Reviewerly/exHarmony huggingface dataset:
Influence of blockchain adoption barriers and drivers on potential application areas in the construction lifecycle: partial least squares structural equation modelling (PLS-SEM) approach

Purpose The construction industry faces public criticism for issues like wastefulness, inefficiency, slim profits, scheduling setbacks, budget overruns, quality concerns, trust deficits, transparency, coordination, communication and fraud. This paper aims to assess the nexus between barriers and drivers for adopting blockchain in construction and its impact on construction lifecycle. Design/methodology/approach A quantitative research approach was used to collect data using a well-structured questionnaire survey. The survey, which used snowball sampling, included 155 Nigerian construction experts that included architects, builders, quantity surveyors and engineers in the built environment. The data were analysed using partial least squares structural equation modelling (PLS-SEM), which allowed for a thorough evaluation of the proposed relationships as well as industry-specific insights. Findings The study's findings validate the conceptual framework established. The results indicate that implementing blockchain across all stages of construction projects has the potential to improve the construction process by 88.2% through its drivers. However, there were no significant relationships found between the barriers to adopting blockchain and the potential application areas in the construction lifecycle. Research limitations/implications This research was carried out in the South-western which is one of the six geo-political zones/regions in Nigeria, using a cross-sectional survey method. The study did not investigate the interdependence of the identified categories of drivers and barriers, limiting a comprehensive understanding of the complex dynamics and interactions influencing blockchain adoption in construction. The study is expected to stimulate further exploration and generate new insights on how blockchain technology (BT) can influence various stages of the construction lifecycle. Practical implications The findings will be immensely beneficial to both professionals and practitioners in the Nigerian construction industry in learning about the potential of BT application in improving the construction lifecycle. Originality/value This paper developed and assessed a conceptual framework by investigating the interrelationships between the constructs. The findings have important implications for the construction industry, as they offer opportunities to improve the construction process and overall lifecycle. The findings are useful for researchers interested in the potential impact of BT on the construction lifecycle and its wider implications.
On the Representation of Dynamic BPMN Process Executions in Knowledge Graphs

Knowledge Graphs (KGs) are a powerful tool for representing domain knowledge in a way that is interpretable for both humans and machines. They have emerged as enablers of semantic integration in various domains, including Business Process Modeling (BPM). However, existing KG-based approaches in BPM lack the ability to capture dynamic process executions. Rather, static components of BPM models, such as Business Process Model and Notation (BPMN) elements, are represented as KG instances and further enriched with static domain knowledge. This poses a challenge as most business processes exhibit inherent degrees of freedom, leading to variations in their executions. To address this limitation, we examine the semantic modeling of BPMN terminology, models, and executions within a shared KG to facilitate the inference of new insights through observations of process executions. We address the issue of representing BPMN models within the concept or instance layer of a KG, comparing potential implementations and outlining their advantages and disadvantages in the context of a human-AI collaboration use case from a European smart manufacturing project.
MixedFusion: An Efficient Multimodal Data Fusion Framework for 3-D Object Detection and Tracking

The performance of environmental perception is critical for the safe driving of intelligent connected vehicles (ICVs). Currently, the most prevalent technical solutions are based on multimodal data fusion to achieve a comprehensive perception of the surrounding environment. However, existing fusion perception methods suffer from issues such as low sensor data utilization and unreasonable fusion strategies, which severely limit their performance in adverse weather conditions. To address these issues, this article proposes a novel multimodal data fusion framework called MixedFusion. In this framework, we introduce two innovative fusion strategies for the data characteristics of each sensor: high-level semantic guidance (HLSG) and multipriority matching (MPM). It not only realizes the efficient utilization of the multimodal data but also further realizes the complementary fusion between the multimodal data. We perform extensive experiments on the nuScenes and K-radar datasets. The experimental results demonstrate that the fusion framework proposed in this article significantly improves the performance of 3-D object detection and tracking in severe weather conditions.
Analysis of Made Lamongan Residential Drainage System Using SWMM (Storm Water Management Model) and HEC-RAS (Hydraulic Engineering Centre-River Analysis System) Models

Made Lamongan Residential is located in an area that has a flat slope and is flanked by two rivers Kali Mengkuli and Kali Gorok, the problem in the study area is that the drainage system is unable to serve rainwater runoff. This is evidenced by the occurrence of inundation during the rainy season with high intensity. The height of the inundation that occurs in the Residential complex ± 60 cm with a reduction in inundation of more than 12 hours. The calculation results showed that there were several sections of the channel that occurred runoff with a planned Q5 flood discharge of 8.14 m3/sec. Based on the results of surveys in the field, many channels were found that had changed their dimensions due to the renovation of residents' houses. In this study, 1D channel hydraulic modelling was carried out using SWMM and HEC-RAS software to obtain a model that was close to the surface runoff conditions at the study site. The purpose of this study is to obtain the greatest handling of inundation in Made Residential in reducing the height of the inundation based on the model to be carried out, as well as obtaining the value of model calibration at the study site.
Elementary Number Theory Problems. Part VIII

Abstract In this paper problems 25, 86, 88, 105, 111, 137–142, and 184–185 from [12] are formalized, using the Mizar formalism [3], [1], [4]. This is a continuation of the work from [5], [6], and [2] as suggested in [8]. The automatization of selected lemmas from [11] proven in this paper as proposed in [9] could be an interesting future work.
Generic hybrid model for breast cancer mammography image classification using EfficientNetB2

Breast cancer is a global health issue that necessitates precise classification for early detection and effective treatment. In recent years, pre-trained models have shown great potential in the field of medical image classification, including breast cancer classification. These models have been trained on extensive datasets, and they possess the ability to capture intricate features and patterns within medical images, facilitating accurate classification. However, some of the models are non-generic. They can be sensitive to dataset biases, leading to over fitting on specific patterns present in the training data, and they equally struggle to handle data from different distributions. In this work, we proposed a generic hybrid model for image classification. The features were extracted from two datasets: the mammographic image analysis society (MIAS) and the INbreast dataset, respectively, through the pre trained EfficientNetB2 architecture. However, three classifiers were used in the image classification of the extracted features: MGSVM, CUBIC SVM, and XGBOOST. Eight evaluation metrics were selected to assess the performance of the proposed models. These metrics include accuracy, precision, F1-score, AUC, sensitivity, false negative rate (FNR), Kappa score, and time complexity. Experimental results show that the hybrid of EfficientNetB2 and the MGSVM classifier is more generic and efficient for breast cancer diagnosis and classification. It exhibits a strong performance when classifying mammography breast images from both datasets, achieving impressive metrics such as an overall accuracy of 99.47%, a sensitivity rate of 99.31%, precision of 99.44%, F1-score of 99.44%, AUC of 99.44%, a low FNR (False Negative Rate) of 0.007, a kappa score of 0.98, and a manageable time complexity of 231.44 seconds on the MIAS Dataset.
DEVELOPMENT OF A QUESTION ANSWERING CHATBOT FOR BLOCKCHAIN DOMAIN

Large Language Models (LLMs), such as ChatGPT, have transformed the field of natural language processing with their capacity for language comprehension and generation of human-like, fluent responses for many downstream tasks. Despite their impressive capabilities, they often fall short in domain-specific and knowledge-intensive domains due to a lack of access to relevant data. Moreover, most state-of-art LLMs lack transparency as they are often accessible only through APIs. Furthermore, their application in critical real-world scenarios is hindered by their proclivity to produce hallucinated information and inability to leverage external knowledge sources. To address these limitations, we propose an innovative system that enhances LLMs by integrating them with an external knowledge management module. The system allows LLMs to utilize data stored in vector databases, providing them with relevant information for their responses. Additionally, it enables them to retrieve information from the Internet, further broadening their knowledge base. The research approach circumvents the need to retrain LLMs, which can be a resource-intensive process. Instead, it focuses on making more efficient use of existing models. Preliminary results indicate that the system holds promise for improving the performance of LLMs in domain-specific and knowledge-intensive tasks. By equipping LLMs with real-time access to external data, it is possible to harness their language generation capabilities more effectively, without the need to continually strive for larger models.
PFENet++: Boosting Few-Shot Semantic Segmentation With the Noise-Filtered Context-Aware Prior Mask

In this work, we revisit the prior mask guidance proposed in “Prior Guided Feature Enrichment Network for Few-Shot Segmentation”. The prior mask serves as an indicator that highlights the region of interests of unseen categories, and it is effective in achieving better performance on different frameworks of recent studies. However, the current method directly takes the maximum element-to-element correspondence between the query and support features to indicate the probability of belonging to the target class, thus the broader contextual information is seldom exploited during the prior mask generation. To address this issue, first, we propose the Context-aware Prior Mask (CAPM) that leverages additional nearby semantic cues for better locating the objects in query images. Second, since the maximum correlation value is vulnerable to noisy features, we take one step further by incorporating a lightweight Noise Suppression Module (NSM) to screen out the unnecessary responses, yielding high-quality masks for providing the prior knowledge. Both two contributions are experimentally shown to have substantial practical merit, and the new model named PFENet++ significantly outperforms the baseline PFENet as well as all other competitors on three challenging benchmarks PASCAL-5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{i}$</tex-math></inline-formula> , COCO-20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{i}$</tex-math></inline-formula> and FSS-1000. The new state-of-the-art performance is achieved without compromising the efficiency, manifesting the potential for being a new strong baseline in few-shot semantic segmentation. Our code will be available at Github.
Computational Group Theory and Quantum-Era Cryptography

This paper provides an overview of the significant role of computational group theory in cryptography. Group theory plays a crucial role in various cryptographic applications, such as key exchange, encryption, and digital signatures. This paper examines the fundamental concepts, algorithms, and applications of computational group theory in cryptography, using polycyclic groups with a focus on key findings and recent developments in quantum-era cryptography.
Deep Learning-Based Anomaly Detection in Cyber-Physical System

Since the introduction of personal computers, software has quickly permeated every aspect of our life. When it comes to cyber-physical systems, software becomes a physical part of the system. When cyber and physical systems work together, new possibilities and difficulties arise. Cyber components offer additional levels of complexity to CPSs, which are already ubiquitous in modern civilization, with software controlling everything from automobiles and aircraft to water purification facilities, smart grids, and trains. In order to prevent costly system outages, researchers in CPS study the methods of spotting unusual patterns of behavior. In this study we present a taxonomy for anomaly detection in CPS which is an open research challenge. We discuss state of art methods used in industrial and spacecraft anomaly detection using Deep Learning models. We introduce a classification scheme for CPS anomaly detection and provide further details on the existing testbed dataset. Lastly the challenges are highlighted for further research direction.
Local Variance-driven Level Set Model with Application to Segment Medical Images

The development of computer vision and medical treatment has driven the development of the medical image. At the same time, as one of the key technologies of medical image postprocessing, the accuracy and robustness of medical image segmentation play a key role in assisting doctors in diagnosis and treatment. In this study, we propose the local variance-driven level set model, which combines the local variance difference and the boundary information dynamically for segmenting medical images. The local variance difference is introduced into the area term to control the role of the area term in the overall energy functional and optimize the ability to resist noise. The edge detection function of is added to detect the edge of organs or lesions more accurately. The application to medical images shows that our local variance-driven level set model is more effective for medical image segmentation. Compared with other level set methods, the local variance-driven level set model is more accurate and more robust to noise.
Quantum Computing and Visualization: A Disruptive Technological Change Ahead

The focus of this Visualization Viewpoints article is to provide some background on quantum computing (QC), to explore ideas related to how visualization helps in understanding QC, and examine how QC might be useful for visualization with the growth and maturation of both technologies in the future. In a quickly evolving technology landscape, QC is emerging as a promising pathway to overcome the growth limits in classical computing. In some cases, QC platforms offer the potential to vastly outperform the familiar classical computer by solving problems more quickly or that may be intractable on any known classical platform. As further performance gains for classical computing platforms are limited by diminishing Moore's Law scaling, QC platforms might be viewed as a potential successor to the current field of exascale-class platforms. While present-day QC hardware platforms are still limited in scale, the field of quantum computing is robust and rapidly advancing in terms of hardware capabilities, software environments for developing quantum algorithms, and educational programs for training the next generation of scientists and engineers. After a brief introduction to QC concepts, the focus of this article is to explore the interplay between the fields of visualization and QC. First, visualization has played a role in QC by providing the means to show representations of the quantum state of single-qubits in superposition states and multiple-qubits in entangled states. Second, there are a number of ways in which the field of visual data exploration and analysis may potentially benefit from this disruptive new technology though there are challenges going forward.
Extracting laboratory test information from paper-based reports

Abstract Background In the healthcare domain today, despite the substantial adoption of electronic health information systems, a significant proportion of medical reports still exist in paper-based formats. As a result, there is a significant demand for the digitization of information from these paper-based reports. However, the digitization of paper-based laboratory reports into a structured data format can be challenging due to their non-standard layouts, which includes various data types such as text, numeric values, reference ranges, and units. Therefore, it is crucial to develop a highly scalable and lightweight technique that can effectively identify and extract information from laboratory test reports and convert them into a structured data format for downstream tasks. Methods We developed an end-to-end Natural Language Processing (NLP)-based pipeline for extracting information from paper-based laboratory test reports. Our pipeline consists of two main modules: an optical character recognition (OCR) module and an information extraction (IE) module. The OCR module is applied to locate and identify text from scanned laboratory test reports using state-of-the-art OCR algorithms. The IE module is then used to extract meaningful information from the OCR results to form digitalized tables of the test reports. The IE module consists of five sub-modules, which are time detection, headline position, line normalization, Named Entity Recognition (NER) with a Conditional Random Fields (CRF)-based method, and step detection for multi-column. Finally, we evaluated the performance of the proposed pipeline on 153 laboratory test reports collected from Peking University First Hospital (PKU1). Results In the OCR module, we evaluate the accuracy of text detection and recognition results at three different levels and achieved an averaged accuracy of 0.93. In the IE module, we extracted four laboratory test entities, including test item name, test result, test unit, and reference value range. The overall F1 score is 0.86 on the 153 laboratory test reports collected from PKU1. With a single CPU, the average inference time of each report is only 0.78 s. Conclusion In this study, we developed a practical lightweight pipeline to digitalize and extract information from paper-based laboratory test reports in diverse types and with different layouts that can be adopted in real clinical environments with the lowest possible computing resources requirements. The high evaluation performance on the real-world hospital dataset validated the feasibility of the proposed pipeline.
Optimizing Tradeoff Between Learning Speed and Cost for Federated Learning-Enabled Industrial IoT

Combination of industrial Internet of Things (IIoT) and federated learning (FL) is deemed as a promising solution to realize Industry 4.0 and beyond. However, scheduling more IIoT devices engaged in FL contributes to accelerated learning speed, but resulting in increased learning cost in terms of energy consumption and model accuracy reduction. In this paper, we investigate the tradeoff between learning speed and cost in a three-layer FL-enabled IIoT system. Particularly, a weighted learning utility function is designed by capturing such a tradeoff. We aim to maximize the weighted learning utility in an FL training round by jointly optimizing the edge association as well as the allocations of resource block, computation capacity, and transmit power of IIoT device. The resulting problem is a non-convex and mixed integer optimization problem, and consequently it is difficult to solve. We thereby decompose the original problem into three subproblems, and then propose an overall alternating optimization algorithm to solve the subproblems iteratively until convergence. Via experimental results, it is demonstrated that the proposed scheme significantly improves the system-wide learning utility as compared to other baseline schemes. It is also shown that the proposed scheme can achieve the optimized tradeoff between learning speed and learning cost.
STUDY OF ENGLISH COMMUNICATIVE COMPETENCE AT THE TERTIARY LEVEL OF EDUCATION BASED ON EUROPEAN FRAMEWORK OF REFERENCE

CEFR has become a very significant framework for the design of language curricula and the assessment of language learning outcomes. The purpose of the study is to know the CEFR level of the graduate candidate at the Tertiary Level of Education. This research used descriptive qualitative method. The researchers took the data from Computer Science Faculty graduate candidates Universitas Duta Bangsa Surakarta in the academic year 2021/2022. There were 359 participants, 346 participants took the test, and 13 participants did not take test. The participant took TOEIC-Like test written by Shin’s book (2015) titled Pre-Intermediate Level Taking the TOEIC: Skills and Strategies, practice test one. The study showed that the highest TOEIC score was 930, and the participant was from the Technical Information study program. Based on TOEIC Test Mapping on the CEFR, the participant was at C1 level or mastery level. There were 12 participants at the B2 level or Vantage Level, and there were 35 participants at the B1 Level. Most participants were at the A2 level or Basic user because 241 participants got a 225-545 TOEIC score (A2) and there were 57 participants at the A1 level.
LinkGuardian: Mitigating the impact of packet corruption loss with link-local retransmission

Packet corruption loss is a serious problem in datacenter networks. A large-scale study by Microsoft reported that the number of packets lost due to corruption is comparable to those lost due to congestion. Previous attempts to mitigate the impact of packet corruption loss seek to avoid the faulty links by routing around them, at the cost of reduced link capacities and disruption to the rest of the network.
DIPSAUCE: Efficient Private Stream Aggregation Without Trusted Parties

Private Stream Aggregation (PSA) schemes are efficient protocols for distributed data analytics. In a PSA scheme, a set of data producers can encrypt data for a central party so that it learns the sum of all encrypted values, but nothing about each individual value. Thus, a trusted aggregator is avoided. However, all known PSA schemes still require a trusted party for key generation. In this paper we propose the first PSA scheme that does not rely on a trusted party. We argue its security against static and mobile malicious adversaries, and show its efficiency by implementing both our scheme and the previous state-of-the-art on realistic IoT devices, and compare their performance. Our security and efficiency evaluations show that it is indeed possible to construct an efficient PSA scheme without a trusted central party. Surprisingly, our results also show that, as side effect, our method for distributing the setup procedure also makes the encryption procedure more efficient than the state of the art PSA schemes which rely on trusted parties.