Reviewerly/exHarmony huggingface dataset:
ArchOnto, a CIDOC-CRM-Based Linked Data Model for the Portuguese Archives

Archives are faced with great challenges due to the vast amounts of data they have to curate. New data models are required, and work is underway. The International Council on Archives is creating the RiC-CM (Records in Context), and there is a long line of work in museums with the CIDOC-CRM (CIDOC Conceptual Reference Model). Both models are based on ontologies to represent cultural heritage data and link them to other information. The Portuguese National Archives hold a collection with over 3.5 million metadata records, described with the ISAD(G) standard. The archives are designing a new linked data model and a technological platform with applications for archive contributors, archivists, and the public. The current work extends CIDOC-CRM into ArchOnto, an ontology-based model for archives. The model defines the relevant archival entities and properties and will be used to migrate existing records. ArchOnto accommodates the existing ISAD(G) information and takes into account its implementation with current technologies. The model is evaluated with records from representative fonds. After the test on these samples, the model is ready to be populated with the semi-automatic transformation of the ISAD records. The evaluation of the model and the population strategies will proceed with experiments involving professional and lay users.
Student Action Recognition Based on Deep Convolutional Generative Adversarial Network

Student action analysis plays an import role in learning and teaching. In order to improve the accuracy of student action recognition in classroom learning, a Deep Convolutional Generative Adversarial Network for Student Action Recognition (DCGANSAR) method is proposed. The method contains two stages: constructing the Deep Convolutional Generative Adversarial Network (DCGAN) to obtain pre-trained weights in the discriminator, and using the discriminator of DCGAN to classify actions. The advantage is that the confrontation between the generator and the discriminator in DCGAN makes the discriminator get stronger, and effective weights are obtained. The pre-trained weights are beneficial for student action recognition, so the accuracy of recognition is improved. Experiments are conducted on the self-built student action dataset. The experimental results demonstrate that the proposed method recognizes student action with high accuracy and fast convergence speed.
Generating Adversarial Surfaces via Band‐Limited Perturbations

Abstract Adversarial attacks have demonstrated remarkable efficacy in altering the output of a learning model by applying a minimal perturbation to the input data. While increasing attention has been placed on the image domain, however, the study of adversarial perturbations for geometric data has been notably lagging behind. In this paper, we show that effective adversarial attacks can be concocted for surfaces embedded in 3D, under weak smoothness assumptions on the perceptibility of the attack. We address the case of deformable 3D shapes in particular, and introduce a general model that is not tailored to any specific surface representation, nor does it assume access to a parametric description of the 3D object. In this context, we consider targeted and untargeted variants of the attack, demonstrating compelling results in either case. We further show how discovering adversarial examples, and then using them for adversarial training, leads to an increase in both robustness and accuracy. Our findings are confirmed empirically over multiple datasets spanning different semantic classes and deformations.
Multi-fidelity meta-optimization for nature inspired optimization algorithms

The last couple of decades have witnessed a steadily increasing applications of nature inspired optimization (NIO) in vast fields such as power engineering, environmental engineering, and civil engineering. Behavioral parameters deeply affect the optimization performance for a NIO algorithm. Meta-optimization is good choice for parameter optimization of NIOs, which uses an optimizer to optimize another optimizer. However, meta-optimization is a very time-consuming process. For this reason, we propose a multi-fidelity strategy based meta-optimization approach to speed up the parameter optimization. Four types of fidelity control functions determine the fidelity level in the course of meta-optimization. We test the proposed method in the meta-optimization systems with diverse meta-NIOs (cuckoo search, fruit fly optimizer, gray wolf optimizer, krill herd, and whale optimization algorithm), diverse optimized-NIOs (cuckoo search, differential evolution, particle swarm optimizer, squirrel search algorithm, and water wave optimizer), and diverse benchmark problems (Ackley-50, Eggholder-2, Michalewicz-5, Shubert-2, Sphere-50, and F1–20). We also apply it to a real-world engineering problem to estimate the source terms of gas emission. Experimental results indicate that multi-fidelity strategy can substantially speed up meta-optimization systems and hence has the potential to be generalized to various NIOs.
Duplicate Question Detection in Question Answer Website using Convolutional Neural Network

Online forums are platforms for gathering, sharing information, and discussing between users on a particular topic. Users in online forums can ask questions about a topic, then other users who are experts on that question would answer the question. However, because users can ask questions in various ways, sometimes they ask questions that other users have previously asked. Therefore, a model is needed to detect the semantic similarity of questions in online forums. In this study, we are using Convolutional Neural Networks (CNN) to detect the semantic similarity of questions. To capture the semantic similarity between questions, we are using Glove pre-trained word embeddings. This word embedding vector is used as an input for CNN, then the output is compared with Siamese Neural Networks. Model Optimization is done using Stochastic Gradient Descent. Our model can achieve accuracy of 79% which proved to be higher than Jaccard Similarity and Multilayer Perceptron algorithms.
Passive concept drift handling via variations of learning vector quantization

Abstract Concept drift is a change of the underlying data distribution which occurs especially with streaming data. Besides other challenges in the field of streaming data classification, concept drift has to be addressed to obtain reliable predictions. Robust Soft Learning Vector Quantization as well as Generalized Learning Vector Quantization has already shown good performance in traditional settings and is modified in this work to handle streaming data. Further, momentum-based stochastic gradient descent techniques are applied to tackle concept drift passively due to increased learning capabilities. The proposed work is tested against common benchmark algorithms and streaming data in the field and achieved promising results.
Hierarchical Multi-Granularity Attention- Based Hybrid Neural Network for Text Classification

Neural network-based approaches have become the driven forces for Natural Language Processing (NLP) tasks.Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural network (ConvNet).RNNs are good at modeling long-term dependencies over input texts, but preclude parallel computation.ConvNets do not have memory capability and it has to model sequential data as un-ordered features.Therefore, ConvNets fail to learn sequential dependencies over the input texts, but it is able to carry out high-efficient parallel computation.As each neural architecture, such as RNN and ConvNets, has its own pro and con, integration of different architectures is assumed to be able to enrich the semantic representation of texts, thus enhance the performance of NLP tasks.However, few investigation explores the reconciliation of these seemingly incompatible architectures.To address this issue, we propose a hybrid architecture based on a novel hierarchical multi-granularity attention mechanism, named Multi-granularity Attention-based Hybrid Neural Network (MahNN).The attention mechanism is to assign different weights to different parts of the input sequence to increase the computation efficiency and performance of neural models.In MahNN, two types of attentions are introduced: the syntactical attention and the semantical attention.The syntactical attention computes the importance of the syntactic elements (such as words or sentence) at the lower symbolic level and the semantical attention is used to compute the importance of the embedded space dimension corresponding to the upper latent semantics.We adopt the text classification as an exemplifying way to illustrate the ability of MahNN to understand texts.The experimental results on a variety of datasets demonstrate that MahNN outperforms most of the state-of-the-arts for text classification.
Solving large-scale support vector ordinal regression with asynchronous parallel coordinate descent algorithms

Ordinal regression is one of the most influential tasks of supervised learning. Support vector ordinal regression (SVOR) is an appealing method to tackle ordinal regression problems. However, due to the complexity in the formulation of SVOR and the high cost of kernel computation, traditional SVOR solvers are inefficient for large-scale training. To address this problem, in this paper, we first highlight a special SVOR formulation whose thresholds are described implicitly, so that the dual formulation is concise to apply the state-of-the-art asynchronous parallel coordinate descent algorithm, such as AsyGCD. To further accelerate the training for SVOR, we propose two novel asynchronous parallel coordinate descent algorithms, called AsyACGD and AsyORGCD respectively. AsyACGD is an accelerated extension of AsyGCD using active set strategy. AsyORGCD is specifically designed for SVOR that it can keep the ordered thresholds when it is training so that it can obtain good performance with lower time. Experimental results on several large-scale ordinal regression datasets demonstrate the superiority of our proposed algorithms.
SphereGAN: Sphere Generative Adversarial Network Based on Geometric Moment Matching and its Applications

We propose a novel integral probability metric-based generative adversarial network (GAN), called SphereGAN. In the proposed scheme, the distance between two probability distributions (i.e., true and fake distributions) is measured on a hypersphere. Given that its hypersphere-based objective function computes the upper bound of the distance as a half arc, SphereGAN can be stably trained and can achieve a high convergence rate. In sphereGAN, higher-order information of data is processed using multiple geometric moments, thus improving the accuracy of the distance measurement and producing more realistic outcomes. Several properties of the proposed distance metric on the hypersphere are mathematically derived. The effectiveness of the proposed SphereGAN is demonstrated through quantitative and qualitative experiments for unsupervised image generation and 3D point cloud generation, demonstrating its superiority over state-of-the-art GANs with respect to accuracy and convergence on the CIFAR-10, STL-10, LSUN bedroom, and ShapeNet datasets.
Indirect associations in learning semantic and syntactic lexical relationships

Computational models of distributional semantics (a.k.a. word embeddings) represent a word’s meaning in terms of its relationships with all other words. We examine what grammatical information is encoded in distributional models and investigate the role of indirect associations. Distributional models are sensitive to associations between words at one degree of separation, such as ‘tiger’ and ‘stripes’, or two degrees of separation, such as ‘soar’ and ‘fly’. By recursively adding higher levels of representations to a computational, holographic model of semantic memory, we construct a distributional model sensitive to associations between words at arbitrary degrees of separation. We find that word associations at four degrees of separation increase the similarity assigned by the model to English words that share part-of-speech or syntactic type. Word associations at four degrees of separation also improve the ability of the model to construct grammatical English sentences. Our model proposes that human memory uses indirect associations to learn part-of-speech and that the basic associative mechanisms of memory and learning support knowledge of both semantics and grammatical structure.
A High-Capacity Reversible Data Hiding Scheme Using Dual-Channel Audio

In recent years, the reversible data hiding (RDH) based on dual stego cover is developing rapidly because of its high capacity and low distortion. For image case, however, two consecutive images of the same image will draw the attention of adversaries during transmission. In this article, we propose a high-capacity RDH scheme using dual-channel audio, by exploiting the natural dual-channel property of the stereo audio. Specifically, we first convert secret message into novenary digits, which could increase the embedding capacity. Then, the magic matrix is used to embed the secret digit into a single-channel audio to generate two single-channel stego-audio. Finally, the two single-channel stego-audio is combined as a convention dual-channel audio. Extensive experiments have demonstrated that our proposed method could significantly boost the stego quality (the SNR is improved by 16% on average), when comparing with the state-of-the-art methods.
Manipulator Package Sorting and Placing System Based on Computer Vision

Nowadays the logistics industry is developing rapidly. This paper proposes and designs a vision-based manipulator express sorting and delivery system for the problem of express stacking. This system combines computer vision, deep learning, manipulator control and motion planning technologies. This paper uses the YOLO algorithm and PCL to determine the position and normal vector of the pick-up point of the package, plans and controls the movement of the manipulator to the target position to suck the package, and uses vision to determine the size of the package and obtain customer information. Compared with RCNN and SDD algorithm, YOLO has a faster detection speed [1], and at the same time, it handles small stacked targets such as express boxes with higher accuracy. Therefore, this system uses the YOLOv3 algorithm to improve the system's extraction accuracy and speed. Finally, the success rate of the system was 75%, and the completion time was about 15s.
Hindi Poetry Classification using Eager Supervised Machine Learning Algorithms

Document management is an essential but critical task. Categorizing these documents into the groups benefits many applications in commercial, industrial and other domains. Manual efforts are reduced by placing documents into its corresponding class. And predicting the category of document. It also reduces the time which otherwise would have required to read the document. Hindi has gained significant value in different fields like information technology, since the last decade due to the multilinguistic talent supported by websites. Natural Language toolkits along with text mining generate speedy, economic and scalable results. In spite of gaining importance in the digital era, Hindi document classification is targeted by very few researchers. Two eager machine learning algorithms are applied on the corpus containing 450 Hindi poems. Poetry/poem gets classified based on terms present in it. The classifiers are evaluated using a misclassification error.
Research and Application of Knowledge Graph in Teaching: Take the database course as an example

Abstract With the rapid development of technologies such as artificial intelligence and deep learning, education informatization has entered the 2.0 era with artificial intelligence as the main feature. As an important part of artificial intelligence technology, knowledge graph provides possibilities for smart education and promotes the innovation and development of smart education. At this stage, the core concepts and knowledge systems of some computer science disciplines need further clarification and improvement. Using a large amount of course-related information to construct an educational knowledge graph, processing and analyzing the knowledge points in the course, extracting the knowledge entities and effectively integrating them, can greatly help to clarify the knowledge system of the subject. The improvement of the teaching quality of this subject is of great significance. Therefore, this paper selects the construction of educational knowledge graph as research content, and uses database courses as examples of graph construction for research. The BIO tagging method is used to construct the database subject dataset, at the same time, it builds knowledge card based on educational knowledge graph to achieve database teaching wisdom, systematic teaching content, integration of knowledge fragments and improve the quality of learning.
Generic Construction of Dual-Server Public Key Encryption With Keyword Search on Cloud Computing

Chen et al. indicated the inner keyword guessing attack coming from the low entropy of keywords, which eliminates the semantic security of most keyword search schemes.Then, they accordingly propose the first dual-server PEKS scheme (abbreviated as DS-PEKS) and its related security models to prevent such attacks.In the DS-PEKS architecture, two non-collusive servers must corporate to execute the keyword search procedure.No individual server has the capability of determining the equivalence between keywords alone, and thus the inner keyword guessing attacks can be avoided.In this article, we found that the security models are lack of soundness and strength, so our first result is to define new stronger and sounder security models which implies all security aspects of original models.Secondly, we also propose a generic construction of DS-PEKS schemes based on IND-CCA2 secure PKE schemes.Finally, we analyze the newly proposed DS-PEKS scheme, and proof its security with the stronger models based on the IND-CCA2 security in the standard model.
Semi-Coupled Synthesis and Analysis Dictionary Pair Learning for Kinship Verification

Kinship verification is an interesting and important problem in the fields of computer vision. In practice, the biggest obstacle in kinship verification is that the representation capability of extracted features may not be powerful due to the significant differences between facial images of family members. To effectively address this problem, we propose a semi-coupled synthesis and analysis dictionary pair learning (SSADL) approach, which can reduce the differences between facial images. Specifically, SSADL jointly learns two view-specific synthesis-analysis dictionary pairs as well as a mapping matrix from the training data of parent and child, with which, the heterogeneous facial images of parent and child can be transformed into coding coefficients of the same subspace, such that the kinship verification task can be conducted using the coding coefficients. Besides, we also design a hard sample based coefficient discriminant term to ensure that the obtained coefficients own favorable discriminability. Experimental results on several publicly used benchmarks show the effectiveness of our proposed approach.
A decision support system to assure high-performance maintenance service

Purpose This study aims to propose a decision support system (DSS) for maintenance management of a service system, namely, a street cleaning service vehicle. Referring to the information flow management, the blockchain technology is integrated in the proposed DSS to assure data transparency and security. Design/methodology/approach The DSS is designed to efficiently handle the data acquired by the network of sensors installed on selected system components and to support the maintenance management. The DSS supports the decision makers to select a subset of indicators (KPIs) by means of the DEcision-MAaking Trial and Evaluation Laboratory method and to monitor the efficiency of performed preventive maintenance actions by using the mathematical model. Findings The proposed maintenance model allows real-time decisions on interventions on each component based on the number of alerts given by sensors and taking into account the annual cost budget constraint. Research limitations/implications The present paper aims to highlight the implications of the blockchain technology in the maintenance field, in particular to manage maintenance actions’ data related to service systems. Practical implications The proposed approach represents a support in planning, executing and monitoring interventions by assuring the security of the managed data through a blockchain database. The implications regard the monitoring of the efficiency of preventive maintenance actions on the analysed components. Originality/value A combined approach based on a multi-criteria decision method and a novel mathematical programming model is herein proposed to provide a DSS supporting the management of predictive maintenance policy.
Transaction Throughput Provisioning Technique for Blockchain-Based Industrial IoT Networks

The proliferation of the IoT in connected society is rapidly expanding into vertical industry sectors due to the ever-increasing ties amongst businesses and economies. As the number of IoT nodes utilized in a network increases, decentralized network infrastructure, and security provisioning mechanisms, primarily enabled by blockchain-based technologies, become more beneficial. However, blockchain-based IoT networks experience transaction throughput degradation due to the platform's cryptographically-based security features, where negotiating with ledger maintainers for faster processing is a must. Existing transaction processing schemes are mainly geared towards digital currency applications. In overcoming these challenges, a novel feeless transaction processing algorithm is proposed for non-cryptocurrency blockchain-based IoT networks. The proposed algorithm enables ledger maintainers in achieving desired processing throughputs for select transactions found in a miner's transaction pool. A utility function is designed to select transactions from miners' transaction pools to form blocks that add a desired operational value for achieving pre-determined production outputs over blockchain-based networks. Furthermore, the proposed scheme will utilize an aging process to increase the likelihood of selecting transactions with larger miners' transaction pool residence times. The simulation and implementation results show that the proposed methodologies increase the processing throughput of desired transactions while preventing transaction processing starvation.