Reviewerly/exHarmony huggingface dataset:
Maximum Signed Θ-Clique Identification in Large Signed Graphs

The maximum clique problem, which is to find the clique with the largest size, can find many real-world applications and is notable for its capability of modeling many combinatorial problems. However, most existing research focuses on processing unsigned graphs, i.e., treat each connection equally. In real applications, edges of graphs are usually associated with signed information, i.e., positive or negative edges, and signed graph analysis has attracted great attentions in the recent. In this paper, we first analyze the disadvantages of existing signed clique models, and then propose a novel clique model, named signed <inline-formula><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> -clique. Given a signed graph <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula> and a subgraph <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> , let <inline-formula><tex-math notation="LaTeX">$d^{+}_{S}(u)$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$d^{-}_{S}(u)$</tex-math></inline-formula> be the number of positive and negative neighbors of vertex <inline-formula><tex-math notation="LaTeX">$u$</tex-math></inline-formula> in <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> . We say a subgraph <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> is a signed <inline-formula><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> -clique if <inline-formula><tex-math notation="LaTeX">$i)$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> is a clique and <inline-formula><tex-math notation="LaTeX">$ii)$</tex-math></inline-formula> each vertex <inline-formula><tex-math notation="LaTeX">$u$</tex-math></inline-formula> in <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> fulfills <inline-formula><tex-math notation="LaTeX">$d^{+}_{S}(u) - d^{-}_{S}(u) \geq \theta$</tex-math></inline-formula> . We show that the problem of identifying the maximum signed <inline-formula><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> -clique is NP-hard. Novel pruning techniques are proposed to reduce the searching space. In addition, efficient searching strategies are developed to scale for large graphs. Comprehensive experiments on 8 real-world datasets are conducted to demonstrate the effectiveness and efficiency of the proposed approaches.
Transformer-based named entity recognition for parsing clinical trial eligibility criteria

The rapid adoption of electronic health records (EHRs) systems has made clinical data available in electronic format for research and for many downstream applications. Electronic screening of potentially eligible patients using these clinical databases for clinical trials is a critical need to improve trial recruitment efficiency. Nevertheless, manually translating free-text eligibility criteria into database queries is labor intensive and inefficient. To facilitate automated screening, free-text eligibility criteria must be structured and coded into a computable format using controlled vocabularies. Named entity recognition (NER) is thus an important first step. In this study, we evaluate 4 state-of-the-art transformer-based NER models on two publicly available annotated corpora of eligibility criteria released by Columbia University (i.e., the Chia data) and Facebook Research (i.e.the FRD data). Four transformer-based models (i.e., BERT, ALBERT, RoBERTa, and ELECTRA) pretrained with general English domain corpora vs. those pretrained with PubMed citations, clinical notes from the MIMIC-III dataset and eligibility criteria extracted from all the clinical trials on ClinicalTrials.gov were compared. Experimental results show that RoBERTa pretrained with MIMIC-III clinical notes and eligibility criteria yielded the highest strict and relaxed F-scores in both the Chia data (i.e., 0.658/0.798) and the FRD data (i.e., 0.785/0.916). With promising NER results, further investigations on building a reliable natural language processing (NLP)-assisted pipeline for automated electronic screening are needed.
A multiple-architecture deep learning approach for nuclear power plants accidents classification including anomaly detection and “don’t know” response

Nuclear power plants (NPPs) are complex systems that are monitored by a team of highly trained operators, that in case of an anomalous event on the NPP, such as an accident, must quickly analyze a high number of variables in order to identify the event. However, due to the great volume of information that must be rapidly analyzed in those situations, misidentifications can occur. Therefore, this study proposes the development of a modular structured, deep learning (DL)-based, NPP accident identification system including anomaly detection and “don’t know” response capability. In order to assess the system capacity, experiments were conducted using a realistic study case using 16 operational situations of main operational Brazilian PWR NPP. As a result, the best configuration of DL models for the system achieved an average accuracy of 100% in terms of correct ‘‘don’t know” identification, while still retaining the accuracy of 99.82% on correct event identifications.
Prototyping Usable Privacy and Security Systems: Insights from Experts

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An Adaptive Consensus Filter for Distributed State Estimation With Unknown Noise Statistics

An adaptive consensus filter for sensor networks with unknown process and measurement noise statistics is proposed in this letter. The variational Bayes(VB) approach is exploited to get local estimates of unknown noise covariances with prior inverse Wishart distributions. A distributed averaging approach on exponential-class densities is applied for consensus on the natural parameters of the unknown predicted error covariance. Consensus on measurements is performed in parallel and the two consensus outcomes are fused. Simulation results demonstrate the effectiveness of the proposed adaptive consensus filter compared to conventional, non-adaptive, consensus filters.
Post-COVID Public Health Surveillance and Privacy Expectations in the United States: Scenario-Based Interview Study

Smartphone-based apps designed and deployed to mitigate the COVID-19 pandemic may become infrastructure for postpandemic public health surveillance in the United States. Through the lenses of privacy concerns and user expectations of digital pandemic mitigation techniques, we identified possible long-term sociotechnical implications of such an infrastructure.We explored how people in the United States perceive the possible routinization of pandemic tracking apps for public health surveillance in general. Our interdisciplinary analysis focused on the interplay between privacy concerns, data practices of surveillance capitalism, and trust in health care providers. We conducted this analysis to achieve a richer understanding of the sociotechnical issues raised by the deployment and use of technology for pandemic mitigation.We conducted scenario-based, semistructured interviews (n=19) with adults in the United States. The interviews focused on how people perceive the short- and long-term privacy concerns associated with a fictional smart thermometer app deployed to mitigate the "outbreak of a contagious disease." In order to elicit future-oriented discussions, the scenario indicated that the app would continue functioning "after the disease outbreak has dissipated." We analyzed interview transcripts using reflexive thematic analysis.In the context of pandemic mitigation technology, including app-based tracking, people perceive a core trade-off between public health and personal privacy. People tend to rationalize this trade-off by invoking the concept of "the greater good." The interplay between the trade-off and rationalization forms the core of sociotechnical issues that pandemic mitigation technologies raise. Participants routinely expected that data collected through apps related to public health would be shared with unknown third parties for the financial gain of the app makers. This expectation suggests a perceived alignment between an app-based infrastructure for public health surveillance and the broader economics of surveillance capitalism. Our results highlight unintended and unexpected sociotechnical impacts of routinizing app-based tracking on postpandemic life, which are rationalized by invoking a nebulous concept of the greater good.While technologies such as app-based tracking could be useful for pandemic mitigation and preparedness, the routinization of such apps as a form of public health surveillance may have broader, unintentional sociotechnical implications for individuals and the societies in which they live. Although technology has the potential to increase the efficacy of pandemic mitigation, it exists within a broader network of sociotechnical concerns. Therefore, it is necessary to consider the long-term implications of pandemic mitigation technologies beyond the immediate needs of addressing the COVID-19 pandemic. Potential negative consequences include the erosion of patient trust in health care systems and providers, grounded in concerns about privacy violations and overly broad surveillance.
A vehicle detection and shadow elimination method based on greyscale information, edge information, and prior knowledge

Vehicle detection is one of the most fundamental aspects of traffic surveillance systems. However, the shadow problem often hinders the accuracy of vehicle detection. Shadows are often mistakenly understood as the parts of a vehicle, causing objects loss, or shape distortion. Collected data with these errors are unreliable, therefore shadow detection and elimination is a key step to promote the accuracy of vehicle detection. This paper proposes a robust vehicle detection method with shadow elimination. The method is divided into two steps: Firstly, we extract foreground regions using a background differential method based on edge information, then we detect and eliminate the shadows from the foreground regions combined with grayscale information, edge information, and prior knowledge. The experimental results show that the proposed method is superior to the faster R-CNN, and SSD methods in terms of real-time performance and vehicle detection accuracy. The proposed method has broad application prospects in the Intelligent Transportation Systems (ITS).
The application of artificial intelligence in software engineering

The field of artificial intelligence (AI) is witnessing a recent upsurge in research, tools development, and deployment of applications. Multiple software companies are shifting their focus to developing intelligent systems; and many others are deploying AI paradigms to their existing processes. In parallel, the academic research community is injecting AI paradigms to provide solutions to traditional engineering problems. Similarly, AI has evidently been proved useful to software engineering (SE). When one observes the SE phases (requirements, design, development, testing, release, and maintenance), it becomes clear that multiple AI paradigms (such as neural networks, machine learning, knowledge-based systems, natural language processing) could be applied to improve the process and eliminate many of the major challenges that the SE field has been facing. This survey chapter is a review of the most commonplace methods of AI applied to SE. The review covers methods between years 1975–2017, for the requirements phase, 46 major AI-driven methods are found, 19 for design, 15 for development, 68 for testing, and 15 for release and maintenance. Furthermore, the purpose of this chapter is threefold; firstly, to answer the following questions: is there sufficient intelligence in the SE lifecycle? What does applying AI to SE entail? Secondly, to measure, formulize, and evaluate the overlap of SE phases and AI disciplines. Lastly, this chapter aims to provide serious questions to challenging the current conventional wisdom (i.e., status quo) of the state-of-the-art, craft a call for action, and to redefine the path forward.
Users’ Cognitive Load: A Key Aspect to Successfully Communicate Visual Climate Information

Abstract The visual communication of climate information is one of the cornerstones of climate services. It often requires the translation of multidimensional data to visual channels by combining colors, distances, angles, and glyph sizes. However, visualizations including too many layers of complexity can hinder decision-making processes by limiting the cognitive capacity of users, therefore affecting their attention, recognition, and working memory. Methodologies grounded on the fields of user-centered design, user interaction, and cognitive psychology, which are based on the needs of the users, have a lot to contribute to the climate data visualization field. Here, we apply these methodologies to the redesign of an existing climate service tool tailored to the wind energy sector. We quantify the effect of the redesign on the users’ experience performing typical daily tasks, using both quantitative and qualitative indicators that include response time, success ratios, eye-tracking measures, user perceived effort, and comments, among others. Changes in the visual encoding of uncertainty and the use of interactive elements in the redesigned tool reduced the users’ response time by half, significantly improved success ratios, and eased decision-making by filtering nonrelevant information. Our results show that the application of user-centered design, interaction, and cognitive aspects to the design of climate information visualizations reduces the cognitive load of users during tasks performance, thus improving user experience. These aspects are key to successfully communicating climate information in a clearer and more accessible way, making it more understandable for both technical and nontechnical audiences.
‘Which part of my group do I represent?’: disability activism and social media users with concealable communicative disabilities

Social media provides marginalized activists multiple options for constructing alternative narratives that challenge mainstream discourse. However, despite these platforms' increasing accessibility, disabled activists may struggle in creating their counter-narrative. Their inability to conform to able-bodied notions of activism leaves their individual experiences out of the discussion of social media activism. This paper addresses this gap by presenting the individual decision-making process of disabled social media users regarding activists' performances in social media as gleaned from the qualitative analysis of in-depth interviews. This process includes three phases that demonstrate the experiences of social media users with communicative concealable disabilities – autistics, hard-of-hearing (HoH), and people who stutter. First, these interviewees present a spectrum of perceptions of disability activism in social media. Second, these perceptions lead to a spectrum of motivations, which mirror the interviewees' various risk-benefit calculations regarding public self-disclosure as disabled. Third, these perceptions and motivations are manifested through interviewees' strategic design of their activist performances in private and public spheres. By rethinking key concepts in social media activism – risk-benefit calculation, slacktivism, and digital storytelling – this process illustrates taken-for-granted assumptions of personalization, visibility and representation, that challenge current discussions on social media activism.
Immersive reading in virtual and augmented reality environment

Purpose This study aims to investigate “immersive reading,” which occurs when individuals read text while in a virtual reality (VR) or augmented reality (AR) environment. Design/methodology/approach In Experiment 1, 64 participants read text passages and answered multiple-choice questions in VR and AR head-mounted displays (HMDs) compared with doing the same task on liquid crystal display (LCD). In Experiment 2, 31 participants performed the same reading tasks but with two VR HMDs of different display quality. Findings Compared with reading on LCD as the baseline, participants reading in VR and AR HMDs got 82% (VR) and 88% (AR) of the information accurately. Participants tended to respond more accurately and faster, though not statistically significant, with the VR HMD of higher pixel density in the speed-reading task. Originality/value The authors observed the speed and accuracy of reading in VR and AR environments, compared with the reading speed and accuracy on an LCD monitor. The authors also compared the reading performance on two VR HMDs that differed in display quality but were otherwise similar in every way.
Puncturable Pseudorandom Sets and Private Information Retrieval with Near-Optimal Online Bandwidth and Time

Imagine one or more non-colluding servers each holding a large public database, e.g., the repository of DNS entries. Clients would like to access entries in this database without disclosing their queries to the servers. Classical private information retrieval (PIR) schemes achieve polylogarithmic bandwidth per query, but require the server to perform linear computation per query, which is a significant barrier towards deployment. Several recent works showed, however, that by introducing a one-time, per-client, off-line preprocessing phase, an unbounded number of client queries can be subsequently served with sublinear online computation time per query (and the cost of the preprocessing can be amortized over the unboundedly many queries). Existing preprocessing PIR schemes (supporting unbounded queries), unfortunately, make undesirable tradeoffs to achieve sublinear online computation: they are either significantly non-optimal in online time or bandwidth, or require the servers to store a linear amount of state per client or even per query, or require polylogarithmically many non-colluding servers. We propose a novel 2-server preprocessing PIR scheme that achieves $$\widetilde{O}(\sqrt{n})$$ online computation per query and $$\widetilde{O}(\sqrt{n})$$ client storage, while preserving the polylogarithmic online bandwidth of classical PIR schemes. Both the online bandwidth and computation are optimal up to a poly-logarithmic factor. In our construction, each server stores only the original database and nothing extra, and each online query is served within a single round trip. Our construction relies on the standard LWE assumption. As an important stepping stone, we propose new, more generalized definitions for a cryptographic object called a Privately Puncturable Pseudorandom Set, and give novel constructions that depart significantly from prior approaches.
Adaptive Central Difference Kalman Filter With Unknown Measurement Noise Covariance and Its Application to Airborne POS

Position and Orientation system (POS), a loosely integrated inertial navigation system (INS) and global positioning system (GPS), can provide high-accuracy motion information for the airborne remote sensing loads, which plays a crucial role in airborne remote sensing imaging. However, the airborne POS often suffers from the harsh environment, such as aircraft maneuver mode and other external disturbance, which will lead to measurement noise unknown and further affects the accuracy of motion parameters. In this paper, an adaptive central difference Kalman filter method based on expectation maximization algorithm is proposed, which can estimate measurement noise adaptively and further improve the performance of POS. A flight experiment is conducted and the results show that the proposed method achieves higher-accuracy motion information by compared with the traditional CDKF method and covariance matching.
Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations

Recent advances have enabled a single neural network to serve as an implicit scene representation, establishing the mapping function between spatial coordinates and scene properties. In this paper, we make a further step towards continual learning of the implicit scene representation directly from sequential observations, namely Continual Neural Mapping. The proposed problem setting bridges the gap between batch-trained implicit neural representations and commonly used streaming data in robotics and vision communities. We introduce an experience replay approach to tackle an exemplary task of continual neural mapping: approximating a continuous signed distance function (SDF) from sequential depth images as a scene geometry representation. We show for the first time that a single network can represent scene geometry over time continually without catastrophic forgetting, while achieving promising trade-offs between accuracy and efficiency.
A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor Scenes

This letter presents a real-time online vision framework to jointly recover an indoor scene’s 3D structure and semantic label. Given noisy depth maps, a camera trajectory, and 2D semantic labels at train time, the proposed deep neural network based approach learns to fuse the depth over frames with suitable semantic labels in the scene space. Our approach exploits the joint volumetric representation of the depth and semantics in the scene feature space to solve this task. For a compelling online fusion of the semantic labels and geometry in real-time, we introduce an efficient vortex pooling block while dropping the use of routing network in online depth fusion to preserve high-frequency surface details. We show that the context information provided by the semantics of the scene helps the depth fusion network learn noise-resistant features. Not only that, it helps overcome the shortcomings of the current online depth fusion method in dealing with thin object structures, thickening artifacts, and false surfaces. Experimental evaluation on the Replica dataset shows that our approach can perform depth fusion at 37 and 10 frames per second with an average reconstruction F-score of 88% and 91%, respectively, depending on the depth map resolution. Moreover, our model shows an average IoU score of 0.515 on the ScanNet 3D semantic benchmark leaderboard. Code and example dataset information is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/suryanshkumar/online-joint-depthfusion-and-semantic</uri> .