Reviewerly/exHarmony huggingface dataset:
Anomaly detection for streaming data based on grid-clustering and Gaussian distribution

A massive amount of real-time and evolving streaming data are produced from various devices and applications. Anomaly detection is one of the main tasks of streaming data mining with many practical applications. However, without prior knowledge, it is difficult to detect the anomaly accurately and quickly. In this paper, we propose an unsupervised anomaly detection algorithm (GC-ADS), which is based on the idea of grid clustering and Gaussian distribution. Specifically, the data space is first segmented using the grid structure, then the data points are mapped to grids and finally grids are clustered. The anomaly can be preliminarily judged according to the cluster density. To solve the problem that clustering cannot distinguish between noise and anomaly, based on the idea of data similarity and Gaussian distribution, a noise recognition model is designed. In addition, a data filtering model based on grid and sliding window is designed to save memory and retain valid information. The proposed method is compared with the state-of-the-art methods on the Numenta anomaly benchmark. Experimental results indicate that GC-ADS detects anomalies more accurately than other methods with less time cost.
The role of social network embeddedness and collective efficacy in encouraging farmers’ participation in water environmental management

Water environmental management (WEM) has a significant influence on the global ecological balance. As an institutional innovation, the River Chief System (RCS) in China has achieved a positive short-term impact on addressing water environmental problems. However, its effects are limited in rural China. As a type of public good, the rural WEM demands the active participation not only of government but also of farmers. Based on the social cognitive and social network theory, this study empirically investigates how rural social networks promote farmers' participation in WEM. Using the survey of 860 farmers in the Yellow and Yangtze River Basin, we employ the double-hurdle model (D-H-M) to craft the primary assessment. The results show that the social network embeddedness facilitates farmers' participation in WEM directly. Collective efficacy plays a full mediation role in the relationship between social network embeddedness and farmers' participation. Moreover, the perceived role of village leaders affects the relationship between social networks and farmers' participation. Our research enriches the application of social network theory in the rural social context and offers an innovative approach to solving farmers' participation problems in WEM.
Artificial Intelligence-Driven Morphology-Based Enrichment of Malignant Cells from Body Fluid

Cell morphology is a fundamental feature used to evaluate patient specimens in pathologic analysis. However, traditional cytopathology analysis of patient effusion samples is limited by low tumor cell abundance coupled with the high background of nonmalignant cells, restricting the ability of downstream molecular and functional analyses to identify actionable therapeutic targets. We applied the Deepcell platform that combines microfluidic sorting, brightfield imaging, and real-time deep learning interpretations based on multidimensional morphology to enrich carcinoma cells from malignant effusions without cell staining or labels. Carcinoma cell enrichment was validated with whole genome sequencing and targeted mutation analysis, which showed a higher sensitivity for detection of tumor fractions and critical somatic variant mutations that were initially at low levels or undetectable in presort patient samples. Our study demonstrates the feasibility and added value of supplementing traditional morphology-based cytology with deep learning, multidimensional morphology analysis, and microfluidic sorting.
Easily Scalable Photonic Tensor Core Based on Tunable Units with Single Internal Phase Shifters

Abstract Photonic neural networks (PNNs) show tremendous potential for artificial intelligence applications due to their higher computational rates than their traditional electronic counterpart. However, the scale‐up of PNN relies on the number of cascaded computing units, which is limited by the accumulated transmission attenuation. Here, a topology of PNN with Mach–Zehnder interferometers based on a single‐tuned phase shifter that implements arbitrary nonnegative or real‐valued matrices for vector‐matrix multiplication is proposed. Compared with the universal matrix mesh, the new configuration exhibits two orders of magnitude lower optical path loss and a twofold reduction in the number of the tunable phase shifter. An 8 × 8 reconfigurable chip is designed and fabricated, and it is experimentally verified that the 2 × 4 nonnegative‐valued matrix and the 2 × 2 real‐valued matrix are implemented in the proposed topology. Higher than 85% inference accuracies are obtained in the Modified National Institute of Standards and Technology handwritten digit recognition tasks with these matrices in the PNNs. Therefore, with much lower optical path loss and comparable computing accuracy, the proposed PNN configuration can be easily scaled up to tackle higher dimensional matrix multiplication, which is highly desired in tasks like voice and image recognition.
High-precision U-Pb geochronology and Bayesian age-depth modeling of the glacial-postglacial transition of the southern Paraná Basin: Detailing the terminal phase of the Late Paleozoic Ice Age on Gondwana

The timing of the terminal deglaciation of the Late Paleozoic Ice Age – LPIA in the Paraná Basin has been under investigation. However, the low availability of high-precision radioisotopic ages prevents the construction of a detailed chronostratigraphic framework necessary to constrain the demise of the glaciation and perform correlation with contemporaneous records across neighboring basins and within the extensive Paraná Basin. This study aims to contribute to the chronostratigraphic detailing of the Carboniferous-Permian succession of the southern Paraná Basin, interval preserving LPIA glacial-postglacial transition. Therefore, we document new high-precision U-Pb radioisotopic ages and reexamine previously published samples through single crystal chemical abrasion, isotope dilution-thermal ionization mass spectrometry (CA-IDTIMS). We integrate all high-precision radioisotopic ages into the first Bayesian age-depth models for the late Carboniferous-early Permian succession recorded in the southern Paraná Basin. Age-depth models were performed on DU-08-RS and HN-120-RS drill cores, located in the Capané and Candiota paleovalleys, respectively. The weighted mean 206Pb/238U date for the Rio Bonito Formation are 297.914 ± 0.10 (T6), 297.495 ± 0.11 (HN), 296.796 ± 0.114 (T1), 296.293 ± 0.098 (T1.5), 296.252 ± 0.117 (T2), and 296.148 ± 0.136 Ma (RB). For the Itararé Group sample, the weighted mean date is 301.809 ± 0.224 Ma (ITA). T1 and T2 high-precision ages turned out to be older than LA-ICP-MS ages published before (Cagliari et al., 2014), and RB and ITA ages younger (Cagliari et al., 2016). Age-depth models constraints the Rio Bonito Formation between Gzhelian and Sakmarian stages, encompassing ~6.9 and ~1.6 Myr in cores DU-08-RS and HN-120-RS, respectively. The results demonstrate that the successions of the Rio Bonito Formation recorded in Capané and Candiota paleovalleys are contemporaneous. They also corroborate to assign the final glaciation to the early Gzhelian (Late Pennsylvanian) and to place the Carboniferous-Permian boundary within the Rio Bonito Formation, in the study area.
A Phishing-Attack-Detection Model Using Natural Language Processing and Deep Learning

Phishing is a type of cyber-attack that aims to deceive users, usually using fraudulent web pages that appear legitimate. Currently, one of the most-common ways to detect these phishing pages according to their content is by entering words non-sequentially into Deep Learning (DL) algorithms, i.e., regardless of the order in which they have entered the algorithms. However, this approach causes the intrinsic richness of the relationship between words to be lost. In the field of cyber-security, the innovation of this study is to propose a model that detects phishing attacks based on the text of suspicious web pages and not on URL addresses, using Natural Language Processing (NLP) and DL algorithms. We used the Keras Embedding Layer with Global Vectors for Word Representation (GloVe) to exploit the web page content’s semantic and syntactic features. We first performed an analysis using NLP and Word Embedding, and then, these data were introduced into a DL algorithm. In addition, to assess which DL algorithm works best, we evaluated four alternative algorithms: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). As a result, it can be concluded that the proposed model is promising because the mean accuracy achieved by each of the four DL algorithms was at least 96.7%, while the best performer was BiGRU with 97.39%.
Assessment of a New Solar Radiation Nowcasting Method Based on FY-4A Satellite Imagery, the McClear Model and SHapley Additive exPlanations (SHAP)

The global warming effect has been accelerating rapidly and poses a threat to human survival and health. The top priority to solve this problem is to provide reliable renewable energy. To achieve this goal, it is important to provide fast and accurate solar radiation predictions based on limited observation data. In this study, a fast and accurate solar radiation nowcasting method is proposed by combining FY-4A satellite data and the McClear clear sky model under the condition of only radiation observation. The results show that the random forest (RF) performed better than the support vector regression (SVR) model and the reference model (Clim-Pers), with the smallest normalized root mean square error (nRMSE) values (between 13.90% and 33.80%), smallest normalized mean absolute error (nMAE) values (between 7.50% and 24.77%), smallest normalized mean bias error (nMBE) values (between −1.17% and 0.7%) and highest R2 values (between 0.76 and 0.95) under different time horizons. In addition, it can be summarized that remote sensing data can significantly improve the radiation forecasting performance and can effectively guarantee the stability of radiation predictions when the time horizon exceeds 60 min. Furthermore, to obtain the optimal operation efficiency, the prediction results were interpreted by introducing the latest SHapley Additive exPlanation (SHAP) method. From the interpretation results, we selected the three key channels of an FY-4A and then made the model lightweight. Compared with the original input model, the new one predicted the results more rapidly. For instance, the lightweight parameter input model needed only 0.3084 s (compared to 0.5591 s for full parameter input) per single data point on average for the 10 min global solar radiation forecast in Yuzhong. Meanwhile, the prediction effect also remained stable and reliable. Overall, the new method showed its advantages in radiation prediction under the condition that only solar radiation observations were available. This is very important for radiation prediction in cities with scarce meteorological observation, and it can provide a reference for the location planning of photovoltaic power stations.
Digital Audio Tampering Detection Based on Deep Temporal–Spatial Features of Electrical Network Frequency

In recent years, digital audio tampering detection methods by extracting audio electrical network frequency (ENF) features have been widely applied. However, most digital audio tampering detection methods based on ENF have the problems of focusing on spatial features only, without effective representation of temporal features, and do not fully exploit the effective information in the shallow ENF features, which leads to low accuracy of audio tamper detection. Therefore, this paper proposes a new method for digital audio tampering detection based on the deep temporal–spatial feature of ENF. To extract the temporal and spatial features of the ENF, firstly, a highly accurate ENF phase sequence is extracted using the first-order Discrete Fourier Transform (DFT), and secondly, different frame processing methods are used to extract the ENF shallow temporal and spatial features for the temporal and spatial information contained in the ENF phase. To fully exploit the effective information in the shallow ENF features, we construct a parallel RDTCN-CNN network model to extract the deep temporal and spatial information by using the processing ability of Residual Dense Temporal Convolutional Network (RDTCN) and Convolutional Neural Network (CNN) for temporal and spatial information, and use the branch attention mechanism to adaptively assign weights to the deep temporal and spatial features to obtain the temporal–spatial feature with greater representational capacity, and finally, adjudicate whether the audio is tampered with by the MLP network. The experimental results show that the method in this paper outperforms the four baseline methods in terms of accuracy and F1-score.
A Novel Hybrid Recommender System for the Tourism Domain

In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order to build a user model and (b) a novel content-based (CB) recommendations component. The second component can in fact itself be considered a hybrid among two different CB algorithms, each exploiting one of two semantic similarity measures: a hierarchy-based and a non-hierarchy based one. The latter is the recently introduced Weighted Extended Jaccard Similarity (WEJS). We note that WEJS is employed for the first time within a recommender algorithm. We incorporate our algorithm within a real, already available at Google Play, tour-planning mobile application for short-term visitors of the popular touristic destination of Agios Nikolaos, Crete, Greece, and evaluate our approach via extensive simulations conducted on a real-world dataset constructed for the needs of the aforementioned mobile application. Our experiments verify that our algorithms result in effective personalized recommendations of touristic points of interest, while our final hybrid algorithm outperforms our exclusively content-based recommender algorithms in terms of recommendations accuracy. Specifically, when comparing the performance of several hybrid recommender system variants, we are able to come up with a “winner”: the most preferable variant of our hybrid recommender algorithm is one using a ⟨four elicitation slates, six shown images per slate⟩ pair as input to its Bayesian elicitation component. This variant combines increased precision performance with a lightweight preferences elicitation process.
Modeling Misspecification as a Parameter in Bayesian Structural Equation Models

Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter—a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its own terms as a measure of absolute model fit and allows for comparing different models fit to the same data. By estimating the degree of misspecification simultaneously with structural parameters, the uncertainty about structural parameters reflects the degree of model misspecification. This results in a model that produces more reliable inference than extant Bayesian structural equation modeling. In addition, the approach estimates the residual covariance matrix that can be the basis for diagnosing misspecifications and updating a hypothesized model. These features are confirmed using simulation studies. Demonstrations with a variety of real-world examples show additional properties of the approach.
Metascience as a Scientific Social Movement

The "reproducibility crisis" has been one of the most significant stories in science in the past 15 years and has led to significant policy changes across the research landscape. Yet, scandals, irreproducible studies, and cries of crisis have occurred for decades in science. This article seeks to explain why the reproducibility crisis has taken root and become a force in science policy in ways previous crises have not. In short, we argue that it was through the scientific, institutional, and cultural efforts of a group of scientific activists we are calling metascientists. Metascience is a scientific social movement that seeks to use quantification and experimentation to diagnose problems in research practice and improve efficiency. It draws together data scientists, experimental and statistical methodologists, and open science activists into a project with both intellectual and policy dimensions. Metascientists have been remarkably successful at winning grants, motivating news coverage, and changing policies at science agencies, journals, and universities. The social movement lens is useful for understanding the popularization and impact of the reproducibility crisis narrative and suggests ways the institutions of science are adapting to meet a changing political and technological landscape.
IRWArt: Levering Watermarking Performance for Protecting High-quality Artwork Images

Increasing artwork plagiarism incidents underscores the urgent need for reliable copyright protection for high-quality artwork images. Although watermarking is helpful to this issue, existing methods are limited in imperceptibility and robustness. To provide high-level protection for valuable artwork images, we propose a novel invisible robust watermarking framework, dubbed as IRWArt. In our architecture, the embedding and recovery of the watermark are treated as a pair of image transformations’ inverse problems, and can be implemented through the forward and backward processes of an invertible neural networks (INN), respectively. For high visual quality, we embed the watermark in high-frequency domains with minimal impact on artwork and supervise image reconstruction using a human visual system(HVS)-consistent deep perceptual loss. For strong plagiarism-resistant, we construct a quality enhancement module for the embedded image against possible distortions caused by plagiarism actions. Moreover, the two-stagecontrastive training strategy enables the simultaneous realization of the above two goals. Experimental results on 4 datasets demonstrate the superiority of our IRWArt over other state-of-the-art watermarking methods. Code: https://github.com/1024yy/IRWArt.
Enhancing Deep Knowledge Tracing with Auxiliary Tasks

Knowledge tracing (KT) is the problem of predicting students’ future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components1 (KCs) but fail to explicitly integrate such intrinsic relations into the final response prediction task. Second, the individualized historical performance of students has not been well captured. In this paper, we proposed AT-DKT to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., question tagging (QT) prediction task and individualized prior knowledge (IK) prediction task. Specifically, the QT task helps learn better question representations by predicting whether questions contain specific KCs. The IK task captures students’ global historical performance by progressively predicting student-level prior knowledge that is hidden in students’ historical learning interactions. We conduct comprehensive experiments on three real-world educational datasets and compare the proposed approach to both deep sequential KT models and non-sequential models. Experimental results show that AT-DKT outperforms all sequential models with more than 0.9% improvements of AUC for all datasets, and is almost the second best compared to non-sequential models. Furthermore, we conduct both ablation studies and quantitative analysis to show the effectiveness of auxiliary tasks and the superior prediction outcomes of AT-DKT. To encourage reproducible research, we make our data and code publicly available at https://github.com/pykt-team/pykt-toolkit 2.
Zero-shot Clarifying Question Generation for Conversational Search

A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been widely studied and considered an effective solution to resolve query ambiguity. Existing work have explored various approaches for clarifying question ranking and generation. However, due to the lack of real conversational search data, they have to use artificial datasets for training, which limits their generalizability to real-world search scenarios. As a result, the industry has shown reluctance to implement them in reality, further suspending the availability of real conversational search interaction data. The above dilemma can be formulated as a cold start problem of clarifying question generation and conversational search in general. Furthermore, even if we do have large-scale conversational logs, it is not realistic to gather training data that can comprehensively cover all possible queries and topics in open-domain search scenarios. The risk of fitting bias when training a clarifying question retrieval/generation model on incomprehensive dataset is thus another important challenge.
PROD: Progressive Distillation for Dense Retrieval

Knowledge distillation is an effective way to transfer knowledge from a strong teacher to an efficient student model. Ideally, we expect the better the teacher is, the better the student performs. However, this expectation does not always come true. It is common that a strong teacher model results in a bad student via distillation due to the nonnegligible gap between teacher and student. To bridge the gap, we propose PROD, a PROgressive Distillation method, for dense retrieval. PROD consists of a teacher progressive distillation and a data progressive distillation to gradually improve the student. To alleviate catastrophic forgetting, we introduce a regularization term in each distillation process. We conduct extensive experiments on seven datasets including five widely-used publicly available benchmarks: MS MARCO Passage, TREC Passage 19, TREC Document 19, MS MARCO Document, and Natural Questions, as well as two industry datasets: Bing-Rel and Bing-Ads. PROD achieves the state-of-the-art in the distillation methods for dense retrieval. Our 6-layer student model even surpasses most of the existing 12-layer models on all five public benchmarks. The code and models are released in https://github.com/microsoft/SimXNS.