DUBLIN--(BUSINESS WIRE)--The "South America Workplace Services Market Forecast to 2028 - COVID-19 Impact and Regional Analysis By Service Type, Organization Size and Large Enterprises, and Vertical" report has been added to ResearchAndMarkets.com's offering.

Consumer Goods and Retail Segment is expected to be the fastest growing during the forecast period for the SAM region.

SAM Workplace Services Market is expected to reach US$ 7680.59 million by 2028 from US$ 3365.00 million in 2021. The market is estimated to grow at a CAGR of 12.5% from 2021 to 2028.

The report provides trends prevailing in the SAM workplace services market along with the drivers and restraints pertaining to the market growth. Rising significance of enterprise mobility is the major factor driving the growth of the SAM workplace services market. However, issues associated with the escalating security concerns hinder the growth of SAM workplace services market.

The market for SAM workplace services market is segmented into service type, organization size, vertical, and country. Based on services type, the market is segmented into end-user outsourcing services, and tech support services core. In 2020, the end-user outsourcing services segment held the largest share SAM workplace market.

Based on organization type the workplace services market is divided into- Small and medium-sized enterprises (SMEs) and large enterprises. Large enterprises is expected to the fastest growing segment over the forecast period. On the basis of vertical the market is segmented into Media and Entertainment, BFSI, Consumer Goods and Retail, Manufacturing, Healthcare and Life Sciences, Education, Telecom- IT and ITES, Energy and Utilities, Government and Public Sector, Others. The Telecom-IT and ITES segment accounts for largest market share in the 2020

The presence of various developing countries in SAM makes this region one of the key markets for the future growth of the workplace services market. The growing population, rising disposable income, high demand for advanced technologies, and huge focus on digital transformation are some of the key factors expected to drive the growth of the workplace services market in SAM.

The high number of confirmed cases and deaths due to COVID-19 in major SAM countries such as Brazil, Peru, Chile, Ecuador, and Argentina have affected the region in 2020. Subsequent to the coronavirus epidemic, IT and software businesses have received a lift in several countries of SAM since digital acceleration, and the need for remote work has accelerated the market growth even in the pandemic. Thus, the workplace services market is not majorly affected during the pandemic.

Accenture, Atos SE, Cognizant, Inc., Fujitsu Limited, HCL Technologies, IBM Corporation, NTT DATA Corporation, Tata Consultancy Services Limited, Unisys Corporation, and Wipro Limited are among some of the leading companies in the SAM workplace services market.

The companies are focused on adopting organic growth strategies such as product launches and expansions to sustain their position in the dynamic market. For instance, in 2020 Fujitsu announced it has signed a two-year agreement with HMRC, under the agreement Fujitsu will provide its Digital Workplace Services to HMRC.

Market Dynamics

Market Drivers

Market Restraints

Market Opportunities

Market Trend

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/rjhwae

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South America Workplace Services Market Forecast to 2028: Unification of Artificial Intelligence (AI) to Revolutionize Workplace Services Business -...

Why dont you go join them? asked Ozioma. Showing up behind Amaka on the balcony, the landlady lit an English-brand cigarette, leaned against the railings, and peered down.

I used to be the dance queen of our village, Ozioma went on, her eyes hazy with nostalgia. Not trying to brag here, but not a single boy could take his eyes off me. My father hated when I danced, though. He threatened to hit me every time he caught me dancing.

Did you listen to him?

Ozioma laughed heartily. Why on earth would a child give up what they love because their parents said no? Eventually, I found a way that could allow me to at least finish the dance.

What was it? asked Amaka.

I would wear an Agbogho Mmuo every time I danced.

What? Amakas eyes widened. The Agbogho Mmuo was the sacred mask of northern Igbo, representing maiden spirits as well as the mother of all living creation.

See, my father had your exact expression when he saw me with the mask. He had no choice but to bow down, to show his respect to the mask and the goddess it embodies. Of course, after I was done with the dance, with the mask stripped off, I would get my share of scolding, said Ozioma, beaming with pride, as if the memory had temporarily brought her back to the days when she was a young girl.

Upon hearing Oziomas story, Amaka felt an idea, blurry and shapeless, darting across his mind like a fish. He scrunched up his face, thinking. The mask

Yes, child. The mask is where my power came from.

Strip off the mask? Strip off the mask, murmured Amaka.

All of a sudden, he leapt to his feet and kissed Ozioma on the cheek. Thank you, oh thank you, my dance queen! He dashed back to his room, leaving behind the hustle and bustle of the parade and a very confused Ozioma.

Maybe spinning a lie and putting it in FAKAs mouth wont make his followers abandon their idol, Amaka told Chi via video chat that afternoon, excited with his new discovery. But stripping off its mask and revealing the hidden puppet master might.

No one knows who the puppet master is, though, Chi replied.

Exactly! Amaka beamed. Cant you see? It means that the puppet master can be anyone.

So, youre suggesting that

I can strip off FAKAs mask and make him any person you want him to be.

Chi fell silent in the video chat.

Youre a fucking genius, Chi finally muttered.

Ndewo, Amaka said, preparing to sign off.

Wait, Chi looked up. It means that you need to create a face that exists in reality.

Yes.

A face that can fool all the anti-fake detectors, added Chi, musing. Think about the color distortion, the noise pattern, the compression rate variation, the blink frequency, the biosignal is it doable?

I need time, said Amaka. And unlimited cloud AI computing power.

Ill get back to you. Chi logged off.

Amaka gazed at his own reflection in the dimming monitor screen. The adrenaline rush that had initially washed over him had faded. He saw on his face not excitement, but exhaustion and an unsettled feeling, as if he had betrayed a guardian spirit watching from above.

In theory anyone could fake a perfect image or video, at least well enough to fool the existing anti-fake detectors. The problem was the costcomputing power.

Fakes and their detectors were engaged in an eternal battle, like Eros and Thanatos. Amaka had his work cut out for him, but he was determined to succeed in achieving his singular goal: the creation of a real, human face.

Read more here:
Artificial Intelligence and the Gods Behind the Masks - WIRED

A new artificial-intelligence technology for heart imaging can potentially improve care for patients, allowing doctors to examine their hearts for scar tissue while eliminating the need for contrast injections required for traditional cardiovascular magnetic resonance imaging.

A team of researchers who developed the technology, including doctors at UVA Health,reports the success of the approach in a new article in the scientific journal Circulation. The team compared its AI approach, known as virtual native enhancement, with contrast-enhanced cardiovascular magnetic resonance scans now used to monitor hypertrophic cardiomyopathy, the most common genetic heart condition. The researchers found that virtual native enhancement produced higher-quality images and better captured evidence of scar in the heart, all without the need for injecting the standard contrast agent required for cardiovascular magnetic resonance scans.

This is a potentially important advance, especially if it can be expanded to other patient groups, said researcher Dr.Christopher Kramer, the chief of the Division of Cardiovascular Medicine at UVA Health, Virginias only designated Center of Excellence by theHypertrophic Cardiomyopathy Association. Being able to identify scar in the heart, an important contributor to progression to heart failure and sudden cardiac death, without contrast, would be highly significant. Cardiovascular magnetic resonance scans would be done without contrast, saving cost and any risk, albeit low, from the contrast agent.

Hypertrophic cardiomyopathy is the most common inheritable heart disease, and the most common cause of sudden cardiac death in young athletes. It causes the heart muscle to thicken and stiffen, reducing its ability to pump blood and requiring close monitoring by doctors.

The new virtual native enhancement technology will allow doctors to image the heart more often and more quickly, the researchers say. It also may help doctors detect subtle changes in the heart earlier, though more testing is needed to confirm that.

The technology also would benefit patients who are allergic to the contrast agent injected for cardiovascular magnetic resonance scans, as well as patients with severely failing kidneys, a group that avoids the use of the agent.

The new approach works by using artificial intelligence to enhance T1-maps of the heart tissue created by magnetic resonance imaging. These maps are combined with enhanced MRI cines, which are like movies of moving tissue in this case, the beating heart. Overlaying the two types of images creates the artificial virtual native enhancement image.

Based on these inputs, the technology can produce something virtually identical to the traditional contrast-enhanced cardiovascular magnetic resonance heart scans doctors are accustomed to reading only better, the researchers conclude. Avoiding the use of contrast and improving image quality in [cardiovascular magnetic resonance] would only help both patients and physicians down the line, Kramer said.

While the new research examined virtual native enhancements potential in patients with hypertrophic cardiomyopathy, the technologys creators envision it being used for many other heart conditions as well.

While currently validated in the [hypertrophic cardiomyopathy] population, there is a clear pathway to extend the technology to a wider range of myocardial pathologies, they write. [Virtual native enhancement] has enormous potential to significantly improve clinical practice, reduce scan time and costs, and expand the reach of [cardiovascular magnetic resonance] in the near future.

The research team consisted of Qiang Zhang, Matthew K. Burrage, Elena Lukaschuk, Mayooran Shanmuganathan, Iulia A. Popescu, Chrysovalantou Nikolaidou, Rebecca Mills, Konrad Werys, Evan Hann, Ahmet Barutcu, Suleyman D. Polat, HCMR investigators, Michael Salerno, Michael Jerosch-Herold, Raymond Y. Kwong, Hugh C. Watkins, Christopher M. Kramer, Stefan Neubauer, Vanessa M. Ferreira and Stefan K. Piechnik.

Kramer has no financial interests in the research, but some of his collaborators are seeking a patent related to the imaging approach. A full list of disclosures is included in the paper.

The research was made possible by work funded by the British Heart Foundation, grant PG/15/71/31731; the National Institutes of Healths National Heart, Lung and Blood Institute, grant U01HL117006-01A1; the John Fell Oxford University Press Research Fund; and the Oxford BHF Centre of Research Excellence, grant RE/18/3/34214. The research was also supported by British Heart Foundation Clinical Research Training Fellowship FS/19/65/34692, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre at The Oxford University Hospitals NHS Foundation Trust, and the National Institutes of Health.

To keep up with the latest medical research news from UVA, subscribe to theMaking of Medicineblog.

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New Artificial Intelligence Technology Poised to Transform Heart Imaging - University of Virginia

Data collection

A data set was formed through structured classroom observations in 20 full-day sessions over 5 months in 2019 at a special school with criteria of ASC for admission in East London. Participants included three teachers (one male, two females), their teaching assistants (all females), and seven children (four males, three females) aged from 6 to 12 years across 3 classes. The childrens P-scales range from P3 to P6; P-scale commonly ranges from P1 to P8, with P1P3 being developmental non-subject-specific levels, and with P4P8 corresponding to expected levels for typical development at ages 5648. In addition, the children are also described as social or language partners on the SCERTS scale used by the school. In our study, none of the participating students were classified as conversational partners. The attributes of the student cohort are presented in Supplementary Table 3.

A coding protocol was developed through an iterative process with the participating teachers, and a grid was used for recording teacher-student interaction observations. Comments and suggestions from the teachers were taken into consideration and reflected throughout the multiple revised drafts and the final versions of the coding protocol and recording grid. For each observation instance, we recorded the student identifier, time stamp, teaching objective, teaching type, the context for this teaching type, the students observed emotional state, teachers communication strategy, and the corresponding student response (outcome). Where applicable we also recorded additional notes and the type of activity (e.g. yoga). Although notes were used for context and interpretation for the data analysis as a whole, they were not included in our machine learning function experiments given their free-form inconsistency. Table 1 details all the subcategories that were considered as inputs to the machine learning models. Up to two teaching types and teacher communications could be attributed to a single observation; the rest of the categories can only be represented by one subtype. For example, an observation coded as "3, academic, giving instruction/modelling, whole class, positive, verbal/gesture, full response (the time stamp is omitted) represents that student no. "3, being in a positive emotional state, fully responded to a teachers verbal and gesture instruction, when teaching was taking place in a whole class environment, its type was modelling and had an overall academic objective. This may refer to an interaction instance where the teacher is delivering a yoga lesson to the whole class: the teacher is demonstrating a yoga move by gesturing while verbally explaining it and asking the students to do the same; the student then responds by doing the move with an observably happy expression.

All observed adult-student interactions during the school day, permitted by the teachers, were recorded. The aim was to rapidly record situation-strategy-outcome data points "in vivo inside and outside the classroom. Locations of the observations outside the classroom include the playground, library, music room, main hall, canteen, therapy rooms, and garden. Overall, these resources were regularly used throughout the observational sessions. The instances recorded for each student vary slightly from 753 to 880 (=780, =45) and in total a sample of 5460 full observations were collected.

From the 5460 observations we collected, only 5001 are distinct. If we ignore the students response, unique observations are reduced to 4880, and if we also ignore the teachers communication strategy, then this number becomes 4357. Hence, there are instances in our data that are overlapping, but this is expected given that teachers and students may perform similarly throughout a specific teaching session. The level of support for each teacher communication strategy is equal to 3128 (709) times for a verbal communication, 1717 (357) for using an object, 1642 (181) for the gesture, 1465 (575) for a physical prompt, and 981 (165) for a picture, where in parentheses we report the number of times the underpinned communication was the only one performed (from a maximum of two communications). Although the small student and teacher sample does not allow for generalisations, we see that teachers tend to verbally engage with students quite frequently (57.29%), either in combination with another communication or as the sole means of communication. The full student response rate for each communication strategy (irrespectively of co-occurrence with another one) is equal to 64.02% (64.90%, 60.68%) for picture, 60.92% (62.48%, 57.73%) for an object, 60.61% (64.34%, 53.56%) for a physical prompt, 57.67% (59.67%, 51.80%) for a gesture, and 53.20% (55.21%, 46.45%) for a verbal communication; the rates in the parentheses are breakdowns for the language and social partner SCERTS classifications, respectively, reaffirming those language partners are in general more responsive, with a more pronounced relative difference when verbal or physical prompts are deployed. In addition, performing two versus one communication is more effective in producing a full student response. In particular, the full, partial, and no response breakdowns for single communications are 50.58%, 21.84%, and 27.58%, compared to 60.01%, 21.82%, and 18.17% for two teacher communications. Although the presence of two communications naturally increases the probability of choosing the correct means of interaction, the current outcome reaffirms the hypothesis that an incorrect communication strategy does not greatly affect the student when a desirable one co-occurs. The observed features with the greatest bivariate correlation with the student response are the negative emotional state of the student (r=0.184, p0.001), the encouragement/praise teaching type (r=0.124, p0.001), and the redirection teaching type (r=0.124, p0.001).

A machine learning classification task aims to learn a function f:Xy, where ({{{bf{X}}}}in {{mathbb{R}}}^{mtimes n}), y{1,,k}m denote the observations (inputs) and the response variable (outcomes), respectively; m, n, k represent the numbers of observations and outcomes, observation categories (features), and outcome classes, respectively. Here, in the most feature-inclusive case, we define X as an aggregation of six feature categories, namely student attributes (age, sex, P-level, SCERTS classification), teaching objective, teaching type, context for teaching type, the students observed emotional state, and teachers communication strategy. All feature categories, apart from age, were coded as c-dimensional tuples of 1s and 0s, where c is the respective number of different subtypes for each category (Table 1), and ones are used to denote the activated subtype(s). Student age was coded as a real number from 0 to 1, using a linear mapping scheme, where 0 and 1 represent 5 and 12 years of age, respectively. The response variable y takes a binary definition representing two classes, a full response output versus otherwise. The rational behind this merging was to generate a more balanced classification task (56.59% full student response labels) as well as alleviate any issues arising from a miscategorisation of partial (21.86%) or no response (21.55%) outcomes.

We train and evaluate the performance of various machine learning functions in predicting the students type of response. We deploy three broadly used classifiers in the literature: (a) a variant of logistic regression (LR)55 that uses elastic net regularisation56 for feature selection, (b) a random forest (RF)57 with 2000 decision trees, and (c) a Gaussian Process (GP)58 with a composite covariance function (or kernel) that we describe below. We devise three problem formulations, where we incrementally add more elements in the observed data (input). In the first instance, we consider all observed categories apart from student attributes. Then, we include student attributes as part of the feature space and, to represent this change, augment method abbreviations with "-. Finally, in both previous setups, we explore autoregression by including the observed data and student responses for up to the previous =5 teacher-student interactions. While performing autoregression, we maintain all three types of recorded student responses in the input data.

Although logistic regression and random forests treat the increased input space without any particular intrinsic additive modelling, the modularity of the GP allows us to specify more customised covariance functions on these different inputs. GP models assume that f:Xy is a probability distribution over functions denoted as (f({{{bf{x}}}}) sim ,{{mbox{GP}}},(mu ({{{bf{x}}}}),k({{{bf{x}}}},{{{bf{x}}}}^{prime} ))), where ({{{bf{x}}}},{{{bf{x}}}}^{prime}) are rows of X, () is the mean function of the process, and k(,) is the covariance function (or kernel) that captures statistical relationships in the input space. We assume that (x)=0, a common setting for various downstream applications59,60,61,62, and use the following incremental (through summation) covariance functions:

$$k({{{bf{x}}}},{{{bf{x}}}}^{prime} )={k}_{{{{rm{SE}}}}}({{{{bf{x}}}}}_{c},{{{{bf{x}}}}}_{c}^{prime}) ,$$

(1)

$$k({{{bf{x}}}},{{{bf{x}}}}^{prime} )={k}_{{{{rm{SE}}}}}({{{bf{a}}}},{{{bf{a}}}}^{prime} )+{k}_{{{{rm{SE}}}}}({{{{bf{x}}}}}_{c},{{{{bf{x}}}}}_{c}^{prime}) ,$$

(2)

$$k({{{bf{x}}}},{{{bf{x}}}}^{prime} )={k}_{{{{rm{SE}}}}}({{{{bf{x}}}}}_{c},{{{{bf{x}}}}}_{c}^{prime})+{k}_{{{{rm{SE}}}}}({{{{bf{x}}}}}_{p},{{{{bf{x}}}}}_{p}^{prime})+{k}_{{{{rm{SE}}}}}({{{{bf{y}}}}}_{p},{{{{bf{y}}}}}_{p}^{prime}) ,,{{mbox{and}}},$$

(3)

$$k({{{bf{x}}}},{{{bf{x}}}}^{prime} )={k}_{{{{rm{SE}}}}}({{{bf{a}}}},{{{bf{a}}}}^{prime} )+{k}_{{{{rm{SE}}}}}({{{{bf{x}}}}}_{c},{{{{bf{x}}}}}_{c}^{prime})+{k}_{{{{rm{SE}}}}}({{{{bf{x}}}}}_{p},{{{{bf{x}}}}}_{p}^{prime})+{k}_{{{{rm{SE}}}}}({{{{bf{y}}}}}_{p},{{{{bf{y}}}}}_{p}^{prime}) ,$$

(4)

where kSE(,) denotes the squared exponential covariance function, xc denotes the current observation including the teachers communication strategy, a is the vector containing student attributes, and xp, yp denote the past observations and student response outcomes, respectively. Therefore, Eq. (1) refers to the kernel in the simplest task formulation where only currently observed data are used, Eq. (2) expands on Eq. (1) by adding a kernel for student attributes, and Eqs. (3) and (4) add kernels for including previous observations and student responses (autoregression). Using an additive problem formulation, where a kernel focuses on a part of the feature space, generates a simpler optimisation task and tends to provide better accuracy63. This is also confirmed by our empirical results.

We apply 10-fold cross-validation as follows. We randomly shuffle the observed samples (5460 in total) and then generate 10 equally sized folds. We use 9 of these folds to train a model, and 1 to test, repeating this training-testing process 10 times, using all formed folds as test sets. By doing this we are solving a task, whereby observations from the same student can exist in both the training and the test sets (although these observations are strictly distinct). That was an essential compromise here given the limited number of different students (7). The same exact training and testing process (and identical data splits) is used for all classification models and problem formulations. We learn the regularisation hyperparameters of logistic regression by cross-validating on the training data; this may result in potentially different choices for each fold. The hyperparameters of the GP models are learned using the Laplace approximation58,64. Performance is assessed using standard classification metrics, and in particular accuracy, precision, recall, and their harmonic mean known as the F1 score. For completeness, we also assess the best-performing model by testing on data from a single student that is not included in the training set, repeating the same process for all students in our cohort (leave-one-student-out, 7-fold cross-validation; see SI for more details).

Ethical approval was granted by the Research Ethics Committee at the Institute of Education, University College London (United Kingdom), where the research was conducted. The parents/guardians of the participating children, the school management, and their teachers gave their written informed consent. All participant information has been anonymised. Raw data and derived data sets were securely stored on the researchers encrypted computer systems with password protection.

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Link:
An artificial intelligence approach for selecting effective teacher communication strategies in autism education | npj Science of Learning -...

And naturally, the closer we get to smart intersections becoming more mainstream, the more technologies to power them go live, some of them with insanely advanced capabilities that nobody would have imagined some 10 years ago.

Iteris, for example, a company providing smart mobility infrastructure management, has come up with the worlds first 1080p high-definition (HD) video and four-dimensional (4D) radar sensor with integrated artificial intelligence (AI) algorithms.

In plain English, this is a traffic monitoring sensor that authorities across the world can install in their systems to get 1080p (thats HD resolution) video as well as 4D radar data using a technology bundling AI algorithms.

This means the new sensor is capable of offering insanely accurate detection, and just as expected, it can spot not only cars, but also trucks, bikes, and many other vehicle types. The parent company says the sensor has been optimized to also detect vulnerable road users, such as pedestrians.

In case youre wondering why a traffic management center (TMC) needs such advanced data, the benefits of this sensor go way beyond the simple approach when someone keeps an eye on the traffic in a certain intersection.

TMCs can be linked to connected cars, so the information collected by the sensor can be transmitted right back on the road where the new-generation vehicles can act accordingly based on the detected information. And this is why AI-powered detection is so important, as it offers extra accuracy, preventing errors and wrong information from being sent to connected cars.

In other words, it can help avoid collisions, reduce the speed when pedestrians are detected, and overall optimize the traffic flow because after all, everybody wants to get rid of traffic jams in the first place.

Were probably still many years away from the moment such complex sensors become more mainstream, but Iteris new idea is the living proof the future is already here. Fingers crossed, however, for authorities across the world to notice how much potential is hiding in this new-gen technology.

The rest is here:
New Traffic Sensor Uses Artificial Intelligence to Detect Any Vehicle on the Road - autoevolution