Reviewerly/exHarmony huggingface dataset:
Aligning Dynamic Social Networks: An Optimization over Dynamic Graph Autoencoder

Social network alignment, aligning different social networks on their common users, is receiving dramatic attentions from both academic and industry. All existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic Graph autoencoder based dynamic social network Alignment approach, referred to as DGA, unfolding the fruitful dynamics of social networks for user alignment. However, it faces challenges in both modeling and optimization: (1) To model the intra-network dynamics, we design a novel dynamic graph autoencoder to learn user embeddings with complex network dynamics. (2) To model the inter-network alignment, we design a unified optimization framework over proposed dynamic graph autoencoders, constructing a common subspace for user alignment across different networks. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed approach substantially outperforms the state-of-the-art methods.
Tree-based Mining of Fine-grained Code Changes to Detect Unknown Change Patterns

In software development, source code is repeatedly changed due to various reasons. Similar code changes are called change patterns. Identifying change patterns is useful to support software development in a variety of ways. For example, change patterns can be used to collect ingredients for code completion or automated program repair. Many research studies have proposed various techniques that detect change patterns. For example, Negara et al. proposed a technique that derives change patterns from the edit scripts. Negara&#x0027;s technique can detect fine-grained change patterns, but we consider that there is room to improve their technique. We found that Negara&#x0027;s technique occasionally generates change patterns from structurally-different changes, and we also uncovered that the reason why such change patterns are generated is that their technique performs text comparisons in matching changes. In this study, we propose a new change mining technique to detect change patterns only from structurally-identical changes by taking into account the structure of the abstract syntax trees. We implemented the proposed technique as a tool, TC2P, and we compared it with Negara&#x0027;s technique. As a result, we confirmed that TC2P was not only able to detect change patterns more adequately than the prior technique but also to detect change patterns that were not detected by the prior technique.
Filtering Deep Convolutional Features for Image Retrieval

In image retrieval, highlighting target object and reducing the influence of background noise remains challenging. To address this problem, we propose a novel weighting method that aggregates deep convolutional features based on filtering, called filtering on spatial channel weighting (FSCW) factors, to represent image contents, and utilize it for image retrieval. There are three main contributions of this study. First, the designed filter can effectively remove the influence of background noise. Second, we propose a new channel selection and spatial weighting method, which can accurately distinguish target object from the background noise. Finally, we designed a new channel weighting strategy to suppress intra-image visual burstiness. Experimental results on benchmark datasets demonstrate that the proposed method effectively enhances discriminative power and outperforms some existing state-of-the-art methods in terms of the mAP metric. Furthermore, the proposed method is superior to some existing algorithms in distinguishing background noise and target object.
Perpetual Interoperability of Legacy ERP and Blockchain in Supply Chain

The advent of blockchain technology has expanded the horizon of many use cases. However, the supply chain industry has still not realized its full potential as the legacy Enterprise Resource Planning (ERP) systems and blockchain are not interoperable. Currently, organizations are running on entirely different governance rules and blockchain versions. Knowledge of every entity involved in the supply chain is limited to their adjacent entities. Thus, a series of disconnected blockchain operates in an isolated ecosystem hindering the industry's growth. The paper's main objective is the implication of blockchain technology in the supply chain industry to accomplish traceability, privacy, transparency, immutability, data standardization, resource optimization, and efficiency. We plan to develop a high-fidelity prototype for end-to-end modeling between ERPs and blockchain. A two-stage architecture is proposed to achieve a balance between privacy and transparency. The business negotiations between two ERPs are automated by off-chain order relay and on-chain settlement. A signing algorithm makes proposal sending through ERP software tamper-proof. The solution for achieving interoperability can be independent of ERP by adding a plug-in module to ERP seamlessly communicating with the blockchain.
Social Distancing and Face Mask Monitoring System Using Deep Learning Based on COVID-19 Directive Measures

We, the entire world is in the lock of a micro size virus named Corona we are in the urge of saving our life rather than the money. This virus had changed the attitude of people from generations together, in this two years people realized that their health worth more than their net worth. We are in an uncertain situation but, we can bring the world back to normal so, we need to follow the guidelines issued by the health organizations so our government insisted people wear the mask and maintain social distance to control the spread of the disease but 90&#x0025; percent of people not following covid guidelines. The main motive in this paper, mask detection on face with social distancing which helps to overcome this pandemic situation. Our proposed system comprises of data processing, data augmentation, image classification using mobilenetv2 and object detection plays a vital role in this paper. The modules are developed using TensorFlow and open-cv python programming to detect the faces with mask. If a person wears a mask they will be in a safe zone and the system shows a green box where if the person doesn&#x0027;t wear a mask, then it will be shown in a red box and with the message of alert as well. Social distancing detection will detect that two or more person in a single frame are walking with maintaining social distancing with at least 2 meters of range with each other using the Euclidean distance method, it will work in a Reliable manner with accurate results during this current situation which will easily help to track the person and collect fine if they violate any government directive guidelines so our system, will prevent the spread of the disease. Every Automation process reduces manual inspection to inspect the people which can be used in public places to control the spread of the virus and this prototype could be used in many places like park, hospital, airports, temples, railway station etc. to control this pandemic situation
Copy-move Image Forgery Localization Using Deep Feature Pyramidal Network

Fake news, frequently making use of tampered photos, has currently emerged as a global epidemic, mainly due to the widespread use of social media as a present alternative to traditional news outlets. This development is often due to the swiftly declining price of advanced cameras and phones, which prompts the simple making of computerized pictures. The accessibility and usability of picture-altering softwares make picture-altering or controlling processes significantly simple, regardless of whether it is for the blameless or malicious plan. Various investigations have been utilized around to distinguish this sort of controlled media to deal with this issue. This paper proposes an efficient technique of copy-move forgery detection using the deep learning method. Two deep learning models such as Buster Net and VGG with FPN are used here to detect copy move forgery in digital images. The two models' performance is evaluated using the CoMoFoD dataset. The experimental result shows that VGG with FPN outperforms the Buster Net model for detecting forgery in images with an accuracy of 99.8% whereas the accuracy for the Buster Net model is 96.9%.
Multilabel learning based adaptive graph convolutional network for human parsing

In human parsing, graph convolutional networks (GCNs), which naturally model the skeleton of the human body as a fixed graph, have been witnessed to obtain remarkable performance. However, the existing methods perform the fixed graph modeling over all the training samples. This may not be an optimal graph for the diversity of the samples that contain various shapes of human parts, complex body postures, severe occlusions and dense crowd, etc. Focusing on this, we propose a new Multilabel Learning based Adaptive Graph Convolutional Network (ML-AGCN) for human parsing. The ML-AGCN includes three modules: adaptive graph generation module, semantic parts based attention module and label consistency loss. Concretely, to effectively deal with the different sizes and connectivities of the optimal graph for different samples, we first propose an adaptive graph generation module based on multilabel learning that contains graph node adaptation (GNA) and graph connection adaptation (GCA). Then, for a more comprehensive node embedding, we design a semantic parts based attention module to optimally fuse fixed graph embeddings and adaptive graph embeddings. Besides, to further explicitly constraint the consistency between the predicted multilabel and the predicted human parsing results, we propose a label consistency loss that can simultaneously refine the human parsing results and optimize the accuracy of the adaptive graph. Extensive experiments on four challenging datasets, including PASCAL-Person-Part, ATR, LIP and CIHP, well demonstrate the effectiveness of our model, and it outperforms other state-of-the-art methods in human parsing.
Digitalisation in the Curricula of Vocational Schools: Text Mining as an Instrument of Curricula Analysis

Abstract The influence of digitalisation on society and the workplace require stakeholders in school development to incorporate digital competencies into school curricula. This study examines the occurrence of digital components in 831 curricula of the vocational schools in Baden-Württemberg, Germany with a text mining approach and the analysis of keywords in context. In total, 200 curricula containing digital features could be identified. The occurrence of digital terms differs notably between the different types of vocational school. The distinct characteristics of digital competencies are represented to a varying degree within the curricula, with a strong focus on the usage of tools for profession-specific tasks and the deployment of the internet as a gateway to information. Ongoing reforms of school curricula need to widen their view on digital competencies and integrate these competencies more broadly into the different types of vocational schools.
Divergent Potentialism: A Modal Analysis With an Application to Choice Sequences

Abstract Modal logic has been used to analyze potential infinity and potentialism more generally. However, the standard analysis breaks down in cases of divergent possibilities, where there are two or more possibilities that can be individually realized but which are jointly incompatible. This paper has three aims. First, using the intuitionistic theory of choice sequences, we motivate the need for a modal analysis of divergent potentialism and explain the challenges this involves. Then, using Beth–Kripke semantics for intuitionistic logic, we overcome those challenges. Finally, we apply our modal analysis of divergent potentialism to make choice sequences comprehensible in classical terms.
A Crest-Factor Optimization Algorithm for Multisine Signals based on the Evolutionary Role Playing Game Theory

In impedance spectroscopy, excitation signals should be limited in their amplitude to fulfil the linearity condition and in their duration to fulfill the stability condition. Multisine excitation signals are suitable because they can reduce the excitation time duration, but they need to be optimized concerning crest factor (CF). A higher challenge is the optimization of the crest factor of multisine signals. In this paper, we propose a hybrid deterministic-heuristic optimization method for CF optimization based on the Evolutionary Role Playing Game Theory able to realize multisine signals with lower and more consistent CF for logarithmically distributed signal frequency components. The novel optimization method needs a short execution time, which is comparable to that of deterministic methods. The method validation has been carried out for bioimpedance measurement. In the case of bioimpedance measurement, the new method reaches a CF with a median of 1.76, which is significantly lower than state-of-the-art methods realizing a CF typically between 1.89 and 2.79.
Leaky Gated Cross-Attention for Weakly Supervised Multi-Modal Temporal Action Localization

As multiple modalities sometimes have a weak complementary relationship, multi-modal fusion is not always beneficial for weakly supervised action localization. Hence, to attain the adaptive multi-modal fusion, we propose a leaky gated cross-attention mechanism. In our work, we take the multi-stage cross-attention as the baseline fusion module to obtain multi-modal features. Then, for the stages of each modality, we design gates to decide the dependency on the other modality. For each input frame, if two modalities have a strong complementary relationship, the gate selects the cross-attended feature, otherwise the non-attended feature. Also, the proposed gate allows the non-selected feature to escape through it with a small intensity, we call it leaky gate. This leaky feature makes effective regularization of the selected major feature. Therefore, our leaky gating makes cross-attention more adaptable and robust even when the modalities have a weak complementary relationship. The proposed leaky gated cross-attention provides a modality fusion module that is generally compatible with various temporal action localization methods. To show its effectiveness, we do extensive experimental analysis and apply the proposed method to boost the performance of the state-of-the-art methods on two benchmark datasets (ActivityNet1.2 and THUMOS14).
Studying the Adoption of Blockchain Technology in the Manufacturing Firms

This chapter studies blockchain technology logistics and supply chain adoption in four manufacturing firms. Semi-structured interviews are conducted, and the results are analyzed using case study methods. Four manufacturing firms are selected for the study. First firms deal with consumer electronics manufacturing, second firms deal with auto components manufacturing, third firms deal with paint manufacturing, and fourth firms deal with consumer electronics, manufacturing, wearables manufacturing. The case study is analysed using cross-case analysis and withing case analysis.
Chess Moves Prediction using Deep Learning Neural Networks

Chess is a game that is popular for high intelligence and strategic thinking. There has been a lot of research on chess for predicting chess moves, applying chess game theory, and automating chess games. The art of playing chess using computer vision can be implemented using various learning algorithms. A class of Deep Learning has the ability to solve problems of predicting chess moves although facing the necessity of huge datasets. The traditional chess algorithm Minimax with the Convolutional Neural Network can perceive and learn the patterns and rules in chess i.e., identification of some small and native tactics of the game, and should be trained on this method with appropriate functions for smarter universal play. CNN when trained with appropriate architecture and validation data can learn to function based on the reasoning in complex logical tasks. Training on 15,00,000 board states in the dataset which is a board state represented as 8x8x14 dimensions. Each board state is given as an input to the input layer of the Convolutional Neural Network. The CNN model tested and validated against the stockfish chess engine achieved the best accuracy of 39.16% for board evaluation. However, this doesn’t reflect the actual accuracy of the model since the evaluation by the model is relative for two different board states. CNN learning the game of chess and based on the result of chess is essentially pre-computation on a given situation.
Query Interpretations from Entity-Linked Segmentations

Web search queries can be ambiguous: is "source of the nile'' meant to find information on the actual river or on a board game of that name? We tackle this problem by deriving entity-based query interpretations: given some query, the task is to derive all reasonable ways of linking suitable parts of the query to semantically compatible entities in a background knowledge base. Our suggested approach focuses on effectiveness but also on efficiency since web search response times should not exceed some hundreds of milliseconds. In our approach, we use query segmentation as a pre-processing step that finds promising segment-based "interpretation skeletons''. The individual segments from these skeletons are then linked to entities from a knowledge base and the reasonable combinations are ranked in a final step. An experimental comparison on a combined corpus of all existing query entity linking datasets shows our approach to have a better interpretation accuracy at a better run time than the previously most effective methods.
The State of Extended Reality Technologies in Language Education and Research

Despite making headway in the entertainment sector, extended reality (XR) remains an experimental technology in many educational contexts. This chapter moves to enrich the understanding of XR for language researchers and educators with a review of the most current established theories, frameworks, and research. An exploration of recommended avenues for future research from recently published articles in VR for education will be discussed to provide readers with an outline of the current research conditions and suggested ways forward. The chapter concludes with an inspection of language learners' experiences using a high-immersion VR system for the purposes of learning paragraph structure. Trends in VR education and research point towards increasingly sustainable applications of the technology in the classroom with identified linguistic and affective benefits. As VR continues to evolve and its applications in language learning become more sophisticated, educators and researchers will need to stay acquainted with the prevailing usages. This chapter aims to assist in that endeavor.
Detecting and Classifying Phishing Websites by Machine Learning

By imitating legitimate websites, phishing websites aim at stealing information from people. This paper thoroughly depicts a method of detecting phishing websites with joint features. Original data was crawled form PhishTank. SVM and bayes methods are mixed in training classifier. Wireshark is used in the process of packet flow detection. After going through training, the classifier can detect 1000 website per second. A data set of 21615 phishing and legitimate websites is used in the comparative study. In addition, 51 features are used to train and test the classifiers.
The chaotic time series prediction method based on sparrow search algorithm optimization

With the maturity of chaos theory and the rapid development of artificial intelligence, the establishment of chaotic time series prediction model based on neural network has become a hot issue. In order to further improve the prediction accuracy of neural network and solve the shortcomings of traditional BP neural network in convergence speed and stability, the chaotic time series prediction method based on the optimization of sparrow algorithm is proposed. The threshold and weight of neural network topology are optimized by using a new Sparrow Search Algorithm to improve the robustness of network prediction, so as to solve the prediction problem of chaotic time series. The experimental results show that the proposed optimization method has been significantly improved in prediction accuracy.
Resource-Aware Edge-Based Stream Analytics

Understanding how machine learning (ML) algorithms can be used for stream processing on edge devices remains an important challenge. Such ML algorithms can be represented as operators and dynamically adapted based on the resources on which they are hosted. Deploying ML algorithms on edge resources often focuses on carrying out inference on the edge, while learning and model development takes place on a cloud data center. In this article, we describe TinyMOA, a modified version of the open-source massive online analytics library for stream processing, that can be deployed across both local and remote edge resources using the Parsl and Kafka systems. Using an experimental testbed, we demonstrate how ML stream-processing operators can be configured based on the resource on which they are hosted, and discuss subsequent implications for edge-based stream-processing systems.
Human Body Pose Distance Image Analysis for Action Recognition

Body pose analysis is an important factor of human action recognition. Recently, the proposed Recurrent Neural Networks (RNNs) and deep ConvNets-based methods are showing good performances in learning sequential information. Despite these good performances, RNN lacks to efficiently learn spatial relation between body parts while deep ConvNets require a huge amount of data for training. We propose a Distance-based Neural Network (DNN) for action recognition in static images. We compute effective distances between a set of body part pairs for a given image and feed to DNN to learn effective representation of complex actions. We also propose Distance-based Convolutional Neural Network (DCNN) to learn representations from 2D images. The distances are rearranged in 2D grayscale image called as a Distance Image. This 2D representation allows the network to learn specific discriminative information between adjacent pixel distance values corresponding to different body part pairs. We evaluate our method on two real-world datasets i.e. UT-Interaction and SBU Kinect Interaction. Results show that our proposed method achieves better performance compared to the state-of-the-art approaches.