Reviewerly/exHarmony huggingface dataset:
MDKE: Multi-level Disentangled Knowledge-Based Embedding for Recommender Systems

Recommender systems aim to dig out the potential interests of users and find out items that might be connected with target users. Accuracy of the recommendation list is crucial for user-oriented applications. Many knowledge-based approaches combine graph neural networks with exploring node structural similarity, while paying little attention to semantically distinguishing potential user interests and item attributes. Therefore, personalized node embeddings fail to be captured after simply aggregating neighborhood information. In this work, we propose Multi-level Disentangled Knowledge-based Embedding (MDKE) for item recommendation. We divide the embedding learning of users and items into disentangled semantic-level and structural-level subspaces. Specifically, semantic-level embeddings correspond to item content vector, which is learned by knowledge graph embedding, and user profile vector that is obtained through a carefully designed preference propagation mechanism. For structural-level embeddings, a graph embedding-based module is proposed to capture local structure features on the redefined knowledge interaction graph. The latent vectors of users and items can be obtained by aggregating the aforementioned multi-level embeddings. Experiments on two real-world datasets verify the effectiveness of MDKE versus a series of state-of-the-art approaches.
Interactive administration service based on HMM speech recognition system

The objective of this study is designing a secure interactive Amazigh speech system over the VoIP network. Our work explores the use of interactive voice response (IVR) system to manage the firewall and backup tasks where a voice platform was created based on HMM automatic speech recognition. To increase the security level the biological voiceprint is adopted for identification and management tasks. The speech system is trained to recognise the five words, 33 alphabets and ten first digits of Amazigh language. We performed experiences by varying IVR and ASR system parameters to manage a distant voice control administration system of the network. Our finding shows that the best performance for admin test is above 80% when the system was trained by using three HMMs and eight GMMs whereas the non-admin recognition rate is less than 5% which demonstrates the security aspect of our system.
Optimal Path Planning of Planetary Rovers with Safety Considerable

Aiming at the problem that dangerous terrain is difficult to avoid in extraterrestrial rover missions, an optimal path planning framework based on vibration information considering terrain constraints is proposed. The terrain hardness function is introduced to model the hardness characteristics of different terrain materials. The pseudo-spectral method is used to parameterize the optimal control problem which includes terrain constraints and the interior point method is used to optimize the parameters, thereby generating an optimal path that avoids obstacles and dangerous terrain at the same time, which is verified in a simulation environment.
Report on the 1st linked archives international workshop (LinkedArchives 2021) at TPDL 2021

The International Workshop on Archives and Linked Data was a satellite event of the 25th International Conference on Theory and Practice of Digital Libraries (TPDL 2021). TPDL 2021 was an online event with free registration, and the same applied to the workshop. Linked Data and Semantic Web technologies offer new possibilities for digital curation and preservation. The growing interest in archival records and the availability of technologies that can take large volumes of data and process them leads archives into the world of Linked Data. The workshop gathered almost 100 researchers and specialists engaged in initiatives crossing Linked Data technologies, Archives, and Cultural Heritage in general, discussing advances and challenges in this area. Overall, the workshop was a very successful event, which may require in the future more than the two two-hour sessions we dedicated to it this year. Date : 13 September, 2021. Website : https://linkedarchives.inesctec.pt/.
Cognitive Artificial Intelligence Computing Modeling Process in Meta Cognitive Architecture Carina

In this paper, Cognitive Artificial Intelligence computing modeling process in Meta cognitive architecture CARINA is implemented. Basically in cognitive sciences, cognitive modeling has become fundamental tool to process. Based on the usage of cognitive architectures, cognitive modeling is designed. For the artificial intelligent agents, CARNIA is most widely used and this is a Meta cognitive architecture which is derived from the Meta cognitive Meta model. This Meta cognitive Meta model is based on the Meta cognitive mechanism which will monitor and control the Meta level. Initially, the cognitive task is selected. Next, the information is described for the cognitive task. By using natural language, the cognitive task is described. GOMS also describes the cognitive task. For the obtained data, decision functions and cognitive functions are described. By using artificial intelligence, the data is computed. Now, the data is transferred from Cognitive Model form GOMS to M + + Language. Now, the data will be performed by using cognitive model in CARNIA. At last testing and maintenance will be done very effectively. From results it can observe that accuracy, cost, errors and observation time gives effective result.
Should I Buy or Should I Pass: E-Commerce Datasets in Portuguese

Text classification is an essential task in Natural Language Processing. The researchers and developers need data in the desired language to build new models and algorithms to develop this task. In this paper, we discuss and make available three datasets of text classification in Portuguese based on data of the B2W group, which is an important contribution to the research in the field.
Masked Face Detection Using Artificial Intelligent Techniques

Nowadays, wearing a face mask is a vital routine in life, but threats are increasing in public due to the advantage of wearing face masks. Existing works do not perfectly detect the human face and also not possible to apply for different faces detection. To overwhelm this issue, in this paper we proposed real-time face mask detection. The proposed work consists of six steps: video acquisition and keyframes selection, data augmentation, facial parts segmentation, pixel-based feature extraction, Bag of Visual Words (BoVW) generation, and face mask detection. In the first step, a set of keyframes are selected using the histogram of gradient (HoG) algorithm. Secondly, data augmentation is involved with three steps as color normalization, illumination correction (parameterized CLAHE), and pose normalization (Angular Affine Transformation). In the third step, facial parts are segmented using the clustering approach i.e., Expectation Maximization with Gaussian Mixture Model (EM-GMM), in which facial regions are segmented into Eyes, Nose, Mouth, Chin, and Forehead. Then, CapsNet based Feature Extraction is performed using CapsNet approach, which performance is higher and lightweight model than the Yolo Tiny V2 and Yolo Tiny V3, and extracted features are constructed into Codebook by Hassanat Similarity with K-Nearest neighbor (H-M with KNN) algorithm. For mask detection, L2 distance function is used. Experiments conducted using Python IDLE 3.8 for the proposed model and also previous works as GMM with Deep learning (GMM + DL), Convolutional Neural Network (CNN) with VGGF, Yolo Tiny V2, and Yolo Tiny V3 in terms of various performance metrics.
Introduction to Pandas

We explored the routines for the creation of NumPy Ndarrays and data visualization. We are reasonably comfortable with both libraries, and we can use the routines to create, store, and visualize data with Python programming. In this chapter, we will be acquainted with the data science library of the Scientific Python Ecosystem, Pandas. We will learn the basic data structures, a few operations, and the recipes of visualization with Matplotlib.
MGMP: Multimodal Graph Message Propagation Network for Event Detection

Multimodal event detection plays a pivotal role in social media analysis, yet remains challenging due to the large differences between images and texts, noisy contexts and the intricate correspondence of different modalities. To address these issues, we introduce a multimodal graph message propagation network (MGMP), a layer-wise approach that aggregates the multi-view context and integrates images and texts simultaneously. In particular, MGMP constructs visual and textual graphs and employs graph neural network (GNN) with an element-wise attention to propagate context and avoid transferring negative knowledge, and multimodal similarity propagation (MSP) follows to propagate complementarity for fusing images and texts. We evaluate MGMP on two public datasets, namely CrisisMMD and SED2014. Extensive experiments demonstrate the effectiveness and superiority of our method.
Learning Knowledge Uncertainty from the Pretrained Language Model

research-article Share on Learning Knowledge Uncertainty from the Pretrained Language Model Authors: Shihan Yang Faculty of Management and Economics, Kunming University of Science and Technology, China Faculty of Management and Economics, Kunming University of Science and Technology, ChinaView Profile , Rui Tang Faculty of Management and Economics, Kunming University of Science and Technology, China Faculty of Management and Economics, Kunming University of Science and Technology, ChinaView Profile Authors Info & Claims ICISE 2021: 2021 the 6th International Conference on Information Systems EngineeringNovember 2021 Pages 37–42https://doi.org/10.1145/3503928.3503936Published:08 March 2022Publication History 1citation57DownloadsMetricsTotal Citations1Total Downloads57Last 12 Months57Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Machine Learning Techniques for Grading of PowerPoint Slides

This paper describes the design and implementation of automated techniques for grading students’ PowerPoint slides. Preparing PowerPoint slides for seminars, workshops, and conferences is one of the crucial activity of graduate and undergraduate students. Educational institutes use rubrics to assess the PowerPoint slides’ quality on different grounds, such as the use of diagrams, text highlighting techniques, and animations. The proposed system describes a method and dataset designed to automate the task of grading students’ PowerPoint slides. The system aims to evaluate students’ knowledge about various functionalities provided by presentation software. Multiple machine learning techniques are used to grade presentations. Decision Tree classifiers gives 100% accuracy while predicting grade of PowerPoint presentation.
Neural network based autonomous control of a speech synthesis system

This work is inspired by the ability of neural systems to control the behavior of specialized actuator mechanisms in living organisms by monitoring the end-effect of their actions. We consider as an example of such an actuator mechanism the human vocal tract where neurons learn to activate its muscles that move the velum, jaw, tongue and the lips, in order to exhibit desired phonetic activity. As a technical approximation to such a setup, we use an artificial neural network (ANN) and a speech synthesizer and we study the capability of the ANN to estimate the synthesizer’s parameters targeting desired speech activity. In this setup, we assume that the training error is obtained by measuring the “perceived distance” between the original (target) and the synthesized speech signals. Thus, the training error needs to be measured after processing the output of the speech synthesizer, instead of measuring it directly at the outputs of the ANN. This operational requirement on error measurement restricts the application of widely used ANN training algorithms that are based on back propagation of gradients but can be met by our earlier proposed “Heuristically Enhanced Gradient Approximation” (HEGA) algorithm. We also propose enhancements to HEGA that further optimize its performance in this demanding application.
Camera-Aware Style Separation and Contrastive Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification (ReID) is a challenging task without data annotation to guide discriminative learning. Existing methods attempt to solve this problem by clustering extracted embeddings to generate pseudo labels. However, most methods ignore the intra-class gap caused by camera style variance, and some methods are relatively complex and indirect although they try to solve the negative impact of the camera style on feature distribution. To solve this problem, we propose a camera-aware style separation and contrastive learning method (CA-UReID), which directly separates camera styles in the feature space with the designed camera-aware attention module. It can explicitly divide the learnable feature into camera-specific and camera-agnostic parts, reducing the influence of different cameras. Moreover, to further narrow the gap across cameras, we design a camera-aware contrastive center loss to learn more discriminative embeddings for each identity. Extensive experiments demonstrate the superiority of our method over the state-of-the-art methods on the unsupervised person ReID task.
Unmanned Aerial Vehicle Knowledge Graph Construction with SpERT

Unmanned aerial vehicles are becoming more and more important in the military field. In recent years, global hot spot military events and local conflicts have fully proved its military value. Since knowledge graph is the information basis of intelligence, how to build a high-quality unmanned aerial vehicle knowledge graph is the focus of this paper. In this work, we propose an effective method to construct a knowledge graph from textual data. We first build the schema manually based on our domain knowledge. We then extract RDF triples with SpERT. Third, we disambiguate the instance by string comparison. Finally, We import the knowledge graph into neo4j for visualization. Our team takes part in the No. 10 evaluation task (i.e., military domain-specific knowledge graph construction for military unmanned aerial vehicles) in CCKS 2021. There are two stages in this evaluation, and our approach achieves the second place in the first stage, i.e., knowledge graph quality evaluation and the third place in the second stage, i.e., knowledge graph usage evaluation.
Data Crawling Approaches for User Discussion Analysis on Web 2.0 Platforms

This article discusses approaches to collecting data from Web 2.0 platforms such as social networks and messengers. The authors propose the implementation of a flexible architecture for a focused web crawler to collect data of users’ discussion in the social network Facebook and the Telegram messenger. The proposed crawler is based on interaction with a platform’s API, get/post requests, and simulating actions in a browser. The authors set up an experiment comparing implementation of proposed data crawling approaches. The data on the COVID-19 virus was collected from Facebook social network and Telegram messenger using RUM Extractor for Facebook and large number open-source Telegram crawler. Developed focused crawler reached the speed of 15 participants per second and 12 posts per second without blocking account when processing a user discussion in Facebook. Telegram crawler showed the speed of 200 participants per second and 300 posts per second without blocking.
Entropy and Fractal Dimension

It seems reasonable to describe the chaotic and unpredictable nature of a dynamical system with probabilistic concepts. It turns out that this is indeed very fruitful, allowing one to quantify the complexity of the dynamics. Therefore, in this chapter we study dynamical systems through the lens of information theory. After introducing the concepts of information and entropy, we look into ways with which to probabilistically describe a deterministic dynamical system. Then, we expand on practically using these concepts to define dimensionality of chaotic sets, a concept that is taken further advantage of in following chapters. Because the structure of chaotic sets is intimately linked with fractal geometry, in this chapter we also introduce fractals.
Large-Scale Nonconvex Optimization: Randomization, Gap Estimation, and Numerical Resolution

We address a large-scale and nonconvex optimization problem, involving an aggregative term. This term can be interpreted as the sum of the contributions of N agents to some common good, with N large. We investigate a relaxation of this problem, obtained by randomization. The relaxation gap is proved to converge to zeros as N goes to infinity, independently of the dimension of the aggregate. We propose a stochastic method to construct an approximate minimizer of the original problem, given an approximate solution of the randomized problem. McDiarmid's concentration inequality is used to quantify the probability of success of the method. We consider the Frank-Wolfe (FW) algorithm for the resolution of the randomized problem. Each iteration of the algorithm requires to solve a subproblem which can be decomposed into N independent optimization problems. A sublinear convergence rate is obtained for the FW algorithm. In order to handle the memory overflow problem possibly caused by the FW algorithm, we propose a stochastic Frank-Wolfe (SFW) algorithm, which ensures the convergence in both expectation and probability senses. Numerical experiments on a mixed-integer quadratic program illustrate the efficiency of the method.
Reconsidering the Challenges of BIS Education in Light of the COVID Pandemic

So far, the biggest challenge for a comprehensive Business Information Systems (BIS) education curriculum was the fast-changing nature of its target market and the resulting demand for a combination of up-to-date technical knowledge, organization-centred mindset, and adaptive skills. However, advances in pedagogical methods, changes in the skills of high-school graduates, and widening online options in the wake of the COVID-19 pandemic brought on a new set of expectations. This situation may be considered an opportunity to address the threat of potentially increasing mismatch and misalignment between competences required by the IS industry labour market and current training contents offered and methods used by higher education institutions. This paper provides a systematic and comprehensive overview of the challenges BIS programs have to face and address. It considers everyday experiences of BIS educators and current best practices as starting point. Then provides an overview of employer and alumni opinion, as well as reviews up-to-date teaching methods related to teaching soft computer skills. It also considers the requirements and opportunities related to an increasingly online-centred situation. Based on these challenges the paper lays down the foundation for a potential curriculum design approach intended to address all of the above issues in an integrated framework.
Adaptive Multi-threshold Image Segmentation Technology Based on Intelligent Algorithm

Image segmentation technology is to assign a label to each pixel in the image. Accurate image segmentation technology can play a key role in various deep learning tasks such as medical analysis and satellite image object detection. The choice of threshold in image segmentation will directly affect the effect of segmentation. The traditional OTSU algorithm has a poor traversal effect, and the segmentation effect is not obvious for images with concentrated histogram distribution. Based on the optimization principle proposed by the intelligent algorithm, this paper treats each pixel value in the picture as an individual, which is a good way to bypass the disadvantages of local optimization. It can complete automatic global optimization according to the original situation of the image to determine the threshold. Complete the image segmentation, and can retain more internal details of the image.
Modified Genetic Algorithm of Global Extremum Search in Combination with Directional Methods

In the paper, the stochastic method of global extremum search is discussed, modified and tested. The method is based on normal distribution modeling and provides covariance matrix adaptation. The method is iterative; a genetic algorithm has been developed on its basis. The coordinates of the trial points of each generation are determined using the ”best” points of the previous generation and the values of standard normal random variables. Thus, at each stage of the search, a normal distribution is simulated, and its parameters (the mean and the covariance matrix) are estimated through the positions of the ”best” points of the previous generation. In this case, there is no need to calculate, store and transform the covariance matrix, which is indisputable advantage of this method. Practice has shown that the dispersion ellipsoid of normal distribution shrinks rapidly with generation number increasing, which can lead to an excessive narrowing the scanning area and obtaining a local extremum instead of a global one. The proposed modification of the method avoids this situation. The trial points are divided into two groups, which are simulated using normal random variables with different standard deviations, at least one of which is greater than 1. Thus, a kind of mutation of the population is carried out, which makes it possible to provide a sufficient number of sample points both near the “best” one and at a distance from it. The modified genetic algorithm is applied to solving the problem of estimating the parameters of nonlinear parametric regression. A successful minimization of the multiextremal function is performed. The stochastic method is used in combination with directional. The numerical results presented confirm the effectiveness of the introduced modification of the genetic algorithm and make it possible to choose from two directed methods the more efficient one for the problem under consideration
Positive-Definite Kernels in Agglomerative Hierarchical Clustering

Positive-definite kernelsPositive-definite kernel are popular in the method of support vector machinesSupport vector machine (e.g., [1, 2]) which is one of the best known technique in supervised classification. We consider their application to agglomerative hierarchical clustering. We should note that positive-definite kernels themselves are independent topic from support vector machines, and indeed no reference to support vector machines is needed herein.