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We describe visual classification by a hierarchy of semantic fragments. In fragment-based classification, objects within a class are represented by common sub-structures selected during training. Here we propose two extensions to the basic fragment-based scheme. The first extension is the extraction and use of feature hierarchies. We describe a method that automatically constructs complete feature hierarchies from image examples, and show that features constructed hierarchically are significantly more informative and better for classification compared with similar non-hierarchical features. The second extension is the use of so-called semantic fragments to represent object parts. The goal of a semantic fragment is to represent the different possible appearances of a given object part. The visual appearance of such object parts can differ substantially, and therefore traditional image similarity-based methods are inappropriate for the task. We show how the method can automatically learn the part structure of a new domain, identify the main parts, and how their appearance changes across objects in the class. We discuss the implications of these extensions to object classification and recognition. Joint work with Prof. Shimon Ullman. ©2006 Microsoft Corporation. All rights reserved.

Well we're back, and in true aussie fashion we are fucking late. Oh well, I'm sure our one fan will be consoled by the fact that this may possibly be our most Australian podcast yet, if only for the mention of Stack Hats. If you don't know what they are you led a deprived and sad life. Anyways, on with the show! Shit we talk about: - STACKHATS!! - We love old school games - Silent Hill Homecoming, it aint no 5 - Sam and Max = God - Scratch that, Mike Patton = God - Duke Forever, RIP - Ahhhh, [url=http://gamingisstupid.com/2009/05/06/the-chair-story-revival/]that chair story[/url]. - We get pissy about Ghostbusters, then at eachother - Where the fuck is our MvC2 demo? - The RC Spotlight: Tits in games. Seriously. - Dillinger Drops his phone number again. Somebody call the bastard. - Listener questions yaaaaayyyy! - AnimuCast! - I know too much about Luchadores Intro Music: Bionic Commando: Rearmed OST - Power Plant Break Music: Selkies the endless obsessions - Between The Buried And Me. 8Bit Version by [url=http://www.youtube.com/user/xTwilightxxPrincessx]xtwilightxxprincessx[/url] Outro Music: Bulb - Illustrated 8 Test

Well we're back, and in true aussie fashion we are fucking late. Oh well, I'm sure our one fan will be consoled by the fact that this may possibly be our most Australian podcast yet, if only for the mention of Stack Hats. If you don't know what they are you led a deprived and sad life. Anyways, on with the show! Shit we talk about: - STACKHATS!! - We love old school games - Silent Hill Homecoming, it aint no 5 - Sam and Max = God - Scratch that, Mike Patton = God - Duke Forever, RIP - Ahhhh, [url=http://gamingisstupid.com/2009/05/06/the-chair-story-revival/]that chair story[/url]. - We get pissy about Ghostbusters, then at eachother - Where the fuck is our MvC2 demo? - The RC Spotlight: Tits in games. Seriously. - Dillinger Drops his phone number again. Somebody call the bastard. - Listener questions yaaaaayyyy! - AnimuCast! - I know too much about Luchadores Intro Music: Bionic Commando: Rearmed OST - Power Plant Break Music: Selkies the endless obsessions - Between The Buried And Me. 8Bit Version by [url=http://www.youtube.com/user/xTwilightxxPrincessx]xtwilightxxprincessx[/url] Outro Music: Bulb - Illustrated 8 Test

We investigate detection, classification, and prognostication of osteosarcoma using artificial intelligence.

The study will be a replication of Maria Abascal´s study "Contraction as a Response to Group Threat: Demographic Decline and Whites’ Classification of People Who Are Ambiguously White" in the German context. Participants willl be shown a graph, depicting the increasing share of migrants in Germany, through a time period with varying length. Later on, participants will be shown faces with ambiguous German appearance, of which they should assess, whether they have a migration background or not.

Overview In Thayer & Stevens (2021), we conducted two experiments investigating the effects of human-animal interaction (HAI) on affect and cognition. In these experiments, participants experienced cognitive (working memory, attentional control) tasks before and after either a three-minute exposure to a dog (HAI condition) or a control task. Self-report measures of affect (mood, anxiety, stress) were collected repeatedly throughout the sessions. While interacting with a dog influenced measures of affect, they did not influence cognition. In addition to the affect and cognition measures, participants wore an Empatica E4 to record heart rate, electrodermal activity, body temperature, etc. However, we did not analyze these data, as the sampling rate was too low for robust measures of heart rate variability (Malik et al. 1996, Laborde et al. 2017). Arce & Gehringer (2021) have investigated machine learning algorithms that take Empatica E4 data and classify whether participants were experiencing stress induction. This offers the opportunity to apply the machine learning algorithms developed to classify stress to the Thayer & Stevens (2021) data and address two specific aims: 1. Assess the predictive accuracy of the machine learning algorithms in classifying intervention exposure 2. Determine which features are most important in classifying exposure Research questions 1. How accurately can machine learning algorithms classify stress intervention exposure? 2. Which algorithms best predict stress intervention exposure? 3. Does the accelerometer data act as a naive feature that gives extra weighting to the different affect states? 4. Which features best predict passive and active stress intervention exposures?

The current study is part of the innovative training network ETUDE (Encompassing Training in fUnctional Disorders across Europe; https://etude-itn.eu/), ultimately aiming to improve the understanding of mechanisms, diagnosis, treatment and stigmatization of Functional Disorders (5). Project 5: Predictive validity of diagnostic criteria of FD used in mental health care. ETUDE programme website https://etude-itn.eu/

Pre-registration for a meta-analysis of the relationship between the after-effects of job insecurity and job insecurity.

Protocol for future scoping review investigating speech pathology interventions for multilingual people with aphasia across the ICF.

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This is a Carlson Dettman presentation to City of Green Bay employees describing the Classification and Compenstation Study.

Global public health continues to face substantial obstacles from mosquito borne diseases, making precise and effective techniques for mosquito species identification necessary. We present a unique method in this article called Mosquito Species Classification through Wingbeat Analysis A Hybrid Machine Learning Approach, which uses wingbeat analysis and deep learning techniques to classify mosquito species. Our approach leverages Convolutional Neural Networks CNNs as the core model to provide robust and dependable classification performance. We make use of an extensive dataset that includes wingbeat recordings from many species of mosquitoes and apply comprehensive pre processing and feature engineering techniques to enhance the models effectiveness. Specifically, we extract and combine features such as zero crossing rate ZCR , root mean square energy RMSE , mel frequency cepstral coefficients MFCC , as well as augmented features derived from audio transformations like add noise, shifting, pitching, and stretching. This combination of handcrafted and augmented features helps to enrich the training data and improve the generalizability of the model. After thorough testing and evaluation, we demonstrate that our CNN based method achieves superior performance in accurately classifying various mosquito species. Our findings underscore the potential of deep learning methods, particularly CNNs, to surpass conventional classification techniques in species identification tasks. Additionally, we highlight the critical role of accurate species classification in vector surveillance and epidemiological research, emphasizing the broader impact of our work on ecological studies and disease control strategies. Mir Irtiqa Farooq | Dr. Dheeraj Chhillar | Mudasir Ahmed Muttoo "Hybrid Machine Learning Approach for Mosquito Species Classification using Wingbeat Analysis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd81085.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/81085/hybrid-machine-learning-approach-for-mosquito-species-classification-using-wingbeat-analysis-a-review/mir-irtiqa-farooq

Document from Tamil Nadu Legislative Assembly Digital Archive

Document from Tamil Nadu Legislative Assembly Digital Archive

The aim of the study is to develop a classification system of different sports that expands existing classification systems by including psychologically relevant dimensions (e.g. effects of sport on subjective well-being or mood). To identify relevant dimensions, a systematic literature review on existing classification systems is combined with a qualitative data analysis of open-ended descriptions of specific sports. For each of the identified dimensions, items will be developed that will be further tested empirically in subsequent studies not outlined here.

In this experiment, participants sort recordings from Expt. 1 into groups based on the speaker’s perceived communicative goal (e.g. to express surprise or make an assertion). To accomplish this, we adapted the classic free classification paradigm (Imai & Garner 1965) from a fixed grid-based task to one where participants have even fewer constraints. This is a suitable task for the current study because it makes no assumptions about the form-meaning mappings that will emerge for participants. Based on the results, we can better understand how tunes relate to one another in terms of perceived meaning, shedding light on questions about tunes in acoustic space and their mapping to linguistic functions.

In this experiment, participants sort recordings from Expt. 1 into groups based on the speaker’s perceived communicative goal (e.g. to express surprise or make an assertion). To accomplish this, we adapted the classic free classification paradigm (Imai & Garner 1965) from a fixed grid-based task to one where participants have even fewer constraints. This is a suitable task for the current study because it makes no assumptions about the form-meaning mappings that will emerge for participants. Based on the results, we can better understand how tunes relate to one another in terms of perceived meaning, shedding light on questions about tunes in acoustic space and their mapping to linguistic functions.

Karnataka Legislative Assembly Debates - Book 574, Pages 399-414

Document number CIA-RDP77-00389R000100070023-4 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP78-04718A000100150043-9 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP79-01206A000100110002-8 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP79M00467A002400080002-6 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP79-00498A000600090017-8 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP75-00793R000200170016-3 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP75-00793R000200170028-0 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP78-04718A001000310001-7 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP78-04914A000100130042-4 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP80-00473A000700020007-0 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R000900010061-5 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R000900010064-2 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001000240034-8 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001000240049-2 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001000240058-2 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001200130005-0 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP84B00890R000400050004-7 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP83-01004R000100130003-7 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R000900010005-7 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001000240019-5 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001000240028-5 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001000240028-5 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001000240028-5 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Star Classification • Language: Hebrew • Author: Ashur Cherry (Toronto, Canada) Hebrew Language Publications are Dedicated in Honour of Professor Binyamin Geva

Star Classification • Language: Hebrew • Author: Ashur Cherry (Toronto, Canada) Hebrew Language Publications are Dedicated in Honour of Professor Binyamin Geva

Here is the western herbs as classified in Chinese medicine. Note for the mention of Amanita muscaria: I meant Amanita phalloides. muscaria is not poisonous, but is hallucinogenic. donation link due to fire https://www.gofundme.com/f/help-mountain-gardens-rebuild?utm_campaign=p_lico%20share-sheet&utm_medium=copy_link&utm_source=customer HD Version of Chart at 2:51 https://docs.google.com/spreadsheets/d/1yiXPSM_pQdbcijdfjn6viotRGhCTjaJV/edit?usp=sharing&ouid=105433261568439406017&rtpof=true&sd=true Thank you.

Reserach team comparing the accuracy of 3 AI models vs. each other vs. human baseline

x, 229 p. : 29 cm

Research reports major concerns over classification and measurement for students with limited English proficiency (LEP). A poor operational definition of the English language proficiency construct and validity concerns about existing language proficiency tests are among these issues. Decisions on including LEP students in large-scale assessments such as the National Assessment of Educational Progress (NAEP) may be directly influenced by some of these factors. Poor relationships between the existing LEP classification codes with English proficiency and achievement test scores raise concern over the validity of the LEP classification system. These factors have contributed to inconsistencies in LEP classification across districts and states. Criteria used for the inclusion of LEP students in NAEP need to be more objectively defined. Based on the recommendations of existing research, the appropriate levels of English language proficiency for participation in NAEP should be determined by reliable and valid English language proficiency measures. With funding through a competitive bidding process authorized under the No Child Left Behind section on "Enhanced Assessment Instruments," there are national efforts currently underway in developing English proficiency tests that can be used to provide valid measures of students' level of English proficiency. These efforts should be guided by the relevant theory and research findings, otherwise past problems relating to the validity of English proficiency tests may recur. Multiple criteria including valid and reliable measures of students' level of English proficiency could help with a more consistent decision-making process for the inclusion of LEP students.

Research studies using booklet classification were implemented by the American College Testing Program to investigate the linkage between the National Assessment of Educational Progress (NAEP) Achievement Levels Descriptions and the cutpoints set to represent student performance with respect to the achievement levels. This paper describes the process and reports the results of the booklet classification study (BCS) implemented for the science achievement levels. It explores the possibility of using booklet classification as a way to set achievement levels by investigating methodologies for computing achievement level cutpoints using booklet classification data. These methodologies were applied to BCS data for science in this study and had been applied to geography and U.S. history. The BCS for science achievement levels involved grades 4 and 8, with 13 panelists for each grade level. Eighteen booklets were selected from NAEP forms, and 22 from other sources. The BCS for science, geography, and U.S. history have all resulted in panelists' classifying student performance at a lower level than plausible values scores indicate. These results indicate that cutpoints computed from booklet classification data would be higher than cutpoints based on the item-by-item rating methods that were used operationally. Procedures using the proportional odds model and nonparametric discriminant analysis were developed as a way to compute Achievement Level cutpoints using booklet classification data. Further refinements to these procedures, especially the nonparametric discriminant analysis, are needed before they could be used operationally to set cutpoints. (Contains 15 tables, 3 figures, and 13 references.) (SLD)

This report contributes to the comprehensive evaluation of the Common Core of Data (CCD) program by providing an analysis of CCD definitions in order to identify potential classification problems. At the time of the evaluation, the CCD program consisted of four surveys. The focus of this evaluation is on the "Public Elementary/Secondary Education Agency Survey." The evaluation process was initiated by researching state statutes and administrative codes to determine the powers, governance, and services provided by agencies in each state. A detailed analysis of CCD definitions reveals that these definitions are generally inadequate. Many are neither exhaustive nor inclusive. The inadequacy and misinterpretation of these definitions leads to unreliable and invalid data. CCD definitions often do not draw a clear distinction between school and agency surveys. The definitions should be revised to be flexible and exhaustive, with clear documentation and explanation provided for instances in which individual arrangements in states differ from conventional approaches. All education surveys that use CCD definitions as their sampling frame should be coordinated. Changes are especially needed in the areas of enrollment and geographic coding. (Contains two tables.) (SLD)

This report contributes to the comprehensive evaluation of the Common Core of Data (CCD) program by providing an analysis of CCD definitions in order to identify potential classification problems. At the time of the evaluation, the CCD program consisted of four surveys. The focus of this evaluation is on the "Public Elementary/Secondary Education Agency Survey." The evaluation process was initiated by researching state statutes and administrative codes to determine the powers, governance, and services provided by agencies in each state. A detailed analysis of CCD definitions reveals that these definitions are generally inadequate. Many are neither exhaustive nor inclusive. The inadequacy and misinterpretation of these definitions leads to unreliable and invalid data. CCD definitions often do not draw a clear distinction between school and agency surveys. The definitions should be revised to be flexible and exhaustive, with clear documentation and explanation provided for instances in which individual arrangements in states differ from conventional approaches. All education surveys that use CCD definitions as their sampling frame should be coordinated. Changes are especially needed in the areas of enrollment and geographic coding. (Contains two tables.) (SLD)