Reviewerly/exHarmony huggingface dataset:
Characterisation of Massive Sulphide Deposits in the Iberian Pyrite Belt Based on the Integration of Digital Outcrops and Multi-Scale, Multi-Source Hyperspectral Data

Geological mapping in difficult-to-access terrain such as open pit mines often relies on remotely sensed data. Hyperspectral data yield valuable geological information, especially when spectral ranges of multiple sensors are used in conjunction. In this contribution we project a number of hyperspectral datasets of an open pit mine covering the visible to near-infrared (VNIR), short-wave infrared (SWIR), and long-wave infrared (LWIR) range from airborne, drone-borne and ground-based acquisitions into a photogrammetric point cloud. The resulting hyperspectral digital outcrop is then used as a basis for data integration in a 3D environment. To discriminate geologic materials in the pit we apply a Gaussian deconvolution to identify the position of diagnostic absorption features in the SWIR and LWIR, and then apply a support vector machine-based classification. Our results agree with known lithologic units and alteration patterns and can be used to guide exploration targeting and mine planning.
Architectural conformance checking for KDM-represented systems

Architecture-Driven Modernization (ADM) is a model-driven reengineering where systems are represented as instances of Knowledge Discovery Metamodel (KDM). KDM is the standard for representing systems in ADM context due to its power for capturing an extensive set of information about software systems. Besides, it is language and platform-independent, so every technique that is able of processing it also present this advantage. A recurrent activity in modernization projects is checking the conformance between the Current Architecture (CA) against the Planned Architecture (PA) in order to identify architectural drifts. The canonical phases of this activity are: (i) specification of the PA with its communication constraints; (ii) ex-traction of the CA, including the relationships among the architectural abstractions; and (iii) comparison between both architectures to identify the drifts. To the best of our knowledge, there is no ACC approach that addresses ACC in ADM context, considering KDM-represented systems. Therefore, we presents an ACC approach to be used in ADM context. We show how KDM can be used in ACC processes for representing the system to be modernized, the PA and the CA. We evaluated Arch-KDM using a small (LabSys-7KLoc) and a medium-size system (FreeMind-84KLoc) and the accuracy of the identification was acceptable.
S2P2: Self-Supervised Goal-Directed Path Planning Using RGB-D Data for Robotic Wheelchairs

Path planning is a fundamental capability for autonomous navigation of robotic wheelchairs. With the impressive development of deep-learning technologies, imitation learning-based path planning approaches have achieved effective results in recent years. However, the disadvantages of these approaches are twofold: 1) they may need extensive time and labor to record expert demonstrations as training data; and 2) existing approaches could only receive high-level commands, such as turning left/right. These commands could be less sufficient for the navigation of mobile robots (e.g., robotic wheelchairs), which usually require exact poses of goals. We contribute a solution to this problem by proposing S2P2, a self-supervised goal-directed path planning approach. Specifically, we develop a pipeline to automatically generate planned path labels given as input RGB-D images and poses of goals. Then, we present a best-fit regression plane loss to train our data-driven path planning model based on the generated labels. Our S2P2 does not need pre-built maps, but it can be integrated into existing map-based navigation systems through our framework. Experimental results show that our S2P2 outperforms traditional path planning algorithms, and increases the robustness of existing map-based navigation systems. Our project page is available at https://sites.google.com/view/s2p2.
VideoDiscovery

We demonstrate an end-to-end intelligent system of short-video generation for live-streaming, namely "VideoDiscovery'', which aims to automatically produce batches of high-value short-videos by discovering and organizing highlight content for commodity delivery. Traditionally, production of high-value short-videos for live-streaming is cost-expensive and time-consuming, which also demands experienced editing skills. To this end, we construct this system with three modules: 1)Semantic segment structuring first decodes live-streaming into a series of semantic candidates including commodity, Q&A, action, multi-modal, etc. 2)Hierarchical search engine performs automatically searches for semantically matching candidate shots from scripts. 3)Script-aware shot assembly is formulated combination problem over a graph of shots, considering temporal constraints and candidate idioms. Specifically, given an input live-streaming, the recommended video results illustrate diverse visual-semantic content, and follow script guidelines. Currently, our system has been launched online for Taobao stores, which enables to generate appealing videos in minutes for advertising and recommendation. The entry of our system is available at https://discovery.aliyun.com/index.
Research and Implementation of the Text Matching Algorithm in the Field of Housing Law and Policy Based on Deep Learning

Machine learning enables machines to learn rules from a large amount of data input from the outside world through algorithms, so as to identify and judge. It is the main task of the government to further emphasize the importance of improving the housing security mechanism, expand the proportion of affordable housing, increase financial investment, improve the construction quality of affordable housing, and ensure fair distribution. It can be seen that the legal system of housing security is essentially a system to solve the social problems brought by housing marketization, and it is an important part of the whole national housing system. More and more attention has been paid to solving the housing difficulties of low- and middle-income people and establishing a housing security legal system suitable for China’s national conditions and development stage. Aiming at the deep learning problem, a text matching algorithm suitable for the field of housing law and policy is proposed. Classifier based on matching algorithm is a promising classification technology. The research on the legal system of housing security is in the exploratory stage, involving various theoretical and practical research studies. Compare the improved depth learning algorithm with the general algorithm, so as to clearly understand the advantages and disadvantages of the improved depth learning algorithm and depth learning algorithm. This paper introduces the practical application of the deep learning model and fast learning algorithm in detail. Creatively put forward to transform it into an independent public law basis or into an independent savings system.
Siammraan: Siamese Multi-Level Residual Attention Adaptive Network for Hyperspectral Videos Tracking

The deep learning based techniques have been widely applied to object tracking in color videos. When these techniques are applied to hyperspectral videos, how to fully explore unique spectral signatures of tracking objects is of crucial importance as well as simultaneously utilizing spatial and temporal information. Different with color videos, hyperspectral videos record continuous spectral reflectance of targets in light wavelength indexed band images and it is more difficult to explore unique spectral feature of tracking objects. Aiming to take advantage of existing object tracking techniques in color videos, a Siamese Multi-level Residual Attention Adaptive Network (SiamMRAAN) is designed to handle 3-band images by using the well-trained ResNet50 as backbone. By grouping hyperspectral videos into several 3-band-image subsets, the proposed SiamMRAAN can be used to explore high-dimensional spectral information. We design a loss function to fuse the tracking results over these subsets to improve the tracking performance. Finally, experiments over 75 hyperspectral videos confirmed that using spectral information is critical to improve the performance of object tracking in color videos, and also demonstrated that the proposed SiamMRAAN based strategy outperforms several compared networks for hyperspectral videos.
An End-to-End Method for Joint Extraction of Tibetan Entity Relations

Entity relation extraction is to find entities and relations from unstructured texts, which is beneficial to the applications of knowledge graphs and question answering systems.The traditional methods handle this task in a pipelined manner which extracts the entities first and then recognizes their relations.This framework may lead to error delivery.In order to tackle this problem, this paper proposes an end-to-end method for joint extraction of Tibetan entity relations which can extract entities and relations at the same time.According to the Tibetan spelling characteristics, this paper processes the Tibetan corpus by word-level and character-level respectively.Combined with part of speech tagging, we use the end-to-end model to convert the entity relation extraction task to the tagging problem.Finally, the experimental results show that the proposed method is better than the baseline.
Haze Mitigation in High-Resolution Satellite Imagery Using Enhanced Style-Transfer Neural Network and Normalization Across Multiple GPUs

Despite recent advances in deep learning approaches, haze mitigation in large satellite images is still a challenging problem. Due to amorphous nature of haze, object detection or image segmentation approaches are not applicable. Also it is practically infeasible to obtain ground truths for training. Bounded memory capacity of GPUs is another constraint that limits the size of image to be processed. In this paper, we propose a style transfer based neural network approach to mitigate haze in a large overhead imagery. The network is trained without paired ground truths; further, perception loss is added to restore vivid colors, enhance contrast and minimize artifacts. The paper also illustrates our use of multiple GPUs in a collective way to produce a single coherent clear image where each GPU dehazes different portions of a large hazy image.
L2RS

This paper aims to advance the performance of industrial ASR systems by exploring a more effective method for N-best rescoring, a critical step that greatly affects the final recognition accuracy. Existing rescoring approaches suffer the following issues: (i) limited performance since they optimize an unnecessarily harder problem, namely predicting accurate grammatical legitimacy scores of the N-best hypotheses rather than directly predicting their partial orders regarding a specific acoustic input; (ii) hard to incorporate various information by advanced natural language processing (NLP) models such as BERT to achieve a comprehensive evaluation of each N-best candidate. To relieve the above drawbacks, we propose a simple yet effective mechanism, Learning-to-Rescore (L2RS), to empower ASR systems with state-of-the-art information retrieval (IR) techniques. Specifically, L2RS utilizes a wide range of textual information from the state-of-the-art NLP models and automatically deciding their weights to directly learn the ranking order of each N-best hypothesis with respect to a specific acoustic input. We incorporate various features including BERT sentence embeddings, the topic vectors, and perplexity scores produced by an n-gram language model (LM), topic modeling LM, BERT, and RNNLM to train the rescoring model. Experimental results on a public dataset show that L2RS outperforms not only traditional rescoring methods but also its deep neural network counterparts by a substantial margin of 20.85% in terms of [email protected] The L2RS toolkit has been successfully deployed for many online commercial services in WeBank Co., Ltd, China's leading digital bank. The efficacy and applicability of L2RS are validated by real-life online customer datasets.
The Influence of Brain Activity on the Interactive Process through Biofeedback Mechanisms in Virtual Reality Environments

This work focuses on the development of a software link interface tool between the Looxid Link Device coupled to the HTC Vive Pro VR HeadSets and the Unity platform, to generate real-time interactivity in virtual reality applications. The software incorporates a dynamic and parameterizable algorithm to be used as a core-engine in the real-time Biofeedback process, recognizing the values of the biological signals registered in each of the EEG channels of the Looxid Link device. The values of EEG frequencies detected in real time can be used to generate elements of interactivity, with different frequencies and intensities.
Rethinking the Prospects of Sustainable Finance and Challenges of Agribusiness Transformation in Nigeria

The chapter explicates the need to rethink the prospects of sustainable finance (SF) for agribusiness transformation in spite of the challenges facing the sector in Nigeria. It extends to highlighting the implications of the nexus on entrepreneurship development. After a triangular data analysis using the world development indicators (2000-2016) and scholarly articles, the authors found that the prospects of SF are enormous: (1) Nigeria has a modest agricultural growth performance in the crop, food, livestock, and cereal production that could support SF; and (2) SF options such as green loans, green bonds, green credit, green investment funds, green mortgage scheme, and other green financial support instruments could be suitable for agribusiness transformation in the country. Also, the content analysis revealed there are 13 challenges facing agribusiness transformation in the country, and these have harmed the vegetation, farmland, and ocean leading to low productivity. The authors contribute to the literature by identifying SF options as a game-changer for agribusiness transformation.
UD Decomposition Based Adaptive UKF for Nonlinear Estimation of States and Parameters

The unscented Kalman filter (UKF) has been a helpful technique which is widely used for nonlinear estimation of states and parameters, fusion of information, target tracking, and fault diagnosis, etc. However, in the case of unknown covariance matrices of process noises and measurement noises, its convergence and performance cannot be guaranteed. To overcome this problem, this paper proposed an adaptive UKF, which is based on the principle of match of covariance between the theoretical matrix and the estimated matrix. A threshold condition is given out to trigger the adaptive process, thus the original stability of UKF will not be decreased. What’s more, a UD decomposition technique is introduced to improve the numerical stability of the adaptive UKF. To compare the performance of the adaptive method and UD decomposition based UKF, an index of degree of mismatch is proposed alongside with root mean square error. Simulation results verify the better accuracy and robustness of the proposed filter.
Monitoring Collective Communication Among GPUs

Communication among devices in multi-GPU systems plays an important role in terms of performance and scalability. In order to optimize an application, programmers need to know the type and amount of the communication happening among GPUs. Although there are prior works to gather this information in MPI applications on distributed systems and multi-threaded applications on shared memory systems, there is no tool that identifies communication among GPUs. Our prior work, ComScribe, presents a point-to-point (P2P) communication detection tool for GPUs sharing a common host. In this work, we extend ComScribe to identify communication among GPUs for collective and P2P communication primitives in NVIDIA's NCCL library. In addition to P2P communications, collective communications are commonly used in HPC and AI workloads thus it is important to monitor the induced data movement due to collectives. Our tool extracts the size and the frequency of data transfers in an application and visualizes them as a communication matrix. To demonstrate the tool in action, we present communication matrices and some statistics for two applications coming from machine translation and image classification domains.
Platform Generation for Edge AI Devices with Custom Hardware Accelerators

In recent years artificial neural networks (NNs) have been at the center of research on data processing. However, their high computational demand often prohibits deployment on resource-constrained Industrial IoT Systems. Custom hardware accelerators can enable real-time NN processing on small-scale edge devices but are generally hard to develop and integrate. In this paper we present a hardware generation approach to rapidly create, test, and deploy entire SoC platforms with application-specific NN hardware accelerators. The feasibility of the approach is demonstrated by the generation of a condition monitoring system for high-speed valves.
Homomorphic Modular Reduction and Improved Bootstrapping for BGV Scheme

Bootstrapping is a crucial subroutine of fully homomorphic encryption (FHE), where a homomorphic encryption scheme evaluates its own decryption circuits. Homomorphic modular reduction is a crucial part of bootstrapping a BGV ciphertext.In this paper, we investigate the homomorphic modular reduction technique. We propose a new homomorphic modular reduction algorithm based on the idea of “blind rotation”. This new homomorphic modular reduction procedure requires no basic homomorphic operations, hence it has lower noise accumulation and more suitable for implementing. Furthermore, we also resort to the blind rotation to construct a new bootstrapping procedure for the BGV scheme. We analyze the noise performance and the computational complexity of our scheme. The results illustrate that our new bootstrapping scheme achieves low noise accumulation so that the lattice approximation factor for the underlying worst-case lattice assumption is smaller than that of Chen and Zhang’s work. Meanwhile, the complexity of our bootstrapping scheme is comparable with their scheme.
Workspace Analysis of a Mobile Robot System for the Ship Section Painting

In order to implement the painting of large unstructured surface in the ship section, the mobile robot system consisting of an automatic guided vehicle (AGV), a scissors elevator, and a 6R industrial painting robot is proposed. To ensure the terminal accuracy and reduce the requirements on cooperative control, the intermittent operation mode will be adopted with the station planning. In order to facilitate the station planning, this paper carried out the analysis of the regular manipulating workspace for the system. Firstly, the reachable workspace is established with the homogeneous transformation matrix and the kinematic analysis. The singularity and accuracy is analyzed considering the distribution of the condition number. Based the projection, the interference is analyzed between the robot and other parts. Finally, boundaries of the regular workspace are formulated for the mobile robot system, which provides a solid foundation for the station planning and efficient painting.
Conceptualizing Embodied Pedagogical Mediation (EPM): The Plávana Project, A Choreographer’s Toolkit.

In an age where circumstance makes the need and plausibility of digital cultural mediation a reality, it becomes necessary to consider the effects such mediation has on pedagogical process. It is our purpose through work with The Plavana Project, to create an artifact that can be used as a digital toolkit for practitioners of tradition, a study tool for its students, and an introduction to the novice for the history and cultural memory carried in its teachings. We hypothesize that through a dedicated focus on Sense of Embodiment (SoE) using the immersive qualities of consumer Virtual Reality (VR), we can place the participant in historically relevant settings and provide an authentic learning experience that is generally only available locally and synchronously. In this study, we apply the fundamentals of digital game design and development, the immersive qualities and philosophies of consumer VR, and the Embodied Pedagogical Mediation (EPM) of current teachers and scholars in the traditional Odissi dance form.
Extraction of Open-PIT Mine Reclamation Area with Convolutional Neural Network

Reclamation of land-damaged areas in open-pit mines is an important means of ecological restoration in mining areas. In order to effectively monitor the reclamation of mining areas, we propose an improved Mask R-CNN method to extract reclamation areas. Since traditional artificial methods and semantic segmentation networks segment the whole image, it leads to more false detections. Mask R-CNN based on instance segmentation avoids the segmentation of the whole image. Moreover, we improve its mask branch to raise accuracy. An open-pit mining area in Ordos City is chosen to be our research area and we use the GF-1 image to make open-pit mine reclamation areas dataset. Similarly, classic semantic segmentation networks are used for comparison, and verified our improved algorithm on the data of GF-1 and JL-1. In comparing with the sub-optimal algorithms, F1 scores have been improved by 1.49% and 2.3% respectively.
Transitioning from structural to nominal code with efficient gradual typing

Gradual typing is a principled means for mixing typed and untyped code. But typed and untyped code often exhibit different programming patterns. There is already substantial research investigating gradually giving types to code exhibiting typical untyped patterns, and some research investigating gradually removing types from code exhibiting typical typed patterns. This paper investigates how to extend these established gradual-typing concepts to give formal guarantees not only about how to change types as code evolves but also about how to change such programming patterns as well. In particular, we explore mixing untyped "structural" code with typed "nominal" code in an object-oriented language. But whereas previous work only allowed "nominal" objects to be treated as "structural" objects, we also allow "structural" objects to dynamically acquire certain nominal types, namely interfaces. We present a calculus that supports such "cross-paradigm" code migration and interoperation in a manner satisfying both the static and dynamic gradual guarantees, and demonstrate that the calculus can be implemented efficiently.
How SAR Image Denoise Affects the Performance of DCNN-Based Target Recognition Method

Currently, deep neural networks have been widely used in the field of SAR target recognition. Many researchers found that deep neural networks have an ability of denoising. In many cases, there is no need to denoise in pre-process. But the denoising ability of deep neural networks can take place of conventional denoising algorithm or not is doubtful. In this article, we explore the effect of image denoising algorithms to SAR target recognition methods based on deep neural networks. Firstly, seven traditional denoising algorithms are selected to process two SAR datasets. And these data are utilized to train two kinds of deep neural networks. After comparing and analyzing the training processes and results, we find that 1) The effect of denoising algorithms is influenced by architectures of neural networks and quality of datasets. It is difficult to find a SAR image denoising algorithm, which can improve the accuracy of any recognition network. Sometimes they even drag down the performance of recognition networks. 2) The deep networks with more layers will have better denoising ability, so the effect of denoising algorithms will decrease. For ResNet, there is no need to add the denoising processing.