Synopsis : Canadian Minister of Sport and Persons with Disabilities Working Group on Gender Equity in Sport are leaders, innovators and influencers who are ready to advance the sport system in Canada.
All Canadians should have the opportunity to get involved and excel in sport, regardless of gender, age and ability. They should be able to do so in an environment free of discrimination or harassment.
In 2016, the Dairy Farmers of Canada and the Canadian Association for the Advancement of Women in Sport and Physical Activity (CAAWS) released the report Women in Sport: Fueling a Lifetime of Participation. It highlighted several areas of gender inequality for Canadian women in sport. According to the research, most Canadian women are missing out on the benefits of sport-while 41 percent of girls aged 3-17 do not participate in sport, this jumps to 84 percent in adult women. Other key findings showed that only 24 percent of all athletic director positions and 17 percent of all head coaching spots in Canadian Interuniversity Sport are held by women, and only 38 percent of senior staff and 29 percent of board members at national and multi-sport organizations are female.
Today, the Honourable Kirsty Duncan, Minister of Science and Minister of Sport and Persons with Disabilities, announced a Working Group on Gender Equity in Sport to address the lack of equity. The working group will be chaired by Guylaine Demers, a renowned professor at Université Laval and president of Égale Action.
Members of the working group will review existing research and data, share personal experiences, discuss the challenges faced by women and girls in sport, examine issues that may affect participation, and propose ideas, approaches and strategies for eliminating barriers and increasing participation.
In Budget 2018, the Government of Canada announced a target to achieve gender equity in sport at every level by 2035. This included an initial commitment of $30 million over three years to support data and research into innovative practices to promote women and girls' participation in sport, and to support national sports organizations in promoting greater inclusion of women and girls in all facets of sport.
The work that will be accomplished by this working group will inform and complement other initiatives aiming to achieve gender equity in sport.
Although many girls participate in sport in their early years, reports show significant declines in sport participation in the transition to adolescence, with a sharper decline in girls' participation rates than boys.
While women and girls make up half of Canada's population, females participate in sport at lower rates than males. Research by CAAWS shows that 79 percent of boys and 70 percent of girls participate in sport, but adolescent girls tend to drop out of sport at a much higher rate than boys. As girls enter adolescence, their overall participation rate falls 22 percent, and school sport participation drops by almost 26 percent.
Women make up only 26.3 percent and 38 percent, respectively, of board members in national sport organizations and multisport service organizations. The proportions are much lower when it comes to occupying board chair positions.
According to data from Sport Canada's Sport Funding and Accountability Framework, from 2012 to 2016, 30 percent of the 706 total major official positions of national sport organizations (NSOs) were held by women in both winter and summer sports. Mainstream sports reported that 30 percent of the major official positions were held by women out of a total of 598 positions. Athletes with a Disability (AWAD) sports reported that 37 percent of major official positions were held by women. Of the 33 major official positions reported by NSOs servicing both mainstream athletes and AWAD, none were held by women.
The working group will discuss the challenges faced by women and girls in coaching, as the percentage of female Olympic and Paralympic national team coaches has always been too low. Canada's Olympic and Paralympic national team coaches are overwhelmingly male. At the London 2012 Olympic Games, 20.43 percent of the national team coaches were female. This dropped to 9.20 percent for the PyeongChang 2018 Olympic Games. Similarly, the percentage of female Paralympic national team coaches was low, at 11.76 percent in 2012 and 9.09 percent in 2018.
The 2018 Gold Coast Commonwealth Games were the most inclusive international games in history: the first such games to achieve gender equality by having the same number of medal events (133) for women and men.
Guylaine is a Full Professor at Laval University and the President of Égale Action, an organization dedicated to the equality of women through sport and physical activity. Her areas of expertise include women in sport, coach education, competency-based training and homophobia in sport.
As Chairperson of the International Paralympic Committee (IPC) Athletes' Council, Chelsey serves on the IPC Board of Governors and the IOC Athlete Commission. She has served as the Athlete Services Officer for the Canadian Paralympic Team Mission Staff for Rio 2016, Toronto 2015, Sochi 2014, and London 2012. She worked for the Toronto 2015 Pan and Parapan Am Games Organizing Committee from 2010 to 2014. Born and raised in Nova Scotia, Chelsey is a passionate athlete advocate for sport and the Paralympic movement, and a motivational speaker who believes in what the power of sport can do to positively change lives.
John is the current head coach of Canada's Men's National Soccer Team. Previously, he was head coach for Canada's Women's National Soccer Team from 2011 to 2017 and led the squad to two Olympic bronze medals at Rio 2016 and London 2012. John was also head coach for the New Zealand Women's National Football Team from 2006 to 2011.
Waneek is currently working with the Assembly of First Nations as the IndigenACTION Ambassador to develop a National Indigenous Sport, Fitness and Wellness Strategy, with an aim to attract Indigenous youth to higher education by building self-esteem and emphasizing a balance between education and sport. In 2015, she was named one of Canada's most influential women in sport by the Canadian Association for the Advancement of Women and Sport and Physical Activity (CAAWS). In 1999, she won the national Tom Longboat Award that recognizes Aboriginal athletes for their outstanding contributions to sport in Canada.
Bruce is an Olympian and former track and field athlete who is now the Co-Chair of the Ontario Minister's Advisory Panel on Ontario's Sport Plan. He is an honorary member of the Canadian Olympic Committee (COC) and an Officer of the Order of Canada (2004). A renowned author and academic, Bruce is currently Vice-President, University of Toronto, and Principal at the University of Toronto Scarborough Campus.
Lorraine is known as a strategic thinker, and has been a strong and well-respected leader in the sport community for many years. Her passion for sports goes back decades. She provides strong leadership around ethics in coaching, and is an advocate for the promotion of coaching programs specifically targeting and tailored to women. Lorraine is currently the Chief Executive Officer at the Coaching Association of Canada.
Nancy worked as a CBC reporter, producer and management executive at 4 Commonwealth Games, 2 Pan American Games and 15 Summer and Winter Olympic Games. As Head of CBC Sports, her role was to oversee the production and lead the negotiations for the Corporation's sports programming. She is the only woman in the world to have held such a position. Last year she coordinated the Gender Equality Review project for the International Olympic Committee; the project's recommendations and call-to-action require gender equality in all facets of the Olympic Movement.
Karin has over 30 years of experience working with national, provincial, territorial and multi-sport organizations in Canada and around the world. She was Executive Director of CAAWS for 14 years, providing leadership for a wide range of initiatives. She developed the Women and Sport Policy Recommendations and examples for CAAWS to present to the Minister of Sport and Persons with Disabilities in November 2017, and worked with Sport Canada on the revision of their Women in Sport Policy and Action Plan released in summer 2009. Karin also volunteered as Secretary Treasurer for eight years on the International Association of Physical Education and Sport for Girls and Women (IAPESGW) and was a member of the COC's Women and Sport Committee for nine years (2008 to 2017).
Allison is an experienced non-profit leader with a passion for social innovation that advances equity and opportunity. She has diverse experiences leading innovative partnerships, programs, fundraising and operations. She brings a holistic perspective and ability to align activities for maximum social impact. Allison believes that sport and physical activity offer powerful opportunities for people to realize their full potential, a conviction that stems from her own experiences with competitive and recreational sports throughout her life. As CEO of CAAWS, Allison seeks to ensure that more women, girls and marginalized Canadians enjoy these myriad benefits, improving the sports system and Canadian society in the process.
Carolyn has worked in the Canadian sport system for over 30 years with a variety of national and provincial sport organizations, and is currently working at the Sport for Life Society. In her current role, she has worked with many national sport organizations in the development of their Long-Term Athlete Development Plan (LTADP) frameworks and Competition Review and Restructuring Plans to ensure that LTADP are embedded into their culture and programs. She also started the Winnipeg Newcomer Soccer and Multi-Sport Academy, working extensively with various immigration organizations to address challenges faced by girls, including enriching sport opportunities for young women in the hope of improving self-confidence.
Adam is one of Canada's most celebrated athletes and has four Olympic medals-the most by a Canadian paddler. He sees his opportunity to affect positive change in Canada's youth as his most rewarding obligation as a Canadian Olympian and public speaker. He also plays a key role in giving athletes a voice at the Canadian Olympic Committee executive level and providing recommendations to the COC on a wide range of issues that affect high-performance athletes.
Hayley is the longest-serving member of Canada's National Hockey Women's Team. She has won four gold medals and one silver medal, making her one of Canada's most decorated Olympians. She is the founder of the Canadian Tire Wickenheiser World Female Hockey Festival (WickFest), where over 2,000 players compete and connect while building positive female hockey experiences across all levels. In addition, she is active with such charities as Spread the Net, Clean Air Champions and Right to Play, and is Chair of the Athletes' Advisory Group at Highmark Interactive. Hayley was appointed an Officer of the Order of Canada in June 2011.
*News Release by Canadian Heritage: Disabled World makes no warranties or representations in connection therewith. PR content may be edited for style, clarity, and/or length. If you are connected with this page and want it corrected/updated please contact us.


text:
package perf

import (
	"fmt"
	"os"

	info "github.com/google/cadvisor/info/v1"
	"github.com/google/cadvisor/stats"
	"github.com/google/cadvisor/utils/sysinfo"
)

type manager struct {
	events      PerfEvents
	onlineCPUs  []int
	cpuToSocket map[int]int
	stats.NoopDestroy
}

func NewManager(configFile string, topology []info.Node) (stats.Manager, error) {
	if configFile == "" {
		return &stats.NoopManager{}, nil
	}

	file, err := os.Open(configFile)
	if err != nil {
		return nil, fmt.Errorf("unable to read configuration file %q: %w", configFile, err)
	}

	config, err := parseConfig(file)
	if err != nil {
		return nil, fmt.Errorf("unable to parse configuration file %q: %w", configFile, err)
	}

	onlineCPUs := sysinfo.GetOnlineCPUs(topology)

	cpuToSocket := make(map[int]int)

	for _, cpu := range onlineCPUs {
		cpuToSocket[cpu] = sysinfo.GetSocketFromCPU(topology, cpu)
	}

	return &manager{events: config, onlineCPUs: onlineCPUs, cpuToSocket: cpuToSocket}, nil
}

func (m *manager) GetCollector(cgroupPath string) (stats.Collector, error) {
	collector := newCollector(cgroupPath, m.events, m.onlineCPUs, m.cpuToSocket)
	err := collector.setup()
	if err != nil {
		collector.Destroy()
		return &stats.NoopCollector{}, err
	}
	return collector, nil
}

redpajama_set_name: RedPajamaGithub


Until recently, New Zealand developers had been slow to adopt the engineered timber structures in contrast to leaders in Australia, USA. Now, more engineers here are recognising engineered wood's advantages with new building information modelling technology for designing commercial buildings.
In August a national conference on engineered wood for commercial and multi-residential building is set to attract hundreds of early adopters as New Zealand moves fast to catch up to our Australian neighbours in sustainable commercial buildings.
"Following trends in Australia and USA, the use of engineered wood is growing as BIM becomes more widely used by complete project teams – from engineers and architects right through to the trades," says John Stulen, conference director for the 3rd Annual Changing Perceptions engineered wood conference on sustainable commercial building.
"The shorter project times have also caught the eye of all of leading trades contractors, especially when their people see BIM in action on a tall wood building project," says Stulen.
"This year we are delighted that 100% of our conference case studies will be outlining New Zealand wood projects," he adds.
Leading architects, Jasmax, designed a stunning apartment building – Merchant Quarter 2 – over a carpark in Grey Lynn, Auckland.


Moths are a vital part of life, yet they're widely misunderstood – and to some even a little scary. Wait, is that moths, or maths? Moth enthusiast Martin Coath and maths fan Katie Steckles share their favourite facts about moths, maths, and the maths of moths. From wing patterns to their evolution, see how maths can explain moths (but you probably won't see any moths explaining maths).


GOLDEN Evander Kane Jersey KNIGHTS at SHARKS10 p.m. ET; NBCSN, CBC, TVASVegas prospects best-of-7 series 2-1SAN JOSE — The Vegas Golden Knights will try and just take a 3-1 sequence lead from the San Jose Sharks in Video game four in the Western Convention Next Spherical at SAP Middle on Wednesday. The Golden Knights defeated the Sharks 4-3 in additional time in Video game three here Monday. San Jose defeated Vegas 4-3 in the second overtime in Sport two at T-Mobile Arena on Saturday. The Golden Knights received Sport one on Friday, 7-0.Groups that take a 3-1 direct in best-of-7 Stanley Cup Playoff sequence have an all-time collection record of 274-28, in keeping with the Elias Athletics Bureau.Listed here are five keys for Activity four:[RELATED: Finish Golden Knights vs. San Jose Sharks collection coverage]1. Desperate SharksIf San Jose loses, it's going to have to defeat Vegas a few straight situations, together with two times over the highway, to get the collection."For them, they are destined to be fired up," Golden Knights forward James Neal mentioned. "They don't want to go back to Vegas down 3-1. So we bought to learn what we are heading up in opposition to over there. They're destined to be a hungry crew, a desperate group. For us, we got to stay over the fuel."Video: Di secting the Golden Knights' Recreation 3 additional time winner2. Lineup alterations for San Jose?Sharks mentor Peter DeBoer said he experienced created no lineup decisions, but it surely appears most likely that rookie defenseman Joakim Ryan will swap Paul Martin which there will be alterations within the very first and third lines. Ryan arrived off the ice early, and Martin stayed on late in what DeBoer referred to as an optional skate.Heart Joe Thornton, who hasn't performed because injuring his proper knee Jan. 23 from the Winnipeg Jets, took rushes within the top rated line but explained he was not participating in in Match four.Forward Evander Kane wasn't on the ice and has been dealing with an upper-body harm Tim Heed Jersey due to the fact late inside the frequent year, but he typically mi ses skates. Joe Pavelski most likely will skate among Kane and, dependant on line rushes, Barclay Goodrow, who performed centre to the third line in Sport three. Chris Tierney, who was middle within the top line in Video game 3, was back again to the 3rd line.three. Exclusive teamsThe winner of each and every recreation from the sequence thus far has won the special-teams battle. Neal said remaining from the penalty box is going to be crucial in Sport 4."It's just not retaliating," he claimed. "It's not punching someone within the head. It truly is not slashing somebody. If another person offers you a whack, you do not whack them back."Vegas was 3-for-10 to the ability perform and 5-for-5 on the penalty destroy from the Recreation one gain, and 2-for-6 to the power participate in and 3-for-4 around the penalty destroy in the Sport 3 acquire. San Jose scored two power-play aims and failed to allow for one in its Activity 2 gain.Video: VGK@SJS, Gm3: Marche sault nets one-timer for PPG4. Vegas' top lineThe initial line of William Karl son, Jonathan Marche sault and Reilly Smith has mixed for 19 details (6 objectives, 13 a sists) in the sequence. Karl son has 3 objectives and 4 a sists, Marche sault has two goals and 4 helps, and Smith has one particular objective and five a sists. Pavelski reported staying from the penalty box would enable slow down that line."You feed those people fantastic players somewhat extra time and let them feel the puck a https://www.fashionssharks.com/san-jose-sharks-jersey/evander-kane-jersey little bit, they begin finding their match," Pavelski stated.five. Fleury factorThe Sharks managed engage in for much in the initially interval in Sport three, but Marc-Andre Fleury created the help you save on all 16 pictures he faced."The superior i sue is we are generating [shots]," DeBoer said. "You bought to present Fleury credit history. He was excellent early, but I feel we just obtained to stick with it. You keep obtaining volume like that, you hope finally it breaks."Video: VGK@SJS, Gm3: Fleury extends his pad to deny HertlGolden Knights projected lineupJonathan Marche sault — William Karl son — Reilly SmithJames Neal — Erik Haula — Alex TuchDavid Perron — Cody Eakin — Tomas TatarTomas Nosek — Pierre-Edouard Bellemare — William CarrierBrayden McNabb — Nate SchmidtShea Theodore — Deryk EngellandJon Merrill — Colin MillerMarc-Andre FleuryMalcolm SubbanScratched: Ryan Carpenter, Ryan Reaves, Oscar Lindberg, Luca Sbisa, Brad Hunt Wounded: NoneSharks projected lineupEvander Kane — Joe Pavelski — Barclay GoodrowTomas Hertl — Logan Couture — Mikkel BoedkerTimo Meier — Chris Tierney — Kevin LabancMarcus Sorensen — Eric Fehr — Melker Karl sonJoakim Ryan — Brent BurnsMarc-Edouard Vlasic — Justin BraunBrenden Dillon — Dylan DeMeloMartin JonesAaron DellScratched: Paul Martin, Joel Ward, Jannik Hansen, Dylan Gambrell Wounded: Joe Thornton (knee), Joonas Donskoi (le sen human body)Position reportThe Golden Knights had the exact same forward traces and protection pairs from Match three in the course of their morning skate. … Donskoi, who skipped Game 3, didn't skate. … Ryan hasn't performed considering that March 16 against the Calgary Flames.
Pizzeria di Misterbianco (CT) cerca pizzaiolo con esperienza nel settore. Requisiti richiesti: Conoscenza degli impasti; Conoscenza del forno a legna ed elettrico; Capacità di lavorare in team. Gli interessanti potranno inviare il loro cv a info@scuolapizzaiolieuropei.it o inviarci un messaggio privato sulla nostra pagina Facebook.


12747789 29 Metal coordination-based inhibitors of adenylyl cyclase: novel potent P-site antagonists. Millennium Pharmaceuticals, Inc.
12372508 20 Hydroxamate based inhibitors of adenylyl cyclase. Part 2: the effect of cyclic linkers on P-site binding. Millennium Pharmaceuticals, Inc.
12372507 30 Hydroxamate based inhibitors of adenylyl cyclase. Part 1: the effect of acyclic linkers on P-site binding. Millennium Pharmaceuticals, Inc.
10937732 15 6-Bicyclopiperazinyl-1-arylsulfonylindoles and 6-bicyclopiperidinyl-1-arylsulfonylindoles derivatives as novel, potent, and selective 5-HT6 receptor antagonists. NPS Allelix Corp.


text:
Q: Laravel Share Variables between multiple Controllers I'm using the AppServiceProvider to share specific admin data with all related views.
Now I'd like to share admin data also with all admin related Controllers to replace Auth::guard('admin')->user()->firstname by $admin->firstname.
$id = Auth::guard('admin')->user()->id;
$admin = Admin::findOrFail($id)->first();

How can I share this piece of code with all related Controllers to get for example admin firstname via $admin->firstname?

A: I would go about creating a global middleware
php artisan make:middleware AdminCarrier

In the middleware, you can add the $admin variable to the $request bag and access it via the Controller.
class AdminCarrier
{
    public function handle($request, Closure $next)
    {
        $id = Auth::guard('admin')->user()->id;
        $admin = Admin::findOrFail($id)->first();

        $request->request->add(['admin' => $admin]);

        return $next($request);
    }
}

In the controller, you can access it via:
$request->admin

Make sure the middleware is global by registering it at Http/Kernel.php

redpajama_set_name: RedPajamaStackExchange


Running from injury – why do runners get injured and how do we stop it?
On Wednesday January 31st I will host a free event at my University to address the topic - why do runners get injured and how do we stop it? Intended to be an interactive seminar between 6 - 8pm, it is open to runners, coaches, clinicians, scientists and members of the general public. The only catch … Continue reading Running from injury – why do runners get injured and how do we stop it?


text:
Lazy Lester
born on 20/6/1933 in Torras, LA, United States
Links www.lazylester.net (English)
Biography Biography
Lazy Lester (born Leslie Johnson,[1] June 20, 1933)[2][3] is an American blues musician, who sings, and plays the harmonica and guitar. His career spans the 1950s to the 2010s.
Best known for regional hits recorded with Ernie Young's Nashville, Tennessee based Excello label, Lester also contributed to songs recorded by Excello label-mates including Slim Harpo, Lightnin' Slim, and Katie Webster. His songs have been covered by (among others) The Kinks, The Flamin' Groovies, Freddy Fender, Dwight Yoakam, Dave Edmunds, Raful Neal, Anson Funderburgh, and The Fabulous Thunderbirds. In the comeback stage of his career (since the late 1980s) he has recorded new albums backed by Mike Buck, Sue Foley, Gene Taylor, Kenny Neal, Lucky Peterson, and Jimmie Vaughan.
In the mid-1950s, Lester was on the margins of the Louisiana blues scene. According to Rolling Stone (February 23, 2006), Buddy Guy, before moving to Chicago, had played in Louisiana "with some of the old masters: Lightnin' Hopkins, Lazy Lester, Slim Harpo." When Guy left for Chicago, in 1957, Lester replaced him, on guitar, in a local band—even though Lester, at the time, did not own such a musical instrument.
Lester's career took off when he found a seat next to Lightnin' Slim on a bus transporting Slim to an Excello recording session. At the studio, the scheduled harmonica player did not appear. Slim and Lester spent the afternoon unsuccessfully trying to find him, when Lester volunteered that he could play the harmonica. Lester's work on that first Lightnin' Slim session led the producer, Jay Miller, to record Lester's solo and also to use him as a multi-instrumentalist on percussion, guitar, bass, and harmonica on sessions headlined by other Miller-produced artists including, notably, Slim Harpo. "Percussion" on these sessions went beyond the traditional drum kit, and included a rolled-up newspaper on a cardboard box.[2]
Miller dubbed Lester "Lazy Lester" because of his laconic, laid-back style.[2]
More than his vocal delivery, Lester is best remembered for songs that were later covered by a wide range of rock, country, blues, and Tex-Mex stars, chiefly, "I'm a Lover Not a Fighter," "I Hear You Knockin'," and "Sugar Coated Love."
Lester stated that he wrote these songs, but almost all are officially credited to Miller, or to Lester and Miller. Lester also stated he received few royalties, which embittered him and made him skeptical of the music industry. By the late 1960s, Lester had given up on the music industry, working manual labor and pursuing his favorite hobby—fishing. Lester moved to Pontiac, Michigan, living with Slim Harpo's sister.
In 1971, Fred Rei and Lester accompanied Slim. Years later, Reif and Lester were both in Michigan, from where Reif orchestrated a comeback. Lester recorded and played around the United States and abroad, backed by blues bands including, frequently, Loaded Dice.
Lester's recordings in this period are on blues labels Alligator and Telarc, alongside releases in Europe.
If disenchanted, Lester retained his harmonica, guitar, and vocal talents (the songwriting that had been muse to The Kinks and Dwight Yoakam having dried up long before). In September 2002, a Lifetime Achievement Award was awarded by the Boston Blues Society.
In 2003, Martin Scorsese included Lester in his blues tribute concert at Radio City Music Hall, a record of which was released as the film and album Lightning in a Bottle. The group photograph inside the album depicted Lester grinning, dead-center among peers and musical progeny including B.B. King, Solomon Burke, Clarence "Gatemouth" Brown, Buddy Guy, Levon Helm, Chuck D, The Neville Brothers, Dr. John, John Fogerty, and Aerosmith.
Lester currently lives in Paradise, California with his girlfriend.[4]
Selected discography
True Blues, circa 1966, Excello LP 8006 SO-1
Lazy Lester Rides Again, 1987, King Snake KS007 (recorded in the UK, May 2528, 1987)
Harp & Soul, 1988, Alligator 4768 (featuring Lucky Peterson and Kenny Neal)
Lazy Lester, 1989, Flyright (France) 007 (previously unreleased 1960s Excello session takes)
I'm a Lover Not a Fighter, 1994, Excello/Ace 518
I Hear You Knockin', 1994, Excello/Avi 3003
All Over You, 1998, Antones ANT 10042 (recorded 1997, featuring Derek O'Brien, Sue Foley, Sarah Brown)
Lazy Lester, (audiophile EP), APO 003 (recorded October 1213, 2000 featuring Henry Gray)
Superharps II, 2001, Telarc 83514 (co-billed with Carey Bell, Raful Neal, Snooky Pryor)
Blues Stop Knockin', 2001, Antones TMG-ANT 0051 (featuring Jimmie Vaughan)
Blues On My Radio, 2004, SWMAF 01 (featuring Louisiana Red)
Family Meeting, 2008, Ruf, double album by Wentus Blues Band
You Better Listen, 2011, Bluestown records, recorded in Norway (Notodden).
The Southern Legends Entertainment & Performing Arts Hall of Fame
San Francisco Blues Festival
List of swamp blues musicians
List of harmonica blues musicians
List of harmonicists
Music of Louisiana
Blues harp
Russell, Tony (1997). The Blues: From Robert Johnson to Robert Cray, Dubai: Carlton Books Limited.
2.0 2.1 2.2 [Lazy Lester at All Music Guide Allmusic biography]
About.com biography - accessed January 2008
Lazy Lester biography at lazylester.net
[Lazy Lester at All Music Guide Lester biography] at Allmusic
This page was last modified 30.03.2014 03:08:05
This article uses material from the article Lazy Lester from the free encyclopedia Wikipedia and it is licensed under the GNU Free Documentation License.

redpajama_set_name: RedPajamaCommonCrawl


text:
import MySQLdb, logging
from .items import *

class GraduationPipeline(object):
    def __init__(self):
        self.logger = logging.getLogger(__name__)
        self.logger.setLevel(logging.INFO)

    def open_spider(self, spider):
        # 连接到数据库。
        try:
            self.connector = MySQLdb.connect(host="localhost", user="root", passwd="123", db="graduation",
                                             use_unicode=True, charset="utf8")
            self.cursor = self.connector.cursor()
            self.logger.info('Conneting to database successfully!')
        except MySQLdb.Error as e:
            self.logger.info('Failed to connect to mysql')

        # class CommentItem(scrapy.Item):
        #     # 评论id主键唯一
        #     comment_id = scrapy.Field()
        #     # 评论所对应的微博文章的id,作为外键
        #     blog_id = scrapy.Field()
        #     # 评论的内容
        #     comment_info = scrapy.Field()
        #     # 评论的点赞数量
        #     comment_thumbup = scrapy.Field()
        #     # 评论的时间
        #     comment_time = scrapy.Field()

        # 如果表不存在就建立新的表blog
        self.cursor.execute(
            'CREATE TABLE IF NOT EXISTS blog (blog_id varchar(200) PRIMARY KEY ,keyword varchar(200) ,post_time datetime,author varchar(200),blog_info text,forward INT ,comment INT ,blog_thumbup INT );',
        )

        # 如果表不存在就建立新的表comment
        self.cursor.execute(
            'CREATE TABLE IF NOT EXISTS comment (comment_id varchar(200) PRIMARY KEY ,blog_id varchar(200),FOREIGN KEY(blog_id) REFERENCES blog(blog_id),comment_time datetime,comment_info text,comment_thumbup INT);',
        )

        # 关闭外键约束
        self.cursor.execute(
            'SET FOREIGN_KEY_CHECKS=0;',
        )
        self.connector.commit()
        self.logger.info('Table check finished!')

    def process_item(self, item, spider):
        if isinstance(item, BlogItem):
            statement = "REPLACE INTO blog VALUES ( %s, %s,%s,%s,%s,%s,%s,%s)"
            self.cursor.execute(statement, (
                item['blog_id'], item['keyword'], item['post_time'], item['author'], item['blog_info'],
                item['forward'], item['comment'], item['blog_thumbup']))
            self.connector.commit()
        elif isinstance(item, CommentItem):
            statement = "REPLACE INTO comment VALUES ( %s, %s,%s,%s,%s)"
            self.cursor.execute(statement, (
                item['comment_id'], item['blog_id'], item['comment_time'], item['comment_info'],
                item['comment_thumbup']))
            self.connector.commit()
        return item


This popular footwear choice combines serious stiffness with superior comfort. The nylon sole is bolstered with a carbon mid-sole insert to provide top-of-the-line stiffness for instant power delivery to the pedals. Pearl Izumi's unique Bi-Directional Boa® dial closure pulls the laces from both sides to ensure equal tension across the top of the foot. A 3-layer bonded seamless upper eliminates hot spots and delivers incredible comfort.


The light of the time - of the life. Are we responsibly for it how long still it will burn? Or does this determine a higher power? But the question is much more important: When will it go out? When will the end of the time expect us? A really important question to which we don't have any clear answer till now! /////////////// THIS IMAGE IS © MARTIN GROHS 2011. ALL RIGHTS RESERVED.
it is never over! trust me! keep smiling! always trok! :) good image!


The "Non-verbal Reasoning" series of workbooks is unique in giving children a comprehensive and progressive learning programme in skills acquisition for Non-verbal Reasoning. They introduce children to recognition, manipulation and movement of shapes, enabling them to develop advanced skills in spatial reasoning.
Want to know when 11+ Non-Verbal Reasoning Year 3/4 Workbook 2 : Including Multiple Choice Test Technique - nzgameshop.com drops below a certain price? Enter your email address and price below and we'll let you know when it drops below that price!


Norman "nbperp" Barth was selected as the new HeroClix Rules Arbitrator, and remained so until HeroClix went into hiatus in late 2008. I managed to track him down, where we talked about HeroClix, comic books, and more.
The Le: Tell us a little about how you got involved in HeroClix at home and eventually as the rules Arbitrator for HeroClix.
Norman: I had played OverPower, the super hero CCG from the late 90s and had been collecting comics since the early 80s. My collection was fairly robust, at least in terms of DC. I saw Infinity [Challenge] when it came out, even had the Spider-Man promo figure but wasn't going to invest in a new collectible game because when I played OverPower, it was something of an obsession. I'd travel around the country to play in major events. All of that was before my kids were born. Now I had 2 kids and while the mechanics seemed interesting, I just wasn't going to let myself go there.
Then Hypertime came out. Superman #380 was my first comic and reading Superman has influenced me in ways I can still see in myself today. So when a DC set came out, I decided I'd buy into it. "But only so that I can have the sculpts" I told my wife. She rolled her eyes at me.
Sure enough, one of each sculpt wasn't enough for the collector in me. I had to have the whole set. I had to have the "Our Worlds at War" limited edition. I had to figure out how to get those cool LEs. So I started to play. I can still remember many of my earliest events. I was able to warrant fellowship of a fairly regular basis, but it took me a while to figure out the strategy of playing the game well. To this day I will never forget how my Darkseid Unique was paralyzed for 6 turns because he kept failing to roll breakaway from an opposing Lackey and then resting (rather than just smacking the Lackey to kingdom come).
Once I started playing, I found a few venues in my area that hosted games. Some were large, like Dream Wizards, in which the people were friendly but competition was fierce. Others were quite small, like Realms of Fantasy, where a typical event had 2 or 3 of us showing up to play. One of the owners of the store was a big MechWarrior player and he was an excellent source for learning core strategies. After a while I was able to hold my own at Realms and was able to finish respectably at Dream Wizards as well.
The judge at Realms told me about how generous WizKids was with its volunteers at conventions, so when the next Wizard World Philly rolled around I signed up as a judge (there were no tests back then, you just needed a sponsor) and volunteered. I had a great time at the con and looked around for a venue needing a judge so that I could keep my status active. I found a nearby store (Other Realms) that had some games on the docket with no judge and within a few weeks I had met with the owner and worked out a plan for building a Heroclix community in the store.
During this time, I spent a lot of time lurking on the internet, particularly in the Judges and Rules forums (at the time, there was one of each of these on both the HCRealms and the WizKids websites). By now, feats were starting to rear their heads so there were a number of debates that raged.
At one of the pre-release events, I had the chance to meet one of the faces behind the name of one of the big posters online. He had a strong grasp of the rules, had a very logical mind, and was a big proponent for rulings that made sense. Often, his posts and those of people arguing with him would go long enough that a relative newbie like me was intimidated to intercede in the discussion, on either side. When I met him, I told him how I respected his opinion and how I nearly always agreed with his view. He asked why I didn't say that online. He felt that he was too often the lone voice in the crowd shouting for consistency and that no one backed him up. I explained as how I had nothing much to say in addition to his opinion. He asked me for a favor – to at least reply with a "Ditto" so that people will know that there was someone who agreed with him.
I took his advice to heart and started posting. At first it was the simple "ditto" to indicate my agreement, but that started drawing me out. In addition, at this time in my life, in the real world, my job had significantly changed. In short, I was being paid to sit at a cubicle for 8 hours a day. If something came up on my project, I had what to do. But other than an emergency situation like that, there was nothing for me to work on and my employer was contractually obligated to not feed me any other work. So now I had lots of time online, besides hitting "dittos" I would also answer questions. I'd start to say more than just ditto, providing other reasons/sources for why the opinion I was agreeing with was right. I even started to disagree with the guy who had asked me to get more involved and we had some wonderful debates. In short, I got involved.
Meanwhile, my venue was growing nicely. It was not (nor would it ever really be) as large as the big players like Dream Wizards where the Premiere Events were being held. But I had gotten involved there too. Never as a judge, but as a facilitator. For those big events, when 60 plus people would be showing up, they had been using an excel spreadsheet to manage player's records. It took anywhere from 20 to 30 minutes between rounds to gather all the scores, enter the data, deal with oddities, seat the next round, pair up the sheets, etc. I spoke with the head judge about me putting together a simple program that could be used to run events. We talked about the necessary features and I went to work on it. I had it in place for its maiden voyage before the next event (apologies – I don't remember what it was, but I think this was just before summer of 2004). During that event, the time between rounds shrunk to about 15 minutes. People were very vocal in their noticing how smoothly it ran. And there were also suggestions for how to make it better, all of which I noted and incorporated into the next version.
Meanwhile, summer-time was rolling around and the head judge from DW had heard through the grapevine that WK was having issues with its tournament software. He asked me if I was planning on going to Wizard World Philly that year and I explained that I was. Using whatever connections he had, he proposed to WK that they give my software a try. While their initial feedback was not positive, apparently the weekend before Philly, the organizer from WK had been at another convention and the existing software performed so poorly that they were willing to give anything else a try. So 4 days before the con, I get a call from the head judge telling me that I should report to the tournament hall in Philly and bring any/everything I needed to run the tournaments running there. Because of the great reception I got from WK, I made the trip that year to most if not all of the following conventions.
By the time the RA posting became available (November 2006, as I recall) I had a decent reputation online as someone knowledgeable about the rules. I had an excellent reputation with many of the WK staffers, as they had seen me at so many of the conventions through the previous years. I simply submitted an application like anyone else. As I recall, they culled the applications down to 4 individuals that were interviewed over the phone. A few weeks later, they called to tell me that I was the selected person.
Nice read. It's always interesting to hear from people that are "behind the scenes". Looking forward to part II: "I need closure on that anecdote!"
Nice to hear Norman's thought process. Ditto about the difference between Magic and Heroclix. Both game styles have their own elegance if done properly. Magic has been refined by a very talented group of people at Wizards, but Heroclix has taken many steps back. Refining a Heroclix type game is unmapped territory, but I have a few ideas.
My best recollection of how OverPower died is this: OP was owned by Fleer/Skybox when Marvel was starting to go bankrupt. So selling the card company(s) started happening. The VP of Marvel Interactive saw potential in the game, so he pulled it from F/S over to Marvel Interactive. He was able to save one guy's job from F/S.
As for me, at the time all this was happening, I was helping with some web site stuff, keeping the record of who played against whom and the database that determined people's scores, and so on. When MI took over, they asked me to stay on board, help as I was, and help with other tasks as they came up.
The MI branch of the house saw a LOT of potential in OP. Remember the MegaPower online sale? That was a test run to see how online sales of cards would go, selling direct to the customer. The plan was that they would radically change the CCG business plan. Build small/easy sets to collect (like Mission Control) and then offer an additional 10-40 cards as exclusive online purchases. The profit potential, especially after the MegaPower numbers started coming in was staggering. The OP team was psyched.
At the same time, a new X-Men movie was coming out. From what I understand, Marvel was offered a licensing deal to develop an X-Men CCG, but only if it meant an exclusive license for Marvel CCGs. Well, despite the numbers showing that the OP method would be better in the long run, the business suits decided at the time to go for the guaranteed money that a contract/license offered them. And thus, OP died.
I bought a bunch of Overpower cards back then. But I never had anyone to play with and eventually got rid of them.
AHHHH! Great read. I can't wait for part II.
great interview, I used to play overpower myself. Don't have them anymore either. I'm not really a card guy though, I just liked marvel. Clix was more my kind of game. Glad too see it's back.
The Spirit: This has been corrected. Thank you for the catch. We'll just refer to it as "Norm's First Mistake". LOL.
Might want to change that first paragraph slightly. The first set was Infinity Challenge, not Infinity Gauntlet. Nice interview.
I was on HCRealms and saw your post and link to this. Had to check it out! Norm got me into Judging and going to CONs and I remember being really thrilled when he became RA.
1. He has a very logical mind and typically will make decisions based on facts rather than emotions.
2. He may be the most ethical person I've ever met. Seriously. I've seen him in situations where the "easy" thing to do could even be rationalized as the "right" thing to do and would clearly benefit him to go ahead and do it. Instead he has chosen to do the "right" thing.
I really appreciate the help he has been to me in keeping Heroclix alive and fun in my little corner of Maryland and when I saw you had an interview with him I had to check it out.
Cool site btw, I'll be coming here a lot now!
Insider information and insight is always cool.
Thanks to Le and Norm.
I see an OverPower card!!!
If I remember correctly one of two things happened with Overpower.
The game was not selling well and the card company dropped the game.
Either the comic license was not renewed or the license was dropped.
I am sorry, but it has been a couple of years since Norm talked about it to me.
excellent interview. keep the past insights coming.
Touch of death I say!!
Overpower! I played that all the time and I still have all my cards. It was the first collectible game I ever played.


text:
\section{Introduction}
Meeting society's needs for graduates skilled in science, technology, engineering, and mathematics (STEM) is of the most important challenges universities face. Innovative student-centered and active learning pedagogical strategies are key tools in addressing these challenges, since they increase retention rates of STEM students and enhance learning outcomes~\cite{olson2012engage,freeman2014active}. Adopting these strategies requires diversifying the use of class time (e.g., opportunities for small group work and greater rates of student-instructor interaction). Instructors need to be able to quantify, analyze, and reflect on their use of class time on student-centered activities to most effectively maximize student learning outcomes. Traditionally, knowing how class time is spent requires bringing in a trained observer to hand-annotate during lecture, for example, using one of the popular classroom annotation schemes~\cite{smith2013classroom,Sawada2002MeasuringRP,Velasco2016CharacterizingIP,Eddy2015PORTAALAC}. Manual annotation, however, cannot scale to meet the need for accurate classroom activity annotation and therefore an automated approach is needed.

In early work, Wang et al.~\shortcite{wang2014automatic} propose training a random forest over the outputs of a modified proprietary wearable device, ``LENA,''~\cite{ford2008lena} to predict ``teacher lecturing,'' ``whole class discussion,'' and ``student group work.'' Owens et al. address the problem of detecting activities in college-level lecture audio using portable audio recording devices~\shortcite{Owens3085}. Their DART system uses binary decision trees with amplitude statistics as features to classify a 4-way label set: ``single voice,'' ``multi voice,'' ``no voice'' and ``other''. Cosbey et al. improve upon DART using deep and recurrent neural network approaches and mel-filterbank features ~\shortcite{cosbey2019deep}. They demonstrate substantial reductions in error on the same data with a gated recurrent unit (GRU) neural network, reporting 31.7\% and 45.1\% reduction in frame-level error over DART for new class sessions from previously seen instructors and new class sessions from previously unseen instructors, respectively.

Li et al. focus on the task of distinguishing teacher from student speaker roles using siamese networks and instructor voice enrollment~\shortcite{Li2020SiameseNN}. In separate work, Li et al. use attention over automatic speech recognition transcripts to improve performance over models using acoustic features on the same task~\shortcite{li2020multimodal}. Donnelly et al. report experiments using close talking noise-canceled microphone data collected from middle school classrooms~\shortcite{donnelly2016automatic}. Of the 17 activity labels, they aim to detect the most common five: ``question \& answer,'' ``procedures and directions,'' ``supervised seatwork,'' ``small group work,'' and ``lecture.''

Other work focuses on identifying specialized classroom activities such as detecting instructor questions~\cite{10.1145/3027385.3027417,10.1007/978-3-030-52237-7_22}, classifying student arguments into ``claims,'' ``evidence,'' and ``warrants''~\cite{lugini-litman-2018-argument}, or analyzing dialogic one-on-one instruction to detect activities such as ``greeting,'' ``guidance,'' and ``commending''~\cite{10.1007/978-3-030-52240-7_62}.

\begin{figure*}
  \centering
  \includegraphics[width=1\textwidth]{figures/slyma1.pdf}
  \caption{Activity trace of an unseen 49 minute class session from an instructor previously seen in training, representative of performance of our best model.}
  \label{fig:trace}
\end{figure*}

The work reported in this manuscript represents significant progress in classroom activity detection, as the first work to operate under the challenging, realistic scenario defined by the following:
\begin{enumerate}
    \item {\bf Fine-grained classification.} We produce fine-grained (9-way) frame level predictions, e.g., distinguishing between instructor and student speech, and question from answer. To aid comparison with the existing literature, we also report 5-way and 4-way variants.
    \item {\bf Practical audio capture.} We only use audio recorded by commodity webcams at the instructor station.
    \item {\bf Transcript free.} We do not rely on automatic speech recognition given the noisy and far-field environment.
    \item {\bf Minimal or no ``enrollment.''} We do not assume any explicit enrollment data from students and assess generalization to previously unseen instructors and classrooms.
\end{enumerate}

To address these challenges we develop and thoroughly evaluate four deep learning models: deep fully connected, dilated temporal convolutional, and both unidirectional and bidirectional gated recurrent unit neural networks.
We also compare the utility of three input feature sets: mel-filterbank features, OpenSmile features, and PASE+ self-supervised embeddings~\cite{ravanelli2020multi}.
We separately report generalization performance to new class sessions from both seen and unseen instructors. In addition to reporting the first fine-grained classroom activity results, we also obtain new state-of-the-art results on 4-way activity classification. Example model output is shown in Fig.~\ref{fig:trace}, using the 5-way classification scheme.
We have made the code for this work publicly available.\footnote{https://github.com/hutchresearch/fine-grained-cad}

\section{Methods} \label{sec:methods}

\subsection{Feature Extraction}
We consider three feature sets. When features from different sets are combined, we align frame centers and concatenate to produce a single feature vector per timestep.

\begin{enumerate}
\item {\it Mel-filterbank:}
We downsample the raw waveform to 16kHz and extract 40 log-scaled mel-filterbank features using the Python audio processing and analysis package librosa~\cite{librosa} with 500ms windows at 10ms offsets, standardizing each frequency bin independently using the training set statistics. Log mel-filterbank features have been widely used in recent years, in applications ranging from automatic speech recognition \cite{pratap2019wav2letter} to speaker verification \cite{wan2018generalized} and audio event detection \cite{fonseca2019learning}. Based on the performance reported in \cite{cosbey2019deep}, we use a significantly longer frame length than is common for these other applications.

\item {\it OpenSMILE:}
Using the open-source speech feature extraction toolkit, OpenSmile~\cite{opensmile2013}, we extract three frame-level features over 50ms frames at a 10ms offset: fundamental frequency, loudness, and voicing probability. We standardize each feature using the training set statistics. The first two features carry key prosodic information; for example, there are often distinctive differences in utterance-final pitch between declarative utterances and various question types. Patterns in the voicing probability may help distinguish between types of activities featuring multiple, single, or no speakers.

\item {\it PASE+ Embeddings:}
\cite{ravanelli2020multi} propose a problem-agnostic speech encoder (PASE+) for raw waveforms that is pretrained with multiple self-supervised tasks, such as predicting Mel-frequency cepstral coefficients and gammatone features, while adding artificial speech distortions to encourage robust representations. PASE features~\cite{Pascual2019} were shown to provide excellent performance for automatic speech recognition (ASR), speaker identification, and speaker emotion classification, before expanding to noise robust ASR in the PASE+ variant. We use the output of a pretrained PASE+, fine-tuned during training, as our third feature set to capture speaker information that is invariant to the noisy classroom environment. Waveforms are normalized before embedding 150ms frames at 10ms offsets.
\end{enumerate}

\subsection{Classifiers}
We design and evaluate four classifiers based on the following deep learning architectures. In each case, we produce frame level posterior probabilities over the activity labels via an output framewise two-layer MLP with Leaky ReLU~\cite{maas2013rectifier} hidden activation and softmax output activation. The overarching network flow is shown in Fig.~\ref{fig:overview}.
\begin{figure}
  \centering
  \includegraphics[width=\columnwidth]{figures/slyma2.pdf}
  \caption{Summary of approach, from input waveforms to output n-way posterior probabilities over activity classes.}
  \label{fig:overview}
\end{figure}

\begin{enumerate}
\item {\it DNN:}
A fully connected, feed-forward Deep Neural Net (DNN). The input to the model is a context window of frame-level features. Specifically, let $x_t$ denote the input features of frame $t$, and $\hat{x}_t^K$ denote the feature-dimension concatenation of frames $x_{t-K},x_{t-K+1},\dots,x_{t-1},x_t,x_{t+1},\dots,x_{t+K}$, then our predictions are given by:
\begin{eqnarray}
    h_{t,0} & = & \text{LReLU}(W_0 \hat{x}_t^k + b_0) \\
    h_{t,1} & = & \text{LReLU}(W_1 h_{t,0} + b_1)     \\
            &\vdots &  \nonumber                      \\
    h_{t,n} & = & \text{LReLU}(W_n h_{t,n-1} + b_n)   \\
    y_t     & = & \text{MLP}(h_{t,n})
\end{eqnarray}
where each $W$ matrix and $b$ vector are learned model parameters, and LReLU denotes an element-wise Leaky ReLU non-linearity.

\begin{figure}
  \centering
  \includegraphics[width=0.4\textwidth]{figures/slyma3.pdf}
  \caption{An example dilated temporal convolution model with dilation increasing by a factor of two each layer.}
  \label{fig:dilated}
\end{figure}

\item {\it DTCNN:}
A dilated temporal (1D) convolutional neural network (DTCNN), a non-causal variant of causal CNNs like WaveNet~\cite{oord2016wavenet}. The temporal dilation of the DTCNN provides the classifier with a significantly larger context around a given frame than standard convolutions. We expect this context benefits the classifier, especially near the boundary between two activities since the large receptive fields are symmetric around each prediction frame. The input to this model is first normalized with a 1-dimensional batch norm layer followed by dropout. Then the data is fed through $L$ layers of 1-dimensional dilated convolution. Our dilated convolution operation is illustrated in Fig.~\ref{fig:dilated}. The dilation factor for each layer is given by $b^{\ell-1}$, where $\ell$ is the CNN layer of the network and $b$ is a tuned hyperparameter. Each CNN layer is composed of a 1-dimensional convolution using the aforementioned dilation factor, leaky ReLU, and 1-dimensional batch norm.

\item {\it GRU:}
Given the significance of sequential information in audio, recurrent neural network architectures are an obvious fit. We train a gated recurrent unit (GRU) network~\cite{cho2014learning}, in order to implicitly condition predictions at time $t$ on all input features up to and including time $t$. Specifically, our activity prediction at time $t$, $y_t$, given the input $x_t$ is defined to be:
\begin{eqnarray}
    r_t & = & \sigma(W_{i,r}x_t + W_{h,r}h_{t-1} + b_{r}) \\
    z_t & = & \sigma(W_{i,z}x_t + W_{h,z}h_{t-1} + b_{z}) \\
    n_t & = & \text{tanh}(W_{i,n}x_t + b_{i,n} + \nonumber
    \\ && \hspace{3em} r_t \circ\ (W_{h,n}h_{t-1} + b_{h,n})) \\
    h_t & = & (1 - z_t) \circ n_t + z_t \circ h_{t-1} \\
    y_t & = & \text{MLP}(h_t)
\end{eqnarray}
where $r$, $z$, and $n$ are the reset, update, and new gates, $\sigma$ is the logistic sigmoid function, and $\circ$ is the Hadamard (element-wise) product. The matrices $W$ and vectors $b$ are learned model parameters. In the multi-layer case, the input of each layer is the post-dropout hidden state of the previous layer. The previous hidden state at the initial time step is a zero-vector.
Note that we also conducted experiments with Long Short-Term Memory (LSTM) networks \cite{hochreiter97}, but found no significant differences in performance as compared to the GRU, and thus do not report LSTM performance.
\item {\it BiGRU:}
Because our predictions are not made online, our system need not be causal: to predict the activity label at frame $t$ we are free to utilize acoustic cues in frames $t'>t$. Due to distinctive patterns of activity and interaction in a classroom future context may provide valuable cues to the current activity; as one example, observing a probable ``instructor answer'' in the near future increases the odds that the current activity is ``student question.'' We therefore experiment with a bidirectional GRU (BiGRU), which conditions on both past and future inputs. In a BiGRU, our prediction state at time $t$, $y_t$, given the input $x_t$ is defined to be:
\begin{eqnarray}
    \overrightarrow{h}_t & = & GRU(x_t, \overrightarrow{h}_{t-1}) \\
    \overleftarrow{h}_t  & = & GRU(x_t, \overleftarrow{h}_{t+1}) \\
    h_t                  & = & W_f \overrightarrow{h}_t + W_b \overleftarrow{h}_t + b \\
    y_t & = & \text{MLP}(h_t)
\end{eqnarray}
where all $\overrightarrow{h}_t$ denote the hidden states of the forward GRU (capturing information in the input from frames before $t$) and all $\overleftarrow{h}_t$ denote the hidden states of the backward GRU (capturing information in the input for frames after). In addition to the parameters of the forward and backward GRUs themselves, matrices $W_f$, $W_b$ and vector $b$ are also learnable parameters of the model.
\end{enumerate}

\subsection{Training and Tuning} \label{subsec:traintune}
We implement all architectures with the popular Python machine learning library PyTorch~\cite{paszke2019pytorch}. To track experimental results and hyperparameter tuning, we leverage Weights \& Biases~\cite{wandb}, a suite of centralized, web-based machine learning developer tools. To reduce the contribution to loss from easy sequences and increase it from underrepresented classes, we use class-balanced~\cite{cui2019class} focal loss~\cite{lin2017focal} with tuned $\beta$ and $\gamma$, reweighting the loss according to an estimated number of per-class samples then further reducing the contribution of well-classified samples to the final aggregated value. This formulation is found to be essential for preventing the network from overpredicting the ``single'' (sgl, 4-way), ``instructor'' (ist, 5-way), and ``instructor lecture'' (l, 9-way) majority class labels. Specifically, the class-balanced focal loss is:
\begin{eqnarray}
    \text{CB}_{\text{focal}}(p, y) = -\frac{1-\beta}{1-\beta^{n_y}}(1-p_y)^\gamma\ \text{log}(p_y).
\end{eqnarray}
Where $p_y$ is the model's posterior probability for target activity class $y$. Note that this differs from the sum of per-class logistic sigmoid activations used in the original work of~\cite{cui2019class} due to the mutually-exclusive nature of our classification task.

We optimize with AdamW~\cite{loshchilov2018decoupled}, a common adaptive gradient method with decoupled weight decay, and tune the learning rate and weight decay. Each network trains for up to 35 epochs using early stopping with a patience of nine epochs. We train over sequences of 3,000 frames for RNN-based classifiers and 12,000 frames for all others -- thirty seconds and two minutes of audio, respectively. Each minibatch is sampled randomly from all sequences in all class sessions, with minibatch size (number of sequences) tuned in $4$-$32$. Larger minibatch sizes require gradient accumulation to fit in GPU memory. To improve stability during training, we clip the L2-norm of the gradients to one and use a learning rate schedule in which the learning rate is halved every three epochs that development set performance does not improve. Training takes 10-20 hours on an Nvidia RTX 2080Ti GPU depending on configuration.

All above hyperparameters are tuned via Bayesian optimization~\cite{snoek}, a principled technique to select optimal hyperparameters for a black-box optimization function, independently for each classifier and labeling scheme combination in order to minimize development set error. Hyperband early stopping~\cite{li2017hyperband} is used to increase throughput in the number of hyperparameter choices we consider by selecting adaptive configurations as framed by a multi-armed bandit resource allocation problem, with four evaluation bands ranging from five to twenty epochs up to a maximum of thirty. For all classifiers, we tune the number of hidden layers, hidden size, and dropout rate. We tune the choice in normalization layers for DNN and DTCNN classifiers between affine and non-affine instance-norm~\cite{ulyanov2016instance}, batch-norm~\cite{ioffe2015batch}, and layer-norm~\cite{ba2016layern} which all serve to stabilize the loss function and improve training time.  Layer-norm performs best in all cases for the DTCNN, while instance-norm and layer-norm perform equally for the DNN. We tune the DTCNN dilation factor between $2$-$2.5$, with the best performance found at $2.1$, and apply dropout~\cite{srivastava2014dropout} immediately before passing the fused embeddings to the classifier and before the final two-layer MLP. When PASE+ embeddings are used, dropout is also applied to the raw audio waveform before generating an embedding.

\begin{table}
\caption{4-, 5- and 9-way Classroom activity labels.}
\label{tab:labels}
\centering
\resizebox{0.75\columnwidth}{!}{%
\begin{tabular}{r|c|c|c}
\toprule
\multicolumn{1}{c|}{\textbf{Activity}} & \textbf{4-Way} & \textbf{5-Way} & \textbf{9-Way} \\ \midrule
instructor announcement      & \multirow{6}{*}{sgl}     & \multirow{4}{*}{ist} & a     \\
instructor lecture           &                          &                      & l     \\
instructor asks question     &                          &                      & iq    \\
instructor answers question  &                          &                      & ia    \\ \cline{3-4}
students asks question       &                          & \multirow{2}{*}{stu} & sq    \\
students answers question    &                          &                      & sa    \\ \cline{2-4}
group work                   & \multicolumn{2}{c|}{grp}                         & g    \\ \cline{2-4}
silence                      & \multicolumn{2}{c|}{sil}                         & s    \\ \cline{2-4}
other                        & \multicolumn{2}{c|}{oth}                         & o    \\ \bottomrule
\end{tabular}}
\end{table}

\subsection{Classroom Activity Labels}
Table~\ref{tab:labels} lists the nine labels used in our fine-grained label set.
It also shows how our fine-grained labels are mapped to 5-way and 4-way simplifications of the task: 5-way distinguishes between student (stu) and instructor (ist), but not question and answer (iq, ia, sq, sa) or announcements (a) and lecture (l), while 4-way merges student and instructor into one ``single voice'' (sgl) category, as was used in~\cite{Owens3085,cosbey2019deep}. Note that ``other'' (oth) refers to unstructured, non-instructional time (e.g. before the class session begins). We train separate models for each activity label set.

\section{Experiments} \label{sec:experiments}
\subsection{Data}
We use a new dataset of classroom audio annotated with the 9-way label scheme (see Table~\ref{tab:labels}). The dataset consists of 58.7 hours of carefully hand-annotated classroom audio. All recorded class sessions are STEM classes at a master's granting university in the US Pacific Northwest, using commodity webcams placed at the instructor workstation, facing the students. It contains recorded class sessions from thirteen courses taught over two quarters by nine professors in eleven classrooms with widely varying size and layout. Occurrences and durations of all the labels in the dataset are enumerated in Table~\ref{tab:datatime}.

\begin{table}[]
\caption{Distribution of event types in data.}
\label{tab:datatime}
\centering
\begin{small}
\resizebox{0.75\columnwidth}{!}{%
\begin{tabular}{c|r|r|r}
\toprule
\multicolumn{1}{l|}{\textbf{Label}} & \multicolumn{1}{l|}{\textbf{Occurences}} & \multicolumn{1}{l|}{\textbf{Duration (min)}} & \multicolumn{1}{l}{\textbf{Percentage}} \\ \midrule
l     & 673 & 1427.59 & 40.0 \\
ia    & 1581& 656.72  & 18.4 \\
g     & 184 & 584.09  & 16.4 \\
a     & 325 & 230.09  &  6.4 \\
o     & 126 & 188.16  &  5.3 \\
iq    & 1202& 187.22  &  5.2 \\
s     & 118 & 115.58  &  3.2 \\
sq    & 766 & 92.47   &  2.6 \\
sa    & 960 & 89.32   &  2.5 \\ \hline
total & 5935& 3571.24 & 100.0 \\ \bottomrule
\end{tabular}}
\end{small}
\end{table}

Three of the professors are withheld to form Test 2 (6.4 hours), to test generalization of the trained system to entirely unseen instructors and classrooms.  Class sessions from the remaining six instructors are split into Train (35.8 hours), Development (8.5 hours), and Test 1 (8.1 hours). Due to IRB constraints, we are unable to publicly release this data.

\begin{table*}
    \caption{Experimental results, reporting mean average precision (mAP), weighted $F_1$ (F) and accuracy (Acc).  Lightness of cells corresponds to better performance by metric, with best scores in \textbf{bold}. Our two baselines are underlined; the second baseline (GRU + mel-filterbank) is the best configuration reported in \cite{cosbey2019deep}.}
\label{tab:main-results}
\centering
\small
\resizebox{\textwidth}{!}{%
\begin{tabular}{ll|ccc|ccc|ccc|ccc|ccc|ccc}
\toprule
 &  & \multicolumn{6}{c|}{\textbf{4-Way}}  & \multicolumn{6}{c|}{\textbf{5-Way}}  & \multicolumn{6}{c}{\textbf{9-Way}} \\
 &  & \multicolumn{3}{c|}{\textbf{Test 1}} & \multicolumn{3}{c|}{\textbf{Test 2}}
    & \multicolumn{3}{c|}{\textbf{Test 1}} & \multicolumn{3}{c|}{\textbf{Test 2}}
    & \multicolumn{3}{c|}{\textbf{Test 1}} & \multicolumn{3}{c}{\textbf{Test 2}} \\
\textbf{Model} & \textbf{Feature(s)}
               & \textbf{mAP} & \textbf{F} & \textbf{Acc}
               & \textbf{mAP} & \textbf{F} & \textbf{Acc}
               & \textbf{mAP} & \textbf{F} & \textbf{Acc}
               & \textbf{mAP} & \textbf{F} & \textbf{Acc}
               & \textbf{mAP} & \textbf{F} & \textbf{Acc}
               & \textbf{mAP} & \textbf{F} & \textbf{Acc} \\ \midrule
\multirow{1}{*}{}
 & \underline{Majority Class}      & .250 & .662 & .764 & .250 & .608 & .724 & .200 & .597 & .716 & .200 & .559 & .687 & .111 & .261 & .433 & .111 & .296 & .466 \\
\midrule
\multirow{7}{*}{DNN}
 & Mel-Filterbank      & \cellcolor[rgb]{1.00,0.64,0.44}.664 & \cellcolor[rgb]{0.99,0.86,0.62}.885 & \cellcolor[rgb]{0.99,0.88,0.63}.897 & \cellcolor[rgb]{0.99,0.57,0.40}.616 & \cellcolor[rgb]{1.00,0.71,0.49}.807 & \cellcolor[rgb]{1.00,0.73,0.51}.832 & \cellcolor[rgb]{0.96,0.42,0.36}.532 & \cellcolor[rgb]{1.00,0.73,0.51}.819 & \cellcolor[rgb]{1.00,0.78,0.54}.851 & \cellcolor[rgb]{0.93,0.35,0.37}.489 & \cellcolor[rgb]{1.00,0.63,0.43}.773 & \cellcolor[rgb]{1.00,0.68,0.46}.806 & \cellcolor[rgb]{0.62,0.18,0.50}.248 & \cellcolor[rgb]{0.55,0.16,0.51}.406 & \cellcolor[rgb]{0.70,0.21,0.48}.524 & \cellcolor[rgb]{0.55,0.16,0.51}.202 & \cellcolor[rgb]{0.59,0.17,0.50}.429 & \cellcolor[rgb]{0.72,0.21,0.48}.538 \\
 & OpenSmile             & \cellcolor[rgb]{1.00,0.64,0.44}.665 & \cellcolor[rgb]{1.00,0.81,0.57}.857 & \cellcolor[rgb]{1.00,0.83,0.59}.876 & \cellcolor[rgb]{0.99,0.55,0.39}.610 & \cellcolor[rgb]{1.00,0.83,0.59}.867 & \cellcolor[rgb]{0.99,0.86,0.61}.886 & \cellcolor[rgb]{0.98,0.47,0.36}.563 & \cellcolor[rgb]{1.00,0.63,0.43}.769 & \cellcolor[rgb]{1.00,0.71,0.49}.820 & \cellcolor[rgb]{0.92,0.34,0.38}.476 & \cellcolor[rgb]{1.00,0.68,0.47}.797 & \cellcolor[rgb]{1.00,0.74,0.51}.838 & \cellcolor[rgb]{0.72,0.21,0.48}.321 & \cellcolor[rgb]{0.60,0.18,0.50}.431 & \cellcolor[rgb]{0.73,0.22,0.47}.542 & \cellcolor[rgb]{0.64,0.19,0.49}.263 & \cellcolor[rgb]{0.77,0.23,0.46}.526 & \cellcolor[rgb]{0.87,0.29,0.41}.614 \\
 & PASE+               & \cellcolor[rgb]{0.96,0.40,0.36}.522 & \cellcolor[rgb]{1.00,0.81,0.57}.860 & \cellcolor[rgb]{1.00,0.83,0.59}.876 & \cellcolor[rgb]{0.94,0.37,0.37}.502 & \cellcolor[rgb]{1.00,0.68,0.47}.798 & \cellcolor[rgb]{1.00,0.64,0.44}.793 & \cellcolor[rgb]{0.90,0.32,0.39}.460 & \cellcolor[rgb]{1.00,0.63,0.43}.769 & \cellcolor[rgb]{1.00,0.68,0.47}.810 & \cellcolor[rgb]{0.87,0.29,0.41}.430 & \cellcolor[rgb]{1.00,0.64,0.44}.774 & \cellcolor[rgb]{1.00,0.65,0.45}.797 & \cellcolor[rgb]{0.66,0.20,0.49}.274 & \cellcolor[rgb]{0.58,0.17,0.50}.421 & \cellcolor[rgb]{0.70,0.21,0.48}.529 & \cellcolor[rgb]{0.56,0.16,0.51}.211 & \cellcolor[rgb]{0.65,0.19,0.49}.459 & \cellcolor[rgb]{0.70,0.21,0.48}.526 \\
 & Mel/OpenSmile         & \cellcolor[rgb]{0.99,0.88,0.64}.814 & \cellcolor[rgb]{0.99,0.94,0.70}.923 & \cellcolor[rgb]{0.99,0.93,0.68}.925 & \cellcolor[rgb]{1.00,0.74,0.51}.723 & \cellcolor[rgb]{0.99,0.88,0.63}.892 & \cellcolor[rgb]{0.99,0.88,0.63}.899 & \cellcolor[rgb]{1.00,0.65,0.45}.670 & \cellcolor[rgb]{1.00,0.78,0.55}.849 & \cellcolor[rgb]{1.00,0.81,0.57}.869 & \cellcolor[rgb]{0.99,0.53,0.38}.600 & \cellcolor[rgb]{1.00,0.78,0.55}.847 & \cellcolor[rgb]{1.00,0.81,0.57}.867 & \cellcolor[rgb]{0.75,0.23,0.46}.336 & \cellcolor[rgb]{0.64,0.19,0.49}.457 & \cellcolor[rgb]{0.79,0.24,0.45}.569 & \cellcolor[rgb]{0.64,0.19,0.49}.261 & \cellcolor[rgb]{0.77,0.23,0.46}.527 & \cellcolor[rgb]{0.87,0.29,0.41}.620 \\
 & Mel/PASE+           & \cellcolor[rgb]{0.99,0.53,0.38}.599 & \cellcolor[rgb]{0.99,0.86,0.62}.885 & \cellcolor[rgb]{0.99,0.86,0.62}.891 & \cellcolor[rgb]{0.98,0.51,0.37}.589 & \cellcolor[rgb]{1.00,0.73,0.51}.821 & \cellcolor[rgb]{1.00,0.68,0.47}.810 & \cellcolor[rgb]{0.98,0.47,0.36}.561 & \cellcolor[rgb]{1.00,0.71,0.49}.812 & \cellcolor[rgb]{1.00,0.74,0.51}.837 & \cellcolor[rgb]{0.97,0.45,0.36}.551 & \cellcolor[rgb]{1.00,0.80,0.56}.852 & \cellcolor[rgb]{1.00,0.81,0.57}.869 & \cellcolor[rgb]{0.66,0.20,0.49}.281 & \cellcolor[rgb]{0.61,0.18,0.50}.440 & \cellcolor[rgb]{0.73,0.22,0.47}.540 & \cellcolor[rgb]{0.66,0.20,0.49}.275 & \cellcolor[rgb]{0.80,0.25,0.44}.549 & \cellcolor[rgb]{0.87,0.29,0.41}.622 \\
 & OpenSmile/PASE+       & \cellcolor[rgb]{0.97,0.45,0.36}.547 & \cellcolor[rgb]{0.99,0.86,0.61}.878 & \cellcolor[rgb]{0.99,0.86,0.62}.892 & \cellcolor[rgb]{0.97,0.44,0.36}.542 & \cellcolor[rgb]{1.00,0.81,0.57}.860 & \cellcolor[rgb]{1.00,0.80,0.56}.861 & \cellcolor[rgb]{0.96,0.40,0.36}.520 & \cellcolor[rgb]{1.00,0.72,0.49}.814 & \cellcolor[rgb]{1.00,0.73,0.51}.830 & \cellcolor[rgb]{0.96,0.43,0.36}.536 & \cellcolor[rgb]{1.00,0.80,0.56}.852 & \cellcolor[rgb]{1.00,0.78,0.55}.856 & \cellcolor[rgb]{0.75,0.23,0.46}.337 & \cellcolor[rgb]{0.75,0.23,0.46}.519 & \cellcolor[rgb]{0.79,0.24,0.45}.576 & \cellcolor[rgb]{0.72,0.21,0.48}.318 & \cellcolor[rgb]{0.82,0.26,0.43}.556 & \cellcolor[rgb]{0.79,0.24,0.45}.570 \\
 & Mel/OpenSmile/Pase+   & \cellcolor[rgb]{0.98,0.51,0.37}.587 & \cellcolor[rgb]{0.99,0.84,0.60}.874 & \cellcolor[rgb]{0.99,0.86,0.61}.888 & \cellcolor[rgb]{0.99,0.57,0.40}.626 & \cellcolor[rgb]{0.99,0.88,0.63}.892 & \cellcolor[rgb]{0.99,0.88,0.63}.900 & \cellcolor[rgb]{0.98,0.51,0.37}.592 & \cellcolor[rgb]{1.00,0.74,0.51}.826 & \cellcolor[rgb]{1.00,0.76,0.52}.842 & \cellcolor[rgb]{0.98,0.49,0.37}.574 & \cellcolor[rgb]{1.00,0.80,0.56}.852 & \cellcolor[rgb]{1.00,0.78,0.55}.859 & \cellcolor[rgb]{0.79,0.24,0.45}.373 & \cellcolor[rgb]{0.79,0.24,0.45}.535 & \cellcolor[rgb]{0.75,0.23,0.46}.555 & \cellcolor[rgb]{0.77,0.24,0.46}.362 & \cellcolor[rgb]{0.87,0.29,0.41}.590 & \cellcolor[rgb]{0.82,0.26,0.43}.591 \\
\midrule
\multirow{7}{*}{DTCNN}
 & Mel-Filterbank      & \cellcolor[rgb]{0.99,0.55,0.39}.612 & \cellcolor[rgb]{0.99,0.86,0.61}.878 & \cellcolor[rgb]{0.99,0.88,0.63}.900 & \cellcolor[rgb]{0.97,0.45,0.36}.550 & \cellcolor[rgb]{1.00,0.72,0.49}.814 & \cellcolor[rgb]{1.00,0.74,0.51}.835 & \cellcolor[rgb]{0.97,0.46,0.36}.553 & \cellcolor[rgb]{1.00,0.73,0.51}.818 & \cellcolor[rgb]{1.00,0.76,0.53}.848 & \cellcolor[rgb]{0.94,0.37,0.37}.504 & \cellcolor[rgb]{1.00,0.76,0.53}.839 & \cellcolor[rgb]{1.00,0.80,0.56}.864 & \cellcolor[rgb]{0.85,0.28,0.42}.410 & \cellcolor[rgb]{0.81,0.25,0.44}.553 & \cellcolor[rgb]{0.77,0.24,0.46}.568 & \cellcolor[rgb]{0.70,0.21,0.48}.313 & \cellcolor[rgb]{0.84,0.27,0.42}.567 & \cellcolor[rgb]{0.77,0.24,0.46}.567 \\
 & OpenSmile             & \cellcolor[rgb]{1.00,0.71,0.49}.703 & \cellcolor[rgb]{0.99,0.91,0.66}.908 & \cellcolor[rgb]{0.99,0.93,0.68}.924 & \cellcolor[rgb]{1.00,0.64,0.44}.666 & \cellcolor[rgb]{0.99,0.93,0.68}.916 & \cellcolor[rgb]{0.99,0.94,0.70}.928 & \cellcolor[rgb]{0.99,0.59,0.41}.640 & \cellcolor[rgb]{1.00,0.81,0.57}.856 & \cellcolor[rgb]{1.00,0.82,0.58}.873 & \cellcolor[rgb]{0.99,0.57,0.40}.618 & \cellcolor[rgb]{0.99,0.84,0.60}.876 & \cellcolor[rgb]{0.99,0.86,0.61}.888 & \cellcolor[rgb]{0.90,0.31,0.39}.452 & \cellcolor[rgb]{0.86,0.28,0.42}.583 & \cellcolor[rgb]{0.87,0.29,0.41}.614 & \cellcolor[rgb]{0.79,0.24,0.45}.375 & \cellcolor[rgb]{0.93,0.35,0.37}.634 & \cellcolor[rgb]{0.94,0.36,0.37}.664 \\
 & PASE+               & \cellcolor[rgb]{1.00,0.80,0.56}.759 & \cellcolor[rgb]{0.99,0.88,0.63}.893 & \cellcolor[rgb]{0.99,0.88,0.64}.901 & \cellcolor[rgb]{1.00,0.63,0.43}.658 & \cellcolor[rgb]{1.00,0.68,0.47}.801 & \cellcolor[rgb]{1.00,0.68,0.46}.806 & \cellcolor[rgb]{1.00,0.72,0.49}.711 & \cellcolor[rgb]{1.00,0.78,0.55}.846 & \cellcolor[rgb]{1.00,0.78,0.54}.851 & \cellcolor[rgb]{0.99,0.59,0.41}.628 & \cellcolor[rgb]{0.99,0.59,0.41}.757 & \cellcolor[rgb]{0.99,0.57,0.40}.762 & \cellcolor[rgb]{0.94,0.36,0.37}.496 & \cellcolor[rgb]{0.87,0.29,0.41}.594 & \cellcolor[rgb]{0.85,0.28,0.42}.604 & \cellcolor[rgb]{0.80,0.25,0.44}.379 & \cellcolor[rgb]{0.77,0.24,0.46}.531 & \cellcolor[rgb]{0.68,0.20,0.49}.514 \\
 & Mel/OpenSmile         & \cellcolor[rgb]{0.99,0.90,0.65}.824 & \cellcolor[rgb]{0.99,0.96,0.72}.934 & \cellcolor[rgb]{0.99,0.95,0.70}.931 & \cellcolor[rgb]{1.00,0.72,0.49}.710 & \cellcolor[rgb]{0.99,0.91,0.66}.907 & \cellcolor[rgb]{0.99,0.88,0.63}.897 & \cellcolor[rgb]{1.00,0.76,0.53}.739 & \cellcolor[rgb]{0.99,0.88,0.64}.897 & \cellcolor[rgb]{0.99,0.88,0.63}.897 & \cellcolor[rgb]{1.00,0.71,0.49}.709 & \cellcolor[rgb]{0.99,0.91,0.66}.908 & \cellcolor[rgb]{0.99,0.90,0.65}.908 & \cellcolor[rgb]{0.84,0.27,0.42}.407 & \cellcolor[rgb]{0.79,0.24,0.45}.536 & \cellcolor[rgb]{0.81,0.25,0.44}.585 & \cellcolor[rgb]{0.77,0.23,0.46}.353 & \cellcolor[rgb]{0.91,0.33,0.38}.616 & \cellcolor[rgb]{0.95,0.38,0.36}.674 \\
 & Mel/PASE+           & \cellcolor[rgb]{1.00,0.78,0.55}.752 & \cellcolor[rgb]{0.99,0.92,0.68}.913 & \cellcolor[rgb]{0.99,0.91,0.66}.914 & \cellcolor[rgb]{0.99,0.59,0.41}.638 & \cellcolor[rgb]{1.00,0.80,0.56}.854 & \cellcolor[rgb]{1.00,0.76,0.53}.847 & \cellcolor[rgb]{1.00,0.72,0.49}.715 & \cellcolor[rgb]{0.99,0.84,0.60}.872 & \cellcolor[rgb]{1.00,0.82,0.58}.873 & \cellcolor[rgb]{0.95,0.38,0.36}.509 & \cellcolor[rgb]{1.00,0.71,0.49}.811 & \cellcolor[rgb]{1.00,0.64,0.44}.791 & \cellcolor[rgb]{0.90,0.31,0.39}.455 & \cellcolor[rgb]{0.84,0.27,0.42}.572 & \cellcolor[rgb]{0.83,0.26,0.43}.595 & \cellcolor[rgb]{0.79,0.24,0.45}.373 & \cellcolor[rgb]{0.68,0.20,0.49}.478 & \cellcolor[rgb]{0.59,0.17,0.50}.472 \\
 & OpenSmile/PASE+       & \cellcolor[rgb]{1.00,0.63,0.43}.661 & \cellcolor[rgb]{1.00,0.83,0.59}.870 & \cellcolor[rgb]{1.00,0.83,0.59}.877 & \cellcolor[rgb]{0.99,0.54,0.39}.602 & \cellcolor[rgb]{0.99,0.86,0.62}.888 & \cellcolor[rgb]{0.99,0.84,0.60}.885 & \cellcolor[rgb]{1.00,0.78,0.55}.755 & \cellcolor[rgb]{0.99,0.86,0.61}.878 & \cellcolor[rgb]{1.00,0.83,0.59}.877 & \cellcolor[rgb]{0.99,0.55,0.39}.610 & \cellcolor[rgb]{1.00,0.68,0.47}.798 & \cellcolor[rgb]{1.00,0.64,0.44}.792 & \cellcolor[rgb]{0.90,0.32,0.39}.459 & \cellcolor[rgb]{0.84,0.27,0.42}.571 & \cellcolor[rgb]{0.79,0.24,0.45}.570 & \cellcolor[rgb]{0.75,0.23,0.46}.335 & \cellcolor[rgb]{0.68,0.20,0.49}.479 & \cellcolor[rgb]{0.55,0.16,0.51}.450 \\
 & Mel/OpenSmile/Pase+   & \cellcolor[rgb]{0.99,0.86,0.61}.792 & \cellcolor[rgb]{0.99,0.93,0.68}.918 & \cellcolor[rgb]{0.99,0.92,0.68}.919 & \cellcolor[rgb]{1.00,0.65,0.45}.673 & \cellcolor[rgb]{1.00,0.73,0.51}.822 & \cellcolor[rgb]{1.00,0.68,0.47}.812 & \cellcolor[rgb]{1.00,0.73,0.51}.719 & \cellcolor[rgb]{0.99,0.86,0.62}.887 & \cellcolor[rgb]{0.99,0.86,0.61}.887 & \cellcolor[rgb]{0.97,0.46,0.36}.553 & \cellcolor[rgb]{1.00,0.68,0.47}.796 & \cellcolor[rgb]{1.00,0.64,0.44}.791 & \cellcolor[rgb]{0.94,0.36,0.37}.498 & \cellcolor[rgb]{0.83,0.26,0.43}.565 & \cellcolor[rgb]{0.84,0.27,0.42}.601 & \cellcolor[rgb]{0.85,0.28,0.42}.413 & \cellcolor[rgb]{0.70,0.21,0.48}.490 & \cellcolor[rgb]{0.66,0.20,0.49}.503 \\
\midrule
\multirow{7}{*}{GRU}
 & \underline{Mel-Filterbank}     & \cellcolor[rgb]{1.00,0.78,0.55}.755 & \cellcolor[rgb]{0.99,0.91,0.66}.906 & \cellcolor[rgb]{0.99,0.91,0.66}.913 & \cellcolor[rgb]{0.99,0.59,0.41}.634 & \cellcolor[rgb]{0.99,0.88,0.64}.897 & \cellcolor[rgb]{0.99,0.88,0.64}.904 & \cellcolor[rgb]{0.99,0.61,0.42}.644 & \cellcolor[rgb]{1.00,0.76,0.53}.837 & \cellcolor[rgb]{1.00,0.78,0.55}.856 & \cellcolor[rgb]{0.96,0.43,0.36}.536 & \cellcolor[rgb]{1.00,0.76,0.52}.833 & \cellcolor[rgb]{1.00,0.78,0.54}.851 & \cellcolor[rgb]{0.85,0.28,0.42}.416 & \cellcolor[rgb]{0.77,0.23,0.46}.528 & \cellcolor[rgb]{0.77,0.24,0.46}.567 & \cellcolor[rgb]{0.75,0.23,0.46}.344 & \cellcolor[rgb]{0.87,0.29,0.41}.589 & \cellcolor[rgb]{0.90,0.32,0.39}.643 \\
 & OpenSmile             & \cellcolor[rgb]{1.00,0.76,0.53}.738 & \cellcolor[rgb]{0.99,0.93,0.68}.921 & \cellcolor[rgb]{0.99,0.96,0.72}.937 & \cellcolor[rgb]{1.00,0.64,0.44}.663 & \cellcolor[rgb]{0.99,0.91,0.66}.908 & \cellcolor[rgb]{0.99,0.92,0.68}.919 & \cellcolor[rgb]{1.00,0.74,0.51}.723 & \cellcolor[rgb]{0.99,0.86,0.62}.885 & \cellcolor[rgb]{0.99,0.88,0.64}.903 & \cellcolor[rgb]{0.99,0.61,0.42}.646 & \cellcolor[rgb]{0.99,0.86,0.61}.881 & \cellcolor[rgb]{0.99,0.86,0.62}.894 & \cellcolor[rgb]{0.89,0.31,0.40}.448 & \cellcolor[rgb]{0.81,0.25,0.44}.554 & \cellcolor[rgb]{0.85,0.28,0.42}.604 & \cellcolor[rgb]{0.81,0.25,0.44}.385 & \cellcolor[rgb]{0.89,0.31,0.40}.603 & \cellcolor[rgb]{0.90,0.31,0.39}.634 \\
 & PASE+               & \cellcolor[rgb]{1.00,0.74,0.51}.726 & \cellcolor[rgb]{0.99,0.93,0.68}.919 & \cellcolor[rgb]{0.99,0.94,0.70}.927 & \cellcolor[rgb]{1.00,0.73,0.51}.717 & \cellcolor[rgb]{0.99,0.93,0.68}.916 & \cellcolor[rgb]{0.99,0.94,0.70}.928 & \cellcolor[rgb]{1.00,0.76,0.52}.731 & \cellcolor[rgb]{0.99,0.88,0.64}.895 & \cellcolor[rgb]{0.99,0.90,0.65}.908 & \cellcolor[rgb]{1.00,0.64,0.44}.664 & \cellcolor[rgb]{0.99,0.90,0.65}.900 & \cellcolor[rgb]{0.99,0.91,0.66}.912 & \cellcolor[rgb]{0.96,0.40,0.36}.521 & \cellcolor[rgb]{0.93,0.35,0.37}.635 & \cellcolor[rgb]{0.94,0.36,0.37}.664 & \cellcolor[rgb]{0.90,0.31,0.39}.452 & \cellcolor[rgb]{0.96,0.40,0.36}.661 & \cellcolor[rgb]{0.97,0.44,0.36}.700 \\
 & Mel/OpenSmile         & \cellcolor[rgb]{1.00,0.82,0.58}.774 & \cellcolor[rgb]{0.99,0.94,0.70}.924 & \cellcolor[rgb]{0.99,0.95,0.70}.932 & \cellcolor[rgb]{1.00,0.66,0.45}.679 & \cellcolor[rgb]{0.99,0.92,0.68}.912 & \cellcolor[rgb]{0.99,0.92,0.68}.920 & \cellcolor[rgb]{1.00,0.78,0.54}.745 & \cellcolor[rgb]{0.99,0.88,0.63}.891 & \cellcolor[rgb]{0.99,0.88,0.63}.900 & \cellcolor[rgb]{1.00,0.68,0.46}.684 & \cellcolor[rgb]{0.99,0.88,0.64}.895 & \cellcolor[rgb]{0.99,0.88,0.64}.902 & \cellcolor[rgb]{0.96,0.40,0.36}.523 & \cellcolor[rgb]{0.88,0.30,0.40}.595 & \cellcolor[rgb]{0.88,0.30,0.40}.625 & \cellcolor[rgb]{0.90,0.31,0.39}.454 & \cellcolor[rgb]{0.93,0.35,0.37}.636 & \cellcolor[rgb]{0.93,0.35,0.37}.656 \\
 & Mel/PASE+           & \cellcolor[rgb]{1.00,0.74,0.51}.725 & \cellcolor[rgb]{0.99,0.94,0.70}.924 & \cellcolor[rgb]{0.99,0.94,0.70}.930 & \cellcolor[rgb]{0.99,0.59,0.41}.640 & \cellcolor[rgb]{0.99,0.91,0.66}.909 & \cellcolor[rgb]{0.99,0.92,0.68}.917 & \cellcolor[rgb]{1.00,0.74,0.51}.726 & \cellcolor[rgb]{0.99,0.88,0.64}.899 & \cellcolor[rgb]{0.99,0.90,0.65}.909 & \cellcolor[rgb]{1.00,0.65,0.45}.674 & \cellcolor[rgb]{0.99,0.90,0.65}.903 & \cellcolor[rgb]{0.99,0.91,0.66}.912 & \cellcolor[rgb]{0.97,0.45,0.36}.546 & \cellcolor[rgb]{0.93,0.35,0.37}.635 & \cellcolor[rgb]{0.91,0.33,0.38}.644 & \cellcolor[rgb]{0.93,0.35,0.37}.482 & \cellcolor[rgb]{0.96,0.43,0.36}.672 & \cellcolor[rgb]{0.96,0.40,0.36}.687 \\
 & OpenSmile/PASE+       & \cellcolor[rgb]{1.00,0.83,0.59}.778 & \cellcolor[rgb]{0.99,0.94,0.70}.926 & \cellcolor[rgb]{0.99,0.95,0.70}.935 & \cellcolor[rgb]{1.00,0.74,0.51}.729 & \cellcolor[rgb]{0.99,0.93,0.68}.916 & \cellcolor[rgb]{0.99,0.94,0.70}.929 & \cellcolor[rgb]{1.00,0.78,0.55}.752 & \cellcolor[rgb]{0.99,0.88,0.64}.899 & \cellcolor[rgb]{0.99,0.90,0.65}.909 & \cellcolor[rgb]{0.99,0.59,0.41}.632 & \cellcolor[rgb]{0.99,0.88,0.64}.897 & \cellcolor[rgb]{0.99,0.90,0.65}.909 & \cellcolor[rgb]{0.98,0.48,0.37}.573 & \cellcolor[rgb]{0.95,0.38,0.36}.650 & \cellcolor[rgb]{0.93,0.35,0.37}.655 & \cellcolor[rgb]{0.93,0.35,0.37}.485 & \cellcolor[rgb]{0.94,0.36,0.37}.642 & \cellcolor[rgb]{0.90,0.32,0.39}.640 \\
 & Mel/OpenSmile/Pase+   & \cellcolor[rgb]{0.99,0.88,0.64}.813 & \cellcolor[rgb]{0.99,0.95,0.70}.929 & \cellcolor[rgb]{0.99,0.95,0.70}.935 & \cellcolor[rgb]{1.00,0.76,0.53}.742 & \cellcolor[rgb]{0.99,0.94,0.70}.922 & \cellcolor[rgb]{0.99,0.95,0.70}.931 & \cellcolor[rgb]{1.00,0.82,0.58}.772 & \cellcolor[rgb]{0.99,0.91,0.66}.907 & \cellcolor[rgb]{0.99,0.91,0.66}.914 & \cellcolor[rgb]{1.00,0.76,0.52}.733 & \cellcolor[rgb]{0.99,0.93,0.68}\textbf{.916} & \cellcolor[rgb]{0.99,0.93,0.68}.921 & \cellcolor[rgb]{0.96,0.43,0.36}.533 & \cellcolor[rgb]{0.92,0.34,0.38}.626 & \cellcolor[rgb]{0.92,0.34,0.38}.651 & \cellcolor[rgb]{0.93,0.35,0.37}.490 & \cellcolor[rgb]{0.94,0.36,0.37}.638 & \cellcolor[rgb]{0.92,0.34,0.38}.652 \\
\midrule
\multirow{7}{*}{BiGRU}
 & Mel-Filterbank      & \cellcolor[rgb]{1.00,0.70,0.48}.697 & \cellcolor[rgb]{0.99,0.91,0.66}.909 & \cellcolor[rgb]{0.99,0.92,0.68}.917 & \cellcolor[rgb]{1.00,0.63,0.43}.660 & \cellcolor[rgb]{1.00,0.78,0.54}.843 & \cellcolor[rgb]{1.00,0.78,0.55}.855 & \cellcolor[rgb]{1.00,0.78,0.54}.748 & \cellcolor[rgb]{0.99,0.86,0.62}.883 & \cellcolor[rgb]{0.99,0.88,0.63}.896 & \cellcolor[rgb]{0.99,0.59,0.41}.630 & \cellcolor[rgb]{0.99,0.86,0.61}.878 & \cellcolor[rgb]{0.99,0.86,0.62}.891 & \cellcolor[rgb]{0.94,0.36,0.37}.492 & \cellcolor[rgb]{0.88,0.30,0.40}.599 & \cellcolor[rgb]{0.88,0.30,0.40}.628 & \cellcolor[rgb]{0.75,0.23,0.46}.338 & \cellcolor[rgb]{0.83,0.26,0.43}.565 & \cellcolor[rgb]{0.81,0.25,0.44}.584 \\
 & OpenSmile             & \cellcolor[rgb]{1.00,0.76,0.53}.741 & \cellcolor[rgb]{0.99,0.93,0.68}.920 & \cellcolor[rgb]{0.99,0.96,0.72}.937 & \cellcolor[rgb]{1.00,0.78,0.54}\textbf{.748} & \cellcolor[rgb]{0.99,0.94,0.70}.922 & \cellcolor[rgb]{0.99,0.96,0.72}.936 & \cellcolor[rgb]{1.00,0.78,0.54}.748 & \cellcolor[rgb]{0.99,0.88,0.64}.895 & \cellcolor[rgb]{0.99,0.90,0.65}.909 & \cellcolor[rgb]{1.00,0.76,0.53}.738 & \cellcolor[rgb]{0.99,0.91,0.66}.905 & \cellcolor[rgb]{0.99,0.91,0.66}.915 & \cellcolor[rgb]{0.90,0.32,0.39}.461 & \cellcolor[rgb]{0.81,0.25,0.44}.553 & \cellcolor[rgb]{0.83,0.26,0.43}.596 & \cellcolor[rgb]{0.87,0.29,0.41}.436 & \cellcolor[rgb]{0.87,0.29,0.41}.591 & \cellcolor[rgb]{0.84,0.27,0.42}.601 \\
 & PASE+               & \cellcolor[rgb]{0.99,0.91,0.66}.831 & \cellcolor[rgb]{0.99,0.97,0.73}.939 & \cellcolor[rgb]{0.99,0.98,0.74}.946 & \cellcolor[rgb]{1.00,0.70,0.48}.696 & \cellcolor[rgb]{0.99,0.94,0.70}.925 & \cellcolor[rgb]{0.99,0.96,0.72}\textbf{.938} & \cellcolor[rgb]{0.99,0.96,0.72}.860 & \cellcolor[rgb]{0.99,0.95,0.70}.930 & \cellcolor[rgb]{0.99,0.95,0.70}.931 & \cellcolor[rgb]{1.00,0.70,0.48}.701 & \cellcolor[rgb]{0.99,0.88,0.63}.891 & \cellcolor[rgb]{0.99,0.84,0.60}.882 & \cellcolor[rgb]{1.00,0.63,0.43}\textbf{.655} & \cellcolor[rgb]{0.97,0.46,0.36}\textbf{.688} & \cellcolor[rgb]{0.96,0.43,0.36}.698 & \cellcolor[rgb]{0.97,0.44,0.36}.541 & \cellcolor[rgb]{0.98,0.48,0.37}.701 & \cellcolor[rgb]{0.97,0.45,0.36}.707 \\
 & Mel/OpenSmile         & \cellcolor[rgb]{0.99,0.90,0.65}.820 & \cellcolor[rgb]{0.99,0.96,0.72}.937 & \cellcolor[rgb]{0.99,0.97,0.73}.941 & \cellcolor[rgb]{1.00,0.76,0.53}.742 & \cellcolor[rgb]{0.99,0.95,0.70}\textbf{.928} & \cellcolor[rgb]{0.99,0.95,0.70}.935 & \cellcolor[rgb]{0.99,0.88,0.63}.809 & \cellcolor[rgb]{0.99,0.90,0.65}.902 & \cellcolor[rgb]{0.99,0.88,0.64}.902 & \cellcolor[rgb]{1.00,0.76,0.53}\textbf{.743} & \cellcolor[rgb]{0.99,0.91,0.66}.910 & \cellcolor[rgb]{0.99,0.91,0.66}.911 & \cellcolor[rgb]{0.97,0.46,0.36}.556 & \cellcolor[rgb]{0.90,0.32,0.39}.613 & \cellcolor[rgb]{0.91,0.33,0.38}.647 & \cellcolor[rgb]{0.90,0.32,0.39}.460 & \cellcolor[rgb]{0.95,0.38,0.36}.652 & \cellcolor[rgb]{0.94,0.36,0.37}.667 \\
 & Mel/PASE+           & \cellcolor[rgb]{0.99,0.94,0.70}.846 & \cellcolor[rgb]{0.99,0.99,0.75}\textbf{.951} & \cellcolor[rgb]{0.99,0.99,0.75}.952 & \cellcolor[rgb]{1.00,0.64,0.44}.667 & \cellcolor[rgb]{0.99,0.91,0.66}.907 & \cellcolor[rgb]{0.99,0.88,0.64}.902 & \cellcolor[rgb]{0.99,0.95,0.70}.856 & \cellcolor[rgb]{0.99,0.95,0.70}.929 & \cellcolor[rgb]{0.99,0.95,0.70}.933 & \cellcolor[rgb]{1.00,0.71,0.49}.709 & \cellcolor[rgb]{0.99,0.91,0.66}.908 & \cellcolor[rgb]{0.99,0.92,0.68}.919 & \cellcolor[rgb]{0.99,0.61,0.42}.645 & \cellcolor[rgb]{0.97,0.44,0.36}.676 & \cellcolor[rgb]{0.96,0.40,0.36}.685 & \cellcolor[rgb]{0.95,0.38,0.36}.509 & \cellcolor[rgb]{0.95,0.40,0.36}.656 & \cellcolor[rgb]{0.93,0.35,0.37}.660 \\
 & OpenSmile/PASE+       & \cellcolor[rgb]{0.99,0.90,0.65}.823 & \cellcolor[rgb]{0.99,0.97,0.73}.941 & \cellcolor[rgb]{0.99,0.97,0.73}.945 & \cellcolor[rgb]{1.00,0.66,0.45}.680 & \cellcolor[rgb]{0.99,0.94,0.70}.923 & \cellcolor[rgb]{0.99,0.94,0.70}.928 & \cellcolor[rgb]{0.99,0.94,0.70}.847 & \cellcolor[rgb]{0.99,0.94,0.70}.922 & \cellcolor[rgb]{0.99,0.94,0.70}.928 & \cellcolor[rgb]{1.00,0.71,0.49}.708 & \cellcolor[rgb]{0.99,0.90,0.65}.901 & \cellcolor[rgb]{0.99,0.91,0.66}.912 & \cellcolor[rgb]{0.99,0.55,0.39}.614 & \cellcolor[rgb]{0.97,0.44,0.36}.677 & \cellcolor[rgb]{0.97,0.44,0.36}\textbf{.701} & \cellcolor[rgb]{0.94,0.37,0.37}.500 & \cellcolor[rgb]{0.97,0.46,0.36}.689 & \cellcolor[rgb]{0.98,0.48,0.37}\textbf{.720} \\
 & Mel/OpenSmile/Pase+   & \cellcolor[rgb]{0.99,0.99,0.75}\textbf{.883} & \cellcolor[rgb]{0.99,0.99,0.75}\textbf{.951} & \cellcolor[rgb]{0.99,0.99,0.75}\textbf{.953} & \cellcolor[rgb]{1.00,0.66,0.45}.681 & \cellcolor[rgb]{0.99,0.92,0.68}.914 & \cellcolor[rgb]{0.99,0.91,0.66}.912 & \cellcolor[rgb]{0.99,0.98,0.74}\textbf{.876} & \cellcolor[rgb]{0.99,0.96,0.72}\textbf{.935} & \cellcolor[rgb]{0.99,0.96,0.72}\textbf{.936} & \cellcolor[rgb]{1.00,0.73,0.51}.717 & \cellcolor[rgb]{0.99,0.93,0.68}\textbf{.916} & \cellcolor[rgb]{0.99,0.93,0.68}\textbf{.923} & \cellcolor[rgb]{0.99,0.59,0.41}.640 & \cellcolor[rgb]{0.96,0.43,0.36}.671 & \cellcolor[rgb]{0.95,0.38,0.36}.678 & \cellcolor[rgb]{0.98,0.47,0.36}\textbf{.565} & \cellcolor[rgb]{0.98,0.49,0.37}\textbf{.706} & \cellcolor[rgb]{0.98,0.47,0.36}.715 \\
\bottomrule
\end{tabular}%
}
\end{table*}

\subsection{Results}

\subsubsection{Model Comparison}
We evaluate each model across single and combined feature sets for each label granularity. The results are shown in Table~\ref{tab:main-results} which reports mean average precision (mAP), class frequency weighted $F_1$-score (F) and accuracy (Acc). This table also includes a baseline performance that uses the training set's empirical class label distribution as the posterior probability of each frame.
We note several observations.

First, as expected, the difficulty of the task increases with the number of labels in the label set. However, the drop off in performance from 4-way to 5-way, which requires differentiating instructor from student voice, was less pronounced than anticipated.
Second, the BiGRU models outperform every other model for every task (although the GRU is often close behind), and in nearly every case more than doubles the percent increase in performance over baseline from 4-way to 9-way when using all features.
Third, feature combinations that include PASE+ embeddings almost always perform better. However, the combination of mel-filterbank and OpenSmile features performs 4.3\% and 7.7\% better by F on the 4-way DNN classifier than mel-filterbank or prosody alone, respectively, and 7.3\% better than PASE+ embeddings alone.
Our last observation on Table~\ref{tab:main-results} is that there is some degradation in performance from Test 1 (unseen class sessions from previously seen instructors) to Test 2 (unseen class sessions from unseen instructors), particularly in mAP, although by F and Acc the model generalizes comparably. The generally good performance on Test~2 provides evidence that the model can generalize to new instructors, buildings, classrooms, courses and disciplines. We leave to future work additional tests of generalization; e.g., to different universities, microphones or microphone placement strategies.

\subsubsection{Confusion Analysis}
To better understand what types of mistakes the model makes, we provide three confusion matrices in Fig.~\ref{fig:confusion-matrix}. On the 9-way matrix we see confusion between lecture and other instructor speech (announcements, instructor questions and answers). This is expected given that these distinctions are largely semantic, but our models use no lexical features. The 5-way confusion matrix shows that students are most often misclassified as instructors, an understandable confusion between the two single voice categories, and that the two most chaotic activities are sometimes confused, with the unstructured ``other'' often misclassified as ``group work.'' Lastly, in the 4-way confusion matrix, we see very strong performance, although some ``group work'' and ``other'' confusion remains.
\begin{figure}
  \centering
  \includegraphics[width=0.30\columnwidth]{figures/slyma4.pdf}
  \includegraphics[width=0.30\columnwidth]{figures/slyma5.pdf}
  \includegraphics[width=0.36\columnwidth]{figures/slyma6.pdf}
  \caption{Confusion matrices for the best overall model (BiGRU, all features) performing 9-way, 5-way, and 4-way classification on Test 1, shown from left to right.}
  \label{fig:confusion-matrix}
\end{figure}

\subsubsection{Precision-Recall Analysis}
Precision-recall curves for the best overall model in each labeling scheme are plotted in Fig.~\ref{fig:pr-curve}. All classes besides ``other'' are able to achieve at least 0.8 recall and 0.8 precision simultaneously, with ``single-voice'' (sgl) and ``instructor'' able to achieve near perfect recall and precision in the 4- and 5-way case, respectively.

\begin{figure}
  \centering
  \includegraphics[width=0.7\columnwidth]{figures/slyma7.pdf}
  \includegraphics[width=0.7\columnwidth]{figures/slyma8.pdf}
  \includegraphics[width=0.7\columnwidth]{figures/slyma9.pdf}
  \caption{Precision-recall curves for the best overall model (BiGRU, all features) on Test 1, using the 4-way (top), 5-way (middle) and 9-way (bottom) label schemes.}
  \label{fig:pr-curve}
\end{figure}

\subsubsection{Aggregate Time on Activity Analysis}
Fig.~\ref{fig:contributions} shows scatter plots comparing the predicted and actual aggregate time spent in each activity for Test 1 and Test 2. Each point represents a class session, while points above (below) the diagonal indicate that the predicted time spent in the event over (under) estimates the true time. Our best model is compared against our strongest baseline, the best model of \cite{cosbey2019deep}. We see dramatically reduced root mean squared error (RMSE, in minutes): 54.9\% lower when averaged uniformly across all categories and both test sets.

\section{Conclusions and Future Work} \label{sec:conclusions}
We present a set of methods for fine-grained classroom activity detection from audio, and extensively evaluate the performance of multiple neural network classifiers, feature sets, and label sets. We find the best performing model to be a BiGRU using mel-filterbank, OpenSmile, and PASE+ features, although other models are competitive. In the majority of cases, self-supervised PASE+ features, alone or combined with other features, yield the best results. The best models report 0.928, 0.916 and 0.706 weighted-$F_1$ for the \mbox{4-,} 5- and 9-way label sets, respectively, when generalizing to previously unseen instructors. Our best model also produces highly accurate estimates of aggregate class time spent on activities: RMSE of 1.3 and 1.6 minutes averaged over activity class labels for the 5-way task when generalizing to unseen class sessions and unseen instructors, respectively -- a 60.2\% and 49.7\% drop in RMSE compared to a strong baseline.

One direction for future work would be to incorporate lexical features from a high quality far-field noise-robust ASR system, which may help differentiate sub-classes of single-speaker audio (e.g., finding characteristic phrases \cite{Zhang10,zhang13} of classroom activities \cite{10.1145/3027385.3027417}). One might consider alternative architectures (e.g., Transformer \cite{vaswani17} variants). Multi-task learning between labels of different granularity may improve performance, further reducing the already small degradation in performance when mapping from fine to coarse-grained predictions. Finally, multi-modal approaches, incorporating both audio and video, may also help to disambiguate by leveraging cross-modal correlations (e.g., hands raising before student question or answer).

\begin{figure}
  \centering
  \includegraphics[width=0.9\columnwidth]{figures/slyma10.pdf}
  \caption{Predicted vs actual (vertical vs horizontal axes) time spent (minutes), by activity, in each Test 1 (left column) or 2 (right column) class session. Orange $X$s denote BiGRU with all features; black $O$s denote the best model reported in \cite{cosbey2019deep}. Root mean squared error (in minutes) between predicted and actual is listed in the upper left of each subfigure.}
  \label{fig:contributions}
\end{figure}

redpajama_set_name: RedPajamaArXiv


text:
Rutgers Tabs Kansas State Transfer Nigel Johnson
campussportswriter | Apr 21, 2015 5:45 pm | Aug 17, 2016 3:53 pm
Nigel Johnson, one of five players to leave the Kansas State program this year, has landed at Rutgers University according to Marcus Helton of DMVelite.com.
Sophomore guard Nigel Johnson has committed to Rutgers. Kansas State transfer will sit and have 2 years of eligibility. @TakingFlight_23
— Marcus Helton (@MarcusHelton) April 21, 2015
Johnson, who saw time at both point and shooting guard, played in 32 games during the 2014-15 season, but started only seven according to K-State's website. He averaged 5.2 points per game, but scored a season-high 20 in a Feb. 23 victory against Kansas and poured in 17 five days later in a win against another ranked conference foe, Iowa State. He also chipped in 1.7 assists per game.
Johnson, whose father Sidney is a former NFL defensive back, will have to sit out a year before he is eligible to play and will be a junior when he first sees action on the court during the 2016-17 season.
Seven current freshman will be juniors along with the newcomer Johnson that year. Mike Williams led that group in scoring this season at just 6.3 points per game.
Johnson, a native of Ashburn, Va., was third on the team in three-pointers made, steals and assists. He left a Wildcat program that has been decimated by three transfers and two dismissals in head coach Bruce Weber's third season.
Leading scorer Marcus Foster was among the five. He announced last week he will be transferring to Creighton.
Here is an interview with Johnson complete with high school highlights just after he joined the Wildcats.
*Featured Photo (above) credit to Scott Sewell-USA TODAY Sports
campussportswriterCAMPUSSPORTS Writer


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