Reviewerly/exHarmony huggingface dataset:
Aligned forgetting mechanism for imbalanced image-to-image translation

作为图像处理领域的热点技术, 图像翻译可将图像由源域迁移至目标域,实现图像风格的转换.然而,在诸如真实人脸与卡通人脸、街道实景与街景分割图等图像翻译应用中,源域与目标域间往往存在巨大差距, 域间的冗余信息和不一致特征会增加转换的难度, 降低转换质量. 本文提出对齐遗忘机制并设计多个损失函数,通过构建简易高效的信息不平衡图像翻译算法框架,实现了域编码与域解码. 具体地,在将源域和目标域信息对齐映射至共享语义空间后,本文在对齐遗忘机制中使用互信息(mutual information)删除域间不一致特征同时保留相同特征. 实验结果表明, 本文方法可有效剔除冗余信息,提高域间信息一致性.此外,本文在多个代表性信息不平衡图像数据集上验证了新算法较已有算法的优越性.
Mobilizing Instrumental Childcare Support for Postpartum Mothers: Needs for and Barriers to Infant-centric Family Informatics Practices in Hong Kong

The availability of effective instrumental support may affect the physical and mental wellness of postpartum mothers. Through an online survey with 84 new mothers in Hong Kong and six-month follow-up interviews with one family, we investigate the practices of our participants mobilizing their support network to help with childcare, their experiences with infant-centric family informatics in the process, and the barriers, needs, and expectations that emerged. Our findings suggest that postpartum mothers may offload different types of babysitting tasks to different caregivers; they try to orchestrate the whole process through assorted communication media but may face a variety of practical and relational challenges. New mothers demand affordable, usable, and accessible information and communication supports to streamline infant-centric family informatics, if applicable. We thus propose a set of design considerations as to how a (connected) system of low-, medium-, and/or high-tech informatics tools could better foster mobilization of instrumental childcare support.
From Environmental Monitoring to Mitigation Action: Considerations, Challenges, and Opportunities for HCI

Noise pollution is among the most consistently cited and highest impact quality-of-life issues in major urban areas across the US, with more than 70 million people estimated to be exposed to noise levels considered harmful. While HCI and CSCW has a relatively rich history of engagement with monitoring such environmental concerns, e.g. through participatory sensing, prior research has not to our knowledge engaged with the process of municipal mitigation. In this paper we present research in this direction, connecting support for pervasive environmental monitoring with civic engagement in mitigation action. Having first identified and described the research space for this engagement, we present empirical data from an ongoing case study focused on two communities living with chronic problem noise. We probe the experiences of residents and representatives of different municipal organizations tasked with addressing their concerns. We find that making and drawing on records of noise is important to residents and authorities, that these groups have misaligned perceptions of how effective current reporting programs are, and that communication between them can be poor. We also find that noise is often one part of more complex issues. We then discuss our findings in light of prior research, and identify a model of civic sensing that highlights opportunities for HCI design and research including: supporting residents' coordinated actions and actions with municipal open data, mediating residents' and authorities' assessments of data quality, and supporting accountability and attributable mitigation action.
CodeFed: Federated Speech Recognition for Low-Resource Code-Switching Detection

One common constraint in the practical application of speech recognition is Code Switching. The issue of code-switched languages is especially aggravated in the context of Indian languages - since most massively multilingual models are trained on corpora that are not representative of the diverse set of Indian languages. An associated constraint with such systems is the privacy-intrusive nature of the applications that aim to collate such representative data. To collectively mitigate both problems, this works presents CodeFed: A federated learning-based code-switching detection model that can be deployed to collaboratively trained by leveraging private data from multiple users, without compromising their privacy. Using a representative low-resource Indic dataset, we demonstrate the superior performance of a collaboratively trained global model that is trained using federated learning on three low-resource Indic languages - Gujarati, Tamil and Telugu and draw a comparison of the model with respect to most current work in the field. Finally, to evaluate the practical realizability of the proposed system, CodeFed also discusses the system overview of the label generation architecture which may accompany CodeFed’s possible real-time deployment.
The Long Way Home: News Values in Stories Told by Appalachian Trail Thru-Hikers on Social Media

Technology use by long-distance hikers provide a fascinating glimpse of the future of HCI research, as it intersects with technology isolation and (non)use. We can understand the use of technology, explore opportunities for rural and outdoor computing, and work with underrepresented communities in these spaces to amplify their stories. In this paper, we focus on the thru-hikers on the Appalachian Trail and the stories they share on Instagram. We apply narrative analysis to understand the story content, and also leverage a News Value framework to describe how they craft their stories. The findings highlight opportunities to understand the different storytelling categories that thru-hikers employ, and the platforms they choose for the telling. We discuss the usefulness of understanding technology and storytelling needs for each type of storyteller, and explore the implications for designing technology and tools to amplify stories from underserved users and to support the telling of compelling stories.
Modeling of Security Fault-Tolerant Requirements for Secure Systems

Security services can keep a system from security breaches for a while, but they are ultimately compromised as the system is deployed and used. This paper describes the modeling of security fault-tolerant (SFT) requirements, which can tolerate the failures of security services for systems. SFT requirements are specified together with the security services requirements so that they tolerate breaches of the security services. This paper addresses an approach for specifying and analyzing SFT requirements using a meta-model. Threats to systems are identified in the requirements specification and analysis phases, and SFT measures against the threats are described with security services. An electronic commerce system is selected to illustrate the approach.
Being there to Learn: Narrative Style and Cross-platform Comparison for 360-degree Educational Videos

360-degree videos hold great potential as learning tools that can provide a sense of presence with instructors. However, much remains to be explored about how these videos should be designed. Across three within-subjects studies, we compared the effects of two different narrative styles, monologue and dialogue, on learning experiences in 360-degree videos filmed in an apple orchard. Study 1 presented the 360-degree videos using virtual reality (VR) with head-mounted displays. Study 2 presented the same content in web-based VR using computer screens. Replicating Study 2, Study 3 further examined users' interaction with the video content using on-screen mouse tracking. Across all three studies, participants preferred a monologue format, and also reported higher physical and social presence in this format. Furthermore, greater physical presence correlated with improved recall of informative content. This suggests that many of the benefits of 360-degree videos can be enjoyed even by students and teachers without access to VR headsets, providing more inclusive and accessible learning opportunities.
Generation of time-frequency entangled photon pairs propagating in separate waveguides in circuit QED setup

Time-frequency entangled photons constitute an important resource for a plethora of applications across the diverse quantum technology landscape. Thus, efficient and tunable setups for the generation of entangled photons are requisite for modern quantum technologies. In this work, we propose a generic cavity QED setup designed for on-demand generation of time-frequency entangled photon pairs, with each photon propagating in a separate waveguide. We outline a potential incarnation of this setup in the microwave superconducting circuit QED architecture. We derive and numerically solve the set of equations of motion governing the evolution of the quantum state of the system, allowing us to examine the photon emission dynamics. Using the Schmidt decomposition of the joint spectral amplitude of the emitted photon pair, we compute the entanglement entropy analyzing its dependence on the system parameters. We outline the potential extension of the proposed scheme for the generation of multiphoton time-frequency entangled states.
Robust sequential adaptive Kalman filter algorithm for ultrashort baseline underwater acoustic positioning

Abstract Affected by dynamic changes in the complex marine environment, ultrashort baseline (USBL) systems may exhibit continuous gross errors in underwater target positioning, resulting in the distortion of the target coordinates. To effectively detect and eliminate continuous gross errors in USBL underwater acoustic positioning, a robust sequential adaptive Kalman filter (RSAKF) algorithm is proposed in this paper. The RSAKF algorithm employs sequential filtering to decompose all measurement updates into multiple submeasurement updates and uses the fading memory weighted average method to estimate the one-step prediction mean square error of the metrics for each submeasurement update. Then, the RSAKF algorithm adopts an adaptive correction method of submeasurement noise variance, which eliminates the influence of continuous gross errors through a more targeted adaptive correction of each submeasurement noise variance. The effectiveness of the algorithm was quantitatively analyzed using a USBL positioning simulation experiment, and the results showed that the continuous gross errors rejection rate of the RSAKF algorithm reached 84.12%. The point error of the RSAKF algorithm is improved by 62.65%, 46.76%, 36.09%, and 26.48% compared with the Kalman filter (KF), KF based on Huber, KF based on Institute of Geodesy and Geophysics, and the maximum correntropy KF, respectively. The USBL positioning remotely operated vehicle experiment was conducted in the South China Sea, and the results showed that the RSAKF has the best filtering accuracy. Simulation and actual measurement experiments verified that the RSAKF algorithm can effectively eliminate the influence of continuous gross errors.
Isotype of the conquest

Abstract This article introduces the Codices of The School of Huejotzingo as early examples of data visualization from sixteenth-century colonial México. The glyphs in the Codices and the technique of repetition are used to represent quantities. A similar approach was introduced four hundred years later in 1930s Vienna that would become known as Isotype. Similar to the Codices, Isotype used pictograms and repetition to represent quantities. A set of principles were established to design Isotype charts that were engaging and memorable. The Codices are analyzed using the principles of Isotype to contextualize and introduce them into the data visualization timeline.
Events in crowded places: A smart service management

In this work we present eSERVANT (eventS in crowdEd places: a smaRt serVice mANagemenT), an ICT hardware and software infrastructure that allows the management of services tied to big events such as concerts, sport matches or fairs that take place in dedicated facilities. Among the main features of eSERVANT there is a vision-based dynamic routing that exploits computer vision techniques to detect people and track their movements within a facility. Thanks to an estimate of room occupancies and inbound/outbound flows, we propose an algorithm for dynamically selecting the best indoor route and avoid hazardous situations. At the same time, we perform user profiling finalized to promoting social networking through recommendation. To assess the usefulness of our infrastructure we established a test case over a six month experimentation and quantitatively evaluated user satisfaction.
WINE: Warning miner for improving bug finders

Bug finders have been actively used to efficiently detect bugs. However, developers and researchers found that the bug finders show high false positive rate. The false positives can be caused by two major reasons: (1) users rejecting warnings and (2) false-positive inducing issues (FPI), i.e., incorrect or incomplete rule implementations. The objective of this study is to reduce warning validation costs for developers of bug finders when they validate the implementation of bug finders to reduce false positives caused by FPI. To achieve the objective, we propose a novel approach, WINE. The key idea of WINE is to extract representative warnings that are structurally equal to other warnings, or structurally contain other warnings from numerous warnings. The rationale behind the approach is that the warnings detected based on structural information and tokens might be equal to each other, or contain other warnings structurally. We evaluated our approach with PMD, an open source bug finder, and 1,008 Java open source projects maintained by Apache Software Foundation. As a result, WINE extracted just about 2% of all warnings. Among the 2% of warnings, we could find the 28 FPIs of PMD. Among them, ten FPIs were already fixed among them. In addition, we simulated our approach in regression testing of PMD with twelve versions changes of PMD (6.25.0 to 6.37.0). As a result, we observed that WINE can effectively reduce the inspection costs by removing about 95% changed warnings. Based on the results, we suggest that WINE could be adopted to improve the bug finders in terms of reducing false positives cause by FPI. In addition, WINE is helpful in the development processes of bug finders to identify false positives and false negatives, especially in regression testing of bug finders.
An efficient Automatic Modulation Classification method based on the Convolution Adaptive Noise Reduction network

Due to the influence of noise in the received signal in non-cooperative communication, it is difficult for existing Automatic modulation classification methods to balance classification accuracy and model complexity. This paper proposes a novel Convolutional Adaptive Noise Reduction (CANR) network, which consists of an Adaptive Noise Reduction (ANR) module and a Convolutional Feature Extraction (CFE) module. The ANR and CFE modules denoise the combined input and capture the spatiotemporal features in the time series. Experiments on benchmark datasets show that the proposed network has the fewest training parameters and state-of-the-art recognition accuracy under the same conditions.
Spatial–contextual variational autoencoder with attention correction for anomaly detection in retinal OCT images

Anomaly detection refers to leveraging only normal data to train a model for identifying unseen abnormal cases, which is extensively studied in various fields. Most previous methods are based on reconstruction models, and use anomaly score calculated by the reconstruction error as the metric to tackle anomaly detection. However, these methods just employ single constraint on latent space to construct reconstruction model, resulting in limited performance in anomaly detection. To address this problem, we propose a Spatial-Contextual Variational Autoencoder with Attention Correction for anomaly detection in retinal OCT images. Specifically, we first propose a self-supervised segmentation network to extract retinal regions, which can effectively eliminate interference of background regions. Next, by introducing both multi-dimensional and one-dimensional latent space, our proposed framework can then learn the spatial and contextual manifolds of normal images, which is conducive to enlarging the difference between reconstruction errors of normal images and those of abnormal ones. Furthermore, an ablation-based method is proposed to localize anomalous regions by computing the importance of feature maps, which is used to correct anomaly score calculated by reconstruction error. Finally, a novel anomaly score is constructed to separate the abnormal images from the normal ones. Extensive experiments on two retinal OCT datasets are conducted to evaluate our proposed method, and the experimental results demonstrate the effectiveness of our approach.
Performance evaluation of automotive dealerships using grouped mixture of regressions

Finite Mixture of Regressions (FMR) are among the most widely used models for dealing with heterogeneity in regression problems. FMR is a model-based clustering approach that models the data by assigning individual observations to one of the K latent regression clusters. In some applications, it is desired to cluster groups of observations together rather than individually. We present an extension to the regular FMR, that we call the Grouped Mixture of Regressions (GMR), which allows for a known group structure among observations, in addition to their possibly unknown heterogeneity. The research is motivated by a large financial longitudinal dataset from a brand of automotive dealerships across the United States. The task was to cluster the dealers based on their financial performance and also improve performance prediction for individual dealers. We derive a fast and salable algorithm for estimating the model parameters using Expectation–Maximization (EM). We also show how the group structure can improve prediction by sharing information among members of each group, as reflected in the posterior predictive density under GMR. The performance of the approach is assessed using both synthetic data as well as the dealership data. Among our findings are the superior predictive performance of GMR and the ability to correctly detect the number of clusters using simple cross-validation.
Ontology-Driven Approach to Scientific and Educational Information Representation

The paper presents an approach to ontological model development for representing information accumulated by various scientific and educational organizations. The process of such organizations functioning is associated with accumulation of a large amount of information, which in turn is used in the process of assessing the quality of their functioning. It is proposed to provide structuring and systematization of such information on the basis of ontological approach, for its further analysis and processing. The general ontology of scientific and educational organizations information identifies several interrelated components, each of which provides a description of the processes occurring in the organization, information accumulated by the organization in the process of its functioning, as well as information on quality assessment of organization. The elements of all the components of general ontology are described. The results of proposed ontology development using the ontological solutions development platform TEDAOS are given.
Self-supervised Learning for Foreground Segmentation with a Few Amount of Labeled Images Using Transformers

Foreground segmentation is a challenging task in computer vision due to the lack of labeled data. Unlike other computer vision tasks, foreground segmentation requires per-pixel labeling, which takes much effort and time. However, recent deep learning models require a large-scale labeled dataset to achieve high accuracy. In this paper, we present a novel self-supervised learning technique that allows a deep learning model to learn robust features and adaptable object representation from the unlabeled dataset and transfer the knowledge to another dataset with a few amount of labeled images. The proposed work is evaluated using two benchmark datasets, namely DAVIS and SegTrack. The proposed method has an f-measure of 0.882 and 0.907 for DAVIS and SegTrack, which is higher than existing deep learning models by 1.95 $$\%$$ on average.
Overview of Gradient Descent Algorithms: Application to Railway Regularity

Optimization is an important branch which aims to conceptualize, analyze, and solve problems of minimization or maximization of a function on a specific dataset. Several optimization algorithms are discussed in machine learning and particularly in deep learning (DL) based systems such as the Gradient Descent (GD) algorithm. Given the importance and the efficiency of the gradient descent algorithm, several research works have made it possible to optimize it and demonstrate its performance, Otherwise, regularity of a train is essential to ensure the continuity of the entire rail system. Non-regularity can spread quickly and influence the rest of the means of transport: rail, road, air, navy etc. In this paper, we perform a comparative study of different optimizations algorithms which are largely used in context of machine learning on the prediction of the regularity of trains, the data used is publicly available. The optimization algorithms studied are Momentum, Adagrad, RMSprop Adam and Adamax. In our context, the overall experimental results obtained show that RMSprop performed better compared to other optimization techniques, while Momentum represents the lowest performances to improve regularity.
The Role of Strategic Entrepreneurship Requirements in Promoting Competitive Advantage at Al-Balqa Applied University

This study aims to identify the role of strategic Entrepreneurship requirements and its various dimensions (entrepreneurial leadership, structural flexibility, entrepreneurial culture, and strategic resource management) in promoting the competitive advantage, including its dimensions: (expanding educational scope, scientific research, mastery of academic performance, benchmarking) at the University Applied Balqa (henceforth BAU). The study used the descriptive and analytical approach. A questionnaire was administered as a study tool and applied this questionnaire to the study community. The total number of the faculty members at BAU was 1560. The sample was selected randomly (269) faculty members. The results showed that there was an impact of the role of the requirements of applying strategic Entrepreneurship on promoting the competitive advantage in BAU. The study concluded with some recommendations.
Impact of COVID-19 on Higher Education: Case Study of a Moroccan Engineering School

Due to the COVID-19 pandemic, the school of information science (ESI) has transitioned to online learning since March 2020. Courses have been delivered using the Moodle LMS and the MS teams tool. Moodle is used to manage learning content, and MS Teams to communicate with students in synchronous mode. This paper focuses on a particular course: “Distributed Systems”, intended for third-year students. We conducted mixed-method research to collect both qualitative and quantitative data two weeks after the course start. We used an anonymous survey to gather students’ feedback on the learning scenario adopted for this course. We proposed twenty-two questions: eighteen closed-ended and four open-ended. The open-ended questions invite students to express all the problems and bad feelings they must struggle in the full distance learning model. Forty-one students answered the survey published on the Moodle platform. Results showed that the MS Teams tool is seen as an important learning media by students. Students also appreciate the quality of learning resources and the individual teacher follow-up provided by distance learning. We also deduced some insights concerning the most adapted activity for learning and assessment. Unfortunately, most students miss the classroom atmosphere and outlined it as the main disadvantage of distance learning. They are also facing many issues to continue their education remotely, with a gender disparity.