Reviewerly/exHarmony huggingface dataset:
An Evaluation Model of UAV Autonomous Capability Based on Cloud Model and AHP

In this paper, we propose a new evaluation model for the autonomous capability of ground attack unmanned aerial vehicle (UAV) using cloud model and analytic hierarchy process (AHP). First, a typical scenario of UAV combat is designed, and the activities and interactions of the scenario are analyzed using the department of defense architecture framework (DoDAF). Second, an evaluation index system for autonomous capability is designed, in which the autonomous capability is divided into five representative aspects. Third, an evaluation method is introduced using cloud model and AHP. Finally, the autonomous capability of three typical ground attack UAVs is evaluated, and the results demonstrate the effectiveness of the proposed model.
Improved YOLOv4 Network for Small Target Detection

In the process of target detection of UAV power inspection, there are some challenges and difficulties owing to the multi-scale targets and object occlusion. The improved YOLOv4 for small target detection is proposed in this article, so as to raise the accuracy of detection results. This paper designs a four-scale feature extraction network, a $$104 \times 104$$ feature extraction channel is added on the original YOLOv4 model. For the purpose of further improving the computational efficiency and the ability of feature information extraction, the SPP network is added to the three-layer features of $$26 \times 26$$ , $$52 \times 52$$ and $$104 \times 104$$ . K-means++ and transfer learning are introduced to improve the training speed and detection accuracy. The experimental result demonstrates that by comparing with SSD and the original YOLOv4 algorithms, the algorithm pointed out in this paper considerably increases the detection accuracy, especially for small target detection.
Visibility Detection Based on Dark Channel Prior and ResNet

In order to make full use of the monitoring video data of the airport and detecting visibility in haze weather, the meteorological visibility detection methods based on DCP and ResNet are proposed respectively in this paper. Firstly, by analyzing the main causes of low visibility in haze weather, the brightness mechanism of fog image is introduced, the visible light scattering attenuation model is deduced, and the DCP method is proposed for visibility detection. Then, for the specific application scenarios of airport video, a data-driven visibility detection method based on ResNet is proposed. Finally, the verification experiment of video data algorithm based on airport monitoring is designed.
Recognition of UAV Image Transmission Signal via Deep Learning Methods

In the last decade, the rapid development of UAV (Unmanned Aerial Vehicle) enterprises has given rise to a great number of drones, and a part of them illegal flights. Due to their low threshold of entry into the air, it is difficult to monitor low and slow UAVs which threaten the airspace safety. Determining the behaviour of UAVs is a challenging area of airspace regulation. Nowadays, DL (Deep Learning) has become a key research component of radiation signal recognition in many regulatory applications. In order to facilitate the solution to illegal UAV intrusion into airspace without locating the perpetrators, we apply deep learning methods in radio signal recognition based on a real-world signal dataset. Further the model of CNN (Convolutional Neural Network) was pruned to obtain a lightweight network for efficient signal recognition. In this paper, we have verified the first time the effectiveness of DL in recognizing drone raw signals. Then, we also take a closer look at the performance of lightweight models and compare acan achieve 98.9% recognition accuracy, which is at least 1.59% higher than CNN. Finally, we discuss open issues in this field.
Dynamic MPC Obstacle Avoidance Based Tube Sparse A* Path Planning Method for UAV Swarm Under Communication Delays

Cooperative path planning plays a vital role for the UAVs in real cooperative combat. However, the reliability and timeliness of the existing path planning methods are usually intractable to guarantee due to the existence of communication delays between UAVs. To deal with this issue, in this paper, considering the communication delays, a distinctive “global planning-local obstacle avoidance” cooperative path planning framework based on the integration of the tube sparse A* algorithm and model predictive control (MPC) is constructed. In global planning phase, the priority decoupling mechanism and receding planning framework are used to incorporate collision avoidance coordination as constraints for the path planning model. Considering the probabilistic distribution of adjacent UAVs’ positions caused by random communication delays, a real-time global tube sparse A* path planning algorithm is customized, in which communication delays are assumed to obey Markov distribution. Subsequently, in local collision avoidance phase, considering the uncertainty of communication delays, some path segments generated by global path planning may still be at risk of collision with adjacent UAVs, the path of UAVs inside the tube will be tracked and adjusted by solving the optimization problem of the distributed MPC model. Lastly, simulation results demonstrate the reliability and timeliness of proposed algorithms.
UAV Path Planning from Human Demonstrations Using Inverse Reinforcement Learning

In UAV path planning tasks, classical algorithms can only solve the tasks where environment is knowable and can be changed into mathematical model. For the tasks with unknown environment information is difficult to solve. However, such tasks can be easily solved by human. In this paper, we first bulid a UAV path planning environment and train the agent with deep reinforcement learning as human demonstration. Then, we use inverse reinforcement learning algorithm and processed human demonstrations to learn human policy. Finally, we test the trained policy in path planning tasks to verify the feasibility.
UAV Cluster Confrontation Decision-Making Algorithm Based on Two-Layer Intelligent Optimization

In this paper, a UAV cluster confrontation decision-making algorithm based on two-layer intelligent optimization is proposed. Firstly, single-UAV strategy set and cluster strategy set are introduced, as well as the reward functions between cluster are constructed through height, speed and angle. Secondly, according to different cluster confrontation methods, the corresponding payoff matrix is calculated. Then, the overall cluster confrontation decision-making algorithm is proposed, along with the analysis of the Nash equilibrium solution in the first-layer intelligent optimization. Finally, a numerical experiment is presented to verify the effectiveness of the proposed algorithm. Based on the research of the two-layer intelligent decision-making algorithm, this paper effectively improves the cooperative decision-making ability of the UAV cluster, and provides a theoretical basis for the intelligent development of the information interaction between the UAV cluster and the single-UAV.
Multi-arm Space Robot Mission Planning Based on Hierarchical Task Network

Aiming at the task requirements of multi-manipulator space robot for typical on-orbit servicing missions, considering complex space environment and the characteristics of many constraints, a multi-constrained task planning method for multi-arm space robot based on Hierarchical Task Network (HTN) is proposed. The network is widely used in the field of intelligent planning because of its simplicity and efficiency. However, the speed at which the HTN planner solves a planning problem depends on the order of the decomposition methods of the problem in the domain knowledge, so the planning results of the HTN planner are generally not optimal solutions. Therefore, Temporal-Difference learning is proposed in HTN. Finally, taking an on-orbit repairing task of a four-arm space robot as an example, the feasibility and effectiveness of the multi-constrained task planning method using the proposed task network are verified by simulation experiments.
Deep Clustering Network with Angular Metric Learning in Navigation

In this paper, we propose a novel method called Deep Clustering Network with Angular Metric Learning (DCN-AML). In DCN-AML, the loss function is composed of three items: reconstruction loss, clustering loss and discriminative loss. DCN-AML firstly non-linearly embeds images into latent spaces through auto-encoder to calculate reconstruction loss and learns the features of original images, according to which clustering probabilities are calculated by generated angular softmax where the parameters are replaced by angles of features, producing the clustering loss and forcing intra-cluster distance smaller. As for discriminative loss, we introduce a new angular metric as embedded regularization with relative entropy minimization to calculate discriminative loss, through which inter-cluster distance will be enlarged. These three parts of loss function are in a joint framework which shows great performance in deep clustering. The extensive experiments on four datasets: MNIST-full, YTF, COIL-20 and FRGC-v2.0 show that DCN-AML has a promising performance.
A High-Precision Dot Matrix Character Recognition Method in Complex Background

Dot matrix character recognition is a challenging task with many difficulties, such as lack of datasets, complex background pattern, character distortion caused by uneven outer packaging of products. In this paper, we propose a new method of synthesizing dot matrix character image, which can automatically generate a large number of datasets and annotation files. The synthesized dot matrix character image is very similar to the real image. On this basis, we propose a high-precision dot matrix character recognition method in complex background, which constructs a new neural network architecture by inserting the spatial transformer network (STN) into the convolutional recurrent neural network (CRNN). The STN can transform an input image to a rectified image by performing explicit spatial transformations, which can help to improve the performance of dot matrix character recognition. Experiments show that the proposed method achieves the state-of-the-art recognition result, without the loss of the inference speed.
A Novel Interacting Multiple Model Based Robust Filter for SINS/GPS

Conventionally, the Kalman filter (KF) is widely used in practical engineering. However, the KF state estimation may be inaccurate when heavy-tailed noises are present. The multiple tasks put forward higher requirements for accuracy, so that filters based on one single model cannot satisfy the requirements always during the entire task. To solve such problems, a novel robust KF based on an interactive multi-model algorithm (IMM-RKF) is proposed. In which, heavy-tailed noises are remodeled as student’s T distribution in advance, and related matrixes are updated adaptively within limited iterations. Furthermore, three different robust sub-filters are introduced to work simultaneously for high precision state estimation. Finally, simulations are conducted by tracking a maneuvering vehicle with SINS/GPS and the results validate the superiority of IMM-RKF.
Perspective Chapter: Re-Inventing Communicative Spaces – A Study to Assess the Shift of Traditional Puppetry Artifacts into Computer-Mediated Objects

Puppetry has always remained a major tool of storytelling for masses. From its inception, this communicative art form has undergone changes, and with the advancement of technological interfaces, its structural space has received a series of modifications. Most puppetry involves storytelling, and its impact is determined by its ability to create patterns into the psycho-spaces of viewers. With changing times, the analog formats of puppetry are getting replaced by the digital objects. These digital objects are computer mediated in nature and have a key characteristic of engaging audiences with multimodal performative interactive system. In the current scenario, under the deep influence of ‘convergence culture,’ the traditional puppetry art form has not remained insulated from the digitized formations. These spaces are allowing a new alternative media output, which is digitized, watched, as well as promoted on digital screens. Under the wider array of networked spaces, this paper is an attempt to theorize the changing landscape of traditional puppetry art form into the corridors of digital objects. These digital objects have a nature of accommodating themselves into multiple channels of communication and triggering a plethora of imaginative spaces for spectators.
Assured and Provable Data Expuncturing in cloud using Ciphertext Policy–Attribute Based Encryption (CP-ABE)

Modern cloud computing strategies greatly reduce investment in infrastructure and data maintenance costs across startup companies and large organizations. This is mainly because of the emergence of the cloud-based IoT paradigm, which allows IoT-based devices to directly upload the data acquired from the environments to the remote cloud and allows the owner of those resources to manage their data through cloud-based applications. However, there are many challenges associated with this kind of data being outsourced, and the cloud server is entirely responsible for securing that data. Since these cloud servers cannot always be fully trusted, securing the deletion of unwanted sensitive data stored in the cloud to prevent data leaking is another challenging issue. As a result, most of the existing technology allows coarse-grained deletion of data on cloud servers. Consequently, cloud data security is jeopardized and new technological development in the cloud environment is hampered. This work implements a Cloud-based, Scalable, Secure unique data Deletion and Verification policy (CSSDV) that uses Ciphertext-Attribute-based encryption to enable fine-grained secure data deletion and data verification. We confirm the CSSDV scheme's security under the standard model and demonstrate its accuracy and efficacy through theoretical analysis and extensive numerical experiment data. The experimental results for eleven attribute each with different sizes demonstrate the superiority of our algorithm in accessing and deleting the files in a secure and scalable manner.
<scp>SmartCLIDE</scp> design pattern assistant: A <scp>decision‐tree</scp> based approach

Abstract Design patterns are well‐known solutions to recurring design problems that are widely adopted in the software industry, either as formal means of communication or as a way to improve structural quality, enabling proper software extension. However, the adoption and correct instantiation of patterns is not a trivial task and requires substantial design experience. Some patterns are conceptually close or present similar design alternatives, leading novice developers to improper pattern selection, thereby reducing maintainability. Additionally, the mis‐instantiation of a GoF (Gang‐of‐Four) design pattern, leads to phenomena such as pattern grime or architecture decay. To alleviate this problem, in this work we propose an approach that can help software engineers to more easily and safely select the proper design pattern, for a given design problem. The approach relies on decision trees, which are constructed using domain knowledge, while options are conveyed to software engineers through an Eclipse Theia plugin. To assess the usefulness and the perceived benefits of the approach, as well as the usability of the tool support, we have conducted an industrial validation study, using various data collection methods, such as questionnaires, focus groups, and task analysis. The results of the study suggest that the proposed approach is promising, since it increases the probability of the proper pattern being selected, and various useful future work suggestions have been obtained by the practitioners.
Students’ perceptions of smartphone use: Institutional policies in Assam, India

Many educational institutions lack well-defined, targeted policies to address problems relating to student smartphone use on campus. In this study, we analyse the patterns of student smartphone use on academic campuses and propose a range of policy measures to address the problems arising from such use. Our research, which draws on primary data collected through a field survey in Assam, India, is one of very few studies in the field to focus on northeast India. Inspired by the literature review, which revealed empirical findings on student smartphone use behaviour, we create and evaluate four policy alternatives: a complete ban on smartphones in educational institutions, the use of student smartphones with proper training, the use of specific smartphones with/without particular applications, and the development of learning platforms and study materials to regulate student smartphone use. Our results show that students who use smartphones prioritise ‘information collection’, although the information they collect may not relate to their studies. We argue that the best policy would be to reduce attention deficiencies by allowing students to use smartphones on campus but not in classrooms. In addition, both teachers and students should receive necessary training on effective smartphone use to exchange academic information.
An Efficient Document Clustering Approach for Devising Semantic Clusters

The rise of superfluous information day by day has made the clustering of information into meaningful sets challenging. We propose an efficient approach for obtaining semantic clusters from a huge volume of documents. The preprocessing based on the lexical ontological information from WordNet helps in reducing the feature space and eliminating synonymy problems among the features. A considerable decrease in computational time is achieved by means of an enhanced k-means clustering algorithm. This algorithm computes the starting centroids using a sorting technique based on Red-Black Tree, thus ensuring efficiency and meaningful clusters. Memoization techniques are utilized in the subsequent stages, to avoid redundant computations. Results indicate that our method produces more meaningful clusters than those employing word embedding models like Word2Vec, FastText, and BERT for feature extraction. The experimental results on the MiniNewsGroup, 20NewsGroup Large dataset, and Reuters-21578 dataset show remarkable achievement in clustering outcomes in terms of purity and execution time. Results on the enormous data collection 20NewsGroup Large dataset show a better NMI (Normalized Mutual Information) score compared to the existing methods.
A grounded theory of selecting lean and agile practices for software development

Abstract The practices of lean and agile methods are important to manage and guide projects development in different situations. However, selecting the appropriate practices of lean and agile methods is a daunting task, owing to the availability of hundreds of practices. This wide variety of available practices bewilders potential users, in terms of the best practices that may yield satisfactory solution when applied to the project development. Lean and agile practices selection model is necessary to guide both academic and industrial experts to choose the appropriate practices for a specific project development. Thus, this study aimed to develop lean and agile practices selection (LAPS) model that presents a clear picture of how developers can use LAPS model to select and adopt different practices from various lean and agile methods. Qualitative research method (e.g., grounded theory) was used to interview 18 software developers across 13 countries. The validated qualitative analysis results showed that the proposed LAPS model consists of two main components, which are (1) lean and agile practices that include 41 best practices related to lean and agile methods and (2) factors that affect the process of lean and agile practices selection including five main factors and 17 subfactors associated with the five main factors. The two case studies conducted assured the validity and applicability of the lean and agile practices selection model.
Direct Delivery of Near by Expiry Product to Underprivileged Peoples (NGO) using Machine Learning Techniques

Farmers may advance their endeavours by using e-Farming as a step. This will help all farmers who need a clear motivation for their plant products as well as end users who need a specific price for each item. In addition to helping them with their daily routine, this will also help less fortunate individuals by providing food for those who are in need. In order to cover the NGO's basic needs while also preventing food waste, other government-based NGO's collaborate with them to get in touch with people who have leftover food that they recently squandered. The system's or application's goal is to create an area where all experts have been removed and the value of green space has been assessed. Finally, we clearly accommodate troubled people through a non-benefit affiliation by using wasted food. Therefore, this system can help end-client thing sureness while similarly spreading out a trust association among purchasers and producers. The remainder of the food is given to the persecuted, NGOs, and wastage/extra food is disposed of in various abilities.
End-to-end multi-granulation causality extraction model

Causality extraction has become a crucial task in natural language processing and knowledge graph. However, most existing methods divide causality extraction into two subtasks: extraction of candidate causal pairs and classification of causality. These methods result in cascading errors and the loss of associated contextual information. Therefore, in this study, based on graph theory, an End-to-end Multi-Granulation Causality Extraction model (EMGCE) is proposed to extract explicit causality and directly mine implicit causality. First, the sentences are represented on different granulation layers, that contain character, word, and contextual string layers. The word layer is fine-grained into three layers: word-index, word-embedding and word-position-embedding layers. Then, a granular causality tree of dataset is built based on the word-index layer. Next, an improved tagREtriplet algorithm is designed to obtain the labeled causality based on the granular causality tree. It can transform the task into a sequence labeling task. Subsequently, the multi-granulation semantic representation is fed into the neural network model to extract causality. Finally, based on the extended public SemEval 2010 Task 8 dataset, the experimental results demonstrate that EMGCE is effective.
TSHD: Topic Segmentation Based on Headings Detection (Case Study: Resumes)

Many unstructured documents contain segments with specific topics. Extracting these segments and identifying their topics helps to access the required information directly. This can improve the quality of many NLP applications such as information extraction, information retrieval, summarization, and question answering. Resumes (CVs) are unstructured documents that have diverse formats. They contain various segments such as personal information, experience, and education. Manually processing resumes to find the most suitable candidates for a particular job is a difficult task. Due to the increased amount of data, it has become very necessary to manipulate resumes by computer to save time and effort. This research presents a new algorithm named TSHD for topic segmentation based on headings detection. We apply the algorithm to extract resume segments and identify their topics. The proposed TSHD algorithm is accurate and addresses many weaknesses in previous studies. Evaluation results show a very high F1 score (about 96%) and a very low segmentation error (about 2%). The algorithm can be easily adapted to deal with other textual domains that contain headings in their segments.