Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Human-AI Collaboration With Descriptions of AI Behavior

People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appropriately rely on AI aids, we propose showing them behavior descriptions, details of how AI systems perform on subgroups of instances. We tested the efficacy of behavior descriptions through user studies with 225 participants in three distinct domains: fake review detection, satellite image classification, and bird classification. We found that behavior descriptions can increase human-AI accuracy through two mechanisms: helping people identify AI failures and increasing people's reliance on the AI when it is more accurate. These findings highlight the importance of people's mental models in human-AI collaboration and show that informing people of high-level AI behaviors can significantly improve AI-assisted decision making.
Martingale Methods for Sequential Estimation of Convex Functionals and Divergences

We present a unified technique for sequential estimation of convex divergences between distributions, including integral probability metrics like the kernel maximum mean discrepancy, $\varphi $ -divergences like the Kullback-Leibler divergence, and optimal transport costs, such as powers of Wasserstein distances. This is achieved by observing that empirical convex divergences are (partially ordered) reverse submartingales with respect to the exchangeable filtration, coupled with maximal inequalities for such processes. These techniques appear to be complementary and powerful additions to the existing literature on both confidence sequences and convex divergences. We construct an offline-to-sequential device that converts a wide array of existing offline concentration inequalities into time-uniform confidence sequences that can be continuously monitored, providing valid tests or confidence intervals at arbitrary stopping times. The resulting sequential bounds pay only an iterated logarithmic price over the corresponding fixed-time bounds, retaining the same dependence on problem parameters (like dimension or alphabet size if applicable). These results are also applicable to more general convex functionals, like the negative differential entropy, suprema of empirical processes, and V-Statistics.
Gait Recognition Based on Locality Preserving Projections

A gait recognition method based on wavelet packet decomposition (WPD) and Locality preserving projections (LPP) is proposed in this paper. The method includes the following steps, pretreatment, feature extraction by WPD and dimensionality reduction by LPP and classification of the test samples to a corresponding class according to the nearest neighbor classifier. The experiment results on the public gait database show the effectiveness of the proposed method.
Supporting Secure Dynamic Alert Zones Using Searchable Encryption and Graph Embedding

Location-based alerts have gained increasing popularity in recent years, whether in the context of healthcare (e.g., COVID-19 contact tracing), marketing (e.g., location-based advertising), or public safety. However, serious privacy concerns arise when location data are used in clear in the process. Several solutions employ searchable encryption (SE) to achieve secure alerts directly on encrypted locations. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting–hidden vector encryption, and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that the optimal encoding is NP-hard, and we provide three heuristics that obtain significant performance gains: gray optimizer, multi-seed gray optimizer and scaled gray optimizer. Furthermore, we investigate the more challenging case of dynamic alert zones, where the area of interest changes over time. Our extensive experimental evaluation shows that our solutions can significantly improve computational overhead compared to existing baselines.
Numerical investigation of airborne transmission in low ceiling rooms under displacement ventilation

This study employs computational fluid dynamics (CFD) simulations to evaluate the risk of airborne transmission of COVID-19 in low-ceiling rooms, such as elevator cabins, under mechanical displacement ventilation. The simulations take into account the effects of the human body's thermal environment and respiratory jet dynamics on the transmission of pathogens. The results of the study are used to propose a potential mitigation strategy based on ventilation thermal control to reduce the risk of airborne transmission in these types of enclosed indoor spaces. Our findings demonstrate that show that as the ventilation rate ( Qv) increases, the efficiency of removing airborne particles ( εp) initially increases rapidly, reaches a plateau at a critical ventilation rate ( Qc), and subsequently increases at a slower rate beyond Qc. Further analysis of the flow and temperature fields reveals that εp is closely linked to the thermal stratification fields, as characterized by the thermal interface height ( hti), the height of the temperature isosurface at T=20.7 ℃ ( hT,20.7), and temperature gradient. The simulations also indicate that the location of infector relative to ventilation inlet/outlet affects Qc and εp,c with higher Qc and lower εp,c observed when infector is in a corner due to potential formation of a local hot spot of high infection risk when infector is near the ventilation inlet. Our findings indicate that the thermal environment, including the temperature difference among the occupants, ventilation, and ambient environment plays a critical role in the transmission of airborne diseases confined spaces.
Flow control of a circular cylinder by using an attached flexible filament

The flow control of a circular cylinder by using a flexible filament has been numerically investigated in this work. The cylinder is either fixed or elastically mounted, and the filament is attached to the base of the cylinder. Its leading end is fixed and its trailing end is free to flap. To execute the numerical simulation and deal with the fluid-structure interaction (FSI) of the filament as well, an improved immersed boundary-lattice Boltzmann method (IB-LBM) is presented. As compared to the conventional IB-LBM for handling the FSI of a filament, the current method can incorporate the mass effect of the filament and no user-defined spring parameter is needed to calculate the interaction force on the filament. After validating the employed method, the effects of the filament on the flow control of the cylinder are systematically examined by varying the bending coefficient (Kb) and length (L) of the filament. The laminar flow with a Reynolds number of 150 is considered in this study. Based on the numeri.
Inpainting borehole images using Generative Adversarial Networks

— In this paper, we propose a GAN-based approach for gap filling in borehole images created by wireline microresistivity imaging tools. The proposed method utilizes a generator, global discriminator, and local discriminator to inpaint the missing regions of the image. The generator is based on an auto-encoder architecture with skip-connections, and the loss function used is the Wasserstein GAN loss. Our experiments on a dataset of borehole images demonstrate that the proposed model can effectively deal with large-scale missing pixels and generate realistic completion results. This approach can improve the quantitative evaluation of reservoirs and provide an essential basis for interpreting geological phenomena and reservoir parameters.
Book Review : Differential Item Functioning Edited by Paul W. Holland and Howard Wainer Hillsdale NJ: Erlbaum, 1993, 453 pp

Dorans and Holland describe the Mantel-Haenszel (MH) and standardization methods in chapter 3. They provide valuable insights on the relations between these methods. I found this chapter to be extremely useful. In chapter 4, Thissen, Steinberg, and Wainer review detection methods based on item response theory (IRT). The review emphasizes detection rather than description, focusing on likelihood-ratio procedures that incorporate recent advances in parameter estimation. Each procedure is applied to real data, and an appendix giving the software code for these examples is included. This chapter is a must for anyone wishing to understand the latest iRT-based detection methods. Bock comments on the preceding two chapters in chapter 5, noting the differences between observed-score methods, such as the MH, and IRT-based methods. In chapter 6, Wainer presents several new &dquo;standardized&dquo; area indices for DIF. These indices use the focal group ability distribution to weight the response function differences. His proposals
An Efficient Approach for Discovering Graph Entity Dependencies (GEDs)

Graph entity dependencies (GEDs) are novel graph constraints, unifying keys and functional dependencies, for property graphs. They have been found useful in many real-world data quality and data management tasks, including fact checking on social media networks and entity resolution. In this paper, we study the discovery problem of GEDs -- finding a minimal cover of valid GEDs in a given graph data. We formalise the problem, and propose an effective and efficient approach to overcome major bottlenecks in GED discovery. In particular, we leverage existing graph partitioning algorithms to enable fast GED-scope discovery, and employ effective pruning strategies over the prohibitively large space of candidate dependencies. Furthermore, we define an interestingness measure for GEDs based on the minimum description length principle, to score and rank the mined cover set of GEDs. Finally, we demonstrate the scalability and effectiveness of our GED discovery approach through extensive experiments on real-world benchmark graph data sets; and present the usefulness of the discovered rules in different downstream data quality management applications.
Inconsistencies in the Definition and Annotation of Student Engagement in Virtual Learning Datasets: A Critical Review

Background: Student engagement (SE) in virtual learning can have a major impact on meeting learning objectives and program dropout risks. Developing Artiﬁcial Intelligence (AI) models for automatic SE measurement requires annotated datasets. However, existing SE datasets suﬀer from inconsistent deﬁnitions and annotation protocols mostly unaligned with the deﬁnition of SE in educational psychology. This issue could be misleading in developing generalizable AI models and make it hard to compare the performance of these models developed on diﬀerent datasets. The objective of this critical review was to explore the existing SE datasets and highlight inconsistencies in terms of diﬀering engagement deﬁnitions and annotation protocols. Methods: Several academic databases were searched for publications introducing new SE datasets. The datasets containing students’ single- or multi-modal data in online or oﬄine computer-based virtual learning sessions were included. The deﬁnition and annotation of SE in the existing datasets were analyzed based on our deﬁned seven dimensions of engagement annotation: sources, data modalities, timing, temporal resolution, level of abstraction, combination, and quantiﬁcation. Results: Thirty SE measurement datasets met the inclusion criteria. The reviewed SE datasets used very diverse and inconsistent deﬁnitions and annotation protocols. Unexpectedly, very few of the reviewed datasets used existing psychometrically validated scales in their deﬁnition of SE. Discussion: The inconsistent deﬁnition and annotation of SE are problematic for research on developing
Recursive Algorithms for Solving a Class of Nonlinear Matrix Equations with Applications to Certain Sensitivity Optimization Problems

This paper is concerned with solving a class of nonlinear algebraic matrix equations. Two recursive algorithms are proposed in terms of matrix difference equations and are studied. A set of initial values is characterized, from which the convergence of the algorithms can be guaranteed. Based on the general results, several effective algorithms are presented to compute $L^2$-sensitivity optimal realizations, as well as Euclidean norm balancing realizations, of a given linear system. A locally exponential convergence property is proved for one of them. As is shown by simulation in this paper, these algorithms prove to be far more practical for digital computer implementation than the gradient flows previously proposed.
Temperature dependence of serrated flows in compression in a bulk-metallic glass

The authors investigated the effect of temperature on the plastic-flow and shear-banding behaviors of a Zr-based bulk-metallic glass in compression. The results indicate that at a subambient temperature (194.5K) the plastic deformation of the bulk-metallic glass is still inhomogeneous and is characterized by the serrated flow and shear banding. But, compared to the ambient-temperature results, the subambient temperature moderates serrations in the plastic flow and leads to the formation of fewer shear bands. In view of the spatiotemporality of shear banding, the authors correlate the plastic-flow behaviors to shear-band patterns.
Electromagnetic data completion and prediction method based on tensor train

In residential environment, electromagnetic power density exceeding a certain value will affect people's livelihood and health. In the monitoring of electromagnetic environmental quality of residential buildings, the grid method is generally used to measure the data value of electromagnetic radiation sources, and the visualization technology is used to display the data of electromagnetic radiation sources in the region. In this paper, we use the method of randomly deploying sensor nodes to sample grid electromagnetic data, which greatly saves the deployment cost of sensor nodes. However, it will lead to data loss and pulse noise interference. Giving that the general electromagnetic data visualization diagram are local smoothing and sparse in transformation domain, we propose to use the tensor form of electromagnetic data to completion/restoration or predict the area grid that cannot be monitored based on the completion theory. The prediction model based on tensor train and algorithm are given. Experimental results show that the method can make the data smoother visually and within a certain accuracy.
An XGBoost risk prediction model of cardiovascular and cerebrovascular diseases with plateau healthcare dataset

This paper aims to build an XGBoost risk prediction model of cardiovascular and cerebrovascular diseases (CVDs) with plateau healthcare dataset. The incidence of cardiovascular and cerebrovascular diseases is very high in plateau areas. And it is difficult to detect partly due to the high cost of professional test. It will have high practical value to build a model to predict the risk of cardiovascular and cerebrovascular diseases by using the common healthcare data (e.g. fundus data). The paper proposes an XGBoost prediction model of CVDs risk with the fundus disease and other healthcare data set. The influence of various fundus disease factors on cardiovascular and cerebrovascular diseases is analyzed in the study. The result suggests that the proposed XGBoost prediction model performs better in terms of accuracy and recall rate compared with other models.
Differentially Private Release of Heterogeneous Network for Managing Healthcare Data

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article, we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet, an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets.
Multi-task Label-wise Transformer for Chinese Named Entity Recognition

Benefiting from the improvement of positional encoding and the introduction of lexical knowledge, Transformer has achieved superior performance than the prevailing BiLSTM-based models in named entity recognition (NER) task. However, existing Transformer-based models for Chinese NER pay less attention to the information captured by the bottom layers of Transformer and the significance of representation subspace where each head of Transformer is projected. In this article, we propose Multi-Task Label-Wise Transformer (MTLWT). From a global perspective, we assign entity boundary prediction (EBP) and entity type prediction (ETP) tasks to the first two layers. In this way, we stimulate lower layers to participate more in constructing character representation. Besides, in each multi-head self-attention (MHSA) layer, we provide a specific focus for each individual head, making the head project into a significant subspace. Experiments on four datasets from different domains show that our proposed model achieves comparable performance with other state-of-the-art models. In particular, MTLWT outperforms the other frameworks without external knowledge on all the datasets.
Digital Nudging: A Systematic Literature Review and Future Research Directions

Digital nudging is omnipresent in users' daily lives and constantly influences decisions in digital choice environments that require users to make decisions such as web-based forms or mobile apps. It uses interface design elements and aims to guide users' behavior in specific directions. So far, there is quite a lot of research in that stream, whereby each article focuses on one specific context in which digital nudging is applied. To assist researchers and practitioners that aim to study or design information systems, we provide an overview of existent research in that stream. Following the grounded-theory method, we categorize 88 publications and reveal ten forms and five contexts of digital nudging. We also provide an overview of relationships between forms of digital nudging and their contexts. Our findings contribute to digital nudging research by offering an in-depth and structured overview of existing research and future research directions.1
13th Workshop on Automating Test Case Design, Selection and Evaluation (A-TEST 2022) co-located with ESEC/FSE conference

The Workshop on Automating Test Case Design, Selection and Evaluation (A-TEST) has provided a venue for researchers and industry members alike to exchange and discuss trending views, ideas, state of the art, work in progress, and scientific results on automated testing. Up until now it has run 13 editions since 2009. The 13th edition of the A-TEST workshop has been performed as an in-person workshop in Singapore during 17 to 18 of November, 2022. This edition of the A-TEST workshop was co-located with ESEC/FSE 2022 conference.
Modularization Technique to Support Software Variability in Cyber-Physical Production Systems

Industries still have problems managing and organizing control software variants for different machine processes in Cyber-Physical Production Systems (CPPSs). They still rely mostly on clone-and-own approaches to create new control software variants when introducing new process variability in reaction to customers’ requirements. This approach not only results in code duplication but over time particularly makes it more difficult to maintain and evolve the software. Due to a lack of modularization, this approach also often results in unnecessary code in delivered software, locked by parameters, which can have a further negative effect on maintenance. In this paper, we discuss modularization approaches to organize control software in CPPSs. Specifically, for IEC 61499-based control software, we propose the combination of explicit variants described in 150% modules, standardized interfaces, and separation of concerns. We discuss how our approach could help industry to decrease the effort for new projects and at the same time get a better overview of the product and process variability of their CPPSs.
TRAPPER:Learning with Weak Supervision for Threat Intelligence Entity Recognition

The emergence of threat intelligence provides more foundation for tracing the source of network attacks, but it also necessitates a significant amount of manual analysis. Although data-driven automatic information extraction can effectively reduce labor consumption, it is limited by a lack of labeled data in the field of threat intelligence. To overcome this limitation, we propose TRAPPER, a threat entity recognition framework that can infer real threat entities from unlabeled threat sentences, avoiding the difficult labeling work. TRAPPER relies on label functions and three components, label aggregator, label predictor, and label expander, which guides the model with weak supervision and uses transfer knowledge as an aid. The label functions permit us to inject expert knowledge into the label aggregator to generate the inputs needed by the label predictor. It enables the label predictor to learn to recognize threat entities. The label expander combines the multi-source noisy label information with the transferred entity recognition semantic knowledge to further expand the entities. Throughout the process, the components promote each other by learning from each other. Comparative experiments on three threat intelligence-related datasets show that our method can effectively identify threat entities and achieve a maximum F1 score improvement of 6.3% over the best baseline.
NHAM: An NFV High Availability Architecture for Building Fault-Tolerant Stateful Virtual Functions and Services

Despite the many advantages of Network Function Virtualization (NFV) technology, the dependability of virtual services must be carefully addressed so that NFV can meet the requirements of commercial carriers. In particular, it is essential to provide mechanisms to ensure their correct and continuous operation. In this work we propose NHAM: an NFV High Availability Module designed within the NFV-MANO (NFV Management and Orchestration) reference model. NHAM allows the creation and management of fault-tolerant virtual network services consisting of stateful VNFs (Virtualized Network Functions) and SFCs (Service Function Chains). The proposed architecture provides fault management, including a choice of recovery mechanisms that can be applied depending on the specific needs of each service. The solution is holistic in the sense that it does not require any modifications of the source code of VNFs/SFCs to make them fault-tolerant. The strategy is based on SFC buffer management coupled with VNF checkpoint/restore, providing high availability in a transparent way. A prototype was implemented and experimental results are presented.