Computing Result – Formes http://formes.asia/ Tue, 22 Nov 2022 14:51:19 +0000 en-US hourly 1 https://wordpress.org/?v=5.9.3 https://formes.asia/wp-content/uploads/2021/06/icon-1-150x150.png Computing Result – Formes http://formes.asia/ 32 32 $60 Dell? These analysts revise the computer maker’s price targets after third-quarter results – Dell Technologies (NYSE:DELL) https://formes.asia/60-dell-these-analysts-revise-the-computer-makers-price-targets-after-third-quarter-results-dell-technologies-nysedell/ Tue, 22 Nov 2022 13:09:05 +0000 https://formes.asia/60-dell-these-analysts-revise-the-computer-makers-price-targets-after-third-quarter-results-dell-technologies-nysedell/ Dell Technologies Inc. Dell reported shows results that exceed expectations and sales results for its third quarter, but issued a weak fourth quarter sales guidance. Dell reported third-quarter revenue of $24.7 billion, beating analysts’ average estimate of $24.53 billion. The company said third quarter revenue was down 6% year over year. Dell reported third-quarter earnings […]]]>

Dell Technologies Inc. Dell reported shows results that exceed expectations and sales results for its third quarter, but issued a weak fourth quarter sales guidance.

Dell reported third-quarter revenue of $24.7 billion, beating analysts’ average estimate of $24.53 billion. The company said third quarter revenue was down 6% year over year. Dell reported third-quarter earnings of $2.30 per share, beating estimates of $1.60 per share.

Dell shares fell 1.1% to $40.62 in premarket trading.

These analysts changed their price targets on Dell after the release of quarterly results.

  • Barclays cut the target share price from $49 to $41. Barclays analyst Tim Long maintained an equal weight rating on the stock.
  • Citigroup cut Dell’s price target from $55 to $53. Citigroup analyst Jim Suva maintained a buy rating on the stock.
  • UBS lowered the price target on Dell from $65 to $60. UBS analyst David Vogt maintained a buy rating on the stock.
  • Wells Fargo cut the price target on Dell from $58 to $52. Wells Fargo analyst Aaron Rakers kept the stock at an overweight.
  • Raymond James raised the price target on Dell from $47 to $50. Raymond James analyst Simon Leopold held the stock up with an outperformance.
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Building a disaster preparedness strategy? Here’s How Top Service Providers Can Help https://formes.asia/building-a-disaster-preparedness-strategy-heres-how-top-service-providers-can-help/ Wed, 16 Nov 2022 18:11:46 +0000 https://formes.asia/building-a-disaster-preparedness-strategy-heres-how-top-service-providers-can-help/ In partnership with Recently, risk modeling firm RMS estimated that Hurricane Ian, which hit Florida, South Carolina, North Carolina, Georgia and Virginia in September, could inflict insured losses on the private market. up to $74 billion. Further inland flooding and storm surges could result in additional losses of $10 billion. The worrying aspect is that […]]]>

In partnership with


Recently, risk modeling firm RMS estimated that Hurricane Ian, which hit Florida, South Carolina, North Carolina, Georgia and Virginia in September, could inflict insured losses on the private market. up to $74 billion. Further inland flooding and storm surges could result in additional losses of $10 billion. The worrying aspect is that this is not a stand-alone event. According to the United States Environmental Protection Agency (EPA), the intensity of North Atlantic cyclones has “increased dramatically over the past 20 years, and eight of the 10 most active years since 1950 have occurred since the mid 1990s”.

Although businesses rely on insurance to cover losses caused by extreme events, they still have to rebuild everything from scratch, and in some cases the insurance coverage may not be sufficient. Therefore, in recent times, companies have started relying more on business continuity plans with disaster preparedness at their core for good reason.

According to Eaton, North American businesses lose $700 billion a year to downtime, with weather-related outages costing the economy between $18 billion and $33 billion a year. The average cost per minute of a data center outage also rose to $8,851, a 38% increase since 2010. Between 2000 and 2017, 19 weather-related network disruptions also affected more than one million customers.

Homemade disaster recovery plans don’t work

Organizations must have robust disaster recovery plans and protocols in place to protect their IT infrastructures against unforeseen and disruptive events. However, that is easier said than done. There are many reasons why organizations fall victim to disasters despite having plans in place. These may include a lack of coordination and communication from IT staff responsible for critical applications, databases and ERP systems, a lack of clearly defined roles and responsibilities, the absence contingency plans in test environments and an inability to accurately estimate the cost of downtime or the cost of recovery.

According to IDC’s white paper, “The State of Ransomware and Disaster Preparedness: 2022,” up to 93% of organizations experienced data-related business disruption in twelve months, forcing many challenge their existing backup and disaster recovery solutions. He also found that inadequate disaster recovery planning led to lost employee productivity, lost revenue, and higher recovery costs (see Figure 1 below). The IDC study, based on a survey of more than 500 respondents from medium and large enterprises in North America and Western Europe, found that following an outage event activity, only 28% of respondents expressed 100% confidence in their backup systems. ‘ to recover the data, and only 29% had 100% confidence in their DR solution to recover the data. As a result, 79% of these organizations have strong disaster recovery strategies in place to prevent further downtime.

Why you need reliable and expert partners to build resilient disaster preparedness strategies

Given the severe impact of unplanned events on business continuity, organizations of all sizes and representing different industries need to work closely with partners with the necessary disaster recovery expertise to get operations back up and running. in no time. Eaton suggests that investing in a disaster prevention application will allow business owners to proactively manage, monitor and determine corrective actions before power outages that can help reduce or avoid the cost of recovery. .

Here are a few reasons why working with a qualified first response service provider can help organizations be optimally prepared for unforeseen disaster scenarios:

  1. The service provider can identify critical areas of your business that need to be running at all times. This helps them develop a contract that covers insurance and training needs, the requirement for additional services in times of crisis, and to put in place the necessary equipment and manpower commensurate with the task at hand. .
  2. The first response service provider can conduct a pre-crisis risk mitigation audit to estimate the potential impact of credible disaster scenarios and identify ways to minimize vulnerability in the event of a disaster.
  3. The service provider may also perform a safety audit and, based on its findings, establish procedures to ensure injury-free repair.
  4. With its on-time disaster recovery expertise, the service provider can help organizations comply with regulations such as Sarbanes-Oxley and OSHA (Occupational Safety and Health Administration) requirements.
  5. The service provider has expertise in the critical staging of support equipment, including generators, equipment and satellite communication networks. It can also assist in the recovery, life extension and/or replacement of equipment with full manufacturing capabilities, regardless of manufacturer.

The good news is that there are reliable and experienced players in the field who have the expertise to estimate the extent of exposure to extreme events and ensure business continuity.

Eaton’s disaster response services, for example, ensure companies have a recovery plan and the support needed to resume production quickly and safely. The company’s field technicians are seasoned experts who can help restore emergency power systems quickly and safely. Eaton’s expertise in implementing a proactive and comprehensive disaster response program will help it achieve its availability goals and avoid financial loss.

The company’s disaster response services provide organizations with a host of benefits, such as remote monitoring services, preventive maintenance and support services, UPS upgrades (kVA, power-saving system, firmware), Eaton replacement batteries, multi-vendor services (support for other manufacturers’ products), and factory-certified spare parts.

For example, Eaton’s Distributed Infrastructure Management (DIM) software, part of the company’s Brightlayer Data Centers suite, provides the tools needed to monitor and manage power devices in physical or virtual. This helps IT managers to remotely monitor, manage and control UPSs, PDUs, servers and other storage devices on the network. It also helps enterprises and SMBs preserve functionality of key assets by disabling them when a shutdown occurs across multiple operating systems.

Eaton’s best products that ensure business continuity and operational resilience

There are several other best-in-class products offered by Eaton that can help make IT infrastructures resilient to extreme weather events and other major disruption vectors. These are:

Surge Protectors: The Isobar® line of surge protectors from Tripp Lite by Eaton protect critical IT equipment with surge protection up to 5700 joules and provide robust EMI/RFI protection. They also offer data line protection, USB charging, metal casing, auto shut-off, and other key features.

Rack cooling: Eaton offers packaged air conditioning units that ensure optimal system performance by eliminating hot spots. They can be modernized and reconfigured to handle expansion, upgrades, virtualization/consolidation projects, increased power density, and new hotspots. Businesses can choose from a range of rack-mounted, portable or row-mounted units depending on their cooling needs.

While rack-mounted units work best in server rooms or data centers with one or two racks, as long as the power density stays below 2 kW per rack, portable units are ideal for cooling a small room. servers, up to five racks or one location. Row units are placed in a rack row, making them highly scalable, as organizations can choose to add as many units as they want based on their cooling needs.

UPS: Eaton offers rack-mount, wall-mount and tower-mount UPS units for a variety of use cases and to provide uninterruptible power to ensure that network closets and intermediate distribution frames (IDFs), which are critical components of IT networks, remain operational at all times.

PDUs: Having the right rackmount PDU can be an essential asset for any business to effectively manage their data center. Eaton offers advanced power distribution units in various form factors and for various use cases to ensure business continuity. While metered PDUs provide metering at the branch or outlet level, managed PDUs offer the more advanced features, such as outlet-level switching (i.e. you can switching it off and on remotely) and monitoring at the outlet.

Charging station and charging cart: Eaton offers charging stations or charging carts to allow businesses to operate their laptops/notebooks, Chromebooks and tablets at all times to ensure operational continuity and avoid interruptions caused by unforeseen events. These high-end charging stations can simultaneously power dozens of devices and are also useful from a cybersecurity perspective. Lockable charging stations protect devices while ensuring the security of sensitive and proprietary data stored on devices or accessed by devices when devices are charged or stored.

For more information on Eaton’s disaster response services, click here.

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Prediction of aircraft surface roughness after coating removal based on optical image and deep learning https://formes.asia/prediction-of-aircraft-surface-roughness-after-coating-removal-based-on-optical-image-and-deep-learning/ Sat, 12 Nov 2022 15:13:21 +0000 https://formes.asia/prediction-of-aircraft-surface-roughness-after-coating-removal-based-on-optical-image-and-deep-learning/ Experimental environment and configuration Table 2 shows the experimental environment of this article. Batch size = 32, max epoch = 100 and the weight of the last epoch is taken as the training result of the model. When using the Adam optimization algorithm, learning rate = 0.001, momentum = 0.9. Table 2 Experimental environment. Comparative […]]]>

Experimental environment and configuration

Table 2 shows the experimental environment of this article. Batch size = 32, max epoch = 100 and the weight of the last epoch is taken as the training result of the model. When using the Adam optimization algorithm, learning rate = 0.001, momentum = 0.9.

Table 2 Experimental environment.

Comparative experiences of different models

In this paper, the prediction performance of three SSEResNet regression models on three datasets of different sizes using a simple gradient descent (GD) optimization algorithm is first compared. The experimental conditions are the original dataset with no data enhancement, a fixed learning rate of 0.0025, 100 epochs, and other identical parameter configurations. In this article, the mean squared error (MSE) loss is used to replace the previous cross-entropy loss used for classification tasks, as an index for evaluating experience. MSE is suitable for regression tasks and is calculated by Eq. (5). Where thereI represents the true value and pI represents the predicted value. Table 3 shows the experimental results of different SSEResNet regression models on different datasets.

$$MSE = frac{1}{n}sumlimits_{i = 1}^{n} {left( {p_{i} – y_{i} } right)^{2} }$$

(5)

Table 3 Experimental results of different SSEResNet on different datasets.

From the experimental data in Table 3, it can be seen that the model should fit the appropriately sized dataset to achieve good results, and the deeper the network layer, the larger the dataset is for the model. This is because the shallow model has insufficient feature extraction and limited image processing capability on large datasets, while the deep model is easy to overfit on small datasets. Considering the training time and prediction performance of the model, the SSEResNet101 regression model and the 8000-image dataset were selected in this paper for the later comparison experiment with other models.

In this article, four optimization methods are compared, then the SSEResNet101 model is compared to seven other CNN backbones. Using the SSEResNet101 regression model, the Adam optimization method is tested under the same conditions as the other three optimization methods commonly used in deep learning, which are SGD, Momentum, and RMSprop. The experimental conditions are a fixed learning rate of 0.0025, 100 epochs and other identical parameter configurations. MSE loss, mean absolute error (MAE) and R-Square (R2) are selected as evaluation indices. Equations (6) and (7) are the calculation methods of MAE and R2, respectively. Where (overline{y}_{i}) is the mean of the label values. When the predicted value equals the label value, MAE is 0, and the larger the error, the larger the MAE value. The range of R-Squared values ​​is [0,1]. If the result is 0, the model-fitting effect is poor; if the result is 1, the model is fully fitted. The larger the R-Squared, the better the model fitting effect. Table 4 shows the experimental results of different optimization methods.

$$MAE = frac{1}{n}sumlimits_{i = 1}^{n} {left| {p_{i} – y_{i} } right|}$$

(6)

$$R2 = 1 – frac{{sumlimits_{i} {(p_{i} – y_{i} )^{2} } }}{{sumlimits_{i} {(overline{ y}_{i} – y_{i} )^{2} } }}$$

(seven)

Table 4 Experimental results of different optimization methods.

The experimental data in Table 4 shows that Adam’s optimization method has better performance than the other three optimization methods with and without data enhancement. Compared with Momentum, RMSprop and the traditional SGD algorithm, Adam incorporates the advantages of Momentum and RMSprop. Among them, the advantage of Momentum is that it can speed up the learning of parameters with the same gradient direction and reduce the updating of parameters with the change of gradient direction, so that the parameters in the same direction can converge quickly. RMSprop is an adaptive learning rate optimization algorithm. The advantage of RMSprop is that at the beginning of training, the learning rate is large, which can accelerate the convergence of the model, while in the later training phase, the learning rate is low, which is beneficial for removing oscillation from the model and avoiding skipping the optimal solution. Therefore, we use Adam’s optimization method to conduct a comparative experiment between SSEResNet101 and seven other CNN core networks. The experimental conditions are a fixed learning rate of 0.0025, 100 epochs and other identical parameter configurations. Table 5 shows the experimental results of the regression prediction models.

Table 5 Experimental results of the “optical image surface roughness” regression models.

The experimental data in Table 5 shows that the MSE and MAE loss values ​​of the model are reduced after the data enhancement, indicating that the data enhancement operation effectively improves the model performance. In effect, the data enhancement operation generates many similar but different training samples by making a series of random changes to the training images, thus enlarging the scale of the training data set. Moreover, these random changes make the model less dependent on certain attributes in the training samples, thus improving the generalizability of the model. Compared with other CNN models, the MSE and MAE values ​​of our model are the smallest and the R2 value is the largest. After using data enhancement, the MSE of our model is only 0.0285, 0.0097 less than ResNet101 and 0.0032 less than SEResNet101. Meanwhile, SEResNet101 was also 0.0065 smaller than ResNet101. These comparison results show that the SE module and the CSPNet reinforcement module play an important role in improving the prediction ability of the model. Indeed, the SE module exploits the correlation of features between channels and the CSPNet reinforcement module helps to extract deep semantic information. After feature fusion and mapping with shallow semantic information, richer semantic information can be obtained. Under the joint action of these two modules, the model can better extract the detailed features from the optical images, and then learn the mapping relationship between these features and the surface roughness.

Influence of learning rate decay strategy on model training and prediction effect

In this paper, we also investigate the effect of different learning rate decay strategies on the model during learning. The MSE loss curves of the validation set, which is trained on the 8000-frame dataset using StepLR, MultiStepLR, CosineAnnealingLR and CosineAnnealing with hot restart, are shown in Fig. 6.

Figure 6

Validation MSE loss curves defined for different learning rate decay strategies.

The experimental results of FIG. 6 show that the learning rate mitigation method of CosineAnnealing with warm restart has the best convergence effect, and the MSE loss is the smallest. The CosineAnnealingLR method has the second best training effect and StepLR has the worst training effect. CosineAnnealing with hot restart can cause the learning rate to drop to a certain value, hot restart, roll back to the original value, then cycle down again. Such a learning rate adjustment method can cause the model that converges to the local optimal solution to jump out of the local optimal solution and continue updating the model until the model reaches the solution global optimum.

To more intuitively show the effect of surface roughness prediction based on optical images and deep learning regression models, we plot the test set validation results as a plot of points from the predicted values ​​of the regression model and the true label values, as shown in Fig. 7.

Picture 7
number 7

Point plot of predicted and actual surface roughness values ​​from the SSEResNet101 regression model. (a) Using StepLR. (b) Using MultiStepLR. (vs) Using CosineAnnealingLR. (d) Using CosineAnnealing with warm restart.

The experimental results of FIG. 7 show that the surface roughness predicted by the regression model for optical images is close to the actual value, which indicates that the regression model we designed has a good prediction effect and can directly and accurately predict the surface roughness of optical images. In particular, the prediction effect of the learning rate attenuation method of CosineAnnealing with hot restart is the best, its MAE test value is 0.245μm, and the surface roughness prediction result is more consistent with the actual value.

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NVIDIA Hosts Third Quarter Financial Results Conference Call https://formes.asia/nvidia-hosts-third-quarter-financial-results-conference-call/ Wed, 02 Nov 2022 21:05:01 +0000 https://formes.asia/nvidia-hosts-third-quarter-financial-results-conference-call/ The CFO’s comment must be provided in writing prior to the call SANTA CLARA, Calif., Nov. 02, 2022 (GLOBE NEWSWIRE) — NVIDIA will host a conference call on Wednesday, November 16 at 2 p.m. PT (5 p.m. ET) to discuss its financial results for the third quarter of fiscal year 2023, which ended on October […]]]>

The CFO’s comment must be provided in writing prior to the call

SANTA CLARA, Calif., Nov. 02, 2022 (GLOBE NEWSWIRE) — NVIDIA will host a conference call on Wednesday, November 16 at 2 p.m. PT (5 p.m. ET) to discuss its financial results for the third quarter of fiscal year 2023, which ended on October 30, 2022.

The call will be webcast live (in listen-only mode) on investor.nvidia.com. The remarks prepared by the company will be followed by a question-and-answer session, which will be limited to questions from financial analysts and institutional investors.

Prior to the call, NVIDIA will provide written comments on its third quarter results from its chief financial officer. This material will be published on investor.nvidia.com immediately following the company’s public earnings announcement at approximately 1:20 p.m. PT.

The webcast will be recorded and available for replay until the company’s conference call to discuss its fourth quarter and fiscal 2023 financial results.

About NVIDIA
Since its creation in 1993, Nvidia (NASDAQ: NVDA) pioneered accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, sparked the era of modern AI, and fueled the creation of the metaverse. NVIDIA is now a comprehensive computing company with industry-shaping data center-scale offerings. More information at https://nvidianews.nvidia.com/.

© 2022 NVIDIA Corporation. All rights reserved. NVIDIA and the NVIDIA logo are trademarks and/or registered trademarks of NVIDIA Corporation in the United States and other countries.

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AI-generated trials are not to be feared (opinion) https://formes.asia/ai-generated-trials-are-not-to-be-feared-opinion/ Mon, 24 Oct 2022 07:08:50 +0000 https://formes.asia/ai-generated-trials-are-not-to-be-feared-opinion/ September 2022 was apparently the month when the artificial intelligence trial angst spilled over into academia, as various media published opinion pieces lamenting the rise of AI writing systems that will ruin the world. student writing and pave the way for unprecedented levels of academic misconduct. Then, on September 23, Academic Twitter exploded in a […]]]>

September 2022 was apparently the month when the artificial intelligence trial angst spilled over into academia, as various media published opinion pieces lamenting the rise of AI writing systems that will ruin the world. student writing and pave the way for unprecedented levels of academic misconduct. Then, on September 23, Academic Twitter exploded in a bit of a panic over this. The firestorm was sparked by a post on the OpenAI subreddit where user Urdadgirl69 claimed to get A’s with essays “written” using artificial intelligence. Professors on Reddit and Twitter expressed frustration and concern about how best to deal with the threat of AI trials. One of the most poignant and widely retweeted laments came from Redditor ahumanlikeyou, who wrote, “Noting something an AI wrote is an incredibly depressing waste of my life.

While all of this was happening online, my Rhetoric and Algorithms undergraduate students and I were running a little AI-generated student writing experiment. After reviewing 22 AI essays that I asked my students to create, I can tell you with certainty that AI-generated essays are nothing to fear. The technology just isn’t there, and I doubt it will be anytime soon. For the aforementioned AI essay activity, I borrowed an assignment sheet from the University of Texas at Austin’s freshman writing class. The assignment asks students to submit a 1,800-2,200 word proposal on a local issue. Students typically tackle issues on campus, coming up with ideas such as “It shouldn’t be so hard to get into computer science classes” or “Tuition should be lower” or “Housing on the campus should be more affordable”. For the purposes of the Rhetoric and Algorithms course, I asked students to rely as much as possible on AI. They were free to create multiple prompts to generate AI outputs. They were even encouraged to use these prompts in their essays. Students were also free to rearrange paragraphs, remove obvious repetitions, and clean up formatting. The main requirement was that they had to make sure most of the essay was “written” by AI.

The students in this class were mostly juniors and seniors, and many were rhetoric and writing majors. They did a great job, putting in a lot of effort. But, in the end, the essays they returned were not good. If I had believed these were real student essays, the best would have won somewhere around a C or C-minus. They met at least the posting requirements, but that was about it. Additionally, many of the essays presented clear red flags for the AI ​​generation: outdated facts about tuition, quotes from former college presidents portrayed as current presidents, fictional professors, and named student organizations that do not exist. Few students in my class have experience in computer programming. As a result, they mainly turned to freely available text generators such as EleutherAI’s GPT-J-6B. Several students have also opted to sign up for free trials of AI writing services such as Jasper AI. However, regardless of the language model used, the results were fairly consistently poor and generally quite obvious in their making.

At the same time, I asked my students to write short thoughts on the quality and difficulty of their AI essays. Almost all of the students said they hated this assignment. They quickly recognized that their AI-generated essays were substandard, and those who used to get top marks were loath to report their results. Students overwhelmingly said that using AI takes a lot more time than just writing their old-fashioned essays. To gain additional insight into the “writing” process, I also asked students to hand in any results collected from the AI ​​text generation “pre-writing”. Students regularly produced 5,000 to 10,000 words (sometimes up to 25,000 words) of results in order to cobble together essays that barely reached the floor of 1,800 words.

There has been a lot written about the supposed awesomeness of AI-generated text. There are even several articles, essays or even high-profile scientific papers or scripts written by AI that highlight this impression. In many of these cases, the “authors” have access to higher quality linguistic models than most students are currently able to use. But, more importantly, my experience with this assignment tells me that it takes a good writer to produce good algorithmic writing. Published examples are usually the beneficiary of professional writers and editors who craft prompts and edit the results in neat form. In contrast, many of my students’ AI-generated essays showed common problems with student writing — uncertainty about the appropriate writing style, problems with organization and transitions, and inconsistent paragraphs. Producing a quality essay with AI requires having sufficient command of the target writing style to create prompts that will cause the model to produce appropriate results. It also requires having strong organizational and editing skills. As a result, the best writers among my students produced the best AI essays, and the developing writers generated essays with many of the same issues that would have been in their authentic writing.

Overall, this exercise tells us that we are not about to receive a flood of algorithmically generated student submissions. It’s just too much work to cheat like that. The activity also tells me that the best defense against AI trials is the same as the best defense against trial deposits – a good homework sheet. If your assignment is “For today’s assignment, please describe the reasons for the American Civil War” (a literal stock prompt from the GPT-J template mentioned above), you are much more likely to get AI or downloaded essay submissions only if you write a detailed assignment sheet specific to the context of your class. The assignment I used for my Rhetoric and Algorithms students was quite a challenge because it asked them to address local issues of concern. There are simply not enough relevant examples in the data from which AI text generators draw to generate plausible essays on this topic as a whole.

Beyond academic misconduct concerns, this activity also showed me that using AI text generation can be part of good writing pedagogy. Two of the most important and hardest things to teach about writing are gender awareness and editing best practices. Developing writers don’t have the experience to understand the subtle differences between different types of essays or assignments. This is why student essays often seem overwritten or understated. Students are often still figuring out how to find the right place and how to adjust their style for different writing activities. Moreover, the usual delay between submission and feedback does little to help develop this intuition. However, rapid creation for AI text generators provides mostly immediate feedback. By experimenting with sentences that do and do not produce appropriate AI outputs, students can develop an idea of ​​how to write differently for different genres and contexts. Finally and unfortunately, most of my students complete their writing assignments in one session just before the deadline. It is difficult to get them to practice editing. The AI-generated text offers an interesting possibility for a sort of educational training exercise. Students might be asked to quickly generate a few thousand words and then turn those words into usable prose. It’s not “writing” in the same way that line drills aren’t basketball. But that doesn’t mean there isn’t a useful educational role here.

Ultimately, higher education will have to tackle AI text generation. Right now, most efforts to address these concerns seem to gravitate towards either AI evangelism or algorithmic desperation. I guess this fits more broadly with the AI ​​discourse. However, neither evangelism nor despair seems to me to be the ideal answer. For those despairing, I think it’s highly unlikely that we are (or soon will be) drowned in AI-generated trials. With today’s technology, it’s just too difficult and time-consuming than writing an essay. At the same time, I am deeply skeptical that even the best models will ever truly allow students to produce writing that far exceeds their current ability. Effective prompt generation and review depends on high-level writing skills. Even if artificial intelligence improves, I wonder to what extent novice writers will be able to steer text generators skillfully enough to produce impressive results. For the same reasons, I also question the enthusiasm of AI evangelists. It’s been just over five years since Google Brain computer scientist Geoffrey Hinton said, “We should stop training radiologists now. It is quite obvious that within five years, deep learning will outperform radiologists. Well, we’re still training radiologists, and there’s no indication that deep learning will replace human doctors anytime soon. In the same way, I strongly suspect that full robotic writing will always and forever be “just around the corner”.

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Apple stock could benefit from higher fiscal fourth-quarter earnings https://formes.asia/apple-stock-could-benefit-from-higher-fiscal-fourth-quarter-earnings/ Fri, 21 Oct 2022 17:43:00 +0000 https://formes.asia/apple-stock-could-benefit-from-higher-fiscal-fourth-quarter-earnings/ Investors will pay attention to Apple(AAPL) September quarter earnings and guidance amid signs of slowing consumer spending. Apple stock tumbled ahead of next week’s report. X Apple plans to announce its fiscal fourth quarter results after the market closes on Thursday. Analysts polled by FactSet are predicting Apple earnings of $1.27 per share on sales […]]]>

Investors will pay attention to Apple(AAPL) September quarter earnings and guidance amid signs of slowing consumer spending. Apple stock tumbled ahead of next week’s report.




X



Apple plans to announce its fiscal fourth quarter results after the market closes on Thursday. Analysts polled by FactSet are predicting Apple earnings of $1.27 per share on sales of $88.7 billion. This would translate to year-over-year growth of 2% in profits and 6% in sales.

For the December quarter, Wall Street forecast Apple earnings of $2.11 a share, up a penny from a year earlier, on sales of $126.6 billion, up 2%.

A key area for investors will be the performance of Apple’s latest smartphones, the iPhone 14 series. Supply chain and retail checks indicate strong sales of the more expensive Pro models offset by average sales of the regular patterns, according to analysts.

Apple Stock Might Get an iPhone 14 Facelift

“We expect the launch of this year’s iPhone 14 to add juice to fiscal fourth-quarter revenue,” Monness Crespi Hardt analyst Brian White said in a note to clients on Friday. However, he noted that “demand for new high-end iPhones and standard models seems more bifurcated this cycle.”

Apple started selling the iPhone 14 series on September 16 after a week of pre-orders.

White views Apple stock as a buy with a 12-month price target of 174.

In afternoon trading in the stock market today, Apple stock rose 1.7% to 145.86.

Sales of high-end smartphones A bright spot

Cowen analyst Krish Sankar said he saw growing risks for iPhone sales through the end of the year.

“High-end smartphones are one of the few areas of the tech landscape that has yet to see a correction in demand, although we believe this divergence may end in the coming quarters,” Sankar said in a note to customers on Friday. He believes Apple shares are outperforming with a price target of 200.

In addition to resilient iPhone sales, Apple should benefit from an improved supply of Mac computers and iPads in the September quarter, Barclays analyst Tim Long said in a note on Thursday. . However, Apple’s services business, particularly the App Store, appears to be weakening, he said.

Longs price Apple shares as equal or neutral weight, with a price target of 155.

Apple’s outlook hardens after the December quarter, Long said. The company faces a series of risks ahead. They include a potential decline in consumer spending and the risk of government regulatory action against Apple’s App Store and other services.

Follow Patrick Seitz on Twitter at @IBD_PSeitz for more stories on consumer technology, software and semiconductor stocks.

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School of Data Science uses Rowdy Datathon to amplify a student-led tradition | UTSA today | UTSA https://formes.asia/school-of-data-science-uses-rowdy-datathon-to-amplify-a-student-led-tradition-utsa-today-utsa/ Wed, 19 Oct 2022 09:39:51 +0000 https://formes.asia/school-of-data-science-uses-rowdy-datathon-to-amplify-a-student-led-tradition-utsa-today-utsa/ “Data science and artificial intelligence are going to be determinants of future success in our current global competition,” the NSA Senior Data Science Authority said. Tony Thrall. “The intelligence community needs to partner with academia and industry even more than ever.” Thrall says he was energized by the turnout and the spirit of the event. […]]]>

“Data science and artificial intelligence are going to be determinants of future success in our current global competition,” the NSA Senior Data Science Authority said. Tony Thrall. “The intelligence community needs to partner with academia and industry even more than ever.”

Thrall says he was energized by the turnout and the spirit of the event.

“NSA wanted to see if we could run a data science competition for students,” said Jianwei Niu, professor of computer science at UTSA, associate dean of University College and faculty member of SDS. “I took from the experience that UTSA had in hosting six or seven hackathons in the past and said, ‘We have a wonderful group of student leaders, so let’s see if they can take up this challenge and organize the first datathon on campus.”

Students who participated in the datathon explored data science issues with their peers and competed to investigate the socioeconomic factors that influence low birthweight and neonatal mortality. The challenge was presented as a commission by a fictitious government agency attempting to project those results to Texas in 2030.

“The Rowdy Datathon was designed with a perspective not found in other similar events,” said Juan Gutierrez, professor and director of the mathematics department at UTSA. “One of the main considerations was to expose data hackers, or ‘dackers’, to the real complexity of analyzing the data.”

Gutiérrez said there is a common misconception — even among college students — that data analysis is just coding. However, he explained that students need to learn skills such as data management, including how to handle large amounts of data or data with errors, as well as ethics in data management.

The competitors’ projects were judged by Gutiérrez and six NSA data analysts. The judges evaluated the teams based on a number of factors such as the quality of the presentation of the results, the correctness of their methodologies and the reproducibility of their results.

“At the end of the day, we want the students who participate in this challenge to grow,” Gutiérrez said. “The next iteration will refine areas that have proven to need more polishing, and with this solid foundation, we are well on our way to making the Rowdy Datathon an example to follow and a notable event in the country.”

Although all participants worked with the same data, they were divided into three streams: beginner, intermediate and advanced. This made the datathon accessible to students of any skill level, explained Roni Maddoxorganizing student specialized in environmental sciences.

“We expect that most people attending these events will either have never touched code or be freshmen and sophomores and have taken a coding course or two,” she said. “So while there is a competitive aspect and we have prizes and awards at the end of the weekend, the focus is definitely on learning.”

Although the datathon is designed to be suitable for beginners, more advanced students found plenty to keep them busy. The event featured workshops and offered students an invaluable opportunity to network and meet other students and professionals in the field.

Niu thinks it’s especially important to accept students of all skill levels in STEM fields such as computer science or data science, which she says can often seem intimidating or inaccessible to many.

“Students often become intimidated when they think about the field of data science, what data science is and if they should get into it or if they are able to,” she said. declared. “This datathon definitely provides a great opportunity for students to try. It’s the best thing we can do to help enrich the program. It’s outside the classroom and it exposes students to challenges of the real world.

Maddox said the planning team is trying to ensure that as much content as possible from the event will be made available to the public in the future. This could include recordings of some of the workshops, or even promoting the dataset and challenges.

The team is also planning future Rowdy Datathons.

“We always want to create as many opportunities as possible for as much of our community as possible,” Maddox said.

The student organizers said the dedication they have to data science students at UTSA reflects the support the organizers have received from their faculty, advisors and mentors at the university and in the community. .

“It’s not in anyone’s job description,” Millison said, “but it wouldn’t be possible without people who really want to see UTSA data science students succeed and have a big impact.”

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Check out the top 10 ASX stocks in Q4 2022 https://formes.asia/check-out-the-top-10-asx-stocks-in-q4-2022/ Mon, 17 Oct 2022 10:12:11 +0000 https://formes.asia/check-out-the-top-10-asx-stocks-in-q4-2022/ 2022 has seen global economies raise interest rates in response to inflation caused by low rates and stimulus measures during the Covid-19 pandemic. Inflation often lowers demand for equities as interest rates become more attractive elsewhere. This can cause panicked investors to divest from publicly traded tech companies, subsequently driving down the price of many […]]]>

2022 has seen global economies raise interest rates in response to inflation caused by low rates and stimulus measures during the Covid-19 pandemic.

Inflation often lowers demand for equities as interest rates become more attractive elsewhere. This can cause panicked investors to divest from publicly traded tech companies, subsequently driving down the price of many tech stocks.

For example, US tech giants listed on the NASDAQ 100 Technology Sector Index, such as Apple, Microsoft and Alphabet, saw declines in value in the first three weeks of the year.

A tech stock refers to any company operating in the technology sector – a space encompassing everything from e-commerce, semiconductors, social media and even cloud computing.

Australian tech stocks, like their international counterparts, also saw a share price slide that continued into early 2022, hitting an eight-month low on January 24 this year.

However, tech stocks are known to be resilient and are likely to improve their performance as market sentiment improves as investors acclimate to rising inflation and rising interest rates. .

Some may see the drop in tech stocks as a chance to buy while the stock price is low in the hope that they will appreciate over time. Note that past performance does not guarantee future results. So if you want to buy these stocks, it is wise to do a thorough technical and fundamental analysis and make sure your position matches your investment plan.

  1. To block
  2. WiseTech Global
  3. computer sharing
  4. Xero
  5. Altium
  6. NEXTDC
  7. Technology One
  8. Liaison group
  9. Irene
  10. Dickerdata

1. Block: $57.48 billion

Block is a global fintech company that was launched in 2009 by Twitter co-founder and former CEO Jack Dorsey.

The company owns several payment brands that target small businesses and consumers, including Square, Cash App and Australia’s BNPL Afterpay platform. Other key brands include decentralized finance platform TBD, music streamlining service Tidal and web hosting service Weebly.

In January 2022, the company completed its $39 billion acquisition of Afterpay, giving it access to 3.6 million active customers in the United States, 3.1 million in Australia and New Zealand, and 600,000 in UK.

2. WiseTech Global: $18.85 billion

WiseTech Global is a logistics software company whose flagship product, CargoWise One, serves as a platform for automating and analyzing supply chain operations.

More than 18,000 logistics organizations in 170 countries use WiseTech’s software, including 24 of the world’s top 25 freight providers and 41 of the world’s top 50 third-party logistics providers.

3. Computer sharing: $14.55 billion

Melbourne-based Computershare is a share registry company that facilitates the transfer of ownership of securities. It was founded in 1978 and is one of Australia’s oldest IT companies.

In addition to share registration services, Computershare assists companies with employee stock ownership plans, stakeholder communications, fund services and corporate governance.

4. Xero: $12.63 billion

Software developer Xero provides cloud-based accounting tools for small business owners and accountants. One of the main selling points of Xero products is the automation of the many accounting and bookkeeping tasks that small businesses have to perform on a regular basis.

Xero has integrated them with more than 1,000 third-party applications to improve the functionality of its software. The company says its products currently have over 3 million subscribers in Australia, New Zealand and the UK.

5. Altium: $4.7 billion

According to Altium, it is currently the world’s leading supplier of printed circuit board (PCB) software after 35 years of research and design work.

75% of the company’s revenue comes from subscriptions, while its revenue comes from a variety of regional sources, including the Americas (55%), Europe (31%), emerging markets (10%) and Asia ( 5%).

6. NEXTDC: $4.5 billion

Data center company NEXTDC markets itself as a customer-centric digital infrastructure provider. The company has nine data centers across Australia and offers over 730 specialized ICT clouds, networks and services in conjunction with its partner ecosystem.

NEXTDC claims to provide its services to some of the world’s leading cloud platforms. The technology company recently won the 2022 Australian Data Center Services Company of the Year award from global consultancy Frost & Sullivan.

7. First technology: $3.64 billion

Technology One’s Software-as-a-Service (SaaS) platform focuses on the financial software needs of businesses and government departments. Major customers include regional governments, universities and museums located throughout Australia and the UK.

The company claims to be one of Australia’s first technology start-ups and launched its first software product, FinanceOne, in 1991. It operates one of Australia’s largest software R&D centers, with a team of over 400 developers.

8. Link Group: $2.15 billion

Founded in 2005 in New Zealand, Link Group provides recordkeeping technology and information solutions to the global financial industry. The company operates across the globe including Australia, India, South Africa, UK and Europe.

Link says it is the largest service provider in Australia’s superannuation administration industry, serving the fourth largest pool of pension funds in the world. It also provides its services to the corporate market, fund managers and the banking industry.

9. Iress: $2.13 billion

Fintech company Iress produces software for the financial services industry, covering areas such as financial advice, trade and market data, investment management and pensions.

The company operates in Asia Pacific, UK, Europe, North America and Africa. According to Iress, more than 10,000 companies and more than 500,000 people use its software.

10. Dicker Data: $1.8 billion

Founded in 1978, Dicker Data is a long-time veteran of the Australian information technology industry with over four decades of experience.

The company is a distributor of hardware, software and cloud services and has an exclusive partner base of over 6,000 resellers. Dicker’s product portfolio encompasses a wide variety of global Tier 1 brands, including Cisco, Citrix, Dell Technologies, Hewlett and Packard Enterprise, HP, Lenovo and Microsoft.

Here’s how to buy, sell, and trade these top 10 promising ASX-listed tech companies:

How to buy and invest in tech stocks

With us, traders and investors can buy any of the top 10 tech stocks, using our stock trading platform by following four steps:

  1. Open a stock trading account or log in

  1. Fund your stock trading account

  1. Open the platform to the stock trading account, go to the “Finder” panel, enter and search for your favorite tech stock

  1. Click on the transaction ticket, where the “in exchange” option will appear. “In exchange” means interacting directly with the relevant exchange

How to trade tech stocks with CFDs

For those who prefer to use leveraged derivatives like CFDs, you can use our market-leading platform to capitalize on long (“buy”) and short (“sell”) price movements.

Trading tech stocks with CFDs with us can be done in a few steps:

  1. Open a CFD trading account or login

  1. Find the tech stock you want to go short or long on

  1. Click “sell” or “buy” in the transaction ticket

  1. Choose your post size

  1. Confirm exchange

Features of tech stock trading with CFDs

  • Leverage: These derivatives allow investors to gain exposure to a relatively large position in a particular asset, with a reduced initial outlay. The use of leverage can, however, be a double-edged sword for those who do not implement proper risk management strategies, because while it maximizes the potential gains reaped from a trade, it can also amplify the losses.

  • Flexibility: This differs from outright buying an asset. CFD trading gives you the opportunity to speculate on the upward or downward price movements of an underlying asset. This means that when trading, you can easily enter short positions if you believe an asset is overvalued and its value will decline.

  • Hedging: A hedge is an investment or transaction to reduce your current risk exposure. This potentially offsets one or more other positions you currently have open. You can implement hedging strategies when trading with us

Although CFD trading has its advantages, you should also be aware that CFD trading involves significant risks, as leverage magnifies profits or losses. You do not own or have any interest in the underlying asset. You have to ask yourself if you understand how CFDs work and if you can afford to take the high risk of losing your money.

Please consider the Margin Trading Product Disclosure (PDS) Statement, Risk Disclosure Notice and Target Market Determination before entering into a CFD transaction with us.

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Phone scammers use CHI computer misfortunes as bait https://formes.asia/phone-scammers-use-chi-computer-misfortunes-as-bait/ Sat, 15 Oct 2022 03:44:00 +0000 https://formes.asia/phone-scammers-use-chi-computer-misfortunes-as-bait/ OMAHA, Neb. (WOWT) – CHI Health’s IT system remains shut down, following a nationwide ransomware attack on its parent company that affected countless patients and healthcare workers. Now some scammers are using the information as an opportunity to prey on consumers. It seems phone scammers are always looking for new ways to get you off […]]]>

OMAHA, Neb. (WOWT) – CHI Health’s IT system remains shut down, following a nationwide ransomware attack on its parent company that affected countless patients and healthcare workers.

Now some scammers are using the information as an opportunity to prey on consumers.

It seems phone scammers are always looking for new ways to get you off the hook, and Collin Warren has answered.

“I had some medicine that I needed to pick up, so why not pick up that phone,” he said. “But you’re still thinking in the back of your head, should I?”

The caller claimed to be in collections for CHI Health.

Collin grew suspicious when she didn’t seem to know where she was calling, so he started recording the call.

“She goes ‘Omh…’, you leave some letters, honey,” Collin said. “The last resort of all [scam attempt] that’s when she told me that I owed money to a place where I had zeroed my account.

Scammers are using CHI Health’s highly publicized computer failure as a trap and the hospital is telling us not to bite. They sent us a statement making it clear that they have bigger concerns than appealing to collections.

Under certain circumstances, CHI Health may contact a patient by phone to confirm information about their care plan, such as insurance or scheduling information. We may request personal and financial information prior to a procedure in order to complete the registration process or when assisting patients to apply for financial assistance.

At this time, due to a ransomware attack, we are not contacting patients to resolve medical bills or request personal financial information.

Third-party collection agencies do not contact patients on behalf of CHI Health at this time.

If a patient receives a call that concerns them, we ask them to hang up and contact their doctor’s office.

Bellevue Police Department community relations coordinator Roger Cox agrees.

“There’s absolutely nothing wrong with saying I’m going to hang up and call your billing office myself,” Cox said. “That’s exactly what I do every time, I could say, ‘thank you so much for your call, I’m just going to hang up now and call back the 800 number I have for you, and then we can discuss this when I’m making the call back.’”

Cox said that even if you don’t fall for the scam, law enforcement still encourages you to let them know by calling your local police department’s non-emergency number.

“Sometimes you call us and it’s something we’ve never heard of before,” Cox said. “We do our best to spread [on social media] what we hear, what are the latest scams, to warn people just to say, be careful.

Most of these patterns are not of local origin, Cox said, even though the caller ID can be read as a local number.

The Federal Trade Commission also encourages people to report phone scams, to keep their database as complete as possible. The FTC also has a program, Consumer Sentinel Network, which compiles data and offers collaborative resources with local law enforcement. But Cox said fraudulent crime is hard to beat.

“I mean the FTC, the feds have created an entire task force to try to find these people and put a stop to them, and even with all the resources they have, they’re still having a horrible time,” he said. said Cox. . “It just seems like it’s getting worse and worse, for every [scam call] you used to have, now you have 10.

Collin took to Facebook and told the social media world what happened, and shared the phone call with us. He wanted to make sure people took him seriously and didn’t fall victim to scammers fishing, or maybe you should call it phishing, during a medical crisis.

“People from Omaha or the metro using CHI, be careful, because [the phone scammers] now know they can try this, and it happened, I got it, today.

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Jackson Walker Celebrates Milestone of 500 Patents Filed by Inventor of Cirrus Logic – Jackson Walker https://formes.asia/jackson-walker-celebrates-milestone-of-500-patents-filed-by-inventor-of-cirrus-logic-jackson-walker/ Thu, 13 Oct 2022 01:09:20 +0000 https://formes.asia/jackson-walker-celebrates-milestone-of-500-patents-filed-by-inventor-of-cirrus-logic-jackson-walker/ Jackson Walker celebrates the momentum of Austin-based Cirrus Logic, a leader in low-power, high-precision mixed-signal processing solutions. With approximately 4,130 issued and pending patents worldwide, the fabless semiconductor provider benefits from strong growth opportunities and the mind of one of the world’s most prolific inventors, John Melanson. Senior technical researcher at Cirrus Logic, John Melanson […]]]>

Jackson Walker celebrates the momentum of Austin-based Cirrus Logic, a leader in low-power, high-precision mixed-signal processing solutions. With approximately 4,130 issued and pending patents worldwide, the fabless semiconductor provider benefits from strong growth opportunities and the mind of one of the world’s most prolific inventors, John Melanson.

Senior technical researcher at Cirrus Logic, John Melanson took another step this year and filed his 500th patent with the US Patent Office. His innovations have helped transform the way humans interact with their devices and have had a positive impact on the environment. Of his 500 filed patents, the Jackson Walker team has proudly participated in the registration of 123. John also has over 60 pending patent applications, of which we are responsible for 30.

“Given the large number of inventions that John has devised, and given that Cirrus Logic technology is present in many mobile devices on the market, a majority of people, whether they know it or not, take the innovations of John with them wherever they go.” said Brian K. Prewitt, Austin partner and leader of the Jackson Walker team representing Cirrus Logic. He added, “It has been an absolute privilege for me to work with John over the past decade to help him and Cirrus Logic protect their industry-leading innovations. It’s a huge honor to have played a small behind-the-scenes role in John’s incredible accomplishments.

Under Brian’s leadership, the team, which includes Austin Temple Keller attorneys Andrea Thai and Tori Emery, has helped Cirrus Logic secure 670 issued U.S. patents and 293 foreign patents since 2011. In fact, the exceptional number of Cirrus Logic’s patent filings over the past few years have featured the company among the 300 patents in 2019 and 2020. Produced by the Intellectual Property Owners Association (IPO) in conjunction with Harrity Analytics, the Patent 300 uses data obtained from the United States Patent and Trademark Office to list the top 300 patent holders granted each year.

Meet Brian

Brian K. Prewitt is an intellectual property attorney specializing in issues relating to patents, trademarks, copyrights, technology development, technology acquisition, strategic alliances, intellectual property licensing , intellectual property due diligence, advice and litigation. With over a decade of experience in intellectual property law and industry experience in the fields of integrated circuit design, semiconductor manufacturing technology, computer architecture and systems Brian has both the legal and technical talents to provide top-notch legal representation to major technology companies around the world.

Meet JW

Since the founding of Jackson Walker in 1887, our attorneys have represented some of the world’s most influential corporations and business leaders. Today, we remain firmly rooted in Texas while serving customers around the world. Our Patents team works with inventors and companies to file and pursue patent applications to protect their inventions, both at home and abroad. Working closely with lawyers in the venture capital and tax disciplines, the Intellectual Property team also drafts and coordinates various technology asset transfers for business development and marketing.

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