Reviewerly/exHarmony huggingface dataset:
Page Object Detection with YOLOF

With the rapid development of information and technology, document digitization has become more critical in many research fields by giving enormous amounts of data. However, computers can not handle a lot of information contained inside physical documents. For that reason, making computers detect objects in document images can help humans have more valuable information such as graphs, captions, or tables. There should be a system capable of detecting various components on document images, especially finding a simply effective object recognition method. Thus, the introduction of YOLOF can be an appropriate method to detect objects in documents because it opens up a simple way to exploit image features, making the object detection problem less computationally intensive, but still maintaining the appropriate accuracy. This paper evaluates the new one-stage YOLOF method on two challenging document datasets: IIIT-AR-13K, UIT-DODV. Our experimental YOLOF model achieves 58.8% and 56% on mAP measurement scores with the IIIT-AR-13K dataset and the UIT-DODV dataset, respectively.
Semi-supervised learning based on intra-view heterogeneity and inter-view compatibility for image classification

To achieve high value of classification results by using a limited number of training samples, designing a multi-view semi-supervised classification model is extremely urgent. Moreover, considering that graph learning can fully capture the complex data structure information, we propose a graph-based multi-view semi-supervised classification approach named as Semi-supervised Classification based on Intra-view Heterogeneity and Inter-view Compatibility (SSC_IHIC). Specifically, the similarity between two samples can be measured based on the Euclidean distance. Considering that heterogeneity exists in intra-view sub-features, hence, the weights are learned adaptively. Besides, based on the inter-view compatibility, the consensus similarity graph is constructed on which the labels are propagated. To verify the effectiveness, the proposed approach is conducted on four data sets including MSRC-v1, Handwritten (HW), Cal101-7 and Cal101-20. From the experimental results, we can see that the classification performance about proposed method outperforms the single view classification methods and the state-of-the-art multi-view classification methods.
Improving Pedestrian Prediction Models With Self-Supervised Continual Learning

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This letter introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In particular, we exploit online streams of pedestrian data, commonly available from the robot&#x2019;s detection and tracking pipeline, to refine the prediction model and its performance in unseen scenarios. To avoid the forgetting of previously learned concepts, a problem known as catastrophic forgetting, our framework includes a regularization loss to penalize changes of model parameters that are important for previous scenarios and retrains on a set of previous examples to retain past knowledge. Experimental results on real and simulation data show that our approach can improve prediction performance in unseen scenarios while retaining knowledge from seen scenarios when compared to naively training the prediction model online.
Aggregable Confidential Transactions for Efficient Quantum-Safe Cryptocurrencies

Confidential Transactions (CT) hide coin amounts even from verifiers without the help of trusted third parties. Aggregable CTs are a scalable category of CTs with “spent coin record trimming”. For example, if Alice sends coins to Bob, who had sent similar coins to Charles, the aggregated transaction shows only that Alice sent coins to Charles by deleting Bob’s coin records. Since the number of spent coin records grows linearly with the number of transactions, faster than the number of accounts, cash systems based on aggregable CTs are highly scalable. However, existing quantum-safe aggregable CT protocols have large unspent coin records, and existing efficient aggregable CTs are vulnerable to quantum attacks. We introduce two aggregable CT protocols, based on new efficient homomorphic zero-knowledge proofs, from either the plain or Module Short Integer Solution (SIS and MSIS) problems, both believed to be secure against quantum adversaries. We further implement the MSIS-based aggregable CT protocol as a C library. Our experiments on 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> transactions show that aggregation reduces the cash system’s size by 40%–54% when the output/input rate is in the range 1/1–2/1. For example, a cash system of 1.73 GB can be reduced to 0.98 GB when the output/input rate is 1.5, which has been the historical real-world average rate.
Predicting Job Titles from Job Descriptions with Multi-label Text Classification

Finding a suitable job and hunting for eligible candidates are important to job seeking and human resource agencies. With the vast information about job descriptions, employees and employers need assistance to automatically detect job titles based on job description texts. In this paper, we propose the multi-label classification approach for predicting relevant job titles from job description texts, and implement the Bi-GRU-LSTM-CNN with different pre-trained language models to apply for the job titles prediction problem. The BERT with multilingual pre-trained model obtains the highest result by F1-scores on both development and test sets, which are 62.20% on the development set, and 47.44% on the test set.
Candidate-plots-based dynamic programming algorithm for track-before-detect

Compared with the classical detect-before-track (DBT) technique, the track-before-detect (TBD) is an effective method to improve the performance of detecting weak targets. Operating directly on the plot-lists data obtained by the detector and plot-extractor, the classical dynamic programming for the TBD algorithm (DP-TBD) confronts the loss of merit function (MF), which will lead to extra degradation of detection performance. Thus, we analyze the influence of relevant factors on the loss of MF, such as target detection probability and clutter. We present results from the analysis that clutter plots near the positions of target-missed detections contribute to ease the loss of MF. The information of target-missed detections is beneficial to prevent the loss of MF. Then, we propose a candidate-plots-based DP-TBD (CP-DP-TBD) method providing candidate plots carrying the information of target-missed detections by the modified MF transfer procedure. Finally, the computational efficiency and the detection performance of CP-DP-TBD are demonstrated by comparing it with other methods in both simulated data and experimental data.
Discovery and Identification of Memory Corruption Vulnerabilities on Bare-Metal Embedded Devices

Memory corruption vulnerabilities remain a prevalent threat on low-cost bare-metal devices.Fuzzing is a popular technique for automatically discovering such vulnerabilities.However, bare-metal devices lack even basic security mechanisms such as Memory Management Unit.Consequently, fuzzing approaches encounter silent memory corruptions with no visible effects, making even discovery difficult.Once discovered, it is also essential to identify the type of observed vulnerability for applying mitigation.Both discovery and identification remain open challenges in the case of fuzzing firmware binaries.This article addresses these problems by proposing an automated instrumentation technique that allows the observation of memory corruption vulnerabilities that are otherwise not observable and facilitates the automated identification of the observed vulnerability.Additionally, we surveyed state-of-the-art IoT fuzzers and analyzed their experimental methodologies.We found that existing approaches have fundamental problems that lead to incorrect or misleading results.To evaluate the effectiveness of IoT fuzzers, it is essential to determine the range and type of vulnerabilities that these fuzzers can discover.Thus, we propose the first ground-truth benchmark suite for IoT fuzzers that enables accurate and consistent evaluation of their vulnerability-finding performance.Our instrumentation framework's efficacy and efficiency in combination with state-of-the-art IoT fuzzers are assessed using the proposed benchmark.
Improve Deep Unsupervised Hashing via Structural and Intrinsic Similarity Learning

Hashing has attracted increasing attention in image retrieval recently due to its storage and computational efficiency. Although several deep unsupervised hashing methods have been proposed lately, their effectiveness is far from satisfactory in practice owing to two drawbacks. On the one hand, they mostly construct binary similarity matrices which could neglect the confidence differences among multiple similarity signals. On the other hand, they ignore the desired properties of hash codes (i.e., independence and robustness). In this paper, we propose an effective unsupervised hashing method called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> ashing via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> tructural and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ntrinsic si <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ilarity learning (HashSIM) to tackle these issues in an end-to-end manner. Specifically, HashSIM utilizes both highly and normally confident image pairs to jointly build a continuous similarity matrix, which guides hash code learning via structural similarity learning. Moreover, inspired by contrastive learning, we impose an intrinsic similarity learning objective, which can maximally satisfy the independence and robustness properties of hash bits. Extensive experiments on three popular benchmark datasets demonstrate that our HashSIM outperforms a broad range of state-of-the-art baselines.
CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning

Humans use causality and hypothetical retrospection in their daily decision-making, planning, and understanding of life events.1 The human mind, while retrospecting a given situation, think about questions such as “What was the cause of the given situation?,” “What would be the effect of my action?,” “What would have happened if I had taken another action instead?,” or “Which action led to this effect?” The human mind has an innate understanding of causality.15 It develops a causal model of the world, which learns with fewer data points, makes inferences, and contemplates counterfactual scenarios.8 The unseen and unknown scenarios are called “counterfactuals.”2
Organisational Culture Attributes Influencing the Adoption of Agile Practices: A Systematic Literature Review

Organisations have been adopting agile practices to deliver information system projects faster and create business value. Despite its advantages, many organisations battle to successfully adopt agile practices. While there are several challenges for agile adoption, organisational culture has been amongst the challenges on adopting agile practices. The objective of this study was to determine the organisational culture attributes which influence the adoption of agile practices within the organisation. The systematic literature review aimed to explore the organisational culture attributes which influence the adoption of agile practices. The review focused on papers published on organisational culture influencing the adoption of agile practices between January 2015 to December 2020. The search strategy retrieved 204 papers of which nine papers were selected for a detailed analysis. The study revealed five factors of organisational culture that influence the adoption of agile practices in the organization as: management control, team collaboration, market, values and creativity. These factors have a number of organisational culture attributes that influence the adoption of agile practices. This study contributes to the body of knowledge by providing organisational culture attributes which influence the adoption of agile practices. The results of the systematic literature review presented in this study will benefit researchers and practitioners of project management.
Privacy and Vulnerable Populations

Abstract Vulnerable populations face unique privacy risks that not only challenge designers’ preconceptions about privacy, these challenges are also frequently overlooked in decisions about privacy design and policy. This chapter defines and describes vulnerable populations and the challenges they face, as well as the research approaches that have traditionally been used to understand and design technologies that respect the privacy needs of vulnerable people. It describes how existing frameworks fail to account for the privacy concerns of people who experience heightened risk. It then introduces alternative ways of thinking about privacy that can help technologists, researchers, policy makers, and designers do a better job of serving the needs of the most vulnerable users of technology. We conclude with concrete guidance around identifying and integrating vulnerable populations into technology design for privacy.
Data-Correlation-Aware Unsupervised Deep-Learning Model for Anomaly Detection in Cyber–Physical Systems

A cyber–physical system (CPS) is a multidimensional complex system integrating computing, communication, and control technologies. Because of their key functionality within the system, CPS requires large robustness and security to ensure its reliable operation. Due to its importance in supporting overall system security, anomaly detection (AD) is likely to continue to play an important role in the CPS security. Moreover, unsupervised AD models based on deep learning have shown better performances in rule training, adaptive update, detection efficiency, and accuracy. Due to the nature of the CPS systems, CPS data is more likely to exhibit implicit correlative relationship among data, which would be vital to exploit for CPS security provisions in more complex data environments. In view of this observation, we propose the data-correlation-aware unsupervised deep learning model for AD in CPS, which uses an undigraph structure to store samples and implicit correlation among samples. We design a dual-autoencoder to train both original features and implicit correlation features among data, and we construct an estimation network using Gaussian mixture model (GMM) to evaluate the probability distribution of samples to complete the anomaly analysis. Experimental results compared the performance with relevant AD models based on deep learning which did not use data-correlation analysis. The results showed that, under some representative application scenarios of CPS considered, data-correlation-aware unsupervised deep-learning model achieved superior results in parameter sensitivity, ablation, relationship between correlation degree and detection performance, visualization, and detection effects.
Learning and Decision-Making from Rank Data

The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then u
AdaBoost-based transfer learning with privileged information

Transfer learning aims to improve the learning of the target domain with the help of knowledge from the source domain. Recently, learning using privileged information (LUPI) has been proposed to learn an accurate classifier with privileged information which is only obtained in the training stage. In this paper, we propose a new AdaBoost-based Transfer Learning with Privileged Information (AdaTLPI) method to solve the transfer learning problem with privileged information, in which AdaBoost is taken into account to combine the weak classifiers into a strong classifier. In the model, we utilize shared parameter to transfer knowledge from the source domain to the target domain. We then incorporate privileged information about the source and target domains into a unified model and AdaBoost is used to learn a strong classifier by combining the obtained weak classifiers. Finally, we present an effective optimization algorithm to solve the proposed objective model and present the boundary of training error of the proposed method. The experimental results manifest that the proposed method can outperform the previous methods.
Robust KPI Anomaly Detection for Large-Scale Software Services with Partial Labels

To ensure the reliability of software services, operators collect and monitor a large number of KPI (Key Performance Indicator) streams constantly. KPI anomaly detection is vitally important for software service management. However, none of supervised learning methods, semi-supervised learning methods, transfer learning methods, or unsupervised learning methods achieve accurate anomaly detection for the large-scale, diverse, dynamically changing KPI streams with little labeling effort. In this paper, we propose PUAD, a PU learning-based method, to achieve accurate KPI anomaly detection requiring a few partial labels. It integrates clustering, PU learning, and semi-supervised learning to minimize labeling effort and improve anomaly detection accuracy simultaneously. Additionally, we propose a novel active learning method that selects the samples most likely to be positive in each iteration to avoid false alarms. We apply 208 real-world KPI streams collected from a large-scale software service provider to evaluate the performance of PUAD, demonstrating that it achieves a close F1-score to supervised learning methods with much fewer manual labels, and greatly outperforms semi-supervised learning methods, transfer learning methods, and unsupervised learning methods.
Band-Area Application Container and Artificial Fish Swarm Algorithm for Multi-Objective Optimization in Internet-of-Things Cloud

Container virtualization methods based on application deployment levels have been widely adopted in cloud-computing environments to implement application construction, deployment, and migration.However, most application containers focus on the interface between the applications and hosts and lack collaboration between application containers.This study proposes a new application container model that contains users, application services, documents, and messages, called Band-area Application Container.A salient feature of the Band-area is that it can express a variety of things in reality, such as organizations or individuals.End users can build a complex and changeable application system through cooperation between the Band-areas.However, the resource allocation of non Internet-of-Thing and Internetof-Thing tasks from the application container is an open issue.The resource allocation method of tasks should not only improve the quality of the user experience, but also reduce energy consumption by improving the resource utilization of the server.To solve this problem, an artificial fish swarm algorithm is proposed to optimize container-based task scheduling.The algorithm considers not only the reliability, processing time overhead, and energy consumption of the task, but also the resource utilization of the servers.Experimental evaluation shows that, compared with the existing three algorithms, the algorithm obtains a better improvement rate in task processing time overhead, energy consumption, reliability, and cluster load balancing.
Degree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graph

Entity alignment, as the vital technique for knowledge graph construction and integration, aims to match entities that refer to the same real-world identity in different knowledge graphs (KGs). Recently, much effort has been devoted to embedding-based methods for entity alignment. For most of such methods, the entity with a high degree is hard to be aligned with its equivalent counterpart with a low degree. This degree difference between equivalent entities poses a great challenge for entity alignment. To solve this problem, a novel entity alignment framework that integrates a graph convolutional network (GCN) based embedding initializer and a degree aware generative adversarial network is proposed. In particular, the embedding initializer utilizes a GCN with highway gates to generate the preliminary embedding of entities based on their topological characteristics in the KGs. By alleviating the relevance between embeddings and degree features, the degree aware GAN mitigates the impact of degree difference and generates the final degree level irrelevant alignment result. To quantify the heterogeneity between the KGs, an evaluation metric called heterogeneity entropy of degree (HED) that represents the degree difference is defined in this paper. Based on HED, the impact of KG heterogeneity on the performance of the proposed DAGCN model is investigated based on WK3l-15k and DBP15k datasets. The experimental results show that the proposed degree aware adversarial graph convolutional network (DAGCN) outperforms other state-of-the-art methods over all metrics, especially when the HED is large.
Entity alignment with adaptive margin learning knowledge graph embedding

A large number of knowledge graphs have been constructed at present. However, there is diversity and heterogeneity among different knowledge graphs. The relation and attribute of the knowledge graph contain rich semantic information, which helps construct the potential semantic representation of the knowledge graph. At present, the method based on knowledge representation is an important method of entity alignment, which can align entities by transforming them into spatial vectors. And it helps to reduce the heterogeneity among different knowledge domains. However, existing methods use the same optimization goal for triples under different relations, ignoring the difference between relationships. In this article, we put forward a kind of entity alignment method based on the TransE model and use adaptive margin strategies in training. At the same time, this paper studies the LSTM encoder model and the BERT pre-training model in the application of entity alignment. To enhance the model’s robustness, we put forward the triple selection strategy based on attribute similarity. Experimental results on real datasets show that this method is significantly improved compared with the baseline model.
Introduction

The goal of this book is to synthesize the existing research on social media and democracy. We present reviews of the literature on disinformation, polarization, echo chambers, hate speech, bots, political advertising, and new media. In addition, we canvass the literature on reform proposals to address the widely perceived threats to democracy. We seek to examine the current state of knowledge on social media and democracy, to identify the many knowledge gaps and obstacles to research in this area, and to chart a course for future research. We hope to advocate for this new field of study and to suggest that universities, foundations, private firms, and governments should commit to funding and supporting this research.