosquitoes relatively to environmental conditions and take the required measures for fighting against the spread of this disease by providing early warning to the hospitals and health institutions to purchase the medicine on time and reminding the citizens to use mosquito nets accordingly. The current study mainly aims to apply machine learning and Internet of Things technologies to help the Ministry of Health(MoH) to have access on the development of malaria mosquitoes and provide early warning information across citizens, hospitals, health institutions and individuals to be prepared accordingly. For modelling the dependency of malaria transmission, we have tested different machine learning classification algorithms for optimizing the prediction accuracy. The data used include the environmental climate and malaria data recorded by METEO Rwanda and Ministry of Health respectively in the period of 8 years (2012–2019) from Bugesera and Huye districts the most malaria endemic district in Rwanda. The results show that the Artificial Neural Network algorithm could perform better than other algorithms tested with 93.9% and 88.2% of training and testing accuracy respectively in Bugesera district, and 88.9% and 62.5% for training and testing accuracies respectively in Huye district. Secondly, an IoT based system was implemented to interact with the predictive model and view the results of prediction in the future on field sensors data via Smartphone, tablet or PC.
Tikhonov-Tuned Sliding Neural Network Decoupling Control for an Inverted Pendulum

This paper introduces the concept of intelligent control using Tikhonov regularization for nonlinear coupled systems. This research is driven by the increasing demand for advanced control techniques and aims to explore the impact of Tikhonov regularization on these systems. The primary objective is to determine the optimal regularization term and its integration with other control methods to enhance intelligent control for nonlinear coupled systems. Tikhonov regularization is a technique employed to adjust neural network weights and prevent overfitting. Additionally, the incorporation of ReLU activation function in the neural network simplifies thearchitecture, avoiding issues like gradient explosion, and optimizes controller performance. Furthermore, sliding surfaces are designed to improve control system stability and robustness. The proposed Tikhonov-tuned sliding neural network (TSN) controller ensures both stability and superior system performance. The methodology emphasizes the importance of determining optimal neural network weights and regularization terms to prevent overfitting, facilitating accurate predictions in inverted pendulum control system applications. To assess the functionality and stability of TSN, this paper employs simulations and experimental implementations to control both the rotary inverted pendulum and the arm-driven inverted pendulum. The results indicate that the proposed TSN methodologies are effective and feasible.
Building the VisSE Corpus of Spanish SignWriting

SignWriting is a system for transcribing sign languages, using iconic depictions of the hands and other body parts, as well as exploiting the possibilities of the page as a two dimensional medium to capture the three-dimensional nature of signs. This goes beyond the usual line-oriented nature of oral writing systems, and thus requires a different approach to its processing. In this article we present a corpus of handwritten SignWriting, a collection of images which transcribe signs from Spanish Sign Language. We explain the annotation schema we have devised, and the decisions which have been necessary to deal with the challenges that both sign language and SignWriting present. These challenges include the transformational nature of symbols in SignWriting, which can rotate and otherwise transform to convey meaning, as well as how to properly codify location, a fundamental part of SignWriting which is completely different to oral writing systems. The data in the corpus is fully annotated, and can serve as a tool for computational training and evaluation of algorithms, as well as provide a window into the nature of SignWriting and the distribution of its features across a real vocabulary. The corpus is freely available online at https://zenodo.org/record/6337885 .
Trajectory Planning and Control Design for Aerial Autonomous Recovery of a Quadrotor

One of the most essential approaches to expanding the capabilities of autonomous systems is through collaborative operation. A separated lift and thrust vertical takeoff and landing mother unmanned aerial vehicle (UAV) and a quadrotor child UAV are used in this study for an autonomous recovery mission in an aerial child–mother unmanned system. We investigate the model predictive control (MPC) trajectory generator and the nonlinear trajectory tracking controller to solve the landing trajectory planning and high-speed trajectory tracking control problems of the child UAV in autonomous recovery missions. On this basis, the estimation of the mother UAV movement state is introduced and the autonomous recovery control framework is formed. The suggested control system framework in this research is validated using software-in-the-loop simulation. The simulation results show that the framework can not only direct the child UAV to complete the autonomous recovery while the mother UAV is hovering but also keep the child UAV tracking the recovery platform at a speed of at least 11 m/s while also guiding the child UAV to a safe landing.
Comprehensive cyber security for port and harbor ecosystems

Global maritime transportation and logistics systems are essential parts of critical infrastructures in every society, and a crucial part of maritime logistics processes are seaports. In the coming years, digitalization and increased levels of autonomy in logistic transport chains are expected to take leaps forward. This development can help create safer, more efficient, more sustainable, and more reliable service chains to meet the requirements for a better quality of life and global prosperity. Port and harbor operations connect the maritime transport to other modes of transportation and enable multimodal transportation. Smart ports play a central role in future transport logistics and supply chains. Digitalization helps improve the efficiency of terminal systems in the processes of these ports. In the best cases, digitalization can also promote the reduction of emissions by optimizing port operations and enhancing cargo and people flows while improving the experience for all stakeholders. The improvement of port processes relies on the development of information and communication technology (ICT) as well as on industrial control systems (ICS) and operation technologies (OT). At the same time, the cyber security of maritime logistics also needs to be addressed. This article presents our findings related to the Sea4Value research goal on cyber security, which is a comprehensive cyber security architecture for port services at the system level. The article emphasizes the importance of a system of systems approach in terms of a comprehensive cyber security management process for port ecosystems. The description and recognition of management steps of every stakeholder are the key elements in this kind of process.
A Novel Noise Clustering Based on Local Outlier Factor

Reducing the impact of outliers is an essential issue in machine learning, including clustering. There are two main approaches to reducing the impact of outliers: one is to build robust models, and the other is to remove outliers through preprocessing. In this paper, we propose a new noise clustering method that combines noise clustering, which builds a model robust to outliers, and local outlier factor, which removes outliers as a preprocessing step. The proposed method is an optimization problem of noise clustering with a weighting of dissimilarities by LOF. Numerical experiments were conducted using four artificial datasets to verify the effectiveness of the proposed method. In the experiments, the proposed method was compared with k-medoids clustering, DBSCAN, and noise clustering. The results show that the proposed method yields good results regarding both clustering performances and detecting outliers. The guideline for determining k and $$\varepsilon $$ among the three parameters D, k, and $$\varepsilon $$ required by the proposed method was also suggested.
A Person Re-Identification Method Based on Multi-Branch Feature Fusion

Due to the lack of a specific design for scenarios such as scale change, illumination difference, and occlusion, current person re-identification methods are difficult to put into practice. A Multi-Branch Feature Fusion Network (MFFNet) is proposed, and Shallow Feature Extraction (SFF) and Multi-scale Feature Fusion (MFF) are utilized to obtain robust global feature representations while leveraging the Hybrid Attention Module (HAM) and Anti-erasure Federated Block Network (AFBN) to solve the problems of scale change, illumination difference and occlusion in scenes. Finally, multiple loss functions are used to efficiently converge the model parameters and enhance the information interaction between the branches. The experimental results show that our method achieves significant improvements over Market-1501, DukeMTMC-reID, and MSMT17. Especially on the MSMT17 dataset, which is close to real-world scenarios, MFFNet improves by 1.3 and 1.8% on Rank-1 and mAP, respectively.
Evaluation and Prediction of Blast-Induced Ground Vibrations: A Gaussian Process Regression (GPR) Approach

Ground vibration is one of the most hazardous outcomes of blasting. It has a negative impact both on the environment and the human population near to the blasting area. To evaluate the magnitude of blasting vibrations, it is important to consider PPV as a fundamental critical base parameter practice in terms of vibration velocity. This study aims to explore the application of different soft computing techniques, including a Gaussian process regression (GPR), decision tree (DT), and support vector regression (SVR), for the prediction of blast-induced ground vibration (PPV) in quarry mining. The three models were evaluated using classical mathematical evaluation metrics (R2, RMSE, MSE, MAE). The result shows that the GPR model achieves an excellent prediction result; with R2 = 0.94, RMSE = 0.0384, MSE = 0.0014, and MAE = 0.0265, it shows high accuracy in predicting PPV. The Shapley additive explanation (SHAP) results emphasize the importance of understanding the interactions between the various factors and their effects on the vibration assessment. The findings can inform the development of more sustainable and environmentally friendly models for predicting blasting vibrations. Using a GPR to simulate and predict blasting-induced ground vibrations is the study’s main contribution. The GPR can capture complicated, non-linear correlations in data, making it ideal for blast-induced ground vibrations, which are dynamic and nonlinear. By using a Gaussian process regression, we can help companies and researchers improve the safety and efficiency in blast-induced ground vibration environments.
A Graph-Based Technique for Securing the Distributed Cyber-Physical System Infrastructure

Spreading digitalization, flexibility, and autonomy of technological processes in cyber-physical systems entails high security risks corresponding to negative consequences of the destructive actions of adversaries. The paper proposes a comprehensive technique that represents a distributed functional cyber-physical system’s infrastructure as graphs: a functional dependencies graph and a potential attacks graph. Graph-based representation allows us to provide dynamic detection of the multiple compromised nodes in the functional infrastructure and adapt it to rolling intrusions. The experimental modeling with the proposed technique has demonstrated its effectiveness in the use cases of advanced persistent threats and ransomware.
Combination Test for Mean Shift and Variance Change

This paper considers a new mean-variance model with strong mixing errors and describes a combination test for the mean shift and variance change. Under some stationarity and symmetry conditions, the important limiting distribution for a combination test is obtained, which can derive the limiting distributions for the mean change test and variance change test. As an application, an algorithm for a three-step method to detect the change-points is given. For example, the first step is to test whether there is at least a change-point. The second and third steps are to detect the mean change-point and the variance change-point, respectively. To illustrate our results, some simulations and real-world data analysis are discussed. The analysis shows that our tests not only have high powers, but can also determine the mean change-point or variance change-point. Compared to the existing methods of cpt.meanvar and mosum from the R package, the new method has the advantages of recognition capability and accuracy.
Towards Semantics for Abstractions in Ontology-Driven Conceptual Modeling

Ontology-driven conceptual models are precise and semantically transparent domain descriptions that enable the development of information systems. As symbolic artefacts, such models are usually considered to be self-explanatory. However, the complexity of a system significantly correlates with the complexity of the conceptual model that describes it. Abstractions of both conceptual models and ontology-driven conceptual models are thus considered to be a promising way to improve the understandability and comprehensibility of those models. Although algorithms for providing abstractions of such models already exist, they still lack precisely formulated formal semantics. This paper aims to provide an approach towards the formalization of the abstraction process. We specify in first-order modal logic one of the graph-rewriting rules for ontology-driven conceptual model abstractions, in order to verify the correctness of the corresponding abstraction step. We also assess the entire network of abstractions of ontology-driven conceptual models and discuss existing drawbacks.
Digital Management of Competencies in Web 3.0: The C-Box® Approach

Management of competencies is a crucial concern for both learners and workers as well as for training institutions and companies. For the former, it allows users to track and certify the acquired skills to apply for positions; for the latter, it enables better organisation of business processes. However, currently, most software systems for competency management adopted by the industry are either organisation-centric or centralised: that is, they either lock-in students and employees wishing to export their competencies elsewhere, or they require users’ trust and for users to give up privacy (to store their personal data) while being prone to faults. In this paper, we propose a user-centric, fully decentralised competency management system enabling verifiable, secure, and robust management of competencies digitalised as Open Badges via notarization on a public blockchain. This way, whoever acquires the competence or achievement retains full control over it and can disclose his/her own digital certifications only when needed and to the extent required, migrate them across storage platforms, and let anyone verify the integrity and validity of such certifications independently of any centralised organisation. The proposed solution is based on C-Box®, an existing application for the management of digital competencies that has been improved to fully support models, standards, and technologies of the so-called Web 3.0 vision—a global effort by major web organisations to “give the web back to the people”, pushing for maximum decentralisation of control and user-centric data ownership.