Reviewerly/exHarmony huggingface dataset:
A study of multi-target image-based displacement measurement approach for field testing of bridges

As the demand for field testing of bridges grows, so does the need to optimize field testing procedures to allow for a simplified testing strategy that can be employed more effectively. With the advent of computer vision, there has been limited research exploring Feature-Based Image Registration (FBIR) methods for structural testing of bridges. In this paper, the potential of a simple FBIR approach to accurately capture submillimeter displacements using consumer-grade cameras will be demonstrated through a field test on a reinforced concrete slab bridge and on a full-scale bridge deck specimen in the laboratory. The internal and external parameters that influence the results of this measurement strategy were investigated by using various camera positions during the laboratory tests and applying different threshold parameters to the Speeded-Up Robust Features algorithm used for the feature detection and matching. The FBIR method demonstrates great potential, producing an average measurement accuracy within 1.6% of conventional displacement sensors during the field test and 3.3% during the laboratory tests. Altogether, the advantages to this image-based measurement approach enhance the load testing strategy to be implemented by bridge owners at much lower costs and with minimal complication and field setup.
Violin plots as visual tools in the meta-analysis of Single-Case Experimental Designs

<p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">Despite the existence of sophisticated statistical methods, systematic reviews regularly indicate that single-case experimental designs (SCEDs) are predominantly analyzed through visual tools. For the quantitative aggregation of results, different meta-analytical techniques are available, but specific visual tools for the meta-analysis of SCEDs are lacking. The present article therefore describes the use of violin plots as visual tools to represent the raw data. We first describe the underlying rationale of violin plots and their main characteristics. We then show how the violin plots can complement the statistics obtained in a quantitative meta-analysis. The main advantages of violin plots as visual tools in meta-analysis are (a) that they preserve information about the raw data from each study, (b) that they have the ability to visually represent data from different designs in one graph, and (c) that they enable the comparison of score distributions from different experimental phases from different studies.
Remote Sensing Scene Graph and Knowledge Graph Matching with Parallel Walking Algorithm

In deep neural network model training and prediction, due to the limitation of GPU memory and computing resources, massive image data must be cropped into limited-sized samples. Moreover, in order to improve the generalization ability of the model, the samples need to be randomly distributed in the experimental area. Thus, the background information is often incomplete or even missing. On this condition, a knowledge graph must be applied to the semantic segmentation of remote sensing. However, although a single sample contains only a limited number of geographic categories, the combinations of geographic objects are diverse and complex in different samples. Additionally, the involved categories of geographic objects often span different classification system branches. Therefore, existing studies often directly regard all the categories involved in the knowledge graph as candidates for specific sample segmentation, which leads to high computation cost and low efficiency. To address the above problems, a parallel walking algorithm based on cross modality information is proposed for the scene graph—knowledge graph matching (PWGM). The algorithm uses a graph neural network to map the visual features of the scene graph into the semantic space of the knowledge graph through anchors and designs a parallel walking algorithm of the knowledge graph that takes into account the visual features of complex scenes. Based on the algorithm, we propose a semantic segmentation model for remote sensing. The experiments demonstrate that our model improves the overall accuracy by 3.7% compared with KGGAT (which is a semantic segmentation model using a knowledge graph and graph attention network (GAT)), by 5.1% compared with GAT and by 13.3% compared with U-Net. Our study not only effectively improves the recognition accuracy and efficiency of remote sensing objects, but also offers useful exploration for the development of deep learning from a data-driven to a data-knowledge dual drive.
Creativity and Design Thinking as Facilitators in Requirements Elicitation

Context: The use of Creativity and Design Thinking (C&amp;DT) techniques favor the generation of new ideas based on the needs of users and stakeholders, and can support software developers during the process of requirements elicitation. Objectives: In this work, we aim to identify C&amp;DT techniques to perform requirements elicitation proposed in the literature and in the industry and investigate the perception of software developers about using these techniques. Methods: We conducted a systematic literature review (SLR) to identify the C&amp;DT techniques in the literature and a regional survey with software development teams from several companies in Brazil to identify which techniques found in the literature are currently being used by organizations. The survey also investigated the level of knowledge that software developers have regarding the C&amp;DT techniques, and whether they agree that the use of these techniques can help to achieve a more effective process of requirements elicitation. Results: In the SLR, we identified 86 C&amp;DT techniques that support requirement elicitation activities. In the survey, most developers outlined that C&amp;DT techniques facilitate requirements elicitation and stated that they have more knowledge and usage experience with DT techniques than creativity techniques. The most used DT techniques mentioned by survey participants were: interview, brainstorming, uses cases, activity analysis, user story, and rapid prototyping, whereas for creativity techniques were: analogies, creativity workshops, focus group, questions list, clarification, none and combining ideas. Conclusions: The results showed that despite the existence of a large number of techniques in the literature, the developers’ lack of knowledge about these techniques makes them not used in the industry. However, the developers’ responses showed that the use of C&amp;DT techniques helps to make requirements elicitation more effective.
Energy-Efficient Distributed Machine Learning at Wireless Edge with Device-to-Device Communication

This paper considers a federated edge learning (FEL) system where a base station (BS) coordinates a set of edge devices to train a shared machine learning model collaboratively. One of the fundamental issues in such systems is maintaining the learning performance with the limited and heterogeneous resource capabilities of edge devices. Our goal is to improve the energy efficiency of edge devices in FEL by mitigating the temporal and spatial heterogeneity of their energy resources. Specifically, to balance the heterogeneous energy levels among edge devices, energy-hungry devices can offload their data to nearby devices that have sufficient energy via device-to-device (D2D) communication links at low transmission overheads. Be-sides, to mitigate the impact of the time-varying energy level of a device, data collected by edge devices can be queued to be processed when sufficient energy is available. To compute the optimal offloading and queuing strategies, we propose an online control algorithm based on Lyapunov optimization to determine the amount of data to be offloaded, queued, and processed at each time slot. Our simulation results on the real-world dataset demonstrate that our approach achieves a better overall energy efficiency than baselines.
Economic By-Products in copper Porphyries: Silver in the Ascutita Cu-Porphyry, Romania

The Ascutita porphyry Cu-Mo prospect is located in the SW Poiana Rusca Mountains, Romania. Current assessment estimates a resource of 110 Mt @ 0.15 % Cu-equivalent. In relatively low-grade deposits such as this, by-products such as Ag (2–3 ppm) are important in determining the economic feasibility of the overall orebody. Assayed Cu and Ag have a clear downhole correlation. In the absence of other silver-bearing minerals, or correlations of Ag with other sulphides (e.g., Pb indicating Ag-bearing galena) this suggests Cu-sulphides are instead the dominant hosts of Ag. Petrography of 21 samples from various drillholes show chalcopyrite is the dominant Cu-sulphide present, spatially associated to two stages of pyrite. LA-ICP-MS analysis determined that both chalcopyrite and Stage 3 pyrite (younger) co-host the Ag. Chalcopyrite hosts Ag mostly in solid solution, whilst pyrite hosts Ag within a variety of complex, sulphosalt micro-inclusions undetectable on an SEM, but apparent on time-resolved LA-ICP-MS spectra. These micro-inclusions lock in Cu and Ag, with a bulk median Ag content of 0.79 ppm. The pyrite also contains mean concentrations of 52 ppm Bi and 46 ppm Se: both typically penalty-incurring elements at smelters. The chalcopyrite has a greater Ag concentration than the pyrite (median = 30.3 ppm with potential for 181 ppm), presumably in solid solution. The lattice-bound Ag partitioned into the chalcopyrite through monovalent, simple substitution of Ag+ for Cu+. Occurrence of Ag in the main Cu-bearing phase at Ascutita raises the possibility of combined recovery, and this study demonstrates the value of trace element analysis in geometallurgical assessments of orebodies.
An Overview of Software Architecture Education

Software architecture comprises the most relevant structure of a software system and is a factor to enable systems to achieve qualities that are crucial to the system's goals. In this sense, teaching software architecture to students and professionals remains a very important task. However, it is still a challenging matter for teachers and learners on different levels and contexts. We also lack an updated overview on how such a challenge has been addressed. This paper presents an overview of software architecture education experiences and initiatives. For this, we systematically examined the most relevant studies addressing software architecture education. After analyzing a total of 50 studies, we discuss how software architecture has been taught, the topic mostly taught, the learning objectives, and the learning methods. Based on our findings, we also present open issues that still remain to be further investigated, aiming to mature software architecture education.
A fast and scalable qubit-mapping method for noisy intermediate-scale quantum computers

This paper presents an efficient qubit-mapping method that redesigns a quantum circuit to overcome the limitations of qubit connectivity. We propose a recursive graph-isomorphism search to generate the scalable initial mapping. In the main mapping, we use an adaptive look-ahead window search to resolve the connectivity constraint within a short runtime. Compared with the state-of-the-art method [15], our proposed method reduced the number of additional gates by 23% on average and the runtime by 68% for the three largest benchmark circuits. Furthermore, our method improved circuit stability by reducing the circuit depth and thus can be a step forward towards fault tolerance.
A 4C Model for Hyflex Classrooms

In last two years, universities around the world have been using hyflex teaching due to COVID-19. This allows students to attend physical/online lectures in a flexible manner. A hyflex class comprises classroom students as well as online students. In this paper, we present a model for hyflex classrooms that highlights 4Cs: Content, Collaboration, Community and Communication. Based on the 4C model, a hyflex classroom has been designed and implemented through various teaching/learning tools or elements. These include the effective use of presentation slides, annotations, chatbox, open education resources, multiple choice exercises, group exercises etc. The effectiveness of these tools/elements were evaluated by means of an initial student survey. These results provide valuable insights into hyflex teaching/learning.
Hypermedea: A Framework for Web (of Things) Agents

Hypermedea is an extension of the JaCaMo multi-agent programming framework to act on Web and Web of Things environments. In this demo, the performance of Hypermedea's Linked Data navigation and planning components are evaluated, both encapsulating computation-intensive algorithms.
Practical analysis on the algorithm of the Cross-Site Scripting Attacks

With the change of information technology over the years, it has become necessary to re-evaluate security risks, various possible errors and responses to them. Well-informed and uninformed internet users who click on the wrong links or even use outdated versions of technologies are the most common causes of various errors resulting in security breaches. There is a growing need to educate users of these technologies to prevent or avoid various errors that pose a high risk of security breaches. Web applications suffer from various security vulnerabilities that can be exploited by hackers to cause harm in different ways. Private data are those which can be directly endangered by these errors, which in one way or another can cause us great personal and financial problems. The various infectious scripts are invisible to the visitor, but they determine the website behavior in response to certain clicks sent by the user. In addition to the World Wide Web, various online scripts are also used to automate processes on a local computer. Each script represents a text containing instructions to be executed by a program or script manager in order to achieve the automated action which is desired. Cross Site Scripting is the attack in which the attacker attempts to create scripts in order to extract data from the victim and utilizing this data for various purposes all to the detriment of the victim. Cross Site Scripting Attack can even rewrite the contents of an HTML page. In this paper we will focus on the attack in question by displaying various details and examples, as well as the attack submission algorithm.
A Comprehensive Review of the Firefly Algorithms for Data Clustering

Separating a given data set into groups (clusters) based on their natural similar characteristics is one of the main concerns in data clustering. A cluster can be defined as a collection of objects which are "homogeneous" between them and are "heterogeneous" to the objects belonging to other clusters. Many areas encounter clustering based applications including the fields of medical, image processing, engineering, economics, social sciences, biology, machine learning and data mining. Clustering goes under unsupervised learning where no labels are given to the learning algorithm, leaving it on its own to find structure in its input. Even though many classical clustering algorithms can be found, most of such suffer from severe drawbacks such as sensitivity over initial cluster centroids and hence can be easily trapped in local optimum solutions. The other main problem with the data clustering algorithms is that it cannot be standardized. On the other hand, clustering can be considered under optimizations which goes to the category of NP hard optimization making more difficult in solving. Addressing such NP hard problems, meta-heuristics play a remarkable role in optimization. Since its appearance from more than a decade ago, Firefly Algorithm (FA), a stochastic meta- heuristic in nature inspired algorithms has shown significant performance in giving solutions to many optimization problems. Hence FA has been used in research addressing the problem of clustering optimization. This chapter forestalls the ability of firefly algorithm in solving data clustering problem. It presents an introduction to clustering and the performance of FA, briefly reviews and summarizes some of the recent firefly-based algorithms used for data clustering with the emphasis on how FA has been combined/ hybridized with other methods to contribute to the problem of data clustering. Further it discusses on different representations, initializations, and the used cluster validation criteria in FA based clustering methods. The chapter also discusses why FA is to be more useful for clustering over other methods and what features made it more suitable for handling the clustering problem compared with other meta-heuristics. Finally, it focuses on the limitations that have been found in the literature on clustering grounded on FA-based applications and discusses possible avenues in future.
A Hybrid African Vulture Optimization Algorithm and Harmony Search: Algorithm and Application in Clustering

Data clustering is one of the necessary research fields in data analysis. Clustering is an unsupervised classification method for assigning data objects to separate groups, which are called clusters. So that the similarity of the data within each cluster and the difference between the cluster data is high, a variety of meta-heuristic algorithms can be used to solve this problem. In this paper, a new algorithm created using a combination of African Vulture Optimization Algorithm (AVOA) and Harmony Search (HA) is used. The proposed algorithm is implemented on the clustering dataset of the UCI machine learning repository. Furthermore, the results obtained from the proposed algorithm are compared with other meta-heuristic algorithms. The experiments show that the proposed method has good and better performance than other optimization algorithms.
Making the futures of AI in education: Why and how imagining the future matters

European Journal of EducationVolume 57, Issue 4 p. 537-541 PERSPECTIVE Making the futures of AI in education: Why and how imagining the future matters Riel Miller, Riel Miller orcid.org/0000-0001-6329-5983 J. Herbert Smith Centre, University of New Brunswick, CanadaSearch for more papers by this authorIlkka Tuomi, Corresponding Author Ilkka Tuomi ilkka.tuomi@meaningprocessing.com orcid.org/0000-0002-4179-7103 Meaning Processing Ltd., Helsinki, Finland Correspondence Ilkka Tuomi, Meaning Processing Ltd., Arkadiankatu 20 A 20, 00100 Helsinki, Finland. Email: ilkka.tuomi@meaningprocessing.comSearch for more papers by this author Riel Miller, Riel Miller orcid.org/0000-0001-6329-5983 J. Herbert Smith Centre, University of New Brunswick, CanadaSearch for more papers by this authorIlkka Tuomi, Corresponding Author Ilkka Tuomi ilkka.tuomi@meaningprocessing.com orcid.org/0000-0002-4179-7103 Meaning Processing Ltd., Helsinki, Finland Correspondence Ilkka Tuomi, Meaning Processing Ltd., Arkadiankatu 20 A 20, 00100 Helsinki, Finland. Email: ilkka.tuomi@meaningprocessing.comSearch for more papers by this author First published: 01 October 2022 https://doi.org/10.1111/ejed.12529Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume57, Issue4Special Issue: Futures of artificial intelligence in educationDecember 2022Pages 537-541 RelatedInformation
FIR digital filter design based on improved artificial bee colony algorithm

The traditional swarm intelligence optimization algorithm is prone to fall into local optimal solutions in finite impulse response (FIR) digital filter design and has slow convergence speed. In order to optimize the design of FIR filter, a FIR digital filter design method based on improved bee colony (ABC) algorithm is proposed. This improved ABC algorithm can adaptively adjust the step size of the selected neighborhood of nectar source location. At the same time, the information of the global optimal solution is used to guide the search of candidate solution, which improves the global search ability of the algorithm. The improved ABC algorithm can balance the conflict between local search ability and global search ability, so it can achieve better optimization effect. The time and space complexity of the algorithm is analyzed in detail. Then, the improved ABC algorithm is used to design three typical FIR digital filters, namely low-pass, band-pass and band-stop filter. The performance of the designed filter is tested by simulation experiments. The experimental results show that compared with other state-of-the-art optimization algorithms, the proposed FIR filter design method has achieved better effect and performance. Meanwhile, the proposed design method has shorter optimization time. The superiority of the proposed method is verified.
Countermeasure to Structured Query Language Injection Attack for Web Applications using Hybrid Logistic Regression Technique

The new generation of security threats has been promoted by real-time applications, where several users develop new ways to communicate on the internet via web applications. Structured Query Language injection Attacks (SQLiAs) is one of the major threats to web application security. Here, unauthorised users usually gain access to the database via web applications. Despite the giant strides made in the detection and prevention of SQLiAs by several researchers, an ideal approach is still far from over as most existing techniques still require improvement, especially in the area of addressing the weak characterisation of input vectors which often leads to low prediction accuracy. To deal with this concern, this paper put forward a hybrid optimised Logistic Regression (LR) model with Improved Term Frequency Inverse Document-Frequency (ITFIDF-LR). To show the effectiveness of the proposed approach, attack datasets is used and evaluated using selected performance metrics, i.e., accuracy, recall, specificity and False Positive Rate. The experimental results via simulation when compared with the benchmarked techniques, achieved performance record of 0.99781 for accuracy, recall and F1-score as well as 0.99782, 0.99409 and 0.00591 for precision, specificity and False Positive Rate (FPR) respectively. This is an indication that the proposed approach is efficient and when deployed is capable of detecting SQLiA on web applications.
Abstract Multivariate Algebraic Function Induced Neural Network Approximations

Here we exhibit multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We study also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by the algebraic sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer. It follows [14].