Reviewerly/exHarmony huggingface dataset:
Centering Health Equity Within COVID-19 Contact Tracing: Connecticut's Community Outreach Specialist Program

The COVID-19 pandemic has disproportionately impacted vulnerable populations, including those who are non-English-speaking and those with lower socioeconomic status; yet, participation of these groups in contact tracing was initially low. Distrust of government agencies, anticipated COVID-19-related stigma, and language and cultural barriers between contact tracers and communities are common challenges.The Community Outreach Specialist (COS) program was established within the Connecticut Department of Public Health (DPH) COVID-19 contact tracing program to encourage participation in contact tracing and address a need for culturally competent care and social and material support among socially vulnerable and non-English-speaking populations in 11 high-burden jurisdictions in Connecticut.In partnership with state and local health departments, we recruited 25 COS workers with relevant language skills from target communities and trained them to deliver contact tracing services to vulnerable and non-English speaking populations.We conducted a cross-sectional analysis using data from ContaCT, DPH's enterprise contact tracing system. Overall, the COS program enrolled 1938 cases and 492 contacts. The proportion of residents reached (ie, called and interviewed) in the COS program was higher than that in the regular contact tracing program for both cases (70% vs 57%, P < .001) and contacts (84% vs 64%, P < .001). After adjusting for client age, sex, race and ethnicity, language, and jurisdiction, we found that the COS program was associated with increased reach for contacts (odds ratio [OR] = 1.52; 95% confidence interval [95% CI], 1.17-1.99) but not for cases (OR = 0.78; 95% CI, 0.70-0.88). Rapid qualitative analysis of programmatic field notes and meeting reports provided evidence that the COS program was feasible and acceptable to clients and contributed to COVID-19 education and communication efforts.A COS program employing a client-centered, community-engaged strategy for reaching vulnerable and non-English-speaking populations was feasible and more effective at reaching contacts than standard COVID-19 contact tracing.
Two-dimensional differential null interferometry of transmission wavefront of axicon lens

• A null interferometric measurement method is proposed for axicon lens. • The method separates the system error with differential reconstruction. • Misalignment error model is established. • The method only uses a pair of axicon lens and is novel and low-cost. • The proposed method achieves an accuracy of ±0.2 wavelength. Axicon lenses are critical in the beam transforming, such as the Bessel beam and hollow beam generation, and its manufacturing accuracy directly affects the quality of the output beam. Aiming at the versatility and high accuracy, we propose a two-dimensional differential null interferometry for measuring transmission wavefront of axicon lens. The method uses a pair of axicon lens to construct a null interferometric setup, with one as the compensator. The two-dimensional difference separates the wavefront information introduced by the compensator and achieves the orthogonal difference of the measured wavefront. Finally, the measured wavefront is reconstructed from the differential wavefront based on the Hudgin slope method. The experiments proved that this method achieves a measurement accuracy of ±0.125 μm with a repeatability of 0.759 nm.
Blockchain Based Multi-Authority Fine-Grained Access Control System With Flexible Revocation

J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing, doi: 10.1109/TSC.2021.3086023
Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modeling

In this study, key geochemical features of magmas formed in eight different tectono-magmatic settings (mid-ocean ridges, oceanic islands, oceanic plateaus, continental flood basalt provinces, intra-oceanic arcs, continental arcs, island arcs, and back-arc basins) are presented that were obtained using a machine-learning-based statistical model. We analyzed geochemical data for volcanic rocks compiled from the global geochemical databases based on statistical model fitting. We used the sparse modeling approach, with which we can objectively identify a small number of fundamental features from a large number of observations. This approach allowed us to identify a small number of representative geochemical features from a total of 857 variables, including major and trace element concentrations, isotope ratios, and all possible ratios and multiplications of elements. Based on the statistical analysis, we present a small number (2–4) of key geochemical features for each tectono-magmatic setting. The extracted geochemical features and associated diagrams can be used to examine geochemical similarities and differences between tectono-magmatic settings and to identify the geochemical characteristics of unknown samples. Based on the extracted geochemical characteristics, we discuss the processes that may lead to the formation of magmas in different tectono-magmatic settings. Our statistical analysis shows that the geochemical signatures of magmas vary with the tectono-magmatic setting, as do the geochemical processes involved in magma generation.
Tolerance-enhanced SU(1,1) interferometers using asymmetric gain

SU(1,1) interferometers play an important role in quantum metrology. Previous studies focus on various inputs and detection strategies with symmetric gain. In this paper, we analyze a modified SU(1,1) interferometer using asymmetric gain. Two vacuum states are used as the input and on–off detection is performed at the output. In a lossless scenario, symmetric gain is the optimal selection and the corresponding phase sensitivity can achieve the Heisenberg limit as well as the quantum Cramer–Rao bound. In addition, we analyze the phase sensitivity with symmetric gain in the lossy scenario. The phase sensitivity is sensitive to internal losses but extremely robust against external losses. We address the optimal asymmetric gain and the results suggest that this method can improve the tolerance to internal losses. Our work may contribute to the practical development of quantum metrology.
Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets, and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional artificial neural network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and exhaust gas temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.
Quantum Fisher Information and Lee‐Yang Zeros with Quantum Coherence of the Isotropic XY$XY$ Model and the Energy Scale

Abstract Lee‐Yang zeros lie on the complex plane of the control parameters for many‐body systems with finite size, such as the transverse magnetic field and the inverse temperature. Here, an anisotropic spin system with nearest‐neighbor interactions for finite‐size parity space is considered. The Lee‐Yang zeros are obtained in anisotropic model for the complex plane of the transverse magnetic field and in the isotropic model for the complex fugacity plane of the quantum coherence. A characteristic function to represent the probe spin coherence for the isotropic model, and study on Lee‐Yang zeros, quantum coherence, and quantum Fisher information at the complex fugacity plane is presented. The relationship between quantum Fisher information and quantum coherence in decoherence channels is demonstrated and it is illustrated that information cannot be acquired at high temperatures. Meanwhile, the ground‐state energy on the complex plane for the anisotropic model is studied and the free energy cumulants with the partition function of the anisotropic model for parity space is calculated.
High-quality binary fringe generation by multi-scale optimization on intensity and phase

The optimization-based binary fringe generation method can greatly improve the quality of projected fringes for structured light measurement. However, the existing phase-based and intensity-based methods are either sensitive to the projector defocus levels or cannot reduce the phase error efficiently, and these two methods only optimize the phase or intensity at a single defocus scale. A new method is proposed to optimize the intensity and phase of binary fringe on multiple defocus scales, to the best of our knowledge, which can effectively reduce the phase error and ensure its robustness. The algorithm is accelerated by block optimization. We also proposed a strategy based on objective index evaluation to simplify the process of selecting the best binary patch. Simulation and experiment results show that the binary fringe generated by the proposed method is of high-phasing quality while keeping robust to different defocus levels of the projector.
Modelling the effects of personal factors on information security awareness

According to research, the number of attacks on the Internet has been increasing each year. Hence, information security awareness is a very significant skill. Accordingly, this study aimed to investigate the effects of students’ personal factors on their information security awareness. The researchers conducted a quantitative study that examines a theoretical model. The data were collected from 684 undergraduate students via three data tools. The effects of variables on information security awareness were explained via path analysis. The mediating role of technology attitude was examined in the relationship between information security awareness and the individual variables for the first time. The findings showed that gender and grade did not directly affect information security awareness levels, while attending information security training, department and technology attitude had a significant effect. On the other hand, some personal factors indirectly affected information security awareness. This analysis offered substantial contributions to the literature in uncovering the effects of variables on students’ information security awareness in a holistic way. The results can guide planning for information security training to increase information security awareness by considering personal factors.
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation

Model-based methods for recommender systems have been studied extensively for years. Modern recommender systems usually resort to 1) representation learning models which define user-item preference as the distance between their embedding representations, and 2) embedding-based Approximate Nearest Neighbor (ANN) search to tackle the efficiency problem introduced by large-scale corpus. While providing efficient retrieval, the embedding-based retrieval pattern also limits the model capacity since the form of user-item preference measure is restricted to the distance between their embedding representations. However, for other more precise user-item preference measures, e.g., preference scores directly derived from a deep neural network, they are computationally intractable because of the lack of an efficient retrieval method, and an exhaustive search for all user-item pairs is impractical.
High-Cardinality Categorical Attributes and Credit Card Fraud Detection

Credit card transactions may contain some categorical attributes with large domains, involving up to hundreds of possible values, also known as high-cardinality attributes. The inclusion of such attributes makes analysis harder, due to results with poorer generalization and higher resource usage. A common practice is, therefore, to ignore such attributes, removing them, albeit wasting the information they provided. Contrariwise, this paper reports our findings on the positive impacts of using high-cardinality attributes on credit card fraud detection. Thus, we present a new algorithm for domain reduction that preserves the fraud-detection capabilities. Experiments applying a deep feedforward neural network on real datasets from a major Brazilian financial institution have shown that, when measured by the F-1 metric, the inclusion of such attributes does improve fraud-detection quality. As a main contribution, this proposed algorithm was able to reduce attribute cardinality, improving the training times of a model while preserving its predictive capabilities.
Imperfect Labels with Belief Functions for Active Learning

Classification is used to predict classes by extracting information from labeled data. But sometimes the collected data is imperfect, as in crowdsourcing where users have partial knowledge and may answer with uncertainty or imprecision. This paper offers a way to deal with uncertain and imprecise labeled data using Dempster-Shafer theory and active learning. An evidential version of K-NN that classifies a new example by observing its neighbors was earlier introduced. We propose to couple this approach with active learning, where the model uses only a fraction of the labeled data, and to compare it with non-evidential models. A new computable parameter for EK-NN is introduced, allowing the model to be both compatible with imperfectly labeled data and equivalent to its first version in the case of perfectly labeled data. This method increases the complexity but provides a way to work with imperfectly labeled data with efficient results and reduced labeling costs when coupled with active learning. We have conducted tests on real data imperfectly labeled during crowdsourcing campaigns.
Advanced Scene Perception for Augmented Reality

Augmented reality (AR), combining virtual elements with the real world, has demonstrated impressive results in a variety of application fields and gained significant research attention in recent years due to its limitless potential [...]
Ten Steps to Design a Counseling App to Reduce the Psychosocial Impact of Human Papillomavirus Testing on the Basis of a User-Centered Design Approach in a Low- and Middle-Income Setting

This study describes the 10 steps followed to produce the information architecture of a user-centered design (UCD) counseling mobile application, the first phase to develop an app. The app aims to reduce the psychosocial impact of the human papillomavirus test result and improve women's knowledge of human papilloma virus and cervical cancer.We used a UCD approach to produce the information architecture of the app (ie, how to organize contents into features). We analyzed field notes, meeting agendas, and documentation produced during each stage of the design process. We described the goals, methods, and outcomes of each step. We also discussed the critical challenges and the strategies to address them.The steps are (1) knowledge, attitudes, and beliefs mapping: reanalysis of team's research findings from prior studies; (2) environmental scanning of apps available on the market; (3) stakeholders' point of view: The International Advisory Committee; (4) potential user's profile: building archetypes through the Persona method; (5) women's interviews: user's preferences and experiences; (6) effective features: scoping review to select app's features that address psychosocial impact; (7) the user journey: ideal interaction with the gynecological service and the counseling app; (8) women's focus groups: using Personas and Scenarios to discuss app's mock-up; (9) women's design sessions: prototype test and card-sorting techniques; and (10) team's design session: translating results into visual objects and features.We provide here detailed descriptions of the UCD process of an app for human papillomavirus-tested women for those venturing into the area of mHealth strategies work. Our experience can be used as a guide for future mHealth app development for a low- and middle-income setting.
Calibrating the Attack to Sensitivity in Differentially Private Mechanisms

This work studies the power of adversarial attacks against machine learning algorithms that use differentially private mechanisms as their weapon. In our setting, the adversary aims to modify the content of a statistical dataset via insertion of additional data without being detected by using the differential privacy to her/his own benefit. The goal of this study is to evaluate how easy it is to detect such attacks (anomalies) when the adversary makes use of Gaussian and Laplacian perturbation using both statistical and information-theoretic tools. To this end, firstly via hypothesis testing, we characterize statistical thresholds for the adversary in various settings, which balances the privacy budget and the impact of the attack (the modification applied on the original data) in order to avoid being detected. In addition, we establish the privacy-distortion trade-off in the sense of the well-known rate-distortion function for the Gaussian mechanism by using an information-theoretic approach. Accordingly, we derive an upper bound on the variance of the attacker’s additional data as a function of the sensitivity and the original data’s second-order statistics. Lastly, we introduce a new privacy metric based on Chernoff information for anomaly detection under differential privacy as a stronger alternative for the (ϵ,δ)-differential privacy in Gaussian mechanisms. Analytical results are supported by numerical evaluations.
Curing the SICK and Other NLI Maladies

Abstract Against the backdrop of the ever-improving Natural Language Inference (NLI) models, recent efforts have focused on the suitability of the current NLI datasets and on the feasibility of the NLI task as it is currently approached. Many of the recent studies have exposed the inherent human disagreements of the inference task and have proposed a shift from categorical labels to human subjective probability assessments, capturing human uncertainty. In this work, we show how neither the current task formulation nor the proposed uncertainty gradient are entirely suitable for solving the NLI challenges. Instead, we propose an ordered sense space annotation, which distinguishes between logical and common-sense inference. One end of the space captures non-sensical inferences, while the other end represents strictly logical scenarios. In the middle of the space, we find a continuum of common-sense, namely, the subjective and graded opinion of a “person on the street.” To arrive at the proposed annotation scheme, we perform a careful investigation of the SICK corpus and we create a taxonomy of annotation issues and guidelines. We re-annotate the corpus with the proposed annotation scheme, utilizing four symbolic inference systems, and then perform a thorough evaluation of the scheme by fine-tuning and testing commonly used pre-trained language models on the re-annotated SICK within various settings. We also pioneer a crowd annotation of a small portion of the MultiNLI corpus, showcasing that it is possible to adapt our scheme for annotation by non-experts on another NLI corpus. Our work shows the efficiency and benefits of the proposed mechanism and opens the way for a careful NLI task refinement.
Learning Optimal Classification Trees Using a Binary Linear Program Formulation

We provide a new formulation for the problem of learning the optimal classification tree of a given depth as a binary linear program. A limitation of previously proposed Mathematical Optimization formulations is that they create constraints and variables for every row in the training data. As a result, the running time of the existing Integer Linear programming (ILP) formulations increases dramatically with the size of data. In our new binary formulation, we aim to circumvent this problem by making the formulation size largely independent from the training data size. We show experimentally that our formulation achieves better performance than existing formulations on both small and large problem instances within shorter running time.
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques

Neural machine translation has significantly pushed forward the quality of the field.However, there are remaining big issues with the output translations and one of them is fairness.Neural models are trained on large text corpora which contain biases and stereotypes.As a consequence, models inherit these social biases.Recent methods have shown results in reducing gender bias in other natural language processing tools such as word embeddings.We take advantage of the fact that word embeddings are used in neural machine translation to propose a method to equalize gender biases in neural machine translation using these representations.We evaluate our proposed system on the WMT English-Spanish benchmark task, showing gains up to one BLEU point.As for the gender bias evaluation, we generate a test set of occupations and we show that our proposed system learns to equalize existing biases from the baseline system.
Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study

Neural generative models have been become increasingly popular when building conversational agents.They offer flexibility, can be easily adapted to new domains, and require minimal domain engineering.A common criticism of these systems is that they seldom understand or use the available dialog history effectively.In this paper, we take an empirical approach to understanding how these models use the available dialog history by studying the sensitivity of the models to artificially introduced unnatural changes or perturbations to their context at test time.We experiment with 10 different types of perturbations on 4 multi-turn dialog datasets and find that commonly used neural dialog architectures like recurrent and transformer-based seq2seq models are rarely sensitive to most perturbations such as missing or reordering utterances, shuffling words, etc.Also, by open-sourcing our code, we believe that it will serve as a useful diagnostic tool for evaluating dialog systems in the future 1 .
Timing Channel in IaaS: How to Identify and Investigate

Recently, the Infrastructure as a Service Cloud (IaaS) (e.g., Amazon EC2) has been widely used by many organizations. However, some IaaS security issues create serious threats to its users. A typical issue is the timing channel. This kind of channel can be a cross-VM information channel, as proven by many researchers. Owing to the fact that it is covert and traceless, the traditional identification methods cannot build an accurate analysis model and obtain a compromised result. We investigated the underlying behavior of the timing channel from the perspective of the memory activity records and summarized the signature of the timing channel in the underlying memory activities. An identification method based on the long-term behavior signatures was proposed. We proposed a complete set of forensics steps including evidence extraction, identification, record reserve, and evidence reports. We studied four typical timing channels, and the experiments showed that these channels can be detected and investigated, even with the disturbances from normal processes.