Reviewerly/exHarmony huggingface dataset:
S-BRRT*: A Spline-based Bidirectional RRT with Strategies under Nonholonomic Constraint

Global path planning in more general scene under the constraint condition has always been the difficulty in the field of path planning. Rapidly-exploration Random Tree and its derived algorithm (RRTs) is a popular planning algorithm of path planning in recent years, which is especially suitable for constrained conditions. A feasible path should consider the model and constraints of its execution object. To this end, we propose S-BRRT*: First, a bidirectional spanning tree is introduced into the basic RRT*. Second, nonholonomic constraints are considered into the program, including internal motion constraints and external collision constraints. Then, a new pruning strategy is put forward to conduct intelligent pruning of BRRT*'s zigzag path, including pruning under collision constraint and pruning under path constraint, so as to select optimal path nodes. Finally, the smooth strategy is proposed, using cubic Bézier curve to fitting the previous pruning path for the first time. Then, we fitting for the second time under collision optimization to ensure the final feasibly smooth path. Our S-BRRT * algorithm can realize the global path planning in a variety of scenarios. It is verified by experiments in sparse common scene as well as the scenarios with dense narrow path. We can conclude that the trajectory maintains robust and conforms to the nonholonomic constraints, meanwhile, we obtain improvement in length and feasibility.
Keyword-Text Graph Representation for Short Text Classification

Short text classification is an essential task in Natural Language Processing. This task is widely applied to many applications, such as spam filtering, question-answering, artificial conversational agent, sentiment analysis, review mining, etc. Short texts usually encounter a great challenge for classification due to data sparseness as they do not provide sufficient contextual information. In this paper, we introduce Keyword-Text Graph Convolutional Networks (KwTGCN) for short text classification. We also propose a method to identify keywords by estimating word distribution over different categories. These category keywords are then used to build a special keyword-text graph of short text corpus. We employ Graph Convolutional Network (GCN) and our keyword-text graph to generate the representation of short text corpus based on the relations of document-keyword and document-word as well as the word co-occurrence. This document, word and keyword representation is further used as an input feature for the next layer of short text classification. The experimental results on multiple benchmark datasets show that our proposed model outperforms the state-of-the-art models for short text classification in multiple attempts.
Modeling Competency Questions-Based Ontology for the Domain of Maize Crop: SIMcOnto

In this present time, there is rapid increase of various forms and structures of information across different domains of real world; for instance, agriculture. Because of this development, information is readily available; however, to retrieve the relevant information becomes a research issue to contend with. This identified research issue is, on the one hand, attributed to the unstructured representation of data, and on the other hand, attributed to the problem of word mismatch. Consequently, and in lieu of this, to retrieve relevant soils and irrigations data for maize crop in a more efficient structure becomes a challenge. Therefore, this research work aims to model soils and irrigations data for maize crop ontologically, which is christened as SIMcOnto. In order to achieve this objective, ontology which is a data modeling technique for complex knowledge representation is exploited. At the end, rule-based ontology is developed using the combined methodologies approach and written using Web Ontology Language (OWL2) in the syntax of RDF/XML. The rules leverage on the validated competency questions (CQs) which are modeled in first-order logic (FOL). During the course of the ontology development, the terminologies and the semantic rules are validated and verified by the domain experts and evaluation techniques. Therefore, the proposed SIMcOnto provides a machine represented knowledge-based modeling for soils and irrigations knowledge of maize crop. It is promising in retrieving a more precise and efficient information.
Big Telemetry Data Analytics for Vehicle Fault Detection

Monitoring the correctness of vehicles in modern traffic is very difficult, considering that the condition of a vehicle is constantly changing due to driving and requires constant checking of various parameters. Systems that would deal with this would have to be accurate in their estimates and very fast given that vehicles constantly generate a large amount of data. A Vehicle Fault Detection system has been proposed as a solution to this problem. The system is distributed and autonomous and uses linear regression as a classifier. The proposed solution was implemented and tested and detailed performance and accuracy analysis were performed.
Channel Discrepancies Adaptive Modulation Recognition Using Domain Adversarial Training

In this paper, we introduce a channel discrepancies adaptive automatic modulation recognition (AMR) method, which employs the domain adversarial training (DAT) to tackle the issue of wireless channel mismatch between training and testing conditions. The channel mismatch is a critical problem for deep learning (DL) based AMR systems. In realistic scenarios, the channel environment mismatch commonly happens and a large mismatch may seriously degrade the recognition accuracy of signals. The introduced channel discrepancies adaptive AMR method consists of a l-dimensional convolutional neural network (1-D CNN) based recognition model and a domain discriminator model. The DAT encourages the 1-D CNN to extract channel invariant features and increase the robustness of the AMR system to new channel environment. We evaluate the proposed method and competition approaches on the popular RadioML2016. 04c and RadioML2016.10a dataset. Experimental results shows that the introduced channel discrepancies adaptive AMR system produce notable better recognition performance than that of the methods without domain adaptation for the channel discrepancies of training and testing datasets.
Combining Deductive and Statistical Explanations in the FRANK Query Answering System

Both symbolic and sub-symbolic AI have their limi-tations, but their combination can be more than the sum of their parts. For instance, statistical machine learning has been hugely successful at classification and decision-making tasks, but not so good at deliberative systematic reasoning nor at explanation. We argue that by combining symbolic and sub-symbolic reasoning into hybrid systems, the whole will be more than the sum of its parts. To illustrate the potential of hybrid AI system, we describe the FRANK query answering system. FRANK infers new knowledge from the diverse and immense knowledge sources on the Web, using a combination of both deductive and statistical reasoning. This enables it to make predictions. For instance, to answer the question “Which country in Europe will have the highest GDP growth rate by 2032?”, it (i) decomposes Europe into its constituent countries, (ii) then for each country uses regression over their previous GDP growth rates to extrapolate each to 2032 and (iii) then returns the country which is predicted to then have the maximum value. The decompositions are explained deductively and the regressions by a prediction model that can be rendered graphically. This explanation of FRANK's reasoning merges deduction and statistics. In this paper, we highlight recent work on FRANK that focus on leveraging hybrid AI to tackle question answering with emphasis on explainability of the inference process and its inferred answers. We aim for whole system reasoning; that is, we are automating the choices of knowledge sources and the planning that constructs the inference process from the facts found in these knowledge sources. We intend that these ‘engineering’ choices are also explained to the user.
Interactive Reinforcement Learning Strategy

The birth of AlphaGo has set off a new wave of reinforcement learning technology. Reinforcement learning has become one of the most popular directions in the field of artificial intelligence. Its essence is the continuous integration and upgrading of various machine learning methods, and the agents continue to trial and error and obtain cumulative rewards. Q-learning is the most commonly used method in reinforcement learning, but it itself has many problems such as less early information, long learning time, low learning efficiency, and repeated trial and error. Therefore, Q-learning cannot be directly applied to the real environment. In response to this problem, the reinforcement learning discussed by the author is an interactive learning method that combines voice commands and Q-learning. This method uses part of the interaction between the agent and the human voice to find a larger target range in the early stage of learning. Then narrow the search range in turn, which can guide the agent to quickly achieve the learning effect and change the blindness of learning. Simulation experiments show that compared with the standard Q-learning algorithm, the proposed algorithm not only improves the convergence speed, shortens the learning time, but also reduces the number of collisions, enabling the agent to quickly find a better collision- free path.
Digital Shadows in Self-Improving System Integration: A Concept U sing Generative Modelling

Self-improving system integration (SISSY) aims at a continuous self-assessment and goal-oriented modification of the integration status of component systems within an overall system constellation. In this article, we argue that the process of self-assessing the conditions and the integration status can be supported by a continuously generated and updated model of the self - the digital shadow. We briefly summarise the concept by showing how this can serve as a basis for establishing the self-improvement part of SISSY and introduce three recent application scenarios, where we apply the technology.
Application dependable interaction module for computer vision-based human-computer interactions

Human-computer interaction (HCI) is a prominent development that provides autonomous and pervasive service broadcasting for in-person communications. Computer vision techniques are assimilated with HCI for human detection and object classification in autonomous communication environments. This article introduces an application dependable interaction module (ADIM) that is optimal in recognizing humans and other autonomous systems for communication. This recognition helps to precisely detect the application demands of the user/system for flawless service broadcast. In this process, a deep belief network is used for the persistent analysis of the behavior of the system/human in the interacting end. The behavior is characterized using touch, voice, and commands/requests for identifying the object at the initial stage. The metrics sharing delay, response latency, interaction failures, recognition ratio, and error are employed to verify the proposed module's performance.
Dense Feature Fusion for Online Mutual Knowledge Distillation

Abstract Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities, Ignoring the middle-level supervision, resulting in low efficiency in training many models. Even if some methods have joined the middle-level supervision, the previous way is to define the characteristic loss, and the effect is general. We propose a new method of middle-level supervision, through the fusion of features between teacher academic network to enter the supervision. The specific method is to fuse the features of the teacher academic network, and establish an auxiliary fusion branch to process the fusion information, so that the feature fusion can effectively strengthen the feature interaction of the teacher academic network. At the same time, we added the normalized integrated distillation of the output, and our method reached the SOTA in online KD. We have done a lot of experiments on cifar-10, cifar-100 and ImageNet datasets, and proved that this method is more effective than other methods in the performance of sub network and fusion classifier, as well as generating meaningful feature maps.
Coherence and entropic uncertainty relation of dipole-coupled qubits under decoherence

Quantum coherence is an essential physical resource in quantum information processing. Recently, an improved unilateral coherence inequation related to the quantum-memory-assisted entropic uncertainty relation was derived with the help of Holevo quantity and mutual information. Here we consider a coupled dipolar system driven by a coherent radiation field, and investigate the dynamics of unilateral coherence and entropic uncertainty of the system under the high temperature noise. The results show that as time increases, the unilateral coherence and the quantum entanglement of the system decrease, whereas the entropic uncertainty of the measured dipole enhances. Moreover, the detuning between the radiation field and the dipoles can induce the periodic oscillations of systemic coherence and entropic uncertainty during time evolution, and the oscillations are positively correlated with the Rabi frequency. Our findings could shed some light on the quantum coherent modulation and precision measurement for the dipole-coupled atomic or molecular system in the noisy environments.
Design and Application of Information System and Technology in Business Management

In order to meet the growing needs of customers and give full play to the function of information system, information system plays an active role in enterprise project management. In order to realize the visual display, sharing and inquiry of the commercial project management information, design the commercial management project management information system based on the network technology, and apply the information system in the project management, it is necessary to improve the standardization and scientific nature of the information system project design and management. In view of this. This paper takes Chengdu Panda Park Project as an example to design the information system for related project management, analyze the content, and discuss the application status of the information system in project management. It can reduce the project cost, promote the better realization of the project objectives and improve the economic benefits.
Feature Selection based on Particle Swarm Optimization Algorithm for Sentiment Analysis Classification

Online media serve as a potential secondary data source for studies on sentiment analysis. The current conditions of the data sources are very different, and it offers a variety of writing systems. Therefore, the results of accuracy in sentiment analysis are very important. An improved approach was proposed to increase the sentiment analysis accuracy based on text pre-processing and Naïve Bayes Classifier algorithm hybrid with Particle Swarm Optimization (NBC-PSO). Furthermore, the proposed algorithm solves the complex background problems about noise data and feature selection that affect the classification performance on sentiment analysis. This proceeded with the classification of positive or negative sentiments on these texts using NBC. Subsequently, the feature selection based on PSO was created to improve the accuracy. The experimental results showed that the proposed approach has a significant effect on sentiment score and polarity detection.
Development of a Machine learning image segmentation-based algorithm for the determination of the adequacy of Gram-stained sputum smear images

Machine learning (ML) prepares and trains a model through supervised or unsupervised learning methods. Sputum, a respiratory tract secretion, is a common laboratory specimen that aids in diagnosing respiratory diseases, including pulmonary tuberculosis (TB). Gram stain is an easy, cost-effective stain, which may be applied to sputum smears to screen out an unsatisfactory sample. ML model may help in screening sputum smears.This collaborative project was carried out from June 2020-July 2021. In this study, a color-based segmentation ML algorithm using K-Means clustering was developed. A library of stained sputum smears was built. The Bartletts criteria (based on neutrophil and squamous cell count) for screening and selecting satisfactory sputum smears were used. A smartphone camera was used to take several photographs of satisfactory, as well as unsatisfactory, smears. The image segmentation algorithm was applied to medical image analysis, color-segmentation of sputum images was done. The hue saturation value (HSV) color ranges were defined on a prototype image. Then, all connected pixels were identified as a single object, and morphological operations were applied.Usage of AI-driven model on the slide-image revealed the slide adequacy as the cell count was acceptable based on Bartlett's criteria. Both the manual cell counts (Range: 126-203 neutrophils, 14-47 squamous cells) and the model counts (Range: 117-242 neutrophils, 14-37 squamous cells) are within acceptable limits.The use of a model to screen a large number of sputum slides may be a boon in resource-limited settings where trained microscopists may not be easily available.
Quantum state tomography as a numerical optimization problem

We present a framework that formulates the quest for the most efficient quantum state tomography scheme as an optimization problem which can be solved numerically. This approach can be applied to a broad spectrum of relevant setups including measurements restricted to a subsystem. To illustrate the power of this method we present results for the six-dimensional Hilbert space constituted by a qubit-qutrit system, which could be realized e.g. by the N-14 nuclear spin-1 and two electronic spin states of a nitrogen-vacancy center in diamond. Measurements of the qubit subsystem are expressed by projectors of rank three, i.e., projectors on half-dimensional subspaces. For systems consisting only of qubits, it was shown analytically that a set of projectors on half-dimensional subspaces can be arranged in an informationally optimal fashion for quantum state tomography, thus forming so-called mutually unbiased subspaces. Our method goes beyond qubits-only systems and we find that in dimension six such a set of mutually-unbiased subspaces can be approximated with a deviation irrelevant for practical applications.
Assessment of Configuration Stability and Variability in Collections of Self-Adaptive Systems

Self-adaptive and self-organising (SASO) systems are typically composed of several (semi-)autonomous subsystems that alter their configuration and the system's structure in response to environmental and internal observations. The overall goal of these changes is mainly two-fold: improving the expected performance or goal achievement and providing increased robustness against disturbances and unforeseen events. In order to establish self-awareness of overall system behaviour as a basis for guided control intervention, we investigate measures to quantify and assess system properties. This paper introduces a novel approach to determine a degree of configuration variability and stability based on external, system-wide observation of configuration variables of the distributed subsystems. We analyse the behaviour of our metrics in two different scenarios and outline the possible advantages of the applicability.
A Research on Face Recognition Open Source Development Framework Based on PyTorch

At present, there are some problems in the development framework of various neural networks for face recognition in the laboratory environment, for example, the development environment is not standard, the dataset is not standard, and it is not friendly to beginners. In order to solve the above problems, this paper presents an open-source face recognition research and development framework based on PyTorch to help beginners quickly complete the experimental environment. First, based on the current mainstream CUDA technology, combined with PyTorch machine learning library, and with general third-party libraries such as OpenCV, a face recognition research and development framework is built. Then, a custom image data collection acquisition mechanism with three-tier filtering capabilities is proposed to help users quickly obtain high-quality datasets for use by the R&D framework. In addition, based on this research and development framework, some methods such as model compression, parameter sharing, were used to speed up model training. Finally, two classical experiments of handwritten numeric recognition and face recognition are implemented in this research and development framework. The test results show that the R&D framework is easy to use and has high practical value.
A System for Practicing Ball/Strike Judgment in VR Environment

The purpose of this study is to develop an easy-to-use ball/strike judgment practice system for inexperienced baseball umpires. The main idea is to provide a practice environment in a Virtual Reality (VR) space. With our system, users observe a pitched ball, perform ball/strike judgment, and review their judgment in a VR space. Since the whole process is completed in VR, users can practice the judgments without preparing a pitcher and catcher. A user investigation in which participants practiced with our system and judged balls thrown by a pitching machine was conducted. The participants responded positively when asked about the usefulness of our system.
Link Prediction with Supervised Learning on an Industry 4.0 related Knowledge Graph

Industry 4.0 requires the integration of many actors to provide correct, personalized, and quick answers to customers. In order to meet this integration, data coming from different actors demand to be semantically integrated and harmonized. In these settings, knowledge graphs have proven to be successful in the task of semantic data integration of distinct data silos. Despite the increasing adoption of knowledge graphs in the Industry 4.0 domain for integrating and harmonizing data, still, all the power of the integrated data is not exploited. In this article, we tackle the problem of knowledge graph completion presenting an approach that applies supervised machine learning algorithms on top of the knowledge graph. In general, observed results indicate that supervised machine learning algorithms perform with an AUC of more than 88%. These outcomes suggest that knowledge graph completion enables to unveil new relations by connecting entities in the knowledge graph. Thus, the discovered relations in the knowledge graph bring added value to the Industry 4.0 domain.