Reviewerly/exHarmony huggingface dataset:
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks

Gradient-based adversarial attacks on neural networks can be crafted in a variety of ways by varying either how the attack algorithm relies on the gradient, the network architecture used for crafting the attack, or both. Most recent work has focused on defending classifiers in a case where there is no uncertainty about the attacker's behavior (i.e., the attacker is expected to generate a specific attack using a specific network architecture). However, if the attacker is not guaranteed to behave in a certain way, the literature lacks methods in devising a strategic defense. We fill this gap by simulating the attacker's noisy perturbation using a variety of attack algorithms based on gradients of various classifiers. We perform our analysis using a pre-processing Denoising Autoencoder (DAE) defense that is trained with the simulated noise. We demonstrate significant improvements in post-attack accuracy, using our proposed ensemble-trained defense, compared to a situation where no effort is made to handle uncertainty.
Reconstruction of Velocity Distribution in Partially-Filled Pipe Based on Non-Uniform Under-Sampling

In the process of research on the flow velocity distribution in a partially filled pipe, the under-sampling of measurement data often occurs. For the first time, this problem is solved by the improved non-uniform B-spline curve fitting approximation (NBSC) method. The main innovation of this method is to reconstruct the flow velocity distribution fitting curve with a small amount of non-uniform feature points containing flow velocity information. First, the curvature of a whole discrete sampled data is analyzed, then the weighted threshold is set, and the sampled points that satisfy the threshold are extracted as the initial velocity distribution feature points. Next the node vectors were constructed according to the initial feature points, and the initial interpolation fitting curves are generated. Secondly, by using the relative deviation between the initial approximation curve and each sampled point, new feature points were added where the curve allowable deviation exceeded the specified tolerance, and then a new interpolation fitting curve was obtained. The above procedure was repeated until the fitting curve reached expected accuracy, thus the appropriate feature points were determined. Experimental results showed that, in the case of the same approximation deviation, the proposed NBSC method can solve the problem of under-sampling of measurement data better.
An Approach to the Estimation of Dimensions from Static Images

The broad goal of this study is the estimation of dimensions of real-world multidimensional objects. The focus is on the human being and its dimensional parameters, primarily the height. The approach to the estimation of the height in a single camera environment is presented and is experimentally verified. The proposed method presumes that the distance from the optical device to the human body and its Vertical Field of View (VFOV) are known. The VFOV has to be calculated or acquired only once for a specific device. The accuracy of the proposed method is validated by a series of test scenarios including, e.g., statistical height measurements.
TiFL: A Tier-based Federated Learning System

Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that exists in both resource and data due to the differences in computation and communication capacity, as well as the quantity and content of data among different clients. We conduct a case study to show that heterogeneity in resource and data has a significant impact on training time and model accuracy in conventional FL systems. To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource and data quantity. To further tame the heterogeneity caused by non-IID (Independent and Identical Distribution) data and resources, TiFL employs an adaptive tier selection approach to update the tiering on-the-fly based on the observed training performance and accuracy overtime. We prototype TiFL in a FL testbed following Google's FL architecture and evaluate it using popular benchmarks and the state-of-the-art FL benchmark LEAF. Experimental evaluation shows that TiFL outperforms the conventional FL in various heterogeneous conditions. With the proposed adaptive tier selection policy, we demonstrate that TiFL achieves much faster training performance while keeping the same (and in some cases - better) test accuracy across the board.
The Absent-Minded Passengers Problem via Computer Algebra

In a delightful article that recently appeared in the American Mathematics Monthly, Norbert Henze and Guenter Last discuss the "Absent-Minded Passengers" Problem, but left open finding an explicit expression for the probability generating function, of the random variable "Number of passengers occupying a wrong seat", when the number of absent-minded passengers is larger than one. This is accomplished in this note, using experimental mathematics and symbolic computation. We also derive explicit expressions for the first 8 moments of the original case of one absent-minded passenger, and indicate how to extend it to the general case
Learning Distributional Programs for Relational Autocompletion

Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DC), which supports both discrete and continuous probability distributions. Within this framework, we introduce DiceML { an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data). To realize this, DiceML integrates statistical modeling and distributional clauses with rule learning. The distinguishing features of DiceML are that it 1) tackles autocompletion in relational data, 2) learns distributional clauses extended with statistical models, 3) deals with both discrete and continuous distributions, 4) can exploit background knowledge, and 5) uses an expectation-maximization based algorithm to cope with missing data. The empirical results show the promise of the approach, even when there is missing data.
Attentional Part-based Network for Person Re-identification

Part-based network is an effective method to improve performance in person re-identification (re-ID). Most existing methods assume the availability of well-aligned person bounding box images as model input. However, automatic detection in some datasets causes misalignment which negatively affects the performance. In this work, we propose an Attentional Part-based CNN (AP-CNN) model which combines learning partial features and attention selection. First, we partition feature map into several horizontal stripes. Second, we use attention selection in each stripe to align the pedestrian images. Inside, we introduce a free-parameter attention model with skip-layer connection which maximizes the complementary information of different levels without increasing the complexity of network. Results on four datasets validate the competitiveness of AP-CNN over the state-of-the-art achieving Rank-1 accuracy of 94.4% on Market-1501, 87.3% on DukeMTMC-ReID, 73.7% on CUHK03-labeled and 72.6% on CUHK03-detected.
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization

Batch Normalization (BN) is one of the most widely used techniques in Deep Learning field. But its performance can awfully degrade with insufficient batch size. This weakness limits the usage of BN on many computer vision tasks like detection or segmentation, where batch size is usually small due to the constraint of memory consumption. Therefore many modified normalization techniques have been proposed, which either fail to restore the performance of BN completely, or have to introduce additional nonlinear operations in inference procedure and increase huge consumption. In this paper, we reveal that there are two extra batch statistics involved in backward propagation of BN, on which has never been well discussed before. The extra batch statistics associated with gradients also can severely affect the training of deep neural network. Based on our analysis, we propose a novel normalization method, named Moving Average Batch Normalization (MABN). MABN can completely restore the performance of vanilla BN in small batch cases, without introducing any additional nonlinear operations in inference procedure. We prove the benefits of MABN by both theoretical analysis and experiments. Our experiments demonstrate the effectiveness of MABN in multiple computer vision tasks including ImageNet and COCO. The code has been released in this https URL.
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques

Single Image Super-Resolution (SISR) task refers to learn a mapping from low-resolution images to the corresponding high-resolution ones. This task is known to be extremely difficult since it is an ill-posed problem. Recently, Convolutional Neural Networks (CNNs) have achieved state of the art performance on SISR. However, the images produced by CNNs do not contain fine details of the images. Generative Adversarial Networks (GANs) aim to solve this issue and recover sharp details. Nevertheless, GANs are notoriously difficult to train. Besides that, they generate artifacts in the high-resolution images. In this paper, we have proposed a method in which CNNs try to align images in different spaces rather than only the pixel space. Such a space is designed using convex optimization techniques. CNNs are encouraged to learn high-frequency components of the images as well as low-frequency components. We have shown that the proposed method can recover fine details of the images and it is stable in the training process.
Modelling Alzheimer’s People Brain Using Augmented Reality for Medical Diagnosis Analysis

Alzheimer’s Disease (AD) in adults is characterized by gradual memory loss for old age people. Alzheimer’s brain have impact on affecting brain, include loss of memory, difficulty in thinking, language and solving problems symptoms. Heart issues, depression, diabetes and high blood pressure pose a higher risk of causing Alzheimer’s disease. The Alzheimer’s disease in the brain proteins build up to form structures known as plaques, which are the abnormal clumps in the brain, and tangles which are the bundles of fibbers in the brain. This leads to loss of brain tissue, nerve cells connections and lead to death of nerve cells. In their brain there is a shortage of chemicals. These chemical messengers around the brain help to transmit signals whose shortage causes the signals do not transmit effectively. The above symptoms were visualized as AR (Augmented Reality) model to assist doctors for medical analysis. Augmented Reality act as a extremity tool for findings, supporting and analyse the Alzheimer’s Disease. Understanding the AR brain model enhance the analysis of the brain with the technical exploration of tracking, visualization technology layer by layer level, integrated feedback about different parts of the brain function which plays a major role in clinical evaluation to treat the Alzheimer’s disease. Microglia are a type of cell that initiate immune responses in the brain and spinal cord. When AD is present, microglia interpret the beta-amyloid plaque as cell injury. To reduce or control the inflammatory response and brain shrinking can be visualized using Augmented Reality.
Computing with vortices: Bridging fluid dynamics and its information-processing capability

Herein, the Karman vortex system is considered to be a large recurrent neural network, and the computational capability is numerically evaluated by emulating nonlinear dynamical systems and the memory capacity. Therefore, the Reynolds number dependence of the Karman vortex system computational performance is revealed and the optimal computational performance is achieved near the critical Reynolds number at the onset of Karman vortex shedding, which is associated with a Hopf bifurcation. Our finding advances the understanding of the relationship between the physical properties of fluid dynamics and its computational capability as well as provides an alternative to the widely believed viewpoint that the information processing capability becomes optimal at the edge of chaos.
An Index of African Monetary Integration (IAMI)

This study improves the African Regional Integration Index (ARII) proposed by the African Union, the African Development Bank, and the United Nations Economic Commission for Africa by providing a theoretical framework and addressing shortcomings related to the weighting and aggregation of the indicator. This article measures monetary integration in the eight African Regional Economic Communities (RECs) by constructing an Index of African Monetary Integration (IAMI). It proposes an optimal currency area as a theoretical framework and uses a panel approach to appreciate the dynamics of the index over different periods of time.
Signal denoising using the minimum-probability-of-error criterion

We address the problem of signal denoising via transform-domain shrinkage based on a novel $\textit{risk}$ criterion called the minimum probability of error (MPE), which measures the probability that the estimated parameter lies outside an $\epsilon$-neighborhood of the actual value. However, the MPE, similar to the mean-squared error (MSE), depends on the ground-truth parameter, and has to be estimated from the noisy observations. We consider linear shrinkage-based denoising functions, wherein the optimum shrinkage parameter is obtained by minimizing an estimate of the MPE. When the probability of error is integrated over $\epsilon$, it leads to the expected $\ell_1$ distortion. The proposed MPE and $\ell_1$ distortion formulations are applicable to various noise distributions by invoking a Gaussian mixture model approximation. Within the realm of MPE, we also develop an extension of the transform-domain shrinkage by grouping transform coefficients, resulting in $\textit{subband shrinkage}$. The denoising performance obtained within the proposed framework is shown to be better than that obtained using the minimum MSE-based approaches formulated within $\textbf{$\textit {Stein's unbiased risk estimation}$}$ (SURE) framework, especially in the low measurement signal-to-noise ratio (SNR) regime. Performance comparison with three state-of-the-art denoising algorithms, carried out on electrocardiogram signals and two test signals taken from the $\textit{Wavelab}$ toolbox, exhibits that the MPE framework results in consistent SNR gains for input SNRs below $5$ dB.
Training Data Stewards in Italy: Reflection on the FAIR RDM Summer School

“Fair Research Data Management” Summer School in Parma focused on the skills gap in Italy for data stewards. A distinct feature of the Summer School was its aim to bring together participants from different backgrounds and from different countries. The paper is a reflection on the organization of the Summer School and the evaluation received by the participants.
P$^2$-GAN: Efficient Style Transfer Using Single Style Image

Style transfer is a useful image synthesis technique that can re-render given image into another artistic style while preserving its content information. Generative Adversarial Network (GAN) is a widely adopted framework toward this task for its better representation ability on local style patterns than the traditional Gram-matrix based methods. However, most previous methods rely on sufficient amount of pre-collected style images to train the model. In this paper, a novel Patch Permutation GAN (P$^2$-GAN) network that can efficiently learn the stroke style from a single style image is proposed. We use patch permutation to generate multiple training samples from the given style image. A patch discriminator that can simultaneously process patch-wise images and natural images seamlessly is designed. We also propose a local texture descriptor based criterion to quantitatively evaluate the style transfer quality. Experimental results showed that our method can produce finer quality re-renderings from single style image with improved computational efficiency compared with many state-of-the-arts methods.
Combining Evolution and Learning in Computational Ecosystems

Abstract Although animals such as spiders, fish, and birds have very different anatomies, the basic mechanisms that govern their perception, decision-making, learning, reproduction, and death have striking similarities. These mechanisms have apparently allowed the development of general intelligence in nature. This led us to the idea of approaching artificial general intelligence (AGI) by constructing a generic artificial animal (animat) with a configurable body and fixed mechanisms of perception, decision-making, learning, reproduction, and death. One instance of this generic animat could be an artificial spider, another an artificial fish, and a third an artificial bird. The goal of all decision-making in this model is to maintain homeostasis. Thus actions are selected that might promote survival and reproduction to varying degrees. All decision-making is based on knowledge that is stored in network structures. Each animat has two such network structures: a genotype and a phenotype. The genotype models the initial nervous system that is encoded in the genome (“the brain at birth”), while the phenotype represents the nervous system in its present form (“the brain at present”). Initially the phenotype and the genotype coincide, but then the phenotype keeps developing as a result of learning, while the genotype essentially remains unchanged. The model is extended to ecosystems populated by animats that develop continuously according to fixed mechanisms for sexual or asexual reproduction, and death. Several examples of simple ecosystems are given. We show that our generic animat model possesses general intelligence in a primitive form. In fact, it can learn simple forms of locomotion, navigation, foraging, language, and arithmetic.
Using Eye Tracking to Assess User Behavior in Virtual Training

Virtual training systems can provide flexible and effective training for interactions with increasingly complex industrial machines. However, existing approaches do not adapt to the adaptive attributes of the user. Being able to track the current state of the user enables a humanization of virtual training system, since it allows analyzing the strain and the cognitive processes of the user and reacting accordingly. In recent years, eye tracking technology has become a widespread research area in human machine interaction. This paper introduces an approach to adapt virtual training systems based on eye tracking analysis. The approach detects specific patterns from eye movements and evaluate the performance of the user based on detected patterns. If the pattern suggests that the user cannot follow the instructions or that the user is distracted, the complexity of the training system can be reduced.
PERANCANGAN SISTEM INFORMASI PELAPORAN POSYANDU LANSIA

The health centre Gondokusuman II was a Yogyakarta Primary Health Centre with two assisted villages, namely Terban and Kotabaru, which has sixteen integrated health post groups for the elderly.Each group of integrated health post sends the results of the patient's health activities once a month to the health centre Gondokusuman II.During this time the reporting used to manage patient data using microsoft excel, but in reporting management found deficiencies namely incomplete reporting, access rights processing elderly patient data can be used by all staff, and in the age column was still filled manually.The purpose of the study was to design a microsoft access 2010 integrated health post based elderly information reporting system in the health centre Nutrition Consultant Unit Gondokusuman II.This study uses a prototype design method as a method of developing management information systems based on microsoft access.The subject of this research was the integrated health post officers of the elderly in the nutrition consultant unit of the health Gondokusuman II.In this study started from the needs of users, applications and information system prototypes that are used as microsoft based desktop access.This prototypes was approved by users, complete filling becomes more complete, access rights can be set, and the age calculation was automatic.
SANST: A Self-Attentive Network for Next Point-of-Interest Recommendation

Next point-of-interest (POI) recommendation aims to offer suggestions on which POI to visit next, given a user's POI visit history. This problem has a wide application in the tourism industry, and it is gaining an increasing interest as more POI check-in data become available. The problem is often modeled as a sequential recommendation problem to take advantage of the sequential patterns of user check-ins, e.g., people tend to visit Central Park after The Metropolitan Museum of Art in New York City. Recently, self-attentive networks have been shown to be both effective and efficient in general sequential recommendation problems, e.g., to recommend products, video games, or movies. Directly adopting self-attentive networks for next POI recommendation, however, may produce sub-optimal recommendations. This is because vanilla self-attentive networks do not consider the spatial and temporal patterns of user check-ins, which are two critical features in next POI recommendation. To address this limitation, in this paper, we propose a model named SANST that incorporates spatio-temporal patterns of user check-ins into self-attentive networks. To incorporate the spatial patterns, we encode the relative positions of POIs into their embeddings before feeding the embeddings into the self-attentive network. To incorporate the temporal patterns, we discretize the time of POI check-ins and model the temporal relationship between POI check-ins by a relation-aware self-attention module. We evaluate the performance of our SANST model with three real-world datasets. The results show that SANST consistently outperforms the state-of-theart models, and the advantage in nDCG@10 is up to 13.65%.
A Non-interactive Shuffle Argument with Low Trust Assumptions

A shuffle argument is a cryptographic primitive for proving correct behaviour of mix-networks without leaking any private information. Several recent constructions of non-interactive shuffle arguments avoid the random oracle model but require the public key to be trusted. We augment the most efficient argument by Fauzi et al. [Asiacrypt 2017] with a distributed key generation protocol that assures soundness of the argument if at least one party in the protocol is honest and additionally provide a key verification algorithm which guarantees zero-knowledge even if all the parties are malicious. Furthermore, we simplify their construction and improve security by using weaker assumptions while retaining roughly the same level of efficiency. We also provide an implementation to the distributed key generation protocol and the shuffle argument.