Reviewerly/exHarmony huggingface dataset:
Pronouns in the Workplace: Developing Sociotechnical Systems for Digitally Mediated Gender Expression

Sharing personal pronouns (such as they/them, she/her, or he/him) in the workplace helps colleagues respectfully address each other. Many companies currently seek to design and implement new software tools to facilitate pronoun-sharing among employees. This paper analyzes the social processes of communication about pronouns in the workplace and identifies best practices for creating and using workplace pronoun-sharing software. We conducted 78 semi-structured qualitative interviews with various stakeholders involved in the launch of pronoun-sharing tools in workplace collaboration software, including transgender and queer people, HR and IT professionals, and LGBTQ advocacy organizations. We used an anthropological approach to qualitatively analyze interview materials and notes from interactions with research participants. We find that sharing personal pronouns is an ongoing communication process rather than a single act of information provision. Pronoun sharing tools encapsulate the tension between dynamic social processes of self-expression and technical systems of classification and information retrieval. People communicate their pronouns differently as they navigate identity expression across social contexts. Sharing pronouns is therefore both an individual expression of self-presentation and a complex act of social communication. Developers must create new methods for building pronoun-sharing tools that equip people to control ongoing social processes of self-expression instead of using an information retrieval approach that treats pronouns merely as stable, unchanging data.
Slowing it Down: Towards Facilitating Interpersonal Mindfulness in Online Polarizing Conversations Over Social Media

Discussions about polarizing topics are essential to have, yet they can easily become hostile, aggressive, or distressing on current social media platforms. Content moderation interventions aim to mitigate this issue, though such approaches are reactionary, removing harmful content only after it has been posted. We conducted a mixed-methods experiment with 40 participants to investigate how a design friction that manipulates the temporal flow during a contentious conversation can foster interpersonal mindfulness, a trait critical for productive communication. Dyads were randomly assigned into the Control Group which received no intervention, and the Experiment Group where participants were limited to sending one message per two-minute interval. Triangulating quantitative and qualitative data from conversation logs, questionnaires, interviews, and computational text analysis, our findings revealed a two-fold effect: Experiment Group participants felt simultaneously frustrated by the intervention as it disrupted the pacing of their conversation and interfered with rapport-building, and appreciative of the intervention as it nudged them towards writing thoughtful and task-focused messages. We discuss implications of these findings for future investigation into the design of temporal interventions to influence interpersonal mindfulness during polarizing online conversations.
Video-to-Text Summarization using Natural Language Processing

Video summarization aims to produce a high-quality text-based summary of videos so that it can convey all the important information or the zest of the videos to users. The process of video summarization involves the conversion of video files to audio files, which are then converted to text files. This entire process is accompanied by the use of transformer architecture of Natural Language Processing. Although a lot of studies have been carried out for text summarization, we present our model, an extractive-video-summarizer, that utilizes state-of-the-art pre-trained ML models and open-source libraries at its core. The extractive-video-summarizer uses the following regime(I) Preparation of a multidisciplinary dataset of videos, (II) Extraction of audios from video files, (III)Text generation from audio files, (IV) Text summarization using extractive summarizers, (V)Entity extraction. We conducted our research primarily on two widely used languages in India - Hindi and English. To conclude, our model performs significantly well and generates tags for videos appropriately.
Adaptive cooperative coevolutionary differential evolution for parallel feature selection in high-dimensional datasets

Abstract In many fields, it is a common practice to collect large amounts of data characterized by a high number of features. These datasets are at the core of modern applications of supervised machine learning, where the goal is to create an automatic classifier for newly presented data. However, it is well known that the presence of irrelevant features in a dataset can make the learning phase harder and, most importantly, can lead to suboptimal classifiers. Consequently, it is becoming increasingly important to be able to select the right subset of features. Traditionally, optimization metaheuristics have been used with success in the task of feature selection. However, many of the approaches presented in the literature are not applicable to datasets with thousands of features because of the poor scalability of optimization algorithms. In this article, we address the problem using a cooperative coevolutionary approach based on differential evolution. In the proposed algorithm, parallelized for execution on shared-memory architectures, a suitable strategy for reducing the dimensionality of the search space and adjusting the population size during the optimization results in significant performance improvements. A numerical investigation on some high-dimensional and medium-dimensional datasets shows that, in most cases, the proposed approach can achieve higher classification performance than other state-of-the-art methods.
Online Automatic English Translation Based on Mobile Edge Computing

The English translation is an important means of converting language as the basis of communication, is gradually becoming an indispensable part of people's daily life.With the rapid progress of science, cultural exchanges between countries in the world have become more and more common.And the advent of the Internet era has led to more frequent exchanges between different languages, and the demand for language translation has gradually increased.How to translate English quickly and accurately is the current challenge of machine translation tasks.In this paper, we investigate how to use the neural network model for the English translation in order to better assist the establishment of the machine translation system.We design an English translation method based on an encoder-decoder structure and an attention mechanism.First, we analyze the characteristics of the LSTM model.Second, we design an English translation framework using the seq2seq model.Third, we combine the attention mechanism to build a more robust translation model to improve the translation performance of the model.Finally, we validate the translation performance of our model on two public datasets and experimental results prove that the method proposed in this paper has good evaluation performance.
Lifelong Learning Architecture of Video Surveillance System

In order to decrease the false negatives and false positives, authors in [8] suggested an unsupervised domain adaptation technique for a single-stage object detector employing weak self-training and adversarial regularization.Moreover, authors in [9] studied a theoretical framework and architecture for designing lifelong learning.Proper training sources from false alarms and/or missed cases can overcome this limitation.However, they are infrequently accessible because of privacy legal problems for personal individual information, and unpredictability of new incoming data, as depicted in Fig. 1.Moreover, trying to update new data to the existing model requires modification of its weights, so that it contradicts the prior under-
Noise Removal Technique for Document Images

The documents may contain noise at the time of scanning, transmission or during converting into it in digital form.The noise may be categorized after finding the features and may find the same patterns in the document image in order to obtain the accurate method for their elimination.The removal of noise from the document image is an important phase for the development of high-quality Optical Character Recognition (OCR) system.To make this possible, this paper presents a robust noise removal method for the noised natural document images.As per proposed algorithm, the connected black pixels are identified and determined the number of pixels they contained.Then from the collected data, density of the black pixels below which all the connected pixels represent noise is calculated.Those pixels having value less then calculated values are removed.The results of proposed approach were tested against Weiner, Median, and Average filters.The value of standard performance measures such as PSNR, F-measure and NRM are 22.77 dB, 82.36 and 1.09 for test image1, 22.46 dB, 96.63% and 1.409 for test image2, 22.79 dB, 96.22% and 0.7927 for test image3, 24.01 dB, 96.63% and 0.3739 for test image4, 24.64 dB, 97.7949, and .4833for test image5.The obtained results of proposed approach are better as compare to average, Weiner and Median filters.
A geometric framework for outlier detection in high‐dimensional data

Abstract Outlier or anomaly detection is an important task in data analysis. We discuss the problem from a geometrical perspective and provide a framework which exploits the metric structure of a data set. Our approach rests on the manifold assumption , that is, that the observed, nominally high‐dimensional data lie on a much lower dimensional manifold and that this intrinsic structure can be inferred with manifold learning methods. We show that exploiting this structure significantly improves the detection of outlying observations in high dimensional data. We also suggest a novel, mathematically precise and widely applicable distinction between distributional and structural outliers based on the geometry and topology of the data manifold that clarifies conceptual ambiguities prevalent throughout the literature. Our experiments focus on functional data as one class of structured high‐dimensional data, but the framework we propose is completely general and we include image and graph data applications. Our results show that the outlier structure of high‐dimensional and non‐tabular data can be detected and visualized using manifold learning methods and quantified using standard outlier scoring methods applied to the manifold embedding vectors. This article is categorized under: Technologies &gt; Structure Discovery and Clustering Fundamental Concepts of Data and Knowledge &gt; Data Concepts Technologies &gt; Visualization
Design and Optimization of Blockchain-Based Distributed Data-Sharing System for Urban Rail Transit

With the wide application of data-driven intelligence technology, the management efficiency and quality of urban rail transit improve considerably. However, due to data privacy and security protection, a large amount of data is still scattered among operators or rail transit departments in the form of data islands. The data barriers between urban rail departments severely hinder the intelligent development of urban rail transit systems. In this study, we design a distributed data-sharing system based on blockchain for urban rail transit system. Data production node in urban rail department, such as servers in data centers, wayside equipment, and onboard equipment, can share their data in a distributed way. Blockchain provides audit and check functions to guarantee data-sharing security. We explore the data-sharing incentive mechanism which has an impact on sharing willingness of data production nodes. To maximize the individual utility of nodes, we formulate an evolutionary game model for data nodes with bounded rationality to adapt their sharing strategies. The uniqueness and stability of the equilibrium of the game are also analyzed theoretically. Extensive experiments are conducted and illustrated that data production nodes can share their data efficiently and safely in our proposed data-sharing system. Our proposed evolutionary game model can determine the most effective data-sharing incentive mechanism.
Trigger or Treat: Using Technology to Facilitate the Perception of Cravings and Corresponding Cues for Achieving Clinical-friendly Drug Psychotherapy

Drug addiction is a chronic condition, marked by compulsive drug use. In previous research, cue exposure and biofeedback technologies proved effective in drug psychotherapy sessions; however, the focus has generally been on the awareness of cravings and the identification of cues. There has been relatively little research on methods aimed at facilitating therapist-patient communication, particularly from a user-centered perspective. In this paper, we describe a qualitative technology probe study exploring the means by which patients identify cues and perceive cravings as well as the way that they communicate with therapists. Our analysis considers the difficulties in cue identification and craving perception, the interactions between the two, and the means by which these characteristics could impact the design of VR support systems in the future.
Vizdat: A Technology Probe to Understand the Space of Discussion Around Data Visualization on Reddit

Visualizations play a considerable role in explaining trends or providing evidence when consuming data online. Whether those visualizations are shared on news outlets or social networks, platforms usually allow readers to discuss their stories in comments sections. For the scope of this work, we studied the online communityr/dataisbeautiful on Reddit. We found that chart creators were using a variety of authoring tools to share their content. Readers of these posts,commenters, used text mainly to discuss and critique the visual content. We noticed a need for a richer mode of communication that would show instead of telling authors what to do. Based on our findings, we introducedVizdat, a lightweight tool and extension to allow users to visualize and reproduce charts in the comments sections of data stories. We usedVizdat as atechnology probe with 11 Reddit users to create data visualization and discuss charts onr/dataisbeautiful. During the four-week field deployment period, we observed howVizdat was used and interviewed the participants. We found thatcommenters saw value in accessing the visualization specifications throughVizdat and used those charts to structure their replies with richer modalities. As a result, visualizationauthors appreciated the feedback and less toxic discussion provided through comments embedded with modified versions of their charts. In our paper, we share these findings and other insights to understand the dynamics of forum discussion around charts.
Two Threshold Smart Irrigation System for Increasing Crop Yield

The objective of this research is to create an automated irrigation system using two threshold method for rice farming in the Jorolot irrigation area, Cimahi City, West Java Province, Indonesia, which addresses the problem of fluctuating water supply. This issue includes water deficits during the dry season, which threatens rice growth, and excess water during the rainy season, leading to flooding and crop destruction. The method used includes one-dimensional hydraulic modeling of irrigation channels to determine two threshold of channel water level elevation, as well as water availability quantification based on flood, normal, and dry clusters. Furthermore, the system's design involved utilizing the Decision Supporting System (DSS) data communication to monitor and respond to dry or flood events in real time, thereby increasing the system's overall performance. The Jorolot irrigation area, which covers 15.5 hectares, sources its water from the Jorolot Dam, with a primary channel length of 1135 m and a secondary channel length of 536 m. The results showed that the water level in the primary canal for normal conditions is 0.1 m&lt;Ynp&lt;0.6 m. Additionally, the proposed system, which allows for real-time water supply monitoring, can increase rice production from two to three harvests per year.
Discovering the Hidden Facts of User-Dispatcher Interactions via Text-based Reporting Systems for Community Safety

Recently, an increasing number of safety organizations in the U.S. have incorporated text-based risk reporting systems to respond to safety incident reports from their community members. To gain a better understanding of the interaction between community members and dispatchers using text-based risk reporting systems, this study conducts a system log analysis ofLiveSafe, a community safety reporting system, to provide empirical evidence of the conversational patterns between users and dispatchers using both quantitative and qualitative methods. We created an ontology to capture information (e.g., location, attacker, target, weapon, start-time, and end-time, etc.) that dispatchers often collected from users regarding their incident tips. Applying the proposed ontology, we found that dispatchers often asked users for different information across varied event types (e.g.,Attacker forAbuse andAttack events,Target forHarassment events). Additionally, using emotion detection and regression analysis, we found an inconsistency in dispatchers' emotional support and responsiveness to users' messages between different organizations and between incident categories. The results also showed that users had a higher response rate and responded quicker when dispatchers provided emotional support. These novel findings brought significant insights to both practitioners and system designers, e.g., AI-based solutions to augment human agents' skills for improved service quality.
PERSPECTIVES OF INDONESIAN UNDERGRADUATE STUDENTS ON READING PROFICIENCY AND COMMUNICATIVE COMPETENCE

The transformation of the world has brought communication into a complex system. This research focused on understanding communication from reading proficiency and communicative competence perspectives. It is aimed to find a correlation between reading proficiency and communicative competence in English as L2. The quantitative method with the correlational study was employed to analyze the data collected. The study was conducted at a private university in Yogyakarta. Thirty students participated in this research. The study employed a survey design using a questionnaire to collect the data. The result indicated a strong connection between reading proficiency and communicative competence. It implies that reading proficiency and communicative competence are connected.Â
Interpreting mereotopological connection

This paper examines ten possible topological interpretations of connection and for each interpretation, identifies sufficient conditions under which a significant class of topological spaces provides models of General Extensional Mereotopology with Closure Conditions (GEMTC) in which some key mereotopological ideas align with their topological analogues. In particular, there is an interpretation under which the non-empty sets of any symmetric topology are a model of GEMTC with alignment between the mereotopological and topological definitions of (self-)connection, open and closed entities, interior, exterior, closure, and boundary.
Advocacy as Access Work: How People with Visual Impairments Gain Access to Digital Banking in India

Research in accessibility and assistive technology often assumes that technology is within easy reach, that is, people with disabilities are able to obtain technologies so long as they are accessible. As a result, less is understood about the challenges that people with disabilities face in obtaining technology in the first place and how they work around these challenges. We reduce this gap by examining the technology access challenges of people with visual impairments in India in the context of digital banking. Through a qualitative study consisting of 30 interviews, we find that participants routinely encountered social and technical challenges that made it difficult to access and use digital banking. To address these challenges, people with visual impairments engaged in advocacy work which consisted of five dimensions: 1) creating awareness, 2) demonstrating competence, 3) escalation, 4) gathering support, and 5) seeking sighted help. We expand on the idea of advocacy as a form of access work performed by people with visual impairments to secure and maintain access to digital banking.
"I Go Beyond and Beyond" Examining the Invisible Work of Home Health Aides

Home health aides are paid professionals who provide long-term care to an expanding population of adults who need it. However, aides' work is often unrecognized by the broader caregiving team despite being in demand and crucial to care---an invisibility reinforced by ill-suited technological tools. In order to understand the invisible work aides perform and its relationship to technology design, we interviewed 13 aides employed by home care agencies in New York City. These aides shared examples that demonstrated the intertwined nature of both types of invisible work (i.e., emotions- and systems-based) and expanded the sociological mechanisms of invisibility (i.e., sociocultural, sociolegal, sociospatial) to include the sociotechnical. Through these findings, we investigate the opportunities, tensions, and challenges that could inform the design of tools created for these important, but often overlooked, frontline caregivers.
EBUNet: a fast and accurate semantic segmentation network with lightweight efficient bottleneck unit

Abstract It has been difficult to achieve a suitable balance between effectiveness and efficiency in lightweight semantic segmentation networks in recent years. The goal of this work is to present an efficient and reliable semantic segmentation method called EBUNet, which is aimed at achieving a favorable trade-off between inference speed and prediction accuracy. Initially, we develop an Efficient Bottleneck Unit (EBU) that employs depth-wise convolution and depth-wise dilated convolution to obtain adequate features with moderate computation costs. Then, we developed a novel Image Partition Attention Module (IPAM), which divides feature maps into subregions and generates attention weights based on them. As a third step, we developed a novel lightweight attention decoder with which to retrieve spatial information effectively. Extensive experiments show that our EBUNet achieves 73.4% mIou and 152 FPS on the Cityscapes dataset and 72.2% mIoU and 147 FPS on the Camvid dataset with only 1.57 M parameters. The results of the experiment confirm that the proposed model is capable of making a decent trade-off in terms of accuracy, inference, and model size. The source code of our EBUNet is available at ( https://github.com/Skybird1101/EBUNet ).