Trending March 2024 # Neckline Finished With Bias Facing # Suggested April 2024 # Top 12 Popular

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One of the simplest neckline applications is a bias-strip neck finish, often known as a bias facing. This method is ideal for edges with gentle curves. Its construction is straightforward, although some hand stitching is necessary. In this instance, a fabric strip with a bias cut for added elasticity completes the neckline edge. The strip may be readily shaped to fit over curving neck edges thanks to the bias cut.

In order to create a neat, finished outside edge, the bias strip is folded upon itself during the application process. The neckline edge is attached by sewing the two lengthwise raw edges together when the garment is folded. The body of the bias strip is then used to enclose all exposed raw edges. Due to the need for folding, the bias strip should be cut at double its width, plus two seam allowances to allow for sewing the bias edges to the neckline edge.

What is “Bias-facing”?

A narrow strip of thin cloth cut on the bias serves as a bias facing because it may be moulded to fit the curve it will finish. On sheer fabrics, bias facings are frequently used to get rid of a wide facing that might show through. Clothing for kids can also have bias facings. On bulky textiles, a bias strip of lining fabric can replace heavy, shaped facings. When finished, a bias facing should be about 12 inches wide.

The bias strip should be cut twice as broad as the desired finishing width plus two seam allowances. The length should be 2 inches longer than the edge’s seam line length. With the incorrect sides together, fold the strip in half along its length. The strip should be pressed with a steam iron to be shaped to meet the edge. Even out the raw edges. Maintaining uniform edges, baste the strip to the garment. The strip should be stretched on the outward curves and eased on the inward curves before being stitched to the garment.

How to sew a neckline with bias facing? Method 1

Although other designs that use different bias-facing techniques might be substituted, the Eucalypt pattern and instructions use this technique. It is better for lighter-weight fabrics since it is a little less bulky than the second approach and uses a thinner bias strip that is 1′′ wide.

Turn your shirt right side out first.

At one end, push the bias strip into a 14” indent.

Right sides together, beginning with the folded end, attach the bias strip to the bodice, aligning the edge with the raw edge of the neckline. Starting at the neckline, begin to pin the bias strip, relaxing it as you go.

Continue all the way around the neck. Cut off the excess bias strip when you reach the folded edge from where you started overlaid by an inch.

Around the neckline, sew every 14′′ from the raw edge.

Reduce the seam allowance to a meagre 1/8” in width.

Press the seam allowance and bias facing up and away from the bodice.

Close to the seam, understitch on the bias facing, passing through both the bias facing and the seam allowance. This aids in keeping the bias facing rolled inside the garment.

Press the bias facing the inside-out fold. Turn your clothing inside out so that the fabric’s wrong side is facing you. With your fingertips, gently fold the bias strip’s raw edge under by ¼”. Continue around the entire neckline, pinning as you go.

Closely sew to the bias facing’s inner folded edge. As a consequence, the stitching should be placed roughly ½′′ from the edge of the neckline.

If you are making a eucalyptus or any sleeveless garment, the sleeves should be sewn using the same technique as the body.

Method 2

The Banksia, Crescent, and Darling Ranges patterns all employ this technique. It employs a broader bias strip that is applied to the neckline after being folded in half. By using this technique, you can avoid manually folding the facing’s raw edge under. It’s a little simpler to build, but it’s also a little bulkier. Once more, this can be used in place of procedure 1 in Eucalyptus or another pattern! Simply increase the width of your bias strip pattern piece to 1 12” to 2”. For example, Banksia uses a 2′′ wide bias strip, but I recommend a 1 ¼′′ wide strip for Eucalypt, which should result in a facing that is just under 12′′ wide.

Method 3

This approach is a little different. Rather than a facing, it is a binding. When binding the raw edge, rather than turning it inside out like the bias facing does, the bias strip will actually be visible from the outside of the garment. This technique is used in the River design, but it can also be used in the Crescent, Eucalyptus, and Bankisa patterns in place of a bias facing technique. In the same manner as with the other instances, we are demonstrating it here in Egypt. Make your bias strips 1 ½ inches wide for this one.


In conclusion, a bias neck facing can be created from the same fabric or a lighter-weight fabric by cutting it diagonally on the bias. When the garment is composed of thick, heavy material, the latter is extremely useful. In relation to fabric, not every material is appropriate for a bias-facing finish. Ironically, bias-strip necklines are either used on clothing made from thick, dense fabrics or on light, flimsy textiles. Since the underside of the facing is visible on the face of the final garment, sheers cannot be used with ordinary facings. A folded bias strip offers a neater, less noticeable option and a finished look on both the right and wrong sides of the garment.

On the other hand, a bias neck facing is effective for heavy clothing when a traditional facing would add too much bulk. In this instance, the correct neck finish is achieved by using a bias strip that is firm enough to keep the neckline edges down but thin enough to avoid adding extra bulk. When a visible topstitch on the face of the garment is not desirable or when bias binding is not an option, bias facing is a fantastic substitute.

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Ten Challenges Facing Cloud Computing

ALSO SEE: Are SaaS/Cloud Computing Vendors Offering Questionable Contracts?

There’s been at least as much healthy skepticism about cloud computing as there has been optimism and real results. And there ought to be, especially as cloud computing moves out of buzzword territory and becomes an increasingly powerful tool for extending IT resources.

To that end, here’s a rundown of ten key things both creators and users of cloud computing should continue to bear in mind.

The good news is that the very nature of the cloud may be compelling more real thought about security – on every level – than before. The bad news is that a poorly written application can be just as insecure in the cloud, maybe even more so.

Cloud architectures don’t automatically grant security compliance for the end-user data or apps on them, and so apps written for the cloud always have to be secure on their own terms. Some of the responsibility for this does fall to cloud vendors, but the lion’s share of it is still in the lap of the application designer.

A cloud computing-based solution shouldn’t become just another passive utility like the phone system, where the owners simply puts a tollbooth on it and charges more and more while providing less and less. In short, don’t give competitors a chance to do an end run around you because you’ve locked yourself into what seems like the best way to use the cloud, and given yourself no good exit strategy. Cloud computing is constantly evolving. Getting your solution in place simply means your process of monitoring and improving can now begin.

We’re probably past the days when people thought clouds were just big server clusters, but that doesn’t mean we’re free of ignorance about the cloud moving forward. There are all too many misunderstandings about how public and private clouds (or conventional datacenters and cloud infrastructures) do and don’t work together, misunderstandings about how easy it is to move from one kind of infrastructure to another, how virtualization and cloud computing do and don’t overlap, and so on.

A good way to combat this is to present customers with real-world examples of what’s possible and why, so they can base their understanding on actual work that’s been done and not just hypotheticals where they’re left to fill in the blanks themselves.

Cloud infrastructures, like a lot of other IT innovations, don’t always happen as top-down decrees. They may happen from the bottom up, in a back room somewhere, or on an employee’s own time from his own PC.

Examples of this abound: consider a New York Times staffer’s experience with desktop cloud computing. Make a “sandbox” space within your organization for precisely this kind of experimentation, albeit with proper standards of conduct (e.g., not using live data that might be proprietary as a safety measure). You never know how it’ll pay off.

The biggest example of this: Amazon EC2. As convenient as it is to develop for the cloud using EC2 as one of the most common types of deployments, it’s also something to be cautious of. Ad-hoc standards are a two-edged sword.

On the plus side, they bootstrap adoption: look how quickly a whole culture of cloud computing has sprung up around EC2. On the minus side, it means that much less space for innovators to create something open, to let things break away from the ad-hoc standards and can be adopted on their own. (Will the Kindle still be around in ten years?) Always be mindful of how the standards you’re using now can be expanded or abandoned.

Challenges Facing Effective Identity Validation In Healthcare

The healthcare system has long faced issues with verifying identity across devices and processes, but today there are even more challenges to consider. The HIMSS Identity Management Task Force (HIMSS IMTF) has made a policy-level recommendation that all healthcare information systems, such as patient portals and electronic health record (EHR) systems, be capable of identity validation of individuals at NIST Level of Assurance 3 (LOA-3) or its equivalent before gaining access to protected information.

Identify Theft Has Become Commonplace

According to the Federal Trade Commission’s estimates, identity theft was again the number one complaint of Americans in 2014. There is no exception for medical records and other protected healthcare information, which is a premium target for thieves and hacker groups. Unfortunately, it is likely that your organization has been considered for an attack or has suffered a security breach in the last five years, making it even more urgent to know your identity validation options moving forward.

Nearly 30 million Americans have had their personal health information (PHI) accessed or accidentally disclosed since 2009, according to the InfoSec Institute. To properly defend your patients’ PHI and prevent this statistic from continuing to rise, existing healthcare security procedures aren’t enough to protect your existing healthcare systems. It’s time to comply with proper mobile and web security policies.

The private sector can no longer wait for the government to update it cybersecurity policies. Legislation isn’t quick enough to get ahead of potential threats to sensitive data. According to the International Information System Security Certification Consortium’s 2024 survey of 1,800 federal information security professionals, the U.S. government hasn’t improved its security posture despite more investments in the area. One of the top reasons for reduced security is that the government is unable to keep pace with modern threats, according to 80 percent of survey respondents.

Confidentiality in Healthcare is Complex

Healthcare organizations looking to secure their data face the critical challenge of PHI confidentiality. Care providers must comply with existing federal laws such as HIPPA, but also cater to patients’ interests to ensure that they are trusted resources for their healthcare needs. Balancing access and HIPPA requirements requires understanding whether or not the individuals accessing systems are who they say they are. This ensures that the only individuals accessing confidential data are the patients and individuals at NIST LOA-3 or its equivalent.

These individual profiles must be accurate enough to provide non-repudiation, or the assurance that the identity of an individual cannot be denied by its owner. At the same time, many patients need to access files on their own without being identified to the healthcare system at large. It’s the patients’ right to remain anonymous. In some cases, they may not need to prove who they are other than that they are served by the healthcare system overall.

Accuracy and Availability of Information is Essential

Whether you’re analyzing mobile security of PHI or other confidential information, note that the data being accessed must be remain entirely accurate and readily available to both medical practitioners and their patients when needed. Security can’t compromise patients’ data integrity, even if you’re ensuring that they are properly treated and all fatal decision errors are avoided.

Patients’ and healthcare providers’ identity validation must be verified quickly to keep pace with the way medical professionals are using mobile devices. The accuracy and availability of PHI is heavily tied to how user-friendly a particular system is. This is an important consideration to keep in mind when adopting the right healthcare security protocols to support your identity validation systems. One solution that addresses this is Samsung KNOX, a manageable, on-device mobile security solution that helps empower healthcare systems to better validate the identity of individuals accessing PHI on a regular basis. KNOX allows healthcare providers to customize Samsung devices into purpose-built appliances, which address these challenges and focus on providing quicker, more effective care for their patients.

Visit Samsung’s Healthcare page to learn how mobile devices with Samsung KNOX can secure your patients’ data and improve your processes.

Ai Bias: A Threat To Women’s Lives?

Artificial Intelligence is either a silver shot for each issue on the planet or the ensured reason for the end of the world, contingent upon whom you address. The fact of the matter is probably going to be unmistakably progressively unremarkable. Artificial intelligence is a tool and like numerous technological breakthroughs before it, it will be utilized for good and for terrible. However, concentrating on potential outrageous situations doesn’t help with our current reality. Artificial intelligence is progressively being utilized to impact the products we purchase and the music and movies we appreciate; to protect our money; and, dubiously, to settle on hiring decisions and procedure criminal behaviour. Somehow or another, it’s a chicken and egg issue. The Western world has been digitized for more, so there are more records for AIs to parse. What’s more, women have been under-represented in numerous different backgrounds, so there is less information, and what information exists is often of lower quality. If we can’t take care of AIs quality information that is free of bias, they will learn and proceed with the partialities we try to dispense with. Frequently the largest datasets accessible are additionally just of such low quality that the outcomes are erratic and unforeseen, for example, racist chatbots on Twitter. The AI field, which is overwhelmingly male, is in danger of duplicating or historical biases and power imbalances. Examples referred to incorporate image recognition services making offensive classifications of minorities, chatbots adopting hate speech, and Amazon technology neglecting to perceive clients with darker skin colors. The predispositions of systems worked by the AI business can be to a great extent credited to the absence of diversity within the field itself. Over 80% of AI professors are men, and just 15% of AI analysts at Facebook and 10% of AI scientists at Google are women. The cosmetics of the AI field is reflective of “a bigger issue across computer science, Stem fields, and even more broadly, society as a whole”, said Danaë Metaxa, a Ph.D. candidate and analyst at Stanford concentrated on issues of internet and democracy. Women included just 24% of the field of computer and data sciences in 2024, as indicated by the National Science Board. Just 2.5% of Google’s workforce is black, while Facebook and Microsoft are each at 4%, and little data exists on trans workers or other gender minorities in the AI field. There are enormous data gaps with respect to the lives and bodies of ladies,” discovers Prof. Dr. Sylvia Thun, chief of eHealth at Charité of the Berlin Institute of Health. Numerous medical algorithms are, for instance, in view of U.S. military workforce information where women in certain regions just speak to 6%. Short of having the option to radically build the number of women serving in the military (and consequently improve the female sample size), Thun recommends that researchers, experts and policy-makers need to cooperate to guarantee that medical applications are gender-informed and think about important information from women. Men at present make up 71% of the applicant pool for AI occupations in the US, as indicated by the 2023 AI Index, a free report on the industry discharged every year. The AI organization recommended additional measures, including publishing compensation for laborers openly, sharing harassment and discrimination transparency reports, and changing enlisting practices to build the number of underrepresented groups at all levels. To all the more likely serve business and society, battling algorithmic bias should be a need. “By 2023, 85% of AI projects will deliver mistaken results because of bias in information, algorithms or the teams answerable for overseeing them. This isn’t only an issue for gender inequality – it additionally undermines the value of AI” as per Gartner, Inc. We have a chance to address these imbalances by driving a more prominent spotlight on incorporation, empowerment and equality. More women working in the innovation business, composing algorithms and taking care of product development will change how we envision and create technology, and how it sounds and looks.

4 Ways To Fight Bias In Grading

We are all subject to implicit bias, the research says, in spite of our best efforts to prevent it. Asian and Black job applicants who “mask” their race on résumés get more callbacks. Black patients seeking medical treatment are consistently “undertreated for pain” compared with White patients. And when a  job applicant’s weight is concealed, they’re perceived as more suitable for employment than heavier candidates.

In the classroom, decades of evidence confirm that teachers are not immune and that some degree of unconscious bias is often at play, especially in the absence of intentional measures to control it. Bias has a way of seeping through the smallest cracks, imperceptibly influencing the way we perceive students, compromising accuracy in grading, and even altering a wide range of educational outcomes.

An inaccurate grade may appear to be a temporary setback, but taken together across a child’s K–12 educational career, biases in grading can have lasting consequences, discouraging students by repeatedly sending the message that their best efforts don’t meet the mark, producing a ripple effect with far-reaching implications—from course placement to scholarships, college admissions, and even job opportunities.

There are ways to course-correct, says David Quinn, an assistant professor of education at the USC Rossier School of Education in a recent study. “The long-term goal is that we want to fundamentally change teachers’ attitudes because that can have downstream impacts on behaviors, and thus on students’ experiences. But we haven’t figured out how to do that yet,” Quinn tells Edutopia. “In the meantime, we need to significantly reduce the impact of those implicit biases.”


In the study, Quinn asked 1,549 preschool through 12th-grade teachers to evaluate two versions of a writing sample. The sample was a short personal narrative written by a fictional second-grade student who described a weekend spent with a sibling.

“The versions were identical in all but one aspect: each used different names for the brother to signal either a Black or a White student author,” Quinn wrote in an article for Education Next. “In one version, the student author refers to his brother as ‘Dashawn,’ signaling a Black author; in the other, his brother is called ‘Connor,’ signaling a White author.” Half the teachers were randomly assigned the Dashawn version, the other half got the Connor version, and they were asked to grade the work based on grade-level standards.

The differences in the writing samples were negligible, but the bias effect was striking: Overall, teachers were 4.7 percentage points less likely to score the “Dashawn” writing sample as meeting or exceeding grade level than the “Connor” sample, and that gap opened up to 8 percentage points for White teachers. Teachers of color, however, did not show obvious bias in the evaluation.


In classrooms, there are “opportunities all the time for teachers’ unconscious racial biases to come out, even when we think that they aren’t,” says Quinn, who taught third and fourth grade in North Las Vegas, Nevada, before moving to K–12 education policy research. “So encouraging teachers to reflect on that and be aware of it is the most important thing for students’ experiences.”

In the study, when teachers rated the two writing samples using a rubric with specific grading criteria—which included standards such as: fails to recount an event, attempts to recount an event, recounts an event in some detail, or provides a well-elaborated recount of an event—instead of a general grade-level scale, the difference in grades was nearly eliminated.


Another simple way to reduce bias is by having colleagues at times check each other’s work—in a quick weekly or biweekly check-in, for example, or in a regular professional learning community environment. “It may be working with grade-level colleagues to compare writing prompts, and to look at how teachers assessed those prompts based on the established rubric,” says Quinn. “There is good evidence that even just the awareness that people’s work will be reviewed for bias decreases the level of bias that is shown in the work.”

Middle school math teacher Jay Wamstead used a clipboard to keep track of his classroom management and conversations for one month, a focused effort “to check the spaces in my pedagogy where bias and prejudice were leaking through.” His list tracked his habits across areas like discipline, calling on raised hands and cold calling, and who he most often chatted or joked around with. “If you keep track of these aspects for a month, you may discover something surprising about the way you interact with students. I know I did,” Wamstead writes. “But it gave me something to work on, a plan to make, and an action item to fix. I know it’s only scratching the surface, but it gave me a place to begin.”

Finally, research shows that implicit bias tends to creep in when we’re tired, overworked, and stressed—factors that besiege many teachers’ professional lives. “We know that implicit bias has more of an influence on behavior when you’re under stress, when you are fatigued, or when there is some type of time constraint,” Quinn concludes. Becoming aware of this tendency during grading is helpful, but when schools genuinely commit to reducing those contextual factors—ensuring that teachers have enough time built into the schedule for evaluations, for example, and that they can do the work in an environment where they’re not distracted and pressured—it can make a real difference.

How To Overcome Position Bias In Recommendation And Search?


In this article, we’re going to discuss the following topics:

Which types of biases do exist, and how to measure them?

Overcoming position bias with Inverse Propensity Weighting and downsides of such approach.

Position-aware Learning is a way to teach your ML model to consider bias while training.

This article was published as a part of the Data Science Blogathon.

Table of Contents Biases in Ranking

Every time you present a list of things, such as search results or recommendations (or autocomplete suggestions and contact lists), to a human being, we can hardly ever impartially evaluate all the items in the list.

Model bias: When you train an ML model on historical data generated by the same model.

In practice, the position bias is the strongest one — and removing it while training may improve your model reliability.

Experiment: Measuring Position Bias

We conducted a small crowd-sourced research about position bias. With a RankLens dataset, we used a Google Keyword Planner tool to generate a set of queries to find a particular movie.

All major crowd-sourcing platforms like Amazon Mechanical Turk and chúng tôi have out-of-the-box templates for typical search evaluation:

But there’s a nice trick in such templates, preventing you from shooting yourself in the foot with position bias: each item must be examined independently. Even if multiple items are present on screen, their ordering is random!

Inverse Propensity Weighting

Relevance: The importance of the item within the current context (like BM25 score coming from ElasticSearch, and cosine similarity in recommendations)

In the MSRD dataset mentioned in the previous paragraph, it’s hard to distinguish the impact of position independently from BM25 relevance as you only observe them combined together.

But how can you estimate the propensity in practice? The most common method is introducing a minor shuffling to rankings so that the same items within the same context (e.g., for a search query) will be evaluated on different positions.

But adding extra shuffling will definitely degrade your business metrics like CTR and Conversion Rate. Are there any less invasive alternatives not involving shuffling?

Position-Aware Learning

A position-aware approach to ranking suggests asking your ML model to optimize both ranking relevancy and position impact at the same time:

On training time, you use item position as an input feature,

In the prediction stage, you replace it with a constant value.

In other words, you trick your ranking ML model into detecting how position affects relevance during the training but zero out this feature during the prediction: all the items are simultaneously being presented in the same position.

But which constant value should you choose? The authors of the PAL paper did a couple of numerical experiments on selecting the optimal value — the rule of thumb is not to pick too high positions, as there’s too much noise.

Practical PAL

The PAL approach is already a part of multiple open-source tools for building recommendations and searches:

ToRecSys implements PAL as a bias-elimination approach to train recommender systems on biased data.

Metarank can use a PAL-driven feature to train an unbiased LambdaMART Learn-to-Rank model.

As the position-aware approach is just a hack around feature engineering, in Metarank, it is only a matter of adding yet another feat

On an MSRD dataset mentioned above, such a PAL-inspired ranking feature has quite a high importance value compared to other ranking features:


The position-aware learning approach is not only limited to pure ranking tasks and position de-biasing: you can use this trick to overcome any other type of bias:

For the presentation bias due to a grid layout, you can introduce a pair of features for an item’s row and column position during the training. But swap them to a constant during the prediction.

The ML model trained with the PAL approach should produce an unbiased prediction. Considering the simplicity of the PAL approach, it can also be applied in other areas of ML where biased training data is a usual thing.

While conducting this research, we made the following main observations:

Position bias can be present even in unbiased datasets.

Shuffling-based approaches like IPW can overcome the problem of bias, but introducing extra jitter in predictions may cost you a lot by lowering business metrics like CTR.

The Position-aware learning approach makes your ML model learn the impact of bias, improving the prediction quality.

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