Reviewerly/exHarmony huggingface dataset:
Gigahertz Sub-Landauer Momentum Computing

We introduce a fast and highly-efficient physically-realizable bit swap. Employing readily available and scalable Josephson junction microtechnology, the design implements the recently introduced paradigm of momentum computing. Its nanosecond speeds and sub-Landauer thermodynamic efficiency arise from dynamically storing memory in momentum degrees of freedom. As such, during the swap, the microstate distribution is never near equilibrium and the memory-state dynamics fall far outside of stochastic thermodynamics that assumes detailed-balanced Markovian dynamics. The device implements a bit-swap operation -- a fundamental operation necessary to build reversible universal computing. Extensive, physically-calibrated simulations demonstrate that device performance is robust and that momentum computing can support thermodynamically-efficient, high-speed, large-scale general-purpose computing that circumvents Landauer's bound.
Evoking the N400 Event-Related Potential (ERP) Component Using a Publicly Available Novel Set of Sentences with Semantically Incongruent or Congruent Eggplants (Endings)

During speech comprehension, the ongoing context of a sentence is used to predict sentence outcome by limiting subsequent word likelihood. Neurophysiologically, violations of context-dependent predictions result in amplitude modulations of the N400 event-related potential (ERP) component. While N400 is widely used to measure semantic processing and integration, no publicly-available auditory stimulus set is available to standardize approaches across the field. Here, we developed an auditory stimulus set of 404 sentences that utilized the semantic anomaly paradigm, provided cloze probability for all stimuli, and was developed for both children and adults. With 20 neurotypical adults, we validated that this set elicits robust N400’s, as well as two additional semantically-related ERP components: the late positivity component and the recognition potential. This stimulus set and the 20 high-density (128-channel) electrophysiological datasets are made publicly available here to promote data sharing and reuse. Future studies that use this stimulus set to investigate sentential semantic comprehension in both control and clinical populations may benefit from the increased comparability and reproducibility within this field of research.
High-Quality Pluralistic Image Completion via Code Shared VQGAN

PICNet pioneered the generation of multiple and diverse results for image completion task, but it required a careful balance between $\mathcal{KL}$ loss (diversity) and reconstruction loss (quality), resulting in a limited diversity and quality . Separately, iGPT-based architecture has been employed to infer distributions in a discrete space derived from a pixel-level pre-clustered palette, which however cannot generate high-quality results directly. In this work, we present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed. The core of our design lies in a simple yet effective code sharing mechanism that leads to a very compact yet expressive image representation in a discrete latent domain. The compactness and the richness of the representation further facilitate the subsequent deployment of a transformer to effectively learn how to composite and complete a masked image at the discrete code domain. Based on the global context well-captured by the transformer and the available visual regions, we are able to sample all tokens simultaneously, which is completely different from the prevailing autoregressive approach of iGPT-based works, and leads to more than 100$\times$ faster inference speed. Experiments show that our framework is able to learn semantically-rich discrete codes efficiently and robustly, resulting in much better image reconstruction quality. Our diverse image completion framework significantly outperforms the state-of-the-art both quantitatively and qualitatively on multiple benchmark datasets.
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning

We investigate the effect of including domain knowledge about a robotic system's causal relations when generating explanations. To this end, we compare two methods from explainable artificial intelligence, the popular KernelSHAP and the recent causal SHAP, on a deep neural network trained using deep reinforcement learning on the task of controlling a lever using a robotic manipulator. A primary disadvantage of KernelSHAP is that its explanations represent only the features' direct effects on a model's output, not considering the indirect effects a feature can have on the output by affecting other features. Causal SHAP uses a partial causal ordering to alter KernelSHAP's sampling procedure to incorporate these indirect effects. This partial causal ordering defines the causal relations between the features, and we specify this using domain knowledge about the lever control task. We show that enabling an explanation method to account for indirect effects and incorporating some domain knowledge can lead to explanations that better agree with human intuition. This is especially favorable for a real-world robotics task, where there is considerable causality at play, and in addition, the required domain knowledge is often handily available.
Parallel Sampling for Efficient High-dimensional Bayesian Network Structure Learning

Score-based algorithms that learn the structure of Bayesian networks can be used for both exact and approximate solutions. While approximate learning scales better with the number of variables, it can be computationally expensive in the presence of high dimensional data. This paper describes an approximate algorithm that performs parallel sampling on Candidate Parent Sets (CPSs), and can be viewed as an extension of MINOBS which is a state-of-the-art algorithm for structure learning from high dimensional data. The modified algorithm, which we call Parallel Sampling MINOBS (PS-MINOBS), constructs the graph by sampling CPSs for each variable. Sampling is performed in parallel under the assumption the distribution of CPSs is half-normal when ordered by Bayesian score for each variable. Sampling from a half-normal distribution ensures that the CPSs sampled are likely to be those which produce the higher scores. Empirical results show that, in most cases, the proposed algorithm discovers higher score structures than MINOBS when both algorithms are restricted to the same runtime limit.
Combining Textual Features for the Detection of Hateful and Offensive  Language

The detection of offensive, hateful and profane language has become a critical challenge since many users in social networks are exposed to cyberbullying activities on a daily basis. In this paper, we present an analysis of combining different textual features for the detection of hateful or offensive posts on Twitter. We provide a detailed experimental evaluation to understand the impact of each building block in a neural network architecture. The proposed architecture is evaluated on the English Subtask 1A: Identifying Hate, offensive and profane content from the post datasets of HASOC-2021 dataset under the team name TIB-VA. We compared different variants of the contextual word embeddings combined with the character level embeddings and the encoding of collected hate terms.
The Call for Environmentally-Based Language Teaching and Green Pedagogy: Climate Actions in Language Education

Environmental education (EE) is one of the content-based language learning pedagogies used to enhance students’ awareness of environmental concerns and their effect on people and animals. It aims to build the language teachers’ and students’ awareness of the importance of EE and green pedagogy in ELT. However, research into integrating EE in language education is still scarce. Therefore, this article presents an analysis of multimodal discourse of the EE imperatives in using content and language integrated learning (CLIL) strategy. Grounded in the video data analysis (VDA) approach, the researchers analysed the recorded talks of two university lecturers who offered their finest teaching approaches, and two environmentalists from “Bye-Bye Plastic Bags Jakarta” who described their significant efforts in handling plastic waste from a 2021 webinar held by the British Council Indonesia. Based on the thematic analysis, the forum suggests: 1) including environmental issues in language curriculum, syllabus, and textbook development, 2) providing environmentally-relevant materials and resources, 3) providing environmental language teacher professional development, and 4) accelerating environmental literacy programs at all levels of education. In other words, the inclusion of green pedagogy and environmental sustainability initiatives in ELT should be well-acknowledged. The methodological and pedagogical strategies in integrating environmental issues in ELT in this study addressed the high demand to catalyse language teachers’ metacognitive awareness in EE and indirectly build students’ metacognitive skills about environmental care.
Differential evolution with perturbation for continuous function optimization

This paper presents differential evolution with perturbation (DEP) for continuous function optimization. Differential evolution is a simple and efficient algorithm. Perturbation is the phenomenon that the motion by the contribution of the main force is disturbed by the influence subsidiary power. In order to show the effectiveness of the proposed algorithm, we compare it to existing algorithms by CEC2017 datasets.
The Solution of Computer Vision for Combating Covid-19

Recently, the Covid-19 pandemic has become very complicated and seriously affecting the economy as well as society in every countries in the world. In this chapter, we explore the solution of Computer Vision for handling the Covid-19 pandemic situation. The given scenarios based on deep learning techniques are used to monitor the traffic of people and vehicles through the checkpoints to control the in-out movement in significant areas. In addition, we also need to pay attention to complying with the regulations on wearing masks and ensuring a safe social distance in public places. From there, the proposed system will effectively support organizations to deal with the Covid-19 pandemic.
Preparing pedagogue-psychologists for innovative activities in the entrepreneurial educational environment of the university

It is emphasized that an entrepreneurial university creates new conditions and opportunities for training personnel capable of performing their basic professional functions with a focus on innovative entrepreneurial activity in educational organizations. The study examines the approaches and methods of preparing future school psychologists for the psychological and pedagogical support of innovative and educational entrepreneurial activities in organizations of general and additional general education. Using the methods of scientific and pedagogical research in an experiment, the possibilities of an entrepreneurial university in the formation of the readiness of future pedagogues-psychologists to perform professional functions in an entrepreneurial innovative, and educational environment are analyzed. There were 132 participants, consisting of 64 students in the control group and 68 participants in the experimental group. The experience of the work of school psychologists in the psychological and pedagogical support of innovative and educational entrepreneurial activities is analyzed, the modeling of which reflects the content and preparation process of students - future pedagogues-psychologists.&#x0D; &#x0D; Keywords: Entrepreneurial university; innovative activity; pedagogue-psychologist; training centers.&#x0D;
The Sandbox Environment for Generalizable Agent Research (SEGAR)

A broad challenge of research on generalization for sequential decision-making tasks in interactive environments is designing benchmarks that clearly landmark progress. While there has been notable headway, current benchmarks either do not provide suitable exposure nor intuitive control of the underlying factors, are not easy-to-implement, customizable, or extensible, or are computationally expensive to run. We built the Sandbox Environment for Generalizable Agent Research (SEGAR) with all of these things in mind. SEGAR improves the ease and accountability of generalization research in RL, as generalization objectives can be easy designed by specifying task distributions, which in turns allows the researcher to measure the nature of the generalization objective. We present an overview of SEGAR and how it contributes to these goals, as well as experiments that demonstrate a few types of research questions SEGAR can help answer.
Funnels: Exact maximum likelihood with dimensionality reduction

Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model. We use the SurVAE framework to construct dimension reducing surjective flows via a new layer, known as the funnel. We demonstrate its efficacy on a variety of datasets, and show it improves upon or matches the performance of existing flows while having a reduced latent space size. The funnel layer can be constructed from a wide range of transformations including restricted convolution and feed forward layers.
Progressive Video Summarization via Multimodal Self-supervised Learning

Modern video summarization methods are based on deep neural networks that require a large amount of annotated data for training. However, existing datasets for video summarization are small-scale, easily leading to over-fitting of the deep models. Considering that the annotation of large-scale datasets is time-consuming, we propose a multimodal self-supervised learning framework to obtain semantic representations of videos, which benefits the video summarization task. Specifically, the self-supervised learning is conducted by exploring the semantic consistency between the videos and text in both coarse-grained and fine-grained fashions, as well as recovering masked frames in the videos. The multimodal framework is trained on a newly-collected dataset that consists of video-text pairs. Additionally, we introduce a progressive video summarization method, where the important content in a video is pinpointed progressively to generate better summaries. Extensive experiments have proved the effectiveness and superiority of our method in rank correlation coefficients and F-score.
Physical Integrity

Abstract Physics-based methods anchor the forensic analysis in physical laws of image and video formation. The analysis is typically based on simplifying assumptions to make the forensic analysis tractable. In scenes that satisfy such assumptions, different types of forensic analysis can be performed. The two most widely used applications are the detection of content repurposing and content splicing. Physics-based methods expose such cases with assumptions about the interaction of light and objects, and about the geometric mapping of light and objects onto the image sensor.
ActionFormer: Localizing Moments of Actions with Transformers

Self-attention based Transformer models have demonstrated impressive results for image classification and object detection, and more recently for video understanding. Inspired by this success, we investigate the application of Transformer networks for temporal action localization in videos. To this end, we present ActionFormer -- a simple yet powerful model to identify actions in time and recognize their categories in a single shot, without using action proposals or relying on pre-defined anchor windows. ActionFormer combines a multiscale feature representation with local self-attention, and uses a light-weighted decoder to classify every moment in time and estimate the corresponding action boundaries. We show that this orchestrated design results in major improvements upon prior works. Without bells and whistles, ActionFormer achieves 71.0% mAP at tIoU=0.5 on THUMOS14, outperforming the best prior model by 14.1 absolute percentage points. Further, ActionFormer demonstrates strong results on ActivityNet 1.3 (36.6% average mAP) and EPIC-Kitchens 100 (+13.5% average mAP over prior works). Our code is available at http://github.com/happyharrycn/actionformer_release.
Interpretable Privacy Preservation of Text Representations Using Vector  Steganography

Contextual word representations generated by language models (LMs) learn spurious associations present in the training corpora. Recent findings reveal that adversaries can exploit these associations to reverse-engineer the private attributes of entities mentioned within the corpora. These findings have led to efforts towards minimizing the privacy risks of language models. However, existing approaches lack interpretability, compromise on data utility and fail to provide privacy guarantees. Thus, the goal of my doctoral research is to develop interpretable approaches towards privacy preservation of text representations that retain data utility while guaranteeing privacy. To this end, I aim to study and develop methods to incorporate steganographic modifications within the vector geometry to obfuscate underlying spurious associations and preserve the distributional semantic properties learnt during training.
Formal Quantum Software Engineering: Introducing the Formal Methods of  Software Engineering to Quantum Computing

Quantum computing (QC) represents the future of computing systems, but the tools for reasoning about the quantum model of computation, in which the laws obeyed are those on the quantum mechanical scale, are still a mix of linear algebra and Dirac notation; two subjects more suitable for physicists, rather than computer scientists and software engineers. On this ground, we believe it is possible to provide a more intuitive approach to thinking and writing about quantum computing systems, in order to simplify the design of quantum algorithms and the development of quantum software. In this paper, we move the first step in such direction, introducing a specification language as the tool to represent the operations of a quantum computer via axiomatic definitions, by adopting the same symbolisms and reasoning principles used by formal methods in software engineering. We name this approach formal quantum software engineering (F-QSE). This work assumes familiarity with the basic principles of quantum mechanics (QM), with the use of Zed (Z) which is a formal language of software engineering (SE), and with the notation and techniques of first-order logic (FOL) and functional programming (FP).
Collider Signatures of Type-X 2HDM + scalar singlet dark matter at HL-LHC

As the 125 GeV Higgs becomes disfavored as a portal to the dark sector, one is motivated to look beyond the SM-Higgs sector, into extended scalar-mediated portal mechanisms. In this work we consider one interesting possibility of such extended scalar sector, namely Type X two Higgs doublet with a scalar singlet dark matter. This model with the advantage of light pseudoscalars also provides the explanation for the observed anomalous magnetic moment of muon. The dark matter phenomenology of this model is unique from the other types of two Higgs doublet models. Therefore, it is quite intriguing to look for signatures specific to this model at the collider experiments. In this work we take up the task of finding suitable final states and regions of parameters space that can be probed at the high-luminosity runs of LHC. We go beyond our rectangular cut-based approach and use Artificial Neural Network which shows remarkable improvement in terms of signal significance.
Snooker Statistics and Zipf's Law

Zipf's law is well known in linguistics: the frequency of a word is inversely proportional to its rank. This is a special case of a more general power law, a common phenomenon in many kinds of real-world statistical data. Here, it is shown that snooker statistics also follow such a mathematical pattern, but with varying (estimated) parameter values. Two types of rankings (prize money earned and centuries scored), and three time-frames (all-time, decade, and year) are considered. The results indicate that the power law parameter values depend on the type of ranking used as well as the time-frame considered. Furthermore, in some cases the resulting parameter values vary significantly over time, for which a plausible explanation is provided.
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience due to their ability to capture non-linear system behavior and extract predictive spatiotemporal patterns. Given their black-box nature however, and the importance of prediction explainability, methods of explainable artificial intelligence (XAI) are gaining popularity as a means to explain the CNN decision-making strategy. Here, we establish an intercomparison of some of the most popular XAI methods and investigate their fidelity in explaining CNN decisions for geoscientific applications. Our goal is to raise awareness of the theoretical limitations of these methods and gain insight into the relative strengths and weaknesses to help guide best practices. The considered XAI methods are first applied to an idealized attribution benchmark, where the ground truth of explanation of the network is known a priori, to help objectively assess their performance. Secondly, we apply XAI to a climate-related prediction setting, namely to explain a CNN that is trained to predict the number of atmospheric rivers in daily snapshots of climate simulations. Our results highlight several important issues of XAI methods (e.g., gradient shattering, inability to distinguish the sign of attribution, ignorance to zero input) that have previously been overlooked in our field and, if not considered cautiously, may lead to a distorted picture of the CNN decision-making strategy. We envision that our analysis will motivate further investigation into XAI fidelity and will help towards a cautious implementation of XAI in geoscience, which can lead to further exploitation of CNNs and deep learning for prediction problems.
Cybersecurity Challenges in the Offshore Oil and Gas Industry: An Industrial Cyber-Physical Systems (ICPS) Perspective

The offshore oil and gas industry has recently been going through a digitalisation drive, with use of `smart' equipment using technologies like the Industrial Internet of Things (IIoT) and Industrial Cyber-Physical Systems (ICPS). There has also been a corresponding increase in cyber attacks targeted at oil and gas companies. Oil production offshore is usually in remote locations, requiring remote access and control. This is achieved by integrating ICPS, Supervisory, Control and Data Acquisition (SCADA) systems, and IIoT technologies. A successful cyber attack against an oil and gas offshore asset could have a devastating impact on the environment, marine ecosystem and safety of personnel. Any disruption to the world's supply of oil and gas (O\&amp;G) can also have an effect on oil prices and in turn, the global economy. This makes it important to secure the industry against cyber threats. We describe the potential cyberattack surface within the oil and gas industry, discussing emerging trends in the offshore sub-sector, and provide a timeline of known cyberattacks. We also present a case study of a subsea control system architecture typically used in offshore oil and gas operations and highlight potential vulnerabilities affecting the components of the system. This study is the first to provide a detailed analysis on the attack vectors in a subsea control system and is crucial to understanding key vulnerabilities, primarily to implement efficient mitigation methods that safeguard the safety of personnel and the environment when using such systems.