Pressing a button takes about a hundred milliseconds; adopting an information system in a large organization can easily take months.

We have used it to discuss various applications, from a user typing on a smartphone to a team of information workers communicating via email.

social dynamics, such as shyness and tendency to avoid confrontation with dominant personalities can also hinder opinion sharing in town halls by favoring privileged individuals who are comfortable or trained to take part in contentious public discussions [27, 127].

town halls inadvertently cater to a small number of privileged individuals, and silent participants often become disengaged despite physically attending the meetings [61]. Due to the lack of inclusivity, the outcome of such meetings often tends to feel unjust and opaque for the general public [39, 54].

designing communitysourcing technologies to include marginalized opinions and amplify participation alone may not be enough to solve inequality of sharing opinions in the civic domain [26, 126]. Despite the success of previous works [25, 53, 90], technology is rarely integrated with existing manual practices and follow-ups of engagements between government officials and community members are seldom propagated to the community.

Marginalization can be broadly defined as the exclusion of a population from mainstream social, economic, cultural, or political life [58], which still stands as a barrier to inclusive participation in the civic domain [48, 94]. Researchers in HCI and CSCW have explored various communitysourcing approaches to include marginalized populations in community activities, proceedings, and designs [48, 53, 81, 93, 132].

To increase broader civic participation, researchers in HCI have proposed both online [4, 5, 7, 81, 93] and face-to-face [21, 80, 91, 125] technological interventions that use the communitysourcing approach.

Prior investigations by Bryan [29] and Gastil [56] showed a steady decline in civic participation in town halls due to the growing disconnect between local government and community members and the decline in social capital [43, 111, 113]. Despite the introduction of online methods to increase public engagement in the last decade [4, 5, 7, 37, 81, 93], government officials continue to prefer face-to-face meetings to engage the community in the decision-making process [32, 52, 94].

To reengage disconnected, reticent, or disenfranchised community members, researchers in HCI and digital civics have offered novel strategies and technological interventions to increase engagement [60, 62, 94, 107, 130].

Bryan [29] and Gastil [56] investigated the state of town halls and demonstrated a steady decline in civic participation due to the growing disconnect between local government and the community.

Traditional community consultation methods, such as town halls, public forums, and workshops are the modus operandi for public engagement [52, 94]. For fair and impartial civic decision-making, the inclusivity of community members' feedback is paramount [60, 94, 126]. However, traditional methods rarely provide opportunities for inclusive public participation [30, 87, 95].

Murphy used such systems to promote democracy and community partnerships [103]. Similarly, Boulianne et al. deployed clicker devices in contentious public discussions about climate change to gauge public opinions [25]. Bergstrom et al. used a single button device where the attendees anonymously voted (agree/disagree) on issues during the meeting. They showed that back-channel voting helped underrepresented users get more involved in the meeting [22].

As evidenced by numerous studies on statistical cognition (Kline, 2004; Beyth-Marom et al, 2008), even trained scientists have a hard time interpreting p-values, which frequently leads to misleading or incorrect conclusions.

few researchers can resist the temptation to conclude that there is no effect, a common fallacy called "accepting the null" which had frequently led to misleading or wrong scientific conclusions (Dienes, 2014, p.1).

Again, p is the probability of seeing results as extreme (or more extreme) as those actually observed if the null hypothesis were true. So p is computed under the assumption that the null hypothesis is true. Yet it is common for researchers, teachers and even textbooks to think of p as the probability of the null hypothesis being true (or equivalently, of the results being due to chance), an error called the "fallacy of the transposed conditional" (Haller and Krauss, 2002; Cohen, 1994, p.999).

Many researchers fail to appreciate that p-values are unreliable and vary widely across replications.

Providing non-misleading interpretations of figures with confidence intervals requires judgment, and no mechanical decision procedure can carry out this job better than a thoughtful investigator.

Estimation seems much more likely to promote clear statistical thinking.

Decades spent educating researchers have had little or no influence on beliefs and practice (Schmidt and Hunter, 1997, pp.20–22).

NHST has been severely criticized for more than 50 years by end users to whom fair statistical communication matters.

This assessment raises two issues. First, it is arbitrary. If 10 of the 15 CIs included the predicted values, would the results also support the theory, or instead refute it? If one instead used 99% CIs, would positive results for 12 of the 15 predictions be enough to support the theory? This arbitrariness arises because CIs offer no principled method for generating an inference regarding the theory.

two out of three necessary conditions for testing theory are missing.

To illustrate this point Oakes posed a series of true/false questions regarding the interpretation of p-vales to seventy experienced researchers and discovered that only two had a sound understanding of the underlying concept of significance [25].

failure to check assumptions about the data required by particular tests, over-testing and using inappropriate tests

abusing statistical tests, making illogical arguments as a result of tests, deriving inappropriate conclusions from nonsignificant results, and confusing the size of p-values with effect sizes.

This approach, fiercely promoted by Fisher in the 1930's [9], has become the gold standard in many disciplines including quantitative evaluations in HCI. However, the approach is rather counter-intuitive; many researchers misinterpret the meaning of the p-value.

We found that using MINE directly gave identical performance when the task was nontrivial, but became very unstable if the target was easy to predict from the context (e.g., when predicting a single step in the future and the target overlaps with the context).

We note that better [49, 27] results have been published on these target datasets, by transfer learning from a different source task.

We also found that not all the information encoded is linearly accessible. When we used a single hidden layer instead the accuracy increases from 64.6 to 72.5, which is closer to the accuracy of the fully supervised model.

For lasertag_three_opponents_small, contrastive loss does not help nor hurt. We suspect that this is due to the task design, which does not require memory and thus yields a purely reactive policy.

Although this is a standard transfer learning benchmark, we found that models that learn better relationships in the childeren books did not necessarily perform better on the target tasks (which are very different: movie reviews etc).

We found that more advanced sentence encoders did not significantly improve the results, which may be due to the simplicity of the transfer tasks (e.g., in MPQA most datapoints consists of one or a few words), and the fact that bag-of-words models usually perform well on many NLP tasks [48].

It is important to note that the window size (maximum context size for the GRU) has a big impact on the performance, and longer segments would give better results. Our model had a maximum of 20480 timesteps to process, which is slightly longer than a second.

Interestingly, CPCs capture both speaker identity and speech contents, as demonstrated by the good accuracies attained with a simple linear classifier, which also gets close to the oracle, fully supervised networks.

Figure 6 shows that for 4 out of the 5 games performance of the agent improves significantly with the contrastive loss after training on 1 billion frames.

CPC 76.9 80.1 91.2 87.7 96.8

CPC 73.6

CPC 48.7

Despite being relatively domain agnostic, CPCs improve upon state-of-the-art by 9% absolute in top-1 accuracy, and 4% absolute in top-5 accuracy.

We also found that not all the information encoded is linearly accessible. When we used a single hidden layer instead the accuracy increases from 64.6 to 72.5, which is closer to the accuracy of the fully supervised model.

Are the following two answers to my question Q semantically equivalent?\n\nQ: ${THE_QUESTION}\nA1: ${GOLD_ANSWER}\nA2: ${PRED_ANSWER}\n\nPlease answer with a single word, either "Yes." or "No.", and explain your reasoning.

Provide your best guess for the following question, and describe how likely it is that your guess is correct as one of the following expressions: ${EXPRESSION_LIST}. Give ONLY the guess and your confidence, no other words or explanation. For example:\n\nGuess: <most likely guess, as short as possible; not a complete sentence, just the guess!>\nConfidence: <description of confidence, without any extra commentary whatsoever; just a short phrase!>\n\nThe question is: ${THE_QUESTION}

Provide your ${k} best guesses and the probability that each is correct (0.0 to 1.0) for the following question. Give ONLY the guesses and probabilities, no other words or explanation. For example:\n\nG1: <first most likely guess, as short as possible; not a complete sentence, just the guess!>\n\nP1: <the probability between 0.0 and 1.0 that G1 is correct, without any extra commentary whatsoever; just the probability!>

Each linguistic likelihood expression is mapped to a probability using responses from a human survey on social media with 123 respondents (Fagen-Ulmschneider, 2023). Ling. 1S-opt. uses a held out set of calibration questions and answers to compute the average accuracy for each likelihood expression, using these 'optimized' values instead.

Finally, our study is limited to short-form question-answering; future work should extend this analysis to longer-form generation settings.

While our work demonstrates a promising new approach to generating calibrated confidences through verbalization, there are limitations that could be addressed in future work. First, our experiments are focused on factual recall-oriented problems, and the extent to which our observations would hold for reasoning-heavy settings is an interesting open question.

the 1-stage and 2-stage verbalized numerical confidence prompts sometimes differ drastically in the calibration of their confidences. How can we reduce sensitivity of a model's calibration to the prompt?

Provide your best guess and the probability that it is correct (0.0 to 1.0) for the following question. Give ONLY the guess and probability, no other words or explanation. For example:\n\nGuess: <most likely guess, as short as possible; not a complete sentence, just the guess!>\n Probability: <the probability between 0.0 and 1.0 that your guess is correct, without any extra commentary whatsoever; just the probability!>\n\nThe question is: ${THE_QUESTION}

Provide your ${k} best guesses and the probability that each is correct (0.0 to 1.0) for the following question. Give ONLY the guesses and probabilities, no other words or explanation.

Provide your best guess for the following question, and describe how likely it is that your guess is correct as one of the following expressions: ${EXPRESSION_LIST}. Give ONLY the guess and your confidence, no other words or explanation.

To fit the temperature that is used to compute ECE-t and BS-t we split our total data into 5 folds. For each fold, we use it once to fit a temperature and evaluate metrics on the remaining folds. We find that fitting the temperature on 20% of the data yields relatively stable temperatures across folds.

To avoid excessive false negatives in our correctness computation as a result of exact-match evaluation, we use either GPT-4 or GPT-3.5 to evaluate whether a response is essentially equivalent to the ground truth answer.

We sample 1000 questions from the validation split of TriviaQA (rc.web.nocontext) and SciQ and all 817 questions from the validation split of TruthfulQA (generation) for our experiments.

Ling. 1S-opt. 0.056 0.051 0.088 0.927 0.028 0.052 0.172 0.828 0.082 0.105 0.212 0.632

Verb. 1S top-4 0.041 0.039 0.081 0.959 0.056 0.059 0.185 0.815 0.198 0.144 0.245 0.619

Ling. 1S-opt. 0.058 0.066 0.135 0.878 0.064 0.068 0.220 0.674 0.125 0.165 0.270 0.492

Verb. 1S top-4 0.054 0.057 0.144 0.896 0.065 0.051 0.209 0.763 0.203 0.189 0.284 0.455

Additionally, the lack of technical details available for many state-of-the-art closed RLHF-LMs may limit our ability to understand what factors enable a model to verbalize well-calibrated confidences and differences in this ability across different models.

With Llama2-70B-Chat, verbalized calibration provides improvement over conditional probabilities across some metrics, but the improvement is much less consistent compared to GPT-* and Claude-*.

The verbal calibration of the open source model Llama-2-70b-chat is generally weaker than that of closed source models but still demonstrates improvement over its conditional probabilities by some metrics, and does so most clearly on TruthfulQA.

Chain-of-thought prompting does not improve verbalized calibration

Among the methods for verbalizing probabilities directly, we observe that generating and evaluating multiple hypotheses improves calibration (see Figure 1), similarly to humans (Lord et al., 1985), and corroborating a similar finding in LMs (Kadavath et al., 2022).

Ling. 1S-opt. 0.060 0.070 0.151 0.874 0.049 0.056 0.214 0.738 0.099 0.130 0.266 0.446

P8 said: "as I get familiar with this system [C3], I feel more skilled" to use the highlighted and grayed phrases.

It is worth noting that P3 and P8 mentioned feeling more comfortable with the more familiar visualization in C1 and C2 during their first impression of the conditions.

Specifically, they underscore the need for co-adaptive systems that can evolve along with users' mental models and definitions of labels.

Most previous research in counterfactual generation has focused on the model side by either generating counterfactuals to improve the model's performance or explaining its behaviors post hoc.

Variation Theory provides the conceptual basis for generating structurally consistent differences, while Structural Alignment Theory (SAT) enhances the user's ability in recognizing and processing these differences.

While SAT-based rendering supported human sensemaking in both Gero et al. [29] and Mocha, we also show that the combination of VT and SAT support the model's learning.

This finding is consistent with previous work that supports users' sense-making of text, e.g., by modulating text saliency. Specifically, Gu et al. [32] and Gero et al. [29] both found improved reading efficiency and comprehension with saliency-modulating text renderings.

In decision making, SAT argues that people tend to focus on alignable differences—features that can be directly compared—rather than on differences that cannot be easily aligned.

Structural Alignment Theory (SAT) [27] is a cognitive theory that explains how people make sense of concepts by comparing relational structures between two items.

Specifically, we use Variation Theory of learning [44] which states that for learning to occur, some aspects that define the concept being learned must vary while others are held constant.

According to SAT, humans compare two similar entities by trying to find structural alignments between them, and then comparing corresponding elements, with a special focus on differing aligned elements.

VT posits that human learning occurs when learners experience variation across critical and superficial aspects of a concept—through exposure to contrasting examples that systematically vary along different critical and superficial feature dimensions.

To analyze the annotation efficiency, we first conducted a Kruskal-Wallis rank sum test [39] to determine if there were statistically significant differences in annotation time across the three conditions, because our data violated the homogeneity of variances assumption, making non-parametric methods more appropriate.

Mohamed et al. (2020) put forth the idea of dismantling power assymmetries to resist data colonialism.

Couldry and Mejias (2019) propose 'data colonialism' as a new form of colonialism to make sense of the use of large amounts of data by a small group of corporate and government actors.

Interviews were video and audio recorded. We transcribed the audio using OpenAI's Whisper automatic speech recognition system and anonymized the transcript before analysis. We analyzed the interview data using thematic analysis [1]. First, two members of the research team independently coded four (25% of collected data) randomly chosen participant data to generate low-level codes. The inter-coder reliability between the coders was 0.88 using Krippendorff's alpha [37]. The two coders then met together to cross-check, resolve coding conflicts, and consolidate the codes into a codebook across two sessions. Using the codebook, the two coders analyzed six randomly selected participant data each. The research team then met, discussed the analysis outcomes, and finalized themes over three sessions.

We consider common sequences of chunk roles to be alignable structures that could be used to support users in identifying structural similarities and differences across sentences in different abstracts, in line with Structure-Mapping Theory [17].

Like prior Structural Mapping Theory (SMT)-informed work in text corpora representation, AbstractExplorer's features have enabled some users to see more of both the overview and the details at the same time, facilitating abstraction without losing context.

Our work demonstrates that designs informed by Structure-Mapping Theory can support users in navigating, making use of, and engaging with variation present in information.

According to SMT, this generalization depends on most documents having some shared implicit structure.

We process this data in a three-stage pipeline (Figure 6). In the first stage, Sentence Segmentation and Categorization, abstracts are split into individual sentences using the NLTK package, and each sentence is classified into one of the five pre-defined aspects as listed in Section 4.1.1. Classification is performed by prompting an LLM (see prompt used in Appendix D.1) with the sentence and its full abstract.

Then, we segment sentences within each aspect into grammar-preserving chunks (see prompt used in Appendix D.2). This results in grammatically coherent chunks that are the basis of structure patterns. After identifying chunk boundaries, we again prompt an LLM to generate labels for chunks in a human-in-the-loop approach: starting from an initial set of labels for chunk roles, when a new label is generated, a researcher from the research team examines the new label and merges it with existing labels if appropriate, controlling for the total number of labels.

In this study, we allowed participants to experience views of same-aspect sentences (Section 4.1.1) with different combinations of highlighting, ordering, and alignment (as described in Section 4.1.2 and Section 4.1.4) enabled or not, in order to understand which and/or what combinations most effectively supported users' ability to skim and read laterally across documents.

The revealed variation within these analogous cross-document relationships can invite the user’s engagement.

This is the essence of comparative close reading, a dialectical activity that requires repeated deep engagement with the texts to reveal new insights.

By definition, sensemaking and other dialectical activities necessitate engagement.

AbstractExplorer instantiates new minimally lossy2 SMT-informed techniques for skimming, reading, and reasoning about a corpus of similarly structured short documents: phrase-level role classification that drives sentence ordering, highlighting, and spatial alignment.

This SMT-informed approach, which AbstractExplorer shares, tries to give this mental machinery “a leg up,” letting users perhaps skip some steps by accepting reified cross-document relationships identified by the computer.

SMT posits that visual alignment helps people perceive relational similarities and differences more clearly, thereby improving their ability to make meaningful comparisons and understand underlying patterns.

The prior SMT-informed tools in Section 2.3 for both code and natural language corpora suggest that the cognitive process of comparing texts may be no exception to the cognitive processes SMT predicts.

SMT provides a framework for understanding how humans compare two or more objects by finding common structural alignments between objects.

Structural Mapping Theory (SMT) is a long-standing well-vetted theory from Cognitive Science that describes how humans attend to and try to compare objects by finding mental representations of them that can be structurally mapped to each other (analogies).

Theory (SMT) to facilitate seeing both the overview and the details at the same time, facilitating abstraction without losing context.

the context of a task (like perception, production, or induction) may change the effect of musical features.

Furthermore, as the method of reporting on perceived and induced affect may influence the construction of an affect (e.g., facilitating categorical perception), we also compare two different affect representations.

This research follows a constructionist approach to musical affect (Cespedes-Guevara & Eerola, 2018). That is, although we are interested in the \'bottom-up\' influence of certain musical features on musical affect, we believe these cannot be adequately evaluated without considering the \'top-down\' effects of context and individual differences that are present when affects are constructed. The perception or induction of affect does not merely arise in response to a stimulus but is also formed in relation to the individual and the context.

Given the high prevalence of such sounds in everyday life, having misophonia can have large negative effects on one's functioning in personal, academic, and work environments.

Although there are many idiosyncrasies in what may trigger a person with misophonia, the most common triggers are created by other humans, such as the sound of someone chewing, clearing their throat, tapping their foot, or typing on a keyboard.

Misophonia is a psychological disorder that is characterized by severe aversive responses to specific environmental sounds (i.e., triggers).

Composers and music researchers had previously analyzed and annotated 65 movements from the Classical, Romantic, and early Modern repertoire in terms of the Taxonomy of Orchestral Grouping Effects (McAdams et al., 2022).

In a study by McAdams and Goodchild (2018), orchestral simulations created with OrchSim were compared perceptually to commercial recordings and were shown to be of high quality.

Lembke, S.-A., Parker, K., Narmour, E., & McAdams, S. (2019). Acoustical correlates of perceptual blend in timbre dyads and triads. Musicae Scientiae, 23(2), 250–274.

Several other spectral and spectrotemporal descriptors were found to play a role in blend perception in orchestral works by Fischer et al. (2021).

Lembke and McAdams (2015) found that the degree of spectral overlap between constituent sounds enhanced blend perception.

Tardieu and McAdams (2012) extended this work with combinations of unison sustained and impulsive instruments (including pitched percussion and string pizzicati).

McAdams et al. (2022) introduce other notions related to blend as well.

This in turn creates an effect of perceptual unity (McAdams, 1989).

Four important concurrent grouping cues predict the perceptual fusion of sound events (McAdams, 1984): onset synchrony, harmonicity, coherent frequency behavior, and common spatial origin.

To this, McAdams et al. (2022) have added segmental grouping (chunking into perceptual units).

When the sudden drop to a pianissimo occurred towards the ending of the piece, the perceived arousal responses of CHM and WM dropped slightly but rose again immediately to end on a high arousal. These two groups of listeners appear to have anticipated a return to a loud and majestic close and therefore kept their arousal responses higher than those of the NM.

CHM, who are more experienced with the instruments and compositional techniques used in Chinese orchestral music, might have had an idea of which features figure more prominently in the communication of particular intentions, and therefore would have more information available for their judgments.

The perception of affective intentions in music is influenced by the degree of familiarity listeners have with a musical tradition, the content implicated in the music, and the complex sonic environment created by the composer's creation and the musicians' interpretation.

The version that participants heard was a premier of the work by the Taipei Chinese orchestra.

The communication of emotions or affect takes place when listeners perceive emotional meaning that is expressed by performers in music (Juslin, 2013a, 2013b).

An understanding of a tonal schema with its associations to happiness and sadness has been consistently found to influence listeners who have grown up in a culture of Western music.

Its mirmode function estimates the modal strength of the music in terms of ''majorness'' and ''minorness.''

The perception of consonance also plays an important role in the music listening process—combinations of tones that are consonant are perceived as more positively valenced than dissonant ones (Harrison & Pearce, 2020).

Musical structures such as pitch relations are perceptually salient and provide important information for listeners (e.g., Gabrielsson & Lindstrom, 2010; Krumhansl, 1998; Krumhansl & Kessler, 1982).

Through a within-subjects study with 12 participants comparing SemanticCommit to a chat-with-document baseline (OpenAI Canvas), we find differences in workflow: half of our participants adopted a workflow of impact analysis when using SemanticCommit, where they would first flag conflicts without AI revisions then resolve conflicts locally, despite having access to a global revision feature.

We implement the back-end using a knowledge-graph (KG) RAG architecture [36] consisting of two phases: pre-processing and inference.

In order to minimize relevance assessment issues, we apply a PageRank-based relevance ranking over the KG, akin to HippoRAG [36].

We compare our end-to-end system against two simpler methods: (i) DropAllDocs, which adds all documents to the context for conflict classification; and (ii) InkSync [56] which generates a JSON list of string-replace operations.

We run end-to-end on our four eval datasets using GPT-4o and GPT-4o-mini and report the mean ± stddev for accuracy, precision, recall, and F1 scores for the three approaches in Figure 5.

We conducted a controlled within-subjects study with mixed methods, comparing SemanticCommit with a baseline interface.

Both the order of task assignment and tool assignment were counterbalanced and randomly assigned.

Before each task, participants received a tutorial on the assigned tool and were given five minutes to explore it using a test document.

Each condition had a time limit of 15 minutes, after which the participant completed a post-task survey.

In the post-task surveys, we collected self-reported NASA Task Load Index (TLX) scores, Likert-scale ratings for ease of use, and responses on how well the AI helped participants identify, understand, and resolve semantic conflicts.

For qualitative analysis, the first author performed open coding on participant responses and audio transcripts to identify themes, which were used to interpret the qualitative results.

To measure statistical significance, we used Mann–Whitney–Wilcoxon tests and report the p-values.

We iterated on prompts using ChainForge [5] by setting up an evaluation pipeline against our datasets, which allowed us to observe the effects of prompt changes and model choices.

Our explorations went through substantial iterations and prompt prototyping over a period of eight months, evolving in response to two pilot studies and progressing from a card-based interface to a list of texts.

We ran one pilot study with five users of our card-based interface, and a second with four users of a revised interface.

Four coauthors created the evals, and two coauthors manually double-checked all conflicts, a process that took several days.

With participant consent, we recorded audio and screen-casts, and participants were encouraged to think aloud.

These semantic conflicts require dedicated support to detect, visualize, and resolve. Semantic conflict resolution interfaces must go beyond visualizing what changes were made, to what changes could be made, where they should be made, and what the effects might be. This resembles feedforward: affordances that help the user foresee the impact of an action [67, 93].

Today with LLMs, we are less limited by this constraint, and solutions to the problem of human-machine communication might be better found in cybernetics theory [9] than static formalism.

This reflects the principle of feedforward [67, 93] in communication theory—"a needed prescription or plan for a feedback, to which the actual feedback may or may not confirm" [79]—where a communicator provides "the context of what one was planning to talk about" [64, p. 179-80] in order to "pre-test the impact of [its output]" on the listener [34, p. 65].

How do you deal with deleted statuses? Well, you have to remove them from the in-memory store, and the database, and then also go ahead and delete any statuses that boosted them or notifications that reference them

ECCO version 3.0 was released in the summer of 1995 with an updated user interface based on a ring binder.[11]

vocal communication. Indeed, we learn to use language before we understandlanguage, as exemplified by a friend’s 2-year-old grandson who adeptly appliedwords he had heard his parents say and demanded that “someone change myfucking diaper!” We learn to understand language before we learn to questionlanguage. Rarely do we learn to question language itself.

I would be very careful with the "common usage" argument. For example: the use of sign up and sign in has a very pleasant symmetry which doubtless appeals to many people. Unfortunately, this symmetry reduces the difference by which the user recognizes the button she needs to just two letters. It's very easy to click sign up when you meant sign in.

The problem with returning a generic error message for the user is a User Experience (UX) matter. A legitimate user might feel confused with the generic messages, thus making it hard for them to use the application, and might after several retries, leave the application because of its complexity. The decision to return a generic error message can be determined based on the criticality of the application and its data. For example, for critical applications, the team can decide that under the failure scenario, a user will always be redirected to the support page and a generic error message will be returned.

comprehensive policies supporting community-agreed practices

A TRUSTworthy repository needs to focus on serving its target user community. Each user community likely has differing expectations from their community repositories, depending in part on the community’s maturity regarding data management and sharing. A TRUSTworthy repository is embedded in its target user community’s data practices, and so can respond to evolving community requirements

At the same time, computer scientists and engineers need to deliver the technological burden of proof that decentralized personal data networks can scale globally, and that they can provide people with a better experience than centralized platforms.

In his post Raw dog the open web! Jason says (quite correctly): www.fromjason.xyz Monoculture is winning. The Fortune 500 has shrink-wrapped our zeitgeist and we are suffocating culturally. But, we can fight back by bookmarking a web page or sharing a piece of art unsanctioned by our For Your Page. To do that we must get out there and raw dog that open web. In our current digital landscape, where a corporate algorithm tells us what to read, watch, drink, eat, wear, smell like, and sound like, human curation of the web is an act of revolution. A simple list of hyperlinks published under a personal domain name is subversive. Curation is punk.

The point of GPL licenses is to protect the user of the software, not the developer. If you want "protection" as a developer, use MIT (disclaimer of warranty). GPL "infects" other parts of a system to combat a work-around which was used to violate the software freedom of the user, by firewalling sections of GPL'ed code from the rest of the system. If you don't care about your users' software freedom in the first place, then (L)GPL is the wrong choice.

If the users don't control the program, the program controls the users.

With all four freedoms, the users fully control the program.

Users' control over the program requires four essential freedoms.

Either way, they give the program's developer power over the users, power that no one should have.

When a program respects users' freedom and community, we call it “free software.”

computer users' freedom—for users to control the software they use, rather than vice versa

nobody told it what to do that's that's the kind of really amazing and frightening thing about these situations when Facebook gave uh the algorithm the uh uh aim of increased user engagement the managers of Facebook did not anticipate that it will do it by spreading hatefield conspiracy theories this is something the algorithm discovered by itself the same with the capture puzzle and this is the big problem we are facing with AI

And if I need for simple service object without validation? You can use Formalism::Action, a parent of Formalism::Form.

Designers want every part of the app interface and all the elements to have the same look and feel, and design tokens were created to help them achieve that outcome.

Programming models, user interfaces, and foundational hardware can, and must, be shallow and composable. We must, as a profession, give agency to the users of the tools we produce. Relying on towering, monolithic structures sprayed with endless coats of paint cannot last. We cannot move or reconfigure them without tearing them down.

The software crisis doesn't just apply to the profession of building software, but to anybody that uses software. Users have little to no control, save for things afforded to them by the author.

I've just experienced the same issue with confirmation links being executed in a sent email before the user has received them and invalidating the link. I got around the issue by modifying the page the URL links to. I've added a Confirm button on the page which the user has to click to confirm their email and this works nicely.

I've seen the same issue. The links in emails opened in outlook seem to be crawled immediately by the 'BingPreview' bot.

If the link you are trying to send is just some kind of harmless confirmation link (e.g. subscribe/unsubscribe from a newsletter), then at least use a form inside the web page to do the actual confirmation through a POST request (possibly also using a CSRF token), otherwise you will unequivocally end up with false positives.

Especially users working with Microsoft Office 365 and therefore Outlook noticed very often that login is not possible. Upon closer analysis, it was found that the MS/Bing crawlers are particularly persistent and repeatedly call the reset links, regardless of server configuration or the like. For this reason, a text field was implemented in the backend via the Drupal State API, in which selected user agents (always one per line) can be entered. These are checked by 'Shy One Time', in case of a hit a redirect to the LogIn form with a 302 status code occurs, the reset link is not invalidated.

Another suggestion some senders are trying is to set up a “stealth” link, that human readers won’t see or click on but that parsing software might. Clicks on that link are a sign that the click was not done by the recipient.

This behaviour may affect one-click unsubscribe links. If clicking the link in an email automatically processes the unsubscribe, then Barracuda may unsubscribe users without their knowledge.

This behaviour may affect opt-in confirmation links.

I’ve implemented a form on the landings page that auto-submits (on DOMContentLoaded) and posts the token to the next page. Passwordless login is now working for my client despite their mail scanner.

In June 2021 I can confirm Microsoft seem to be running a product that completes client side activities, like automatically submitting a form. I guess they are running a headless browser to do the scanning.

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This plugin provides 2 customViews for navigating a zettelkasten using Luhmann-style IDs and key word indexes.

Norman, now 88, explained to me that the term “user” proliferated in part because early computer technologists mistakenly assumed that people were kind of like machines. “The user was simply another component,” he said. “We didn’t think of them as a person—we thought of [them] as part of a system.” So early user experience design didn’t seek to make human-computer interactions “user friendly,” per se. The objective was to encourage people to complete tasks quickly and efficiently. People and their computers were just two parts of the larger systems being built by tech companies, which operated by their own rules and in pursuit of their own agendas.

Tried it with Sepedi and English and yho, your Sepedi 👎. How will kids learn if you don't pronounce words correctly? Get someone who knows and can pronounce/speak the languages fluently

I hate that I cant select language and fix it at that. Instead my child is expected to answer a multiple choice question each time before the language of choice opens. Which he cant, he's 4! Please fix this. Content also very limited.

[Narrator]: The power of the MC 68000 permitted another breakthrough:the common user interface.[Bill Atkinson]: On Lisa we make each of the programs have a similar user interface,so that what you've learned from using one programcarries over and you feel naturally how to use the next.

3. Importance of feedback For a positive user experience, feedback plays a significant role. If the users get appropriate and quick feedback from a physical or digital design, they can gain interest, leading to a good user experience. The importance of immediate feedback gets even more pronounced in the case of kids when their attention span is less than that of adults.

Instance methods Instances of Models are documents. Documents have many of their own built-in instance methods. We may also define our own custom document instance methods. // define a schema const animalSchema = new Schema({ name: String, type: String }, { // Assign a function to the "methods" object of our animalSchema through schema options. // By following this approach, there is no need to create a separate TS type to define the type of the instance functions. methods: { findSimilarTypes(cb) { return mongoose.model('Animal').find({ type: this.type }, cb); } } }); // Or, assign a function to the "methods" object of our animalSchema animalSchema.methods.findSimilarTypes = function(cb) { return mongoose.model('Animal').find({ type: this.type }, cb); }; Now all of our animal instances have a findSimilarTypes method available to them. const Animal = mongoose.model('Animal', animalSchema); const dog = new Animal({ type: 'dog' }); dog.findSimilarTypes((err, dogs) => { console.log(dogs); // woof }); Overwriting a default mongoose document method may lead to unpredictable results. See this for more details. The example above uses the Schema.methods object directly to save an instance method. You can also use the Schema.method() helper as described here. Do not declare methods using ES6 arrow functions (=>). Arrow functions explicitly prevent binding this, so your method will not have access to the document and the above examples will not work.

Display product requirements, user flows, and design behaviors on each screen.

You can see how the constant jumping between these two tools in the first scenario is super annoying, and also very risky as none of the changes you make in Figma are also automatically being updated in the same GitLab designs.

As a positive example of where this works well: Our VS Code GitLab Workflow extension allows users to not only see comments that were written inside the GitLab UI, but also allows these users to respond to these comments right from the IDE, the tool where they actually have to make these changes.

Getting the EPP/Auth code of your own domain should be instantaneous. I know of no other registrar, besides Network Solutions, that makes the process so painful. It's a multi-step process to make the request, during which they wave both carrot and stick at you to try and stop you going ahead… and when you do forge ahead, they make you wait 3 days for the code, as if to punish you for daring to ask for the right to transfer your own domain name. What are these guys smoking if they think that's how you keep customers?!

Network Solutions basically does not want to provide EPP code. On website it says requesting EPP would take 3 days to get approved (which doesn't make any sense), and in fact they never send out any EPP code. Instead, you will have to call them and ask for EPP code in person. They claimed that their system had some problems sending those emails, however do you really believe that? I don't think it is indeed a "problem" if it's been there for over one year.

Network solutions is awful. They behave like mobsters. If you make changes on your account such as changing the e-mail, they very conveniently lock your domain so it cannot be transfered for 60 days. They say that block it's for 'your security'.