Reviewerly/exHarmony huggingface dataset:
Enabling secure time-series data sharing via homomorphic encryption in cloud-assisted IIoT

A growing number of Industrial Internet of Things (IIoT) devices and services collect massive time-series data related to production, monitoring and maintenance. To provide ubiquitous access, scalability and sharing possibilities, the IIoT applications utilize the cloud to store collected data streams. However, secure storing of the massive and continuously generated data poses significant privacy risks, including data breaches for IIoT applications. Alongside, we need to protect the utility of the data streams by allowing benign services to access and run analytics securely and selectively. To address this, we propose SmartCrypt, a data storing and sharing system that supports scalable analytics over the encrypted time-series data. SmartCrypt enables users to secure and fine-grain sharing of their encrypted data. Additionally, SmartCrypt guarantees data confidentiality in the presence of unauthorized parties by allowing end-to-end encryption using a novel symmetric homomorphic encryption scheme. We perform extensive experiments on a real-world dataset primarily to assess the feasibility of SmartCrypt for secure storing and sharing of IIoT data streams. The results show that SmartCrypt reduces query time by 17%, reduces range query time by 32%, improves throughput by 9% and scalability by 20% over the best performed scheme in the state-of-the-art.
The doge worth 88 billion dollars: A case study of Dogecoin

In the modern financial system, the ability to create money is in the hands of a few central institutions. Blockchain networks, and by extension cryptocurrencies, were created with the promise of giving that power to users. The most well-known example of a blockchain technology achieving such decentralization is Bitcoin, but its popularity has arguably been matched by an alternative-currency named Dogecoin. Unlike other cryptocurrencies, which have marketed themselves on differentiating technical features, Dogecoin’s allure likely stems from its cultural roots as a meme. Where cryptocurrency is typically regarded as a difficult topic to grasp, the introduction of Doge’s (2013) most popular meme, into the crypto-space increased crypto’s accessibility to new participants. Consequently, Dogecoin exists in two economies: the financial economy and the cultural meme economy, with the latter having unprecedented tangible impacts on the former. Dogecoin’s unique cultural significance provides an example of how blockchain can succeed in promoting alternative money systems. At its peak in 2021, Dogecoin achieved a market capitalization of $88 billion. Where analysis of the Dogecoin phenomenon is lacking in the current literature, we will fill that gap with a case study of Dogecoin. By studying Dogecoin as a combination of money and meme, we can further our understanding of how to better promote social finance initiatives through the virality of memes.
Photonic reinforcement learning based on optoelectronic reservoir computing

Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot control, internet advertising, and elastic optical networks. However, the computational cost of reinforcement learning with deep neural networks is extremely high and reducing the learning cost is a challenging issue. We propose a photonic on-line implementation of reinforcement learning using optoelectronic delay-based reservoir computing, both experimentally and numerically. In the proposed scheme, we accelerate reinforcement learning at a rate of several megahertz because there is no required learning process for the internal connection weights in reservoir computing. We perform two benchmark tasks, CartPole-v0 and MountanCar-v0 tasks, to evaluate the proposed scheme. Our results represent the first hardware implementation of reinforcement learning based on photonic reservoir computing and pave the way for fast and efficient reinforcement learning as a novel photonic accelerator.
On cross-lingual retrieval with multilingual text encoders

Pretrained multilingual text encoders based on neural transformer architectures, such as multilingual BERT (mBERT) and XLM, have recently become a default paradigm for cross-lingual transfer of natural language processing models, rendering cross-lingual word embedding spaces (CLWEs) effectively obsolete. In this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a number of diverse language pairs. We first treat these models as multilingual text encoders and benchmark their performance in unsupervised ad-hoc sentence- and document-level CLIR. In contrast to supervised language understanding, our results indicate that for unsupervised document-level CLIR-a setup with no relevance judgments for IR-specific fine-tuning-pretrained multilingual encoders on average fail to significantly outperform earlier models based on CLWEs. For sentence-level retrieval, we do obtain state-of-the-art performance: the peak scores, however, are met by multilingual encoders that have been further specialized, in a supervised fashion, for sentence understanding tasks, rather than using their vanilla 'off-the-shelf' variants. Following these results, we introduce localized relevance matching for document-level CLIR, where we independently score a query against document sections. In the second part, we evaluate multilingual encoders fine-tuned in a supervised fashion (i.e., we learn to rank) on English relevance data in a series of zero-shot language and domain transfer CLIR experiments. Our results show that, despite the supervision, and due to the domain and language shift, supervised re-ranking rarely improves the performance of multilingual transformers as unsupervised base rankers. Finally, only with in-domain contrastive fine-tuning (i.e., same domain, only language transfer), we manage to improve the ranking quality. We uncover substantial empirical differences between cross-lingual retrieval results and results of (zero-shot) cross-lingual transfer for monolingual retrieval in target languages, which point to "monolingual overfitting" of retrieval models trained on monolingual (English) data, even if they are based on multilingual transformers.
Toward Fully Autonomous Seismic Networks: Backprojecting Deep Learning-Based Phase Time Functions for Earthquake Monitoring on Continuous Recordings

Abstract Accurate and (near) real-time earthquake monitoring provides the spatial and temporal behaviors of earthquakes for understanding the nature of earthquakes, and also helps in regional seismic hazard assessments and mitigations. Because of the increase in both the quality and quantity of seismic data, an automated earthquake monitoring system is needed. Most of the traditional methods for detecting earthquake signals and picking phases are based on analyses of features in recordings of an individual earthquake and/or their differences from background noises. When seismicity is high, the seismograms are complicated, and, therefore, traditional analysis methods often fail. With the development of machine learning algorithms, earthquake signal detection and seismic phase picking can be more accurate using the features obtained from a large amount of earthquake recordings. We have developed an attention recurrent residual U-Net algorithm, and used data augmentation techniques to improve the accuracy of earthquake detection and seismic phase picking on complex seismograms that record multiple earthquakes. The use of probability functions of P and S arrivals and potential P and S arrival pairs of earthquakes can increase the computational efficiency and accuracy of backprojection for earthquake monitoring in large areas. We applied our workflow to monitor the earthquake activity in southern California during the 2019 Ridgecrest sequence. The distribution of earthquakes determined by our method is consistent with that in the Southern California Earthquake Data Center (SCEDC) catalog. In addition, the number of earthquakes in our catalog is more than three times that of the SCEDC catalog. Our method identifies additional earthquakes that are close in origin times and/or locations, and are not included in the SCEDC catalog. Our algorithm avoids misidentification of seismic phases for earthquake location. In general, our algorithm can provide reliable earthquake monitoring on a large area, even during a high seismicity period.
Backdoor attacks-resilient aggregation based on Robust Filtering of Outliers in federated learning for image classification

Federated Learning is a distributed machine learning paradigm vulnerable to different kind of adversarial attacks, since its distributed nature and the inaccessibility of the data by the central server. In this work, we focus on model-poisoning backdoor attacks, because they are characterized by their stealth and effectiveness. We claim that the model updates of the clients of a federated learning setting follow a Gaussian distribution, and those ones with an outlier behavior in that distribution are likely to be adversarial clients. We propose a new federated aggregation operator called Robust Filtering of one-dimensional Outliers (RFOut-1d), which works as a resilient defensive mechanism to model-poisoning backdoor attacks. RFOut-1d is based on an univariate outlier detection method that filters out the model updates of the adversarial clients. The results on three federated image classification dataset show that RFOut-1d dissipates the impact of the backdoor attacks to almost nullifying them throughout all the learning rounds, as well as it keeps the performance of the federated learning model and it outperforms that state-of-the-art defenses against backdoor attacks.
Partial Label Learning with competitive learning graph neural network

Partial Label Learning (PLL) is a weakly supervised learning framework where each instance may be associated with more than one candidate label, among which only one is true. Traditionally, the PLL problem is solved by removing the false candidate labels based on the instance relationship, while the potentially useful information between instances and labels as well as the potential candidate label relationship is ignored. In this paper, a new PLL framework PL-CGNN is proposed, which treats the instances with false labels as noise, and the PLL is reformulated to remove the noise instances. First of all, the feature of each label class is approximately represented by the center point of all the related instances. The significant operation enables the similarity between instances and labels measurable. Next, all the candidate labels for each instance compete for the biggest similarity. To further improve the robustness of the model, the competition procedure for the most similar label is extended to the neighbors of this instance. The label with the most wins is the final ground-truth one. The relationship between candidate labels guides the situation that the competition process develops into. Through iterative competitive learning, each label class approaches the true value. Experiments carried out on diverse datasets show that the performance of the PL-CGNN model is outstanding.
Dataset for understanding the effort and performance of external auditors during the COVID-19 crisis: A remote audit analysis

The onset of the COVID-19 pandemic at the end of 2019 has dramatically changed the life of both individuals and corporate businesses. Besides the health fears of the pandemic, there are sizable challenges for some jobs that habitually deliver their services face-to-face, disrupting the quality of service. In particular, external auditors, the most critical and reliable accreditors of financial reports, are in an unprecedented situation to conduct audit works and collect sufficient and appropriate audit evidence. To capture how COVID-19 affects auditors in Saudi Arabia, an online questionnaire was developed and distributed from April to August 2021 via email to all 417 auditors/audit firms registered with the Saudi Organization for Chartered and Professional Accountants (SOCPA). This dataset contains responses from 193 auditors, in four major sections: demographic information (7 items); audit effort during COVID-19 (3 items); auditor performance during COVID-19 (7 items); and remote audit proficiency (6 items). It may serve for theoretical and practical assessment for auditors in the time of COVID-19, for example, evaluating how auditors conduct their work during the lockdown and how remote audit proficiency impacts the effort and performance of auditors. In addition, it can be used to assess the differences between auditors by considering the moderating effect of their personal characteristics on the impact of remote audit proficiency on both effort and performance.
Methods for Eliciting Informative Prior Distributions: A Critical Review

Eliciting informative prior distributions for Bayesian inference can often be complex and challenging. Although popular methods rely on asking experts probability-based questions to quantify uncertainty, these methods are not without their drawbacks, and many alternative elicitation methods exist. This paper explores methods for eliciting informative priors categorized by type and briefly discusses their strengths and limitations. Most of the review literature in this field focuses on a particular type of elicitation approach. The primary aim of this work, however, is to provide a more complete yet macro view of the state of the art by highlighting new (and old) approaches in one clear easy-to-read article. Two representative applications are used throughout to explore the suitability, or lack thereof, of the existing methods, one of which highlights a challenge that has not been addressed in the literature yet. We identify some of the gaps in the present work and discuss directions for future research.
A new S-box construction method meeting strict avalanche criterion

S-box is the main security component in lightweight block ciphers. Finding new algorithms and technical applications to generate S-box is still a current research topic. We proposed an S-box construction method that satisfies the strict avalanche criterion. It includes a nonlinear layer and a diffusion layer. These layers enable the constructed S-box to maintain good nonlinearity, differential uniformity, strict avalanche criterion, and a high number of algebraic terms. Secondly, we listed 20 existing classic 4 × 4 S-boxes and tested their cryptographic properties such as nonlinearity, difference uniformity, strict avalanche criterion, algebraic degree, algebraic item number, and fixed point. At the same time, the test results are compared with the proposed S-box. The results show that these cryptographic properties of our proposed S-box are superior to other S-boxes. In addition, we evaluated the proposed S-box hardware implementation overhead. To evaluate our S-box more objectively, we tested the above 20 S-boxes hardware implementation overhead under the same conditions, and the results show that the S-box we constructed achieved a good balance between cryptographic properties and hardware overhead.
SEASum: Syntax-Enriched Abstractive Summarization

Compared to traditional RNN-based models, abstractive summarization systems based on Pre-trained Language Models (PTMs) achieve dramatic improvements in readability. Thus, in the field of abstractive summarization, more attention should be devoted to the faithfulness issue that predicted summaries are not factually consistent with source texts. To alleviate this disadvantage, we propose a novel S yntax- E nriched A bstractive Sum marization (SEASum) framework, which utilizes graph attention networks (GATs) to introduce syntactic features of source texts to generate faithful summaries. In the SEASum framework, the PTM-based semantic encoder encodes word sequence, while the GAT-based syntactic encoder captures explicit syntax, i.e., part-of-speech tags, parse trees, and dependency-based relative positions of source documents. A feature fusion module is introduced to incorporate encoded syntactic features into the summarization framework. Based on the proposed SEASum framework, we develop two summarization models: 1) parallel SEASum model, in which the semantic encoder and syntactic encoder work in parallel, a multi-head attention module fused two-stream features for the following decoding process; 2) cascaded SEASum model, which takes contextual word embeddings from semantic encoder as node embeddings for the syntactic encoder and employs highway networks to regulate information flow. Experimental results on CNN/DailyMail and Reddit-TIFU (short) datasets show our parallel SEASum model and cascaded SEASum model outperform state-of-the-art abstractive summarization approaches in the faithfulness measurement. The results also demonstrate that cascaded SEASum model is more effective than parallel SEASum model in boosting faithfulness. • A syntax-enhanced abstractive summarization framework, SEASum, is proposed. • SEASum incorporates syntactic features, i.e. dependency parse trees, POS tags, etc. • A parallel and a cascaded model are developed based on the SEASum framework. • Two SEASum-based models are tested on CNN/DM and Reddit-TIFU (short) datasets. • Both SEASum models achieve improvements on ROUGE and faithfulness metrics.
Detecting abnormality with separated foreground and background: Mutual Generative Adversarial Networks for video abnormal event detection

As one of the most important tasks in intelligent video analysis, video abnormal event detection has been extensively studied. Prior arts have made a great process in designing frameworks to capture spatio-temporal features of video frames. However, video frames usually consist of various objects. It is challenging to grasp the nuances of anomalies against noisy backgrounds. To tackle the bottleneck, we propose a novel Foreground–Background Separation Mutual Generative Adversarial Network (FSM-GAN) framework. The FSM-GAN permits the separation of video frames into the foreground and background. The separated foreground and background are utilized as the input of mutual generative adversarial networks, which transform raw-pixel images in optical-flow representations and vice versa. In the networks, the background is regarded as known conditions and the model focuses on learning the high-level spatio-temporal foreground features to represent the event with the given conditions during the mutual adversarial training. In the test stage, these high-level features instead of low-level visual primitives are utilized to measure the abnormality in the semantic level. Compared with state-of-the-art methods and other abnormal event detection approaches, the proposed framework demonstrates its effectiveness and reliability across various scenes and events.
Object-Agnostic Transformers for Video Referring Segmentation

Video referring segmentation focuses on segmenting out the object in a video based on the corresponding textual description. Previous works have primarily tackled this task by devising two crucial parts, an intra-modal module for context modeling and an inter-modal module for heterogeneous alignment. However, there are two essential drawbacks of this approach: (1) it lacks joint learning of context modeling and heterogeneous alignment, leading to insufficient interactions among input elements; (2) both modules require task-specific expert knowledge to design, which severely limits the flexibility and generality of prior methods. To address these problems, we here propose a novel Object-Agnostic Transformer-based Network, called OATNet, that simultaneously conducts intra-modal and inter-modal learning for video referring segmentation, without the aid of object detection or category-specific pixel labeling. More specifically, we first directly feed the sequence of textual tokens and visual tokens (pixels rather than detected object bounding boxes) into a multi-modal encoder, where context and alignment are simultaneously and effectively explored. We then design a novel cascade segmentation network to decouple our task into coarse-grained segmentation and fine-grained refinement. Moreover, considering the difficulty of samples, a more balanced metric is provided to better diagnose the performance of the proposed method. Extensive experiments on two popular datasets, A2D Sentences and J-HMDB Sentences, demonstrate that our proposed approach noticeably outperforms state-of-the-art methods.
PS-Tree: A piecewise symbolic regression tree

The symbolic methods have recently regained popularity due to their reasonable interpretability compared to neural network-based artificial intelligence techniques. The regression tree is such a symbolic method that divides the feature space into several subregions and builds a simple response surface model, such as a constant value or a linear model, for each subregion. However, this strategy may fail when nonlinear structures exist in the subregions. To overcome this problem, this paper proposes a new regression model, named piecewise symbolic regression tree (PS-Tree). Instead of using constant values or linear models as the leaf nodes, PS-Tree builds symbolic regressors for the leaf nodes or subregions. In addition to that, we also propose an adaptive space partition strategy by dynamically adjusting the partition of the space to alleviate the problem caused by incorrect partitioning. PS-Tree is applied to 122 synthetic and real-world datasets, and the results show that it outperforms several state-of-the-art regression methods.
Adaptive reversible data hiding algorithm for interpolated images using sorting and coding

Existing reversible data hiding (RDH) algorithms based on the interpolation technology (IT) have high embedding capacities and visual qualities. However, they usually embed secret data directly into interpolated pixels sequentially, resulting in that the visual qualities may not be optimal. To address this problem, an adaptive IT-based RDH algorithm is proposed. First, for a given interpolated image, the amount of data that can be embedded into each interpolated pixel is sorted in the ascending order. Next, the secret data to be embedded are recoded and sequentially embedded into interpolated pixels after overflow/underflow preprocessing based on the obtained index. The experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art RDH algorithms based on IT. On average, the average peak signal-to-noise ratio (PSNR) of our algorithm is improved by approximately 25%. In comparison with traditional RDH algorithms using histogram shifting and pixel value ordering, our algorithm also achieves a higher PSNR. In addition, our algorithm can resist histogram and regular singular steganalysis attacks. These results clearly demonstrate that the proposed algorithm is effective.