Reviewerly/exHarmony huggingface dataset:
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation

This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised solution cross pseudo supervision is the unsatisfactory precision of foreground classes, leading to a degenerated result under barely-supervised learning. In this paper, we propose a novel Compete-to-Win method (ComWin) to enhance the pseudo label quality. In contrast to directly using one model's predictions as pseudo labels, our key idea is that high-quality pseudo labels should be generated by comparing multiple confidence maps produced by different networks to select the most confident one (a compete-to-win strategy). To further refine pseudo labels at near-boundary areas, an enhanced version of ComWin, namely, ComWin+, is proposed by integrating a boundary-aware enhancement module. Experiments show that our method can achieve the best performance on three public medical image datasets for cardiac structure segmentation, pancreas segmentation and colon tumor segmentation, respectively. The source code is now available at https://github.com/Huiimin5/comwin.
ALiSNet: Accurate and Lightweight Human Segmentation Network for Fashion E-Commerce

Accurately estimating human body shape from photos can enable innovative applications in fashion, from mass customization, to size and fit recommendations and virtual try-on. Body silhouettes calculated from user pictures are effective representations of the body shape for downstream tasks. Smartphones provide a convenient way for users to capture images of their body, and on-device image processing allows predicting body segmentation while protecting users privacy. Existing off-the-shelf methods for human segmentation are closed source and cannot be specialized for our application of body shape and measurement estimation. Therefore, we create a new segmentation model by simplifying Semantic FPN with PointRend, an existing accurate model. We finetune this model on a high-quality dataset of humans in a restricted set of poses relevant for our application. We obtain our final model, ALiSNet, with a size of 4MB and 97.6$\pm$1.0$\%$ mIoU, compared to Apple Person Segmentation, which has an accuracy of 94.4$\pm$5.7$\%$ mIoU on our dataset.
Mining for Cost Awareness in the Infrastructure as Code Artifacts of Cloud-based Applications: an Exploratory Study

Context: The popularity of cloud computing as the primary platform for developing, deploying, and delivering software is largely driven by the promise of cost savings. Therefore, it is surprising that no empirical evidence has been collected to determine whether cost awareness permeates the development process and how it manifests in practice. Objective: This study aims to provide empirical evidence of cost awareness by mining open source repositories of cloud-based applications. The focus is on Infrastructure as Code artifacts that automate software (re)deployment on the cloud. Methods: A systematic search through 152,735 repositories resulted in the selection of 2,010 relevant ones. We then analyzed 538 relevant commits and 208 relevant issues using a combination of inductive and deductive coding. Results: The findings indicate that developers are not only concerned with the cost of their application deployments but also take actions to reduce these costs beyond selecting cheaper cloud services. We also identify research areas for future consideration. Conclusion: Although we focus on a particular Infrastructure as Code technology (Terraform), the findings can be applicable to cloud-based application development in general. The provided empirical grounding can serve developers seeking to reduce costs through service selection, resource allocation, deployment optimization, and other techniques.
Multi-Server Secure Aggregation with Unreliable Communication Links

In many distributed learning setups such as federated learning (FL), client nodes at the edge use individually collected data to compute local gradients and send them to a central master server. The master server then aggregates the received gradients and broadcasts the aggregation to all clients, with which the clients can update the global model. In this paper, we consider multi-server federated learning with secure aggregation and unreliable communication links. We first define a threat model using Shannon's information-theoretic security framework and propose a novel scheme called Lagrange Coding with Mask (LCM), which divides the servers into groups and uses Coding and Masking techniques. LCM can achieve a trade-off between the uplink and downlink communication loads by adjusting the number of servers in each group. Furthermore, we derive the lower bounds of the uplink and downlink communication loads, respectively, and prove that LCM achieves the optimal uplink communication load, which is unrelated to the number of collusion clients.
Magnitude Invariant Parametrizations Improve Hypernetwork Learning

Hypernetworks, neural networks that predict the parameters of another neural network, are powerful models that have been successfully used in diverse applications from image generation to multi-task learning. Unfortunately, existing hypernetworks are often challenging to train. Training typically converges far more slowly than for non-hypernetwork models, and the rate of convergence can be very sensitive to hyperparameter choices. In this work, we identify a fundamental and previously unidentified problem that contributes to the challenge of training hypernetworks: a magnitude proportionality between the inputs and outputs of the hypernetwork. We demonstrate both analytically and empirically that this can lead to unstable optimization, thereby slowing down convergence, and sometimes even preventing any learning. We present a simple solution to this problem using a revised hypernetwork formulation that we call Magnitude Invariant Parametrizations (MIP). We demonstrate the proposed solution on several hypernetwork tasks, where it consistently stabilizes training and achieves faster convergence. Furthermore, we perform a comprehensive ablation study including choices of activation function, normalization strategies, input dimensionality, and hypernetwork architecture; and find that MIP improves training in all scenarios. We provide easy-to-use code that can turn existing networks into MIP-based hypernetworks.
Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees

An $\varepsilon$-approximate quantile sketch over a stream of $n$ inputs approximates the rank of any query point $q$ - that is, the number of input points less than $q$ - up to an additive error of $\varepsilon n$, generally with some probability of at least $1 - 1/\mathrm{poly}(n)$, while consuming $o(n)$ space. While the celebrated KLL sketch of Karnin, Lang, and Liberty achieves a provably optimal quantile approximation algorithm over worst-case streams, the approximations it achieves in practice are often far from optimal. Indeed, the most commonly used technique in practice is Dunning's t-digest, which often achieves much better approximations than KLL on real-world data but is known to have arbitrarily large errors in the worst case. We apply interpolation techniques to the streaming quantiles problem to attempt to achieve better approximations on real-world data sets than KLL while maintaining similar guarantees in the worst case.
Density Devolution for Ordering Synthetic Channels

Constructing a polar code is all about selecting a subset of rows from a Kronecker power of $[^1_1{}^0_1]$. It is known that, under successive cancellation decoder, some rows are Pareto-better than the other. For instance, whenever a user sees a substring $01$ in the binary expansion of a row index and replaces it with $10$, the user obtains a row index that is always more welcomed. We call this a "rule" and denote it by $10 \succcurlyeq 01$. In present work, we first enumerate some rules over binary erasure channels such as $1001 \succcurlyeq 0110$ and $10001 \succcurlyeq 01010$ and $10101 \succcurlyeq 01110$. We then summarize them using a "rule of rules": if $10a \succcurlyeq 01b$ is a rule, where $a$ and $b$ are arbitrary binary strings, then $100a \succcurlyeq 010b$ and $101a \succcurlyeq 011b$ are rules. This work's main contribution is using field theory, Galois theory, and numerical analysis to develop an algorithm that decides if a rule of rules is mathematically sound. We apply the algorithm to enumerate some rules of rules. Each rule of rule is capable of generating an infinite family of rules. For instance, $10c01 \succcurlyeq 01c10$ for arbitrary binary string $c$ can be generated. We found an application of $10c01 \succcurlyeq 01c10$ that is related to integer partition and the dominance order therein.
Non-exemplar Class-incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features

Non-exemplar class-incremental learning refers to classifying new and old classes without storing samples of old classes. Since only new class samples are available for optimization, it often occurs catastrophic forgetting of old knowledge. To alleviate this problem, many new methods are proposed such as model distillation, class augmentation. In this paper, we propose an effective non-exemplar method called RAMF consisting of Random Auxiliary classes augmentation and Mixed Feature. On the one hand, we design a novel random auxiliary classes augmentation method, where one augmentation is randomly selected from three augmentations and applied on the input to generate augmented samples and extra class labels. By extending data and label space, it allows the model to learn more diverse representations, which can prevent the model from being biased towards learning task-specific features. When learning new tasks, it will reduce the change of feature space and improve model generalization. On the other hand, we employ mixed feature to replace the new features since only using new feature to optimize the model will affect the representation that was previously embedded in the feature space. Instead, by mixing new and old features, old knowledge can be retained without increasing the computational complexity. Extensive experiments on three benchmarks demonstrate the superiority of our approach, which outperforms the state-of-the-art non-exemplar methods and is comparable to high-performance replay-based methods.
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value

Data valuation is a powerful framework for providing statistical insights into which data are beneficial or detrimental to model training. Many Shapley-based data valuation methods have shown promising results in various downstream tasks, however, they are well known to be computationally challenging as it requires training a large number of models. As a result, it has been recognized as infeasible to apply to large datasets. To address this issue, we propose Data-OOB, a new data valuation method for a bagging model that utilizes the out-of-bag estimate. The proposed method is computationally efficient and can scale to millions of data by reusing trained weak learners. Specifically, Data-OOB takes less than 2.25 hours on a single CPU processor when there are $10^6$ samples to evaluate and the input dimension is 100. Furthermore, Data-OOB has solid theoretical interpretations in that it identifies the same important data point as the infinitesimal jackknife influence function when two different points are compared. We conduct comprehensive experiments using 12 classification datasets, each with thousands of sample sizes. We demonstrate that the proposed method significantly outperforms existing state-of-the-art data valuation methods in identifying mislabeled data and finding a set of helpful (or harmful) data points, highlighting the potential for applying data values in real-world applications.
Pointwise Maximal Leakage on General Alphabets

Pointwise maximal leakage (PML) is an operationally meaningful privacy measure that quantifies the amount of information leaking about a secret $X$ to a single outcome of a related random variable $Y$. In this paper, we extend the notion of PML to random variables on arbitrary probability spaces. We develop two new definitions: First, we extend PML to countably infinite random variables by considering adversaries who aim to guess the value of discrete (finite or countably infinite) functions of $X$. Then, we consider adversaries who construct estimates of $X$ that maximize the expected value of their corresponding gain functions. We use this latter setup to introduce a highly versatile form of PML that captures many scenarios of practical interest whose definition requires no assumptions about the underlying probability spaces.
Educational trends in the conditions of digital society development

The article is devoted to the analysis of educational trends in the conditions of digital society development. It is noted that the key resource of the digital society is citizens who can effectively and efficiently use digital technologies both for their own needs and for the achievement and implementation of economic and social goals. This requires the formation of digital skills and competencies of specialists, which cannot be realized without the transformation of education. Digital transformations are based on digital trends, the analysis of which makes it possible to predict the development of specific economic, technological and even social phenomena in the future. The modern educational trends affecting the Russian education system are determined based on the directions of development of digital technologies. These include: distance and online learning, blended learning, lifelong learning, informal education, cloud technologies in education, gamification of learning, virtual, augmented and mixed reality, mobile technologies in learning, STEM education, robotics and 3D technologies in education. It is emphasized that the awareness of teachers about modern educational trends will increase their motivation and understanding of the need for their own professional development, which opens up broad prospects for deepening the professionalism of teachers and improving the efficiency of the educational process as a whole.
Analisis Pengelolaan Limbah Bahan Berbahaya dan Beracun (B3) di Indonesia

Hazardous and Toxic Materials are substances, energy, and/or other components which due to their nature, concentration and/or amount, either directly or indirectly, can pollute and/or damage the environment, endanger the environment, health and survival of humans and living thing.. Hazardous and toxic waste materials are. The management of this hazardous and toxic waste is very crucial given the impact it will have on human health. So this study aims to analyze the management of Hazardous and Toxic Materials (B3) waste in Indonesia. This study uses a literature review method with a qualitative approach. This literature review is by searching for data, selecting the necessary data, analyzing it, and making conclusions from the results of data analysis. The results obtained are that the management of hazardous and toxic waste in the stages of collection, separation, storage, transportation and processing of waste must be carried out properly and safely.
Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

In order to improve the state monitoring and adaptive control capability of inertial stabilization platforms (ISPs) with unknown loads, it is necessary to estimate the dynamic parameters comprehensively online. However, most current online estimation methods regard the system as a linear dual-inertia model which neglects the backlash and nonlinear friction torque. It reduces the accuracy of the model and leads to incomplete and low accuracy of the estimated parameters. The purpose of this research is to achieve a comprehensive and accurate online estimation of multiple parameters of ISPs and lay a foundation for state monitoring and adaptive control of ISPs. First, a dual-inertia model containing backlash and nonlinear friction torque of the motor and load is established. Then, the auto-regressive moving average (ARMA) model of the motor and load is established by the forward Euler method, which clearly expresses the online identification formula of the parameters. On this basis, the adaptive identification method based on the recursive extended least squares (RELS) algorithm is used to realize the online estimation of multiple parameters. The simulation and experimental results show that the proposed adaptive multi-parameter estimation method can realize the simultaneous online identification of the moment of inertia of the load, the damping coefficient of motor and load, the transmission stiffness, the Coulomb friction torque of motor and load, and the backlash, and the steady-state error is less than 10%. Compared with the traditional linear dual-inertia model, the similarity between the model based on the proposed adaptive parameter estimation algorithm and the actual system is increased by 65.3%.
University as a space for intensification of “collective thought action”: At the intersection of the concepts of “knowledge management” by Nonako–Takeuchi and the SMD approach of G.P. Shchedrovitsky

The purpose of this paper is to describe conceptually (in the discourse of G.P. Shchedrovitsky, I. Nonaka, H. Takeuchi) the parameters and the type of university capable of being a space of collective thinking intensification, i.e. such innovation platform where it is possible not only to solve the “main question” of modern university: “How to match thinking and activity?”, but also to find the balance between the laws of ‘business’ and ‘science’ functioning. We describe the ‘project part’ — the first stage of organizing such a space, thanks to which it becomes possible to intensify the processes of knowledge creation and exchange in the university, as well as the process of forming a more open innovation corporate culture. The methodological basis here is organized communication, which allows the formation of an open system of horizontal social links within the university for the implementation of its innovative activities.
A Diachronic study from 1880 to 1940s: Phonological phenomenon in Korean Language textbooks for Japanese learner

This study examined the phonological phenomenon description aspect in Korean language textbooks published for Japanese learner during the modern period. The Korean language textbooks published from 1880 to 1940 were divided into five periods. In the 1880s, the principle of the occurrence of phonological phenomena was not accurately recognized, and it was explained in Japanese grammatical terms such as Onbin and Voiced sound. However, it was seen that the environment or process in which phonological phenomena gradually occurred was clearly recognized and described in later generations. From the 1911s to the 1920s, it describes the most types of phonological phenomena, and it can be seen that one phonological phenomenon is explained in detail. From the 1921s to the 1930s, the types of phonological phenomena dealt with in textbooks decreased in number but there were also textbooks that presented new phonological phenomena that were not covered in the previous period. It have been included in the textbook by newly identifying the phonological phenomenon that could not be identified in the previous period through research on the phonological phenomenon. From the 1931s to the 1940s, the phonological phenomena were dealt with in detail and considered the Korean language norms. The contents are very close to today's Korean language norms. Through this study, the diachronic changes in phonological phenomena in Korean language textbooks were examined thoroughly.
Reflections on the future of broker-dealer compliance

The chapter speculates about the future challenges and developments facing compliance in the brokerage industry. First, much of compliance’s future depends upon the future of the brokerage industry itself, which has seen a steady shrinkage in the number of broker-dealers. Second, compliance in broker-dealers, as in organizations in general, is affected by the related pressures of cost and technology, which could reduce the number of compliance officers as compliance tasks are automated. Third and finally, there is a growing behavioral and organizational literature explaining misconduct in organizations with which compliance officers should familiarize themselves because it could prove useful to them when they design compliance programs and advocate for compliance policies and strategies in broker-dealers.
Energy-Efficiency in Cloud Datacenters: A Survey

Energy efficiency in data centers refers to how effectively any virtualized data center providing cloud administrations may use or use energy. Due to data centers' continued high electrical power consumption, current computer systems have strict energy efficiency requirements to optimize of Energy Utilization. Consequently, the idea of “green cloud computing” was born. Energy efficiency in the clouds, particularly in cloud data centers, were themes covered in many survey articles. In this Survey we focused on energy efficiency of the clouds by using cooling, VM allocation and migration techniques, scheduling techniques, AI technologies to aid in the construction of the green cloud. Our survey also covered the cloud services offered in Saudi Arabia and their comparison. We found that Saudi cloud providers is categorized into different classes, (A), (B), and (C), all of these classes are service providers that qualified to deal with individuals, private sector, non-profit sector, and government sector.
Mitigation of Straggler in Virtual Machine Stack Using Supervised Learning Methodology

Hadoop is an inexpensive analytical tool as compared to the other distributed storage in market as it does not need any standalone machines and works on group of commodity hardware. It is a distributed storage system along with this it achieves parallelization of larger data collections. With MapReduce, HDFS (Hadoop distributed file system) provides solution for the system where processing huge datasets is a requirement. Few of the main reasons of stragglers in assorted Hadoop clusters are load inconsistency during storing, resource friction throughout scheduling tasks, hardware downturn due to excessive usage, as well as software configuration issues while managing the cluster. Hadoop's performance lows down in a heterogeneous network due to the technical heterogeneity. We used a supervised machine learning (ML) technique to identify straggler nodes in an eminently distributed network in this article. The suggested technique identifies the proper slow-running job (Straggler) in the network and assign it to other node in the stack to complete the operation with quick succession. Virtual Machine (VM) identifier, network bandwidth consumption, number of processors and its load, memory load and other parameters included in the full data set are utilized for recognition. Various feature extraction methodologies have been utilized to develop its training system. The whole data set was processed for heterogenous features on the dataset. We analyzed our approach using our suggested classifier after doing comprehensive empirical work. As out-turn, the system outperforms using typical machine learning models in classification performance.