USAA savings account rates are well below what consumers can find with high-yield savings accounts at other financial institutions.

USAA Savings Rate (March 2024)

The latest San Antonio local news from mySA.com

Is the USAA app down?

If you&#x27;re looking for a Sector - Tech fund category, then a potential option is USAA Science &amp; Technology Fund (USSCX). USSCX bears a Zacks Mutual Fund Rank of 3 (Hold), which is based on various forecasting factors like size, cost, and past performance.

Is USAA Science & Technology Fund (USSCX) a Strong Mutual Fund Pick Right Now?

USAA offers a variety of credit cards including some that earn cash back, travel rewards and offer intro APRs.

The best USAA credit cards of March 2024

For the most talked about sports franchise in the world, the spotlight will again be on giving back. NBC 5 caught up with the woman in charge of the Dallas Cowboys brand, who cooked up the Salvation Army halftime show spotlighted many years ago and is being recognized for something else.

Charlotte Jones recognized by NFL and USAA – NBC 5 Dallas-Fort Worth

A VA loan is a military benefit that doesn’t require a down payment or private mortgage insurance (PMI). They also typically carry lower interest rates than conventional home loans. However, there are specific requirements to qualify for a VA home loan.

10 Best VA Loan Lenders of November 2023 | Money

Here is our list of the 15 best car insurance companies in 2023.

15 Best Car Insurance Companies of 2023

The most affordable car insurance in Arizona is State Farm, which costs an average of $1,093 per year for comprehensive coverage. Drivers with a clean driving record pay an average of $117 per month in Arizona. High-risk drivers will pay an average of $2,223 annually in Arizona.

Best Car Insurance in Arizona for 2023

Lakeland Financial Corp. (LKFN) on Wednesday reported net income of $25.3 million in its third quarter. The Warsaw, Indiana-based bank said it had earnings

Lakeland Financial: Q3 Earnings Snapshot

USAA, likely the most prolific and consistent of sponsors of catastrophe bonds, is back with what will be the 42nd we have listed from the company,

USAA targets $300m multi-peril reinsurance with Residential Re 2023-2 cat bond

USAA

usaa

Is Crypto Just Another Misleading Strategy by The Re-Elected Trump?

Is Crypto net nog 'n misleidende strategie deur die herverkose Trump?

Yenidən seçilmiş Trampın kriptovalyutası başqa bir yanlış strategiyadırmı?

¿Son las criptomonedas simplemente otra estrategia engañosa del reelecto Trump?

Crypto Berriro hautatutako Trumpek egindako beste estrategia engainagarri bat besterik ez da?

A Crypto csak egy újabb félrevezető stratégiája az újraválasztott Trumpnak?

האם קריפטו היא רק עוד אסטרטגיה מטעה של טראמפ שנבחר מחדש?

暗号通貨は再選されたトランプによるもう一つの誤解を招く戦略なのか？

Дали Crypto е само уште една погрешна стратегија на реизбраниот Трамп?

Adakah Crypto Hanya Satu Lagi Strategi Mengelirukan oleh Trump yang Dipilih Semula?

¿Crypto Huk pantachiq estrategiallachu The Re-Elected Trumppa?

ක්‍රිප්ටෝ නැවත තේරී පත් වූ ට්‍රම්ප්ගේ තවත් නොමඟ යවන උපාය මාර්ගයක් ද?

Liệu tiền điện tử có phải chỉ là một chiến lược gây hiểu lầm khác của Trump tái đắc cử?

Bitcoinized Fossil Fuel

Биткоинизированное выкапнёвае паліва

Combustible fósil bitcoinizado

Combustible fossile bitcoinisé

דלק מאובן שעבר ביטקוין

ビットコイン化された化石燃料

Bahan Api Fosil Bitcoin

Ibicanwa bya Bitcoin

பிட்காயின் செய்யப்பட்ட புதைபடிவ எரிபொருள்

Сӯзишвории истихроҷи биткоинизатсияшуда

เชื้อเพลิงฟอสซิล Bitcoin

I-Bitcoinized Fossil Fuel

Author:
(1) Sharif Elcott, equal contribution of Google Japan (Email: selcott@google.com);
(2) J.P. Lewis, equal contribution of Google Research USA (Email: jplewis@google.com);
(3) Nori Kanazawa, Google Research USA (Email: kanazawa@google.com);
(4) Christoph Bregler, Google Research USA (Email: bregler@google.com). Table of Links Abstract and Introduction
Background and Related Work
Method
Results and Failure Cases
Limitations and Conclusion, Acknowledgements and References ABSTRACT Alpha matting is widely used in video conferencing as well as in movies, television, and social media sites. Deep learning approaches to the matte extraction problem are well suited to video conferencing due to the consistent subject matter (front-facing humans), however training-based approaches are somewhat pointless for entertainment videos where varied subjects (spaceships, monsters, etc.) may appear only a few times in a single movie – if a method of creating ground truth for training exists, just use that method to produce the desired mattes. We introduce a training-free high quality neural matte extraction approach that specifically targets the assumptions of visual effects production. Our approach is based on the deep image prior, which optimizes a deep neural network to fit a single image, thereby providing a deep encoding of the particular image. We make use of the representations in the penultimate layer to interpolate coarse and incomplete "trimap" constraints. Videos processed with this approach are temporally consistent. The algorithm is both very simple and surprisingly effective. CCS CONCEPTS • Computing methodologies → Image processing; • Applied computing → Media arts. KEYWORDS Alpha matting, deep learning, visual effects. ACM Reference Format: Sharif Elcott, J.P. Lewis, Nori Kanazawa, and Christoph Bregler. 2022. TrainingFree Neural Matte Extraction for Visual Effects. In SIGGRAPH Asia 2022 Technical Communications (SA ’22 Technical Communications), December 6–9, 2022, Daegu, Republic of Korea. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3550340.3564230 1 INTRODUCTION Alpha matte extraction refers to the underconstrained inverse problem of finding the unknown translucency or coverage 𝛼 of a foreground object [Wang and Cohen 2007]. Matte extraction is widely used to provide alternate backgrounds for video meetings, as well as to produce visual effects (VFX) for movies, television, and social media. However, it is not always recognized in the research literature that these two applications have significantly different requirements. This paper introduces a neural matte extraction method specifically addressed to the assumptions and requirements of VFX. pecifically addressed to the assumptions and requirements of VFX. Matting for video calls requires real-time performance and assumes a single class of subject matter, front-facing humans. This is a "train once and use many times" situation for which it is possible and advantageous to obtain training data. Video call matting often assumes a fixed camera, and may require a "clean plate" image of the room without the participant. On the contrary, VFX production has the following assumptions and requirements: • Diverse and often rare ("one-off") subject matter. For example the youtube video [Godzilla vs. Cat 2021] (30M views) involves mattes of a cat, ships, and wreckage. A physical prop such as a crashed alien spaceship might only be used in a few seconds in a single movie, never to be seen again. Thus, gathering a ground-truth training dataset for deep learning is often pointless: if there is a method to generate the ground-truth mattes for training, just use this method to produce the desired mattes – no need to train a model! • Visual effects frequently involves moving cameras as well as extreme diversity of moving backgrounds. For example, an actor might be filmed as they run or travel in a vehicle any place on earth, or on set with extraterrestrial props in the near background. Thus, even for the major case of repeated subject matter (humans), gathering a representative training dataset is more challenging than in the case of video calls. • Real-time performance is not required. Instead, the on-set filming time is to be minimized, due to the combined cost of the actors (sometimes with 7-figure salaries) and movie crew. It is often cheaper to employ an artist to work days to "fix it in post" rather than spend a few minutes on-set (with actors and crew waiting) to address an issue. • Clean-plates are not a desirable approach for matting, and are often not possible. With moving cameras, a motion control rig is required to obtain a clean plate. The indoor use of motion control for clean plates is expensive due to the previous principle (minimizing on-set time). Moreover, this approach is generally not feasible outdoors, for reasons including background movement (e.g. plants moved by wind) and changing lighting (moving clouds blocking the sun). Our solution, a matte extraction approach employing the deep image prior [Ulyanov et al. 2018], addresses these requirements. It has the following characteristics and contributions: • It is a deep neural network matte extraction method targeted towards the requirements of VFX rather than video meetings. To our knowledge, this is the first deep neural matte extraction method that requires neither training data nor expensive on-set image capture to support the matting process (i.e. clean plates or greenscreens). • It relies exclusively on the given image or video along with coarse "trimaps" (Fig. 1) that are easily created during post production using existing semi-automatic tools. Professional software such as Nuke or After Effects is typically used, however a familiar example is the "Select Subject" tool in Photoshop [Adobe 2018], followed by dilation/erosion and automated with "Actions". • It does not require a clean plate, thus allowing extractions from outdoor footage. • It does not require greenscreens, yet can produce detailed high quality mattes even when the foreground and background subject have similar colors (green hair in Fig. 1). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Sharif Elcott, equal contribution of Google Japan (Email: selcott@google.com); (2) J.P. Lewis, equal contribution of Google Research USA (Email: jplewis@google.com); (3) Nori Kanazawa, Google Research USA (Email: kanazawa@google.com); (4) Christoph Bregler, Google Research USA (Email: bregler@google.com). Author: Author: (1) Sharif Elcott, equal contribution of Google Japan (Email: selcott@google.com); (2) J.P. Lewis, equal contribution of Google Research USA (Email: jplewis@google.com); (3) Nori Kanazawa, Google Research USA (Email: kanazawa@google.com); (4) Christoph Bregler, Google Research USA (Email: bregler@google.com). Table of Links Abstract and Introduction Background and Related Work Method Results and Failure Cases Limitations and Conclusion, Acknowledgements and References Abstract and Introduction Abstract and Introduction Background and Related Work Background and Related Work Method Method Results and Failure Cases Results and Failure Cases Limitations and Conclusion, Acknowledgements and References Limitations and Conclusion, Acknowledgements and References ABSTRACT Alpha matting is widely used in video conferencing as well as in movies, television, and social media sites. Deep learning approaches to the matte extraction problem are well suited to video conferencing due to the consistent subject matter (front-facing humans), however training-based approaches are somewhat pointless for entertainment videos where varied subjects (spaceships, monsters, etc.) may appear only a few times in a single movie – if a method of creating ground truth for training exists, just use that method to produce the desired mattes. We introduce a training-free high quality neural matte extraction approach that specifically targets the assumptions of visual effects production. Our approach is based on the deep image prior, which optimizes a deep neural network to fit a single image, thereby providing a deep encoding of the particular image. We make use of the representations in the penultimate layer to interpolate coarse and incomplete "trimap" constraints. Videos processed with this approach are temporally consistent. The algorithm is both very simple and surprisingly effective. CCS CONCEPTS • Computing methodologies → Image processing; • Applied computing → Media arts. KEYWORDS Alpha matting, deep learning, visual effects. ACM Reference Format: Sharif Elcott, J.P. Lewis, Nori Kanazawa, and Christoph Bregler. 2022. TrainingFree Neural Matte Extraction for Visual Effects. In SIGGRAPH Asia 2022 Technical Communications (SA ’22 Technical Communications), December 6–9, 2022, Daegu, Republic of Korea. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3550340.3564230 1 INTRODUCTION Alpha matte extraction refers to the underconstrained inverse problem of finding the unknown translucency or coverage 𝛼 of a foreground object [Wang and Cohen 2007]. Matte extraction is widely used to provide alternate backgrounds for video meetings, as well as to produce visual effects (VFX) for movies, television, and social media. However, it is not always recognized in the research literature that these two applications have significantly different requirements. This paper introduces a neural matte extraction method specifically addressed to the assumptions and requirements of VFX. pecifically addressed to the assumptions and requirements of VFX. Matting for video calls requires real-time performance and assumes a single class of subject matter, front-facing humans. This is a "train once and use many times" situation for which it is possible and advantageous to obtain training data. Video call matting often assumes a fixed camera, and may require a "clean plate" image of the room without the participant. On the contrary, VFX production has the following assumptions and requirements: • Diverse and often rare ("one-off") subject matter. For example the youtube video [Godzilla vs. Cat 2021] (30M views) involves mattes of a cat, ships, and wreckage. A physical prop such as a crashed alien spaceship might only be used in a few seconds in a single movie, never to be seen again. Thus, gathering a ground-truth training dataset for deep learning is often pointless: if there is a method to generate the ground-truth mattes for training, just use this method to produce the desired mattes – no need to train a model! • Visual effects frequently involves moving cameras as well as extreme diversity of moving backgrounds. For example, an actor might be filmed as they run or travel in a vehicle any place on earth, or on set with extraterrestrial props in the near background. Thus, even for the major case of repeated subject matter (humans), gathering a representative training dataset is more challenging than in the case of video calls. • Real-time performance is not required. Instead, the on-set filming time is to be minimized, due to the combined cost of the actors (sometimes with 7-figure salaries) and movie crew. It is often cheaper to employ an artist to work days to "fix it in post" rather than spend a few minutes on-set (with actors and crew waiting) to address an issue. • Clean-plates are not a desirable approach for matting, and are often not possible. With moving cameras, a motion control rig is required to obtain a clean plate. The indoor use of motion control for clean plates is expensive due to the previous principle (minimizing on-set time). Moreover, this approach is generally not feasible outdoors, for reasons including background movement (e.g. plants moved by wind) and changing lighting (moving clouds blocking the sun). Our solution, a matte extraction approach employing the deep image prior [Ulyanov et al. 2018], addresses these requirements. It has the following characteristics and contributions: • It is a deep neural network matte extraction method targeted towards the requirements of VFX rather than video meetings. To our knowledge, this is the first deep neural matte extraction method that requires neither training data nor expensive on-set image capture to support the matting process (i.e. clean plates or greenscreens). • It relies exclusively on the given image or video along with coarse "trimaps" (Fig. 1) that are easily created during post production using existing semi-automatic tools. Professional software such as Nuke or After Effects is typically used, however a familiar example is the "Select Subject" tool in Photoshop [Adobe 2018], followed by dilation/erosion and automated with "Actions". • It does not require a clean plate, thus allowing extractions from outdoor footage. • It does not require greenscreens, yet can produce detailed high quality mattes even when the foreground and background subject have similar colors (green hair in Fig. 1). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Training-Free Neural Matte Extraction for Visual Effects: Abstract and Introduction

Authors:
(1) Clement Lhos, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA;
(2) Emek Barıs¸ Kuc¸uktabak, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA;
(3) Lorenzo Vianello, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA;
(4) Lorenzo Amato, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and The Biorobotics Institute, Scuola Superiore Sant’Anna, 56025 Pontedera, Italy and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy;
(5) Matthew R. Short, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA;
(6) Kevin Lynch2, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA;
(7) Jose L. Pons, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA. Table of Links Abstract and I Introduction II Methods III Results IV Discussion V. Conclusion, Acknowledgment, and References IV. DISCUSSION In this paper, we evaluated a deep-learning model to predict the weight distribution (i.e., stance interpolation factor) of a user wearing a lower-limb exoskeleton during several walking conditions; we compared the exoskeleton controller performance when this stance interpolation factor was estimated with our deep-learning model (αˆ ) and measured with ground truth values (treadmill force plates or FSR sensor pads, α). This work highlights the viability and limitations of using deep-learning predictions to detect changes in the gait state using only joint kinematics information without ground reaction sensors. Utilizing a history of kinematic data was shown to enhance the model’s accuracy (Fig. 3 and Tab. I) in comparison to using instantaneous values, as seen in a similar study [14]. These findings show the advantage of utilizing additional temporal data from previous time steps, enhancing the network’s performance in both training and testing phases. Importantly, including additional data did not significantly alter the model’s prediction speed; we observed comparable prediction times when using a history of kinematic data with respect to the instantaneous value predictions. As a result, the model was implemented to achieve real-time performance. This is crucial for its use in the control of an exoskeleton, as predictions need to be faster than the main control loop. Achieving an average prediction time of 0.57 ms ensured the real-time usability of the system, as the main control loop runs usually significantly slower than 1 ms on lower-limb exoskeletons (e.g., 3 ms for the exoskeleton controller in our study). It is noteworthy that, without using TensorFlow Lite (i.e., employing the classic TensorFlow library), the average prediction time with a history of kinematic data is 67.1 ± 13.5 ms, which cannot be used in most real-time applications. Furthermore, the proposed method provides accurate predictions of the stance interpolation factor for previous and unseen users (Fig. 3 and Tab. I), demonstrating another aspect of its usability. Requiring training data for every exoskeleton user is not convenient or realistic, particularly in physical rehabilitation as a patient’s time spent receiving therapy must be prioritized. In the context of machine learning for robotic control, using the predictions in a closed loop can affect the performance due to error propagation in real time [20]. Therefore, it was critical to evaluate the closed-loop performance of our system, using several conditions. We demonstrated that the deep-learning approach produces a similar stance interpolation factor compared to treadmill force plates during walking with haptic transparent control; these findings generalize for walking speeds between 0.14 and 0.47 m/s (Fig. 4). To characterize the effect of the deep-learning predictions on the performance of the exoskeleton controller, we assessed both haptic rendering of nonzero impedance and haptic transparent modes during treadmill walking. Moreover, we evaluated the extent to which these results generalize to overground walking. During treadmill walking, mean interaction torque errors were similar between deep-learning predictions and treadmill force plate measurements. Evaluating the performance of the controller during more naturalistic, overground walking, the deep-learning predictions also facilitated similar interaction torque error profiles (Fig. 7A) and hip and knee kinematics (Fig. 7B). While the overall performance of the exoskeleton controller was comparable when implementing our deeplearning predictions and ground reaction sensor values, we did observe a delay-like effect in the stance interpolation factor estimation which likely resulted in some performance discrepancies at specific phases of the gait cycle. Specifically, we observed a longer stance duration when walking with the deep-learning predictions at slower speeds (Fig. 4); this was associated with a longer transition between left and right single stance (i.e., double stance to swing). During treadmill and overground walking, at the knee joint, we observed higher interaction torque error at the beginning of the swing phase (around 60-70% of the cycle, Fig. 6A) with the deep-learning predictions. This increase in interaction torque error was more noticeable during overground walking, and also observed at the hip joint. In this condition, the use of crutches may have promoted additional lateral movement which was not measured by the exoskeleton [21]. For both treadmill and overground walking, the discrepancies in interaction torque errors relative to their respective ground truths could be due to the deep-learning model requiring users to change their kinematics before the stance interpolation factor changes. This is different from using force sensors, which can detect changes in weight distribution independent of kinematics recorded by the exoskeleton. One way to improve the detection of changes in weight distribution in the deep-learning model is to incorporate the frontal plane IMU angle of the backpack in addition to the sagittal plane angles from the backpack and joint encoders. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Clement Lhos, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; (2) Emek Barıs¸ Kuc¸uktabak, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; (3) Lorenzo Vianello, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; (4) Lorenzo Amato, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and The Biorobotics Institute, Scuola Superiore Sant’Anna, 56025 Pontedera, Italy and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy; (5) Matthew R. Short, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA; (6) Kevin Lynch2, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; (7) Jose L. Pons, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA. Authors: Authors: (1) Clement Lhos, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; (2) Emek Barıs¸ Kuc¸uktabak, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; (3) Lorenzo Vianello, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; (4) Lorenzo Amato, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and The Biorobotics Institute, Scuola Superiore Sant’Anna, 56025 Pontedera, Italy and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy; (5) Matthew R. Short, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA; (6) Kevin Lynch2, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; (7) Jose L. Pons, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA. Table of Links Abstract and I Introduction Abstract and I Introduction II Methods II Methods III Results III Results IV Discussion IV Discussion V. Conclusion, Acknowledgment, and References V. Conclusion, Acknowledgment, and References IV. DISCUSSION In this paper, we evaluated a deep-learning model to predict the weight distribution (i.e., stance interpolation factor) of a user wearing a lower-limb exoskeleton during several walking conditions; we compared the exoskeleton controller performance when this stance interpolation factor was estimated with our deep-learning model (αˆ ) and measured with ground truth values (treadmill force plates or FSR sensor pads, α). This work highlights the viability and limitations of using deep-learning predictions to detect changes in the gait state using only joint kinematics information without ground reaction sensors. Utilizing a history of kinematic data was shown to enhance the model’s accuracy (Fig. 3 and Tab. I) in comparison to using instantaneous values, as seen in a similar study [14]. These findings show the advantage of utilizing additional temporal data from previous time steps, enhancing the network’s performance in both training and testing phases. Importantly, including additional data did not significantly alter the model’s prediction speed; we observed comparable prediction times when using a history of kinematic data with respect to the instantaneous value predictions. As a result, the model was implemented to achieve real-time performance. This is crucial for its use in the control of an exoskeleton, as predictions need to be faster than the main control loop. Achieving an average prediction time of 0.57 ms ensured the real-time usability of the system, as the main control loop runs usually significantly slower than 1 ms on lower-limb exoskeletons (e.g., 3 ms for the exoskeleton controller in our study). It is noteworthy that, without using TensorFlow Lite (i.e., employing the classic TensorFlow library), the average prediction time with a history of kinematic data is 67.1 ± 13.5 ms, which cannot be used in most real-time applications. Furthermore, the proposed method provides accurate predictions of the stance interpolation factor for previous and unseen users (Fig. 3 and Tab. I), demonstrating another aspect of its usability. Requiring training data for every exoskeleton user is not convenient or realistic, particularly in physical rehabilitation as a patient’s time spent receiving therapy must be prioritized. In the context of machine learning for robotic control, using the predictions in a closed loop can affect the performance due to error propagation in real time [20]. Therefore, it was critical to evaluate the closed-loop performance of our system, using several conditions. We demonstrated that the deep-learning approach produces a similar stance interpolation factor compared to treadmill force plates during walking with haptic transparent control; these findings generalize for walking speeds between 0.14 and 0.47 m/s (Fig. 4). To characterize the effect of the deep-learning predictions on the performance of the exoskeleton controller, we assessed both haptic rendering of nonzero impedance and haptic transparent modes during treadmill walking. Moreover, we evaluated the extent to which these results generalize to overground walking. During treadmill walking, mean interaction torque errors were similar between deep-learning predictions and treadmill force plate measurements. Evaluating the performance of the controller during more naturalistic, overground walking, the deep-learning predictions also facilitated similar interaction torque error profiles (Fig. 7A) and hip and knee kinematics (Fig. 7B). While the overall performance of the exoskeleton controller was comparable when implementing our deeplearning predictions and ground reaction sensor values, we did observe a delay-like effect in the stance interpolation factor estimation which likely resulted in some performance discrepancies at specific phases of the gait cycle. Specifically, we observed a longer stance duration when walking with the deep-learning predictions at slower speeds (Fig. 4); this was associated with a longer transition between left and right single stance (i.e., double stance to swing). During treadmill and overground walking, at the knee joint, we observed higher interaction torque error at the beginning of the swing phase (around 60-70% of the cycle, Fig. 6A) with the deep-learning predictions. This increase in interaction torque error was more noticeable during overground walking, and also observed at the hip joint. In this condition, the use of crutches may have promoted additional lateral movement which was not measured by the exoskeleton [21]. For both treadmill and overground walking, the discrepancies in interaction torque errors relative to their respective ground truths could be due to the deep-learning model requiring users to change their kinematics before the stance interpolation factor changes. This is different from using force sensors, which can detect changes in weight distribution independent of kinematics recorded by the exoskeleton. One way to improve the detection of changes in weight distribution in the deep-learning model is to incorporate the frontal plane IMU angle of the backpack in addition to the sagittal plane angles from the backpack and joint encoders. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Weight Distribution Estimation in Lower-Limb Exoskeletons via Deep Learning: Discussion

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. Table of Links Abstract and Introduction
Grassroots Protocols
Grassroots Implementation of Grassroots Dissemination
Applications of Grassroots Dissemination Supporting Digital Sovereignty
Future Work & Discussion
References
A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems
B. All-to-All Dissemination is not Grassroots
C. Proofs References 1 Martin R Albrecht, Jorge Blasco, Rikke Bjerg Jensen, and Lenka Mareková. Mesh messaging in large-scale protests: Breaking bridgefy. In Cryptographers’ Track at the RSA Conference, pages 375–398. Springer, 2021. 2 Juan Benet. Ipfs-content addressed, versioned, p2p file system. arXiv preprint arXiv:1407.3561, 2014. 3 Sonja Buchegger, Doris Schiöberg, Le-Hung Vu, and Anwitaman Datta. Peerson: P2p social networking: early experiences and insights. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems, pages 46–52, 2009. 4 Vitalik Buterin et al. A next-generation smart contract and decentralized application platform. white paper, 3(37), 2014. 5 Manuel Castells. The city and the grassroots: a cross-cultural theory of urban social movements. Number 7. Univ of California Press, 1983. 6 Miguel Castro, Barbara Liskov, et al. Practical byzantine fault tolerance. In OsDI, volume 99, pages 173–186, 1999. 7 Gregory Chockler, Roie Melamed, Yoav Tock, and Roman Vitenberg. Constructing scalable overlays for pub-sub with many topics. In Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing, pages 109–118, 2007. 8 Gregory Chockler, Roie Melamed, Yoav Tock, and Roman Vitenberg. Spidercast: a scalable interest-aware overlay for topic-based pub/sub communication. In Proceedings of the 2007 inaugural international conference on Distributed event-based systems, pages 14–25, 2007. 9 Bram Cohen. Incentives build robustness in bittorrent. In Workshop on Economics of Peer-to-Peer systems, volume 6, pages 68–72. Berkeley, CA, USA, 2003. 10 Alan Demers, Karin Petersen, Mike Spreitzer, Doug Terry, Marvin Theimer, and Brent Welch. The bayou architecture: Support for data sharing among mobile users. In 1994 First Workshop on Mobile Computing Systems and Applications, pages 2–7. IEEE, 1994. 11 Bitcoin Developer. P2p network, Retrieved 20222. URL: https://developer.bitcoin.org/ devguide/p2p_network.html. 12 Ethereum Developer. go-ethereum/params/bootnodes.go, Retrieved 2023. URL: https://github.com/ethereum/go-ethereum/blob/master/params/bootnodes.go. 13 Adem Efe Gencer, Soumya Basu, Ittay Eyal, Robbert van Renesse, and Emin Gün Sirer. Decentralization in bitcoin and ethereum networks. In International Conference on Financial Cryptography and Data Security, pages 439–457. Springer, 2018. 14 Gabriele Goldacker. Digitale souveränität [white paper of kompetenzzentrum öffentliche it (öfit)]. retrieved april 20, 2021, 2017. 15 Idit Keidar, Eleftherios Kokoris-Kogias, Oded Naor, and Alexander Spiegelman. All you need is dag, 2021. arXiv:2102.08325. 16 Idit Keidar, Oded Naor, and Ehud Shapiro. Cordial miners: A family of simple and efficient consensus protocols for every eventuality. arXiv preprint arXiv:2205.09174, 2022. 17 Antony Lewis. A gentle introduction to self-sovereign identity. 2017. URL: https://bitsonblocks.net/2017/05/17/a-gentle-introduction-to-self-sovereign-identity/. 18 Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2019. 19 Romualdo Pastor-Satorras, Claudio Castellano, Piet Van Mieghem, and Alessandro Vespignani. Epidemic processes in complex networks. Reviews of modern physics, 87(3):925, 2015. 20 Julia Pohle and Thorsten Thiel. Digital sovereignty. Internet Policy Review: Journal on Internet Regulation, 9(4):1–19, 2020. 21 Aravindh Raman, Sagar Joglekar, Emiliano De Cristofaro, Nishanth Sastry, and Gareth Tyson. Challenges in the decentralised web: The mastodon case. In Proceedings of the Internet Measurement Conference, pages 217–229, 2019. 22 Vinay Setty, Maarten van Steen, Roman Vitenberg, and Spyros Voulgaris. Poldercast: Fast, robust, and scalable architecture for p2p topic-based pub/sub. In ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing, pages 271–291. Springer, 2012. 23 Devavrat Shah et al. Gossip algorithms. Foundations and Trends® in Networking, 3(1):1–125, 2009. 24 Ehud Shapiro. Multiagent transition systems: Protocol-stack mathematics for distributed computing. arXiv preprint arXiv:2112.13650, 2021. 25 Ehud Shapiro. Sovereign personal cryptocurrencies: A grassroots foundation for a digital economy. arXiv preprint arXiv:2202.05619, 2022. 26 Marc Shapiro, Nuno Preguiça, Carlos Baquero, and Marek Zawirski. Conflict-free replicated data types. In Symposium on Self-Stabilizing Systems, pages 386–400. Springer, 2011. 27 Robert Shostak, Marshall Pease, and Leslie Lamport. The byzantine generals problem. ACM Transactions on Programming Languages and Systems, 4(3):382–401, 1982. 28 Ion Stoica, Robert Morris, David Karger, M Frans Kaashoek, and Hari Balakrishnan. Chord: A scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM computer communication review, 31(4):149–160, 2001. 29 Maofan Yin, Dahlia Malkhi, Michael K Reiter, Guy Golan Gueta, and Ittai Abraham. Hotstuff: Bft consensus with linearity and responsiveness. In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, pages 347–356, 2019. 30 Shoshana Zuboff. The age of surveillance capitalism: The fight for a human future at the new frontier of power: Barack Obama’s books of 2019. Profile books, 2019. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. Table of Links Abstract and Introduction Grassroots Protocols Grassroots Implementation of Grassroots Dissemination Applications of Grassroots Dissemination Supporting Digital Sovereignty Future Work & Discussion References A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems B. All-to-All Dissemination is not Grassroots C. Proofs Abstract and Introduction Abstract and Introduction Grassroots Protocols Grassroots Protocols Grassroots Implementation of Grassroots Dissemination Grassroots Implementation of Grassroots Dissemination Applications of Grassroots Dissemination Supporting Digital Sovereignty Applications of Grassroots Dissemination Supporting Digital Sovereignty Future Work & Discussion Future Work & Discussion References References A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems B. All-to-All Dissemination is not Grassroots B. All-to-All Dissemination is not Grassroots C. Proofs C. Proofs References 1 Martin R Albrecht, Jorge Blasco, Rikke Bjerg Jensen, and Lenka Mareková. Mesh messaging in large-scale protests: Breaking bridgefy. In Cryptographers’ Track at the RSA Conference, pages 375–398. Springer, 2021. 2 Juan Benet. Ipfs-content addressed, versioned, p2p file system. arXiv preprint arXiv:1407.3561, 2014. 3 Sonja Buchegger, Doris Schiöberg, Le-Hung Vu, and Anwitaman Datta. Peerson: P2p social networking: early experiences and insights. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems, pages 46–52, 2009. 4 Vitalik Buterin et al. A next-generation smart contract and decentralized application platform. white paper, 3(37), 2014. 5 Manuel Castells. The city and the grassroots: a cross-cultural theory of urban social movements. Number 7. Univ of California Press, 1983. 6 Miguel Castro, Barbara Liskov, et al. Practical byzantine fault tolerance. In OsDI, volume 99, pages 173–186, 1999. 7 Gregory Chockler, Roie Melamed, Yoav Tock, and Roman Vitenberg. Constructing scalable overlays for pub-sub with many topics. In Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing, pages 109–118, 2007. 8 Gregory Chockler, Roie Melamed, Yoav Tock, and Roman Vitenberg. Spidercast: a scalable interest-aware overlay for topic-based pub/sub communication. In Proceedings of the 2007 inaugural international conference on Distributed event-based systems, pages 14–25, 2007. 9 Bram Cohen. Incentives build robustness in bittorrent. In Workshop on Economics of Peer-to-Peer systems, volume 6, pages 68–72. Berkeley, CA, USA, 2003. 10 Alan Demers, Karin Petersen, Mike Spreitzer, Doug Terry, Marvin Theimer, and Brent Welch. The bayou architecture: Support for data sharing among mobile users. In 1994 First Workshop on Mobile Computing Systems and Applications, pages 2–7. IEEE, 1994. 11 Bitcoin Developer. P2p network, Retrieved 20222. URL: https://developer.bitcoin.org/ devguide/p2p_network.html. 12 Ethereum Developer. go-ethereum/params/bootnodes.go, Retrieved 2023. URL: https://github.com/ethereum/go-ethereum/blob/master/params/bootnodes.go. 13 Adem Efe Gencer, Soumya Basu, Ittay Eyal, Robbert van Renesse, and Emin Gün Sirer. Decentralization in bitcoin and ethereum networks. In International Conference on Financial Cryptography and Data Security, pages 439–457. Springer, 2018. 14 Gabriele Goldacker. Digitale souveränität [white paper of kompetenzzentrum öffentliche it (öfit)]. retrieved april 20, 2021, 2017. 15 Idit Keidar, Eleftherios Kokoris-Kogias, Oded Naor, and Alexander Spiegelman. All you need is dag, 2021. arXiv:2102.08325. 16 Idit Keidar, Oded Naor, and Ehud Shapiro. Cordial miners: A family of simple and efficient consensus protocols for every eventuality. arXiv preprint arXiv:2205.09174, 2022. 17 Antony Lewis. A gentle introduction to self-sovereign identity. 2017. URL: https://bitsonblocks.net/2017/05/17/a-gentle-introduction-to-self-sovereign-identity/. 18 Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2019. 19 Romualdo Pastor-Satorras, Claudio Castellano, Piet Van Mieghem, and Alessandro Vespignani. Epidemic processes in complex networks. Reviews of modern physics, 87(3):925, 2015. 20 Julia Pohle and Thorsten Thiel. Digital sovereignty. Internet Policy Review: Journal on Internet Regulation, 9(4):1–19, 2020. 21 Aravindh Raman, Sagar Joglekar, Emiliano De Cristofaro, Nishanth Sastry, and Gareth Tyson. Challenges in the decentralised web: The mastodon case. In Proceedings of the Internet Measurement Conference, pages 217–229, 2019. 22 Vinay Setty, Maarten van Steen, Roman Vitenberg, and Spyros Voulgaris. Poldercast: Fast, robust, and scalable architecture for p2p topic-based pub/sub. In ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing, pages 271–291. Springer, 2012. 23 Devavrat Shah et al. Gossip algorithms. Foundations and Trends® in Networking, 3(1):1–125, 2009. 24 Ehud Shapiro. Multiagent transition systems: Protocol-stack mathematics for distributed computing. arXiv preprint arXiv:2112.13650, 2021. 25 Ehud Shapiro. Sovereign personal cryptocurrencies: A grassroots foundation for a digital economy. arXiv preprint arXiv:2202.05619, 2022. 26 Marc Shapiro, Nuno Preguiça, Carlos Baquero, and Marek Zawirski. Conflict-free replicated data types. In Symposium on Self-Stabilizing Systems, pages 386–400. Springer, 2011. 27 Robert Shostak, Marshall Pease, and Leslie Lamport. The byzantine generals problem. ACM Transactions on Programming Languages and Systems, 4(3):382–401, 1982. 28 Ion Stoica, Robert Morris, David Karger, M Frans Kaashoek, and Hari Balakrishnan. Chord: A scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM computer communication review, 31(4):149–160, 2001. 29 Maofan Yin, Dahlia Malkhi, Michael K Reiter, Guy Golan Gueta, and Ittai Abraham. Hotstuff: Bft consensus with linearity and responsiveness. In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, pages 347–356, 2019. 30 Shoshana Zuboff. The age of surveillance capitalism: The fight for a human future at the new frontier of power: Barack Obama’s books of 2019. Profile books, 2019.

Grassroots Distributed Systems for Digital Sovereignty: References

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Conclusion & Future Work and References

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Methodology

This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Salim Chouaki, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (2) Oana Goga, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (3) Hamed Haddadi, Imperial College London, Brave Software; (4) Peter Snyder, Brave Software. Table of Links Abstract and Introduction Background Measurement Methodology Results Limitations Related Work Conclusion, Acknowledgments, and References Appendix 7 CONCLUSION In this paper, we presented the first systematic study of the privacy properties of the advertising systems of five popular search engines: Two traditional ones, Google and Bing, and three private ones, DuckDuckGo, StartPage, and Qwant. We investigated whether, and to which extent, search engines through their advertising systems, engage in privacyharming behaviors that allow cross site tracking. Despite the privacy intentions and promises of private search engines, our findings reveal the failure of privacyfocused search engines to fully protect users’ privacy during ad interactions. Users on all measured search engines, including the privacy-focused ones, are subject to navigation-based tracking by third parties. We find that all search engines engage in bounce tracking when clicking on ads, where users are sent through several redirectors before reaching the ads’ destination websites. While private search engines themselves do not engage in user tracking, their reliance on traditional advertising systems (Microsoft or Google) renders users susceptible to tracking by those systems. Although we cannot directly attribute this tracking to the search engines themselves, it is evident that they are enabling it through their reliance on Microsoft and Google’s advertising systems. Inspecting the privacy policies of the search engines in light of our findings reveals interesting disparities. While our results demonstrate that Microsoft is capable of tracking DuckDuckGo users when they click on ads, DuckDuckGo asserts that Microsoft does not associate ad-click data with user profiles. On the other hand, Qwant, which also relies on Microsoft advertising for a significant fraction of its ads, do not document the utilization of ad-click data by Microsoft and whether it is used to enhance user profiles. Similarly, StartPage explicitly states that clicking on ads subjects users to the data collection policies of other websites. Our study highlights the need for increased attention to privacy protection within the advertising systems of search engines. One potential solution to protect users’ privacy for private search engines would be to reduce their reliance on third-party advertising systems. Developing their own advertising platform could provide greater control over privacy practices, although the feasibility and complexity of such an approach remain uncertain. Alternatively, private search engines could collaborate with advertising systems such as Microsoft and Google, forging partnerships that proactively tackle privacy concerns. For instance, private search engines could negotiate agreements with the ad provider that prevent redirecting users who click on ads placed within private search engines to additional third parties. This approach would minimize the extent of third-party tracking, limiting it to the ad provider only. Moreover, search engines like StartPage and Qwant could follow the lead of DuckDuckGo by seeking agreements with advertising systems to prevent the use of ad-click identifiers for user profile enrichment. These proactive steps would enhance user privacy while maintaining advertising partnerships with larger platforms. ACKNOWLEDGMENTS This research was supported in part by the French National Research Agency (ANR) through the ANR-17-CE23-0014, ANR-21-CE23-0031-02, and MIAI@Grenoble Alpes ANR19-P3IA-0003 grants and by the EU through the 101041223, 101021377, and 952215 grants. REFERENCES [1] Last accessed September 11, 2023. Ad Block engine in Rust. https://www.npmjs.com/package/adblock-rs [2] Last accessed September 11, 2023. Disconnect Entity List. https://github.com/mozilla-services/shavar-prodlists/blob/master/disconnect-entitylist.json [3] Last accessed September 11, 2023. DuckDuckGo seach engine. https://duckduckgo.com/ [4] Last accessed September 11, 2023. EasyList. https://easylist.to/easylist/easylist.txt [5] Last accessed September 11, 2023. EasyPrivacy. https://easylist.to/easylist/easyprivacy.txt [6] Last accessed September 11, 2023. MovieLens. https://movielens.org/ [7] Last accessed September 11, 2023. Puppeteer. https://www.npmjs.com/package/puppeteer [8] Last accessed September 11, 2023. Puppeteer Extra Plugin Stealth. https://www.npmjs.com/package/puppeteer-extra-plugin-stealth [9] Last accessed September 11, 2023. Qwant seach engine. https://www.qwant.com/ [10] Last accessed September 11, 2023. StartPage seach engine. https://www.startpage.com/ [11] Brave. Last accessed September 11, 2023. What are the best private search engines?https://brave.com/learn/no-tracking-search-engine/ [12] Jordi Castellà-Roca, Alexandre Viejo, and Jordi Herrera-Joancomartí. 2009. Preserving user’s privacy in web search engines. Computer Communications 32, 13 (2009), 1541–1551. https://doi.org/10.1016/j. comcom.2009.05.009 [13] Quan Chen, Panagiotis Ilia, Michalis Polychronakis, and Alexandros Kapravelos. 2021. Cookie Swap Party: Abusing First-Party Cookies for Web Tracking. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21). Association for Computing Machinery, New York, NY, USA, 2117–2129. https://doi.org/10.1145/3442381.3449837 [14] Google Click Identifier (GCLID): Definition. Last accessed September 11, 2023. Google Click Identifier (GCLID): Definition. https://support.google.com/google-ads/answer/9744275 [15] Nurullah Demir, Daniel Theis, Tobias Urban, and Norbert Pohlmann. 2022. Towards Understanding First-Party Cookie Tracking in the Field. [16] MDN Web Docs. Last accessed September 11, 2023. The Anchor element - ping attribute. https://developer.mozilla.org/en-US/docs/Web/HTML/Element/a#ping [17] MDN Web Docs. Last accessed September 11, 2023. Redirections in HTTP. https://developer.mozilla.org/en-US/docs/Web/HTTP/Redirections [18] DuckDuckGo and Microsoft. Last accessed September 11, 2023. DuckDuckGo Help Pages -Company Ads by Microsoft on DuckDuckGo Private Search. https://help.duckduckgo.com/duckduckgo-helppages/company/ads-by-microsoft-on-duckduckgo-private-search/ [19] EasyList. Last accessed September 11, 2023. Overview. https://easylist.to/ [20] Github. Last accessed September 11, 2023. PyEnchant. https://pyenchant.github.io/pyenchant/ [21] Google. Last accessed: September 11, 2023. Stats and Analysis. https://trends.google.com/trends [22] Saikat Guha, Bin Cheng, and Paul Francis. 2010. Challenges in Measuring Online Advertising Systems. In Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement (Melbourne, Australia) (IMC ’10). Association for Computing Machinery, New York, NY, USA, 81–87. https://doi.org/10.1145/1879141.1879152 [23] Aniko Hannak, Piotr Sapiezynski, Arash Molavi Kakhki, Balachander Krishnamurthy, David Lazer, Alan Mislove, and Christo Wilson. 2013. Measuring Personalization of Web Search. In Proceedings of the 22nd International Conference on World Wide Web (Rio de Janeiro, Brazil) (WWW ’13). Association for Computing Machinery, New York, NY, USA, 527–538. https://doi.org/10.1145/2488388.2488435 [24] Google Analytics Help. Last accessed September 11, 2023. Common questions about Google Ads Clicks and Analytics Sessions. https://support.google.com/analytics/answer/4588454?hl=en [25] Microsoft Help. Last accessed September 11, 2023. Auto-tagging of Microsoft Click ID. https://help.ads.microsoft.com/apex/index/3/en/60000 [26] Martin Koop, Erik Tews, and Stefan Katzenbeisser. 2020. In-Depth Evaluation of Redirect Tracking and Link Usage. Proceedings on Privacy Enhancing Technologies 2020 (10 2020), 394–413. https://doi.org/10.2478/popets-2020-0079 [27] Milica Mihajlija. Last accessed September 11, 2023. Cookies Having Independent Partitioned State (CHIPS). https://developer.chrome.com/docs/privacy-sandbox/chips/ [28] Milica Mihajlija. Last accessed September 11, 2023. Cookies Having Independent Partitioned State (CHIPS) origin trial. https://developer.chrome.com/blog/chips-origin-trial/ [29] NordVPN. Last accessed September 11, 2023. The best private search engines for secure browsing. https://nordvpn.com/blog/privatesearch-engines/ [30] DuckDuckGo Help Pages. Last accessed September 11, 2023. Company - Advertising and Affiliates. https://help.duckduckgo.com/duckduckgohelp-pages/company/advertising-and-affiliates/ [31] DuckDuckGo Help Pages. Last accessed September 11, 2023. Privacy - Anonymous Localized Results. https://help.duckduckgo.com/privacy/anonymous-localized-results/ [32] Qwant. Last accessed September 11, 2023. Legal information. https://about.qwant.com/en/legal/confidentialite [33] Audrey Randall, Peter Snyder, Alisha Ukani, Alex C Snoeren, Geoffrey M Voelker, Stefan Savage, and Aaron Schulman. 2022. Measuring UID smuggling in the wild. In Proceedings of the 22nd ACM Internet Measurement Conference. 230–243. [34] Shamma Rashed, Tasnim Said, Amal Abdulrahman, Arsiema Yohannes, and Monther Aldwairi. 2022. Evaluating Web Search Engines Results for Personalization and User Tracking. (2022). https://doi.org/10.48550/ARXIV.2211.11518 [35] Franziska Roesner, Tadayoshi Kohno, and David Wetherall. 2012. Detecting and Defending against Third-Party Tracking on the Web. In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (San Jose, CA) (NSDI’12). USENIX Association, USA, 12. [36] Xuehua Shen, Bin Tan, and ChengXiang Zhai. 2007. Privacy Protection in Personalized Search. SIGIR Forum 41, 1 (jun 2007), 4–17. https://doi.org/10.1145/1273221.1273222 [37] StartPage. Last accessed September 11, 2023. Can I advertise on StartPage? https://support.startpage.com/hc/en-us/articles/5076181310612-Can-I-advertise-on-Startpage- [38] StartPage. Last accessed September 11, 2023. Privacy Policy. https://www.startpage.com/en/privacy-policy [39] WebKit. Last accessed September 11, 2023. Tracking Prevention Policy. https://webkit.org/tracking-prevention-policy/ [40] Yabo Xu, Ke Wang, Benyu Zhang, and Zheng Chen. 2007. PrivacyEnhancing Personalized Web Search (WWW ’07). Association for Computing Machinery, New York, NY, USA, 591–600. https://doi.org/10.1145/1242572.1242652

Privacy Risks of Search Engine Advertising Systems: Conclusion, Acknowledgments, and References

A Comparative Algorithm Audit of Conspiracies on the Net: Conclusion and Bibliography

Introduction Mobile banking is not what it used to be just a few years ago. Customers around the world are discovering new kinds of experiences that minimize visits to physical branches (or eliminate them altogether) in favor of hyper-convenient, feature-rich apps that let you track expenses, build a budget, or even trade stocks and crypto. quicker to welcome these new experiences, like the EU, Russia and Latin America. On the other hand, we can see some of the markets, like the US, resisting change owing to legacy banking systems and stricter legislation. But the common truth is that mobile banking is not only here to stay - it will never be the same again. In this article we will look at some of the current mobile banking trends that are redefining your day-to-day financial operations, as well as new approaches to building mobile banking apps and their potential future. Trends Security Let’s first talk about the issue that prevents someone from trying mobile banking in the first place: the safety of their data and funds. As mobile devices introduced new security features like on-device encryption and biometric authentication (first with fingerprints, later with the face and even with your iris), mobile banking has seen an explosion in adoption since it first became a concept with the breakthrough of the smartphone in 2008. Biometrics But progress in that area hasn’t stopped. On the contrary, cybersecurity engineers are constantly introducing new security measures aiming at preventing attacks from happening in the first place and boosting devices’ underlying security. The pandemic has exacerbated the need for security methods that rely on non-fungible traits of the bank account owner rather than passwords or secret codes that can be copied. For example, voice recognition technology has made great strides in becoming available for use in the mobile banking context, and some companies have successfully launched pilots. At Sinara Bank, we aim to build trust in our biometric solutions as we adopt the “One-time contact” approach to offering our retail banking services. This means the client needs to complete a one-time biometric authentication procedure in our branch, and subsequent banking services will be provided on a remote-first basis. We are also experimenting with offering initial biometric authentication without any branch visits at all, and are seeing a positive dynamic with adoption. Voice biometrics is increasingly getting adopted by US bank call centers to securely authenticate account holders who can finally ditch learning a secret phrase or punching in their credit card number on the line. HSBC UK claims it was able to prevent almost £1 billion in customer funds from being stolen by malicious actors thanks to its adoption of voice authentication technology. These “voiceprint” models can be effectively scaled to mobile apps and even combined with existing biometric authentication methods like fingerprint and face recognition to create a unique biometric identity for every customer that is simply impossible to spoof. Real-Time Monitoring Banks also turn to Big Data models to learn how to identify and stop attacks in real time by monitoring millions of active processes across branches and security systems and identifying patterns that would indicate an attack vector. Data analytics derived from banking mobile apps can be a principal contributor to ensuring the security of the banking system. Features Banking apps are more feature-rich than ever. Here are some of the innovations that keep banning customers coming back: Superapps Banking apps have evolved from simply allowing users to check their balance and conduct basic transfer operations to offering the full scope of banking services and even third-party integrations that simplify everyday tasks and empower a healthier financial lifestyle. These bank apps offer instant chat-style customer support, built-in loan instruments and financial expenses dashboards that help customers track their spending habits and plan ahead, automatic bill payment, and sometimes even public transportation payment options, airline tickets and cinema and theater tickets, typically offered as part of the bank’s loyalty program. Of course, customers increasingly find banking apps with better UX to be more appealing. According to internal research by Sinara Bank, only 7% of our clients took advantage of remote APR deposits in 2019. In 2022, that number was 60%. We are seeing a similar dynamic with pre-approved loans as well. This year alone, our app’s MAU and DAU have jumped 11% and 20% respectively. In addition to that, almost all of our clients transitioned to mobile-only banking, abandoning desktop versions and bank branch visits altogether, with just under 10% of our client base sticking to the legacy desktop web application to manage their finances. Overall penetration of our mobile banking app skyrocketed from 40% to 75% over the past few years, with even the most skeptical groups like elderly depositors finally moving on. We can confidently say that clients overwhelmingly prefer a remote-first banking approach, and we responded accordingly with all new features being remote and mobile-only. More Ways To Pay NFC payments are arguably the most convenient way to pay for goods and services - just tap with your phone and you’re done. It’s seamless, secure and blazing fast, but also not the only way to pay with a smartphone. The banking apps of 2023 need to support more ways of payment to offer customers choice and flexibility, plus, retail chains may create special cashback programs that incentivize customers to use one payment method over the other. QR code payments are a fantastic alternative to NFC payments for customers who, for example, don’t have an NFC-enabled device. This way, they can quickly pay by pointing their camera at the QR code at the checkout counter and deduct funds directly from their bank account. Another popular way to pay is digital wallets and bank-enabled BNPL services which have skyrocketed in recent years as players like Klarna, ShopPay and Afterpay introduced simple loan-based payments on websites, typically with zero interest. Banks are creating hybrid services that combine easy-to-use digital wallets that do not require card credentials with BNPL options that incentivize quick purchases and create an additional revenue stream. Instant Transfers Gone are the days when you needed someone's sixteen-digit bank account number and other lengthy details to conduct a basic wire transfer, which would be executed within several banking business days. At Sinara Bank, we introduced instant operations like C2C, C2B and me-2-me pulls several years ago. Now, we’ve added phone number and QR code transfers between individuals and organizations. We already hit a turnover of $4 million on these operations, up from zero, and we are currently testing instant phone number-based cross-border transfers as well. This trend is global: for example, the US’ Federal Reserve is currently testing its instant transfer system called FedNow that would finally allow the aging US banking system to catch up with the modern world. Of course, other phone number-based transfer applications like Zelle aren’t quick to give up their market shares. Virtual Assistants In the advent of virtual assistants, users rely on Siri, Google Assistant, and even on brand new products like Bing Chat to simplify their interactions with devices and boost productivity, and banks are implementing the same functionality in banking apps to speed up routine tasks like sending quick transfers to relatives or paying bills. Artificial intelligence AI is all the rage, and banks can take advantage of it to perform deeper financial analysis of its customers and offer more personalized financial products. Some companies like Affirm already use AI to accurately score potential borrowers with limited credit history. Crypto and Assets Digital currency is not only for enthusiasts anymore. Governments around the world are flocking to legalize and take advantage of all that digital assets have to offer. Even the United States is piloting a so-called “digital dollar” that should unlock next-generation banking capabilities for millions of retail clients. Sinara Bank is proud to be one of the Russian banks partaking in the digital ruble pilot program. But industry-wide adoption does not stop with analogs of regular currency - many banking apps are starting to integrate with popular cryptocurrencies like Bitcoin, Ethereum and USDT. Some of the leading global banks that are opening their doors to crypto include Revolut, Ally Bank, USAA and even Bank of America., Banking-as-a-Service Neobanks have recently sprung up across the financial landscape to provide competition to traditional banks by undercutting them and introducing features previously unheard of. These novel institutions often lack branches and boast convenient apps with features that younger customers have come to expect from digitally native financial services like PayPal. In response, banks are turning to Banking-as-a-Service (BaaS) platforms to create customized neobank-like experiences that rely on their own infrastructure, diversify their offerings and attract more customers. An everyday example of BaaS solutions are airline credit cards that one might use regularly to accumulate miles and fly cheaper. These cards are issued by banks, not airlines, and in this scenario, both of them reap the benefits. Another example of BaaS are neobank-esque financial companies like Chime that rely on the infrastructure of a legacy bank (in this case, Bancorp Bank, N.A. and Stride Bank, N.A.) to provide services that the underlying banks do not, thus netting those banks more clients. Open Banking Similarly to BaaS, open banking involves the bank opening up some of its tightly kept infrastructure to a third party, but unlike BaaS, it is designed to integrate information from your account with a third party app, like a financial management and planning app that keeps track of your expenses for you. An example of this is Wallet by BudgetBakers, which can synchronize with your bank account to unlock insights into your spending, projected income and overall financial outlook. Conclusion Any banking app can check your balance. So if you want your app to stand out in this increasingly crowded feel, think of ways to pack as many features as possible in the simplest app possible. The future of mobile banking is that of peace-of-mind security that customers don’t have to think twice about, and of infinite flexibility that allows them to manage their entire financial life from a single app. And with more artificial intelligence tools becoming available every day, it’s up to the banks and app developers to take advantage of it to create apps customers will rely on throughout their entire financial journey.

Stories, News & Mentions about USAA

Usaa’s Stories on HackerNoon

Usaa's News & Mentions