Reviewerly/exHarmony huggingface dataset:
Methodology for Developing Resilient Information Systems in Cyber Conflict Environment

Abstract Successful cyber defence in the challenging cyber conflict environment depends on many factors but a crucial one is the use of a resilient system for cyber defence which is highly interoperable with those of partner countries and organisations. This paper presents a methodology of developing information systems capabilities resilient and interoperable in cyber conflict environment. The applied approach for defining the system design of information infrastructure is based on NATO models. For the architectural configuration, the NATO Technical Reference Model is applied and the NATO Common Operating Environment Component Model (NCOECM) is used for defining the information infrastructure components of the system design. NATO Technical Reference Model defines system services necessary for the information infrastructure of organizations. Functionalities needed by users and based on system services are also identified using reference models like workstations, servers, terminals and others, which are connected in an integrated information infrastructure. By implementing the proposed methodology a model of architectural configuring of an information infrastructure component has been developed – the Security Information and Event Management (SIEM) Server functional configuration.
Right Thoughts and Right Action: How to Make Agile Teamwork Effective

Teamwork is critical in many industrial sectors. When creating complex software solutions, most companies and public institutions organize work in cross-functional teams and follow the principles of agile development. This approach to knowledge-intensive work seeks to empower team members, ensures that the most competent people make decisions, and manages uncertainty by allowing members to learn and adapt as the work progresses. Advice on teamwork is abundant. For example, the Google re:Work model offers advice to development teams in the form of five key factors for successful teams, including psychological safety, structure and clarity, and teamwork that the team members consider meaningful. There is also general advice from years of studies of teamwork and from empirical studies on agile development teams. However, there has yet to be a model that draws together the knowledge from all of these sources and specifically focuses on the effectiveness of agile teamwork. To fill this gap, we have developed an Agile Teamwork Effectiveness Model (ATEM). Our model is based on a review of empirical studies on agile development teams, general studies of effective teams and teamwork, and practitioner advice. We also incorporated findings from our own two case studies and 22 focus groups. Though primarily intended for collocated agile software development teams, the increasing adoption of agile methods outside IT departments may make the model valuable for other agile workplaces.
ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations

Early diagnosis of disease can lead to improved health outcomes, including higher survival rates and lower treatment costs. With the massive amount of information available in electronic health records (EHRs), there is great potential to use machine learning (ML) methods to model disease progression aimed at early prediction of disease onset and other outcomes. In this work, we employ recent innovations in neural ODEs combined with rich semantic embeddings of clinical codes to harness the full temporal information of EHRs. We propose ICE-NODE (Integration of Clinical Embeddings with Neural Ordinary Differential Equations), an architecture that temporally integrates embeddings of clinical codes and neural ODEs to learn and predict patient trajectories in EHRs. We apply our method to the publicly available MIMIC-III and MIMIC-IV datasets, and we find improved prediction results compared to state-of-the-art methods, specifically for clinical codes that are not frequently observed in EHRs. We also show that ICE-NODE is more competent at predicting certain medical conditions, like acute renal failure, pulmonary heart disease and birth-related problems, where the full temporal information could provide important information. Furthermore, ICE-NODE is also able to produce patient risk trajectories over time that can be exploited for further detailed predictions of disease evolution.
Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems

Interactive Recommender Systems (IRSs) have attracted a lot of attention, due to their ability to model interactive processes between users and recommender systems. Numerous approaches have adopted Reinforcement Learning (RL) algorithms, as these can directly maximize users' cumulative rewards. In IRS, researchers commonly utilize publicly available review datasets to compare and evaluate algorithms. However, user feedback provided in public datasets merely includes instant responses (e.g., a rating), with no inclusion of delayed responses (e.g., the dwell time and the lifetime value). Thus, the question remains whether these review datasets are an appropriate choice to evaluate the long-term effects of the IRS. In this work, we revisited experiments on IRS with review datasets and compared RL-based models with a simple reward model that greedily recommends the item with the highest one-step reward. Following extensive analysis, we can reveal three main findings: First, a simple greedy reward model consistently outperforms RL-based models in maximizing cumulative rewards. Second, applying higher weighting to long-term rewards leads to a degradation of recommendation performance. Third, user feedbacks have mere long-term effects on the benchmark datasets. Based on our findings, we conclude that a dataset has to be carefully verified and that a simple greedy baseline should be included for a proper evaluation of RL-based IRS approaches.
What Do Graph Convolutional Neural Networks Learn?

Graph neural networks (GNNs) have gained traction over the past few years for their superior performance in numerous machine learning tasks. Graph Convolutional Neural Networks (GCN) are a common variant of GNNs that are known to have high performance in semi-supervised node classification (SSNC), and work well under the assumption of homophily. Recent literature has highlighted that GCNs can achieve strong performance on heterophilous graphs under certain "special conditions". These arguments motivate us to understand why, and how, GCNs learn to perform SSNC. We find a positive correlation between similarity of latent node embeddings of nodes within a class and the performance of a GCN. Our investigation on underlying graph structures of a dataset finds that a GCN's SSNC performance is significantly influenced by the consistency and uniqueness in neighborhood structure of nodes within a class.
Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models

Explanations for \emph{black-box} models help us understand model decisions as well as provide information on model biases and inconsistencies. Most of the current explainability techniques provide a single level of explanation, often in terms of feature importance scores or feature attention maps in input space. Our focus is on explaining deep discriminative models at \emph{multiple levels of abstraction}, from fine-grained to fully abstract explanations. We achieve this by using the natural properties of \emph{hyperbolic geometry} to more efficiently model a hierarchy of symbolic features and generate \emph{hierarchical symbolic rules} as part of our explanations. Specifically, for any given deep discriminative model, we distill the underpinning knowledge by discretisation of the continuous latent space using vector quantisation to form symbols, followed by a \emph{hyperbolic reasoning block} to induce an \emph{abstraction tree}. We traverse the tree to extract explanations in terms of symbolic rules and its corresponding visual semantics. We demonstrate the effectiveness of our method on the MNIST and AFHQ high-resolution animal faces dataset. Our framework is available at \url{https://github.com/koriavinash1/SymbolicInterpretability}.
A New Identity-Based Encryption Scheme for Secure Sharing of Medical Data in the Cloud Using Blockchain

Abstract With the development of information technology (IT), electronic health (e-health) systems are used as a common approach to record patients' medical data. Given that medical information is an essential asset of individuals and the health system, there are severe concerns about secure sharing and preserving this information's privacy. In this paper, we propose a new identity-based encryption (IBE) method by bilinear pairings to ensure the confidentiality of patients' data and their privacy in cloud health systems; in this way, this plan also provides authentication for users using challenge-response mechanisms. In addition, the proposed scheme, using blockchain techniques, ensures integrity and precise access control for shared data. The correctness of the proposed protocol is verified, and its security is formally proven in the standard model. The implementation of our scheme is performed in Java, and the results show that the proposed scheme reduces the computational overhead compared to previous similar methods.
Developing virtual fingerprint: identifying trustworthy interaction behaviors of web users

Abstract Trustworthy interaction refers to the interaction process between human and network information systems that is believable and credible. In recent years, network security events frequently happened caused by human-web interaction lacking trustworthiness, which brings about some security risks such as private information leakage, resource abuse, property or reputation loss, etc. It is a significant network security concern threatening people. The problem is typically approached from the viewpoint of identifying the trustworthiness of the credentials submitted by interactors. There are little few studies that have addressed it in terms of the trustworthiness of the interaction behavior by interactors. We propose that the interaction behavior pattern of interactors on the website is a virtual fingerprint (VF) to discriminate the trustworthiness of the identity based on relevant psychological and behavioral theories. Using 481,838 weblog data from a travel service website, we demonstrate the effectiveness of the proposed VF. First, the individual differences in interaction behavior of interactors on the website are characterized by numerical analysis and statistical inference. Then, 15 interaction behavior features are constructed from weblogs to create a VF, which can discriminate the trustworthiness of the identity by identifying the interaction behaviors of the interactors on the website. Of these, a machine learning algorithm (i.e., the eXtreme gradient boosting) is applied to build the identification model for interaction behaviors. The results show that the VF proposed in this study has better discrimination of trustworthiness compared to those approaches that use other kinds of behavior features. And, the eXtreme gradient boosting used performs more efficiently than the algorithm of Decision Tree and Random Forest. Specifically, the errors of discriminating both untrustworthy and trustworthy identities are as low as 0.19% and 3.30%, respectively. Also, the accuracy of identifying interaction behaviors is as good as 99.63%.
CHERCHE: A New Tool to Rapidly Implement Pipelines in Information Retrieval

In this demo paper, we present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. Our aim is to propose an easy to plug tool capable to execute, simple but strong, state-of-the-art information retrieval models. To do so, we have integrated classical models based on lexical matching but also recent models based on semantic matching. Indeed, a large number of models available on public hubs can be now tested on information retrieval tasks with only a few lines. CHERCHE is oriented to newcomers into the neural information retrieval field that want to use transformer-based models in small collections without struggling with heavy tools. The code and documentation of CHERCHE is public available at https://github.com/raphaelsty/cherche
Adaptive Frequency-Based Fully Hyperbolic Graph Neural Networks

Abstract Graph Convolutional Networks (GCNs) have attracted broad attention from industry and academia, for which GCNs have demonstrated powerful ability to model the irregular data, e.g., skeletal data and graph-structured data. The most existing effective model may be the fully hyperbolic graph neural network. However, it involves a large number of parameters, thus consuming considerable computing resources. In this paper, we propose a model based on adaptive frequency filter and corresponding optimizer in hyperbolic space. The adaptive frequency can learn the different frequency components of the embeddings of the nodes in graph, which adaptively adjust the beneficial signals of high-frequency and low-frequency. And the optimizer is based on a subset of the orthogonal constraint, which is dedicated for the adaptive frequency with less parameters. Consequently, our model need only to optimize the less parameters in hyperbolic space and meanwhile prevent the distortion caused by conventional manifold GCN. Experimental results show that our method achieves substantial improvements and outperforms the state-of-the-art performance in terms of node classification.
A Stochastic Game Approach to Masking Fault-Tolerance: Bisimulation and Quantification

We introduce a formal notion of masking fault-tolerance between probabilistic transition systems based on a variant of probabilistic bisimulation (named masking simulation). We also provide the corresponding probabilistic game characterization. Even though these games could be infinite, we propose a symbolic way of representing them, such that it can be decided in polynomial time if there is a masking simulation between two probabilistic transition systems. We use this notion of masking to quantify the level of masking fault-tolerance exhibited by almost-sure failing systems, i.e., those systems that eventually fail with probability 1. The level of masking fault-tolerance of almost-sure failing systems can be calculated by solving a collection of functional equations. We produce this metric in a setting in which the minimizing player behaves in a strong fair way (mimicking the idea of fair environments), and limit our study to memoryless strategies due to the infinite nature of the game. We implemented these ideas in a prototype tool, and performed an experimental evaluation.
The Saudi Information System Law and its Effectiveness (A Study of the Degree to Which Major Saudi Organizations Apply the Best Practices of Information Systems)

Information Security Law is the body of legal rules, codes, and standards that require companies to protect that information and the information systems that process it from unauthorized access. The government's development, issuance, and maintenance of law requirements help to ensure that standards are met, which, in turn, supports the country's safety, productivity, and economic development. The purpose of this paper is to assess the adequacy of the current Saudi laws and regulations related to information security and the extent to which information security laws impacted companies' application of information security best practices. The Saudi laws related to information security were studied and compared with the American information security regulations in the first part of the research. Then the experimental setting was used to perform the search by designing an electronic questionnaire to reveal the impact of Saudi information security laws on the reality of information security in companies. The questionnaire consists of 3 general questions and 11 questions regarding corporate practices that support the application of information security laws. The questionnaire was distributed to a random sample of information security workers in a number of major Saudi companies, and forty participants responded, and the largest proportion of respondents were executives, and the respondents' years of experience ranged between 1 and 40 years. It is concluded from the research that having stricter and flawless information security laws provides a strong foundation for corporate policies, and a safe investment environment.
Penerapan Data Mining Pengelompokan Menu Makanan dan Minuman Berdasarkan Tingkat Penjualan Menggunakan Metode K-Means

Data mining can be used to find solutions in making sales decisions to increase sales. Sales data storage stores many sales transaction records, where each document provides products purchased by customers in each sales transaction. A problem began to arise with an excess stockpiling of materials. The number of fluctuating sales causes the stock of available materials to be unstable and can directly impact consumers. Mistakes in predicting sales caused the coffee shop to buy large quantities of material stock, which were not widely used or sold out, so the supply of these materials swelled in the warehouse. One way to be implemented is by applying data mining because there are ways and methods to meet needs, one of which is the need for extensive information, then the information that we can use to determine quality in determining a decision. Therefore, it is hoped that this research can help Dpom Coffee minimize material stock inventory management cases such as shortages and excesses and make policies to increase sales by grouping menus based on sales levels using the K-means algorithm. Based on the results of processing the sales dataset at Dpom Coffee, it produces 3 clusters, namely Cluster 1 with eight menus with low sales levels, cluster 2 with 40 menus with moderate sales levels, and cluster 3 with seven menus with high sales levels. The accuracy or performance of the k-means algorithm results in a Davies Bouldin index value of 0.457.&#x0D;
Anomaly-aware multiple instance learning for rare anemia disorder classification

Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations. Multiple Instance Learning (MIL) has shown to be an effective solution, yet it suffers from low accuracy and limited explainability. Although the inclusion of attention mechanisms has addressed these issues, their effectiveness highly depends on the amount and diversity of cells in the training samples. Consequently, the poor machine learning performance on rare anemia disorder classification from blood samples remains unresolved. In this paper, we propose an interpretable pooling method for MIL to address these limitations. By benefiting from instance-level information of negative bags (i.e., homogeneous benign cells from healthy individuals), our approach increases the contribution of anomalous instances. We show that our strategy outperforms standard MIL classification algorithms and provides a meaningful explanation behind its decisions. Moreover, it can denote anomalous instances of rare blood diseases that are not seen during the training phase.
PENGEMBANGAN SISTEM PENGENDALIAN ARUS KAS MENGGUNAKAN METODE DIRECT CASH FLOW (STUDI KASUS : BADAN KESATUAN BANGSA DAN POLITIK DAERAH PROVINSI LAMPUNG)

The study, entitled Accounting Information Systems Cash Flow Control Direct Cash Flow Method (Case Study: National Unity and Political Agency of Lampung Province) in the process of controlling the flow of funds in the finances of the National Unity and Political Agency of Lampung Province found problems including the recording process on the finance department uses a ledger (manual recording). Using the ledger (recording) causes the recording to be less effective and also takes a long time. This study aims to facilitate the finance department in the process of managing funds. Data collection techniques include observation, interviews, documentation, and literature review. The system development method used in this research is the Prototype method using UML (Unified Modeling Language) as a tool to analyze the system to be made including Use Case Diagrams, Activity Diagrams and Class Diagrams. The development of this system uses the PHP programming language, Codeigniter framework version 3.1, Sublime as a Text editor version 3.1.1 (64 bit), and MySQL database version 11.11 (64 bit) as a database management system. The results of this study are able to build web-based applications and can manage financial transactions and present financial reports quickly and well, which has been tested by Blackbox and obtained a percentage with a value of 100%, and results that the system can run according to user needs and is feasible to use workable
Effects of Situation-Based Learning on Sentence Writing Ability of EFL Students in Thailand

Situation-based learning is a meaningful learning approach that provides a connection between the classroom and practice in the real world.This study represents an attempt to investigate the effects of situation-based learning on students' sentence writing ability of English as a Foreign Language (EFL) and explore students' opinions toward situation-based learning.The samples were 52 students majoring in English Education.The research instruments consisted of a learning achievement test for pre-testing and post-testing, lesson plans, situation-based tasks, a questionnaire, and a semi-structured interview form.The data were analyzed using quantitative and qualitative methods.The quantitative method applied descriptive statistics including Mean, Min, Max, and Standard Deviation (S.D.), and the qualitative method applied content analysis.The results of this study showed that situation-based learning improved students' English sentence writing ability in terms of simple sentences, compound sentences, complex sentences, and compound-complex sentences.The results also indicated that situation-based learning enhanced students' sentence writing in terms of sentence completion, complete thought, word use, grammar, and punctuation.Students' opinions toward situation-based learning were explored in three areas: classroom climate, classroom activities, and sentence writing improvement.The results showed that the most advantageous area was sentence writing improvement, while the least adventurous area was classroom climate.This study suggests that situation-based learning should be applied at other educational levels to help students transfer knowledge from the classroom into practice in real life.It is recommended to investigate situation-based learning in terms of problem-solving strategies that students apply to solve problems in real life situations.