Reviewerly/exHarmony huggingface dataset:
Assessment of Barriers to the Implementation of Smart Contracts in Construction Projects—Evidence from Turkey

Despite their promising potential, the level of implementation of smart contracts is not at the desired level. To expedite the acceptance and deployment of smart contracts, the barriers to the implementation of smart contracts should be revealed. Past studies, however, do not provide a comprehensive theoretical basis due to several methodological drawbacks. Thus, this study aims to identify and assess the barriers to the implementation of smart contracts by considering the inherent characteristics of the construction industry. An in-depth literature review was initially conducted to extract all barriers proposed in the literature. Then, focus group discussion (FGD) sessions were conducted with the participation of the construction practitioners. In the FGD session, the results of the literature review were validated, and nine additional barriers were proposed. Finally, a total of 20 barriers under five categories was proposed for the smart contract adoption in the construction industry. Then, a questionnaire survey was conducted with the participation of 15 construction practitioners. Lastly, fuzzy VIKOR analysis was performed to assess the criticality of the implementation barriers. This study indicates that the construction companies should overcome not only technical barriers but also managerial barriers. Changes in the processes arisen due to smart contract implementation prevent the construction from implementing a smart contract, since the employees show resistance to these changes. Furthermore, the companies believe that they can lose their bargaining power with smart contracts, and they do not want to lose their power. Bottlenecks are considered the most critical barrier from a technological perspective, and companies are concerned about the problems resulting from them. Although this study provides insights into the barriers to smart contracts in the construction industry, all the respondents are from Turkey. Therefore, some of the findings of this study can be specific to the Turkish construction industry.
Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM

The wind tunnel balance signal detection system is widely employed in aerospace applications for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature compensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial population position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.
Semantic and sentiment trajectories of literary masterpieces

The paper deals with semantic and sentiment trajectories of literary masterpieces (we used corpora of 12 languages of various language families), composed of individual embeddings or n-grams. We ascertain that, for all languages, semantic and sentiment trajectories are markedly chaotic: positive largest Lyapunov exponents; 'entropy-complexity' pairs belonging to the 'chaotic' area of the respective plane; the distinctive 'chaotic' drop of the number of false nearest neighbours at a particular value of an embedding dimension. The Russian language turns out to be more 'chaotic' than, for example, the English one; we attribute this fact to the free order of words. The Esperanto language, for various 'approaches' to different Indo-European languages. The results do not corroborate its claim to be equidistant from all languages. However, it seems to be equidistant from all Indo-European languages. These characteristics are utilised in order to develop a method to compare styles of an original masterpiece and its translations (to automatically assess translation quality). It appears that machine translations are still worse than human ones, however, for example, the Facebook translation is comparable with them.
A study of polarization compensation for quantum networks

Abstract The information-theoretic unconditional security offered by quantum key distribution has spurred the development of larger quantum communication networks. However, as these networks grow so does the strong need to reduce complexity and overheads. Polarization-based entanglement distribution networks are a promising approach due to their scalability and no need for trusted nodes. Nevertheless, they are only viable if the birefringence of all-optical distribution fibres in the network is compensated to preserve the polarization-based quantum state. The brute force approach would require a few hundred fibre polarization controllers for even a moderately sized network. Instead, we propose and investigate four different realizations of polarization compensation schemes that can be used in quantum networks. We compare them based on the type of reference signals, complexity, effort, level of disruption to network operations and performance on a four-user quantum network.
Parameter estimation in non-linear chemical processes: an opposite point-based differential evolution (OPDE) approach

Abstract In recent years, evolutionary algorithms have been gaining popularity for finding optimal solutions to non-linear multimodal problems encountered in many engineering disciplines. Differential evolution (DE), an evolutionary algorithm, is a novel optimization method capable of handling nondifferentiable, non-linear, and multimodal objective functions. DE is an efficient, effective, and robust evolutionary optimization method. Still, DE takes large computational time to optimize the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This paper introduces a modification to the original DE that enhances the convergence rate without compromising solution quality. The proposed opposite point-based differential evolution (OPDE) algorithm utilizes opposite point-based population initialization, in addition to random initialization. Such an improvement reduces computational effort. The OPDE has been applied to benchmark test functions and high-dimensional non-linear chemical engineering problems. The proposed method of population initialization accelerates the convergence speed of DE, as indicated by the results obtained using benchmark test functions and non-linear chemical engineering problems.
Bodily surveillance: Singapore’s COVID-19 app and technological opportunism

ABSTRACTSingapore won early kudos for its ‘gold standard’ handling of the COVID-19 pandemic back in February 2020. It was praised globally for its ability to activate an effective contact tracing system. Riding on this success, the government introduced ‘TraceTogether’, a mobile phone app to enhance contact tracing efforts, using a technology that leverages the Bluetooth feature on smartphones to track proximity between users and record their physical encounters. This paper contends that the roll-out of the app is a form of ‘technological opportunism’ to enhance greater bodily surveillance over its citizens during a time of crisis. The low number of downloads of the app initially (at 20%), before persuasion-coercion strategies were applied to lift the take-up rate to 90%, belies the assumption that surveillance is genuinely widely accepted. This paper details key responses to the app in Singapore, and the government’s decision to make it mandatory during the heart of the pandemic between 2020 to 2022. It considers the implications of technological opportunism, taking advantage of a pandemic to continue in the journey of turning citizens into what Michel Foucault would refer to as ‘subjectified bodies’ to be traced, tracked and codified.KEYWORDS: COVID-19tracing apptechnological optimismsurveillancedocile bodiesgovernmentality Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. GovTech: https://www.tech.gov.sg/2. Little India is a district in Singapore where South Asian migrant workers frequent during their leisure hours. On the night of 8 December 2013, Sakthivel Kumarvelu, a 33-year-old Indian construction worker, was not permitted to board a private chartered bus that would take him back to his dormitory, as he was deemed intoxicated by the driver and conductor. He attempted to run after the bus but was instead run over.by it when it rounded a corner. Kumarvelu was pinned under the bus and eventually died while trapped.Attempts to free him were in vain and his fellow workers blamed the driver and conductor, leading toviolence on the streets and damage to vehicles. The unrest was eventually quelled by Special Operations Command police officers, but the incident brought to light tough working conditions faced by migrantworkers in Singapore and the undercurrents of discrimination they face.
The study of various registration methods based on maximal stable extremal region and machine learning

ABSTRACTThis review article aims to discuss contemporary and traditional image registration methods, which involve establishing correspondences between two or more images of a same object. Two popular methods based on local features are maximal stable extremal region (MSER) and deep learning (DL). After evaluating various registration methods, it has been determined that MSER and DL algorithms are well organised, convenient and highly accurate. These methods can effectively handle different aspects of images, such as size and viewpoint, and are currently in trend for image and video registration due to their excellent repeatability, efficiency and feature extraction capabilities. The article encompasses a comprehensive analysis of approximately 35 registration algorithms based on MSER and DL, spanning 2004–2023. MSER and DL techniques can be modified to specific requirements at different stages of the registration process. A comparative study was also conducted to assess the advantages and disadvantages of implementing each unique registration mechanism.KEYWORDS: Deep learningmaximal stable extremal regionimage registrationinterest pointfeature miningfeature correspondence Disclosure statementNo potential conflict of interest was reported by the author(s).
Applying blockchain to quality food products: a marketing perspective

Purpose The purpose of this paper is to enrich the knowledge about blockchain (BC) technology implementation in the agri-food industry by providing an interpretive framework of the key marketing opportunities and challenges, related to the adoption of BC for Geographical Indication (GI) products. Design/methodology/approach The study adopts an explorative qualitative research design through the cognitive mapping technique applied to the cognition of different market players involved in agri-food BC projects: farmers, distributors, companies and consultancies. Findings This study presents a comprehensive examination of the marketing impacts of BC across various marketing objectives, including product enhancement, brand positioning, consumer relationships, market access and supply chain relationships. It highlights the capability of BC to facilitate data-enabled ecosystems within the agri-food sector, involving supply chain actors and control agencies. Additionally, the study sheds light on the challenges (technological, collaborative, political, financial and organizational) associated with the implementation of BC in the marketing of agri-food products. Research limitations/implications This work provides a comprehensive examination of the relevance of BC in the marketing activities of firms, particularly in the context of quality food products. It highlights the main areas of impact and effects and emphasizes the complexity of the phenomenon, which extends beyond its technical issues. Furthermore, it offers a systematic exploration of the challenges associated with the adoption of BC in marketing activities, thus contributing to a broader understanding of the implications of BC adoption in companies' marketing strategies. Practical implications The practical implications for this work addresses both GI companies and policy makers. Implications for companies relate to the market benefits associated with the implementation of BC, which allow further strengthening of market positioning, relationships of trust within the supply chain and integration between physical and digital market channels. The study also systematizes the challenges underlying the implementation of BC projects. The implications for policy makers regard the role they have to play in BC projects at regulatory, financial and policy levels. Originality/value Studies focusing on BC applications in marketing are still limited and characterized by a very narrow perspective (especially in the food industry). This study contributes to the conceptual design of the marketing applications of BC in the agri-food sector. The value of the study also lies in having framed the marketing impacts of BC in a holistic perspective, along with the technological and non-technological challenges that are related to the integration of BC in marketing strategy and operations.
Seeking alternatives in music education: The effects of mobile technologies on students’ achievement in basic music theory

This study aimed to investigate the effect of mobile technology with music education content that supports basic music theory teaching on secondary school students’ achievement levels and learning. The study was carried out using a mixed-methods sequential explanatory design on sixth-grade secondary school students studying in Turkey (experimental group n = 43 and control group n = 40). The experimental group students attended an 8-week music lesson prepared through mobile technologies called NoteWorks (the names of the tunes, the letter notation, and their positions on the piano), Rhythm Cat (rhythm studies), GarageBand (arrangement studies), and Kids Piano (playing the melody by remembering). Basic music theory subjects were taught to the control group using traditional music teaching methods. Quantitative data showed that the music lesson activities developed and enriched with mobile technologies created a significant difference in the students in the experimental group’s basic music theory subjects’ achievement test scores. Post-intervention assessments (semi-structured interviews) increased students’ motivation levels, willingness to study outside the classroom, communication, musical development, and willingness to participate in the lesson. The research concludes with several recommendations and highlights points that need further attention in mobile technology research.
Compositional functional regression and isotemporal substitution analysis: Methods and application in time-use epidemiology

The distribution of time that people spend in physical activity of various intensities has important health implications. Physical activity (commonly categorised by the intensity into light, moderate and vigorous physical activity), sedentary behaviour and sleep, should not be analysed separately, because they are parts of a time-use composition with a natural constraint of 24 h/day. To find out how are relative reallocations of time between physical activity of various intensities associated with health, herewith we describe compositional scalar-on-function regression and a newly developed compositional functional isotemporal substitution analysis. Physical activity intensity data can be considered as probability density functions, which better reflects the continuous character of their measurement using accelerometers. These probability density functions are characterised by specific properties, such as scale invariance and relative scale, and they are geometrically represented using Bayes spaces with the Hilbert space structure. This makes possible to process them using standard methods of functional data analysis in the L2 space, via centred logratio (clr) transformation. The scalar-on-function regression with clr transformation of the explanatory probability density functions and compositional functional isotemporal substitution analysis were applied to a dataset from a cross-sectional study on adiposity conducted among school-aged children in the Czech Republic. Theoretical reallocations of time to physical activity of higher intensities were found to be associated with larger and more progressive expected decreases in adiposity. We obtained a detailed insight into the dose-response relationship between physical activity intensity and adiposity, which was enabled by using the compositional functional approach.
Named Entity Recognition for Few-Shot Power Dispatch Based on Multi-Task

In view of the fact that entity nested and professional terms are difficult to identify in the field of power dispatch, a multi-task-based few-shot named entity recognition model (FSPD-NER) for power dispatch is proposed. The model consists of four modules: feature enhancement, seed, expansion, and implication. Firstly, the masking strategy of the encoder is improved by adopting whole-word masking, using a RoBERTa (Robustly Optimized BERT Pretraining Approach) encoder as the embedding layer to obtain the text feature representation, and an IDCNN (Iterated Dilated CNN) module to enhance the feature. Then the text is cut into one Chinese character and two Chinese characters as a seed set, the score for each seed is calculated, and if the score is greater than the threshold value ω, they are passed to the expansion module as candidate seeds; next, the candidate seeds need to be expanded left and right according to offset γ to obtain the candidate entities; finally, to construct text implication pairs, the input text is used as a premise sentence, the candidate entity is connected with predefined label templates as hypothesis sentences, and the implication pairs are passed to the RoBERTa encoder for the classification task. The focus loss function is used to alleviate label imbalance during training. The experimental results of the model on the power dispatch dataset show that the precision, recall, and F1 scores of the recognition results in 20-shot samples are 63.39%, 61.97%, and 62.67%, respectively, which is a significant performance improvement compared to existing methods.
AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement

With the continuous expansion of the darknet and the increase in various criminal activities in the darknet, darknet traffic identification has become increasingly essential. However, existing darknet traffic identification methods rely on all traffic characteristics, which require a long computing time and a large amount of system resources, resulting in low identification efficiency. To this end, this paper proposes an autoencoder-based darknet traffic identification method (AE-DTI). First, AE-DTI maps the feature values to pixels of a two-dimensional grayscale image after deduplication and denoising of the darknet traffic dataset. Then, AE-DTI designs a new feature selection algorithm (AE-FS) to downscale the grayscale graph, and AE-FS trains a feature scoring network, which globally scores all the features based on the reconstruction error to select the features with scores greater than or equal to a set threshold value. Finally, AE-DTI uses a one-dimensional convolutional neural network with a dropout layer to identify darknet traffic on the basis of alleviating overfitting. Experimental results on the ISCXTor2016 dataset show that, compared with other dimensionality reduction methods (PCA, LLE, ISOMAP, and autoencoder), the classification model trained with the data obtained from AE-FS has a significant improvement in classification accuracy and classification efficiency. Moreover, AE-DTI also shows significant improvement in recognition accuracy compared with other models. Experimental results on the CSE-CIC-IDS2018 dataset and CIC-Darknet2020 dataset show that AE-DTI has strong generalization.
Machine Learning Techniques for Human Activity Recognition Using Wearable Sensors

The HAR refers to a system that monitors human behaviors and activities by collecting data from a wide range of distinct kinds of wearable sensors. This system is known as human activity recognition. HAR enables in improving Health, Quality of life, Biomedical, and Mental Health. Recently, there has been an increase in demand for a range of HAR techniques, including those that make use of deep learning, machine learning, and CNN algorithms. Specifically, this need has been on the rise in the United States. In the most recent few years, this field of research has achieved major advancements, and there is still a substantial amount of research being carried out. Human activity recognition (HAR) has seen tremendous growth in the disciplines of pervasive computing, human–computer interaction, and human behavior analysis. Recently, machine learning (ML) algorithms have proven effective to predict a variety of human actions using time-series data from wearable sensors and cell phones. Even though ML-based techniques were excellent at identifying activities, managing time series data remains difficult. Time-series data still has a lot of problems, including hard feature extraction, extremely biased data, etc. Furthermore, manual feature engineering is a key component of the bulk of HAR approaches. Since each measurement made by wearable sensors like gyroscopes and accelerometers has a timestamp associated with it, time series data is the format utilized for data on human activity. The raw sensor data must be processed to obtain the necessary temporal properties for HAR. In-depth feature engineering and data pre-processing are required for the majority of HAR approaches, which in turn necessitates subject-matter expertise. These techniques are time-consuming and application-specific. This paper begins with an in-depth discussion of HAR, then moves on to a discussion of the various machine learning methods, and then concludes with a discussion of wearable sensors and the applications that can be used with them.
Linguistic Driven Feature Selection for Text Classification as Stop Word Replacement

The common corpus optimization method "stop words removal" is based on the assumption that text tokens with high occurrence frequency can be removed without affecting classification performance.Linguistic information regarding sentence structure is ignored as well as preferences of the classification technology.We propose the Weighted Unimportant Part-of-Speech Model (WUP-Model) for token removal in the pre-processing of text corpora.The weighted relevance of a token is determined using classification relevance and classification performance impact.The WUP-Model uses linguistic information (part of speech) as grouping criteria.Analogous to stop word removal, we provide a set of irrelevant part of speech (WUP-Instance) for word removal.In a proof-of-concept we created WUP-Instances for several classification algorithms.The evaluation showed significant advantages compared to classic stop word removal.The tree-based classifier increased runtime by 65% and 25% in performance.The performance of the other classifiers decreased between 0.2% and 2.4%, their runtime improved between -4.4% and -24.7%.These results prove beneficial effects of the proposed WUP-Model.