Reviewerly/exHarmony huggingface dataset:
A building regulation question answering system: A deep learning methodology

Regulations play an important role in assuring the quality of a building’s construction and minimizing its adverse environmental impacts. Engineers and the like need to retrieve regulatory information to ensure a building conforms to specified standards. Despite the availability of search engines and digital databases that can be used to store regulations, engineers, for example, are unable to retrieve information for domain-specific needs in a timely manner. As a consequence, users often have to deal with the burden of browsing and filtering information, which can be a time-consuming process. This research develops a robust end-to-end methodology to improve the efficiency and effectiveness of retrieving queries pertaining to building regulations. The developed methodology integrates information retrieval with a deep learning model of Natural Language Processing (NLP) to provide precise and rapid answers to user’s questions from a collection of building regulations. The methodology is evaluated and a prototype system to retrieve queries is developed. The paper’s contribution is therefore twofold as it develops a: (1) methodology that combines NLP and deep learning to be able to address queries raised about the building regulations; and (2) chatbot of question answering system, which we refer to as QAS4CQAR. Our proposed methodology has powerful feature representation and learning capability and therefore can potentially be adopted to building regulations in other jurisdictions.
COEGAN

Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to train, and several aspects of the model should be previously designed by hand. Neuroevolution is a well-known technique used to provide the automatic design of network architectures which was recently expanded to deep neural networks. COEGAN is a model that uses neuroevolution and coevolution in the GAN training algorithm to provide a more stable training method and the automatic design of neural network architectures. COEGAN makes use of the adversarial aspect of the GAN components to implement coevolutionary strategies in the training algorithm. Our proposal was evaluated in the Fashion-MNIST and MNIST dataset. We compare our results with a baseline based on DCGAN and also with results from a random search algorithm. We show that our method is able to discover efficient architectures in the Fashion-MNIST and MNIST datasets. The results also suggest that COEGAN can be used as a training algorithm for GANs to avoid common issues, such as the mode collapse problem.
Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images

Driven by the urgent demand for managing remote sensing big data, large-scale remote sensing image retrieval (RSIR) attracts increasing attention in the remote sensing field. In general, existing retrieval methods can be regarded as visual-based retrieval approaches that search and return a set of similar images to a given query image from a database. Although these retrieval methods have delivered good results, there is still a question that needs to be addressed: can we obtain the accurate semantic labels of the returned similar images to further help analyzing and processing imagery? To this end, in this article, we redefine the image retrieval problem as visual and semantic retrieval of images. Especially, we propose a novel deep hashing convolutional neural network (DHCNN) to retrieve similar images and classify their semantic labels simultaneously in a unified framework. In more detail, a convolutional neural network (CNN) is used to extract high-dimensional deep features. Then, a hash layer is perfectly inserted into the network to transfer the deep features into compact hash codes. In addition, a fully connected layer with a softmax function is performed on the hash layer to generate the probability distribution of each class. Finally, a loss function is elaborately designed to consider the label loss of each image and similarity loss of pairs of images simultaneously. Experimental results on three remote sensing data sets demonstrate that the proposed method can achieve state-of-art retrieval and classification performance.
Reinforcement learning-based adaptive PID controller for DPS

A conventional PID controller for the DPS has limitations due to fixed gains and dependence on manual adjustment for its gains. Therefore, several previous studies developed a fuzzy-based adaptive PID controller for the DPS which tunes the gains based on the fuzzy logic. However, the fuzzy logic has its disadvantages due to a manual definition of fuzzy rules and fuzzy variables. To overcome those limitations, a deep reinforcement learning algorithm is adopted to learn the efficient adaptive gain-tuning strategy without human intuition behind since it does not require any prior knowledge about the dynamics of a ship or DPS. Finally, it is shown that the proposed system can result in better station-keeping performance without deterioration in its control efficiency.
UAV Maneuvering Target Tracking in Uncertain Environments Based on Deep Reinforcement Learning and Meta-Learning

This paper combines deep reinforcement learning (DRL) with meta-learning and proposes a novel approach, named meta twin delayed deep deterministic policy gradient (Meta-TD3), to realize the control of unmanned aerial vehicle (UAV), allowing a UAV to quickly track a target in an environment where the motion of a target is uncertain. This approach can be applied to a variety of scenarios, such as wildlife protection, emergency aid, and remote sensing. We consider a multi-task experience replay buffer to provide data for the multi-task learning of the DRL algorithm, and we combine meta-learning to develop a multi-task reinforcement learning update method to ensure the generalization capability of reinforcement learning. Compared with the state-of-the-art algorithms, namely the deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3), experimental results show that the Meta-TD3 algorithm has achieved a great improvement in terms of both convergence value and convergence rate. In a UAV target tracking problem, Meta-TD3 only requires a few steps to train to enable a UAV to adapt quickly to a new target movement mode more and maintain a better tracking effectiveness.
Improved Shuffled Jaya algorithm for sizing optimization of skeletal structures with discrete variables

Jaya algorithm is a simple and efficient population-based metaheuristic algorithm. Besides its simplicity, it has free from any algorithm-specific parameters. Although it has these advantages, the Jaya algorithm suffers from some shortcomings including unwanted premature convergence and the possibility of being trapped in local minima due to insufficient population diversity. To alleviate these handicaps, this paper proposes an Improved Shuffled based Jaya (IS-Jaya) algorithm. The proposed optimization method uses the concept of shuffling process to gain superior exploration capability in the search mechanism. A mechanism that causes to escape from local minima is also incorporated into the original Jaya algorithm. The efficiency of the IS-Jaya algorithm is tested on discrete optimization problems and compared to those of Jaya algorithm, self-adaptive multi-population-based Jaya (SAMP-Jaya), and some other state-of-art optimization methods. Optimization results show that the proposed optimization method can be an effective tool for solving discrete size optimization of skeletal structures.
Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images

Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. The proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.
Concentration-concentration fractal modelling: A novel insight for correlation between variables in response to changes in the underlying controlling geological-geochemical processes

Fractal/multifractal classification models applied to regional geochemical data are typically based on relationships between geochemical concentrations and mathematical, statistical or geometrical properties such as sample numbers, surface areas, perimeter and distance. There is, however, another component of geochemical datasets that is implicitly addressed using multi-variate methods such as factor analysis or various forms of clustering. This is the degree of correlation and changes in the nature of the correlation between elements as the processes affecting sample geochemistry change. In regional datasets, much of the change is related to differences in parent lithology or regolith type, but also relates to the effects of mineralisation or contamination. Where two elements display systematic changes in their mutual correlation in response to different ‘forcings’, joint modelling of such bivariate relationships can provide new ‘dimensions’ to data interpretation. Bivariate probability plots indicate distinct population breaks representing the intersections between different sub-populations within the two variables. This concept is extended in this study to fractal analysis and a proposed concentration-concentration (C–C) fractal model. A soil dataset from Cyprus was studied in this research to demonstrate and interpret the C–C results associated with copper (Cu)-related VMS mineralisation considering the geological-geochemical evidence. The C–C models generated based on the relation between Cu and indium (In) were compared to the established number-size (N-S), concentration-area (C-A), spectrum-area (S-A) and singularity models. All the models were applied to the same datasets, but in different formats, i.e., raw data, and multi-variate approaches of Box-Cox transformed data, factor analysis (FA) and centred log-ratio (clr) transformed data scores. This is to visualise and evaluate the robustness of the C–C model comparing to uni- and multi-variate studies. Therefore, based on the maps classified by the C–C model and the geology in Cyprus comparing to the mentioned models, the robustness of the C–C fractal model was demonstrated. In addition, applying Monte Carlo simulation on each class characterised by all the models applied, demonstrated a remarkably high certainty of the thresholds obtained by the C–C fractal model.
How pre-service teachers perceive their 21st-century skills and dispositions: A longitudinal perspective

Research-based discussions about 21st-century skills are currently needed; 21st-century skills refer to skills that today's students are expected to possess for successful future careers. The ways students perceive these skills or what kind of dispositions they have in this regard are significant. This paper provides an overview of the development of pre-service teachers' perceived 21st-century skills and dispositions. The quantitative data was collected in three phases during 2014, 2015, and 2016 at three Finnish universities. The number of respondents at each measurement point varied from 209 to 267. Data were analysed using latent growth curve modeling. The study focuses on students' perceptions of three areas related to 21st-century skills: learning skills, collaboration dispositions, and skills to use ICT. The results show that the three areas evolved in different ways. Learning skills and collaboration dispositions show up as yearly assessments that remain at the same level, with small differences among respondents, unlike skills to use ICT with bigger yearly changes. The measured areas also appear as separate entities throughout the bachelor's studies, with small or non-significant correlations. These results reveal important new perspectives on how pre-service teachers perceive 21st-century skills and how perceptions evolve during teacher education.
Towards Optimal Topology Aware Quantum Circuit Synthesis

We present an algorithm for compiling arbitrary unitaries into a sequence of gates native to a quantum processor. As CNOT gates are error-prone for the foreseeable Noisy-Intermediate-Scale Quantum devices era, our A* inspired algorithm minimizes their count while accounting for connectivity. We discuss the formulation of synthesis as a search problem as well as an algorithm to find solutions. For a workload of circuits with complexity appropriate for the NISQ era, we produce solutions well within the best upper bounds published in literature and match or exceed hand tuned implementations, as well as other existing synthesis alternatives. In particular, when comparing against state-of-the-art available synthesis packages we show 2.4× average (up to 5.3×) reduction in CNOT count. We also show how to re-target the algorithm for a different chip topology and native gate set while obtaining similar quality results. We believe that tools like ours can facilitate algorithmic exploration and guide gate set discovery for quantum processor designers, as well as being useful for optimization in the quantum compilation tool-chain.
Multitask Learning Strengthens Adversarial Robustness

Although deep networks achieve strong accuracy on a range of computer vision benchmarks, they remain vulnerable to adversarial attacks, where imperceptible input perturbations fool the network. We present both theoretical and empirical analyses that connect the adversarial robustness of a model to the number of tasks that it is trained on. Experiments on two datasets show that attack difficulty increases as the number of target tasks increase. Moreover, our results suggest that when models are trained on multiple tasks at once, they become more robust to adversarial attacks on individual tasks. While adversarial defense remains an open challenge, our results suggest that deep networks are vulnerable partly because they are trained on too few tasks.
All at Once: Temporally Adaptive Multi-frame Interpolation with Advanced Motion Modeling

Recent advances in high refresh rate displays as well as the increased interest in high rate of slow motion and frame up-conversion fuel the demand for efficient and cost-effective multi-frame video interpolation solutions. To that regard, inserting multiple frames between consecutive video frames are of paramount importance for the consumer electronics industry. State-of-the-art methods are iterative solutions interpolating one frame at the time. They introduce temporal inconsistencies and clearly noticeable visual artifacts. Departing from the state-of-the-art, this work introduces a true multi-frame interpolator. It utilizes a pyramidal style network in the temporal domain to complete the multi-frame interpolation task in one-shot. A novel flow estimation procedure using a relaxed loss function, and an advanced, cubic-based, motion model is also used to further boost interpolation accuracy when complex motion segments are encountered. Results on the Adobe240 dataset show that the proposed method generates visually pleasing, temporally consistent frames, outperforms the current best off-the-shelf method by 1.57 dB in PSNR with 8 times smaller model and 7.7 times faster. The proposed method can be easily extended to interpolate a large number of new frames while remaining efficient because of the one-shot mechanism.
Asymmetric Supervised Consistent and Specific Hashing for Cross-Modal Retrieval

Hashing-based techniques have provided attractive solutions to cross-modal similarity search when addressing vast quantities of multimedia data. However, existing cross-modal hashing (CMH) methods face two critical limitations: 1) there is no previous work that simultaneously exploits the consistent or modality-specific information of multi-modal data; 2) the discriminative capabilities of pairwise similarity is usually neglected due to the computational cost and storage overhead. Moreover, to tackle the discrete constraints, relaxation-based strategy is typically adopted to relax the discrete problem to the continuous one, which severely suffers from large quantization errors and leads to sub-optimal solutions. To overcome the above limitations, in this article, we present a novel supervised CMH method, namely Asymmetric Supervised Consistent and Specific Hashing (ASCSH). Specifically, we explicitly decompose the mapping matrices into the consistent and modality-specific ones to sufficiently exploit the intrinsic correlation between different modalities. Meanwhile, a novel discrete asymmetric framework is proposed to fully explore the supervised information, in which the pairwise similarity and semantic labels are jointly formulated to guide the hash code learning process. Unlike existing asymmetric methods, the discrete asymmetric structure developed is capable of solving the binary constraint problem discretely and efficiently without any relaxation. To validate the effectiveness of the proposed approach, extensive experiments on three widely used datasets are conducted and encouraging results demonstrate the superiority of ASCSH over other state-of-the-art CMH methods.
Detection of Breast Cancer From Whole Slide Histopathological Images Using Deep Multiple Instance CNN

Histopathological Whole Slide Imaging (WSI) has become a standard in the detection of breast cancer. Automated image analysis methods attempt to reduce the workload from the clinicians and Convolutional Neural Networks (CNNs) are a popular choice for this purpose. However, size of a WSI image typically is approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$40,000\times 40.000$ </tex-math></inline-formula> pixels (can reach up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100,000\times 100.000$ </tex-math></inline-formula> pixels). CNNs cannot handle such large images. Moreover, downscaling a WSI image causes degradation of small-scale visual information. Hence, a large number of small patches (containing critical visual information) from a WSI image are extracted by a trained pathologist and are used for training. However, it requires massive amounts of time to precisely search and label appropriate class-representative patches. To address this issue, a Deep Multiple Instance Learning (MIL) based CNN framework has been introduced in this paper. In the proposed framework every slide is represented as a bag of extracted patches. Only the bag label is used for training, thus eliminating the requirement to provide patchwise labels. The patches inherit the label of the bag containing them. A WSI image (i.e. a bag) is labeled benign if all its patches are benign and labeled malignant even if a single patch contains malignant cells. Learning can be carried out at the bag level even with noisy patch labels. Performance of this method was evaluated using the BreakHis, IUPHL and UCSB breast cancer datasets where 93.06%, 96.63%, 95.83% accuracy was achieved respectively.
Global Distance-Distributions Separation for Unsupervised Person Re-identification

Supervised person re-identification (ReID) often has poor scalability and usability in real-world deployments due to domain gaps and the lack of annotations for the target domain data. Unsupervised person ReID through domain adaptation is attractive yet challenging. Existing unsupervised ReID approaches often fail in correctly identifying the positive samples and negative samples through the distance-based matching/ranking. The two distributions of distances for positive sample pairs (Pos-distr) and negative sample pairs (Neg-distr) are often not well separated, having large overlap. To address this problem, we introduce a global distance-distributions separation (GDS) constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view. We model the two global distance distributions as Gaussian distributions and push apart the two distributions while encouraging their sharpness in the unsupervised training process. Particularly, to model the distributions from a global view and facilitate the timely updating of the distributions and the GDS related losses, we leverage a momentum update mechanism for building and maintaining the distribution parameters (mean and variance) and calculate the loss on the fly during the training. Distribution-based hard mining is proposed to further promote the separation of the two distributions. We validate the effectiveness of the GDS constraint in unsupervised ReID networks. Extensive experiments on multiple ReID benchmark datasets show our method leads to significant improvement over the baselines and achieves the state-of-the-art performance.
BATS: Binary ArchitecTure Search

This paper proposes Binary ArchitecTure Search (BATS), a framework that drastically reduces the accuracy gap between binary neural networks and their real-valued counterparts by means of Neural Architecture Search (NAS). We show that directly applying NAS to the binary domain provides very poor results. To alleviate this, we describe, to our knowledge, for the first time, the 3 key ingredients for successfully applying NAS to the binary domain. Specifically, we (1) introduce and design a novel binary-oriented search space, (2) propose a new mechanism for controlling and stabilising the resulting searched topologies, (3) propose and validate a series of new search strategies for binary networks that lead to faster convergence and lower search times. Experimental results demonstrate the effectiveness of the proposed approach and the necessity of searching in the binary space directly. Moreover, (4) we set a new state-of-the-art for binary neural networks on CIFAR10, CIFAR100 and ImageNet datasets. Code will be made available.