Reviewerly/exHarmony huggingface dataset:
Effects of annotation quality on model performance

Supervised machine learning generally requires pre-labelled data. Although there are several open access and pre-annotated datasets available for training machine learning algorithms, most contain a limited number of object classes, which may not be suitable for specific tasks. As previously available pre-annotated data is not usually sufficient for custom models, most of the real world applications require collecting and preparing training data. There is an obvious trade-off between annotation quality and quantity. Time and resources can be allocated for ensuring superior data quality or for increasing the quantity of the annotated data. We test the degree of the detrimental effect caused by the annotation errors. We conclude that while the results deteriorate if annotations are erroneous; the effect - at least while using relatively homogeneous sequential video data - is limited. The benefits from the increased annotated data set size (created by using imperfect auto-annotation methods) outweighs the deterioration caused by annotated data.
Integrated Circuit Design of an Improved Discrete Chaotic Map by Averaging Multiple Seed Maps

In this paper, a novel method is proposed to build an improved 1-D discrete chaotic map circuit by averaging the output of multiple seed maps. The design is done in a 65 nm CMOS process and is applicable to other technology nodes as well. We show the results for two and three seed maps but the general scheme is compatible with any number of seed maps. The chaotic properties are evaluated using the bifurcation diagram, Lyapunov exponent, Shannon entropy and correlation coefficient. We have found that by having a suitable combination of seed maps, the proposed averaging scheme has a much wider chaotic range with better chaotic performance than constituent seed maps. This can be useful for many applications such as reconfigurable logic gate, chaos-based cryptography, side-channel attack mitigation, random number generation, and so on, where a robust and wide chaotic range is desired.
ST-FMT*: A Fast Optimal Global Motion Planning for Mobile Robot

This article introduces a secure tunnel fast marching tree motion planning algorithm (ST-FMT*) to provide a secure and optimal path quickly for a mobile robot. The proposed ST-FMT* consists of preprocessing and exploring procedures, which are responsible for establishing a secure tunnel and optimizing the path, respectively. In the preprocessing process, the generalized Voronoi graph is adopted to build an equidistant roadmap and generates an initial collision-free solution rapidly. Then, a secure tunnel is established via the minimum distance from the obstacles to the initial solution to facilitate the concentration of sampling. In the exploration process, the FMT* with uniform sampling within the secure tunnel is utilized to find the optimal solution with high computational efficiency. The theoretical analyses of the ST-FMT* are provided. In a series of scenarios evaluation, the ST-FMT* exhibits fast convergence to the optimal solution in different environments. Besides, the effectiveness of the ST-FMT* is verified in a transportation experiment using a Turtlebot2 mobile robot.
Texture Segmentation Benchmark

The Prague texture segmentation data-generator and benchmark (mosaic.utia.cas.cz) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods. The benchmark verifies segmenter performance characteristics on potentially unlimited monospectral, multispectral, satellite, and bidirectional texture function (BTF) data using an extensive set of over forty prevalent criteria. It also enables us to test for noise robustness and scale, rotation, or illumination invariance. It can be used in other applications, such as feature selection, image compression, query by pictorial example, etc. The benchmark's functionalities are demonstrated in evaluating several examples of leading previously published unsupervised and supervised image segmentation algorithms. However, they are used to illustrate the benchmark functionality and not review the recent image segmentation state-of-the-art.
Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations

Distribution-based imaging is a promising methodology mainly to differentiate suspicious regions from surrounding tissues by applying a distribution to the images vertically or horizontally, ideally in both directions. The methodology is very useful for contouring and highlighting desired regions even under near-zero contrast conditions; it also leads to flexible segmentation of the lesions by parametric kernels and provides robust results when supported by solid post-segmentation protocols. Given these benefits, what we propose in this research is a specialized fuzzy 2-means algorithm enhanced by parametric distribution-based imaging framework to offer novel solutions for multiple-sclerosis (MS) identification and segmentation from flair MRI images. The interchangeable distributions employed in this research are Rayleigh, Weibull, Gamma, Exponential and Chi-square, which all are mathematically transmuted from Nakagami distribution. The Nakagami m-parameter is defining the shape of the distributions unless a special parameter exists; while the highlighted areas are segmented by fuzzy 2-means. All parameters are optimized using a set of MICCAI 2016 MS lesion segmentation challenge taken by Siemens Verio 3T scanner and 0.8245 dice score is achieved by Nakagami-Gamma. However, when the optimized framework is tested by other 4 sets with same resolution and size properties, the highest average dice score 0.7113 is obtained by Nakagami–Rayleigh; while Nakagami-Gamma transmutation is resulted in 0.7112 dice score with significantly better sensitivity.
Multi-agent reinforcement learning with directed exploration and selective memory reuse

Many tasks require cooperation and coordination of multiple agents. Multi-agent reinforcement learning (MARL) can effectively learn solutions to these problems, but exploration and local optima problems are still open research topics. In this paper, we propose a new multi-agent policy gradient method called decentralized exploration and selective memory policy gradient (DecESPG) that addresses these issues. DecESPG consists of two additional components built on policy gradient: 1) an exploration bonus component that directs agents to explore novel observations and actions and 2) a selective memory component that records past trajectories to reuse valuable experience and reinforce cooperative behavior. Experimental results verify that the proposed method learns faster and outperforms state-of-the-art MARL algorithms.
A fast diagonal distance metric learning approach for large-scale datasets

Distance metric learning (DML) aims to learn distance metrics that reflect the interactions between features and labels. Due to the high computational complexity, existing DML models are unsuitable for large-scale datasets. This study proposes a DML approach for large-scale problems by reducing the number of variables, utilizing sparse structures of the optimization problems, and taking advantage of large-scale computation platforms. The proposed approach treats DML as a linear space transformation problem and suggests that a full DML matrix can be approximated by a diagonal matrix in many cases. We solve the diagonal DML problem along with its ℓ1 and ℓ2 regularizations via linear and quadratic programming. To facilitate large-scale learning problems, we design a MapReduce framework to build triplets, which are encapsulations of triple data points used for the optimization problem, and develop a weighting mechanism for triplets according to their contributions to the whole distance distortion. Experiments show that the proposed approach is fast in large-scale DML applications with comparable accuracy to much more time-consuming full-matrix models. Since the approach is implemented with the Scala language based on the Spark platform, it can be used directly by productive Java applications, which makes it highly practical for large-scale datasets.
How to Frame Understanding in Mathematics: A Case Study Using Extremal Proofs

Abstract The frame concept from linguistics, cognitive science and artificial intelligence is a theoretical tool to model how explicitly given information is combined with expectations deriving from background knowledge. In this paper, we show how the frame concept can be fruitfully applied to analyze the notion of mathematical understanding. Our analysis additionally integrates insights from the hermeneutic tradition of philosophy as well as Schmid’s ideal genetic model of narrative constitution. We illustrate the practical applicability of our theoretical analysis through a case study on extremal proofs. Based on this case study, we compare our analysis of proof understanding to Avigad’s ability-based analysis of proof understanding.
A novel brightness preserving joint histogram equalization technique for contrast enhancement of brain MR images

Low contrast is a challenging factor in brain magnetic resonance (MR) images due to its structural complexity. Histogram equalization (HE) approach is often used in enhancing the contrast in brain MR images. However, the spatial information is not taken into account in this approach. Further, the problem of preserving structural details while retaining the brightness is also an important concern. To solve these, we suggest a novel stationary wavelet transform based brightness preserving joint histogram equalization (SWT-BPJHE) scheme for brain MR image contrast enhancement. Our contributions are – i) use of SWT to extract the low sub-band wavelength coefficients from the low contrast input image for enhancement, ii) to isolate the high frequency wavelength coefficients from enhancement, retaining the structural details, iii) to preserve brightness. The suggested scheme is experimented with synthetic brain MR images from BrainWeb and clinical images from Howard Whole Atlas databases. The performance is evaluated in terms of several validation indices followed by statistical analysis. The outcomes reveal the superiority of the suggested scheme in comparison to state-of-the-art methods.
Learning Communication for Cooperation in Dynamic Agent-Number Environment

The number of agents in many multiagent systems in the real world, such as storage robots and drone cluster systems, continually changes. Still, most current multiagent reinforcement learning (RL) algorithms are limited to fixed network dimensions, and prior knowledge is used to preset the number of agents in the training phase, which leads to a poor generalization of the algorithm. In addition, these algorithms use centralized training to solve the instability problem of multiagent systems. However, the centralized learning of large-scale multiagent RL algorithms will lead to an explosion of network dimensions, which in turn leads to very limited scalability of centralized learning algorithms. To solve these two difficulties, in this article propose a group centralized training and decentralized execution-unlimited dynamic agent-number network (GCTDE-UDAN). First, since we use the attention mechanism to select several leaders and establish a dynamic number of teams, and the UDAN performs a nonlinear combination of all agents' Q values when performing value decomposition, it is not affected by changes in the number of agents. Moreover, our algorithm can unite any agent to form a group and conduct centralized training within the group, avoiding network dimension explosion caused by the global centralized training of large-scale agents. Finally, we verified on the simulation and experimental platform that the algorithm can learn and perform cooperative behaviors in many dynamic multiagent environments.
Comparison of Common Ground Models for Human--Computer Dialogue

Common ground processes [26] can improve performance in communication tasks [72, 42, 43, 24], and understanding these processes will likely benefit human--computer dialogue interfaces. However, there are multiple proposed theories with different implications for interface design. Fusaroli and Tylén [40] achieved a direct comparison by designing two models: one based on alignment theory and the other based on complementarity theory that encapsulated interpersonal synergy and audience design. The current research used these models, extending them to differentiate between interpersonal synergy and audience design. Few studies have tested multiple common ground models against tasks representative of envisioned human--computer interaction (HCI) applications. We report on four such tests, which allowed examination of generalizability of findings. Results supported the complementarity models over the alignment model, and were suggestive of the audience design variant of complementarity, providing guidance for HCI design that differs from contemporary approaches.
Highly Scalable Distributed Architecture for NoSQL Datastore Supporting Strong Consistency

Introducing a strong consistency model into NoSQL data storages is one of the most interesting issues nowadays. In spite of the CAP theorem, many NoSQL systems try to strengthen the consistency to their limits to better serve the business needs. However, many consistency-related problems that occur in popular data storages are impossible to overcome and enforce rebuilding the whole system from scratch. Additionally, providing scalability to those systems really complicates the matter. In this paper, a novel data storage architecture that supports strong consistency without loosing scalability is proposed. It provides strong consistency according to the following requirements: high scalability, high availability, and high throughput. The proposed solution is based on the Scalable Distributed Two-Layer Data Store which has proven to be a very efficient NoSQL system. The proposed architecture takes into account the concurrent execution of operations and unfinished operations. The theoretical correctness of the architecture as well as experimental evaluation in comparison to traditional mechanisms like locking and versioning is also shown. Comparative analysis with popular NoSQL systems like MongoDB and MemCached is also presented. Obtained results show that the proposed architecture presents a very high performance in comparison to existing NoSQL systems.
A species-based flower pollination algorithm with increased selection pressure in abiotic local pollination and enhanced intensification

Flower Pollination Algorithm (FPA) is a bio-inspired metaheuristic that simulates pollination behavior of flowers. FPA is introduced to solve global optimization problems. Subsequently, it has been applied to a variety of problems. The present study introduces some new extensions and modifications for FPA. In this respect, first, abiotic pollination mechanism of FPA is modified. Secondarily, in order to control convergence speed, a step size function that is used in both global and local pollination along with the randomness factor is adopted. Finally, FPA is extended as a species-based algorithm by partitioning whole population into smaller-sized groups that independently search for promising regions. Performances of the proposed extensions are analyzed by using the well-known unconstrained function optimization problems and Morrison and De Jong’s field of cones function. Finally, non-parametric statistical tests are conducted to demonstrate possible significant improvements over standard FPA. As shown by these statistically verified results, the first FPA modification with the proposed selection mechanism and step size function achieves the best results in global optimization problems while the species-based FPA modification is found as a promising algorithm to solve multi-modal problems of De Jong’s field of cones function.
A Chi-MIC Based Adaptive Multi-Branch Decision Tree

Since the decision trees (DTs) have an advantage over “black-box” models, such as neural nets or support vector machines, in terms of comprehensibility, such that it might merit improvement for further optimization. The node splitting measures and pruning methods are primary among the techniques that can improve the generalization abilities of DTs. Here, we introduced the unequal interval optimization for node splitting, as well as the local chi-square test for tree pruning. This new method was named an adaptive multi-branch decision tree (CMDT). 11 benchmark data sets with different scales were chosen from UCI Machine Learning Repository and coupled with 12 classifiers to evaluate the CMDT algorithm. The results showed that CMDT can be more reliable than the twelve comparative approaches, especially for imbalanced datasets. We also discussed the performance metrics and the weighted decision-making table in unbalanced data sets. The CMDT algorithm can be found here: <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/chenyuan0510/CMDT</uri></u> .
Engaging People with Lived Experience in Co-design of Future Palliative Care Services

(2021). Engaging People with Lived Experience in Co-design of Future Palliative Care Services. International Journal of Public Administration: Vol. 44, Engaging Vulnerable Populations in the Co-Production of Public Services, pp. 778-789.
HySync: Hybrid Federated Learning with Effective Synchronization

Federated learning schedules mobile devices to train data locally and upload updates of model parameters to the cloud for aggregation, typically using synchronous algorithms. The synchronous algorithms have been shown to achieve better prediction accuracy as compared to asynchronous algorithms which are more flexible in scheduling but fluctuate greatly in accuracy with the change of staleness. We present HySync, a hybrid algorithm for federated learning to improve the scheduling efficiency and the stability of accuracy. The evaluation result shows that HySync can update the global model flexibly without waiting for each selected client to finish training work. The training time of HySync is 33% shorter than the synchronous algorithm. At the same time, in the case of highly concurrent updates, HySync can control the staleness to half of that of the asynchronous algorithm, leading to 5% higher accuracy than the asynchronous algorithm and the same accuracy as the synchronous algorithm.
How Do Graph Relabeling Algorithms Improve Memory Locality?

Relabeling algorithms aim to improve the poor memory locality of graph processing by changing the order of vertices. This paper analyses the functionality of three state-of-the-art relabeling algorithms: SlashBurn, GOrder, and Rabbit-Order for real-world graphs.
Blind motion deblurring via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mi>L</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:math> sparse representation

The method of blind deblurring based on machine learning can effectively deal with the blurred images in the real world. However, existing multi-level architectures can lead to problems such as the inability to reserve edges, the expected introduction of artifacts and ghosts when deblurring. Multi methods have found that using L0 norm to realize image sparse representation can help keeping main structure of images. In this paper, an edge extraction module based on L0 sparse representation is proposed to preserve the edge of images, which is embedded in a multi-scale recurrent network(SRN). When the current scale transmits information to the next scale, edge enhancement is performed using the edge extraction module. Furthermore, considering the correlation among pixels and the correlation among channels, we introduce dual-attention mechanism into the encoder-decoder structure. The deblurring experiment was carried out on the GOPRO dataset. Comparing with 5 state-of-art methods qualitatively and quantitatively, the experimental results show that the proposed method can better preserve the image edges and effectively avoid the artifact of the image. And the peak signal-to-noise ratio of the proposed method are improved compared with other methods.
Deep Multi-Scale Features Learning for Distorted Image Quality Assessment

Image quality assessment (IQA) aims to estimate human perception based image visual quality. Although existing deep neural networks (DNNs) have shown significant effectiveness for tackling the IQA problem, it still needs to improve the DNN- based quality assessment models by exploiting efficient multi- scale features. In this paper, motivated by the human visual system (HVS) combining multi-scale features for perception, we propose to use pyramid features learning to build a DNN with hierarchical multi-scale features for distorted image quality prediction. Our model is based on both residual maps and distorted images in luminance domain, where the proposed network contains spatial pyramid pooling and feature pyramid from the network structure. Our proposed network is optimized in a deep end-to-end supervision manner. To validate the effectiveness of the proposed method, extensive experiments are conducted on four widely-used image quality assessment databases, demonstrating the superiority of our algorithm.
A Semi-Automatic Method for Extracting Small Ground Fissures from Loess Areas Using Unmanned Aerial Vehicle Images

Remote sensing-based ground fissure extraction techniques (e.g., image classification, image segmentation, feature extraction) are widely used to monitor geological hazards and large-scale artificial engineering projects such as bridges, dams, highways, and tunnels. However, conventional technologies cannot be applied in loess areas due to their complex terrain, diverse textural information, and diffuse ground target boundaries, leading to the extraction of many false ground fissure targets. To rapidly and accurately acquire ground fissures in the loess areas, this study proposes a data processing scheme to detect loess ground fissure spatial distributions using unmanned aerial vehicle (UAV) images. Firstly, the matched filter (MF) algorithm and the first-order derivative of the Gaussian (FDOG) algorithm were used for image convolution. A new method was then developed to generate the response matrices of the convolution with normalization, instead of the sensitivity correction parameter, which can effectively extract initial ground fissure candidates. Directions, the number of MF/FDOG templates, and the efficiency of the algorithm are comprehensively considerate to conclude the suitable scheme of parameters. The random forest (RF) algorithm was employed for the step of the image classification to create mask files for removing non-ground-fissure features. In the next step, the hit-or-miss transform algorithm and filtering algorithm in mathematical morphology is used to connect discontinuous ground fissures and remove pixel sets with areas much smaller than those of the ground fissures, resulting in a final binary ground fissure image. The experimental results demonstrate that the proposed scheme can adequately address the inability of conventional methods to accurately extract ground fissures due to plentiful edge information and diverse textures, thereby obtaining precise results of small ground fissures from high-resolution images of loess areas.