Reviewerly/exHarmony huggingface dataset:
Quantum Zeno Effect in Open Quantum Systems

Abstract We prove the quantum Zeno effect in open quantum systems whose evolution, governed by quantum dynamical semigroups, is repeatedly and frequently interrupted by the action of a quantum operation. For the case of a quantum dynamical semigroup with a bounded generator, our analysis leads to a refinement of existing results and extends them to a larger class of quantum operations. We also prove the existence of a novel strong quantum Zeno limit for quantum operations for which a certain spectral gap assumption, which all previous results relied on, is lifted. The quantum operations are instead required to satisfy a weaker property of strong power-convergence. In addition, we establish, for the first time, the existence of a quantum Zeno limit for open quantum systems in the case of unbounded generators. We also provide a variety of physically interesting examples of quantum operations to which our results apply.
Few-Shot Transformation of Common Actions into Time and Space

This paper introduces the task of few-shot common action localization in time and space. Given a few trimmed support videos containing the same but unknown action, we strive for spatio-temporal localization of that action in a long untrimmed query video. We do not require any class labels, interval bounds, or bounding boxes. To address this challenging task, we introduce a novel few-shot transformer architecture with a dedicated encoder-decoder structure optimized for joint commonality learning and localization prediction, without the need for proposals. Experiments on re-organizations of the AVA and UCF101-24 datasets show the effectiveness of our approach for few-shot common action localization, even when the support videos are noisy. Although we are not specifically designed for common localization in time only, we also compare favorably against the few-shot and one-shot state-of-the-art in this setting. Lastly, we demonstrate that the few-shot transformer is easily extended to common action localization per pixel.
On Use of Deep Learning for Side Channel Evaluation of Black Box Hardware AES Engine

With the increasing demand for security and privacy, there has been an increasing availability of cryptographic acclerators out of the box in modern microcontrollers, These accelerators are optimised and often black box. Thus, proper evaluation against vulnerabilities like side-channel attacks is a challenge in absence of architecture information and thus leakage model. In this paper, we show the use of deep learning based side-channel attack can overcome this challenge, allowing evaluation of black box AES hardware engine on a secure microcontroller, without the knowledge of precise leakage model information. Our results report full key recovery with only 3,000 traces under a profiling setting.
A Framework for Document-level Cybersecurity Event Extraction from Open Source Data

With the rapid development of the Internet, the number of cyber threats increases exponentially. More and more cyber threats come from new and unexpected sources, leading organizations and individuals to facing more security risks and vulnerabilities. Automatically obtaining and structuring security information from cybersecurity news can help security analysts to identify useful information more quickly. Most existing studies on extracting security events merely focused on the event detection task, aiming to discover and categorize cybersecurity events from the plain text. However, such event detection methods cannot capture useful information such as who performed the cyberattack, when the data breach event happened, who was the victim, etc. These arguments of a cybersecurity event are needed for analysts to get cybersecurity event details directly. Several studies have tried to extract rich semantic information of cybersecurity events, but they merely focused on extracting event arguments within the sentence scope. These studies still have limitations when the event arguments needed to recognize spread across multiple sentences. In this paper, we proposed a framework that effectively extracts cybersecurity events at the document-level from cybersecurity news, blogs and announcements. We model the document level event extraction task as a sequence tagging problem. The goal is to identify the related arguments of cybersecurity events from documents. Firstly, we get the characters embedding and incorporate the word information into the character representations. Then we design a sliding window mechanism to get the cross-sentence context information. Finally, we predict the label of each character. We build a Chinese cybersecurity dataset and use three methods to evaluate our method, and the experimental results demonstrate the effectiveness of the proposed model.
Agoraphilic Navigation Algorithm Under Dynamic Environment

This article presents a summary of the work done on the development of a new algorithm for mobile robot navigation in unknown dynamic environment. The developed humanlike algorithm uses a free-space attraction (Agoraphilic) concept for robot navigation. The algorithm presented in this article is an advanced development of the Agoraphilic navigation algorithm. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for free spaces toward the goal (solutions) to follow. The original Agoraphilic while it was able to overcome the limitations of the traditional algorithms had its own limitations in navigating robots in environments cluttered with moving obstacles. The new Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE) was developed to overcome those limitations. ANADE consists of seven main modules reported in this article. The objects tracking and objects prediction methodologies developed for the algorithm estimate future growing free-space passages toward the goal. The algorithm generates a time-varying single attractive force to pull the robot through the free space toward the predicted (future) growing free-space passages leading to the goal. The new algorithm was tested, not only through simulation, but also through experimental work. Summary of the experimental results is presented and discussed in this article.
Multi-Scale Feedback Feature Refinement U-Net for Medical Image Segmentation

Designing a novel and efficient architecture is the thrust of medical image segmentation. In this paper, we introduce a novel network named Multi-scale Feedback Feature Refinement U-Net (MFFRU-Net) for medical image segmentation. We design a simple and effective multi-scale feedback structure. Up-sampling and 1 × 1 convolution are used to feedback the feature maps of different scales in the decoder to the encoder, so that multiple high-level and low-level features are fused to obtain more representative features. Specifically, we propose a feature refinement module (FRM) based on the dual attention mechanism in the middle layer of the network. FRM block can enhance the use of spatial and channel information of image features. We evaluate the MFFRU-Net on two datasets. Comprehensive experimental results show that the proposed method is superior to the original U-Net method and other advanced methods.
A Fairness Evaluation of Automated Methods for Scoring Text Evidence Usage in Writing

Automated Essay Scoring (AES) can reliably grade essays at scale and reduce human effort in both classroom and commercial settings. There are currently three dominant supervised learning paradigms for building AES models: feature-based, neural, and hybrid. While feature-based models are more explainable, neural network models often outperform feature-based models in terms of prediction accuracy. To create models that are accurate and explainable, hybrid approaches combining neural network and feature-based models are of increasing interest. We compare these three types of AES models with respect to a different evaluation dimension, namely algorithmic fairness. We apply three definitions of AES fairness to an essay corpus scored by different types of AES systems with respect to upper elementary students' use of text evidence. Our results indicate that different AES models exhibit different types of biases, spanning students' gender, race, and socioeconomic status. We conclude with a step towards mitigating AES bias once detected.
Evaluation of Denoising Algorithms for Source Camera Linking

In the source camera linking (SCL) tasks, a large number of images taken by the same camera is not available, then forensic investigation must be carried out using only residual noises extracted from each natural image without any knowledge about their source camera. Therefore, an efficient denoising algorithm and/or noise enhancement function are required to estimate accurately Sensor Pattern Noise (SPN). In this paper we provide a systematic evaluation of common-used denoising algorithms with different parameters under a SCL task. The denoising algorithms considered are locally adaptive window-based denoising and the Block-matching 3D (BM3D) denoising. The SCL task used for evaluation is image clustering based on their source camera, in which we construct three sets with natural images taken by 5, 10 and 15 different source cameras. Linkage clustering algorithm with Ward modality is applied to group the images by their source camera. The experimental results show that the BM3D denoising with standard $$(\sigma )$$ deviation of $$5$$ provides the best performance. Said method achieved a clustering accuracy of 98%, 96% and 85% for 5, 10 and 15 cameras respectively.
Evaluating Mixed-Initiative Procedural Level Design Tools Using a Triple-Blind Mixed-Method User Study

Results from a triple-blind mixed-method user study into the effectiveness of mixed-initiative tools for the procedural generation of game levels are presented. A tool which generates levels using interactive evolutionary optimisation was designed for this study which (a) is focused on supporting the designer to explore the design space and (b) only requires the designer to interact with it by designing levels. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimisation algorithm. A rigorous user study was designed which compared the experiences of designers using the mixed-initiative tool to designers who were given a tool which provided completely random level suggestions. The designers using the mixed-initiative tool showed an increased engagement in the level design task, reporting that it was effective in inspiring new ideas and design directions. This provides significant evidence that procedural content generation can be used as a powerful tool to support the human design process.
Reliability of self-rated experience and confidence as predictors for students’ performance in software engineering

Abstract Students’ experience is used in empirical software engineering research as well as in software engineering education to group students in either homogeneous or heterogeneous groups. To do so, students are commonly asked to self-rate their experience, as self-rated experience has been shown to be a good predictor for performance in programming tasks. Another experience-related measurement is participants’ confidence (i.e., how confident is the person that their given answer is correct). Hence, self-rated experience and confidence are used as selector or control variables throughout empirical software engineering research and software engineering education. In this paper, we analyze data from several student experiments conducted in the past years to investigate whether self-rated experience and confidence are also good predictors for students’ performance in model comprehension tasks. Our results show that while students can somewhat assess the correctness of a particular answer to one concrete question regarding a conceptual model (i.e., their confidence), their overall self-rated experience does not correlate with their actual performance. Hence, the use of the commonly used measurement of self-rated experience as a selector or control variable must be considered unreliable for model comprehension tasks.
Topology and Content Co-Alignment Graph Convolutional Learning

In traditional graph neural networks (GNNs), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learning. In reality, network topology and node content each provide unique and important information, and they are not always consistent because of noise, irrelevance, or missing links between nodes. A pure topology-driven feature aggregation approach between unaligned neighborhoods may deteriorate learning from nodes with poor structure-content consistency, due to the propagation of incorrect messages over the whole network. Alternatively, in this brief, we advocate a co-alignment graph convolutional learning (CoGL) paradigm, by aligning topology and content networks to maximize consistency. Our theme is to enforce the learning from the topology network to be consistent with the content network while simultaneously optimizing the content network to comply with the topology for optimized representation learning. Given a network, CoGL first reconstructs a content network from node features then co-aligns the content network and the original network through a unified optimization goal with: 1) minimized content loss; 2) minimized classification loss; and 3) minimized adversarial loss. Experiments on six benchmarks demonstrate that CoGL achieves comparable and even better performance compared with existing state-of-the-art GNN models.
Neural Network for Real-Time Object Detection on FPGA

Object detection is one of the most active research and application areas of neural networks. In this article we combine FPGA and neural networks technologies to solve the real-time object recognition problem. The article discusses the integration of the YOLOv3 neural network on the DE10-Nano FPGA. Slightly worse indicators of the main metrics (mAP, FPS, inference time) when operating a neural network on a De10-Nano board in comparison with more expensive solutions based on GPUs, are offset by differences in the cost and dimensions of the FPGA board used. Based on the results of the study of various methods for converting neural networks to FPGA, it was concluded that this architecture is applicable for solving problems of detecting objects on a video stream in real time.
How much do Twitter posts affect voters? Analysis of the multi-emotional charge with affective computing in political campaigns

Artificial intelligence has been applied to different sectors in the government from data analysis to predictive analytics, as well as, policing, combating the COVID-19 pandemic, and political election campaigns. In the academic field, the exploitation and understanding of data generated in social media have mostly focused on unimodal sentiment analysis, based on one-dimensional sentiment analysis. This research proposes an analysis of the emotional charge for the U.S. presidential elections in 2020, based on a hybrid approach that combines affective computing and classic statistical analysis. We analyze the multi-emotional charge of candidates and voters, as well as the potential relationship of the candidates' emotions on the voters. Through this analysis is possible to determine the degree of agreement between candidates and voters. Future research is proposed for the area of affective computing in political campaigns.
Proof-Theoretic Semantics and Inquisitive Logic

Prawitz (1971) conjectured that proof-theoretic validity offers a semantics for intuitionistic logic. This conjecture has recently been proven false by Piecha and Schroeder-Heister (2019). This article resolves one of the questions left open by this recent result by showing the extensional alignment of proof-theoretic validity and general inquisitive logic. General inquisitive logic is a generalisation of inquisitive semantics, a uniform semantics for questions and assertions. The paper further defines a notion of quasi-proof-theoretic validity by restricting proof-theoretic validity to allow double negation elimination for atomic formulas and proves the extensional alignment of quasi-proof-theoretic validity and inquisitive logic.
RECAL: Reuse of Established CNN Classifier Apropos Unsupervised Learning Paradigm

Recently, clustering with a deep network framework has attracted the attention of several researchers in the computer vision community. The Deep framework gains extensive attention due to its efficiency and scalability towards large-scale and high-dimensional data. In this paper, we transform supervised CNN classifier architecture into an unsupervised clustering model, called RECAL ( https://arxiv.org/abs/1906.06480 ). It jointly learns discriminative embedding subspace and cluster labels. RECAL is made up of feature extraction layers that are convolutional, followed by unsupervised classifier layers which is fully connected. A multinomial logistic regression function (softmax) stacked on top of classifier layers. We train this network using stochastic gradient descent (SGD) optimizer. However, the successful implementation of our model is revolved around the design of loss function. Our loss function uses the heuristics that true partitioning entails lower entropy, given that the class distribution is not heavily skewed. This is a trade-off between the situations of “skewed distribution” and “low-entropy”. To handle this, we have proposed classification entropy and class entropy, which are the two components of our loss function. In this approach, the size of the mini-batch should be kept high. Experimental results indicate the consistent and competitive behavior of our model for clustering well-known digit, multi-viewed object, and face datasets. Moreover, we use this model to generate unsupervised patch segmentation for multi-spectral LISS-IV images. We observe that it is able to distinguish built-up areas, wetland, vegetation and water-body from the underlying scene.
High Resolution Solar Image Generation Using Generative Adversarial Networks

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly (AIA) 0304-Å images. The Ultraviolet (UV)/Extreme Ultraviolet observations like the SDO/AIA 0304-Å images were only made available to scientists in the late 1990s even though the magnetic field observations like the SDO/HMI have been available since the 1970s. Therefore, by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images, we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. After model training, we evaluated the model on the test data. The results show that our deep learning models are capable of generating high resolution (1024 × 1024 pixels) SDO/AIA0304 images from SDO/HMI line of sight magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate SDO/AIA 0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation.
Prioritizing security over usability: Strategies for how people choose passwords

Abstract Passwords are one of the most common security technologies that people use everyday. Choosing a new password is a security decision that can have important consequences for end users. Passwords can be long and complex, which prioritizes the security-focused aspects of a password. They can also be simple—easy to create, remember, and use—which prioritizes the usability aspects of the password. The tradeoff between password security versus usability represents competing constraints that shape password creation and use. We examined an ecologically valid dataset of 853 passwords entered a total of 2533 times by 134 users into 1010 websites, to test hypotheses about the impact of these constraints. We found evidence that choices about password complexity reflect an emphasis on security needs, but little support for the hypothesis that users take day-to-day ease of use of the password into account when creating it. There was also little evidence that password creation policies drive password choices.
Performance Study of Minimax and Reinforcement Learning Agents Playing the Turn-based Game Iwoki

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Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation

Convolutional neural networks typically perform poorly when the test (target domain) and training (source domain) data have significantly different distributions. While this problem can be mitigated by using the target domain data to align the source and target domain feature representations, the target domain data may be unavailable due to privacy concerns. Consequently, there is a need for methods that generalize well despite restricted access to target domain data during training. In this work, we propose an adversarial semantic hallucination approach (ASH), which combines a class-conditioned hallucination module and a semantic segmentation module. Since the segmentation performance varies across different classes, we design a semantic-conditioned style hallucination module to generate affine transformation parameters from semantic information in the segmentation probability maps of the source domain image. Unlike previous adaptation approaches, which treat all classes equally, ASH considers the class-wise differences. The segmentation module and the hallucination module compete adversarially, with the hallucination module generating increasingly "difficult" stylized images to challenge the segmentation module. In response, the segmentation module improves as it is trained with generated samples at an appropriate class-wise difficulty level. Our results on the Cityscapes and Mapillary benchmark datasets show that our method is competitive with state of the art work. Code is made available at https://github.com/gabriel-tjio/ASH.