Reviewerly/exHarmony huggingface dataset:
A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.
A Novel Attention-Based Network for Fast Salient Object Detection

In the current salient object detection network, the most popular method is using U-shape structure. However, the massive number of parameters leads to more consumption of computing and storage resources which are not feasible to deploy on the limited memory device. Some others shallow layer network will not maintain the same accuracy compared with U-shape structure and the deep network structure with more parameters will not converge to a global minimum loss with great speed. To overcome all of these disadvantages, we propose a new deep convolution network architecture with three contributions: (1) using smaller convolution neural networks (CNNs) to compress the model in our improved salient object features compression and reinforcement extraction module (ISFCREM) to reduce parameters of the model. (2) introducing channel attention mechanism to weigh different channels for improving the ability of feature representation. (3) applying a new optimizer to accumulate the long-term gradient information during training to adaptively tune the learning rate. The results demonstrate that the proposed method can compress the model to 1/3 of the original size nearly without losing the accuracy and converging faster and more smoothly on six widely used datasets of salient object detection compared with the others models. Our code is published in https://gitee.com/binzhangbinzhangbin/code-a-novel-attention-based-network-for-fast-salientobject-detection.git
Case Method in the Study of Information Technologies and IT Project Management

The paper shows the features of the application of the case method in the study of the discipline of IT project management for students majoring in the field of Information Technology. This work analyzes some of the difficulties associated with the limited use of case studies in the field of IT. An original solution to the problem of combining flexible technologies for the development of the project itself on the basis of a case study and methods of studying and applying special software for IT project management presented by authors. The conceptual scheme of the main stages of work with a case, interrelations and content from an initial acquaintance, discussion in groups and acceptance of intermediate decisions, to planning of operations, calendar planning of works and resources were shown. However, the role of special software for visualization of separate stages of project execution management in the MS Project environment at all stages of the life cycle was explained. The combination of individual analytical work of the students, acquisition of teem job skills, the study of MS Project tools, IT project management methods at different stages allows to achieve the planned learning outcomes and develop interdisciplinary competencies.
Urban street scene analysis using lightweight multi-level multi-path feature aggregation network

Urban street scene analysis is an important problem in computer vision with many off-line models achieving outstanding semantic segmentation results. However, it is an ongoing challenge for the research community to develop and optimize the deep neural architecture with real-time low computing requirements whilst maintaining good performance. Balancing between model complexity and performance has been a major hurdle with many models dropping too much accuracy for a slight reduction in model size and unable to handle high-resolution input images. The study aims to address this issue with a novel model, named M2FANet, that provides a much better balance between model’s efficiency and accuracy for scene segmentation than other alternatives. The proposed optimised backbone helps to increase model’s efficiency whereas, suggested Multi-level Multi-path (M2) feature aggregation approach enhances model’s performance in the real-time environment. By exploiting multi-feature scaling technique, M2FANet produces state-of-the-art results in resource-constrained situations by handling full input resolution. On the Cityscapes benchmark data set, the proposed model produces 68.5% and 68.3% class accuracy on validation and test sets respectively, whilst having only 1.3 million parameters. Compared with all real-time models of less than 5 million parameters, the proposed model is the most competitive in both performance and real-time capability.
Intelligent Path Planning Approach for Autonomous Mobile Robot

This paper presents a new path planning algorithm for an autonomous mobile robot. It is desired that the robot reaches its goal in a known or partially known environment (e.g., a warehouse or an urban environment) and avoids collisions with walls and other obstacles. To this end, a new, efficient, simple, and flexible path finder strategy for the robot is proposed in this paper. With the proposed strategy, the optimal path from the robot’s current position to the goal position is guaranteed. The environment is represented as a grid-based map, which is then divided into a predefined number of subfields to reduce the number of required computations. This leads to a reduction in the load on the controller and allows a real-time response. To evaluate the flexibility and efficiency of the proposed strategy, several tests were simulated with environments of different sizes and obstacle distributions. The experimental results demonstrate the reliability and efficiency of the proposed algorithm.
Refining Weakly-Supervised Free Space Estimation Through Data Augmentation and Recursive Training

Free space estimation is an important problem for autonomous robot navigation. Traditional camera-based approaches rely on pixel-wise ground truth annotations to train a segmentation model. To cover the wide variety of environments and lighting conditions encountered on roads, training supervised models requires large datasets. This makes the annotation cost prohibitively high. In this work, we propose a novel approach for obtaining free space estimates from images taken with a single road-facing camera. We rely on a technique that generates weak free space labels without any supervision, which are then used as ground truth to train a segmentation model for free space estimation. We study the impact of different data augmentation techniques on the performances of free space predictions, and propose to use a recursive training strategy. Our results are benchmarked using the Cityscapes dataset and improve over comparable published work across all evaluation metrics. Our best model reaches $$83.64\%$$ IoU ( $$+2.3\%$$ ), $$91.75\%$$ Precision ( $$+2.4\%$$ ) and $$91.29\%$$ Recall ( $$+0.4\%$$ ). These results correspond to $$88.8\%$$ of the IoU, $$94.3\%$$ of the Precision and $$93.1\%$$ of the Recall obtained by an equivalent fully-supervised baseline, while using no ground truth annotation. Our code and models are freely available online.
Factors influencing students’ satisfaction and its impact on their loyalty for the University: Case Study at The University of Jordan-Aqaba Branch

This study aimed to measure the impact of several antecedent factors on student’s satisfaction (i.e., academic aspects, non-academic aspects, program issues, reputation, access, quality of university facility, university location and social network services) and the mediator factor of students’ satisfactions' impact on student’s loyalty on the university of Jordan-Aqaba Brunch. Measurement tool was developed to examine the relationship between the study variables. The sample of 379 was used from the university of Jordan-Aqaba brunch students. Results indicated that academic aspects, non-academic aspects, reputation, university location and social network services impacted students' satisfaction directly. In the other hand, program issues, access and quality of university facility did not impact students' satisfaction. However, there was a positive impact of students' satisfaction on students' loyalty.
Closed frequent itemsets mining based on It-Tree

In the last decade, the amount of collected data, in various computer science applications, has grown considerably. These large volumes of data need to be analysed in order to extract useful hidden knowledge. This work focuses on association rule extraction. This technique is one of the most popular in data mining. Nevertheless, the number of extracted association rules is often very high, and many of them are redundant. In this paper, we propose an algorithm, for mining closed itemsets, with the construction of an it-tree. This algorithm is compared with the DCI (direct counting &amp; intersect) algorithm based on min support and computing time. CHARM is not memery-efficient. It needs to store all closed itemsets in the memory. The lower min-sup is, the more frequent closed itemsets there are so that the amounts of memory used by CHARM are increasing.
ONCE UPON A TIME THERE LIVED BORIS VASILYEVICH

Dear readers! Here is a text that is unusual in its genre. This is a dialogue with my imaginary interlocutor, the cat Kuzya. From time to time I post these texts on Facebook. But it's one thing to publish on the net and quite another to publish in a scientific journal. I don’t know how my colleagues will perceive this genre. The cat Kuzya and I continue our conversation about our friends. This time we have chosen for discussion the person of Doctor of Philosophy, Professor Markov Boris Vasilievich. Boris Vasilievich is a well-known person in Russian philosophy and does not need an introduction. In addition, he is close to us in his life attitudes. So we decided to «hook» him, as they say, for a living. However, we do not act on the principle of «beat your own so that others are afraid». We treat everyone with care and try not to cross the border of personal space. Therefore, we do not strive to «distort » our counterpart on personal grounds. In addition, Boris Vasilyevich turned out to be a painfully difficult person. In a word, «tough nut to crack». But why surprise, there are practically no others among our friends.
An Unsupervised Dual Attention Method for 3D Medical Image Registration

Medical image registration has always been a research hotspot in medical image processing. Compared with other fields (such as image segmentation), the training parameter space is very large and often lacks expert supervised correspondence annotations. These two challenges lead to slow image registration progress. We present a novel unsupervised medical image registration model, which incorporates channel and spatial weight module and spatial structure attention module. Firstly, we weigh the channels and spaces in the convolutional network process, which is an improvement to the Convolution structure, it can output the attention map in turn from the input feature map along the two independent dimensions of the channel and space, and then multiply the attention map by the input feature map for adaptive feature refinement. Then, we focus on the 3D characteristics of medical images, the proposed spatial structure attention can aggregate the spatial information of medical images to avoid the loss of important information during the convolution process, which is more conducive to retaining the content information of the input images. In addition, we use a learnable activation function in the network to improve the experimental results without adding more parameters. Combine the above parts with a convolution neural network to form our network. Finally, on LPBA40 and OASIS datasets, the method proposed by this work outperformed most work.
Introduction to Recommender Systems

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Zhuang Characteristic Culture Detection Based on Improved YOLOV3-SPP Network

In order to enhance the dissemination power of Zhuang culture and enhance the influence of traditional Chinese culture, this article proposes a data set containing the characteristic cultural products of the Zhuang nationality for the unique cultural development of the Zhuang nationality. The data set has a total of 1890 images and contains three categories, namely Zhuang Five-color glutinous rice, Zhuang Bronze drum, and Zhuang Hydrangea. At the same time, the YOLOV3-SPP network based on the darknet53 model is improved, and the ResNet101 structure is introduced to improve the robustness of the network. The verification results on the Zhuang characteristic data set show that the improved YOLOV3-SPP network has an accuracy rate of 92.78% on the Zhuang characteristic data set, which is 1.28% higher than the original YOLOV3-SPP network. The experimental results show that the improved YOLOV3-SPP structure can better realize the detection of the characteristic culture of the Zhuang nationality.
Parallelization of Sequential Pattern Sampling

In the last years, the field of data mining has undergone extensive work on patterns discovery by sampling techniques. Recently, these sampling methods have been applied to sequential data that are complex in nature. The complexity of these data lies in their structure, which has a notable impact on the speed of the computation which is time consuming with huge database. In this paper, we show how to use the BSP (Bulk Synchronous Parallel) programming model to improve the efficiency of sequential pattern sampling methods. Indeed, we propose a parallel algorithm that operates on sequential databases that are knowingly distributed in order to accelerate the computation time. The analyses show the positive impact of the framework on the execution time of the method.
The continua of sound qualities for Tanya Tagaq's katajjaq sounds

Tanya Tagaq is an experimental vocalist originally from Cambridge Bay, Nunavut who regularly employs Inuit katajjaq (throat singing), as well as screams, moans, cries, and whispers. Tagaq characterizes her katajjaq sounds along two dimensions: 1) whether it is produced through inhalation or exhalation, and 2) whether it is a low, deep growl or a high, pure pitch. Her characterizations focus on sound qualities, indicating an importance of timbre in her music. This chapter suggests that the measurement of acoustic properties—specifically, RMS energy (loudness), estimated f0 (pitch), periodicity (noisiness), and spectral centroid (brightness)—of Tanya Tagaq’s vocal sounds are best situated on a continua of relative values for various acoustic properties, rather than a timbral opposition chart. These continua provide a nuanced way of describing the subtle, but important, differences between Tagaq’s sounds and allow for an in-depth comparison of the sounds. Although this chapter focuses on the music of Tanya Tagaq, this method of analyzing sound qualities may also be useful for other music where changes in sound qualities are elemental.
NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation

The complementary item recommender system (CIRS) recommends the complementary items for a given query item. Existing CIRS models consider the item co-purchase signal as a proxy of the complementary relationship, due to the lack of human-curated labels from the huge transaction records. These methods represent items in a complementary embedding space and model the complementary relationship as a point estimation of the similarity between items vectors. However, co-purchased items are not necessarily complementary to each other. For example, customers may frequently purchase bananas and bottle water within the same transaction, but these two items are not complementary. Hence, using co-purchase signals directly as labels will aggravate the model performance. On the other hand, model evaluation will not be trustworthy if the labels for evaluation are not reflecting the true complementary relatedness. To address the above challenges from noisy labeling of the co-purchase data, we model the co-purchases of two items as a Gaussian distribution, where the mean denotes the co-purchases from the complementary relatedness, and covariance denotes the co-purchases from the noise. To do so, we represent each item as a Gaussian embedding and parameterize the Gaussian distribution of co-purchases by the means and covariances from item Gaussian embedding. To reduce the impact of the noisy labels during evaluation, we propose an independence test-based method to generate a trustworthy label set with certain confidence. Our extensive experiments on both the publicly available dataset and the large-scale real-world dataset justify the effectiveness of our proposed model in complementary item recommendations compared with the state-of-the-art models.
Grafting Heterogeneous Neural Networks for a Hierarchical Object Classification

Convolutional neural networks (CNNs) are deep learning architectures used for image classification that have been improved in recent years to increase their accuracies and reduce their computation times. Hierarchical approaches are based on a step-by-step strategy and aim to optimize performance on difficult tasks by solving successive subtasks. The gain provided by these solutions must be relativized with the explosion in the number of parameters they imply, which makes their implementation on embedded systems difficult. New constraints also appear in the choice of the architectures of the branches when one seeks to have a global network providing predictions at different levels. We propose a strategy that allows the merging of heterogeneous CNNs by following a hierarchical approach in which the information extracted by first-level networks can be fed back at any location into second-level networks. Despite the differences in the number and size of the feature maps, such grafting can be done by using clustering, dimension reduction, and interpolation techniques. This strategy eliminates the computational redundancy induced by the recalculation of low-level features. The proposed grafting approach significantly reduces the inference time of the second network without impacting the accuracy. Tests performed on MNIST, CIFAR-10, and PlantVillage datasets with several CNNs illustrate the possibility of implementation in various situations. Our solution allows us to consider in an innovative way the implementation of hierarchical solutions on devices with limited capacities.
Requirements engineering and enterprise architecture-based software discovery and reuse

Organizations’ business processes need to be adapted in response to changing internal and external environments, which are becoming increasingly complex. We target in this research work the exploitation of a software capability profile based on requirements engineering and enterprise architecture to respond to stakeholder requirements and efficiently reuse existing technical solutions. We propose in this work an exploitation methodology based on the alignment of enterprise architecture actions with a requirement engineering process. These latter evolve together helping to investigate the highest compatibility of the desired functionalities and their related constraints. Our contribution aims to produce a ready-to-use application based on the defined requirements and the selected software capability profiles for accelerating business application development. Implementation and a case study are proposed to demonstrate the effectiveness of this approach.
Independent assessment of the quality of conditions for implementing educational activities in the region: statistical analysis of the results

Relevance. Currently, the most popular public administration mechanism in the field of education is the assessment of the quality of educational services rendered, an important component of which is an independent assessment of the quality of conditions for implementing educational activities. The use of the ECSI methodology based on structural equation modeling (SEM) can make an important contribution to the development of mechanisms for the independent assessment of the quality of education (IAQE). The research purpose is to study the possibilities of using the method of statistical data analysis for an independent assessment of the quality of conditions for implementing educational activities in the process of clarifying and confirming the results of monitoring studies in education. Materials and methods. Structural equation modeling was used to study data obtained in 1,143 educational institutions of the Sverdlovsk region (Russian Federation) located in 30 municipal districts, 25 cities, 4 closed administrative-territorial entities. The general population of respondents selected for the IAQE was 350,950 people, including parents (legal representatives) – 298,485 people (85%) and students aged over 14 – 52,465 people (15%). Research results. An original methodology for statistical analysis of data from an independent assessment of the quality of education has been developed. The method of structural equation modeling revealed statistically significant links between indicators with integral criteria of structural SEM-models of the IAQE. In particular, the structural SEM-model of the IAQE has a single negative value of the regression coefficient r=-1.32 for the indicator "Availability of information about the organization's teachers on its official website", and the structural SEM-model of the IAQE criterion "Comfort of the conditions in which educational activities are carried out" found the smallest value of the regression coefficient r=0.36 for the indicator "Availability of conditions for organizing the training and education of students with disabilities and disabled people". These results of the statistical analysis of the IAQE allow identifying directions for improving educational and management practices in the region. Conclusion. The developed original methodology can be used to clarify and confirm the results of monitoring studies in the field of independent assessment of the quality of conditions for implementing educational activities in the regions, especially in cases of any questions about the results of the studies conducted and obtaining controversial results.