Reviewerly/exHarmony huggingface dataset:
Understanding ore-forming conditions using machine reading of text

Mineral exploration reports assist present day exploration by providing valuable observations about the geological environments in which mineral deposits form. Querying and aggregating historical data can assist in reducing future exploration risks and costs. However, since the reports are written in unstructured text, it is a challenging task to derive meaningful geological information without manually reading through a large collection of reports, which is a formidable task for geologists. In this study, geological information relevant to mineralisation and ore-forming conditions is automatically extracted from such under-utilised exploration reports. This is achieved by constructing knowledge graphs that describe geological entities and their relations as they appear in exploration reports. Natural language processing and deep learning methods are used to automatically extract and label geological terms with the correct entity types and establish the relationships between these entities. In this research, six dominant entity types are considered, namely, geographical location, geological timescale, stratigraphic unit, rock type, ore and deposit type, and contained minerals. Two knowledge graphs are constructed for two high-quality mineral exploration reports, one for iron ore and the other for gold deposit, to illustrate the effectiveness of our methodology. The knowledge graphs are then assessed by determining whether the contents of the source reports were depicted accurately, specifically the labelled geological terms in the nodes and their associations in node connections. The results show that the structured information stored in the knowledge graphs faithfully represent the contents of the source reports, matching well with the domain knowledge. The proposed methods are capable of rapidly and robustly transforming text data into a structured form, the untapped area towards geological knowledge mining.
Automated assessment of glomerulosclerosis and tubular atrophy using deep learning

In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance, which is currently based on the pathologists' histological evaluations on renal biopsies in addition to clinical data. In this work, we present an automated algorithm, called RENTAG (Robust EvaluatioN of Tubular Atrophy & Glomerulosclerosis), for the segmentation and classification of glomerular and tubular structures in histopathological images. The proposed novel strategy combines the accuracy of a level-set with the semantic segmentation of convolutional neural networks to detect the glomeruli and tubules contours. In the TEST set, our method exhibited excellent performance in both glomeruli (dice score: 0.9529) and tubule (dice score: 0.9174) detection and outperformed all the compared methods. To the best of our knowledge, the RENTAG algorithm is the first fully automated method capable of quantifying glomerulosclerosis and tubular atrophy in digital histological images. The developed software can be employed for the analysis of pre-transplantation biopsies to support the pathologists' diagnostic activity.
Scheme for Universal High-Dimensional Quantum Computation with Linear Optics

Photons are natural carriers of high-dimensional quantum information, and, in principle, can benefit from higher quantum information capacity and noise resilience. However, schemes to generate the resources required for high-dimensional quantum computing have so far been lacking in linear optics. Here, we show how to generate GHZ states in arbitrary dimensions and numbers of photons using linear optical circuits described by Fourier transform matrices. Combining our results with recent schemes for qudit Bell measurements, we show that universal linear optical quantum computing can be performed in arbitrary dimensions.
KRanTi: Blockchain‐based farmer's credit scheme for agriculture‐food supply chain

Abstract The advent of agro‐industrialization has helped the agricultural food supply chain (AFSC) in introducing ease and innovation to other facets. In addition, Blockchain (BC) technology is revolutionizing each distributed data‐related application by adding authentication and trust among the system's stakeholders. Inspired by the above‐mentioned argument, this article proposes a system, KRanTi , to promote the supply chain of agricultural food by BC embedding across the 5G network. The 5G network upgrades the data connectivity by enhancing network infrastructure efficiency. In KRanTi , we present an efficient credit system that enables associated farmers to purchase the requisite agricultural raw product of higher quality without the pressure of instant payment. A score‐based farm‐food quality assurance system is then proposed, which guarantees optimal quality grading. Trust, transparency, and traceability are preserved through the implementation of Smart Contracts (SC) distributed across public Ethereum BC. The cost‐effective off‐line data storage service in AFSC is offered through the use of the Inter‐Planetary File System (IPFS), a shared storage system to hold all data from stakeholders. Results show that the efficiency of the proposed scheme KRanTi is improved compared with traditional schemes with metrics such as less than 1 millisecond latency with 99.99% reliability compared with LTE with 5G, scalability, packet failure ratio, BC performance, cost‐quality ratio, and data storage contrast.
A survey on generative adversarial network-based text-to-image synthesis

The task of text-to-image synthesis is a new challenge in the field of image synthesis. In the earlier research, the task of text-to-image synthesis is mainly to achieve the alignment of words and images by the way of retrieval based on the sentences or keywords. With the development of deep learning, especially the application of deep generative models in image synthesis, image synthesis achieves promising progress. The Generative adversarial networks (GANs) are one of the most significant generative models, and GANs have been successfully applied in computer vision, natural language processing and so on. In this paper, we review and summarize the recent research in GANs-based text-to-image synthesis, and provide a summary of the development of classic and advanced models. The input of the GANs-based text-to-image synthesis is not only the general text description as earlier studies, also includes scene layout and dialog text. The typical structure of each categories is elaborated. The general text-based image synthesis is the most commonly in the text-to-image synthesis, and it is subdivided into three groups based on the improvements of text information utilization, network structure and output control conditions. Through the survey, the detailed and logical overview of the evolution of GANs-based text-to-image synthesis is presented. Finally, the challenged problems and the future development of text-to-image synthesis are discussed.
A novel dynamic quantum secret sharing in high-dimensional quantum system

This paper proposed a novel dynamic quantum secret sharing protocol in high-dimensional quantum system. Via transmitting the particles circularly and local unitary operations, the dealer and all agents can share the multi-particle entangled GHZ state in high-dimensional quantum system. The proposed protocol allows a dealer to share the predetermined dits without directly distributing any piece of shares to agents. To recover the secrets, dealer and all agents only need to perform the single-particle measurement operation and then implement the simple modular arithmetic operation according to their corresponding measurement results. Besides, in the proposed protocol, only a little fraction of entangled GHZ states are employed for eavesdropping check instead of lots of decoy (or detecting) particles used in previous protocols. Since the protocol is designed in the high-dimensional quantum system, it makes our protocol have higher resource capacity and better security in detecting the illegal eavesdropper than former protocols designed in two-dimensional quantum system. Security analyses indicate that the proposed protocol is immune to general attacks of intercept-and-resend, entangle-and-measure, collusion, and revoked a dishonest agent. Furthermore, the efficiency of proposed DQSS protocol in noise environment is deduced through quantum fidelity. The obtained results indicate that the efficiency of proposed protocol decreases with the increase in channel noise parameter and it has a quite different efficiency in four types of noise, i.e., dit-flip, d-phase-flip, amplitude-damping and depolarizing.
Blind and safety-enhanced dual watermarking algorithm with chaotic system encryption based on RHFM and DWT-DCT

This study proposes a novel blind watermarking algorithm based on radial harmonic Fourier moments (RHFMs) as well as discrete wavelet transform and discrete cosine transform (DWT-DCT) to achieve copyright protection. A non-adjacent coupled map lattice chaotic system was used to improve the safety in the watermark embedding process. First, a zero-watermark was obtained by performing DCT on the original image. Meanwhile, the direction information of the origin was calculated and embedded in the RHFM domain. During normal watermark embedding, the image-embedded direction information was transformed by a one-level DWT and the low-frequency sub-band was divided into 8×8 non-overlapping blocks. Each block was transformed by DCT, and two low-frequency coefficients were selected to embed the encrypted watermark. Experimental results demonstrated that the proposed algorithm is robust against rotation and other attacks. The analysis also showed that the proposed algorithm satisfies the invisibility of watermarking algorithms and has good security performance.
Manipulator Motion Planning for Part Pickup and Transport Operations From a Moving Base

Mobile manipulators are being deployed for transporting parts between machines and work stations in warehouses and shop floors. To increase the efficiency of operations, these mobile manipulators are required to complete the tasks as fast as possible. Picking up parts with the manipulator while the mobile base is moving decreases the time required to complete the transportation task and increases the efficiency of operations. However, motions of the manipulator on a moving platform can be risky, and hence, it is desired that the manipulator starts and ends its motions as close as possible to the part being picked up. In this article, we present a bidirectional sampling-based scheme for generating such manipulator trajectories for a given mobile base trajectory for pickup and transportation. Our approach implicitly determines the location of the mobile base where the manipulator motion starts and ends as well as where grasping happens. It also determines which grasping pose to use for picking up the part. Furthermore, we have presented the techniques to reduce the manipulator motion time (span time) and the computation time. Our approach enables us to reduce span time on average by 35% with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16\times $ </tex-math></inline-formula> reduction in the computation time compared to the RRT-based baseline methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Transportation of objects is a crucial application in industrial and warehouse environments. Conveyor belts and AGVs are typically used for such applications as they provide an efficient mode of transportation of a large number of objects. However, they may not provide the flexibility which mobile manipulators bring in for small-scale operations. The method presented in this article provides a way to increase the efficiency of operation for mobile manipulators for transportation tasks. We attempt to reduce the risks of the manipulator colliding with expensive equipment and moving obstacles by making sure that the manipulator moves only when needed for picking up objects. Moreover, the method can be used to pick up and transport a variety of parts in different ways using a two-fingered gripper. The method can also easily incorporate different types of grippers and mobile platforms.
Multi-modal 3D object detection by 2D-guided precision anchor proposal and multi-layer fusion

3D object detection, of which the goal is to obtain the 3D spatial structure information of the object, is a challenging topic in many visual perception systems, e.g., autonomous driving, augmented reality, and robot navigation. Most existing region proposal network (RPN) based 3D object detection methods generate anchors in the whole 3D searching space without using semantic information, which leads to the problem of inappropriate anchor size generation. To tackle the issue, we propose a 2D-guided precision anchor generation network (PAG-Net). Specifically speaking, we utilize a mature 2D detector to get 2D bounding boxes and category labels of objects as prior information. Then the 2D bounding boxes are projected into 3D frustum space for more precise and category-adaptive 3D anchors. Furthermore, current feature combination methods are early fusion, late fusion, and deep fusion, which only fuse features from high convolutional layers and ignore the data missing problem of point clouds. To obtain more efficient fusion of RGB images and point clouds features, we propose a multi-layer fusion model, which conducts nonlinear and iterative combinations of features from multiple convolutional layers and merges the global and local features effectively. We encode point cloud with the bird’s eye view (BEV) representation to solve the irregularity of point cloud. Experimental results show that our proposed approach improves the baseline by a large margin and outperforms most of the state-of-the-art methods on the KITTI object detection benchmark.
iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV

Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.
ClothTiles: A Prototyping Platform to Fabricate Customized Actuators on Clothing using 3D Printing and Shape-Memory Alloys

Emerging research has demonstrated the viability of on-textile actuation mechanisms, however, an easily customizable and versatile on-cloth actuation mechanism is yet to be explored. In this paper, we present ClothTiles along with its rapid fabrication technique that enables actuation of clothes. ClothTiles leverage flexible 3D-printing and Shape-Memory Alloys (SMAs) alongside new parametric actuation designs. We validate the concept of fabric actuation using a base element, and then systematically explore methods of aggregating, scaling, and orienting prospects for extended actuation in garments. A user study demonstrated that our technique enables multiple actuation types applied across a variety of clothes. Users identified both aesthetic and functional applications of ClothTiles. We conclude with a number of insights for the Do-It-Yourself community on how to employ 3D-printing with SMAs to enable actuation on clothes.
Automatically Matching Bug Reports With Related App Reviews

App stores allow users to give valuable feedback on apps, and developers to find this feedback and use it for the software evolution. However, finding user feedback that matches existing bug reports in issue trackers is challenging as users and developers often use a different language. In this work, we introduce DeepMatcher, an automatic approach using state-of-the-art deep learning methods to match problem reports in app reviews to bug reports in issue trackers. We evaluated DeepMatcher with four open-source apps quantitatively and qualitatively. On average, DeepMatcher achieved a hit ratio of 0.71 and a Mean Average Precision of 0.55. For 91 problem reports, DeepMatcher did not find any matching bug report. When manually analyzing these 91 problem reports and the issue trackers of the studied apps, we found that in 47 cases, users actually described a problem before developers discovered and documented it in the issue tracker. We discuss our findings and different use cases for DeepMatcher.
The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods

The variety of accents has posed a big challenge to speech recognition. The Accented English Speech Recognition Challenge (AESRC2020) is designed for providing a common testbed and promoting accent-related research. Two tracks are set in the challenge – English accent recognition (track 1) and accented English speech recognition (track 2). A set of 160 hours of accented English speech collected from 8 countries is released with labels as the training set. Another 20 hours of speech without labels is later released as the test set, including two unseen accents from another two countries used to test the model generalization ability in track 2. We also provide baseline systems for the participants. This paper first reviews the released dataset, track setups, baselines and then summarizes the challenge results and major techniques used in the submissions.
Smart contracts for automated control system in Blockchain based smart cities

Nowadays, smart applications are increasing day by day to improve the standard of living in smart cities. A modern-day smart city is characterized by the presence of numerous smart Information and Communication Technology (ICT)-enabled services such as automated healthcare, automatic building monitoring, home automation, smart parking, traffic management, data security, among others. Such cities employ multitudes of Internet of Things (IoT) devices to collect and share data between trusted users by means of a centralized intermediary for monitoring and control of the myriad automatic activities. However, a centralized intermediary is plagued by issues such as single point of failure, risk of data loss, man-in-the-middle attack, and so forth. Blockchain-based smart contracts for automated control in smart cities provide a decentralized and secure alternative. In this paper, an Ethereum based system design for decentralized applications in smart cities has been proposed that enables systems to share data without an intermediary between trusted and non-trusted stakeholders using Ethereum based self-executing contracts. Such contracts allow automated multi-step workflows for smart applications. Two use cases, have been considered namely smart healthcare and smart building monitoring, as proof of stake of the proposed Ethereum based contract. The performance of the proposed scheme for these use cases has been presented with Keccack 256 transaction hash, the total number of transactions, gas consumed by each contract. Such an attempt is a worthwhile addition to state of the art as evident from the results presented herein. The modeling simulation and analysis of hashing power shows that for hashing power greater than 55% the probability of double spending attack reaches to 42% maximum. So it is concluded that the probability of double spending increases with the increase of transaction values.
Heterogeneous Graph Propagation Network

Graph neural network (GNN), as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. Recently, some works attempt to generalize GNN to heterogeneous graph which contains different types of nodes and links. Heterogeneous graph neural networks (HeteGNNs) usually follow two steps: aggregate neighbors via single meta-path and then aggregate rich semantics via multiple meta-paths. However, we discover an important semantic confusion phenomenon in HeteGNNs, i.e., with the growth of model depth, the learned node embeddings become indistinguishable, leading to the performance degradation of HeteGNNs. We explain semantic confusion by theoretically deriving that HeteGNNs and multiple meta-paths based random walk are essentially equivalent. Following the theoretical analysis, we propose a novel Heterogeneous graph Propagation Network (HPN) to alleviate the semantic confusion. Specically, the semantic propagation mechanism of HPN absorbes nodes local semantic with a proper weight during aggregating process, which makes HPN capture the characteristics of each node and learn distinguishable node embedding with deeper HeteGNN architecture. Then, the semantic fusion mechanism is designed to learn the importance of meta-path and fuse them judiciously. Extensive experimental results on three datasets show the superior performance of the proposed HPN over the state-of-the-arts.