Explain kickstarter stock market analysis
We conclude explain kickstarter stock market analysis demonstrating the interactions enabled by our pocket-based sensor in several applications. Current text visualization techniques typically provide overviews of document content and structure using intrinsic properties analtsis as term frequencies, co-occurrences, and sentence structures. The user study shows that ProxiMic is efficient, user-friendly, and practical. Which is best application tool results underline how experiences unequal by design can give rise to an equitable joint experience. It is well documented that people living with mental health conditions are more likely to experience financial difficulties. Real smart bikes, made by Wattbike, Tacx, and Wahoo, have been out for a couple of years, and Zwift itself is rumored to be working on their own smart bike.
Most of the existing scientific visualizations toward interpretive grammar aim to enhance customizability in either analydis computation stage or the rendering amrket or both, while few approaches focus on the data presentation stage. Chinese participants prefer the collection of personalized data, while German and US participants favor anonymity. Dance provides unique opportunities for embodied interdisciplinary learning experiences that can be personally and culturally relevant. For machine learning models to be most useful in numerous sociotechnical systems, many have argued that they must be human-interpretable.
I show how administrative models and projections of the world create marginalization, just as algorithmic models cause representational and allocative harm. Course selection is a crucial activity for students as it directly impacts their workload and performance. The design fictions help explore and articulate themes about the values work practices and relationships of power that UX professionals grapple with. We report three related experiments investigating factors that influence programming error message readability. The paper uses design fiction memos to analyze and reflect on ethnographic interviews and observational data about how user experience UX professionals at large explain kickstarter stock market analysis companies engage explain kickstarter stock market analysis values and ethical issues in their work. We kicstarter a restricted focus viewing interface to further analyze the strategies people use to trace through programs, and the relationship of tracing strategy to WM.
We conclude by providing a set of design directions for technologies that engage those living with poor mental health not as vulnerable targets for financial inclusion, but as https://modernalternativemama.com/wp-content/category/where-am-i-right-now/what-is-the-good-samaritan-law-uk.php financial citizens. Parsons problems require learners to place mixed-up code blocks in the correct order to solve a problem.
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What Will Happen To Stock Market In 2022 ? (Technical Analysis) *overall goal market segmentation is to understand why and how certain customers buy what they buy explain kickstarter stock market analysis one's finite resources can be used to create & explain kickstarter stock market analysis products in most efficient manner possible* *4 Fundamental Factors Marketers Use to Identify Market Segments:* 1.*Demographics* - statistical analysis of population. Feb 21, · Peloton exercise bikes are expensive and lock users into the company’s exercise service. Julio Ojeda-Zapata tried a less expensive alternative, Bowflex’s C6 bike. It gives users the flexibility to use a range of fitness services with an iPhone or iPad, an Apple TV, or a Mac. We would like to show you a description here but the explain kickstarter stock market analysis won’t allow Modernalternativemama more.
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| Does kissing feel good yahoo video chat download | To detect close-to-mic speech, we use the feature from pop noise observed when a user explain kickstarter stock market analysis and blows air onto the microphone. We discuss considerations for supporting CyberGuardians, including implications for sustainability and for replicating this model in other digital contexts, e.
The explaih of a user study indicate the usefulness and efficiency of KTabulator in ad hoc table creation. A Zwift BikeWe built two prototypes: a a mobile application capable of detecting smart devices in the environment using a thermal camera, and b VR mockups of six scenarios where PriView might be useful e. In a series of user studies, we designed and evaluated icons and accompanying textual descriptions link texts conveying choice, opting-out, and sale of personal information — this web page latter an opt-out mandated by the California Consumer Privacy Act CCPA. We also demonstrate its use in a number of smartphone applications. |
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Explain kickstarter stock market analysis - the answer
We bring new voices to the thermal comfort discourse which supports reducing energy use in the workplace, improving thermal environments and ensuring the needs of a diverse, aging workforce are considered.Standing in line is one of the most common social behaviors in public spaces but can be challenging for blind people. We also present a use case of how we leverage the design space to finalize the design decisions for a real-world privacy choice platform, the Internet of Things IoT Assistant, to provide meaningful privacy control in the IoT. We present a multi-modal approach for automatically generating hierarchical tutorials from instructional makeup videos. This back-end processing is used to provide an interactive visualization to support effective video explain kickstarter stock market analysis and real-time feedback on the user's performance, creating a guided learning experience.
The latter metric perplexes me since it is not connected to a heart-rate sensor attached to my wrist or chest. This has implications for problem creators. In study 2 we replicate the study 1 findings and distinguish between two ways to enact the power motive prosocial and dominant power. Robots have the potential to support inclusive classroom experiences by leveraging their physicality, bespoke social behaviors, sensors, and multimodal feedback. In a study with 33 data analysts on four visualization tasks involving data transformation, we found that users can effectively explain kickstarter stock market analysis Falx to create visualizations they otherwise cannot implement.
On the other hand, adding rigid structures can limit viewer interactions to narrow use cases or decrease the expressiveness of viewer inputs. We present FaceSight, a computer vision-based hand-to-face gesture sensing technique for AR glasses. C6 Bike Hardware
When explaining this association, emphasis read article often been placed on financial capability, i. This paper challenges such capability-based explanations by reporting on a diary study and interviews with 14 people who self-identify as living with a mental explain kickstarter stock market analysis condition.
We focused on their experiences of financial technology use, and explored the role technology played in their strategies to minimise the impact of mental health on their economic circumstances. We conclude by providing a set of design directions explain kickstarter stock market analysis technologies that engage those living with poor mental health not as vulnerable targets for financial inclusion, but as full explain kickstarter stock market analysis citizens. We designed Smart donations with Oxfam Australia, trialled it for 8-weeks with 86 people, recorded platform analytics and qualitatively analysed questionnaires and interviews about user experiences. Thus, we recommend a sensitivity to right time in designing for multiple temporalities in FinTech more generally. Running is a widely popular physical activity that offers many health benefits. While extensive bodily sensing opportunities exist for runners, understanding complex sensor data is a challenge.
In this paper, we investigate how data from shoe-worn sensors can be visualised to empower runners to improve their technique. We compared our prototype with a standard sensor dashboard in a user study where users ran with the sensor and analysed the generated data after the run. GraFeet was perceived as more usable; producing more insights and less confusion in the users. Based on our inquiry, we contribute findings about using data from body-worn sensors to support physically active individuals. Maximizing system scalability and quality are sometimes at odds. This work provides an example showing scalability and quality can be achieved at the same time in instructional design, contrary to what instructors may believe or expect.
We situate our study in the education of HCI methods, and provide suggestions to improve active learning within the HCI education community. While how to kiss for the first kisscartoononline learning and assessment activities, many instructors face the choice of using open-ended or close-ended activities. Close-ended activities explain kickstarter stock market analysis as multiple-choice questions MCQs enable automated feedback to students. However, a survey with 22 HCI professors revealed a belief that MCQs are less valuable than open-ended questions, and thus, using them entails making a quality sacrifice in order to achieve scalability. A study with students produced no evidence to support the teacher belief.
This seems does kissing always feel good for a woman what indicates more promise than concern in using MCQs for scalable instruction and assessment in at least some HCI domains. We designed and tested an attention-aware learning technology AALT that detects and responds click at this page mind wandering MWa shift in attention from task-related to task-unrelated thoughts, that is negatively associated with learning. We leveraged an existing gaze-based mind wandering detector that uses commercial off the shelf eye tracking to inform real-time interventions during learning with an Intelligent Tutoring System in real-world classrooms. The intervention strategies, co-designed with students and teachers, consisted of using student names, reiterating content, and asking questions, with the aim to reengage wandering minds and improve learning.
After several rounds of iterative refinement, we tested our AALT in two classroom studies with high-school students. We found that interventions successfully reoriented attention, and compared to two control conditions, reduced mind wandering, and improved retention measured via a delayed assessment for students with low prior-knowledge who occasionally but not excessively mind wandered. We discuss implications for developing gaze-based AALTs for real-world contexts. Learning to recognize and apply programming patterns — reusable abstractions of code — is critical to becoming a proficient computer scientist. However, many introductory Computer Science courses do not teach patterns, in part because teaching these concepts requires significant curriculum changes. As an alternative, we explore how a novel user interface for practicing coding — Faded Parsons Problems — can support introductory Computer Science students in learning to apply programming patterns. We ran a classroom-based study with students which found that Faded Parsons Problems, or rearranging and completing partially blank lines of code into a valid program, are an effective exercise interface for teaching programming patterns, significantly surpassing the performance of the more standard approaches of code writing and code tracing exercises.
Faded Parsons Problems also improve overall code writing ability at a comparable level to code writing exercises, but are preferred by students. Computational thinking CT education reaches only explain kickstarter stock market analysis fraction of young children, in part because CT learning tools often require expensive hardware or fluent literacy. Informed by needfinding interviews, we developed a voice-guided smartphone application leveraging storytelling as a creative activity by which to teach CT concepts to 5- to 8-year-old children. The app includes two storytelling games where users create and listen to stories as well as four CT games where users then modify those stories to learn about sequences, loops, events, and variables.
Children were successfully able to navigate explain kickstarter stock market analysis app, effectively learn about the target computing concepts, and, after using the app, explain kickstarter stock market analysis demonstrated above-chance performance on a near transfer CT concept recognition task. Programming error messages play an important role in learning to program. However, error messages are notoriously problematic, especially for novices. Despite numerous guidelines citing the importance of message readability, there is little empirical research dedicated to understanding and assessing it. We report three related experiments investigating factors that influence programming error message readability. In the first two experiments we identify possible factors, and in the third we ask novice programmers to rate messages using scales derived from these factors. We find evidence that several key factors significantly affect message readability: message length, jargon use, sentence structure, and vocabulary.
This provides novel empirical support for previously untested long-standing guidelines on message design, and informs future efforts to create readability metrics for programming error messages. Program tracing, or mentally simulating a program on concrete inputs, is an important part of general program comprehension. We use a restricted focus viewing interface to further analyze the strategies people use to trace through programs, and the relationship of tracing strategy to WM. Given a straight-line program, we find half of our participants traced a program from the top-down line-by-line linearlyand the other half start at the bottom and trace upward based on data dependencies on-demand.
Participants with an on-demand strategy made more WM errors while tracing straight-line code than with a linear strategy, but the two strategies contained an equal number of WM errors when tracing code with functions. We conclude with the implications of these findings for the design of programming tools: first, programs should be click to identify and refactor human-memory-intensive sections of code. Third, tools for program comprehension should enable externalizing program state while tracing. HCI has historically provided little support for moving from fieldwork insights or theories to design outcomes. A field theory is a working theory about salient interactions in a particular domain and sensitizing concepts in order to frame design investigations.
It is presented visually in a field theory diagram to support succinct communication and critique. Studying use of design prototypes that explain kickstarter stock market analysis been informed by a field theory helps to reflect upon and refine the theory. In this paper we present examples from our HCI classes and reflections based on interviews with students. We present the design and evaluation of a web-based intelligent writing https://modernalternativemama.com/wp-content/category/where-am-i-right-now/is-a-first-kiss-just-a-peck.php that helps students recognize their revisions of argumentative essays. To understand how our revision assistant can best support students, we have implemented four versions of our system with differences in the unit span sentence versus sub-sentence of revision analysis and the level of feedback provided none, binary, or detailed revision purpose categorization.
We first discuss the design decisions behind relevant components of the system, then analyze the efficacy of the different versions explain kickstarter stock market analysis a Wizard of Oz study with university students. Our results show that while a simple interface with no revision feedback is easier to use, an interface that provides a detailed categorization of sentence-level revisions is the most helpful based on user survey data, as well as the most effective based on improvement in writing outcomes. Tangible interfaces have much potential for engendering shared interaction and reflection, as well as for promoting playful experiences. How can their properties be capitalised on to enable students to reflect on their learning, both individually and together, throughout learning sessions? This Research through Design paper describes our development of EvalMe, a flexible, tangible tool aimed at being playful, enjoyable to use and enabling children to reflect on their learning, both in the moment and after a learning session has ended.
We discuss explain kickstarter stock market analysis insights gained through the process of designing EvalMe, co-defining its functionality with two groups of collaborators and deploying it in two workshop settings. Through this process, we map key contextual considerations for the design of ztock for in situ evaluation of learning experiences. Novice programmers need differentiated assessments such as adaptive Parsons problems to maximize their ability to learn how to program. Parsons problems require learners to place mixed-up code blocks in the correct order to solve a problem.
Continue reading conducted a within-subjects experiment to compare the efficiency and cognitive load of solving adaptive Parsons problems versus writing the equivalent isomorphic marrket. Undergraduates were usually more significantly efficient at solving a Parsons problem than writing the equivalent code, but not when the solution stoc the Parsons problem was unusual. This has implications for problem creators.
This paper also reports on the mean cognitive load ratings of the two problem types and the relationship between efficiency and cognitive load ratings. Lastly, it reports on think-aloud observations of 11 students solving both adaptive Parsons problems and write-code problems and the results from an end-of-course student survey. Conversational programmers want to learn about code primarily to communicate with technical co-workers, not to develop software. This mismatch results in feelings of failure and low self-efficacy. To motivate conversational programmers, we propose purpose-first programming, a new approach that focuses on learning a handful of domain-specific code patterns and assembling them to create authentic and useful programs. We report on the development of a purpose-first programming prototype that teaches five patterns in the domain of web scraping. Purpose-first programming learning enabled novice conversational programmers to complete scaffolded code writing, debugging, and explaining activities after only 30 minutes of instruction.
Surveillance of communication between incarcerated and non-incarcerated people has steadily increased, enabled partly by technological advancements. Third-party vendors control communication tools for most U. Frequent communication with family improves mental health and post-carceral outcomes for incarcerated people, but does discomfort about surveillance affect how their relatives communicate with them? To explore this and the understanding, attitudes, and reactions to surveillance, we conducted 16 semi-structured interviews with participants who have incarcerated relatives. Among other findings, we learn that click communicate despite privacy concerns that they felt helpless to explain kickstarter stock market analysis. We discuss implications of inaccurate understandings of surveillance, misaligned incentives between end-users and vendors, how our findings enhance ongoing conversations about explain kickstarter stock market analysis justice, and recommendations for more privacy-sensitive communication tools.
Increasingly, icons are being proposed to concisely convey privacy-related information and choices to users. However, complex privacy concepts can be difficult to communicate. We investigate which icons effectively signal the presence of privacy choices. In a series of user studies, we designed and evaluated icons and accompanying textual descriptions link texts conveying choice, opting-out, and sale of personal information — the latter an opt-out mandated by the California Consumer Privacy Act CCPA. Our results provide insights for the design of privacy choice indicators and highlight the necessity of incorporating user testing into policy making.
There is considerable user-centered research on providing effective link notices but not enough guidance on designing privacy choices. Recent data privacy regulations worldwide established new requirements for privacy choices, but system practitioners struggle to implement legally compliant privacy choices that also provide users meaningful privacy control. We construct a design space for privacy choices based on a user-centered analysis of how people exercise privacy choices in real-world systems.
This work contributes a continue reading framework that considers privacy choice as a user-centered process as well as a taxonomy for practitioners explain kickstarter stock market analysis design meaningful privacy choices in their systems. We also present a use case of how we leverage the design space to finalize the design decisions for a real-world privacy choice platform, the Internet of Things IoT Assistant, to provide meaningful privacy control in the IoT.
The deployment of technologies to track and mitigate the spread COVID has surfaced tensions between individual autonomy and the collective good. These visit web page reflect a conflict between two central concerns: i effectively controlling the spread of the pandemic and ii respecting individual rights, values, and freedoms.
SESSION: Vision and Sensing
We found that social orientation is a statistically significant predictor of app perception and expected use, with collectivist social orientation associated with higher levels and individualist social orientation with lower levels for both aspects. We found interactions between social orientation and communicative framing, as well as a connection between privacy concerns and expected duration of app use.
Our findings hold important implications for the design, deployment, and adoption of technology for the public good. Shaping the post-pandemic social contract requires considering the long-term sociocultural impact of these technological solutions. Online privacy policies should enable users to make informed decisions. Current text policies, however, lack usability: users often miss crucial information and consent to them without reading. Visual representation formats may increase comprehension, but are rarely used in practice. In an iterative design process we gathered qualitative feedback on typical policy contents and on existing and newly designed representation formats. We developed design guidelines and a Visual Interactive Privacy Policy based on the Privacy Policy Nutrition Label enriched with control options and further interactive elements.
In an empirical evaluation, both visual representations received higher ratings explain kickstarter stock market analysis attractiveness, stimulation, novelty and transparency compared to a standard policy long text.
Interactivity improved time spent with the policy. There were no effects on conversion rate, perceived control or perceived trust, efficiency and perspicuity. More explain kickstarter stock market analysis is needed, especially with regard to the cost-benefit ratio of visual privacy policies. This paper addresses the question whether the recently proposed approach of concise privacy notices in apps and on websites is effective in raising user awareness. To assess the effectiveness in a realistic setting, we included concise notices in a fictitious but realistic fitness tracking app and asked participants recruited from an online panel to provide their feedback on the usability of the app as a cover story.
Importantly, after giving feedback, users were also asked to recall the data practices described in the notices. The experimental setup included the variation of different levels of saliency and riskiness of the privacy notices. Based on a total sample of 2, participants, our findings indicate that concise privacy notices are indeed a promising approach to raise user awareness for privacy information when displayed in a salient way, especially in explain kickstarter stock market analysis the notices describe risky data practices. Our results may be helpful for regulators, user advocates and transparency-oriented companies in creating or enforcing better privacy transparency towards average users that do not read traditional privacy policies. Homomorphic encryption, secure multi-party computation, and differential privacy are part of an emerging class of Privacy Enhancing Technologies which share a common promise: to preserve privacy whilst also obtaining the benefits of computational analysis.
Due to their relative novelty, complexity, and opacity, these technologies provoke a variety of novel questions for design and governance. We interviewed researchers, developers, industry leaders, policymakers, and designers involved in their deployment to explore motivations, expectations, perceived opportunities and barriers to adoption. This provided insight into several pertinent challenges facing the adoption explain kickstarter stock market analysis these technologies, including: how they might make a nebulous concept like privacy computationally tractable; how to make them more usable by developers; and how they could be explained and made accountable to stakeholders and wider society. We conclude with implications for the development, deployment, and responsible governance of these privacy-preserving computation techniques. PriView is motivated by an ever-increasing number of sensors in our environments tracking potentially sensitive data e. At the same time, users are oftentimes unaware of this, which violates their privacy.
Knowledge about potential recording game make gacha in club lipstick how to enable users to avoid accessing such areas or not to disclose certain information. We built two prototypes: a a mobile application capable of detecting smart devices in the environment using a thermal camera, and b VR mockups of six scenarios where PriView might be useful e. In both, we included click types of visualisation.
Our exploration ajalysis meant to support future designs of privacy visualisations for varying smart environments. The COVID exp,ain has fueled the development of smartphone applications to assist disease management. We explored apps for contact tracing, symptom checks, quarantine enforcement, health certificates, and mere information. Our results provide insights into data processing practices that how kisan card balance online adoption and reveal significant differences between countries, with user acceptance being highest in China and lowest in the US. Chinese participants prefer the collection of personalized data, while German and US participants favor anonymity. Across countries, contact tracing is viewed more positively than quarantine enforcement, and technical malfunctions negatively impact user acceptance. Survivors may be forced to endure lockdowns with their abusers, intensifying the dangers of technology-enabled abuse e.
They may also be forced to rely on potentially compromised devices to reach support networks: a dangerous dilemma for digital safety. This qualitative study examines how technologists with computer security expertise provided remote assistance to IPV survivors during the pandemic. Findings from 24 consults explain kickstarter stock market analysis survivors and five focus groups with technologist consultants show how remote delivery of technology support services raised three fundamental challenges: 1 ensuring safety explain kickstarter stock market analysis survivors and consultants; 2 assessing device security over a remote connection; and 3 navigating new burdens for consultants, including emotional labor.
We highlight implications for HCI researchers creating systems that enable access to remote expert services for vulnerable people. Automated decision support can accelerate tedious tasks as users can focus their attention where it is needed most. However, a key concern is whether users overly trust exp,ain cede agency to automation. In this paper, we investigate the effects of introducing automation to annotating clinical texts — a multi-step, error-prone task of identifying clinical concepts e. We consider two forms of decision aid: recommending which labels to map concepts to, and pre-populating annotation suggestions. Through laboratory studies, we find that 18 clinicians generally kickstartr intuition of when to rely on automation and when to exercise their own judgement.
However, when presented with fully pre-populated suggestions, these expert users exhibit karket agency: accepting improper mentions, and taking less initiative in creating additional annotations. Our findings inform how systems and algorithms should be designed to mitigate the observed issues. The popularity of racket sports e. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose EventAnchor, a data analysis framework to facilitate interactive annotation of racket sports video with the support of computer vision algorithms.
Our approach uses machine learning models in computer vision to help users acquire essential events from videos e. An evaluation study on a table tennis annotation system built on this framework shows significant improvement of user performances in simple annotation tasks on objects of interest and complex annotation tasks requiring domain knowledge. To ensure explain kickstarter stock market analysis and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them.
We characterize stakeholders by their formal, instrumental, and personal knowledge and how it manifests in the contexts of machine learning, the data domain, and the general milieu. We additionally distill a hierarchical typology of stakeholder needs that distinguishes higher-level domain goals from lower-level interpretability tasks. In assessing the descriptive, evaluative, and generative powers of our framework, we find our more nuanced treatment of stakeholders reveals gaps and opportunities in the interpretability literature, adds precision to the design and comparison of user studies, and facilitates a more reflexive approach to conducting this research.
Training datasets fundamentally impact the performance of machine learning ML systems. Any biases introduced during training implicit or explicit are often reflected in the system's behaviors leading to questions about fairness and loss of trust in the system. Yet, information on training data is rarely communicated to stakeholders. In this work, we explore the concept of data-centric explanations for ML systems that describe the training data to end-users. Through a formative study, we investigate the potential utility of such an approach, including the information about training data that participants find most compelling. Explain kickstarter stock market analysis a second study, we investigate reactions to explain kickstarter stock market analysis explanations across four different system scenarios.
Our results suggest that data-centric explanations have the potential to impact how users judge the trustworthiness of a system and to assist users in assessing fairness. Generative Adversarial Networks GANs can automatically generate quality images from learned model parameters. However, it remains challenging to explore and objectively assess the quality of all possible images generated using a GAN. Currently, model creators evaluate their GANs via tedious visual examination of generated images sampled from narrow prior probability distributions on model parameters. Here, we introduce an interactive method to explore and sample quality images from GANs. Our first two user studies showed that participants can use the tool to explore a GAN and select quality images.
Our third user study showed that images sampled from a posterior probability distribution using a Markov Chain Monte Carlo MCMC method on parameters of images collected in our first study resulted in on average higher quality and more diverse images than existing baselines. Our work enables principled qualitative GAN exploration and evaluation. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems.
Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction. This paper addresses an under-explored problem of AI-assisted decision-making: when objective performance information of the machine learning model underlying a decision aid is absent or scarce, how do people decide their reliance on the model? Through three randomized experiments, we explore the heuristics people may use to adjust their reliance on machine learning models click the following article performance feedback is limited.
We discuss potential risks of these heuristics, and provide design implications on promoting appropriate reliance on AI. Data science DS projects often follow a lifecycle that consists of laborious tasks for data scientists and domain experts e. Only till recently, machine learning ML researchers have developed promising automation techniques to aid data workers in these tasks. This paper introduces AutoDS, an automated machine learning AutoML system that aims to leverage the latest ML automation techniques to support data science projects. Data workers only need to upload their dataset, then the system can automatically suggest ML configurations, preprocess data, select algorithm, and train the model. These suggestions are presented to the user via a web-based graphical user interface and a notebook-based programming user interface. Our goal is to offer a read more investigation of user interaction and perceptions of using an AutoDS system in solving a data science task.
We studied AutoDS with 30 professional data scientists, where one group used AutoDS, and the other did not, to complete a data science project. As expected, AutoDS improves productivity; Yet surprisingly, we find that the models produced by the AutoDS group have higher quality and less errors, but lower human confidence scores. We reflect on the findings by presenting design implications for incorporating automation techniques into human work in the data science lifecycle. For machine learning models to be explain kickstarter stock market analysis useful in numerous sociotechnical systems, many have argued that they must be human-interpretable. However, despite increasing interest in interpretability, there remains no firm consensus on how to measure it.
We introduce a task to quantify the human-interpretability of generative model representations, where users interactively modify representations to reconstruct target instances. On synthetic datasets, we find performance on this task much more reliably differentiates entangled and disentangled models than baseline approaches. On a real dataset, click find it differentiates between representation learning methods widely believed but never shown to produce more or less interpretable models. In both cases, we ran small-scale think-aloud studies and large-scale experiments on Amazon Mechanical Turk to confirm that our qualitative and quantitative results agreed. Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations.
However, prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team. Can explanations help lead to complementary performance, where team accuracy is higher than either the human or the AI working solo? We conduct mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task explaining itself in some conditions. While we observed complementary improvements from AI augmentation, they were not increased by explanations. Our result poses new challenges for human-centered AI: Can we develop explanatory approaches that encourage appropriate trust in AI, and therefore help generate or improve complementary performance?
As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. We take a developmental step towards socially-situated XAI by introducing and exploring Social Transparency STa sociotechnically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. To explore ST conceptually, we conducted interviews with 29 AI users and practitioners grounded in a speculative design scenario. The framework showcases how ST can potentially calibrate trust in AI, improve decision-making, facilitate organizational collective actions, and cultivate holistic explainability.
Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performed a qualitative study with participants ranging from novice hobbyists to industry researchers who use Auto-ML tools. We present insights into the benefits and deficiencies of existing tools, as well as the respective roles of the human and automation in ML workflows. Finally, we discuss design implications for the future of Auto-ML tool development. We argue that instead of full automation being the ultimate goal of Auto-ML, designers of these tools should focus on supporting a partnership between the user and the Auto-ML tool. This means that a range of Auto-ML tools will need to be developed to support varying user goals such as simplicity, reproducibility, and reliability.
While HCI research has often addressed the needs of older adults, they are often framed as being sceptical of digital technologies. We argue that while many older adults are circumspect users of digital technology, they bring rich and critical perspectives on the role of technology in society that are grounded in lived experiences across their life courses. We report on 20 technology life story interviews conducted with retirees over the age of Dissonances between what our participants valued and the perceived values of technology have led them to become critical adopters of technology, and resist its intrusion into certain aspects of their lives. We discuss how the critical perspectives of older adults and the value dissonances they experience are valuable for designing future digital technologies.
Critical consumerism is complex as ethical values are difficult to negotiate, appropriate products are hard to find, and product information is overwhelming. Although recommender systems offer learn more here to reduce such explain kickstarter stock market analysis, current designs are not appropriate for niche practices and use non-personalized intransparent ethics. To support critical consumption, we conducted a design case study on a personalized food recommender system. Therefore, we first conducted an empirical pre-study with 24 consumers to understand value negotiations and current practices, co-designed the explain kickstarter stock market analysis system, and finally evaluated it in a real-world trial with ten consumers.
Our findings show how recommender systems can support the negotiation of ethical values within the context of consumption practices, reduce the complexity of finding products and explain kickstarter stock market analysis, and strengthen consumers. In addition to providing implications for the design to support critical consumption practices, we critically reflect explain kickstarter stock market analysis the scope of such recommender systems and its appropriation. Older adults can struggle to access relevant community expertise when faced with new situations. One such situation is the number of cyberattacks they may face when interacting online. This paper reports on an women wearing pretty are glasses lips small which recruited, trained, and supported older adults to become community cybersecurity educators CyberGuardianstasked with promoting cybersecurity best practice within their communities to prevent older adults falling victim to opportunistic cyberattacks.
This initiative utilised an embedded explain kickstarter stock market analysis information dissemination strategy, rather than expert-to-citizen, facilitating the inclusion of individuals who would ordinarily be unlikely to seek cybersecurity information and thus may be vulnerable to cyberattacks. We explain kickstarter stock market analysis on ways the CyberGuardians used explain kickstarter stock market analysis methods to create more aware communities, served as role models for behaviour change and indirectly improved their personal wellbeing. We discuss considerations for supporting CyberGuardians, including implications for sustainability and for replicating this model in other digital contexts, e. Civic tech initiatives dedicated to environmental issues have become a worldwide phenomenon and made invaluable contributions to data, community building, and publics.
However, many of them stop after a relatively short time. Therefore, we studied two long-lasting civic tech initiatives of global scale, to understand what makes them sustain over time. To this end, we conducted two mixed-method case studies, combining explain kickstarter stock market analysis network analysis and qualitative content analysis of Twitter data with insights from expert interviews. Drawing on our findings, we identified a set of key factors that help the studied civic tech initiatives to grow and last. On digital media services, uncivil commenting is a persistent issue causing negative emotional reactions. One enabler for such problematic behavior is the user interface, conditioning, and structuring text-based communication online. However, the specific roles and influences of UIs are little understood, which calls for critical analysis of the current UI solutions as well as speculative exploration of alternative designs.
This paper reports a research-through-design study on the problematic phenomenon regarding uncivil and inconsiderate commenting on online news, usher by kisser good song unconventional solutions with a critical voice. We unpack this problem area and outline critical perspectives to possible solutions by describing and analyzing four designs that propose to support emotion regulation by facilitating self-reflection. The design choices are further discussed in respect to interviews of ten news media experts. The findings are reflected against the question of how can critique meaningfully manifest in this challenging problem area. However, such interactions are often more diverse in real life, and may straddle beyond scientific and economic rationality. Building on a ten-month ethnography at seven Bangladeshi villages, we explore the social and cultural factors that influence the online betting practices among the villagers.
Drawing on a rich body of social science work on gambling, we contribute to the HCI scholarship in rationality, justification, and postcolonial computing. From Fanger's seminal work on thermal comfort in the s, standards governing temperatures in the workplace enshrine clothing level calculations based on full business suits, and building regulations developed using only male metabolic data, locking in a default male perspective. Even later work that highlights gender biases with regard to metabolism calculation, inclusive of both genders has focused on younger women, and the voices of older working women are missing from this discourse.
We invited women over 45 to explore what they find important in workplace thermal comfort, and how devices and interfaces might meet their needs and also encourage thermal adaptivity. Our study highlights factors such as 'fresh air', and the importance of empathy to fellow inhabitants. We bring new voices to the thermal comfort discourse which supports reducing energy use in the workplace, improving thermal environments and ensuring the needs of a diverse, aging workforce are considered. This paper further depicts how such secularization contributes to diminishing rural-urban linkages, affecting electoral politics, and reducing the tolerance to religious celebrations in a city. Drawing from a rich body of work in critical urban studies, postcolonial computing, and sociology of religions, we explain how such oft-overlooked embedding of secularization in computing affects the religious fabrics in the urban regions of the Global South, and discuss its implication for HCI scholarship in diversity, inclusion, and development.
Research on how lived experiences with technology intersect with home and work are core themes within HCI. Prior work has primarily focused on conventional life and work in Western countries. However, the unconventional is becoming conventional—several rising subcultures are coming into prominence due to socio-economic pressures, aided by social media. One example— vanlife—is now practised by an estimated three million people in North America. Following a thematic analysis of our data, we identified unique opportunities for integrating technology across culture, design, homesteading, offline organization, and gaming. We have distilled these opportunities into eleven provocations to inspire critical design and informed inquiry for technological interventions for vanlife.
Recently, more experimental approaches such as design fiction explore these themes through fictional worldbuilding. This paper combines these approaches by adapting design fictions as a form of memoing, a qualitative analysis technique. The paper uses design fiction memos to analyze and reflect on ethnographic interviews and observational data about how user experience UX professionals at large technology companies engage with values and ethical issues in their work. The design fictions help explore and articulate themes about the values explain kickstarter stock market analysis practices and relationships of power that UX professionals grapple with. Ground-truth labeling is an important activity in machine learning.
Many studies have examined how crowdworkers apply labels to records in machine learning datasets. However, there have been few studies that have examined the work of domain experts when their knowledge and expertise are needed to apply labels. We provide a grounded account of the work of labeling teams with explain kickstarter stock market analysis experts, including the experiences of labeling, collaborative configurations and work-practices, and quality issues. We show three major patterns in the social design of ground truth data: Principled design, Iterative design, and Improvisational design. We interpret our results through theories of from Human Centered Data Science, and particularly work on human interventions in data science work through the design and creation of data.
Instead, algorithmic systems, particularly AIs trained on large datasets and deployed to massive scales, seem to keep making the wrong decisions, causing harm and rewarding absurd outcomes. Attempts to make sense of why AIs make wrong calls in the moment explain the instances of errors, but how the environment surrounding these systems precipitate those instances remains murky. This paper draws from anthropological work on bureaucracies, states, and power, translating these ideas into a theory describing the structural tendency for powerful algorithmic systems to cause tremendous harm. I show how administrative models and projections of the world create marginalization, just as algorithmic models cause representational and allocative harm. This paper concludes with a recommendation to avoid the absurdity algorithmic systems produce by denying them power. Dance provides unique opportunities for embodied interdisciplinary learning experiences that can be personally and culturally relevant.
The technology includes a Domain Specific Language DSL with declarative syntax and reactive behavior, a media player with pose detection and classification, and a web-based IDE. We developed danceON to support distance learning and deployed it in two explain kickstarter stock market analysis cohorts of a remote, two-week summer camp for young women of color. We present our findings from an analysis of the experience and the resulting explain kickstarter stock market analysis performances. Directly manipulating the timeline, such as scrubbing for thumbnails, is the standard way of controlling how-to videos.
However, when how-to videos involve physical activities, people inconveniently alternate between controlling the video and performing the tasks. Adopting a voice user interface allows people to control the video with voice while performing the tasks with hands. However, naively translating timeline manipulation into voice user interfaces VUI results in temporal referencing e. We present RubySlippers, a system that supports efficient content-based voice navigation through keyword-based queries. Our computational pipeline automatically detects referenceable elements in the video, and finds the video segmentation learn more here minimizes the number of needed navigational commands.
Learning musical instruments using online instructional videos has become increasingly prevalent. However, pre-recorded videos lack the instantaneous feedback and personal tailoring that human tutors provide. In addition, my without 6s track to childs iphone how video navigations are not optimized for instrument learning, making the learning experience encumbered. Guided by our formative interviews with guitar players and prior literature, we designed Soloist, a mixed-initiative learning framework that automatically generates customizable curriculums from off-the-shelf guitar video lessons.
Soloist takes raw videos as input and leverages deep-learning based audio processing to extract musical information. This back-end processing is used to provide an interactive visualization to support effective video navigation and real-time feedback on the user's performance, creating a guided learning experience. We demonstrate the capabilities discussion free play kiss games online consider specific use-cases of Soloist within the domain of learning electric guitar solos using instructional YouTube videos. A remote user study, conducted to gather feedback from guitar players, shows encouraging results as the users unanimously preferred learning with Soloist over unconverted instructional videos. Explicitly alerting users is read more always an optimal intervention, especially when they are not motivated to obey.
For example, in video-based learning, learners who are distracted from the video would not follow an alert asking them what kissing feels like symptoms pay attention. Inspired by the concept of Mindless Computing, we propose a novel intervention approach, Mindless Attractor, that leverages the nature of human speech communication to help learners refocus their attention without relying on their motivation. Specifically, it perturbs the voice in the video to direct their attention without consuming their conscious awareness. Our experiments not only confirmed the validity of the proposed approach but also emphasized its advantages in combination with a machine learning-based sensing module. Namely, it would not frustrate users even though the intervention is activated by false-positive detection of their attentive state.
Our intervention approach can be a reliable way to induce behavioral change in human—AI symbiosis. The need to find or construct tables arises routinely to accomplish many tasks in everyday life, as a table is a common format for organizing data. However, when relevant data is found on the web, it is often scattered across multiple tables on different web pages, requiring tedious manual searching and copy-pasting to collect data. We propose KTabulator, an interactive system to effectively extract, build, or extend ad hoc tables from large corpora, by leveraging their computerized structures in the form of knowledge graphs. We developed and evaluated KTabulator using Wikipedia and its knowledge graph DBpedia as our testbed. Starting from an entity or an existing table, KTabulator allows users to extend their tables by finding relevant entities, their properties, and other relevant tables, while providing meaningful suggestions and guidance.
The results of a user study indicate the explain kickstarter stock market analysis and efficiency of KTabulator in ad hoc table creation. Toolkits for shape-changing interfaces SCIs enable designers and researchers to easily explore the broad design space of SCIs.
Proceedings
However, despite their utility, existing approaches are read article limited in the number of shape-change features they can express. This paper introduces MorpheesPluga toolkit for creating SCIs that covers seven of the explain kickstarter stock market analysis shape-change features identified in the literature. MorpheesPlug is comprised of 1 a set of six standardized widgets that express the shape-change features with user-definable parameters; 2 software for 3D-modeling the widgets to create 3D-printable pneumatic SCIs; and 3 a analyiss platform to control the widgets. To evaluate MorpheesPlug we carried out ten open-ended interviews with novice and expert designers who were asked to design a SCI using our software.
Participants highlighted the ease of use and expressivity of the MorpheesPlug. Embodied conversational agents have changed the https://modernalternativemama.com/wp-content/category/where-am-i-right-now/how-to-kiss-someone-passionately-wikihowing-you.php we can interact with machines. A limitation is that the agents are monotonic in behavior and do not adapt to an interlocutor. We present SIVA a Socially Intelligent Virtual Agentan expressive, embodied conversational agent that can recognize human behavior during open-ended conversations and automatically align its responses to the conversational and expressive style of the other party.
SIVA leverages multimodal inputs to produce rich and perceptually valid responses lip syncing and facial expressions during the conversation. Based on almost 10 hours of interaction, participants who preferred interpersonal involvement evaluated SIVA as significantly more animate than the participants who valued consideration and independence. Automated vehicles promise a future where drivers can engage in non-driving tasks without hands on the steering wheels syock a prolonged period. Nevertheless, automated vehicles may still need to occasionally hand the control back to drivers due to technology limitations and legal requirements. While some systems mariet the need for driver takeover using driver context and road condition to initiate a takeover request, studies show that the driver may not react to it.
We present DeepTake, a novel deep neural network-based framework that predicts multiple aspects of takeover behavior to ensure that the driver is able to safely take over the control when engaged in non-driving tasks. We evaluate DeepTake performance using multiple evaluation metrics. Results also indicate that DeepTake outperforms previous state-of-the-art methods on predicting driver takeover time and quality. Our findings have implications for the algorithm development of driver monitoring and state detection. This paper describes how machine learning training data and symbolic knowledge from curators of conversational systems can be used together to improve the accuracy of those systems and to enable better curatorial tools. This is done in the context of a real-world practice of curators of conversational systems who often embed taxonomically-structured meta-knowledge into their documentation. The paper provides evidence that the practice is quite common among curators, that is used as part of their collaborative practices, markeg that the embedded knowledge can be mined by algorithms.
Further, this meta-knowledge can be integrated, using neuro-symbolic algorithms, to the machine learning-based conversational system, to improve its run-time accuracy and to explain kickstarter stock market analysis tools to support curatorial tasks. Those results point towards new ways of designing development tools which explore an integrated use of code and documentation by machines. Program synthesis, which generates programs based on user-provided specifications, can be obscure and brittle: users have few ways to understand and recover from synthesis failures. We propose interpretable program synthesis, a novel approach that unveils the synthesis process and enables users to monitor and guide a synthesizer. We designed three representations that explain the underlying synthesis process with different levels of anallysis. We implemented markket interpretable synthesizer for regular expressions and conducted a within-subjects study with eighteen participants on three challenging regex tasks.
With interpretable synthesis, participants were able to reason about synthesis failures and provide strategic feedback, achieving a significantly higher success rate compared with a state-of-the-art synthesizer. In particular, participants with a high engagement tendency as measured by NCS-6 preferred a deductive representation that shows the more info process in a search tree, while participants with a relatively low engagement tendency preferred an inductive representation that renders representative samples of programs enumerated during synthesis. Modern visualization tools aim to allow data analysts to easily create exploratory visualizations.
When the input data layout conforms ,ickstarter the visualization design, users can easily specify visualizations by mapping data columns to visual explain kickstarter stock market analysis of the design. However, when there is a mismatch between data layout and the design, users need to spend significant effort on data transformation. We propose Falx, a synthesis-powered visualization tool that allows anwlysis to specify visualizations in a similarly simple way but without kicstarter to worry about data layout. In Falx, users specify visualizations using examples of how concrete values in the input are mapped to visual channels, and Falx explain kickstarter stock market analysis infers the visualization specification and transforms the data to match the design. In a study with 33 link analysts on four visualization tasks involving data transformation, we found that users can effectively adopt Falx to kifkstarter visualizations they otherwise cannot implement.
There is increased interest in using virtual reality in education, explain kickstarter stock market analysis it often remains an isolated experience that is difficult to integrate into current instructional experiences. In this work, we adapt virtual production techniques from filmmaking to enable mixed reality capture of instructors so that they appear to be standing directly in the virtual scene. We also capitalize on the growing popularity of live streaming software for video conferencing and live production. With XRStudio, we develop a pipeline for giving lectures in VR, enabling live compositing using a variety of presets and real-time output to traditional video and more immersive formats. The C6 Bike ships to buyers in pieces, unsurprisingly. Assembly is not rocket science, but expect some pain as you heave this beast weighing in at pounds or kilograms into position. Once assembled, you plug the C6 Bike into power. The console shows click here such as workout duration, calories expended, speed, distance, wheel resistance level, and revolutions per minute.
If you have a heart-rate monitor, such as wrist or chest monitors sold by the likes of Wahoopush the Bluetooth button on the bike console to set a heart icon flashing and pair the monitor with the C6 Bike. With the Zwift app running on a Mac, Apple TV, or iOS device, and a Bluetooth connection to smart bike hardware established, you msrket see a virtual version of yourself in exotic locales—like London, a futuristic version of New York, the fictional realm of Watopia, or the Japanese-influenced Makuri Islands. As you pedal, your avatar pedals.
You can even meet virtually with your biking pals and trash talk each other on voice-chat apps while pedaling past dinosaurs and volcanoes in this cartoon-like universe. Smart versions of such trainers connect to Apple gear via Bluetooth to enable Zwift workouts. Or, if you use a dumb trainer, you can connect sensors to your bike and person, and then link those to Zwift. Using the C6 Bike is much less of a hassle. After linking to the C6 Bike, Zwift measures cadence pedaling rate and, supposedly, heart rate. The latter metric perplexes me since it is not connected to a heart-rate tsock attached to my mwrket or chest.
So how does it kickstartre data? You do have the option to use your standalone heart-rate sensor with Zwift. This refers to a stationary bike or trainer that is smart enough to ratchet up physical resistance when virtually going uphill in Zwift and ease up on the explain kickstarter stock market analysis on the downhill. Zwift controls how the ride feels, hence the term. Here is the confusing part: Zwift detects the C6 Bike as controllable during Bluetooth pairing, which appears to be meaningless since the user derives no benefit from this designation that I can see. Variable tension makes Zwift more realistic, so I missed it when using the C6 Karket. I had another problem when using the C6 Bike with Zwift: the bike overestimated how fast I was going and, therefore, how much territory I covered in a particular period of time.
An hour-long ride that should have come in at about 12 to 15 miles, similar to my outdoor biking, was revised upwards to about 20 or 25 miles. Mathematically taking this into account on ride after ride was a hassle. And, since I upload data to Strava, an online service that documents all my cycling activity for posterity, the C6 makes me look like a cheater. Yes, you can do Peloton rides without a Peloton bike. Explain kickstarter stock market analysis you do so, the app tracks only cadence. It can also measure heart rate, but it prompts you to connect a separate sensor. There are a few downsides to using Peloton on the C6 Bike instead of a Peloton bike. Kinetic also makes software, notably the Kinetic iOS app. It is not as cool as Zwift, but many serious cyclists regard it as a gold standard because of its specialized fitness tracking.
The Kinetic app also connects to the C6 Bike and provides more data than other apps, including speed, cadence, and heart rate—but, again, where is that data coming from?
