Reviewerly/exHarmony huggingface dataset:
Resampling algorithm based on sample similarity and variation coefficient

To solve the problem of imbalanced data classification, a resampling algorithm based on sample similarity and variation coefficient is proposed. For minority samples, new samples are synthesized with bias according to the similarity between samples. For majority samples, the boundary region is determined according to their variation coefficients, and the samples in the non-boundary region are deleted randomly. By the above two methods, original data set is reconstructed, and sample scales of two classes become more balanced. The identification ability of minority samples and overall classification accuracy can be improved effectively by scientifically guiding oversampling and undersampling. Finally, the imbalanced data sets were adopted for verification by using the kernel extreme learning machine (KELM) as classifier. Experimental results show that the overall performance of the proposed algorithm is superior to other comparison algorithms in dealing with imbalanced data classification.
Learning and Characterizing Fully-Ordered Lattice Automata

Traditional automata classify words from a given alphabet as either good or bad. In many scenarios, in particular in formal verification, a finer classification is required. Fully-ordered lattice automata (FOLA) associate with every possible word a value from a finite set of values such as $$\{0,1,2,\ldots ,k\}$$ . In this paper we are interested in learning formal series that can be represented by FOLA. Such a series can be learned by a straight forward extension of the $${\textbf {L}}^*$$ algorithm. However, this approach does not take advantage of the special structure of a FOLA. In this paper we investigate FOLAs and provide a Myhill-Nerode characterization for FOLAs, which serves as a basis for providing a specialized algorithm for FOLAs, which we term $${\textbf {FOL}}^*$$ . We compare the performance of $${\textbf {FOL}}^*$$ to that of $${\textbf {L}}^*$$ on synthetically generated FOLA. Our experiments show that $${\textbf {FOL}}^*$$ outperforms $${\textbf {L}}^*$$ in the number of states of the obtained FOLA, the number of issued value queries (the extension of membership queries to the quantitative setting), and the number of issued equivalence queries.
A Hybrid Pattern Knowledge Graph-Based API Recommendation Approach

There are a large number of application program interfaces (APIs) on the Internet. Due to frequently updating APIs, programmers are prompted to frequently consult API documents in the process of software development. However, the traditional query approaches of the documents have certain limitations. For example, the programmers need to know the API name as a prerequisite and are often unable to obtain the expected search results because of the difference in understanding between the description of the problem and the description of the documents. Only these “known-unknown” information can be queried, and it is difficult to query the “unknown-unknown” information. To address the limitations, we establish the knowledge graph of software source code combined with knowledge which is derived from the documents, Github project code warehouse, Stack overflow platform, and local project code warehouse. Moreover, we propose a hybrid pattern knowledge graph-based API recommendation approach for programmers to complete the query task of unknown-unknown information. Finally, we constructed large-scale real experiments. Evaluation results prove that the proposed approach significantly outperforms the state-of-the-art approach.
Knowledge State Networks for Skill Assessment in Atomic Learning

We introduce a new framework for fast and effective knowledge state assessments in the context of personalized, skill-based online learning. We use knowledge state networks – specific neural networks trained on assessment data of previous learners – to predict the full knowledge state of other learners from only partial information about their skills. In combination with a matching assessment strategy for asking discriminative questions we demonstrate that our approach leads to a significant speed-up of the assessment process – in terms of the necessary number of assessment questions – in comparison to standard assessment designs. In practice, the methods enable personalized, skill-based online learning also for skill ontologies of fine granularity without deteriorating the associated learning experience by a lengthy assessment process.
Discriminative Correlation Filter for Visual Tracking based on Asymmetric Relative Entropy

In recent years, in the field of target tracking based on correlation filters, people mostly build correlation filter models to improve the tracking defects caused by boundary effects and filter degradation, and achieve good results to a certain extent. However, most of the model regularization terms are constructed based on response graph approximation problems, or are measured by some simpler linear measures. In this paper, we get rid of the traditional linear measurement method, and use more complex relative entropy to fit the response in the online tracking model in terms of model construction. We propose a correlation filter based on asymmetric relative entropy (RECF) to solve the problem of filter degradation during long-term tracking and improve the robustness of the model. And compare 10 advanced tracker algorithms in recent years on the three data of UAV123, DTB70 and UAVDT-M to show the excellent performance of RECF in this paper.
Implementation of Blockchain Technology to Enhance Last Mile Delivery Models with Sustainability Perspectives

The advancement in technology, such as, Smart Logistics, IoT, RFID, sensors, and 5G, resulted in the evolution of Industry 4.0 that has started gaining a lot of popularity among different sectors like last mile delivery. This is important as the rising demand for such technology enabled platforms has been found to be necessary for fulfilling the opt for e-commerce services to support the retail outlets. The literature shows that to relax the pressure on the last mile sector, blockchain technology can be an effective solution both to protect the firm financial aspects and sustainability requirements. To ensure efficiency in the system and success in the implementation of blockchain technology into the last mile delivery sector, it is essential to study the various factors and capabilities of blockchain to handle the existing problems and requirements to analyze the efficiency of this integration. The focus areas of this paper are mainly to identify the impact of applying blockchain technology to support the last mile delivery of goods. The impacted areas focus mainly on the efficiency of the process and its leverage on the costs, both administrative and operational, and level of sustainability achieved. The proposed platform has enabled the enhancement of the integration of blockchain into the last mile delivery. The proposed smart contract system is designed to efficiently assign the orders from the demander to the respective fleet providers with the help of miners. This assignment is made possible by considering the various aspects that have been stored into the system, namely geographical location, the proximity to the destination of delivery along the route of delivery, size of the parcels, and capacity of the fleet.
Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement

With the development of science and technology, graph convolutional network has made great progress in improving the accuracy of action recognition. However, there still exists some deficiencies in current methods. Firstly, the human skeleton point coordinates entering into the network are barely refined, which may cause large error. Secondly, the second-order infor-mation(the length and direction of bones), which can reflect action characteristics discriminatively, is rarely used. To solve the above issues, a two stream graph convolutional network with pose refinement for skeleton based action recognition is proposed. Besides, we use an adaptive block to to help improve the accuracy. We test our method on Kinetics dataset and the experiment show it can get better results than some recent methods, which plays a positive role in future research.
O2SNet: Unsupervised Ship Instance Segmentation from Optical to SAR Images Via Domain Adaptation

Considering insufficient data and difficulty of labeling in Synthetic Aperture Radar (SAR) images, we propose a method for unsupervised SAR ship instance segmentation based on domain adaptation. Compared with optical images, unsupervised learning in SAR images faces the difficulties of insufficient data and lacking detail features. The proposed method O2SNet contains both sample and model adaptation module. The former builds GeminiGAN to convert optical data into SAR-style training samples. In addition, the latter uses optical target features in source domain to guide the unsupervised learning of the SAR images in target domain through prototype alignment. The O2SNet proposed in this paper constructs a novel domain adaptation framework. Experiments show that it has achieved 25.9 and 25.3 points higher Average Precision (AP) in unsupervised detection and segmentation tasks on SAR dataset of HRSID, which has exceeded state-of-the-art domain adaptation methods.
An Anchor-Free Target Detection Algorithm Combining Attention and Dilation Convolution

Aiming at the problem of insufficient target detection capability in CenterNet, an improved target detection model combining attention and cavity convolution is proposed. Firstly, in order to improve the ability of the network to obtain the semantic and location features of the target, an improved nonlocal attention mechanism module (CANL) is designed to capture the remote dependence of the target in the image along the channel domain and the spatial domain, respectively. Secondly, a multi-scale feature extraction network based on dilation convolution (MSNet) is designed to improve the expression ability of the network to different scale targets, the residual structure is used to fuse the receptive field features of multiple scales in parallel, and the feature information obtained by the target in the image at multiple scales is retained. Finally, the proposed algorithm is verified on PASCAL VOC dataset. The detection accuracy of the proposed algorithm is 2.65 % higher than that of the baseline algorithm CenterNet, which effectively improves the performance of the anchorless object detection algorithm.
Monocular Pseudo-LiDAR Point Cloud Extrapolation Based on Iterative Hybrid Rendering

Recently, a pseudo-LiDAR point cloud extrapolation algorithm equipped with stereo cameras has been introduced, bridging the gap between the expensive 3D sensor LiDAR and relatively cheap 2D sensor camera in autonomous driving. In this paper, we explore an approach to further bridge this gap using only a monocular camera and extrapolate a wide field of view 3D point cloud from a limited 2D view. However, this task is extremely challenging as it requires inferring the occluded contents in the scene. To this end, we propose a ‘render-refine-iterate-fuse’ framework that takes advantage of both image view synthesis and image inpainting techniques, guiding the neural network to learn the potential spatial distribution. In addition, we design a hybrid rendering scheme to ensure that the visible content moves in a geometrically correct manner and fills the pixels caused by occlusion. Benefitting from the proposed framework, our approach achieves significant improvements on the pseudo-LiDAR point cloud extrapolation task. The gap between LiDAR and cameras is further bridged, showing an economical and practical application in the environment perception module of autonomous driving. The experimental results evaluated on the KITTI dataset demonstrate that our approach achieves superior quantitative and qualitative performance.
Brain cancer classification using elman recurrent neural network with genetic algorithm optimization

Elman Recurrent Neural Network (ERNN) is a model that accommodates network output to be network input to produce the next network output. Genetic algorithms are computational approaches to solving problems that are modeled after the process of biological evolution. The purpose of this research is to classify brain cancer from Magnetic Resonance Imaging (MRI) brain with ERNN model. To improve the accuracy of the classification results, the ERNN model parameters are optimized using genetic algorithms. The data used in the model is the result of extracting MRI images using Gray Level Co-occurrence Matrix (GLCM) method, which consists of 13 variables, those are contrast, correlation, energy, homogeneity, entropy, sum of square variance, Inverse Difference Moment (IDM), sum average, sum entropy, sum variance, difference entropy, maximum probability, and dissimilarity. Classification results are evaluated using accuracy, sensitivity, and specificity on training and testing data. The study has perfect results on training data with no misclassification, while on the testing data the results are also very satisfying with sensitivity, specificity, and accuracy values are 100%, 93.33%, 96.43%.
XRtic: A Prototyping Toolkit for XR Applications using Cloth Deformation

This paper presents XRtic, a prototyping toolkit enabling real-world cloth deformations to be used in novel ways in eXtended Reality (XR) applications. XRtic was developed based on the insights gathered from semi-structured interviews with XR developers. It consists of custom-made actuators that can be attached to regular clothing, a controller bus system, and a controller interface. Using our toolkit, users can design and integrate different cloth deformation types synchronised with virtual content in a plug-and-play manner. Along with a technical analysis of the actuation behaviour of the XRtic actuators, we present the findings gathered from a user study with eight XR developers, focusing on the usability of the system and creative support. Overall, participants found it an easy-to-use toolkit that supports iterative and rapid prototyping, and enables cloth to be deformed in unique ways in synchronisation with XR applications. Based on the findings, we also report limitations and future work relating to our system.
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning

Multiagent reinforcement learning (MARL) can solve complex cooperative tasks. However, the efficiency of existing MARL methods relies heavily on well-defined reward functions. Multiagent tasks with sparse reward feedback are especially challenging not only because of the credit distribution problem, but also due to the low probability of obtaining positive reward feedback. In this paper, we design a graph network called Cooperation Graph (CG). The Cooperation Graph is the combination of two simple bipartite graphs, namely, the Agent Clustering subgraph (ACG) and the Cluster Designating subgraph (CDG). Next, based on this novel graph structure, we propose a Cooperation Graph Multiagent Reinforcement Learning (CG-MARL) algorithm, which can efficiently deal with the sparse reward problem in multiagent tasks. In CG-MARL, agents are directly controlled by the Cooperation Graph. And a policy neural network is trained to manipulate this Cooperation Graph, guiding agents to achieve cooperation in an implicit way. This hierarchical feature of CG-MARL provides space for customized cluster-actions, an extensible interface for introducing fundamental cooperation knowledge. In experiments, CG-MARL shows state-of-the-art performance in sparse reward multiagent benchmarks, including the anti-invasion interception task and the multi-cargo delivery task.
Data-Interactive Innovations as the Heart of Market of Data (MoDAT)

The data marketplace is not only for data trading, but it is also a place where the smartness of stakeholders meets the smartness of the data. Not only the data themselves as exchangeable resources, but also the analytical skills, the users’ requests, and the knowledge are traded among stakeholders toward innovations. In light of this, communication design in the market is important for leading data collaboration. In this chapter, we design the data marketplace as an innovation-driven environment for value exchange via data. Moreover, we give an overview of the history of the data marketplace, discuss the business conditions and environment surrounding recent data in the world through our survey, and identify the problems.
Multiple Object Tracking in aerial vehicle overhead video

This paper proposes a multiple Object Tracking algorithm for drone overhead video, which solves some of the specific problems in this field. By studying the characteristics of small and dense targets in the UAV overhead shooting video, using the self-supervision technology to innovate the dynamic mask structure, combined with the existing multiple Object Tracking idea of first detection and then tracking, we designed our multiple Object Tracking algorithm, and finally trained and tested on the Visdrone dataset, which got good results and proved the superiority of our algorithm in the UAV overhead video.
ALPY PLUS - Adaptive Model Oriented to Pathway Planning in Virtual Learning System

This paper presents an adaptive model called ALPY PLUS to enrich the dynamic planning of learning resources with user characteristics and those of her/his context, in order to provide a personalized course in a virtual environment. We describe the proposed architecture, the visual prototype, together with the main components, actions and services required by the adaptive model.
A Defect Heterogeneous Risk Assessment Method with Misclassification Cost

Existing software defect prediction techniques do not pay enough attention to the different cost impacts caused by misclassification and cannot prioritize modules with high defect risk. To address these problems, a defect heterogeneity risk assessment method with misclassification cost (DHRA) is proposed. Firstly, noise samples, discrete samples, and high-dimensional features are processed. Secondly, the imbalance ratio and the ratio of misclassification of different categories are calculated to obtain the overall misclassification cost (MC). The heterogeneous classifiers are selected based on the evaluation metric and MC. Then, the defective assessment value is calculated by multiplying the voting weight matrix based on misclassification cost and the base probability matrix. Finally, the defect risk grade of the module is assessed based on the defect risk table. The experimental results show that DHRA outperforms other methods in terms of accuracy, F-score, and Matthews correlation coefficient. The defect risk grade obtained by this method can accurately reflect the potential defects of the samples.
Analogical Proportions, Multivalued Dependencies and Explanations

Analogical proportions are statements of the form "a is to b as c is to d". They deal simultaneously with the similarities and differences between items, and they may be considered as a building block of analogical inference. This short paper establishes the existence of a close linkage between analogical proportions and (weak) multivalued dependencies in databases, thus providing an unexpected bridge between two distant areas of research: analogical reasoning and database design. (Weak) multivalued dependencies express a form of contextual logical independence. Besides, analogical proportions, which heavily rely on the comparison of items inside pairs and to the pairing of pairs exhibiting identical changes on attributes, are also a tool for providing adverse example-based explanations. Lastly, it is suggested that this may be applied to a data set reporting decisions in order to detect if some decision is unfair with respect to a sensitive variable (fairness being a matter of independence).
Hierarchical Spatial Pyramidal Feature Representation Learning for Facial Expression Recognition

As the recognition of facial expressions has attracted increasing attention due to its important applications in several fields (security, human-machine interaction, marketing, etc.), thanks to the detection of different possible expressions (happiness, sadness, etc.), this technology is gaining more attention. This article presents a method for the recognition of facial expressions by exploiting spatial information. In this method, the LBP descriptor is proposed to extract local characteristics, and a hierarchical spatial pyramid model is used to capture spatial information in an image. This method enriches the semantic description of visual information, which improves the recognition rate of facial expressions. The experimental analysis was carried out on the CK+ facial expression dataset, which includes seven categories of expressions—“happy,” “sad,” “surprise,” “contempt,” “fear,” “disgust,” “anger”—and it has been given state-of-the-art performance. The recognition results of the system are very satisfactory.
Workshop Pengelolaan Jurnal dalam Lingkup Prodi Pendidikan Matematika

Abstract: Community service is an effort to share knowledge about journal management for journal managers so that they can manage journals properly and correctly. This service is carried out by holding a workshop followed by a journal review. used are workshops and journal surgery. The workshop was held twice and continued with journal review to find out the shortcomings of each journal. The methods used are workshops and journal surgery. The results obtained during the journal management workshop were that the participants knew the management of the journal from a managerial perspective as well as from an article editing perspective. It is hoped that after obtaining the journal management workshop, participants can prepare their journals for national and international accreditation. Improving the quality of scientific journal management is one of the basic components that greatly affects the quality of published journals. Journal managers who are not equipped with good journal management skills will certainly not be able to carry out a good journal management process. This Community Service (PkM) activity carried out in the Mathematics Education Study Program is very useful for journal managers. Especially for managers who have planned to submit their journals to register with the National Journal Accreditation (Arjuna) BRIN. This PkM is also an opportunity for journal managers to correct deficiencies in their respective journals. After understanding what are the shortcomings in the journals that are managed, it will be easier to fix them. Abstrak: Pengabdian kepada masyarakat merupakan salah satu upaya untuk sharing pengetahuan mengenai pengelolaan jurnal bagi para pengelola jurnal agar dapat mengelola jurnal dengan baik dan benar. Pengabdian ini dilakukan dengan mengadakan workshop dilanjutkan dengan bedah jurnal. yang digunakan adalah workshop dan bedah jurnal. Workshop dilaksanakan sebanyak dua kali dan dilanjutkan dengan bedah jurnal untuk mengetahui kekurangan dari masing-masing jurnal. Hasil yang diperoleh selama workshop pengelolaan jurnal adalah peserta mengetahui pengelolaan jurnal dari segi managerial maupun dari segi penyuntingan artikel. Kegiatan Pengabdian kepada Masyarakat (PkM) yang dilaksanakan di Prodi Pendidikan Matematika ini, sangat bermanfaat sekali bagi para pengelola jurnal. Terutama bagi pengelola yang sudah merencanakan untuk mengajukan jurnalnya mendaftar pada Akreditasi Jurnal Nasional (Arjuna) BRIN. PkM ini juga menjadi ajang bagi para pengelola jurnal untuk memperbaiki kekurangan-kekurangan pada jurnal mereka masing-masing. Setelah memahami apa yang menjadi kekurangan pada jurnal yang dikelola, maka akan lebih mudah untuk memperbaikinya.