Reviewerly/exHarmony huggingface dataset:
Individual and Group-level considerations of Actionable Recourse

The advent of machine learning in several critical fields, such as banking, healthcare, and criminal justice, has inspired research into improving robustness, trustworthiness, and transparency in the models. Actionable Recourse is one such tool that enables the negatively impacted users to receive a favorable outcome by providing recommendations of cost-efficient changes to their features. Current recourse methodologies optimize for proximity, sparsity, validity, and distance-based costs. Actionability takes both individual and group-level signals. A critical component of actionability is the consideration of User Preference to guide the recourse generation process. These preferences can take several forms, and we introduce three such preferences to capture the individual difficulty of user actions. Additionally, feasibility and plausibility should be considered as a fixed set of pre-specified constraints. We argue that plausibility draws strong signals from group-level population information, which must be considered to achieve low-cost recourses across protected groups. Recoursability is an active research area, and plausibility becomes an essential direction for further research.
Navigating the Limits of AI Explainability: Designing for Novice Technology Users in Low-Resource Settings

Share on Navigating the Limits of AI Explainability: Designing for Novice Technology Users in Low-Resource Settings Author: Chinasa T. Okolo Computer Science, Cornell University, USA Computer Science, Cornell University, USA 0000-0002-6474-3378View Profile Authors Info & Claims AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and SocietyAugust 2023Pages 959–961https://doi.org/10.1145/3600211.3604759Published:29 August 2023Publication History 0citation59DownloadsMetricsTotal Citations0Total Downloads59Last 12 Months59Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Searching for a target in a natural scene does not allow for robust recall of scene or target details that are irrelevant to response expectations

Humans can easily form memories of naturalistic scenes with rich visual details and recreate them in drawings, but how robust are incidental memories of scenes when participants only expect to search for an object within them? Tests of incidental working memory for simple search stimuli (e.g., letters, colored shapes) show that even an attended target’s identity cannot be accurately retrieved when participants only expect to report its location, a phenomenon termed attribute amnesia. We predict that this form of expectation-based memory selectivity will also be present for both the whole scene and the search target when participants are asked to search for an object in a scene and only report its location. In our experiments, participants located wall art or pillows within novel indoor scenes for 43 trials before being unexpectedly prompted to draw the entire scene (Experiment 1) or the wall art which was just the search target (Experiment 2). Naïve raters matched participants’ drawings to the actual scene or object. In both experiments, the failure to draw accurately on the surprise trial was dramatic. Most drawings lacked visual details to be sufficiently recognizable, were not in the same basic category as the scene or object, and some drawers even submitted empty canvases. On the next trial, after developing an expectation to draw detailed scenes or objects, the same drawers produced highly recognizable scenes and objects. Experiment 3 further showed that these memory failures were not merely attributable to surprise-related interference. Thus, despite being capable of reconstructing visual details from memory, response-irrelevant details of scenes, or even attended-to objects themselves, may not be encoded to begin with. Rather, participants primarily encode information relevant to current goals and expectations. We theorize that this is a fundamental property of cognitive computations designed to optimize task performance and minimize resource use.
Global Mean Position Perception of Multiple Spatially-Separated Ensembles

The visual system is remarkably efficient at extracting summary statistics from the environment, which drive the overall perception of a scene and inform judgment of individual objects within it. Most ensemble research focuses on the perception of one specific characteristic (e.g., average size) from a particular group of stimuli, but natural visual environments usually consist of many groups of objects distributed over space. Here we ask how people perceive the mean position of all stimuli when stimuli are clustered into two distinct groups. Specifically, we evaluate how the cardinality and precision of the perceptual groups influence global mean position perception. We designed a paradigm utilizing the mean summary task used in data visualization. Participants were asked to make intuitive estimations of the global mean position of two spatially separated groups of dots. Both within and across 6 experiments, we varied the number of dots in each group as well as the spatial spread of the groups. We found that people reliably overweight the group of smaller cardinality, including if it is a singleton group (i.e., an outlier). We also found that people overweight the group with smaller spatial spread despite it being perceived as less numerous. We find that an “edge effect” can potentially explain the bias – with people reporting the percent distance between the edges, rather than the centers, of the two groups. This would suggest that people aggregate at the level of groups, rather than individual objects. If people are using an (incorrect) edge strategy, this also leads to a possible way of resolving the bias by altering the underlying reference frame people use, which could improve accuracy in reading data visualizations.
Ensemble machine learning for screening cardiovascular diseases in electronic health records

Background and Aims: Machine Learning (ML) methods are increasingly proposed for cardiovascular disease (CVD) screening in Electronic Health Records (EHRs). Among all ML methods, "Ensembles of machine learning models" (EMLs), which work by combining multiple predictions into one, are often acknowledged to provide the best predictive performances. However, the evidence for using EMLs in the context of CVD screening within EHRs has not been systematically reviewed yet.
Benefits of the use of ICTs to achieve significant learning in students with NEAE

The objective is to determine the benefits of the use of ICTs as a teacher's resource to achieve significant learning in students with NEAE of the School of Basic Education "18 de Noviembre" of Loja. The use of a mixed methodology is proposed, which adopts quantitative and qualitative approaches, which obeys the action research line. The sample is made up of 30 teachers who provide information on the subject in a validated questionnaire, then the application of the instruments was carried out with the proposed sample. At the end of this theoretical study we conclude that, initially, teachers do not integrate ICTs in the classroom, however, later it was observed that the correct integration of ICT tools in the classroom allows students to achieve better grades in the different activities. , but there are still drawbacks such as lack of teacher training, inadequate infrastructure, and resistance to the use of ICTs.
Brain-optimized models reveal increase in few-shot concept learning accuracy across human visual cortex

A concept manifold is the collection of all possible brain activity patterns evoked by stimuli exemplifying the concept. A recent theory (Sorscher et al., 2022) identified several geometric elements of concept manifolds that accurately predicts their distinguishability under few-shot learning. Here, we use this theory to characterize the representational geometry of the same set of visual concepts in direct brain data (the Natural Scene Dataset, a large fMRI dataset of response to natural images), in accurate end-to-end encoding models predicting this neural activity, and in neural networks trained to classify a separate set of concepts. This direct comparison demonstrates that the brain organizes concepts in a manner very different from artificial networks trained as visual concept classifiers. Although few-shot accuracy of visual concepts tends to increase in both brains and artificial networks with generalized ascension (toward anterior areas in brains, deeper layers in networks), we find that the geometry of concept manifolds in early visual areas (e.g. V1) is more similar to the last (readout) layers of neural networks than to lower layers. This suggests that the human visual system may be subject to very different learning pressures than those that arise in supervised training for core object recognition in artificial neural networks.
Research on Alumni Resources Development and Alumni Association Work in Colleges and Universities under "Integrated Media" Environment

Integrating alumni resources, building interactive platforms and carrying out alumni work through modern tools such as integrated media and meta-universe are of great significance in promoting interconnected interaction and mutual assistance between alumni and their alma mater as well as the society. The study has sorted out the construction of alumni associations in Chinese colleges and universities, focused on collating the advantages of the melting media in promoting the development of alumni resources and improving the efficiency of alumni associations, promoting the transformation of alumni service mode, as well as fostering the culture of alumni, etc. The study explores and establishes the paths and ways how to make use of the advantages of the melting media to carry out the work of alumni associations in the new environment, including the construction of a cloud-based data-tracking and management system, forming a This includes building a cloud-based data tracking management system, forming a "1+M+N" alumni work team, and creating a new IP of the alumni association, so as to realise the effective output of alumni work.
EASY E-VOTE: An Ethereum-based Voting System using IPFS and MetaMask for Student Government Election

EASY E-VOTE is a student government election voting system that utilized Ethereum blockchain, IPFS (Interplanetary File System), and MetaMask. This paper addressed challenges in traditional e-voting systems by introducing blockchain technology. The Ethereum blockchain provided security and immutability, while IPFS ensured efficient and distributed data storage. MetaMask offered a user-friendly interface. Moreover, the study reviewed literature on e-voting system security, immutability, data storage, distribution, privacy, and anonymity. As part of its data gathering, the study encompassed smart contract testing and a mock election using the proposed system. These experiments demonstrated system functionality and effectiveness. Overall, EASY E-VOTE offered a promising solution for student government elections, addressing limitations of traditional e-voting systems. Blockchain, IPFS, and MetaMask contributed to system security, data integrity, and user-friendliness, making it a viable alternative for transparent and trustworthy elections.
The Mechanical Psychologist: Leveraging AI for Detecting Predatory Behaviour in Online Interactions

Share on The Mechanical Psychologist: Leveraging AI for Detecting Predatory Behaviour in Online Interactions Author: Darren Cook Institute for Risk and Uncertainty, University of Liverpool, United Kingdom and Dyson School of Design Engineering, Imperial College London, United Kingdom Institute for Risk and Uncertainty, University of Liverpool, United Kingdom and Dyson School of Design Engineering, Imperial College London, United Kingdom 0000-0002-6810-0281View Profile Authors Info & Claims AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and SocietyAugust 2023Pages 1010–1012https://doi.org/10.1145/3600211.3604734Published:29 August 2023Publication History 0citation29DownloadsMetricsTotal Citations0Total Downloads29Last 12 Months29Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Literasi Investasi Untuk Generasi Millenial Di Gresik

The purpose of the community service activities that have been carried out is related to literacy and investment. The investment literacy intended in this community service is investment literacy in the stock investment. This stock investment was introduced to the millennial generation to provide an overview regarding the importance of investing for young peopleand then the implementation of this stock investment for them. This investment literacy is carried out by the millennial generation in Gresik City. This community service activity is carried out in two stages, First, literacy stage by providing literacy to the importance of investing in the millennial generation. second, the implementation stage by investing through a securities company to be able to invest in stocks, because to be able to invest in shares a minimum of one hundred thousand rupiah.
Does the visual system temporally demarcate the complex flow of liquids?

Imagine watching water rush through a ramp dotted with planks. We may readily recognize the viscosity of the liquid and its non-rigid boundaries, but here we ask: Does the visual system temporally segment the complex dynamics of liquids into discrete events, reflecting a prediction hierarchy? To address this, we simulated nine brief naturalistic animations of liquids interacting with 25 planks in a 5-by-5 maze formation. The liquid flowed from the top of the planks, starting either from the left, middle, or right. Studying event segmentation in this setting poses a unique challenge: Unlike a typical physical scene with rigid bodies (e.g., colliding balls), there is no obvious pattern for temporally demarcating the complex branching and splashing flow of the liquid through the planks. Instead, we take a data-driven approach using a recent performance-based task for discovering event boundaries. We transiently (120 ms) slowed down each animation at one of 18 different time points and recruited participants (N=30 per scene and time point combination) to press a key when they noticed a pause, with the idea that detection accuracy would be decreased at event boundaries. Chi-squared tests revealed a significant effect of time points on the number of detections in five of the nine animations; three others were marginally significant. Fisher's exact tests comparing participants’ detection counts in each time point and the original videos (without slow-downs) indicated that 1-2 time windows in a typical video were as undetectable as the original video. Finally, we found that pixel-distances resulting from slow-downs significantly correlated with detection rates (r = .55). Future work should systematically decouple the effects of pixel change and potential event boundaries in perception of liquid dynamics. Spontaneous event segmentation suggests that the visual system may build predictive “event models” of near-future dynamics of otherwise non-linear, complex liquid flow.
Comparison of Signal to Noise in Vision and Imagery for qualitatively different kinds of stimuli

Brain activity during mental imagery is often characterized as a reactivation of visual activity. Brain areas vary considerably in their response to qualitatively different visual stimuli, but it is currently unknown if these effects are preserved during mental imagery. To investigate this issue, we tested if the activity profile across different visually responsive brain areas remains stable when subjects imagine two qualitatively different kinds of stimuli. Specifically, we conducted a 7T fMRI experiment in which subjects viewed and imagined simple (bars and crosses) and complex (natural scene images and artwork) stimuli, and calculated signal-to-noise ratios (SNR) in individual voxels during imagery and vision. All 8 subjects of the Natural Scenes Dataset (NSD) experiment (Allen et al., 2022) took part in this additional scan session. For every vision run, there were 2 corresponding imagery runs. Significant differences in the SNR profile were observed across the two imagery runs for simple stimuli, alluding to a potential practice effect. We thus focused subsequent analyses on data from the second run only. We used an AlexNet-based encoding model to sort voxels according to their preferred network layer. We then calculated median SNR during vision and imagery and for both stimulus types as a function of network layer preference. During vision, median voxelwise SNR for simple stimuli was greater than for complex stimuli in voxels that preferred lower network layers, whereas, for voxels that preferred higher network layers, SNR for complex stimuli was greater. We observed the same trend during imagery, although the SNR mean and variance across layers was greatly reduced relative to vision. We conclude that while vision enjoys much higher SNR than imagery, the effect of stimulus type on SNR is preserved by the transformation from seen to imagined representations.
Pengaruh Model Discovery Elerning terhadap Materi Sistem Pernapasan untuk Meningkatkan Kecerdasan Sosial Peserta Didik

Creative thinking must be developed in the learning process, so that students are accustomed to developing their creativity. According to Fadly (2014: 166) media is a component of learning resources or physical vehicles that contain instructional material among students which can stimulate students to learn. to convey messages or learning materials. In addition, learning media is useful for attracting students' interest in learning material and increasing student understanding. The goal is to apply an interesting and student-centered learning model with learning activities that activate students to construct subject matter so as to improve learning outcomes. One learning model that can overcome these problems is Discovery Learning. Discovery is a learning model that was developed based on a constructivism view.&#x0D; Keywords: Discovery Elerning Model, Respiratory System Material, Social Intelligence&#x0D;
Assessing Visual Short-Term Memory in 5- to 12-Month-Old Infants Using an Eye-Tracking Change-Localization Task at Set Sizes Three and Four

Visual short-term memory (VSTM), a subcomponent of working memory, is a memory system that briefly stores and maintains visual information across disruptions that may occur with eye movements and eye blinks (Luck, 2007). Research using the simultaneous stream change detection task has examined infants’ VSTM with arrays of as many as 6 items (Ross-Sheehy et al., 2003). More precise one-shot change-localization tasks have primarily tested infants’ VSTM for arrays of two items (Oakes et al., 2013). Here, we assessed VSTM in a group of 54 5- to 12-month-old infants using an eye-tracking change localization procedure. Here, infants saw trials with the following sequence: a 500-ms sample array of three or four (set sizes) colored circles, followed by a 300-ms delay array with a blank screen, and finally a 2000-ms test array in which one circle chosen at random changes color. We calculated change localization scores during the test phase for each infant on each trial by dividing the duration of infants’ looking at the changed item by their looking at all items (3 or 4 items) combined. Successful detection and localization of the changed item is indicated by above chance change localization scores, .33 for set size 3, and .25 for set size 4. Infants’ mean change localization was significantly above chance for both set sizes three, t(53) = 3.45, p = .001, and four, t(53) = 5.28, p < .001. In addition, change localization at set size three was significantly correlated with age, r = .30, p = .03, but not set size four, r = .12, p = .40, indicating increased sensitivity to change at set size three during infancy. These results add to our growing understanding of the development of VSTM in infancy, and demonstrate the utility of our change localization task with larger set sizes.
A Variable Step Crow Search Algorithm and Its Application in Function Problems

Optimization algorithms are popular to solve different problems in many fields, and are inspired by natural principles, animal living habits, plant pollinations, chemistry principles, and physic principles. Optimization algorithm performances will directly impact on solving accuracy. The Crow Search Algorithm (CSA) is a simple and efficient algorithm inspired by the natural behaviors of crows. However, the flight length of CSA is a fixed value, which makes the algorithm fall into the local optimum, severely limiting the algorithm solving ability. To solve this problem, this paper proposes a Variable Step Crow Search Algorithm (VSCSA). The proposed algorithm uses the cosine function to enhance CSA searching abilities, which greatly improves both the solution quality of the population and the convergence speed. In the update phase, the VSCSA increases population diversities and enhances the global searching ability of the basic CSA. The experiment used 14 test functions,2017 CEC functions, and engineering application problems to compare VSCSA with different algorithms. The experiment results showed that VSCSA performs better in fitness values, iteration curves, box plots, searching paths, and the Wilcoxon test results, which indicates that VSCSA has strong competitiveness and sufficient superiority. The VSCSA has outstanding performances in various test functions and the searching accuracy has been greatly improved.
Development of Web-Based Vehicle Service Application Program on CV X In Bekasi

The increasing number of motorized vehicles, followed by the addition of new brands and types of vehicles, is certainly one of the factors causing the development of the automotive world in Indonesia and reflects the increasingly fierce competition in the automotive world. The research was conducted at CV X Bekasi with qualitative descriptive research methods. Data obtained from interviews the data sources were obtained from documents and informants. Research informants were managers, staff and main documents of CV X. Data analysis was carried out using interactive analysis techniques, which included data collection, data presentation, data reduction and conclusion drawing. So far, the manufacture of work orders is still manual (handwritten), so there are still frequent errors in customer service and data. Often the wrong number of spare parts causes the car repair process to be hampered. Can provide output in the form of Work Orders (SPK), Invoices and Reports.