Reviewerly/exHarmony huggingface dataset:
Reorder Buffer Contention: A Forward Speculative Interference Attack for Speculation Invariant Instructions

Speculative side-channel attacks access sensitive data and use transmitters to leak the data during wrong-path execution. Various defenses have been proposed to prevent such information leakage. However, not all speculatively executed instructions are unsafe: Recent work demonstrates that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">speculation invariant</i> instructions are independent of speculative control-flow paths and are guaranteed to eventually commit, regardless of the speculation outcome. Compile-time information coupled with run-time mechanisms can then selectively lift defenses for speculation invariant instructions, reclaiming some of the lost performance. Unfortunately, speculation invariant instructions can easily be manipulated by a form of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">speculative interference</i> to leak information via a new side-channel that we introduce in this paper. We show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">forward</i> speculative interference where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">older</i> speculative instructions interfere with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">younger speculation invariant instructions</i> effectively turns them into transmitters for secret data accessed during speculation. We demonstrate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">forward speculative interference</i> on actual hardware, by selectively filling the reorder buffer (ROB) with instructions, pushing speculative invariant instructions in-or-out of the ROB <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on demand</i> , based on a speculatively accessed secret. This reveals the speculatively accessed secret, as the occupancy of the ROB itself becomes a new speculative side-channel.
Using Venn Diagrams to Evaluate Digital Contact Tracing: Panel Survey Analysis

Mitigation of the spread of infection relies on targeted approaches aimed at preventing nonhousehold interactions. Contact tracing in the form of digital proximity tracing apps has been widely adopted in multiple countries due to its perceived added benefits of tracing speed and breadth in comparison to traditional manual contact tracing. Assessments of user responses to exposure notifications through a guided approach can provide insights into the effect of digital proximity tracing app use on managing the spread of SARS-CoV-2.The aim of this study was to demonstrate the use of Venn diagrams to investigate the contributions of digital proximity tracing app exposure notifications and subsequent mitigative actions in curbing the spread of SARS-CoV-2 in Switzerland.We assessed data from 4 survey waves (December 2020 to March 2021) from a nationwide panel study (COVID-19 Social Monitor) of Swiss residents who were (1) nonusers of the SwissCovid app, (2) users of the SwissCovid app, or (3) users of the SwissCovid app who received exposure notifications. A Venn diagram approach was applied to describe the overlap or nonoverlap of these subpopulations and to assess digital proximity tracing app use and its associated key performance indicators, including actions taken to prevent SARS-CoV-2 transmission.We included 12,525 assessments from 2403 participants, of whom 50.9% (1222/2403) reported not using the SwissCovid digital proximity tracing app, 49.1% (1181/2403) reported using the SwissCovid digital proximity tracing app and 2.5% (29/1181) of the digital proximity tracing app users reported having received an exposure notification. Most digital proximity tracing app users (75.9%, 22/29) revealed taking at least one recommended action after receiving an exposure notification, such as seeking SARS-CoV-2 testing (17/29, 58.6%) or calling a federal information hotline (7/29, 24.1%). An assessment of key indicators of mitigative actions through a Venn diagram approach reveals that 30% of digital proximity tracing app users (95% CI 11.9%-54.3%) also tested positive for SARS-CoV-2 after having received exposure notifications, which is more than 3 times that of digital proximity tracing app users who did not receive exposure notifications (8%, 95% CI 5%-11.9%).Responses in the form of mitigative actions taken by 3 out of 4 individuals who received exposure notifications reveal a possible contribution of digital proximity tracing apps in mitigating the spread of SARS-CoV-2. The application of a Venn diagram approach demonstrates its value as a foundation for researchers and health authorities to assess population-level digital proximity tracing app effectiveness by providing an intuitive approach for calculating key performance indicators.
Artificial intelligence in research and development for sustainability: the centrality of explicability and research data management

Abstract Sustainability constitutes a focal challenge and objective of our time and requires collaborative efforts. As artificial intelligence brings forth substantial opportunities for innovations across industry and social contexts, so it provides innovation potential for pursuing sustainability. We argue that (chemical) research and development driven by artificial intelligence can substantially contribute to sustainability if it is leveraged in an ethical way. Therefore, we propose that the ethical principle explicability combined with (open) research data management systems should accompany artificial intelligence in research and development to foster sustainability in an equitable and collaborative way.
Unbiased Graph Embedding with Biased Graph Observations

Graph embedding techniques are pivotal in real-world machine learning tasks that operate on graph-structured data, such as social recommendation and protein structure modeling. Embeddings are mostly performed on the node level for learning representations of each node. Since the formation of a graph is inevitably affected by certain sensitive node attributes, the node embeddings can inherit such sensitive information and introduce undesirable biases in downstream tasks. Most existing works impose ad-hoc constraints on the node embeddings to restrict their distributions for unbiasedness/fairness, which however compromise the utility of the resulting embeddings. In this paper, we propose a principled new way for unbiased graph embedding by learning node embeddings from an underlying bias-free graph, which is not influenced by sensitive node attributes. Motivated by this new perspective, we propose two complementary methods for uncovering such an underlying graph, with the goal of introducing minimum impact on the utility of the embeddings. Both our theoretical justification and extensive experimental comparisons against state-of-the-art solutions demonstrate the effectiveness of our proposed methods.
Evaluating the trade-off between privacy, public health safety, and digital security in a pandemic

Abstract COVID-19 has impacted all aspects of everyday normalcy globally. During the height of the pandemic, people shared their (PI) with one goal—to protect themselves from contracting an “unknown and rapidly mutating” virus. The technologies (from applications based on mobile devices to online platforms) collect (with or without informed consent) large amounts of PI including location, travel, and personal health information. These were deployed to monitor, track, and control the spread of the virus. However, many of these measures encouraged the trade-off on privacy for safety. In this paper, we reexamine the nature of privacy through the lens of safety focused on the health sector, digital security, and what constitutes an infraction or otherwise of the privacy rights of individuals in a pandemic as experienced in the past 18 months. This paper makes a case for maintaining a balance between the benefit, which the contact tracing apps offer in the containment of COVID-19 with the need to ensure end-user privacy and data security. Specifically, it strengthens the case for designing with transparency and accountability measures and safeguards in place as critical to protecting the privacy and digital security of users—in the use, collection, and retention of user data. We recommend oversight measures to ensure compliance with the principles of lawful processing, knowing that these, among others, would ensure the integration of privacy by design principles even in unforeseen crises like an ongoing pandemic; entrench public trust and acceptance, and protect the digital security of people.
VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment

Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc. State-of-the-art (SOTA) Ontology Alignment systems typically use naive domain-dependent approaches with handcrafted rules or domainspecific architectures, making them unscalable and inefficient.In this work, we propose VeeAlign, a Deep Learning based model that uses a novel dual-attention mechanism to compute the contextualized representation of a concept which, in turn, is used to discover alignments.By doing this, not only is our approach able to exploit both syntactic and semantic information encoded in ontologies, it is also, by design, flexible and scalable to different domains with minimal effort.We evaluate our model on four different datasets from different domains and languages, and establish its superiority through these results as well as detailed ablation studies.The code and datasets used are available at https://github.com/Remorax/VeeAlign.
A novel method for causal structure discovery from EHR data and its application to type-2 diabetes mellitus

Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.
Sequential Data–Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development

Sequential information in electronic medical records is valuable and helpful for patient outcome prediction but is rarely used for patient similarity measurement because of its unevenness, irregularity, and heterogeneity.We aimed to develop a patient similarity framework for patient outcome prediction that makes use of sequential and cross-sectional information in electronic medical record systems.Sequence similarity was calculated from timestamped event sequences using edit distance, and trend similarity was calculated from time series using dynamic time warping and Haar decomposition. We also extracted cross-sectional information, namely, demographic, laboratory test, and radiological report data, for additional similarity calculations. We validated the effectiveness of the framework by constructing k-nearest neighbors classifiers to predict mortality and readmission for acute myocardial infarction patients, using data from (1) a public data set and (2) a private data set, at 3 time points-at admission, on Day 7, and at discharge-to provide early warning patient outcomes. We also constructed state-of-the-art Euclidean-distance k-nearest neighbor, logistic regression, random forest, long short-term memory network, and recurrent neural network models, which were used for comparison.With all available information during a hospitalization episode, predictive models using the similarity model outperformed baseline models based on both public and private data sets. For mortality predictions, all models except for the logistic regression model showed improved performances over time. There were no such increasing trends in predictive performances for readmission predictions. The random forest and logistic regression models performed best for mortality and readmission predictions, respectively, when using information from the first week after admission.For patient outcome predictions, the patient similarity framework facilitated sequential similarity calculations for uneven electronic medical record data and helped improve predictive performance.
Video Text Tracking With a Spatio-Temporal Complementary Model

Text tracking is to track multiple texts in a video, and construct a trajectory for each text. Existing methods tackle this task by utilizing the tracking-by-detection framework, i.e., detecting the text instances in each frame and associating the corresponding text instances in consecutive frames. We argue that the tracking accuracy of this paradigm is severely limited in more complex scenarios, e.g., owing to motion blur, etc., the missed detection of text instances causes the break of the text trajectory. In addition, different text instances with similar appearance are easily confused, leading to the incorrect association of the text instances. To this end, a novel spatio-temporal complementary text tracking model is proposed in this paper. We leverage a Siamese Complementary Module to fully exploit the continuity characteristic of the text instances in the temporal dimension, which effectively alleviates the missed detection of the text instances, and hence ensures the completeness of each text trajectory. We further integrate the semantic cues and the visual cues of the text instance into a unified representation via a text similarity learning network, which supplies a high discriminative power in the presence of text instances with similar appearance, and thus avoids the mis-association between them. Our method achieves state-of-the-art performance on several public benchmarks. The source code is available at https://github.com/lsabrinax/VideoTextSCM.
Distributed Quantum inner product estimation

As small quantum computers are becoming available on different physical platforms, a benchmarking task known as cross-platform verification has been proposed that aims to estimate the fidelity of states prepared on two quantum computers. This task is fundamentally distributed, as no quantum communication can be performed between the two physical platforms due to hardware constraints, which prohibits a joint SWAP test. In this paper we settle the sample complexity of this task across all measurement and communication settings. The essence of the task, which we call distributed quantum inner product estimation, involves two players Alice and Bob who have k copies of unknown states ρ,σ (acting on ℂd) respectively. Their goal is to estimate Tr(ρσ) up to additive error ε∈(0,1), using local quantum operations and classical communication. In the weakest setting where only non-adaptive single-copy measurements and simultaneous message passing are allowed, we show that k=O(max{1/ε2,√d/ε}) copies suffice. This achieves a savings compared to full tomography which takes Ω(d3) copies with single-copy measurements. Surprisingly, we also show that the sample complexity must be at least Ω(max{1/ε2,√d/ε}), even in the strongest setting where adaptive multi-copy measurements and arbitrary rounds of communication are allowed. This shows that the success achieved by shadow tomography, for sample-efficiently learning the properties of a single system, cannot be generalized to the distributed setting. Furthermore, the fact that the sample complexity remains the same with single and multi-copy measurements contrasts with single system quantum property testing, which often demonstrate exponential separations in sample complexity with single and multi-copy measurements.
Putting Words in BERT’s Mouth: Navigating Contextualized Vector Spaces with Pseudowords

We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond to word senses.By inducing a contextualized "pseudoword" as a stand-in for a static embedding in the input layer, and then performing masked prediction of a word in the sentence, we are able to investigate the geometry of the BERT-space in a controlled manner around individual instances.Using our method on a set of carefully constructed sentences targeting ambiguous English words, we find substantial regularity in the contextualized space, with regions that correspond to distinct word senses; but between these regions there are occasionally "sense voids"-regions that do not correspond to any intelligible sense. 1 1 Our code and dataset are available at https://github.com/tai314159/PWIBM-Putting-Words-in-Bert-s-Mouth 1 BERT(The event is in October.)Learn pseudoword in place of that is customized to reconstruct .static inputs: contextualized outputs: Run masked prediction with the modified input vector.BERT(The event is in [MASK].)
“It seemed like an annoying woman”: On the Perception and Ethical Considerations of Affective Language in Text-Based Conversational Agents

Previous research has found that task-oriented conversational agents are perceived more positively by users when they provide information in an empathetic manner compared to a plain, emotionless information exchange.However, users' perception and ethical considerations related to a dialog systems' response language style have received comparatively little attention in the field of human-computer interaction.To bridge this gap, we explored these ethical implications through a scenario-based user study.127 participants interacted with one of three variants of an affective, task-oriented conversational agent, each variant providing responses in a different language style.After the interaction, participants filled out a survey about their feelings during the experiment and their perception of various aspects of the chatbot.Based on statistical and qualitative analysis of the responses, we found language style played an important role in how humanlike participants perceived a dialog agent as well as how likable.Language style also had a direct effect on how users perceived the use of personal pronouns 'I' and 'You' and how they projected gender onto the chatbot.Finally, we identify and discuss ethical implications.In particular we focus on what factors/stereotypes influenced participants' impressions of gender, and what trade-offs a more human-like chatbot brings.
Inflate and Shrink:Enriching and Reducing Interactions for Fast Text-Image Retrieval

By exploiting the cross-modal attention, cross-BERT methods have achieved state-of-the-art accuracy in cross-modal retrieval.Nevertheless, the heavy text-image interactions in the cross-BERT model are prohibitively slow for large-scale retrieval.Late-interaction methods trade off retrieval accuracy and efficiency by exploiting cross-modal interaction only in the late stage, attaining a satisfactory retrieval speed.In this work, we propose an inflating and shrinking approach to further boost the efficiency and accuracy of late-interaction methods.The inflating operation plugs several codes in the input of the encoder to exploit the text-image interactions more thoroughly for higher retrieval accuracy.Then the shrinking operation gradually reduces the text-image interactions through knowledge distilling for higher efficiency.Through an inflating operation followed by a shrinking operation, both efficiency and accuracy of a late-interaction model are boosted.Systematic experiments on public benchmarks demonstrate the effectiveness of our inflating and shrinking approach.
Cloud Property Graph: Connecting Cloud Security Assessments with Static Code Analysis

In this paper, we present the Cloud Property Graph (CloudPG), which bridges the gap between static code analysis and runtime security assessment of cloud services. The CloudPG is able to resolve data flows between cloud applications deployed on different resources, and contextualizes the graph with runtime information, such as encryption settings. To provide a vendor- and technology-independent representation of a cloud service's security posture, the graph is based on an ontology of cloud resources, their functionalities and security features. We show, using an example, that our CloudPG framework can be used by security experts to identify weaknesses in their cloud deployments, spanning multiple vendors or technologies, such as AWS, Azure and Kubernetes. This includes misconfigurations, such as publicly accessible storages or undesired data flows within a cloud service, as restricted by regulations such as GDPR.
Unterschiedliche Komplexitätsebenen bei der Integration texturierter extra-terrestrischer Geländedaten in Spiele-Engines für Augmented und Virtual Reality Anwendungen im Bildungsbereich

Abstract Elevation and visual data from Chang’E-2, Mars Viking, and MOLA were transformed into 3D models and environments using unity and unreal engine to be implemented in augmented (AR) and virtual reality (VR) applications, respectively. The workflows for the two game development engines and the two purposes overlap, but have significant differences stemming from their intended usage: both are used in educational settings, but while the AR app has to run on basic smartphones that students from all socio-economic backgrounds might have, the VR requires high-end PCs and can therefore make use of respective devices’ potential. Hence, the models for the AR app are reduced to the necessary components and sizes of the highest mountains on Luna and Mars, whereas the VR app contains several models of probe landing sites on Mars, a landscape containing the entire planet at multiple levels of detail and a complex environment. Both applications are enhanced for educational use with annotations and interactive elements. This study focuses on the transfer of scientific data into game development engines for the use in educational settings using the example of scales in extra-terrestrial environments.
CVAE-based Re-anchoring for Implicit Discourse Relation Classification

Training implicit discourse relation classifiers suffers from data sparsity.Variational AutoEncoder (VAE) appears to be the proper solution.It is because that VAE is able to automatically generate inexhaustible varying samples by self supervision, and facilitates data augmentation.However, our experiments show that the utilization of VAE results in severe performance degradation.We ascribe this phenomenon to erroneous sampling.To address the issue, we use Conditional VAE (CVAE) to estimate the risk of erroneous sampling.Moreover, we develop a re-anchoring method which migrates the anchor of sampling area of VAE to reduce the risk.The experiments on PDTB v2.0 demonstrate that, compared to the RoBERTabased baseline, re-anchoring yields substantial improvements.In addition, we prove that re-anchoring can cooperate with other auxiliary strategies (transfer learning and interactive attention mechanism) to further improve the classification performance.