Reviewerly/exHarmony huggingface dataset:
Detecting and Measuring Security Risks of Hosting-Based Dangling Domains

Public hosting services provide convenience for domain owners to build web applications with better scalability and security. However, if a domain name points to released service endpoints (e.g., nameservers allocated by a provider), adversaries can take over the domain by applying the same endpoints. Such a security threat is called "hosting-based domain takeover''. In recent years, a large number of domain takeover incidents have occurred; even well-known websites like the subdomains of microsoft.com have been impacted. However, until now, there has been no effective detection system to identify these vulnerable domains on a large scale. In this paper, we fill this research gap by presenting a novel framework, HostingChecker, for detecting domain takeovers. Compared with previous work, HostingChecker expands the detection scope and improves the detection efficiency by: (i) systematically identifying vulnerable hosting services using a semi-automated method; and (ii) effectively detecting vulnerable domains through passive reconstruction of domain dependency chains. The framework enables us to detect the subdomains of Tranco sites on a daily basis. We evaluate the effectiveness of HostingChecker and eventually detect 10,351 subdomains from Tranco Top-1M apex domains vulnerable to domain takeover, which are over 8× more than previous findings. Furthermore, we conduct an in-depth security analysis on the affected vendors, like Amazon and Alibaba, and gain a suite of new insights, including flawed implementation of domain ownership validation. Following responsible disclosure processes, we have reported issues to the security response centers of affected vendors, and some (e.g., Baidu and Tencent) have adopted our mitigation.
Synthesizing Event-Centric Knowledge Graphs of Daily Activities Using Virtual Space

Artificial intelligence (AI) is expected to be embodied in software agents, robots, and cyber-physical systems that can understand the various contextual information of daily life in the home environment to support human behavior and decision making in various situations. Scene graph and knowledge graph (KG) construction technologies have attracted much attention for knowledge-based embodied question answering meeting this expectation. However, collecting and managing real data on daily activities under various experimental conditions in a physical space are quite costly, and developing AI that understands the intentions and contexts is difficult. In the future, data from both virtual spaces, where conditions can be easily modified, and physical spaces, where conditions are difficult to change, are expected to be combined to analyze daily living activities. However, studies on the KG construction of daily activities using virtual space and their application have yet to progress. The potential and challenges must still be clarified to facilitate AI development for human daily life. Thus, this study proposes the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VirtualHome2KG</i> framework to generate synthetic KGs of daily life activities in virtual space. This framework augments both the synthetic video data of daily activities and the contextual semantic data corresponding to the video contents based on the proposed event-centric schema and virtual space simulation results. Therefore, context-aware data can be analyzed, and various applications that have conventionally been difficult to develop due to the insufficient availability of relevant data and semantic information can be developed. We also demonstrate herein the utility and potential of the proposed VirtualHome2KG framework through several use cases, including the analysis of daily activities by querying, embedding, and clustering, and fall risk detection among older adults based on expert knowledge. As a result, we are able to develop a support tool that detects the fall risk with 1.0 precision, 0.6 recall, and 0.75 F1-score and visualize it with an explanation of its rationale. Using the cases explored in this work, we also clarify and classify the challenges that future research on synthetic KG generation systems should resolve in terms of simulation, schema, and human activity. Finally, we discuss the potential solutions for implementing advanced applications to support our daily life.
Free Speech and the Legal Prohibition of Fake News

Western European liberal democracies have recently enacted laws that prohibit the diffusion of fake news on social media. Yet, many consider that such laws are incompatible with freedom of expression. In this paper, I argue that democratic governments have strong pro tanto reasons to prohibit fake news, and that doing so is compatible with free speech. First, I show that fake news disrupts a mutually beneficial form of epistemic dependence in which members of the public are engaged with journalists. Second, I contend that laws against fake news enhance rather than thwart personal autonomy. If these suggestions are plausible, then the same considerations about truth and autonomy that underpin the value of free speech give us strong reasons to prohibit fake news.
Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels

A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. Its success is largely attributed to the GP's analytical tractability, robustness, and natural inclusion of uncertainty quantification. Unfortunately, the use of exact GPs is prohibitively expensive for large datasets due to their unfavorable numerical complexity of [Formula: see text] in computation and [Formula: see text] in storage. All existing methods addressing this issue utilize some form of approximation-usually considering subsets of the full dataset or finding representative pseudo-points that render the covariance matrix well-structured and sparse. These approximate methods can lead to inaccuracies in function approximations and often limit the user's flexibility in designing expressive kernels. Instead of inducing sparsity via data-point geometry and structure, we propose to take advantage of naturally-occurring sparsity by allowing the kernel to discover-instead of induce-sparse structure. The premise of this paper is that the data sets and physical processes modeled by GPs often exhibit natural or implicit sparsities, but commonly-used kernels do not allow us to exploit such sparsity. The core concept of exact, and at the same time sparse GPs relies on kernel definitions that provide enough flexibility to learn and encode not only non-zero but also zero covariances. This principle of ultra-flexible, compactly-supported, and non-stationary kernels, combined with HPC and constrained optimization, lets us scale exact GPs well beyond 5 million data points.
Software-defined networking enabled big data tasks scheduling: A tabu search approach

The growth of information technology along with the revolution of the industry and business has led to the generation of an enormous amount of data. This big data needs a platform beyond the traditional data possessing context that relies on some computational servers communicating through a network in its lower layer. One of the most important challenges in data processing is how to transfer the big batches of data between the servers to achieve fast responsiveness. Consequently, the underlying network plays a critical role in the performance of a big data analysis platform. Ideally, this network must use the shortest path that has the lowest amount of load, to transfer the large-scale data. To address this issue, we propose a software-defined networking (SDN) enabled scheduling method that uses the tabu search algorithm to schedule big data tasks. The proposed algorithm not only considers data locality but also uses the network traffic status for efficient scheduling. Our extensive simulative study in the Mininet emulator shows that the proposed scheme gives high performance and minimizes job completion time.
Research on smart city data encryption and communication efficiency improvement under federated learning framework

To improve the data communication processing capability of smart city informatization construction and defend against malicious joint attacks of internal communication participants, we studied the use of function encryption, blockchain, differential privacy and other technologies to defend against weight disclosure, participation in collusion attacks, single point failures and other issues in the federal learning process, and introduced edge computing and asynchronous communication in the classic federal learning framework, Improve the communication efficiency of the model while ensuring the accuracy. The research results show that the accuracy of FE-BDP algorithm can maintain above 95% for a long time, and the change is not significant with the increase of the number of users, indicating that the algorithm has strong stability. The loss value of FE-LDP model is significantly smaller than that of other models and can be stabilized below 0.05. The data aggregation time of blockchain encryption technology is less than 1.0s. The edge-asynchronous communication framework can be applied to multiple urban scenarios and achieve effective data communication, with the highest accuracy rate of 93.87%and the lowest communication cost of 1315.29s. The research results show that the security encryption fusion technology can effectively protect user data privacy, and the edge-asynchronous communication framework has obvious effect on improving communication efficiency, which has important application value for promoting the construction of smart city informatization.
Development of a patients’ satisfaction analysis system using machine learning and lexicon-based methods

Abstract Background Patients’ rights are integral to medical ethics. This study aimed to perform sentiment analysis and opinion mining on patients’ messages by a combination of lexicon-based and machine learning methods to identify positive or negative comments and to determine the different ward and staff names mentioned in patients’ messages. Methods The level of satisfaction and observance of the rights of 250 service recipients of the hospital was evaluated through the related checklists by the evaluator. In total, 822 Persian messages, composed of 540 negative and 282 positive comments, were collected and labeled by the evaluator. Pre-processing was performed on the messages and followed by 2 feature vectors which were extracted from the messages, including the term frequency–inverse document frequency (TFIDF) vector and a combination of the multifeature (MF) (a lexicon-based method) and TFIDF (MF + TFIDF) vectors. Six feature selectors and 5 classifiers were used in this study. For the evaluations, 5-fold cross-validation with different metrics including area under the receiver operating characteristic curve (AUC), accuracy (ACC), F1 score, sensitivity (SEN), specificity (SPE) and Precision-Recall Curves (PRC) were reported. Message tag detection, which featured different hospital wards and identified staff names mentioned in the study patients’ messages, was implemented by the lexicon-based method. Results The best classifier was Multinomial Naïve Bayes in combination with MF + TFIDF feature vector and SelectFromModel (SFM) feature selection (ACC = 0.89 ± 0.03, AUC = 0.87 ± 0.03, F1 = 0.92 ± 0.03, SEN = 0.93 ± 0.04, and SPE = 0.82 ± 0.02, PRC-AUC = 0.97). Two methods of assessment by the evaluator and artificial intelligence as well as survey systems were compared. Conclusion Our results demonstrated that the lexicon-based method, in combination with machine learning classifiers, could extract sentiments in patients’ comments and classify them into positive and negative categories. We also developed an online survey system to analyze patients’ satisfaction in different wards and to remove conventional assessments by the evaluator.
Scim: Intelligent Skimming Support for Scientific Papers

Scholars need to keep up with an exponentially increasing flood of scientific papers. To aid this challenge, we introduce Scim, a novel intelligent interface that helps experienced researchers skim – or rapidly review – a paper to attain a cursory understanding of its contents. Scim supports the skimming process by highlighting salient paper contents in order to direct a reader's attention. The system's highlights are faceted by content type, evenly distributed across a paper, and have a density configurable by readers at both the global and local level. We evaluate Scim with both an in-lab usability study and a longitudinal diary study, revealing how its highlights facilitate the more efficient construction of a conceptualization of a paper. We conclude by discussing design considerations and tensions for the design of future intelligent skimming tools.
X-CHAR

End-to-end deep learning models are increasingly applied to safety-critical human activity recognition (HAR) applications, e.g., healthcare monitoring and smart home control, to reduce developer burden and increase the performance and robustness of prediction models. However, integrating HAR models in safety-critical applications requires trust, and recent approaches have aimed to balance the performance of deep learning models with explainable decision-making for complex activity recognition. Prior works have exploited the compositionality of complex HAR (i.e., higher-level activities composed of lower-level activities) to form models with symbolic interfaces, such as concept-bottleneck architectures, that facilitate inherently interpretable models. However, feature engineering for symbolic concepts-as well as the relationship between the concepts-requires precise annotation of lower-level activities by domain experts, usually with fixed time windows, all of which induce a heavy and error-prone workload on the domain expert. In this paper, we introduce X-CHAR , an eXplainable Complex Human Activity Recognition model that doesn't require precise annotation of low-level activities, offers explanations in the form of human-understandable, high-level concepts, while maintaining the robust performance of end-to-end deep learning models for time series data. X-CHAR learns to model complex activity recognition in the form of a sequence of concepts. For each classification, X-CHAR outputs a sequence of concepts and a counterfactual example as the explanation. We show that the sequence information of the concepts can be modeled using Connectionist Temporal Classification (CTC) loss without having accurate start and end times of low-level annotations in the training dataset-significantly reducing developer burden. We evaluate our model on several complex activity datasets and demonstrate that our model offers explanations without compromising the prediction accuracy in comparison to baseline models. Finally, we conducted a mechanical Turk study to show that the explanations provided by our model are more understandable than the explanations from existing methods for complex activity recognition.
Random forest-based IDS for IIoT edge computing security using ensemble learning for dimensionality reduction

The industrial internet of things (IIoT) is the IoT application in the industrial sphere. It aims to get smart devices involved in industrial sectors to enhance their operations. In addition to IoT vulnerabilities, IIoT introduces more severe security flaws. Therefore, intrusion detection systems (IDS) are being developed to avoid catastrophic intrusions. This research proposes a machine learning (ML)-based IDS for IIoT edge computing security. We employed Pearson's correlation coefficient (PCC) and isolation forest (IF) to reduce the computational cost and training. We implemented the IF to eliminate outliers and the PCC for the feature selection. Feature engineering improves ML models' accuracy (ACC) and detection rate (DR). The random forest (RF) classifier was applied to enhance the IDS performances. Our approach showed significant results on the Bot-IoT dataset with 100% DR and 99.99% ACC. The obtained results show that our approach has many advantages compared to other models.
Feature-based approach and sequential pattern mining to enhance quality of Indonesian automatic text summarization

Indonesian automatic text summarization research is developed rapidly. The quality, especially readability aspect, of text summary can be reached if the meaning of the text can be maintained properly. Therefore, this research aims to enhance the quality of extractive Indonesian automatic text summarization with considering the quality of structured representation of text. This research uses sequential pattern mining (SPM) to produce This research use SPM to produce sequence of words (SoW) as structured text representation using PrefixSpan algorithm. Then, SPM is combined with feature-based approach using sentence scoring method to produce summary. The experiment result using IndoSum dataset shows that even though the combination of SPM and sentence scoring can increase the precision value of recall-oriented understudy for gisting evaluation (ROUGE)-1, ROUGE-2, and ROUGE-L, from 0.68 to 0.76, 0.54 to 0.69, and 0.51 to 0.72. Especially, combination of SPM and Sentence Scoring can enhance precision, recall, and f-measure of ROUGE-L that consider the order of word occurance in measurement. SPM increases ROUGE-L f-measure value of sentence scoring from 0.32 to 0.36. Moreover, combination of sentence scoring and SPM is better than SumBasic that used as feature-based approach in the previous Indonesian text summarization research.
Conditional synthetic food image generation

Generative Adversarial Networks (GAN) have been widely investigated for image synthesis based on their powerful representation learning ability. In this work, we explore the StyleGAN and its application of synthetic food image generation. Despite the impressive performance of GAN for natural image generation, food images suffer from high intra-class diversity and inter-class similarity, resulting in overfitting and visual artifacts for synthetic images. Therefore, we aim to explore the capability and improve the performance of GAN methods for food image generation. Specifically, we first choose StyleGAN3 as the baseline method to generate synthetic food images and analyze the performance. Then, we identify two issues that can cause performance degradation on food images during the training phase: (1) inter-class feature entanglement during multi-food classes training and (2) loss of high-resolution detail during image downsampling. To address both issues, we propose to train one food category at a time to avoid feature entanglement and leverage image patches cropped from high-resolution datasets to retain fine details. We evaluate our method on the Food-101 dataset and show improved quality of generated synthetic food images compared with the baseline. Finally, we demonstrate the great potential of improving the performance of downstream tasks, such as food image classification by including high-quality synthetic training samples in the data augmentation.
SoK: Anonymous Credentials

Anonymous credentials are a powerful tool for making assertions about identity while maintaining privacy and have been the subject of study for many years. The interest in anonymous credentials has intensified in recent years as the Internet and Web have become more and more interwoven into the fabric of our daily lives, causing large scale concerns about privacy. In particular, users are now wanting to reduce the amount of personal information they share in order to gain access to services. Since their introduction by Chaum (Comm. ACM 1985) there have been a plethora of results attempting to instantiate them with the first fully anonymous scheme being realised by Camenish and Lysyanskaya (EUROCRYPT 2001). Since this breakthrough result, there have been several newer schemes that have been proposed that not only improve on the Camenish-Lysyanskaya scheme but also introduce new features. In fact there have been a large variety of extensions proposed that have led to a seemingly incomparable landscape of schemes. In this paper, we review the many properties of anonymous credential systems, and systematically categorise and compare the approaches in the literature. Our analysis allows us to highlight gaps, open questions and directions for future research in the space of anonymous credentials.
Metaverse

In this chapter, the main study focus will be on the emerging opportunities and challenges to the metaverse in digital marketing domain and will provide more detailed scoping review of the concept. The findings from the literature conceptualize the prevailing opportunities and upcoming challenges for metaverse in digital marketing and offers guidance in the form of future research directions. The opportunities to metaverse discussed in this chapter can prove to be gamechanger for digital marketing field. It is clear that metaverse will give new dimension to marketing, especially to digital marketing, and if it is used to its potential can change the landscape of marketing industry for good. Although a number of opportunities are presented by metaverse, on the flip side it also has some challenges which need to be faced and sorted to provide seamless and uninterrupted services to users and to earn profits at the same time.
Observation of entanglement transition of pseudo-random mixed states

Random quantum states serve as a powerful tool in various scientific fields, including quantum supremacy and black hole physics. It has been theoretically predicted that entanglement transitions may happen for different partitions of multipartite random quantum states; however, the experimental observation of these transitions is still absent. Here, we experimentally demonstrate the entanglement transitions witnessed by negativity on a fully connected superconducting processor. We apply parallel entangling operations, that significantly decrease the depth of the pseudo-random circuits, to generate pseudo-random pure states of up to 15 qubits. By quantum state tomography of the reduced density matrix of six qubits, we measure the negativity spectra. Then, by changing the sizes of the environment and subsystems, we observe the entanglement transitions that are directly identified by logarithmic entanglement negativities based on the negativity spectra. In addition, we characterize the randomness of our circuits by measuring the distance between the distribution of output bit-string probabilities and the Porter-Thomas distribution. Our results show that superconducting processors with all-to-all connectivity constitute a promising platform for generating random states and understanding the entanglement structure of multipartite quantum systems.