Reviewerly/exHarmony huggingface dataset:
Validation of Play Fairway Analysis of the geothermal potential of Camas Prairie, south-central Idaho, by an exploration well

Abstract Play Fairway Analysis (PFA) methodology was adapted for geothermal exploration at Camas Prairie, Idaho. Geophysical data, structural and geologic mapping, volcanic rock ages and vent locations, and the distribution of thermal springs and wells all indicated a relatively high geothermal potential along the southern margin of the Prairie. An exploration well (USU Camas-1) was drilled to a depth of 618.3 m to validate the PFA. A permeable zone was encountered at ~ 357.5 m with a maximum measured temperature of ~ 80 °C, which was suppressed following the injection of cold water. A moderate transmissivity of ~ 0.25–1 cm 2 /s estimated from an injection test as well a seasonal artesian flow at ~ 0.7 L/s corroborate the presence of a permeable zone. The existence of a lacustrine clay seal was confirmed near the bottom of the basin-fill sediment occupying the upper 314 m of the well. Geothermometers suggest the USU Camas-1 well water equilibrated at a reservoir temperature of ~ 120 °C. Based on the locations of both thermal and cold wells, geothermal fluids appear to be flowing upward along one or both of two fault systems. The presence of young basalts and elevated helium isotope ratios suggest that the heat source of Camas Prairie is magmatic. However, the faults may be acting as a conduit for geothermal fluids to rise from great depth without a shallow magmatic source being present. Camas Prairie is a promising area for geothermal development, but the relatively low reservoir temperatures indicate this resource may not be suitable for electric generation. Perhaps the best use would be for heating.
Reversible Data Hiding with Neighboring-Prediction-Errors Aided Sorting and CNN Prediction

In the area of reversible data hiding (RDH), multiple-histograms modification (MHM) has been widely recognized as one of the most high-performance techniques. With MHM, the correlation between the prediction-error (PE) and the local complexity (LC) can be well exploited for pixel sorting based data embedding, which is very important in MHM-based RDH algorithms for the obtained high image quality and the well-preserved embedding capacity. However, since PE and LC are usually obtained using different algorithms, their correlation may not be strong enough. In this paper, a novel correlation measurement is proposed by exploring the relationship of neighboring PEs to improve the performance of pixel sorting. In the proposed work, we first divide a cover image into non-overlapping cells of two pixels, and segment the pixel cells into two layers for layer-wise data embedding. Then, in the process of prediction, pixels in one layer are estimated from pixels in the other layer using a convolutional neural network (CNN), the loss function of which is designed to minimize not only the PE itself, but also the difference of PEs within the same pixel cell. Finally, both the difference of PEs and the LC are employed for pixel sorting to minimize the embedding distortion introduced by MHM. Extensive experiments demonstrate the effectiveness of the proposed method.
Novel Distributed Architecture for Frequent Pattern Mining using Spark Framework

The Apriori algorithm is one of the association rule mining algorithms which is commonly used in retail data analysis to find patterns in itemsets. However, as the amount of data generated by retailers continues to increase, traditional approaches may no longer be sufficient. To address this challenge, In this paper, we have proposed a distributed architecture implementation of the Apriori algorithm in the Hadoop ecosystem using Spark. The approach involves three phases: categorising and partitioning customer data based on seasons, clustering customers based on behaviour and preferences, and applying the Apriori algorithm to obtain frequent itemset patterns and association rules. By implementing this approach in a distributed architecture, we are able to efficiently analyse large datasets and make accurate predictions about customer needs and preferences, which has important implications for retail strategy and we anticipate that this approach will be particularly useful in the context of retail data analysis where large amounts of data must be processed quickly and accurately.
Segmentation quality assessment by automated detection of erroneous surface regions in medical images

Despite the advancement in deep learning-based semantic segmentation methods, which have achieved accuracy levels of field experts in many computer vision applications, the same general approaches may frequently fail in 3D medical image segmentation due to complex tissue structures, noisy acquisition, disease-related pathologies, as well as the lack of sufficiently large datasets with associated annotations. For expeditious diagnosis and quantitative image analysis in large-scale clinical trials, there is a compelling need to predict segmentation quality without ground truth. In this paper, we propose a deep learning framework to locate erroneous regions on the boundary surfaces of segmented objects for quality control and assessment of segmentation. A Convolutional Neural Network (CNN) is explored to learn the boundary related image features of multi-objects that can be used to identify location-specific inaccurate segmentation. The predicted error locations can facilitate efficient user interaction for interactive image segmentation (IIS). We evaluated the proposed method on two data sets: Osteoarthritis Initiative (OAI) 3D knee MRI and 3D calf muscle MRI. The average sensitivity scores of 0.95 and 0.92, and the average positive predictive values of 0.78 and 0.91 were achieved, respectively, for erroneous surface region detection of knee cartilage segmentation and calf muscle segmentation. Our experiment demonstrated promising performance of the proposed method for segmentation quality assessment by automated detection of erroneous surface regions in medical images.
New multi-UAV formation keeping method based on improved artificial potential field

Formation keeping is important for multiple Unmanned Aerial Vehicles (multi-UAV) to fully play their roles in cooperative combats and improve their mission success rate. However, in practical applications, it is difficult to achieve formation keeping precisely and obstacle avoidance autonomously at the same time. This paper proposes a joint control method based on robust H∞ controller and improved Artificial Potential Field (APF) method. Firstly, we build a formation flight model based on the “Leader-Follower” structure and design a robust H∞ controller with three channels X, Y and Z to eliminate dynamic uncertainties, so as to realize high-precision formation keeping. Secondly, to fulfill obstacle avoidance efficiently in complex situations where UAVs fly at high speed with high inertia, this paper comes up with the improved APF method with deformation factor considered. The judgment criterion is proposed and applied to ensure flight safety. In the end, the simulation results show that the designed controller is effective with the formation keeping a high accuracy and in the meantime, it enables UAVs to avoid obstacles autonomously and recover the formation rapidly when coming close to obstacles. Therefore, the method proposed here boasts good engineering application prospect.
Digital Technologies as a Means of Improving the Efficiency of Higher Education

Today, the practical application of digitalisation in higher education and finding ways to optimize the use of digital technologies to improve the management of the educational process is a relevant issue for the modern research paradigm. This research aims to study the unique ways in which digital technologies are utilised in higher education and create a conceptual framework for their adoption to enhance the efficiency of the educational process. The study applied the method of expert assessments using a semi-structured questionnaire, with two groups of respondents acting as experts. The questionnaire responses were evaluated using the Likert scale, and the Student’s t-test was used for statistical analysis of the research results. Both students and teachers recognise blended learning as the most effective method for organising the educational process since it combines traditional distance learning with the implementation of digital educational technologies. The respondents recognised the communicational aspect to be the most crucial advantage of digitalisation. The study findings are summarised into a conceptual model of digitalisation in higher education, comprising interconnected semantic blocks of generation, accumulation, and management of educational information.
Exploring Multilingual Intent Dynamics and Applications

Multilingual Intent Detection and explore its different characteristics are major field of study for last few years. But, detection of intention dynamics from text or voice, especially in the Indian multilingual contexts, is a challenging task. So, my first research question is on intent detection and then I work on the application in Indian Multilingual Healthcare scenario. Speech dialogue systems are designed by a pre-defined set of intents to perform user specified tasks. Newer intentions may surface over time that call for retraining. However, the newer intents may not be explicitly announced and need to be inferred dynamically. Hence, here are two crucial jobs: (a) recognizing newly emergent intents; and (b) annotating the data of the new intents in order to effectively retrain the underlying classifier. The tasks become specially challenging when a large number of new intents emerge simultaneously and there is a limited budget of manual annotation. We develop MNID (Multiple Novel Intent Detection), a cluster based framework that can identify multiple novel intents while optimized human annotation cost. Empirical findings on numerous benchmark datasets (of varying sizes) show that MNID surpasses the baseline approaches in terms of accuracy and F1-score by wisely allocating the budget for annotation. We apply intent detection approach on different domains in Indian multilingual scenarios - healthcare, finance etc. The creation of advanced NLU healthcare systems is threatened by the lack of data and technology constraints for resource-poor languages in developing nations like India. We evaluate the current state of several cutting-edge language models used in the healthcare with the goal of detecting query intents and corresponding entities. We conduct comprehensive trials on a number of models different realistic contexts, and we investigate the practical relevance depending on budget and the availability of data on English.
Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training

Actor-critic deep reinforcement learning (DRL) algorithms have recently achieved prominent success in tackling various challenging reinforcement learning (RL) problems, particularly complex control tasks with high-dimensional continuous state and action spaces. Nevertheless, existing research showed that actor-critic DRL algorithms often failed to explore their learning environments effectively, resulting in limited learning stability and performance. To address this limitation, several ensemble DRL algorithms have been proposed lately to boost exploration and stabilize the learning process. However, most of existing ensemble algorithms do not explicitly train all base learners towards jointly optimizing the performance of the ensemble. In this paper, we propose a new technique to train an ensemble of base learners based on an innovative multi-step integration method. This training technique enables us to develop a new hierarchical learning algorithm for ensemble DRL that effectively promotes inter-learner collaboration through stable inter-learner parameter sharing. The design of our new algorithm is verified theoretically. The algorithm is also shown empirically to outperform several state-of-the-art DRL algorithms on multiple benchmark RL problems.
Understanding the Generalization Ability of Deep Learning Algorithms: A Kernelized Rényi's Entropy Perspective

Recently, information-theoretic analysis has become a popular framework for understanding the generalization behavior of deep neural networks. It allows a direct analysis for stochastic gradient / Langevin descent (SGD/SGLD) learning algorithms without strong assumptions such as Lipschitz or convexity conditions. However, the current generalization error bounds within this framework are still far from optimal, while substantial improvements on these bounds are quite challenging due to the intractability of high-dimensional information quantities. To address this issue, we first propose a novel information theoretical measure: kernelized Rényi's entropy, by utilizing operator representation in Hilbert space. It inherits the properties of Shannon's entropy and can be effectively calculated via simple random sampling, while remaining independent of the input dimension. We then establish the generalization error bounds for SGD/SGLD under kernelized Rényi's entropy, where the mutual information quantities can be directly calculated, enabling evaluation of the tightness of each intermediate step. We show that our information-theoretical bounds depend on the statistics of the stochastic gradients evaluated along with the iterates, and are rigorously tighter than the current state-of-the-art (SOTA) results. The theoretical findings are also supported by large-scale empirical studies.
Deep Partial Multi-Label Learning with Graph Disambiguation

In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate accurate confidence scores from candidate labels, have been prevalent to deal with PML problems. However, we observe that existing graph-based PML methods typically adopt linear multi-label classifiers and thus fail to achieve superior performance. In this work, we attempt to remove several obstacles for extending them to deep models and propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN). Specifically, we introduce the instance-level and label-level similarities to recover label confidences as well as exploit label dependencies. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels; then, we train the deep model to fit the numerical labels. Moreover, we provide a careful analysis of the risk functions to guarantee the robustness of the proposed model. Extensive experiments on various synthetic datasets and three real-world PML datasets demonstrate that PLAIN achieves significantly superior results to state-of-the-art methods.
BEA-Net: Body and Edge Aware Network With Multi-Scale Short-Term Concatenation for Medical Image Segmentation

Medical image segmentation is indispensable for diagnosis and prognosis of many diseases. To improve the segmentation performance, this study proposes a new 2D body and edge aware network with multi-scale short-term concatenation for medical image segmentation. Multi-scale short-term concatenation modules which concatenate successive convolution layers with different receptive fields, are proposed for capturing multi-scale representations with fewer parameters. Body generation modules with feature adjustment based on weight map computing via enlarging the receptive fields, and edge generation modules with multi-scale convolutions using Sobel kernels for edge detection, are proposed to separately learn body and edge features from convolutional features in decoders, making the proposed network be body and edge aware. Based on the body and edge modules, we design parallel body and edge decoders whose outputs are fused to achieve the final segmentation. Besides, deep supervision from the body and edge decoders is applied to ensure the effectiveness of the generated body and edge features and further improve the final segmentation. The proposed method is trained and evaluated on six public medical image segmentation datasets to show its effectiveness and generality. Experimental results show that the proposed method achieves better average Dice similarity coefficient and 95% Hausdorff distance than several benchmarks on all used datasets. Ablation studies validate the effectiveness of the proposed multi-scale representation learning modules, body and edge generation modules and deep supervision.
BN-DRISHTI: Bangla Document Recognition Through Instance-Level Segmentation of Handwritten Text Images

Handwriting recognition remains challenging for some of the most spoken languages, like Bangla, due to the complexity of line and word segmentation brought by the curvilinear nature of writing and lack of quality datasets. This paper solves the segmentation problem by introducing a state-of-the-art method (BN-DRISHTI (Code and Demo: https://github.com/crusnic-corp/BN-DRISHTI )) that combines a deep learning-based object detection framework (YOLO) with Hough and Affine transformation for skew correction. However, training deep learning models requires a massive amount of data. Thus, we also present an extended version of the BN-HTRd dataset comprising 786 full-page handwritten Bangla document images, line and word-level annotation for segmentation, and corresponding ground truths for word recognition. Evaluation on the test portion of our dataset resulted in an F-score of $$99.97\%$$ for line and $$98\%$$ for word segmentation. For comparative analysis, we used three external Bangla handwritten datasets, namely BanglaWriting, WBSUBNdb_text, and ICDAR 2013, where our system outperformed by a significant margin, further justifying the performance of our approach on completely unseen samples
Localization of image fragments with high frequency intensity oscillation

&lt;p&gt;The problem of detecting image fragments characterized by high-frequency fluctuations in spatial intensity in the general case has not been previously considered in the literature. The article researches a sequence of known and new algorithms that allows detection and localization of such fragments. The geometric localization of the fragments is based on the Hough transform of the pixel array of the external contours of the connected components. The components connecting becomes possible due to the use of the oscillation function proposed by the authors. The oscillation function turns out to be an effective tool for highlighting intensity fluctuations zones in an image and is superior in reliability to alternative methods for detecting such zones, based, for example, on gradient methods. The article demonstrates examples of localization of the image fragments with different levels of background complexity.&lt;/p&gt;
Hotel room access control: an NFC approach ecotourism framework

Purpose This study aims to highlight that security and flexibilities remain the main points of contention in the cordiality business. This research points to planning a framework that empowers hotel users to get to the room using a mobile access key. Advancing secured facilities, mobile phone “Near Field Communication” (NFC) innovation as the entrance device by carrying out an application containing an imitated mobile key for explicit verification access is used. Design/methodology/approach The proposed system is evaluated by triangulation of experimental, numerical and rational evaluation using partial least square structural equation modeling (PLS-SEM) with Malaysian hotel guests and employees. Findings The discoveries with the hypothesis supported validated that the suggested solution can eliminate physical cards, boost protection and encourage a contactless ecosystem. Theoretical, management and societal contributions are discussed here. Research limitations/implications This experiment comes with the constraints that it was conducted in only two hotels and does not fully reflect the choices of a wider range of travellers. Secondly, the cost of existing NFC smart locks is still relatively high, and along with the development of technology, the price will decrease when supply exceeds demand. Practical implications To promote high-security attributes, NFC technology as the access system by implementing an application containing an emulated smart key for specific authentication access is used. The host-card emulation enables cost-effectiveness profit and initiating a defence system in the pandemic era. Social implications To promote high-security attributes, NFC technology is used as the access system by implementing an application containing an emulated smart key for specific authentication access. The host-card emulation enables cost-effectiveness profit and initiating a defence system in the pandemic era. Originality/value The novelty of this study comes from the use of commonly available smartphone NFC features that are yet to be applied in the tourism ecosystem. The research provokes the applied concept of mobile smartkeys.
Applying User Experience and Usercentered Design Software Processes in Undergraduate Mobile Application Development Teaching

Agile methods in undergraduate courses have been explored by various authors looking to close the gap between industry and professional profiles. We have structured an Android application development course based on a tailored agile process for development of educational software tools. This process is based on both Scrum and Extreme Programming in combination with User Experience (UX) and User-Centered Design (UCD) approaches. The course is executed in two phases: the first half of the course's semester presents theory on agile and mobile applications development, the latter half is managed as a workshop where students develop for an actual client. The introduction of UX and UCD exploiting the close relationship with stakeholders expected from an agile process can enhance Quality in Use features. Since 2019 two of the projects have been extended in agreement between the client and students. Students, clients and users have found value in the generated products.
Particle Swarm Optimization with Average Individuals Distance‐Incorporated Exploitation

Particle swarm optimization (PSO) is a popular optimization technique known for its simplicity and effectiveness. This paper introduces a variant that achieves a better balance between exploration and exploitation, named DiPSO. DiPSO incorporates a novel strategy based on trends in mean distance between individuals for local exploitation control. Experiments on 29 benchmark functions demonstrate that DiPSO consistently outperforms the state‐of‐the‐art variant of PSO. Convergence analysis reveals that DiPSO achieves faster convergence and superior solutions. These results highlight the effectiveness of DiPSO in solving optimization problems. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.