Reviewerly/exHarmony huggingface dataset:
Event-Triggered Sequential Fusion Filter for Nonlinear Multi-Sensor Systems With Correlated Noise Based on Observation Noise Estimation

In this paper, a sequential fusion filter estimator based on event-triggered (ET) multi-sensor nonlinear systems and Cubature Kalman filter, that is ETCKF-SF, is studied, in which the observation noise of each sensor is synchronously correlated, and the observation noise of each sensor is correlated with the process noise at the previous moment. Because the energy of each sensor in the network system is limited, in order to reduce unnecessary sensor energy loss, an ET mechanism is used to reduce the communication rate from the sensor to the fusion center. Sequential fusion filter estimator based on ET mechanism multi-sensor nonlinear systems and innovation analysis is studied, firstly. At the same time, in the design process of the sequential fusion filter, the estimator of the observation noise based on the ET mechanism will also be designed. Secondly, the numerical implementation of the proposed algorithm will be derived based on the third-degree spherical-radial rule Cubature Kalman filter (CKF). Finally, the proposed algorithm is compared with the extended Kalman filter (EKF) based on Taylor series expansion. The effectiveness of the proposed algorithm is verified by the comparison of simulation examples.
Phishing simulation exercise in a large hospital: A case study

Phishing is a major threat to the data and infrastructure of healthcare organizations and many cyberattacks utilize this socially engineered pathway. Phishing simulation is used to identify weaknesses and risks in the human defences of organizations. There are many factors influencing the difficulty of detecting a phishing email including fatigue and the nature of the deceptive message.A major Italian Hospital with over 6000 healthcare staff performed a phishing simulation as part of its annual training and risk assessment. Three campaigns were launched at approx. 4-month intervals, to compare staff reaction to a general phishing email and a customized one.The results show that customization of phishing emails makes them much more likely to be acted on. In the first campaign, 64% of staff did not open the general phish, significantly more than the 38% that did not open the custom phish. A significant difference was also found for the click rate, with significantly more staff clicking on the custom phish. However, the campaigns could not be run as intended, due to issues raised within the organization.Phishing simulation is useful but not without its limitations. It requires contextual knowledge, skill and experience to ensure that it is effective. The exercise raised many issues within the Hospital. Successful, ethical phishing simulations require coordination across the organization, precise timing and lack of staff awareness. This can be complex to coordinate. Misleading messages containing false threats or promises can cause a backlash from staff and unions. The effectiveness of the message is dependent on the personalization of the message to current, local events. The lessons learned can be useful for other hospitals.
Understanding levels of technology integration: A TPACK scale for EFL teachers to promote 21st-century learning

Education worldwide has emphasized 21st-century competencies, including language competence, computer competence, and thinking skills. Research on Technological Pedagogical Content Knowledge (TPACK), essential teacher knowledge, has attempted to address the need for technology integration to support thinking skills. However, existing TPACK assessments have not intended to help teachers understand the levels of technology integration in teaching English as a foreign language (EFL). Therefore, this study proposed a two-dimensional TPACK scale, allowing EFL teachers to assess their TPACK in integrating technology and thinking skills. In total, 525 EFL teachers responded to this survey online. Scores of this scale were collected to test and establish validity and reliability. The statistical evidence showed that this instrument has high reliability and validity and is helpful for understanding levels of technology integration. The results showed that the EFL teachers were less confident in their TPACK teaching higher-order thinking skills. The EFL teachers in different cultures reported different confidence levels in TPACK and thinking skills (F(6, 518) = 7.83, p < .001). The high-achieving EFL teachers reported high TPACK self-efficacy (r = .210, p < .05). This TPACK survey would be helpful for EFL teachers to understand their development of TPACK in integrating technology and thinking skills in teaching English.
Understanding Data Visualization Design Practice

Professional roles for data visualization designers are growing in popularity, and interest in relationships between the academic research and professional practice communities is gaining traction. However, despite the potential for knowledge sharing between these communities, we have little understanding of the ways in which practitioners design in real-world, professional settings. Inquiry in numerous design disciplines indicates that practitioners approach complex situations in ways that are fundamentally different from those of researchers. In this work, I take a practice-led approach to understanding visualization design practice on its own terms. Twenty data visualization practitioners were interviewed and asked about their design process, including the steps they take, how they make decisions, and the methods they use. Findings suggest that practitioners do not follow highly systematic processes, but instead rely on situated forms of knowing and acting in which they draw from precedent and use methods and principles that are determined appropriate in the moment. These findings have implications for how visualization researchers understand and engage with practitioners, and how educators approach the training of future data visualization designers.
An Efficient Track-Before-Detect for Multi-PRF Radars With Range and Doppler Ambiguities

This article considers the detection and tracking of weak targets in multiple pulse repetition frequency (multi-PRF) radars using the track-before-detect (TBD) technique. By exploring the measurement independence among different PRFs, we decompose the joint multi-PRF and multiframe optimization problem into several lower dimensional maximizations, each of which corresponds to an intra-PRF multiframe processing. An efficient two-stage TBD procedure with a parallel structure is proposed for the multi-PRF radars. Specifically, in the first stage, a constraint inequality is derived analytically and used to decouple the measurement ambiguities from the nonlinear conversion relationship between polar and Cartesian coordinates. The intra-PRF multiframe integration can then be carried out concurrently using the ambiguous measurements and the target-like measurement plot sequences with different ambiguities are extracted for different PRFs. In the second stage, a covariance combination fusion-based inter-PRF joint disambiguation and estimation algorithm is proposed to solve the ambiguities of the plot sequences and output high-accuracy target tracks. Simulation experiments show that the proposed algorithm can provide a good detection performance and higher tracking accuracy with much lower complexity.
Deep Extreme Learning Machines Based Two-Phase Spatiotemporal Modeling for Distributed Parameter Systems

Accurate and robust modeling of complex distributed parameter systems (DPSs) is a challenge for three reasons: 1) they have infinite-dimensional characteristics; 2) they are time/space coupled; and 3) there are model uncertainties. In this article, a two-phase spatiotemporal (S/T) modeling framework based on deep extreme learning machine (DELM) is proposed for DPSs. The modeling process consists of two S/T models in two phases: Phase I: a DELM model and Phase II: a Karhunen–Loève (KL) based ELM (KL-ELM) model. In phase I, the DELM model is constructed by combing the multilayer ELM (ML-ELM), ELM, and kernel-based ELM (K-ELM) to approximate the dominant S/T dynamics of DPSs. Since DPSs have an infinite-dimensional characteristic that can hardly be handled directly, ML-ELM is first employed to transform the infinite-dimensional systems into finite-dimensional systems. Then, the ELM model is adopted to further approximate the finite-dimensional systems to ensure the model can predict future dynamic behavior. Finally, the K-ELM is used to reconstruct the infinite-dimensional systems, which can be considered as the inverse process of ML-ELM. Thus, the final DELM model can be used for prediction in both space and time directions. In phase II, a KL-ELM model is constructed to compensate for modeling errors caused by reconstruction error or unknown nonlinear dynamics. By integrating the obtained DELM and KL-ELM models, the proposed two-phase S/T model can be constructed. Experiments on a typical industrial thermal process verified that the proposed method may work better in complex DPSs.
Introduction to quantum non-reciprocal interactions: from non-Hermitian Hamiltonians to quantum master equations and quantum feedforward schemes

These lecture notes from the 2019 Les Houches Summer School on Quantum Information Machines provides a pedagogical introduction to the theory of non-reciprocal quantum interactions and devices. The goal is connect various approaches and concepts, including Hamiltonians encoding synthetic gauge fields, scattering descriptions, quantum master equations, and non-Hermitian Hamiltonians. The importance of having both non-trivial synthetic gauge fields and dissipation for obtaining non-reciprocal interactions is stressed. Connections to broader topics such as quantum reservoir engineering and the quantum theory of continuous-measurement based feedforward are also discussed.
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation

This work presents our end-to-end (E2E) automatic speech recognition (ASR) model targetting at robust speech recognition, called Integraded speech Recognition with enhanced speech Input for Self-supervised learning representation (IRIS). Compared with conventional E2E ASR models, the proposed E2E model integrates two important modules including a speech enhancement (SE) module and a self-supervised learning representation (SSLR) module. The SE module enhances the noisy speech. Then the SSLR module extracts features from enhanced speech to be used for speech recognition (ASR). To train the proposed model, we establish an efficient learning scheme. Evaluation results on the monaural CHiME-4 task show that the IRIS model achieves the best performance reported in the literature for the single-channel CHiME-4 benchmark (2.0% for the real development and 3.9% for the real test) thanks to the powerful pre-trained SSLR module and the fine-tuned SE module.
Photonic reservoir computer based on frequency multiplexing

Reservoir computing is a brain-inspired approach for information processing, well suited to analog implementations. We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states. The system processes 25 comb lines simultaneously (i.e., 25 neurons), at a rate of 20 MHz. We illustrate performances on two standard benchmark tasks: channel equalization and time series forecasting. We also demonstrate that frequency multiplexing allows output weights to be implemented in the optical domain, through optical attenuation. We discuss the perspectives for high-speed, high-performance, low-footprint implementations.
Improved Methods to Aid Unsupervised Evidence-Based Fact Checking for Online Heath News

False information in the domain of online health related articles is of great concern, which has been witnessed abundantly in the current pandemic situation of Covid-19. Recent advancements in the field of Machine Learning and Natural Language Processing can be leveraged to aid people in distinguishing false information from the truth in the domain of online health articles. Whilst there has been substantial progress in this space over the years, research in this area has mainly focused on the sphere of political news. Health fake news is markedly different from fake news in the political context as health information should be evaluated against the most recent and reliable medical resources such as scholarly repositories. However, one of the challenges with such an approach is the retrieval of the pertinent resources. In this work, we formulate two techniques for the retrieval of the most relevant authoritative and reliable medical content from scholarly repositories which can be used to assess veracity of an online health article. The first technique is an unsupervised method of generating queries from claims which are extracted from an online health article. We propose a three-step approach for it and illustrate that our method is able to generate effective queries which can be used for retrieval of information from medical knowledge databases. The second method involves a filtering approach for extracting the most relevant information for the claims. We show how this can be achieved with the help of state of the art transformer models and illustrate it's effectiveness over other methods.
An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling

K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One of the K-means algorithm’s main concerns is to find out the initial optimal centroids of clusters. It is the most challenging task to determine the optimum position of the initial clusters’ centroids at the very first iteration. This paper proposes an approach to find the optimal initial centroids efficiently to reduce the number of iterations and execution time. To analyze the effectiveness of our proposed method, we have utilized different real-world datasets to conduct experiments. We have first analyzed COVID-19 and patient datasets to show our proposed method’s efficiency. A synthetic dataset of 10M instances with 8 dimensions is also used to estimate the performance of the proposed algorithm. Experimental results show that our proposed method outperforms traditional kmeans++ and random centroids initialization methods regarding the computation time and the number of iterations.
Weaponizing reproductive rights: a mixed-method analysis of White nationalists’ discussion of abortions online

According to the Great Replacement conspiracy theory, nonwhites, globalists and elites are plotting to eliminate the white race and its dominance through anti-white policies and increased immigration. In that context, abortion among white women is perceived by white nationalists (WN) as a betrayal of their ‘biological’ and ‘traditional’ gender role – procreation of white babies. While WN condemn abortion among white women as a murderous sin, at times they encourage the practice among nonwhites to solve demographic threats to white dominance. In this study, we use mixed methods, combining unsupervised machine learning with close textual analysis of 30,725 posts including the term ‘abortion’ published on the WN website Stormfront between 2001 and 2017. We identify three broad themes: White genocide, focused on the conspiracy theory and detailing the active actors in its alleged execution; political, focused on political agendas and laws; and WN reproductive reasoning, articulating and justifying the contradiction between supporting abortion for nonwhites but not for whites via politics of difference that emphasize nonwhites’ supposed inferior morality. We discuss WN’s unique and explicitly racist discourse around a medical topic like abortion, a staple of the conservative and religious right for decades, and how it is used to alleviate their cognitive dissonance resulting from their dual-stance on abortion. Such discourse could be harnessed to recruit members into the movement and normalize extreme, racist ideologies.
Deepfake Network Architecture Attribution

With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial Network (GAN) models and obtain high accuracies. While encouraging, these works are restricted to model-level attribution, only capable of handling images generated by seen models with a specific seed, loss and dataset, which is limited in real-world scenarios when fake images may be generated by privately trained models. This motivates us to ask whether it is possible to attribute fake images to the source models' architectures even if they are finetuned or retrained under different configurations. In this work, we present the first study on Deepfake Network Architecture Attribution to attribute fake images on architecture-level. Based on an observation that GAN architecture is likely to leave globally consistent fingerprints while traces left by model weights vary in different regions, we provide a simple yet effective solution named by DNA-Det for this problem. Extensive experiments on multiple cross-test setups and a large-scale dataset demonstrate the effectiveness of DNA-Det.
Manas

Today deep learning is widely used for building software. A software engineering problem with deep learning is that finding an appropriate convolutional neural network (CNN) model for the task can be a challenge for developers. Recent work on AutoML, more precisely neural architecture search (NAS), embodied by tools like Auto-Keras aims to solve this problem by essentially viewing it as a search problem where the starting point is a default CNN model, and mutation of this CNN model allows exploration of the space of CNN models to find a CNN model that will work best for the problem. These works have had significant success in producing high-accuracy CNN models. There are two problems, however. First, NAS can be very costly, often taking several hours to complete. Second, CNN models produced by NAS can be very complex that makes it harder to understand them and costlier to train them. We propose a novel approach for NAS, where instead of starting from a default CNN model, the initial model is selected from a repository of models extracted from GitHub. The intuition being that developers solving a similar problem may have developed a better starting point compared to the default model. We also analyze common layer patterns of CNN models in the wild to understand changes that the developers make to improve their models. Our approach uses commonly occurring changes as mutation operators in NAS. We have extended Auto-Keras to implement our approach. Our evaluation using 8 top voted problems from Kaggle for tasks including image classification and image regression shows that given the same search time, without loss of accuracy, Manas produces models with 42.9% to 99.6% fewer number of parameters than Auto-Keras' models. Benchmarked on GPU, Manas' models train 30.3% to 641.6% faster than Auto-Keras' models.
Using Transfer Learning for Code-Related Tasks

Deep learning (DL) techniques have been used to support several code-related tasks such as code summarization and bug-fixing. In particular, pre-trained transformer models are on the rise, also thanks to the excellent results they achieved in Natural Language Processing (NLP) tasks. The basic idea behind these models is to first pre-train them on a generic dataset using a self-supervised task (e.g., filling masked words in sentences). Then, these models are fine-tuned to support specific tasks of interest (e.g., language translation). A single model can be fine-tuned to support multiple tasks, possibly exploiting the benefits of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transfer learning</i> . This means that knowledge acquired to solve a specific task (e.g., language translation) can be useful to boost performance on another task (e.g., sentiment classification). While the benefits of transfer learning have been widely studied in NLP, limited empirical evidence is available when it comes to code-related tasks. In this paper, we assess the performance of the Text-To-Text Transfer Transformer (T5) model in supporting four different code-related tasks: (i) automatic bug-fixing, (ii) injection of code mutants, (iii) generation of assert statements, and (iv) code summarization. We pay particular attention in studying the role played by pre-training and multi-task fine-tuning on the model's performance. We show that (i) the T5 can achieve better performance as compared to state-of-the-art baselines; and (ii) while pre-training helps the model, not all tasks benefit from a multi-task fine-tuning.
Re2G: Retrieve, Rerank, Generate

As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.However, for tasks that require a large amount of knowledge, nonparametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements.Recent models such as RAG and REALM have introduced retrieval into conditional generation.These models incorporate neural initial retrieval from a corpus of passages.We build on this line of research, proposing Re 2 G, which combines both neural initial retrieval and reranking into a BART-based sequenceto-sequence generation.Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval.To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output.We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9% to 34% over the previous state-of-the-art on the KILT leaderboard.We make our code available as open source 1 .
A Data-Driven Approach for University Public Opinion Analysis and Its Applications

In the era of mobile Internet, college students increasingly tend to express their opinions and views through online social media; furthermore, social media influence the value judgments of college students. Therefore, it is vital to understand and analyze university online public opinion over time. In this paper, we propose a data-driven architecture for analysis of university online public opinion. Weibo, WeChat, Douyin, Zhihu and Toutiao apps are selected as sources for collection of public opinion data. Crawler technology is utilized to automatically obtain user data about target topics to form a database. To avoid the drawbacks of traditional methods, such as sentiment lexicon and machine learning, which rely on a priori knowledge and complex handcrafted features, the Word2Vec tool is used to perform word embedding, the LSTM-CFR model is proposed to realize Chinese word segmentation and a convolutional neural network (CNN) is built to automatically extract implicit features in word vectors, ultimately establishing the nonlinear relationships between implicit features and the sentiment tendency of university public opinion. The experimental results show that the proposed model is more accurate than SVM, RF, NBC and GMM methods, providing valuable information with respect to public opinion management.