Reviewerly/exHarmony huggingface dataset:
PropInit: Scalable Inductive Initialization for Heterogeneous Graph Neural Networks

Graph Neural Networks (GNNs) require that all nodes have initial representations which are usually derived from the node features. When the node features are absent, GNNs can learn node embeddings with an embedding layer or use pre-trained network embeddings for the initial node representations. However, these approaches are limited because i) they cannot be easily extended to initialize new nodes that are added to the graph for inference after training and ii) they are memory intensive and store a fixed representation for every node in the graph. In this work, we present PropInit a scalable node representation initialization method for training GNNs and other Graph Machine Learning (ML) models on heterogeneous graphs where some or all node types have no natural features. Unlike existing methods that learn a fixed embedding vector for each node, PropInit learns an inductive function that leverages the metagraph to initialize node representations. As a result, PropInit is fully inductive and can be applied, without retraining, to new nodes without features that are added to the graph. PropInit also scales to large graphs as it requires only a small fraction of the memory requirements of existing methods. On public benchmark heterogeneous graph datasets, using various GNN models, PropInit achieves comparable or better performance to other competing approaches while needing only 0.01% to 2% of their memory consumption for representing node embeddings. We also demonstrate PropInit's effectiveness on an industry heterogeneous graph dataset for fraud detection and achieve better classification accuracy than learning full embeddings while reducing the embedding memory footprint during training and inference by 99.99%
Certified Data Removal in Sum-Product Networks

Data protection regulations like the GDPR or the California Consumer Privacy Act give users more control over the data that is collected about them. Deleting the collected data is often insufficient to guarantee data privacy since it is often used to train machine learning models, which can expose information about the training data. Thus, a guarantee that a trained model does not expose information about its training data is additionally needed. In this paper, we present Unlearnspn– an algorithm that removes the influence of single data points from a trained sum-product network and thereby allows fulfilling data privacy requirements on demand.
BlabKG: a Knowledge Graph for the Blue Amazon

The Blue Amazon represents a large area off the coast of Brazil. The region is important for economic reasons, as it holds the majority of Brazil's oil and gas resources, as well as for its biological, geological and environmental features. Not much information about this region, however, is organized in systematic and computer-readable ways. In this context, we built a knowledge graph for the Blue Amazon based on a set of 496 scientific paper abstracts on the subject. The corpus includes a large number of topics, such as biodiversity, natural resources and climate change. Entities and relationships were automatically extracted from text through the use of OpenIE (a relationship triple extraction method) and were combined using word embeddings. The generated knowledge graph was then evaluated by comparing it to the source documents. Results show that key knowledge was correctly extracted, but also that there is still room for improvement, specially when it comes to weeding out undesirable or context-dependent data.
Using Image Upscaling Methods in Digital Platforms to Reduce Internet Usage

Image scaling has played an essential role today, especially in file sharing, such as on social media. Image scaling is a process for changing the size of a digital image. There are a lot of methods in image scaling that are being used in digital platforms. The standard image scaling being used is the upscaling method. Image upscaling also requires a lot of time to process, making it slower to finish upscaling the photos. This paper has proposed an optimized upscaling method where the upscaling process only focuses on the object in the images. By using the Object Detection AI, the Image Upscaling Method will only focus on the object inside the picture. Theoretically using image upscaling can reduce the time needed to finish the upscaling process. Before implementing this object-focused AI in the image upscaling method, there will be done a comparison of several upscaling methods to determine which upscaling method has the best quality. The results of comparison between some image upscaling method shows that the waifu2x has the best image quality. Not only the image quality, this waifu2x also has one of the smallest image size produced by the algorithm compared to the others. In this experiment, there is a limitation in experimenting the algorithm because using waifu2x requires CUDA Hardware Acceleration by Nvidia. Without using CUDA, the time needed to finish the upscaling process requires longer time.
Community Privacy using the Sparse Vector Technique for Graph Statistics

Various attacks have occurred to extract information on a specific person from social networks. Differential privacy (DP) is one of the solutions for privacy disclosure issues. However, the privacy issue in social networks makes people reluctant to provide their data. This circumstance causes a lack of data for data analysis. DP in small data degrades data utility more than in big data when we add the same amount of noise. We propose Community Attributes Privacy-preserving Method (CAPM) using the sparse vector technique that maintains a constant privacy level even in small data to mitigate this issue in this paper. CAPM obfuscates raw graph data to protect the network structure in a small network. This technique can improve the data utility performance compared to the existing model. We also suggest a privacy parameter that sets the privacy budget based on the similarity of communities in a network to reflect the network topology and contribute to raising the accuracy of a synthetic graph. In a node privacy view, we inject noise into the edges of central nodes in a community. Finally, we evaluate CAPM with real networks regarding statistical utility and privacy protection. We show that CAPM has an error rate of the number of edges up to 20 percent and its structural entropy is less than 17 percent of the error rate on average. CAPM improves the average clustering coefficient by 82 percent from the recent modeling algorithm. In addition, a maximum 18 percent error rate in modularity outperforms the baseline whose 43 percent of error rate. The evaluation results show that the CAPM generates synthetic social graphs targeting their relations of communities and performs better in data utility.
URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud

The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modelling (URegM) which predicts the impact of code smell refactoring on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively.
Web e-Learning: Automated Essay Assessment Based on Natural Language Processing Using Vector Space Model

Automatic essay assessment aims to determine the value of essay and provides feedback based on a computer system. Using a computer-based system can make essay assessments more objective so that the students are more satisfied with the results they get. The assessment of the learning outcomes is very important for web e-learning systems. The application of the right algorithm can provide the right assessment results. This article aims to develop a web e-Learning system with the automatic essay answer scoring application based on natural language processing (NLP) with the text processing. It consists of tokenizing, stopward removal and stemming by using a vector space model algorithm to calculate the word weights. The feasibility validation test phase of application system was carried out by comparing between manual assessment and automatic assessment. The results of manual calculation shows the similaritty with a percentage of 82%. while the level of similarity of the automatic calculation system is 81.6%. The calculation value of similarity to all sample questiosn tested has a significant proximity.
An Analysis of Cyber Breaches and Effects on Shareholder Wealth

AbstractCyber data breaches are an evolving risk facing all firms and threatens their ability to conduct business activity. Due to a lack of available information, very little is known about the frequency and severity of data breaches and the financial markets’ responses to them. This study uses a unique proprietary data set that includes cyber data breach information for 3,992 publicly traded firms during the 1990 to 2019 period. We find cyber breaches vary with time and cyber criminals focus on particular industries, types of information, access sources, and countries. We find significant short run negative abnormal returns around the announcement of cyber events and larger negative returns in industries with higher frequencies of attacks and attacks involving personal financial information. Additionally, we find increasing negative cumulative abnormal returns up to 250 days after the announcement.Keywords: Cyberbreachshareholder valuestock returnsevent studyJEL: M2C58D22K24 AcknowledgementsThe authors wish to thank Advisen Ltd. providing us with a comprehensive database of cyber data events using both their “Standard Loss Feed Data Set” and access to their “Cyber OverVue” application for company specific frequency and severity profiles. The authors also wish to thank Dylan Vogt and David Ratigan for their help with some of the data analysis and two anonymous referees for their comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the authors.Notes1 Scott Stransky, Assistant Vice President and Principal scientist in AIR Research and Modeling group http://www.ambest.com/video/MediaArchive.aspx?lid=5991296858001&vid=60312270010012 Hacker attacks with ransom demands on companies grow in the country, CE Noticias Financieras (Latin America), 8/22/21.3 Mandiant Video Seminar, “Ransomware Ups Its Game: Understanding Multifaceted Extortion Attacks & How To Protect Against Them”, September 16, 2021.4 “Most Company Hacks Not Disclosed to SEC Despite Guidance”, Wall Street Journal, Feb 29, 2019.5 First notice date is defined as the earliest date when the incident was known to have occurred: this may be the mailing date of notification letters, the date an incident is reported to the public, the date of a news article, the date an investigation began from a law firm or regulator, or a specifically mentioned “discover” date.6 See Fama (Citation1998) for a discussion on the differences between CARs and BHARs.7 Advisen’s Cyber OverVue program enables real-time decision-making about the cyber risk of organizations. The program allows insurance company clients to evaluate a company’s known loss experience in terms of frequency and severity, as well as how their cyber program compares to their peers.8 The t statistic is based on the crude dependence adjustment method as described in Brown and Warner (Citation1985) and Pandey and Kumari (Citation2021). The Z statistic is based on the Patell Z statistic, Patell (Citation1976).
A new method for solving the mobile payment scheduling problem using harris hawks optimization algorithm during the COVID-19 pandemic

The Coronavirus Disease 2019 (COVID-19) epidemic is causing once-in-a-century upheavals in global civilization. Payment systems have advanced lately, from simple cash or credit card transactions to various forms of mobile payment systems. This transformation is occurred due to COVID 19 and shifts in the economy, the growth of social networks, technical advancements on the Internet, and the increased usage of mobile devices. Throughout COVID19, this article offers a unique approach to the payment scheduling issue, which seeks out a timetable that enhances the project's stakeholders' benefit. Both the sponsor and the contractor in a project want to have a strong payment plan on their own. To create an equal schedule between the sponsor and the development team, the timing of payments and the completion periods of project activities are decided concurrently. The Harris hawks optimization method is designed to tackle the problem because of its high NP-hardness. Harris hawks optimization is a novel meta-heuristic nature-inspired optimizer inspired by how Harris hawks hunt food in nature. By comparing the suggested Harris hawks optimization optimizer to existing nature-inspired methods, the efficacy of the suggested Harris hawks optimization optimizer is determined. The Harris hawks optimization algorithm appears to be highly promising based on the statistical findings and comparisons. The MATLAB simulator's simulation findings confirm the algorithm's superiority over earlier efforts regarding energy, cost, delay time, and net value.
High-probability photon catalysis with non-linear photon beam splitter

Photon catalysis (PC) is an intriguing quantum optical operation wherein the same number of photons are added to and subsequently subtracted from the relevant optical system. It is an important tool that facilitates photon number manipulation. However, most existing PC methods are suitable only for photonic states residing within few-photon subspace. In addition, the probability of PC decreases exponentially with increase in the number n of the Fock state |n〉. Thus, this study proposed a high probability PC scheme for optical quantum states with large Fock state population based on nonlinear beam splitters. The results indicate that the fidelity and the probability of PC are both substantially improved. Moreover, in case of detector inefficiency, the scheme of PC with non-linear beam splitter was more robust. Thus, the proposed method provides a useful tool for manipulating optical quantum states with large Fock number state population.
Design and implementation of a WEBGIS-based recommendation system based on context-awareness for tourism planning

Design and implementation of a WEBGIS-based recommendation system based on context-awareness for tourism planning
PIMS: An Efficient Process Integrity Monitoring System Based on Blockchain and Trusted Computing in Cloud-Native Context

With the advantages of lightweight and high resource utilization, cloud-native technology with containers as the core is gradually becoming the mainstream technical architecture for information infrastructure.However, malware attacks such as Doki and Symbiote threaten the container runtime's security.Malware initiates various types of runtime anomalies based on process form (e.g., modifying the process of a container, and opening the external ports).Fortunately, dynamic monitoring mechanisms have proven to be a feasible solution for verifying the trusted state of containers at runtime.Nevertheless, the current routine dynamic monitoring mechanisms for baseline data protection are still based on strong security assumptions.As a result, the existing dynamic monitoring mechanism is still not practical enough.To ensure the trustworthiness of the baseline value data and, simultaneously, to achieve the integrity verification of the monitored process, we combine blockchain and trusted computing to propose a process integrity monitoring system named IPMS.Firstly, the hardware TPM 2.0 module is applied to construct a trusted security foundation for the integrity of the process code segment due to its tamper-proof feature.Then, design a new format for storing measurement logs, easily distinguishing files with the same name in different containers from log information.Meanwhile, the baseline value data is stored on the blockchain to avoid malicious damage.Finally, trusted computing technology is used to perform fine-grained integrity measurement and remote attestation of processes in a container, detect abnormal containers in time and control them.We have implemented a prototype system and performed extensive simulation experiments to test and analyze the functionality and performance of the PIMS.Experimental results show that PIMS can accurately and efficiently detect tampered processes with only 3.57% performance loss to the container.
GraphCWGAN-GP: A Novel Data Augmenting Approach for Imbalanced Encrypted Traffic Classification

Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network (GAN) method can generate new samples by learning the feature distribution of the original samples, it is confronted with the problems of unstable training and mode collapse.To this end, a novel data augmenting approach called GraphCWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then, the minority class data is augmented with our proposed model, which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally, the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN (CGAN), Wasserstein GAN-Gradient Penalty (WGAN-GP) and GraphCWGAN-GP, respectively.Compared with the state-of-the-art GAN methods, the GraphCWGAN-GP cannot only control the modes of the data to be generated, but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision, recall and F1-Score of the minority class in the balanced dataset augmented in this paper have improved by more than 2.37%, 3.39% and 4.57%, respectively.
A Linear Homomorphic Proxy Signature Scheme Based on Blockchain for Internet of Things

The mushroom growth of IoT has been accompanied by the generation of massive amounts of data.Subject to the limited storage and computing capabilities of most IoT devices, a growing number of institutions and organizations outsource their data computing tasks to cloud servers to obtain efficient and accurate computation while avoiding the cost of local data computing.One of the most important challenges facing outsourcing computing is how to ensure the correctness of computation results.Linearly homomorphic proxy signature (LHPS) is a desirable solution to ensure the reliability of outsourcing computing in the case of authorized signing right.Blockchain has the characteristics of tamper-proof and traceability, and is a new technology to solve data security.However, as far as we know, constructions of LHPS have been few and far between.In addition, the existing LHPS scheme does not focus on homomorphic unforgeability and does not use blockchain technology.Herein, we improve the security model of the LHPS scheme, and the usual existential forgery and homomorphic existential forgery of two types of adversaries are considered.Under the new model, we present a blockchain-based LHPS scheme.The security analysis shows that under the adaptive chosen message attack, the unforgeability of the proposed scheme can be reduced to the CDH hard assumption, while achieving the usual and homomorphic existential unforgeability.Moreover, compared with the previous LHPS scheme, the performance analysis shows that our scheme has the same key size and comparable computational overhead, but has higher security.
Technique for Multi-Pass Turning Optimization Based on Gaussian Quantum-Behaved Bat Algorithm

The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field.The main objective of this operation is to minimize the unit production cost.This paper proposes a Gaussian quantum-behaved bat algorithm (GQBA) to solve the problem of multi-pass turning operation.The proposed algorithm mainly includes the following two improvements.The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification.The second improvement is to use a Gaussian distribution instead of the uniform distribution to update the positions of the quantum-behaved bats, thus performing a more accurate search and avoiding premature convergence.The performance of the presented GQBA is demonstrated through numerical benchmark functions and a multi-pass turning operation problem.Thirteen classical benchmark functions are utilized in the comparison experiments, and the experimental results for accuracy and convergence speed demonstrate that, in most cases, the GQBA can provide a better search capability than other algorithms.Furthermore, GQBA is applied to an optimization problem for multi-pass turning, which is designed to minimize the production cost while considering many practical machining constraints in the machining process.The experimental results indicate that the GQBA outperforms other comparison algorithms in terms of cost reduction, which proves the effectiveness of the GQBA.
Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models' outcomes could result in misleading conclusions that ultimately could become very dangerous.For these reasons, it is important to rely on expert knowledge when applying these methods.However, not every user can count on this specific expertise; non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems, but they need basic guidelines to obtain the most out of AI models.The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features.The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering.As a result, 9 papers that tackle AI algorithm recommendation through tangible and traceable rules and heuristics were collected.The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.