Reviewerly/exHarmony huggingface dataset:
Global Dynamic Path Planning Based on Fusion of A<sup>*</sup> Algorithm and Dynamic Window Approach

To elevate capabilities of global optimal and real-time obstacle avoidance of mobile robots, a novel method based on fusion of $A^{\star}$ algorithm and dynamic window approach is proposed. Firstly, the principle and work process of $A^{\star}$ algorithm is introduced and simulation experiments in MATLAB are used to compare A$^{\star}$ algorithm and Dijkstra algorithm, then the dynamic window approach based on the evaluation function is applied to implement dynamic path planning to guarantee the ability of obstacle avoidance as holding the global optimality of path. Finally, the experimental results in ROS system verified the effectiveness of global path planning algorithm and the fusion algorithm in a simulation environment.
Blind Stereoscopic Image Quality Assessment Accounting for Human Monocular Visual Properties and Binocular Interactions

Human visual perceptual model is a key factor for evaluating stereoscopic image quality. This paper focuses on the contributions of monocular and binocular properties on quality perception and proposes a novel blind stereoscopic image quality assessment model by comprehensively digging the relationship between visual features and quality perception. The statistical quality-aware monocular features are extracted from both left view and right view to reveal monocular quality perception, including the color statistical features which are missed in most previous models, while the multiple features of the summation signal and the entropy features of the difference signal are extracted to quantify the binocular quality perception. Finally, support vector regression (SVR) is utilized to train a regression model based on the extracted features and the subjective scores. Three public databases, LIVE 3D Phase I, LIVE 3D Phase II, and MCL 3D Database, are adopted to prove the effectiveness of the proposed model. Experimental results demonstrate that the proposed model is superior to other existing state-of-the-art quality metrics.
Generated Data With Sparse Regularized Multi-Pseudo Label for Person Re-Identification

Recently, Generative Adversarial Network (GAN) has been adopted to improve person re-identification (person re-ID) performance through data augmentation. However, directly leveraging generated data to train a re-ID model may easily lead to over-fitting issue on these extra data and decrease the generalisability of model to learn true ID-related features from real data. Inspired by the previous approach which assigns multi-pseudo labels on the generated data to reduce the risk of over-fitting, we propose to take sparse regularization into consideration. We attempt to further improve the performance of current re-ID models by using the unlabeled generated data. The proposed Sparse Regularized Multi-Pseudo Label (SRMpL) can effectively prevent the over-fitting issue when some larger weights are assigned to the generated data. Our experiments are carried out on two publicly available person re-ID datasets (e.g., Market-1501 and DukeMTMC-reID). Compared with existing unlabeled generated data re-ID solutions, our approach achieves competitive performance. Two classical re-ID models are used to verify our sparse regularization label on generated data, i.e., an ID-embedding network and a two-stream network.
Obstacle Avoidance of Two-Wheeled Mobile Robot based on DWA Algorithm

Autonomous mobile robot is an integrated system which integrates many functions such as environment perception, dynamic strategy and planning, behavior control and execution. In recent years, mobile robot technology has played an important role in many fields such as industry, agriculture, medicine, aerospace and aviation. Intelligent robots require more and more real-time, adaptability and optimization for unknown environments. Only with the ability of autonomous action can they realize their value. Robot obstacle avoidance has been widely studied since the emergence of mobile robots, but at the beginning, this technology has not been studied as a separate technology. Intelligent obstacle avoidance is one of the hotspots and difficulties in the field of research. Intelligent obstacle avoidance is able to effectively avoid obstacles and reach the end point according to certain methods according to the information collected from obstacles. People usually use path planning to realize it. Path planning is used to decide how a robot walks in the environment that is the key technology to realize the intelligent robot. Path planning is used to guide the action and behavior of a mobile robot to reach its target location. According to some optimization criteria, an optimal path from the starting point to the target point can be found in the navigation range of a mobile robot, and obstacles can be avoided. The path has some characteristics, such as the shortest distance or the least time. This paper discussed the issue of obstacle avoidance of two-wheeled mobile robot based on DWA algorithm. By building maps, placing obstacles and testing the DWA algorithm on ROS, it is concluded that the DWA algorithm can be well applied in the field of robot obstacle avoidance.
Fractionation of cadmium isotope caused by vapour-liquid partitioning in hydrothermal ore-forming system: A case study of the Zhaxikang Sb–Pb–Zn–Ag deposit in Southern Tibet

Here, we conducted a case study of the Zhaxikang Sb–Pb–Zn–Ag deposit to explore Cd isotopic fractionation mechanisms in hydrothermal ore-forming system. The δ114/110Cd values of sphalerite systematically decrease from ore mineralization stage 1 (−0.30‰ to 1.01‰; average value = 0.17‰; n = 4) through stage 2 (−0.51‰ to −0.09‰; average value = −0.23‰; n = 3) to stage 3 (−0.34‰ to −0.23‰; average value = −0.285‰; n = 2). A simple Rayleigh distillation models this temporally decreasing trend resulted from Cd isotopic fractionation that is most likely related to vapor-liquid dynamics of ore-forming fluid. This mechanism for Cd isotopic fractionation is further augmented by the general geochemical characteristics and fluid inclusion data of sulfides and cogenetic gangue minerals. Firstly, the sphalerite has low Cd concentrations (1183–2199 ppm), correspondingly high Zn/Cd ratios (248–421), and large Cd isotopic variation range with relatively lower δ114/110Cd values (−0.51‰ to 1.01‰). Commonly, sphalerite from sedimentary exhalative systems possess these characteristics caused by vapor-liquid interactions. Secondly, fluid inclusion data from the Pb–Zn sulfides and cogenetic carbonate alteration minerals indicates that vapor-liquid two-phase inclusions are in dominance (more than 90%). Meanwhile, the vapor-liquid ratios (20–50%) of these two-phase inclusions are consistent with those of the established Cd (25.48–55.07%) and Zn (26.70–48.70%) isotopic Rayleigh distillation model. To conclude, vapor-liquid partitioning is main cause for observed Cd isotopic variations in Zhaxikang deposit, and is demonstrated as an important Cd isotopic fractionation mechanism in hydrothermal ore-forming system.
Hardware Implementation for Multiple Activation Functions

In a neural network, the activation function defines the output of that node. In this paper, we propose a novel hardware implementation for AI hardware accelerators to support three popularly used activation functions, including Hyperbolic Tangent, Sigmoid and ReLU. Based on this hardware implementation, the users can configure the activation functions of nodes at the execution time. Therefore, a higher flexibility for AI hardware computation can be provided. Implementation results show that the proposed hardware works well in practice.
Blockchain Applications with Privacy using Efficient Multiparty Computation Protocols

Blockchain technology provides a distributed solution, but not privacy of data used. Data privacy is included with the help of secure multiparty computation protocols and which in turn increases the complexity of application. This paper provides an efficient solution for blockchain technology with privacy by including a novel optimization for secure computation protocols.
Generation of Schubert polynomial series via nanometre-scale photoisomerization in photochromic single crystal and double-probe optical near-field measurements

Abstract Generation of irregular time series based on physical processes is indispensable in computing and artificial intelligence. In this report, we propose and demonstrate the generation of Schubert polynomials, which are the foundation of versatile permutations in mathematics, via optical near-field processes introduced in a photochromic crystal of diarylethene combined with a simple photon detection protocol. Optical near-field excitation on the surface of a photochromic single crystal yields a chain of local photoisomerization, forming a complex pattern on the opposite side of the crystal. The incoming photon travels through the nanostructured photochromic crystal, and the exit position of the photon exhibits a versatile pattern. We emulated trains of photons based on the optical pattern experimentally observed through double-probe optical near-field microscopy, where the detection position was determined based on a simple protocol, leading to Schubert matrices corresponding to Schubert polynomials. The versatility and correlations of the generated Schubert matrices could be reconfigured in either a soft or hard manner by adjusting the photon detection sensitivity. This is the first study of Schubert polynomial generation via physical processes or nanophotonics, paving the way for future nano-scale intelligence devices and systems.
Secure and efficient sharing of authenticated energy usage data with privacy preservation

As a technological innovation, smart grid improves electricity services in terms of substantiality, economics, efficiency, and reliability. This owes to the bi-directional communication property which not only enables the fine-grained energy usage data to be available for different entities but also facilitates the automated grid management. However, sharing such energy usage data with other parties could potentially cause the leakage of customers' sensitive information. In addition, the data are vulnerable to be tampered with by any internal or external attacker such that the value of data could be destroyed. Therefore, it is crucial to preserve customers' privacy and provide authenticity and verifiability while sharing energy usage data with other parties. In this paper, we propose a bilinear-map accumulator based redactable signature scheme (RSS-BMA) which allows customers to safeguard their privacy while guaranteeing the verifiability of shared data. Furthermore, the batch-data-block verification property of our design enhances not only efficiency but also security by prohibiting additional redaction to a batch of data blocks. We analyze the efficiency and security of our proposed scheme extensively, and the results indicate our construction is more practical than others.
Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions

Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem and test it on two benchmarks; one for Modern Standard Arabic (MSA), and another for the 24 major Arabic dialects. We are able to show that our new system outperforms state-of-the-art approaches by achieving 93% F1-score on the MSA benchmark and 82% on the dialectical one. This is achieved by utilizing contextualized word representations (ELMo embeddings) trained on a text corpus containing MSA and dialectic sentences. This in combination with a pairwise fine-grained similarity layer, helps our question-to-question model to generalize on dialects while being trained only on our newly developed question-to-question MSA data.
Process Knowledge Graph Construction Method for Process Reuse

Aiming at the problems of process knowledge reuse and sharing led by the difficulty of unified representation of complex and diverse process knowledge, a process knowledge graph construction method for process reuse is proposed. Firstly, to ensure the accuracy and universality of data schema for process knowledge, the basic schema of process knowledge graph is constructed based on step-nc. Secondly, to improve process knowledge graph basic schema, the process knowledge graph extensive schema is established through process knowledge analysis and process knowledge combination. Meanwhile, to construct the process knowledge graph schema, the existing rules of experience are represented by SWRL language. Moreover, to instantiate the process knowledge graph schema, the process cases are analyzed under the guidance of process knowledge graph schema and the similarity between process cases is computed by latent semantic analysis technology. And then, process cases are transformed into the structured process knowledge graph representation, and process knowledge graph data is obtained. Finally, the process knowledge graph construction application platform is developed to verify the feasibility of the proposed method.
New heuristic algorithms for the Dubins traveling salesman problem

Abstract The problem of finding a shortest curvature-constrained closed path through a set of targets in the plane is known as Dubins traveling salesman problem (DTSP). Applications of the DTSP include motion planning for kinematically constrained mobile robots and unmanned fixed-wing aerial vehicles. The difficulty of the DTSP is to simultaneously find an order of the targets and suitable headings (orientation angles) of the vehicle when passing the targets. Since the DTSP is known to be NP-hard there is a need for heuristic algorithms providing good quality solutions in reasonable time. Inspired by standard methods for the TSP we present a collection of such heuristics adapted to the DTSP. The algorithms are based on a technique that optimizes the headings of the targets of an open or closed subtour with given order. This is done by discretizing the headings, constructing an auxiliary network and computing a shortest path in the network. The first algorithm for the DTSP uses the order of the targets obtained from the solution of the Euclidean TSP. A second class of algorithms extends an open subtour by successively adding a new target and closes the tour if all targets have been added. A third class of algorithms starts with a closed subtour consisting of few targets and successively inserts a new target into the tour. The individual algorithms differ by the number of headings to be optimized in each iteration. Extensive simulation results show that the proposed methods are competitive with state-of-the-art methods for the DTSP concerning performance and superior concerning running time, which makes them applicable also to large-scale scenarios.
Efficient FPGA-Based Reconfigurable Accelerators for SIMON Cryptographic Algorithm on Embedded Platforms

With the advancement of embedded computing, many applications are becoming common on these devices, which have stringent constraints and requirements. FPGA-based systems are currently the most promising avenue to support applications on embedded platforms. Furthermore, it is imperative to ensure the security of the applications running on these highly constrained embedded devices. In this paper, we introduce novel, unique, and efficient FPGA-based reconfigurable accelerators (both hardware and software) for the SIMON lightweight cryptographic algorithm on embedded devices, considering the associated constraints and requirements. Both our hardware and software designs are generic, parameterized, and scalable; thus, without changing the internal architectures, our designs can be reconfigured on-the-fly: to perform either the encryption or the decryption; and to process varying block sizes and varying key sizes. Our embedded designs are highly flexible and reconfigurable, hence can be utilized for various applications with diverse security requirements. We also introduce the system-level architecture for our FPGA-based hardware/software SIMON designs, with unique techniques to overcome memory limitations and memory access latency. Experiments are performed to evaluate the feasibility and efficiency of our proposed accelerators on embedded platforms. Our reconfigurable hardware designs achieve 59 times speedup compared to its software counterparts.
Path Planning for Obstacle Avoidance of Manipulators Based on Improved Artificial Potential Field

In this paper, artificial potential field is applied to real-time path planning for obstacle avoidance of manipulators. In this approach, a collision-free path from the initial state to target is planned in three-dimensional space. Artificial potential field is widely used in obstacle avoidance for manipulators, with the advantages of simple structure and real-time performance, but there is a local minima problem in this method. Firstly, the artificial potential field method is used to determine the joint-space path of manipulator, and the path is smoothed to ensure the flexibility of joint-motion. Secondly, the local minima problem is decomposed into two categories: unreachable target and local minima. Virtual obstacles and targets are set to avoid the problem. Finally, regarding time as the optimization target, simulated annealing algorithm is used to search the optimal parameters of artificial potential field. The simulation results verify the effectiveness and adaptability of the algorithm.
Temporal condition pattern mining in large, sparse electronic health record data: A case study in characterizing pediatric asthma

Abstract Objective This study introduces a temporal condition pattern mining methodology to address the sparse nature of coded condition concept utilization in electronic health record data. As a validation study, we applied this method to reveal condition patterns surrounding an initial diagnosis of pediatric asthma. Materials and Methods The SPADE (Sequential PAttern Discovery using Equivalence classes) algorithm was used to identify common temporal condition patterns surrounding the initial diagnosis of pediatric asthma in a study population of 71 824 patients from the Children’s Hospital of Philadelphia. SPADE was applied to a dataset with diagnoses coded using International Classification of Diseases (ICD) concepts and separately to a dataset with the ICD codes mapped to their corresponding expanded diagnostic clusters (EDCs). Common temporal condition patterns surrounding the initial diagnosis of pediatric asthma ascertained by SPADE from both the ICD and EDC datasets were compared. Results SPADE identified 36 unique diagnoses in the mapped EDC dataset, whereas only 19 were recognized in the ICD dataset. Temporal trends in condition diagnoses ascertained from the EDC data were not discoverable in the ICD dataset. Discussion Mining frequent temporal condition patterns from large electronic health record datasets may reveal previously unknown associations between diagnoses that could inform future research into causation or other relationships. Mapping sparsely coded medical concepts into homogenous groups was essential to discovering potentially useful information from our dataset. Conclusions We expect that the presented methodology is applicable to the study of diagnostic trajectories for other clinical conditions and can be extended to study temporal patterns of other coded medical concepts such as medications and procedures.
Video Recognition of American Sign Language Using Two-Stream Convolution Neural Networks

Sign language uses manual-visual to convey meaning. The style is expressed through manual sign flow in combination with non-manual elements. Sign gestures interpreted in the meaning of words, letters, and numbers. This study proposed Two-stream Convolutional Neural Networks (CNN) to recognize and classify words in hand motion images of video form. Two-stream CNN works with two processes, namely spatial and temporal stream. Spatial flow detects edges and overall global features. While temporal flow identifies local action features in stacked optical flow images of 10 frames, each stream passed Softmax function. Average Fusion function combines both of streams. Two-stream separated training reduced computing time and overcome resource limitations. In building a CNN two-stream model, a specific configuration is needed to update the weight during training such as VGG - SGD, Resnet - Adam, Resnet - SGD, Xceptionnet - Adam, and Xceptionnet - SGD. The result gave the best precision used Xceptionnet SGD of spatial flow and Xceptionnet Adam of temporal flow configuration. The architecture gave precision 89.4% of a combination of one choice or Top1 is 89.4% and 99.4% of the five choices or Top5.
Richpedia: A Comprehensive Multi-modal Knowledge Graph

Large-scale knowledge graphs such as Wikidata and DBpedia have become a powerful asset for semantic search and question answering. However, most of the knowledge graph construction works focus on organizing and discovering textual knowledge in a structured representation while paying little attention to the proliferation of visual resources on the Web. To improve the situation, in this paper, we present Richpedia, aim to provide a comprehensive multi-modal knowledge graph by distributing sufficient and diverse images to textual entities in Wikidata. We also set RDF links (visual semantic relations) between image entities based on the hyperlinks and descriptions in Wikipedia. The Richpedia resource is accessible on the Web via a faceted query endpoint and provides a pathway for knowledge graph and computer vision tasks, such as link prediction and visual relation detection.
Image Caption Generation Using A Deep Architecture

Recently, image captioning is a new challenging task that has gathered widespread interest. The task involves generating a concise description of an image in natural language and is currently accomplished by techniques that use a combination of computer vision (CV), natural language processing (NLP), and machine learning methods.In this paper, we presented a model that generates natural language description of an image. We used a combination of convolutional neural networks to extract features and then used recurrent neural networks to generate text from these features. We incorporated the attention mechanism while generating captions. We evaluated the model on MSCOCO database. The obtained results are promising and competitive.
Sentiment Analysis of Twitter Data in Online Social Network

Sentiment Analysis is the procedure of computationally deciding if a bit of composing is certain, negative or nonpartisan. It's otherwise called supposition mining, inferring the sentiment or frame of mind of a user. In this paper, an attempt has been made to propose analysis method for sentiment of twitter dataset. In proposed method polarity of each tweet is calculate to distinguish whether tweet is positive or negative. A sentiment polarity is the emotions of user such as angry, sad, happy and joy. The proposed mechanism has been implemented in Python.