An evaluation of the impacts of construction professionals’ conflict on performance in the Nigerian construction industry

PurposeThis study aims to evaluate impacts of construction professionals (CPs) conflict on performance in the Nigerian construction industry (NCI).Design/methodology/approachA quantitative method was used for this research. Questionnaires were sent to various CPs in Southwestern part of Nigeria. A total of 150 questionnaires were sent out, while 135 were gotten back from the partakers. The data received from the partakers were computed by applying descriptive and exploratory factor analysis.FindingsIn this study, conflict leads to the abandonment of the CPs’ task being rated highest by the participants. This was followed by conflict that results in insufficient communication, generates job pressure, results to frustrations and displeasure among the CPs, helps in early problem identification, causes work damage among professionals, helps in solving professional organization problems, improves productivity of professionals, improves communication among the professionals and so on.Research limitations/implicationsThis paper is limited to CPs that are members of professional bodies in Nigeria, and only 135 participants participated. Though, this paper suggests that a mixed-method approach should be utilized in further studies with a wider coverage.Practical implicationsThe findings from this paper will increase the understanding of CPs in Nigeria on various impacts of conflict on performance in the construction industry, most specifically the professional bodies. Moreover, this study will increase the knowledge of CPs to always avoid whatever that leads to the abandonment of their tasks. Additionally, this study will benefit the CPs to avoid insufficient communication among themselves, in order to accomplish great performance and efficiency in their respective professional bodies.Originality/valueSince previous studies on impacts of construction conflict in Nigeria were only focused on contractors and consultants in construction project, this current study filled the gap by evaluating the impacts of CPs’ conflict on performance in the NCI. Also, the method of analysis used for this study is exemptional because previous studies have overlooked the method. However, it is recommended that CPs must communicate more with others so as to ensure favorable conflict effects on performance.

Attention Optimized Deep Generative Adversarial Network for Removing Uneven Dense Haze

The existing dehazing algorithms are problematic because of dense haze being unevenly distributed on the images, and the deep convolutional dehazing network relying too greatly on large-scale datasets. To solve these problems, this paper proposes a generative adversarial network based on the deep symmetric Encoder-Decoder architecture for removing dense haze. To restore the clear image, a four-layer down-sampling encoder is constructed to extract the semantic information lost due to the dense haze. At the same time, in the symmetric decoder module, an attention mechanism is introduced to adaptively assign weights to different pixels and channels, so as to deal with the uneven distribution of haze. Finally, the framework of the generative adversarial network is generated so that the model achieves a better training effect on small-scale datasets. The experimental results showed that the proposed dehazing network can not only effectively remove the unevenly distributed dense haze in the real scene image, but also achieve great performance in real-scene datasets with less training samples, and the evaluation indexes are better than other widely used contrast algorithms.

Population-based Ensemble Learning with Tree Structures for Classification

<p>Ensemble learning is one of the most powerful extensions for improving upon individual machine learning models. Rather than a single model being used, several models are trained and the predictions combined to make a more informed decision. Such combinations will ideally overcome the shortcomings of any individual member of the ensemble. Most ma- chine learning competition winners feature an ensemble of some sort, and there is also sound theoretical proof to the performance of certain ensem- bling schemes. The benefits of ensembling are clear in both theory and practice. Despite the great performance, ensemble learning is not a trivial task. One of the main difficulties is designing appropriate ensembles. For exam- ple, how large should an ensemble be? What members should be included in an ensemble? How should these members be weighted? Our first contribution addresses these concerns using a strongly-typed population- based search (genetic programming) to construct well-performing ensem- bles, where the entire ensemble (members, hyperparameters, structure) is automatically learnt. The proposed method was found, in general, to be significantly better than all base members and commonly used compari- son methods trialled. With automatically designed ensembles, there is a range of applica- tions, such as competition entries, forecasting and state-of-the-art predic- tions. However, often these applications also require additional prepro- cessing of the input data. Above the ensemble considers only the original training data, however, in many machine learning scenarios a pipeline is required (for example performing feature selection before classification). For the second contribution, a novel automated machine learning method is proposed based on ensemble learning. This method uses a random population-based search of appropriate tree structures, and as such is em- barrassingly parallel, an important consideration for automated machine learning. The proposed method is able to achieve equivalent or improved results over the current state-of-the-art methods and does so in a fraction of the time (six times as fast). Finally, while complex ensembles offer great performance, one large limitation is the interpretability of such ensembles. For example, why does a forest of 500 trees predict a particular class for a given instance? In an effort to explain the behaviour of complex models (such as ensem- bles), several methods have been proposed. However, these approaches tend to suffer at least one of the following limitations: overly complex in the representation, local in their application, limited to particular fea- ture types (i.e. categorical only), or limited to particular algorithms. For our third contribution, a novel model agnostic method for interpreting complex black-box machine learning models is proposed. The method is based on strongly-typed genetic programming and overcomes the afore- mentioned limitations. Multi-objective optimisation is used to generate a Pareto frontier of simple and explainable models which approximate the behaviour of much more complex methods. We found the resulting rep- resentations are far simpler than existing approaches (an important con- sideration for interpretability) while providing equivalent reconstruction performance. Overall, this thesis addresses two of the major limitations of existing ensemble learning, i.e. the complex construction process and the black- box models that are often difficult to interpret. A novel application of ensemble learning in the field of automated machine learning is also pro- posed. All three methods have shown at least equivalent or improved performance than existing methods.</p>

Population-based Ensemble Learning with Tree Structures for Classification

<p>Ensemble learning is one of the most powerful extensions for improving upon individual machine learning models. Rather than a single model being used, several models are trained and the predictions combined to make a more informed decision. Such combinations will ideally overcome the shortcomings of any individual member of the ensemble. Most ma- chine learning competition winners feature an ensemble of some sort, and there is also sound theoretical proof to the performance of certain ensem- bling schemes. The benefits of ensembling are clear in both theory and practice. Despite the great performance, ensemble learning is not a trivial task. One of the main difficulties is designing appropriate ensembles. For exam- ple, how large should an ensemble be? What members should be included in an ensemble? How should these members be weighted? Our first contribution addresses these concerns using a strongly-typed population- based search (genetic programming) to construct well-performing ensem- bles, where the entire ensemble (members, hyperparameters, structure) is automatically learnt. The proposed method was found, in general, to be significantly better than all base members and commonly used compari- son methods trialled. With automatically designed ensembles, there is a range of applica- tions, such as competition entries, forecasting and state-of-the-art predic- tions. However, often these applications also require additional prepro- cessing of the input data. Above the ensemble considers only the original training data, however, in many machine learning scenarios a pipeline is required (for example performing feature selection before classification). For the second contribution, a novel automated machine learning method is proposed based on ensemble learning. This method uses a random population-based search of appropriate tree structures, and as such is em- barrassingly parallel, an important consideration for automated machine learning. The proposed method is able to achieve equivalent or improved results over the current state-of-the-art methods and does so in a fraction of the time (six times as fast). Finally, while complex ensembles offer great performance, one large limitation is the interpretability of such ensembles. For example, why does a forest of 500 trees predict a particular class for a given instance? In an effort to explain the behaviour of complex models (such as ensem- bles), several methods have been proposed. However, these approaches tend to suffer at least one of the following limitations: overly complex in the representation, local in their application, limited to particular fea- ture types (i.e. categorical only), or limited to particular algorithms. For our third contribution, a novel model agnostic method for interpreting complex black-box machine learning models is proposed. The method is based on strongly-typed genetic programming and overcomes the afore- mentioned limitations. Multi-objective optimisation is used to generate a Pareto frontier of simple and explainable models which approximate the behaviour of much more complex methods. We found the resulting rep- resentations are far simpler than existing approaches (an important con- sideration for interpretability) while providing equivalent reconstruction performance. Overall, this thesis addresses two of the major limitations of existing ensemble learning, i.e. the complex construction process and the black- box models that are often difficult to interpret. A novel application of ensemble learning in the field of automated machine learning is also pro- posed. All three methods have shown at least equivalent or improved performance than existing methods.</p>

Highly Efficient and Thermally Stable Eu2+-Doped Ca9Nd(PO4)7 Phosphor for Near-Ultraviolet White-Emission LED Applications

Various Eu2+-based Ca9Nd(PO4)7 (CNP:xEu2+, with different x values) materials are prepared via facile solid-state reaction. Their crystal structures are investigated in detail by means of the Rietveld refinement. The structure of CNP:Eu2+ with a trigonal lattice is analogous to that of β-Ca3(PO4)2. Therefore, Eu2+ ions tend to incorporate calcium sites in the host. All the obtained samples can be excited using near ultraviolet (nUV) light to present blue-green emission. An optimal dopant concentration is verified at x = 0.8 with a large critical interaction radius (11.21 Å). The mechanism of the concentration quenching effect is assigned to the multipole-multipole interaction. CNP:xEu2+ possesses a short decay lifetime of ∼60 μs and can endure severe working conditions thanks to its great thermal stability. The relative photoluminescence (PL) intensity of CNP:0.8Eu2+ can retain 84.75% of the pristine intensity measured at room temperature, and the relative intensity remains as high as 69.97% at 423 K. The CNP:Eu2+ phosphors also show great performance in the WLED demonstration. The correlated color temperature (CCT) of the prototype device is 3404 K, with an extremely high Ra (97.6). Therefore, CNP:xEu2+ could be regarded as a promising alternative to blue green phosphors in nUV chip-based WLED applications.

Siamese anchor-free object tracking with multiscale spatial attentions

Recently, object trackers based on Siamese networks have attracted considerable attentions due to their remarkable tracking performance and widespread application. Especially, the anchor-based methods exploit the region proposal subnetwork to get accurate prediction of a target and make great performance improvement. However, those trackers cannot capture the spatial information very well and the pre-defined anchors will hinder robustness. To solve these problems, we propose a Siamese-based anchor-free object tracking algorithm with multiscale spatial attentions in this paper. Firstly, we take ResNet-50 as the backbone network to generate multiscale features of both template patch and search regions. Secondly, we propose the spatial attention extraction (SAE) block to capture the spatial information among all positions in the template and search region feature maps. Thirdly, we put these features into the SAE block to get the multiscale spatial attentions. Finally, an anchor-free classification and regression subnetwork is used for predicting the location of the target. Unlike anchor-based methods, our tracker directly predicts the target position without predefined parameters. Extensive experiments with state-of-the-art trackers are carried out on four challenging visual object tracking benchmarks: OTB100, UAV123, VOT2016 and GOT-10k. Those experimental results confirm the effectiveness of our proposed tracker.

Experimental Certification of Quantum Entanglement Based on the Classical Complementary Correlations of Two-Qubit States

Quantum entanglement is one of the essential resources in quantum information processing. It is of importance to verify whether a quantum state is entangled. At present, a typical quantum certification focused on the classical correlations has attracted widespread attention. Here, we experimentally investigate the relation between quantum entanglement and the classical complementary correlations based on the mutual information, Pearson correlation coefficient and mutual predictability of two-qubit states. Our experimental results show the classical correlations for complementary properties have strong resolution capability to verify entanglement for two qubit pure states and Werner states. We find that the resolution capability has great performance improvement when the eigenstates of the measurement observables constitute a complete set of mutually unbiased bases. For Werner states in particular, the classical complementary correlations based on the Pearson correlation coefficient and mutual predictability can provide the ultimate bounds to certify entanglement.

Machine learning and deep learning strategies in drug repositioning

: Drug repositioning is to find novel usages for existing drugs. It plays an important role in drug discovery, especially in the pre-clinical stages. Compared with the traditional drug discovery approaches, computational approaches can save time and reduce cost significantly. Since drug repositioning relies on existing drug-, disease-, and target-centric data, many machine learning (ML) approaches have been proposed to identify useful information from multiple data resources. Deep learning (DL) is a subset of ML and appears in drug repositioning much later than basic ML. Nevertheless, DL methods have shown great performance in predicting potential drugs in many studies. In this article, we review the commonly used basic ML and DL approaches in drug repositioning. Firstly, the related databases are introduced, while all of them are publicly available for researchers. Two types of pre-processing steps, calculating similarities and constructing networks based on those data, are discussed. Secondly, the basic ML and DL strategies are illustrated separately. Thirdly, we review the latest studies about the applications of basic ML and DL in identifying potential drugs through three paths: drug-disease associations, drug-drug interactions, and drug-target interactions. Finally, we discuss the limitations in current studies and suggest several directions of future work to address those limitations.

A clinical specific BERT developed using a huge Japanese clinical text corpus

Generalized language models that are pre-trained with a large corpus have achieved great performance on natural language tasks. While many pre-trained transformers for English are published, few models are available for Japanese text, especially in clinical medicine. In this work, we demonstrate the development of a clinical specific BERT model with a huge amount of Japanese clinical text and evaluate it on the NTCIR-13 MedWeb that has fake Twitter messages regarding medical concerns with eight labels. Approximately 120 million clinical texts stored at the University of Tokyo Hospital were used as our dataset. The BERT-base was pre-trained using the entire dataset and a vocabulary including 25,000 tokens. The pre-training was almost saturated at about 4 epochs, and the accuracies of Masked-LM and Next Sentence Prediction were 0.773 and 0.975, respectively. The developed BERT did not show significantly higher performance on the MedWeb task than the other BERT models that were pre-trained with Japanese Wikipedia text. The advantage of pre-training on clinical text may become apparent in more complex tasks on actual clinical text, and such an evaluation set needs to be developed.

Improving Text-to-Code Generation with Features of Code Graph on GPT-2

Code generation, as a very hot application area of deep learning models for text, consists of two different fields: code-to-code and text-to-code. A recent approach, GraphCodeBERT uses code graph, which is called data flow, and showed good performance improvement. The base model architecture of it is bidirectional encoder representations from transformers (BERT), which uses the encoder part of a transformer. On the other hand, generative pre-trained transformer (GPT)—another multiple transformer architecture—uses the decoder part and shows great performance in the multilayer perceptron model. In this study, we investigate the improvement of code graphs with several variances on GPT-2 to refer to the abstract semantic tree used to collect the features of variables in the code. Here, we mainly focus on GPT-2 with additional features of code graphs that allow the model to learn the effect of the data stream. The experimental phase is divided into two parts: fine-tuning of the existing GPT-2 model, and pre-training from scratch using code data. When we pre-train a new model from scratch, the model produces an outperformed result compared with using the code graph with enough data.