Reviewerly/exHarmony huggingface dataset:
Shuffle Gaussian Mechanism for Differential Privacy

We study Gaussian mechanism in the shuffle model of differential privacy (DP). Particularly, we characterize the mechanism's R\'enyi differential privacy (RDP), showing that it is of the form: $$ \epsilon(\lambda) \leq \frac{1}{\lambda-1}\log\left(\frac{e^{-\lambda/2\sigma^2}}{n^\lambda} \sum_{\substack{k_1+\dotsc+k_n = \lambda; \\k_1,\dotsc,k_n\geq 0}}\binom{\lambda}{k_1,\dotsc,k_n}e^{\sum_{i=1}^nk_i^2/2\sigma^2}\right) $$ We further prove that the RDP is strictly upper-bounded by the Gaussian RDP without shuffling. The shuffle Gaussian RDP is advantageous in composing multiple DP mechanisms, where we demonstrate its improvement over the state-of-the-art approximate DP composition theorems in privacy guarantees of the shuffle model. Moreover, we extend our study to the subsampled shuffle mechanism and the recently proposed shuffled check-in mechanism, which are protocols geared towards distributed/federated learning. Finally, an empirical study of these mechanisms is given to demonstrate the efficacy of employing shuffle Gaussian mechanism under the distributed learning framework to guarantee rigorous user privacy.
Measuring Class-Imbalance Sensitivity of Deterministic Performance Evaluation Metrics

The class-imbalance issue is intrinsic to many real-world machine learning tasks, particularly to the rare-event classification problems. Although the impact and treatment of imbalanced data is widely known, the magnitude of a metric's sensitivity to class imbalance has attracted little attention. As a result, often the sensitive metrics are dismissed while their sensitivity may only be marginal. In this paper, we introduce an intuitive evaluation framework that quantifies metrics' sensitivity to the class imbalance. Moreover, we reveal an interesting fact that there is a logarithmic behavior in metrics' sensitivity meaning that the higher imbalance ratios are associated with the lower sensitivity of metrics. Our framework builds an intuitive understanding of the class-imbalance impact on metrics. We believe this can help avoid many common mistakes, specially the less-emphasized and incorrect assumption that all metrics' quantities are comparable under different class-imbalance ratios.
Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads

Distributed Stream Processing systems have become an essential part of big data processing platforms. They are characterized by the high-throughput processing of near to real-time event streams with the goal of delivering low-latency results and thus enabling time-sensitive decision making. At the same time, results are expected to be consistent even in the presence of partial failures where exactly-once processing guarantees are required for correctness. Stream processing workloads are oftentimes dynamic in nature which makes static configurations highly inefficient as time goes by. Static resource allocations will almost certainly either negatively impact upon the Quality of Service and/or result in higher operational costs. In this paper we present Phoebe, a proactive approach to system auto-tuning for Distributed Stream Processing jobs executing on dynamic workloads. Our approach makes use of parallel profiling runs, QoS modeling, and runtime optimization to provide a general solution whereby configuration parameters are automatically tuned to ensure a stable service as well as alignment with recovery time Quality of Service targets. Phoebe makes use of Time Series Forecasting to gain an insight into future workload requirements thereby delivering scaling decisions which are accurate, long-lived, and reliable. Our experiments demonstrate that Phoebe is able to deliver a stable service while at the same time reducing resource over-provisioning.
Klasifikasi Kerusakan Barang Dengan Menggunakan Komparasi Algoritma C4.5 Dan Naive Bayes

Process control is needed to prevent goods from being damaged both in the production and delivery processes. Therefore, in this study, a classification of conditions was carried out whether the goods that experienced the incident could still be used or not. This study uses a classification model with the C4.5 and Naïve Bayes algorithms, by evaluating using the confusion matrix method, the best accuracy in the C4.5 algorithm is 73.9% recall 72.9% and 88% precision, with these results it can be said that algorithm C 4.5 is good to be implemented in this decision support system model
ANALYSIS OF PARADIGM OF EDUCATION IN THE MODERN EDUCATION SYSTEM

Shaping a person as personalities requires from the society of constant and conciously organizined improvements of public education system. Such practice of transformation of the established forms of unbringing impossible without full tilts on scientifically - theoretical psy chological knowledge of regularities of development of child. Essence really humanistic rela tions to unbringing a child denominated in the thesis of its activity as enjoying full rights, rather then object of process of education. Consequently, it is important so build a pedagogi cal process to managed child activity, organizining its active by completion of independent and responsible actions The Teacher can and obliged to help a grow person to pass this - always unique and independent- a way morally and social development Education presents itself not adjustment a children, teenagers, to available forms sociality, not tailoring under the certain standard.
A scheme for multipartite entanglement distribution via separable carriers

The ability to reliably distribute entanglement among the nodes of a network is an essential requirement for the development of effective quantum communication protocols and the realization of useful quantum networks. It has been demonstrated, in different contexts, that two remote systems can be entangled via local interactions with a carrier system that always remains in a separable state with respect to such distant particles. We develop a strategy for entanglement distribution via separable carriers that can be applied to any number of network nodes to achieve various entanglement distribution patterns. We show that our protocol results in multipartite entanglement, while the carrier mediating the process is always in a separable state with respect to the network. We provide examples showcasing the flexibility of our approach and propose a scheme of principle for the experimental demonstration of the protocol.
Systematic Literature Review: Rancang Bangun Image Digital Watermarking

Digital Image Watermarking is a technology used in information security applications such as protection of digital image content and copyright protection. Digital image watermarking is an interesting area of research because it protects multimedia data from unauthorized access. To design an efficient and robust digital image watermarking system, a balance must be maintained. In this Systematic Literature Review (SLR), we conduct a brief review of the literature on digital image watermarking on journal papers published from 2018. Watermarking algorithms can be classified based on different criteria. Depending on the particular application, the watermarking algorithm must meet certain requirements. The expected result of this research is to know what digital image watermarking is, what it is for and is it useful to do digital image watermarking.
Ensembling over Classifiers: a Bias-Variance Perspective

Ensembles are a straightforward, remarkably effective method for improving the accuracy,calibration, and robustness of models on classification tasks; yet, the reasons that underlie their success remain an active area of research. We build upon the extension to the bias-variance decomposition by Pfau (2013) in order to gain crucial insights into the behavior of ensembles of classifiers. Introducing a dual reparameterization of the bias-variance tradeoff, we first derive generalized laws of total expectation and variance for nonsymmetric losses typical of classification tasks. Comparing conditional and bootstrap bias/variance estimates, we then show that conditional estimates necessarily incur an irreducible error. Next, we show that ensembling in dual space reduces the variance and leaves the bias unchanged, whereas standard ensembling can arbitrarily affect the bias. Empirically, standard ensembling reducesthe bias, leading us to hypothesize that ensembles of classifiers may perform well in part because of this unexpected reduction.We conclude by an empirical analysis of recent deep learning methods that ensemble over hyperparameters, revealing that these techniques indeed favor bias reduction. This suggests that, contrary to classical wisdom, targeting bias reduction may be a promising direction for classifier ensembles.
A Flexible Diffusion Model

Diffusion (score-based) generative models have been widely used for modeling various types of complex data, including images, audios, and point clouds. Recently, the deep connection between forward-backward stochastic differential equations (SDEs) and diffusion-based models has been revealed, and several new variants of SDEs are proposed (e.g., sub-VP, critically-damped Langevin) along this line. Despite the empirical success of the hand-crafted fixed forward SDEs, a great quantity of proper forward SDEs remain unexplored. In this work, we propose a general framework for parameterizing the diffusion model, especially the spatial part of the forward SDE. An abstract formalism is introduced with theoretical guarantees, and its connection with previous diffusion models is leveraged. We demonstrate the theoretical advantage of our method from an optimization perspective. Numerical experiments on synthetic datasets, MINIST and CIFAR10 are also presented to validate the effectiveness of our framework.
The entropy cones of $W_N$ and $W_N^d$ states

The quantum entropy cones (QEC) for $W_N$ states of qubits and $W_N^d$ states of qudits are computed. These cones emerge as symmetrized quantum entropy cones (SQEC) for arbitrary $N$ and $d$. Directed graph models are presented which describe the SQEC for $W_N$ states and $W_N^d$ states. Monogamous mutual information (MMI) is violated for all $N>3$.
Better Incentives for Proof-of-Work

This work proposes a novel proof-of-work blockchain incentive scheme such that, barring exogenous motivations, following the protocol is guaranteed to be the optimal strategy for miners. Our blockchain takes the form of a directed acyclic graph, resulting in improvements with respect to throughput and speed. More importantly, for our blockchain to function, it is not expected that the miners conform to some presupposed protocol in the interest of the system's operability. Instead, our system works if miners act selfishly, trying to get the maximum possible rewards, with no consideration for the overall health of the blockchain.
Benchmarking Constraint Inference in Inverse Reinforcement Learning

When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints. In many real-world problems, however, the constraints are often hard to specify mathematically and unknown to the RL agents. To tackle these issues, Inverse Constrained Reinforcement Learning (ICRL) empirically estimates constraints from expert demonstrations. As an emerging research topic, ICRL does not have common benchmarks, and previous works tested algorithms under hand-crafted environments with manually-generated expert demonstrations. In this paper, we construct an ICRL benchmark in the context of RL application domains, including robot control, and autonomous driving. For each environment, we design relevant constraints and train expert agents to generate demonstration data. Besides, unlike existing baselines that learn a deterministic constraint, we propose a variational ICRL method to model a posterior distribution of candidate constraints. We conduct extensive experiments on these algorithms under our benchmark and show how they can facilitate studying important research challenges for ICRL. The benchmark, including the instructions for reproducing ICRL algorithms, is available at https://github.com/Guiliang/ICRL-benchmarks-public.
Transmission performance in compressed medical images using turbo code

Medical imaging is now an essential support for screening, diagnosis, treatment protocols implementation, patient monitoring, operative preparation and post-operative control. In addition, scientific and technological advances make it possible to set up new imaging methods, often complementary to the existing ones, but also to gradually improve their accuracy. The result is an increase, in the acquisitions number made for the same patient and for information produced for each examination. Since these images must be kept for a certain period, the storage space required for archiving all this data is constantly evolving and images are often viewed locally, and it can be viewed remotely through networks with limited bandwidth such as the long term evolution (LTE) mobile network. The use of compression quickly proves to be essential, whether to facilitate storage or for these data mass browsing remotely. The results of the work carried out in this article are mainly focused on the medical images compression by the set partitioning hierarchical trees (SPIHT) method, which, in fact, allow a significant reduction for data. We are also interested in the transmission of these images on an LTE mobile radio channel in a way that can provide a high bitrate with good transmission quality, by exploiting the channel coding technique, which is effective in combating the noise introduced during the transmission of these images.
Online progressive instance-balanced sampling for weakly supervised object detection

Based on multiple instance detection networks (MIDN), plenty of works have contributed tremendous efforts to weakly supervised object detection (WSOD). However, most methods neglect the fact that the overwhelming negative instances exist in each image during the training phase, which would mislead the training and make the network fall into local minima. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this paper. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module combining random sampling and IoU-balanced sampling progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances for making the network focus on positive instances. Extensive experimental results on the PASCAL VOC 2007 and 2012 datasets demonstrate the proposed method can significantly improve the baseline, which is also comparable to many existing state-of-the-art results. In addition, compared to the baseline, the proposed method requires no extra network parameters and the supplementary training overheads are small, which could be easily integrated into other methods based on the instance classifier refinement paradigm.
Practical Deepfake Detection: Vulnerabilities in Global Contexts

Recent advances in deep learning have enabled realistic digital alterations to videos, known as deepfakes. This technology raises important societal concerns regarding disinformation and authenticity, galvanizing the development of numerous deepfake detection algorithms. At the same time, there are significant differences between training data and in-the-wild video data, which may undermine their practical efficacy. We simulate data corruption techniques and examine the performance of a state-of-the-art deepfake detection algorithm on corrupted variants of the FaceForensics++ dataset. While deepfake detection models are robust against video corruptions that align with training-time augmentations, we find that they remain vulnerable to video corruptions that simulate decreases in video quality. Indeed, in the controversial case of the video of Gabonese President Bongo's new year address, the algorithm, which confidently authenticates the original video, judges highly corrupted variants of the video to be fake. Our work opens up both technical and ethical avenues of exploration into practical deepfake detection in global contexts.
Image anomalies detection using transfer learning of resnet-50 convolutional neural network

With the quick advancement of keen fabricating, information-based blame determination has pulled in expanding attention. As one of the foremost prevalent strategies of diagnosing errors, deep learning has accomplished exceptional comes about. Be that as it may, due to the truth that the estimate of the seeded tests is little in diagnosing mistakes, the profundities of the deep learning (DL) models for fault conclusion are shallow compared to the convolutional neural network in other regions (including ImageNet), which limits the accuracy of the final prediction. In this paper, ResNet-50 with a 25 convolutional layer depth has been proposed to diagnose anomalous images. Trained ResNet-50 applies ImageNet as a feature extractor to diagnose errors. It was proposed on three sets of data which are the bottle, the spoon, and the carton, and the proposed method was achieved. The prediction accuracy of the data set was 99%, 95% and 90%, respectively.
The Complexity of the Co-Occurrence Problem

Let $S$ be a string of length $n$ over an alphabet $\Sigma$ and let $Q$ be a subset of $\Sigma$ of size $q \geq 2$. The 'co-occurrence problem' is to construct a compact data structure that supports the following query: given an integer $w$ return the number of length-$w$ substrings of $S$ that contain each character of $Q$ at least once. This is a natural string problem with applications to, e.g., data mining, natural language processing, and DNA analysis. The state of the art is an $O(\sqrt{nq})$ space data structure that -- with some minor additions -- supports queries in $O(\log\log n)$ time [CPM 2021]. Our contributions are as follows. Firstly, we analyze the problem in terms of a new, natural parameter $d$, giving a simple data structure that uses $O(d)$ space and supports queries in $O(\log\log n)$ time. The preprocessing algorithm does a single pass over $S$, runs in expected $O(n)$ time, and uses $O(d)$ space in addition to the input. Furthermore, we show that $O(d)$ space is optimal and that $O(\log\log n)$-time queries are optimal given optimal space. Secondly, we bound $d = O(\sqrt{nq})$, giving clean bounds in terms of $n$ and $q$ that match the state of the art. Furthermore, we prove that $\Omega(\sqrt{nq})$ bits of space is necessary in the worst case, meaning that the $O(\sqrt{nq})$ upper bound is tight to within polylogarithmic factors. All of our results are based on simple and intuitive combinatorial ideas that simplify the state of the art.
Adherence to treatment: a major problem in the control of arterial hypertension

Arterial hypertension (AH) is a polygenic syndrome and comprises several pathophysiological aspects such as genetic predisposition, where heredity plays a considerable role in the origin of the increase in blood pressure levels. AH is the most frequent disease in Brazilian society, and its exact prevalence is not known throughout the country, and it contributes significantly to a high rate of cardiovascular mortality throughout the country. The evaluation of a patient with AH includes confirmation of the diagnosis, suspicion and identification of a secondary cause, in addition to checking the risk of a cardiovascular attack. Non-adherence to treatment can also be linked to the undesirable effects of antihypertensive drugs, financial barriers, lack of motivation of the patient to treat an asymptomatic disease, long-term treatment, influence on quality of life, inadequate relationship with the health team, among other diverse factors. Thus, the present study aims to search the scientific literature to analyze data on the impact on individual health of patients considered hypertensive who do not adhere to the treatment of arterial hypertension. A bibliographic survey of publications related to studies on arterial hypertension and treatment adherence through the data platform on Google Scholar, Scielo and LILACS was carried out between the years 1990 to 2020. Analyzing the information present in several articles researched in the literature, it is I need to recognize that the patient is not just hypertensive, but consider that he is inserted in a socio-cultural context and that he has his particularities.
Multi-Mode Correlation in a Concurrent Parametric Amplifier

A concurrent parametric amplifier consisting of two pump beams is used to investigate the possibility of generating multi-mode correlation and entanglement. The existence of three-mode entanglement is demonstrated by analyzing the violation degree of three-mode entanglement criteria, including the sufficient criterion, i.e., two-condition and optimal single-condition criterion, and necessary and sufficient criterion, i.e., positivity under partial transposition (PPT) criterion. Besides, two-mode entanglement generated from any pair is also studied by using the Duan criterion and PPT criterion. We find that three-mode entanglement and two-mode entanglement of the two pairs are present in the whole parameter region. Our results pave the way for the realization and application of multi-mode correlation and entanglement based on the concurrent parametric amplifiers.
Bregman Power k-Means for Clustering Exponential Family Data

Recent progress in center-based clustering algorithms combats poor local minima by implicit annealing, using a family of generalized means. These methods are variations of Lloyd's celebrated $k$-means algorithm, and are most appropriate for spherical clusters such as those arising from Gaussian data. In this paper, we bridge these algorithmic advances to classical work on hard clustering under Bregman divergences, which enjoy a bijection to exponential family distributions and are thus well-suited for clustering objects arising from a breadth of data generating mechanisms. The elegant properties of Bregman divergences allow us to maintain closed form updates in a simple and transparent algorithm, and moreover lead to new theoretical arguments for establishing finite sample bounds that relax the bounded support assumption made in the existing state of the art. Additionally, we consider thorough empirical analyses on simulated experiments and a case study on rainfall data, finding that the proposed method outperforms existing peer methods in a variety of non-Gaussian data settings.
Pengaruh Kebijakan Harga terhadap Minat Mahasiswa Perguruan Tinggi di Kota Pagar Alam (Studi Kasus Pada 5 Perguruan Tinggi di Kota Pagar Alam STIT Pagar Alam ITBis Lembah Dempo, STKIP Muhammadiyah Pagar Alam, STEBIS Pagar Alam, Sekolah Tinggi Teknologi Pa

This study aims to determine the effect of price policy on the interest of college students in Pagar Alam City. The method used in sampling is proposive sampling. The sample in this study amounted to 50 people who were taken randomly from 5 universities in the city of Pagar Alam. The collection is done by using a questionnaire method that has been tested for validity and reliability. Based on the results of the t test on the price policy variable, it was obtained at 2,949, while in this study it was 2,949, so it can be said &gt; , and it can be concluded that in the t test in this study "there is a negative and significant influence between the price policy variable on the interest variable". So it can be concluded that the price policy has a negative and significant effect on the interest of college students in Pagar Alam City. This means that Universities in Pagar Alam City should further increase their reconsideration of price policies in order to attract students' interest.