Reviewerly/exHarmony huggingface dataset:
Fuzzy clustering method with approximate orthogonal regularization

As a widely used unsupervised learning method, clustering model plays an indispensable role in exploring data structures. Spectral analysis is a useful technique for clustering problems. The spectral analysis could yield good results when used in conjunction with fuzzy clustering models. However, the existing methods are limited by factors such as the definition of the similarity graph and predetermined number of clusters. In this paper, we propose a new fuzzy clustering model with the approximate orthogonal regularization. To obtain the relatively stable results, we reconstruct the Laplacian matrix with geometrically-nearest-neighbor similarity measurement. This new Laplacian matrix can represent the properties of datasets. For this constrained optimization problem, by applying the indicator function to simplify it as an unconstrained optimization problem. Then we propose an algorithm based on Alternating Direction Method of Multipliers (ADMM) and theoretically prove its convergence. We make some empirical experiments on both synthetic and real datasets. The experimental results demonstrate the model's effectiveness and showcase the desirable properties of the resulting solutions.
SlicedLocator: Code vulnerability locator based on sliced dependence graph

Machine learning-based fine-grained vulnerability detection is an important technique for locating vulnerable statements, which assists engineers in efficiently analyzing and fixing the vulnerabilities. However, due to insufficient code representations, code embeddings, and neural network design, current methods suffer low vulnerability localization performance. In this paper, we propose to address these shortcomings by presenting SlicedLocator, a novel fine-grained code vulnerability detection model that is trained in a dual-grained manner and can predict both program-level and statement-level vulnerabilities. We design the sliced dependence graph, a new code representation that not only preserves rich interprocedural relations but also eliminates vulnerability-irrelevant statements. We create attention-based code embedding networks that are trained with the entire model to extract vulnerability-aware code features. In addition, we present a new LSTM-GNN model as a fusion of semantic modeling and structural modeling. Experiment results on a large-scale C/C++ vulnerability dataset reveal that SlicedLocator outperforms state-of-the-art machine learning-based vulnerability detectors, especially in terms of localization metrics.
Unveiling the Blockchain Technology: An Analysis of Adoption and Inventions

The chapter explains the Blockchain and its application in cryptocurrency and in various sectors. It gives an insight into the level of adoption of Blockchain technology globally based upon industry, country, and component. China is leading all nations worldwide, followed by the United States. The study will help to understand future research regarding its applications in different sectors of the economy. The study will also help to understand the significance and complications regarding risk and regulation. Its adoption in the logistics and supply chain is meant to achieve error-free communication and efficient tracking management.
Mitigating risk of failure in information technology projects: Causes and mechanisms

This paper shows how causes and mechanisms behind past information technology (IT) project failures can be used for systematic risk mitigation in new IT projects. This is significant because successful IT projects are needed to realise the benefit potential of digitalisation, whereas failed IT projects overspend resources and underdeliver benefits. In this paper we a) identify factors and causes that lead to IT project failure, b) analyse the consistency over time of the identified factors and causes, c) expose mechanisms of failure by analysing failure factors, causes, and common features of IT projects, and d) show how this knowledge can be used in IT project risk evaluations. The paper uses hermeneutic literature review, statistical analysis of failure factors in the literature, content analysis of the reviewed literature, and process tracing.
Evaluating visual encoding quality of a mixed reality user interface for human–machine co-assembly in complex operational terrain

During human–machine collaboration in manufacturing activities, it is important to provide real-time annotations in the three-dimensional workspace for local workers who may lack relevant experience and knowledge. For example, in MR assembly, workers need to be alerted to avoid entering hazardous areas when manually replacing components. Recently, many researchers have explored various visual cues for expressing physical task progress information in the MR interface of intelligent systems. However, the relationship between the implantation of visual cues and the balance of interface cognition has not been well revealed, especially in tasks that require annotating hazardous areas in complex operational terrains. In this study, we developed a novel MR interface for an intelligent assembly system that supports local scene sharing based on dynamic 3D reconstruction, remote expert behavior intention recognition based on deep learning, and local personnel operational behavior visual feedback based on external bounding box. We compared the encoding results of the proposed MR interface with 3D annotations combined with 3D sketch cues (3DS), which combines 3D spatial cues (3DSC) and 3DS combined with adaptive cues (AVC), through a case study. We found that for physical tasks that require specific area annotations, 3D annotations with context (3DAC) can better improve the quality of manual work and regulate the cognitive load distribution of the MR interface more reasonably.
Mineral prospectivity mapping integrated with geological map knowledge graph and geochemical data: A case study of gold deposits at Raofeng area, Shaanxi Province

The knowledge graph in geological applications is highly regarded, and its usefulness requires the support of geological survey data. Therefore, this paper proposes a mineral prospectivity mapping (MPM) method that integrates geological map knowledge graphs with geochemical data. The geological map knowledge graph is used in the vectorized form as knowledge features in the training of the mineral prediction model. It is also used for knowledge-based ore-forming interpretation of prospective areas, achieving bidirectional application of knowledge in prediction and interpretation of prediction results. Unlike most knowledge graphs, the geological map knowledge graph represents spatial correlations and geological features in the form of “entity-relation-entity” triplets, with geological map units as entities and assigned geological attributes. Based on this, an MPM process driven by knowledge graph and data is proposed: (1) Convert the geological map knowledge graph into low-dimensional vectors using node2VEC. (2) Align geological knowledge with geochemical data spatially through knowledge embedding gridization, achieving a forward fusion of knowledge and data. (3) Collect the integrated data using window sampling as the training dataset for DenseNet to train the mineral prospection model. A case study was conducted to demonstrate the advantages of the method for producing a potential map linked to gold mineralization in the Rao Feng area, Shaanxi Province, China. The results show that the prospective areas with knowledge graph embedding and geochemical data are better and delineate six unmineralized prospective areas. Ore-forming interpretations based on knowledge graph information were conducted on the prospective areas, demonstrating strong consistency between the ore-forming geological features of the prediction areas and known mineral deposits.
Who is who in the age of service robots: The impact of robots’ demand for user identification in human-to-robot interactions

In the not-so-distant future, when we are expected to be increasingly engaged in interactions with service robots, it can also be expected that such interactions will require that we identify ourselves. That is to say, given that identification demands occur frequently when we use computers, mobile telephones and websites, it is expected that this will be the case also for service robots. So far, however, the effects of such demands have not been examined in existing research on human-to-robot interactions. Yet, identification is an issue with potentially profound implications, and demands for identification appear to have mainly a negative charge in many settings beyond human-to-robot interactions. Therefore, the purpose of the present study is to explore what happens when a user of a service robot is asked by the robot to identify him/herself. To this end, two experiments were conducted in which a service robot’s demand for user identification was manipulated (no demand for identification before usage vs. demand for identification before usage). The results indicate that the robot’s demand for identification attenuated perceived robot warmth, usefulness, and trust; enhanced privacy concerns; and reduced the overall evaluation of the robot. A proposed mechanism behind these outcomes, priming, was assessed empirically. The results with respect to the latter indicate that a robot’s request for identification makes accessible mainly negatively charged material about identification in other contexts than human-to-robot interactions, and that this material informs responses to the robot.
ASER: Adapted squared error relevance for rare cases prediction in imbalanced regression

Abstract Many real‐world data mining applications involve using imbalanced datasets to obtain predictive models. Imbalanced data can hinder the model performance of learning algorithms in rare cases. Although there are many well‐researched classification task solutions, most of them cannot be directly applied to regression task. One of the challenges in imbalanced regression is to find a suitable evaluation and optimization standard that can improve the predictive ability of the model without severe model bias. Based on the importance of rare cases, this study proposes a new evaluation metric called adapted squared error relevance (ASER) by defining new relevance function and weighting functions. This metric weights data points by defining the importance of rare cases and assigns different weights to losses of the same size at different rare cases, thus enabling the model selected by this evaluation metric to better predict rare cases. ASER is compared with SER on 32 real datasets and 9 simulated datasets to verify the predictive performance of the selected model at rare cases. The experimental results show that the new evaluation metric ASER can obtain a high prediction performance at rare cases, while also not losing too much prediction accuracy in common cases.
Cyber Incident Response Managerial Approaches for Enhancing Small–Medium-Size Enterprise's Cyber Maturity

There is a research gap in the explanation of cyber incident response approaches in management to increase cyber maturity for small–medium-size enterprises (SMEs). Therefore, based on the literature analysis, the chapter aims to (1) provide cyber incident response characteristics, (2) show the importance for SMEs, (3) identify cyber incident response feasibility and causal factors, (4) provide scenarios for consideration to create an incident response plan (IRP), and (5) discuss the cyber incident response and managerial approaches in SMEs. The authors used content analysis of scientific and professional articles to develop the theoretical foundation of incident response approaches in management for SMEs. The authors start from the fundamentals to obtain knowledge and understanding of the latest threats and opportunities, and how to defend themselves using the limited capacity of resources might be the starting point to building an extensive incident response capability. Incident response capabilities and maturity levels vary widely between various organisations. There is no simple one-size-fits-all process for incident response; each case is unique and requires continuous refinement. Differentiation and adaptation to different types of SMEs are pivotal to developing cyber maturity and defining requirements that fit the market’s needs and are therefore more efficient in achieving the goal of increasing cyber security (CS) among business management. SMEs may not have a mature IRP, but at least one readiness indicator could lead to the preparation of a mature IRP. Implementation of the secure undertakings and information processes requires using modern information and communication technologies, incident response processes, and other modules that could enhance support for decision-making processes in management. The approach requires a systematic approach to issues related to constructing these solutions. The authors highlight that building efficient incident response approaches in management to improve cyber maturity will begin with infrastructure and people factors.
Smoothed noise contrastive mutual information neural estimation

Information Noise Contrastive Estimation (InfoNCE) is a popular neural estimator of mutual information (MI). While InfoNCE has demonstrated impressive results in representation learning, the estimation can be significantly off. While the original estimator is known to underestimate the MI due to the logn upper bound, where n is the sample size, we show that some subsequent fix can cause the MI estimate to overshoot apparently without any bound. We propose a novel MI variational estimator, smoothed InfoNCE, that resolves the issues by smoothing out the contrastive estimation. Experiments on high-dimensional Gaussian data confirm that the proposed estimate can break the logn curse without overshooting.
Review of Load Balancing Algorithms Inspired by Artificial Bee Colony Algorithm in the Cloud Computing

Research interest in cloud-based load balancing and task scheduling has grown rapidly over the last few years. To be more precise, load balancing of virtual machine tasks (VMs) that require nature-inspired algorithms has become an area of particular interest. It is essential to ensure load balancing between VMs as this avoids overloading and underloading VMs, aspects that can cause issues like high-power consumption, increased execution time, and elevated response times, all of which can ultimately cause a system failure. Swarm intelligence is critical when dealing with issues that are difficult to solve and must be overcome using traditional and mathematical techniques. The algorithm was developed by Karaboga in 2005 and is inspired by the foraging behaviors of an artificial bee colony. This algorithm is extremely robust, convergent, and flexible. Another researcher applied the ABC method to load balancing in order to enhance it. In-depth research on load balancing in cloud computing utilizing the ABC method is presented in this review paper. Moreover, this work also discusses some fundamental ideas on the intelligence and characteristics of the swarm. Additionally, the paper provided a detailed explanation of cloud computing, its services, and components, as well as the concept and objectives of load balancing in the cloud
A deformation measurement method based on surface texture information of rocks and its application

Users of the digital image correlation method are faced with the problem of poor operability, low repeatability, and lack of standardized specifications for spraying speckles. To solve the problem, the research proposed a rock deformation measurement method that obviates the need to spray speckles. A local binary model was established by using the local binary pattern (LBP) operator based on deep texture features on rock surfaces. The resulting LBP digital speckle pattern can substitute artificial speckle patterns and demonstrates high quality and strong applicability. Based on the LBP digital speckle pattern, the target tracking algorithm was employed to achieve non-contact measurement of the dynamic displacements of rocks. The feasibility and effectiveness of the algorithm in practical application were verified by conducting shear tests on granite and siltstone. Test results show that the deformation characteristics in the displacement nephograms are in line with the measured data pertaining to rock fracturing and conform to the basic characteristics of the shear failure of rocks. The deformation measurement method based on surface texture information can realize non-contact displacement measurement of rocks under conditions without speckles: this obviates the influence of the quality of sprayed speckles on the accuracy of the measurement of deformation.
Chronicles of Exploration: Examining the Materiality of Computational Artifacts

Artistic computing learning environments have been of central importance in the exploration of how to support equity and inclusion in computing. Explorations within e-textiles, music, and interactive media, for example, have created diverse opportunities for learning how to program while creating culturally relevant artifacts. However, there is a gap in our understanding of the design processes of learners in these constructionist environments, including how the computational artifacts and their components impact the learning processes and the ways they build meaning and agency with computing. We advocate for research to attend more closely to the materiality of the computational materials to understand how they impact the social and cultural dimensions in which students are learning. In this paper, we present an analysis of 6 high school learners’ experiences within a co-designed arts and computing curriculum. Our analyses highlight how the materiality of the components impacted the ways in which learners developed personal and epistemological connections to computing based on how it enabled them to connect to their interests, represent their ideas, engage with their community, and overcome or navigate around challenges to get to their final designs. We demonstrate how centralizing the materiality in the design of computational construction kits can inform how we support agency and engagement with computing.
A Comparative Analysis Between SciTokens, Verifiable Credentials, and Smart Contracts: Novel Approaches for Authentication and Secure Access to Scientific Data

Managing and exchanging sensitive information securely is a paramount concern for the scientific and cybersecurity community. The increasing reliance on computing workflows and digital data transactions requires ensuring that sensitive information is protected from unauthorized access, tampering, or misuse. This research paper presents a comparative analysis of three novel approaches for authenticating and securing access to scientific data: SciTokens, Verifiable Credentials, and Smart Contracts. The aim of this study is to investigate the strengths and weaknesses of each approach from trust, revocation, privacy, and security perspectives. We examine the technical features and privacy and security mechanisms of each technology and provide a comparative synthesis with the proposed model. Through our analysis, we demonstrate that each technology offers unique advantages and limitations, and the integration of these technologies can lead to more secure and efficient solutions for authentication and access to scientific data.
Data-Centric Operational Design Domain Characterization for Machine Learning-Based Aeronautical Products

We give a first rigorous characterization of Operational Design Domains (ODDs) for Machine Learning (ML)-based aeronautical products. Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the parameters that define an ODD can be explicitly captured, together with a categorization of the data that ML-based applications can encounter in operation, whilst identifying their system-level relevance and impact. Specifically, we discuss how those data categories are useful to determine: the requirements necessary to drive the design of ML Models (MLMs); the potsince operating outside the flightential effects on MLMs and higher levels of the system hierarchy; the learning assurance processes that may be needed, and system architectural considerations. We illustrate the underlying concepts with an example of an aircraft flight envelope.
A Low-Cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks

We present a highly compact run-time monitoring approach for deep computer vision networks that extracts selected knowledge from only a few (down to merely two) hidden layers, yet can efficiently detect silent data corruption originating from both hardware memory and input faults. Building on the insight that critical faults typically manifest as peak or bulk shifts in the activation distribution of the affected network layers, we use strategically placed quantile markers to make accurate estimates about the anomaly of the current inference as a whole. Importantly, the detector component itself is kept algorithmically transparent to render the categorization of regular and abnormal behavior interpretable to a human. Our technique achieves up to $${\sim }96\%$$ precision and $${\sim }98\%$$ recall of detection. Compared to state-of-the-art anomaly detection techniques, this approach requires minimal compute overhead (as little as $$0.3\%$$ with respect to non-supervised inference time) and contributes to the explainability of the model.
Hybrid path planning based on adaptive visibility graph initialization and edge computing for mobile robots

This paper presents a new initialization method that combines adaptive visibility graphs and the A* algorithm to improve the exploration, accuracy, and computing efficiency of hybrid path planning for mobile robots. First, segments/links in the full visibility graphs are removed randomly in an iterative and adaptive manner, yielding adaptive visibility graphs. Then the A* algorithm is applied to find the shortest paths in these adaptive visibility graphs. Next, high-quality paths featuring low fitness values are chosen to initialize the subsequent heuristic optimization in hybrid path planning. Specifically, in the present study, the genetic algorithm (GA) is implemented on a CPU/GPU edge computing device (Jetson AGX Xavier) to exploit its massively parallel processing threads, and the strategy for judicious CPU/GPU resource utilization is also developed. Numerical experiments are conducted to determine proper hyperparameters and configure GA with balanced performance. Various optimal paths with differential consideration of practical factors for robot path planning are obtained by the proposed method. Compared to the other benchmark methods, ours significantly improves the diversity of initial path and exploration, optimization accuracy, and computing speed (within 5 s with most less than 2 s). Furthermore, real-time experiments are carried out to demonstrate the effectiveness and application of the proposed algorithm on mobile robots.