Reviewerly/exHarmony huggingface dataset:
A Neighborhood Encoding for Subgraph Queries in Graph Databases

Subgraph isomorphism search is a fundamental problem in querying graph-like structured data. It consists to enumerate the subgraphs of a data graph that match a query graph. It is an NP-complete problem that knows several investigations. Most of them extend Ullmann's backtracking algorithm and rely on filtering and pruning mechanisms to reduce the search space. Most of these solutions focus on how to alleviate the searching step of the algorithm, identified as the most costly part, with various techniques and data structures. However, little effort is devoted to reduce the cost of the filtering step. In this paper, we take a completely different approach that relies on a constant time pruning mechanism while keeping Ullman's backtracking subgraph search subroutine. The main idea is to aggregate the semantic and topological information that surround a vertex into a simple integer. This simple neighbourhood encoding reduces the time complexity of vertex filtering from cubic to quadratic. We evaluate our approach on several real-word datasets and compare it with the state of the art algorithms.
KBQA: Accelerate Fuzzy Path Query on Knowledge Graph

Fuzzy path query is widely used to find the deep association of entities in many real-world applications such as knowledge graph answering and social network analysis. However, existing engines fail to support fuzzy path queries on large property graphs due to the imprecise string matching and indefinite search space. In this paper, we propose an extremely fast graph query engine KBQA, which can perform semantic matching in both entities and properties, and search arbitrarily long paths efficiently. Facing the performance problem, KBQA designs two-phase filtering strategy to accelerate candidate selection. Also, bitwise operations are adopted for fast graph exploration. Furthermore, KBQA adaptively prunes unpromising search paths based on path similarity. Extensive experiments show that KBQA outperforms all state-of-the-art graph databases by 2 $$\times $$ $$\sim $$ 10 $$\times $$ and searches all 6-hop paths within ten seconds. Our system has been applied in the ICT field and has achieved remarkable results.
Streaming Data Analytics for Feature Importance Measures in Concept Drift Detection and Adaptation

Numerous applications require the ability to detect and adapt to concept drifts in streaming data on the fly. This is challenged by limited computational resources and access to archival storage. In this paper, we study features that capture the evolving relationship between raw data features and target labels, and techniques to extract those features. In particular, we focus on the relationship between feature importance measures in streaming data and predictability performance of the main classifier. For this, we consider two groups of feature importance measures: impurity-based and permutation-based, both of which are computed over an auxiliary online gradient boosted decision trees ensemble that runs in parallel to the main classifier in processing the same data stream. We found strong evidence that feature importance measures follow the long-term trend of the performance metrics even if the data streams are non-stationary or deviate from the performance metrics in short-term. Our study also shows that classification models that process data with constant or monotonic rate of drift, are robust in terms of stationary nature of feature importance measures and learner's predictability performance. Moreover, we found evidence for more consistency and reliability of permutation feature importance measurements over impurity-based ones if data exhibits periodic or non-monotonic rates of drift, or if this knowledge is not known a priori. Our study and results indicate that the feature importance measures considered are viable sources of information for concept drift detection and adaptation problems. This has been established through a solution to these problems we developed based on vector error-correction analysis.
Compliance and Data Lifecycle Management in Databases and Backups

From the United States' Health Insurance Portability and Accountability Act (HIPAA) to the European Union's General Data Protection Regulation (GDPR), there has been an increased focus on individual data privacy protection. Because multiple enforcement agencies (such as legal entities and external governing bodies) have jurisdiction over data governance, it is possible for the same data value to be subject to multiple (and potentially conflicting) policies. As a result, managing and enforcing all applicable legal requirements has become a complex task. In this paper, we present a comprehensive overview of the steps to integrating data retention and purging into a database management system (DBMS). We describe the changes necessary at each step of the data lifecycle management, the minimum functionality that any DBMS (relational or NoSQL) must support, and the guarantees provided by this system. Our proposed solution is 1) completely transparent from the perspective of the DBMS user; 2) requires only a minimal amount of tuning by the database administrator; 3) imposes a negligible performance overhead and a modest storage overhead; and 4) automates the enforcement of both retention and purging policies in the database.
S2CTrans: Building a Bridge from SPARQL to Cypher

In graph data applications, data is primarily maintained using two models: RDF (Resource Description Framework) and property graph. The property graph model is widely adopted by industry, leading to property graph databases generally outperforming RDF databases in graph traversal query performance. However, users often prefer SPARQL as their query language, as it is the W3C's recommended standard. Consequently, exploring SPARQL-to-Property-Graph-Query-Language translation is crucial for enhancing graph query language interoperability and enabling effective querying of property graphs using SPARQL. This paper demonstrates the feasibility of translating SPARQL to Cypher for graph traversal queries using graph relational algebra. We present the S2CTrans framework, which achieves SPARQL-to-Cypher translation while preserving the original semantics. Experimental results with the Berlin SPARQL Benchmark (BSBM) datasets show that S2CTrans successfully converts most SELECT queries in the SPARQL 1.1 specification into type-safe Cypher statements, maintaining result consistency and improving the efficiency of data querying using SPARQL.
A Key-Value Based Approach to Scalable Graph Database

An increasing number of applications are modeling data as property graphs. In various scenarios, the scale of data can differ significantly, ranging from thousands of nodes/relationships to tens of billions of nodes/relationships. While distributed native graph databases can cater to the management and query requirements of large-scale graph data sets, they tend to be relatively cumbersome for small-scale data sets. This motivates us to develop a lightweight, scalable graph database capable of handling data across different scales. In this paper, we propose a method for constructing a graph database based on key-value storage, outlining the process of mapping graph data to key-value storage and executing graph queries on the key-value storage. We implemented and open-sourced a graph database based on RocksDB, namely KVGDB, which can manage data in an embedded fashion and be easily scaled to distributed environments. Experimental results demonstrate that KVGDB can effectively meet the management and query requirements of graph data sets, even at the scale of billions of nodes/relationships.
Semantically Constitutive Entities in Knowledge Graphs

Knowledge graphs are repositories of facts about a world. In this work, we seek to distill the set of entities or nodes in a knowledge graph into a specified number of constitutive nodes, whose embeddings would be retained. Intuitively, the remaining accessory nodes could have their original embeddings "forgotten", and yet reconstitutable from those of the retained constitutive nodes. The constitutive nodes thus represent the semantically constitutive entities, which retain the core semantics of the knowledge graph. We propose a formulation as well as algorithmic solutions to minimize the reconstitution errors. The derived constitutive nodes are validated empirically both in quantitative and qualitative means on three well-known publicly accessible knowledge graphs. Experiments show that the selected semantically constitutive entities outperform those selected based on structural properties alone.
Towards a Workload Mapping Model for Tuning Backing Services in Cloud Systems

With the increasing advent of applications and services adopting cloud-based technologies, generic automated tuning techniques of database services are gaining much attraction. This work identifies and proposes to overcome the potential challenges associated with deploying a tuning service as part of Platform-as-a-Service (PaaS) offerings for tuning of backing services. Offering an effective database tuning service requires such tuners whose architecture can support tuning multiple databases and numerous database versions deployed on various types of underlying hardware configurations with varying VM plans. Tuners that offer such capabilities usually attempt to leverage experiences gathered previously. By taking advantage of relevant past experiences, tuners classify the current workload to the most pertinent workload seen recently. In this work, a five-layered, fully connected neural network with ReLU activation function is being employed as the classification model to classify data points into relevant workload classes. The categorical cross-entropy function is employed as the loss function and optimized using Adam optimizer. The work handles the challenges related to the cold-start problem, issues in mapping, and cascading errors. The proposed solution can overcome these issues in a large-scale production environment. The results show that the model has 93.3% accuracy in 93.8% F1-score as compared to the previous model like Ottertune.
An approach to internal threats detection based on sentiment analysis and network analysis

Years into the insider threat, it remains an universal challenge to predict and defend. Concerning this problem, there has been a multitude of solutions, including the detection of sentiment fluctuations of an entity to predict its abnormality degree. Previous research solely focus on the analysis of individual anomalies, but ignore the sociality of individual sentiments. Therefore, in this paper, we propose an approach to internal threats detection based on sentiment and network analysis. Above all, we use Natural Language ToolKit (NLTK) to analyze the sentiment of each preprocessed email, and thus we establish sentiment communication network. The graphs are reconstructed into series and reshaped as matrices. By the singular value decomposition (SVD), the matrix is decomposed into base networks, and we analyze the change in importance of important base networks over time to detect the company’s internal conditions. When anomalies occur, we establish combination networks which consist of base networks and can represent the abnormal character patterns of this time. Then compare the eigenvector centrality of both combination networks and total network to identify the employees related to this anomaly. The experiment shows that it can locate the anomaly-related employees accurately, and according to the network graph, the top few of anomaly-related employees are associated in group internal threats. Our approach provides the anomaly-related rank of employees and the abnormal character pattern of the sentiment communication network at the moment of anomaly.
An intelligent transport system capable of collecting or foraging with many robotic vehicles: An intelligent computing paradigm

Self-driving vehicles are increasingly vital for various environmental tasks, such as data gathering, remote sensing, and mapping. They are particularly important in transportation, where advanced robotic vehicles can operate effectively without human oversight in challenging conditions. One exciting application is using collaborative autonomous robotic vehicles to locate specific points of interest like accidents, congestion, rain, fog, or icy roads. This article explores multi-robot systems for transportation tasks, considering interference that may impact the robot group's performance. A mathematical model is analysed to quantify the influence of interference and transformed into a dimensionless form for analysis. The system is studied numerically, and a neural soft computing technique, LMA-NN, is proposed. The technique is established on a neural network. For performance evaluation of the LMA-NN technique, different performance benchmarks such as mean square error and absolute error with reference solution are employed. The results are also presented graphically.
Quantum Scientific Computing

Quantum computing began in 1980 with Paul A. Benioff’s theoretical feasibility study. Since then, it has brought a wealth of publications on quantum information theory, quantum technologies, algorithms, and potential applications. In this paper, we give a brief overview of developments in this field, focusing on their potential impact on scientific computing in the post-quantum era.
Enhancing Urban Traffic Management through Predictive Modelling and Drone-Captured Image Analysis for Smart Traffic Lights

This research paper explores the utilization of predictive modelling and drone-captured image analysis to enhance urban traffic management in the context of smart traffic lights.The study focuses on employing advanced machine learning techniques, including LSTM and GRU architectures, to predict traffic flow patterns.Comparative analysis is conducted by evaluating the performance of these deep learning models against traditional algorithms such as Linear Regression, Gradient Boosting Regressor, and Random Forest Regressor.Metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared values are utilized to quantify the predictive accuracy of these models.Experimental results reveal that the LSTM model achieves an MAE of 6.32 and an RMSE of 12.76, while the GRU model yields an MAE of 6.50 and an RMSE of 13.12.These values outperform traditional algorithms, emphasizing the effectiveness of the proposed models in improving traffic flow predictions.The dataset comprises drone-captured images of urban traffic scenes, enabling the extraction of relevant features for accurate predictions.Findings underscore the potential of the proposed models in advancing intelligent traffic management systems.
Optimizing quantum algorithms on bipotent architectures

Vigorous optimization of quantum gates has led to bipotent quantum architectures, where the optimized gates are available for some qubits but not for others. However, such gate-level improvements limit the application of user-side pulse-level optimizations, which have proven effective for quantum circuits with a high level of regularity, such as the ansatz circuit of the quantum approximate optimization algorithm (QAOA). In this paper, we investigate the trade-off between hardware-level and algorithm-level improvements on bipotent quantum architectures. Our results for various QAOA instances on two quantum computers offered by IBM indicate that the benefits of pulse-level optimizations currently outweigh the improvements due to vigorously optimized monolithic gates. Furthermore, our data indicate that the fidelity of circuit primitives is not always the best indicator for the overall algorithm performance; their gate type and schedule duration should also be taken into account. This effect is particularly pronounced for QAOA on dense portfolio optimization problems, since their transpilation requires many swap gates, for which efficient pulse-level optimization exists. Our findings provide practical guidance on optimal qubit selection on bipotent quantum architectures and suggest the need for improvements of those architectures, ultimately making pulse-level optimization available for all gate types.
Self-perception of learning basic skills in the use of information and communications technology in university students with intellectual disabilities

Educational transformation in the university environment requires updating the basic skills of all university students to the educational moment in which they find themselves. This study aimed to determine the basic skills in the use of information and communications technology that were identified as known, both conceptually and procedurally, in 15 university students with intellectual disabilities. To ascertain the importance of digital skills and information and communications technology tools, a descriptive, cross-sectional, pre-post study was conducted. Descriptive results and statistical analyses are presented with a t-test for independent samples for the variables with a normal distribution, assuming a hypothesis that the post values would be greater than those obtained pre. It was confirmed that there is an increase in the perceived procedural knowledge and skills in different digital tools based on participants' self-perception (p < 0.0001).
1. Introduction and Welcome

This collection of reflective essays is a treasure trove of advice, reflection and hard-won experience from experts in the field of open and distance education. Each chapter offers tried-and-tested advice for nascent academic writers, delivered with personal, rich, and wonderful stories of the authors’ careers, their process, their research and their writing, and the struggles and triumphs they have encountered in the course of their careers.
Automation Bias and the “Goldilocks Effect” in Auditing Blockchain

ABSTRACT Blockchain technology appears to have unique characteristics such as inherent trust, immutability, transparency, and tighter security that might facilitate the audit process. Using a survey of AICPA members, we examine responses to the evaluation of audit evidence related to management assertions in a blockchain-enabled environment. We posit three possible effects pertaining to reliance on audit evidence in blockchain-enabled environments: (1) Goldilocks Effect (no automation bias) or the ideal scenario where auditors exhibit “just right” levels of professional skepticism and due care resulting in an optimal level of auditing; (2) Halo Effect or positive automation bias, which potentially results in over-reliance on evidence and, in turn, underauditing and; (3) Hype Effect or negative automation bias, which potentially results in under-reliance on evidence and, in turn, overauditing. Our results suggest that respondents to our survey are predominantly influenced by the Halo Effect, indicating a propensity for positive automation bias across management assertions.
A hybrid quantum–classical neural network for learning transferable visual representation

Abstract State-of-the-art quantum machine learning (QML) algorithms fail to offer practical advantages over their notoriously powerful classical counterparts, due to the limited learning capabilities of QML algorithms, the constrained computational resources available on today’s noisy intermediate-scale quantum (NISQ) devices, and the empirically designed circuit ansatz for QML models. In this work, we address these challenges by proposing a hybrid quantum–classical neural network (CaNN), which we call QCLIP, for Quantum Contrastive Language-Image Pre-Training. Rather than training a supervised QML model to predict human annotations, QCLIP focuses on more practical transferable visual representation learning, where the developed model can be generalized to work on unseen downstream datasets. QCLIP is implemented by using CaNNs to generate low-dimensional data feature embeddings followed by quantum neural networks to adapt and generalize the learned representation in the quantum Hilbert space. Experimental results show that the hybrid QCLIP model can be efficiently trained for representation learning. We evaluate the representation transfer capability of QCLIP against the classical Contrastive Language-Image Pre-Training model on various datasets. Simulation results and real-device results on NISQ IBM_Auckland quantum computer both show that the proposed QCLIP model outperforms the classical CLIP model in all test cases. As the field of QML on NISQ devices is continually evolving, we anticipate that this work will serve as a valuable foundation for future research and advancements in this promising area.
Accounting Data Analytics in R: A Case Study Using Tidyverse

ABSTRACT Data analytics (DA) has gained widespread attention in accounting and auditing recently. Despite the significant potential presented by DA programming languages such as R and Python, their application remains limited. This study presents the tidyverse approach of R for DA in the field of accounting. This approach can greatly benefit undergraduate or graduate accounting students without any coding knowledge. In addition, a teaching case suitable for auditing, accounting information systems, or accounting/auditing analytics classes is presented for instructors to adopt in their courses. Evidence from classrooms suggests that this approach is effective in teaching R for DA and in introducing advanced DA tools for the next generation of accountants. JEL Classifications: C8; Y1.
A Java Library for Itemset Mining with Choco-solver

While traditional data mining techniques have been used extensively for discovering patterns in databases, they are not always suitable for incorporating user-specified constraints.To overcome this issue, new research has began connecting Data Mining to Constraint Programming (CP).Such fertilization leads to a flexible way to tackle data mining tasks, such as itemset or association rule mining.In this paper, we introduce a new library for solving itemset mining problems with Choco-solver. Constraint Programming (CP)Constraint Programming (CP) is a powerful paradigm for solving combinatorial optimization problems (Rossi et al., 2006).It provides a declarative approach to problem-solving by defining a set of variables, domains, and constraints that capture the problem's requirements.CP solvers explore the space of possible solutions systematically, leveraging powerful search algorithms and constraint propagation techniques to efficiently find valid solutions.The flexibility of CP allows for modeling a wide range of problems, including scheduling (Baptiste et al., 2001), resource allocation (Zhang et al., 2013), and planning (Van Beek &Chen, 1999).Its ability to handle complex constraints, discrete variables, and global properties makes it particularly suitable for tackling real-world problems.CP has demonstrated remarkable success in various domains, offering a high-level modeling language and a diverse set of solving techniques.Its integration with other optimization methods and technologies further enhances its applicability and effectiveness.Overall, Constraint Programming is a valuable tool for addressing challenging optimization problems, offering a powerful approach to problem modeling, solving, and decision support.