Reviewerly/exHarmony huggingface dataset:
Flexible Bayesian quantile curve fitting with shape restrictions under the Dirichlet process mixture of the generalized asymmetric Laplace distribution

We propose a flexible Bayesian semiparametric quantile regression model based on Dirichlet process mixtures of generalized asymmetric Laplace distributions for fitting curves with shape restrictions. The generalized asymmetric Laplace distribution exhibits more flexible tail behaviour than the frequently used asymmetric Laplace distribution in Bayesian quantile regression. In addition, nonparametric mixing over the shape and scale parameters with the Dirichlet process mixture extends its flexibility and improves the goodness of fit. By assuming the derivatives of the regression functions to be the squares of the Gaussian processes, our approach ensures that the resulting functions have shape restrictions such as monotonicity, convexity and concavity. The introduction of shape restrictions prevents overfitting and helps obtain smoother and more stable estimates of the quantile curves, especially in the tail quantiles for small and moderate sample sizes. Furthermore, the proposed shape‐restricted quantile semiparametric regression model deals with sparse estimation for regression coefficients using the horseshoe+ prior distribution, and it is extended to cases with group‐specific curve estimation and censored data. The usefulness of the proposed models is demonstrated using simulated datasets and real applications.
Decision Support System Cattle Weight Prediction using Artificial Selected Weighting Method

Animal agriculture in Indonesia is a very important need. Beef production in Indonesia experienced fluctuations from 2015 to 2019. In that period, 2016 reached its highest point with 518,484 tons. Even so, the use of technology for animal husbandry is needed to help farmers improve the quality and quantity of livestock products. One of the needs of farmers is cattle scales. This scale is expensive so not all farmers can afford this. They weighing cattle by bringing it to the cattle market or a place that has been facilitated by the government. This causes farmers have to require transportation just only weighing cows. Another way is approximating the weight, but this method can only be done by someone who has high experience. The aim of this paper to propose a system for predicting the weight of cattle using a novel artificial selected weighting method. This method is based on hybrid image processing results combined with Danish Schoorl, Schoorl Indonesia, Winter Europe/Scheiffer, and Winter Indonesia calculations. The initial stage is image processing, the results of the parameters obtained will be calculated using Denmark Schoorl, Schoorl Indonesia, Winter Europe/Scheiffer, and Winter Indonesia formula. Calculation obtained will be learned by comparing it with the actual weight of livestock. The results of this learning process obtained weights that can be used to predict cattle weight. The results show using an artificial selected weighting method, the accuracy of prediction can be increased.
A Slimmer and Deeper Approach to Network Structures for Image Denoising and Dehazing

Image denoising and dehazing are representatives of low-level vision tasks. The right trade-off between depth and computational complexity of convolutional neural networks (CNNs) is of significant importance to these problems. Wider feature maps and deeper network are beneficial for better performance, but would increase their complexity. In this paper, we explore a new way in network design, to encourage more convolution layers while decrease the width of feature maps. Such slimmer and deeper architectures can enhance the performance while maintain the same level of computational costs. We experimentally evaluate the performances of the proposed approach on denoising and dehazing, and the results demonstrate that it can achieve the state-of-the-art results on both quantitative measures and qualitative performances. Further experiments also indicate that the proposed approach can be adapted for other image restoration tasks such as super-resolution.
Adversarial Detection on Graph Structured Data

Graph Neural Networks (GNNs) has achieved tremendous development on perceptual tasks in recent years, such as node classification, graph classification, link prediction, etc. However, recent studies show that deep learning models of GNNs are incredibly vulnerable to adversarial attacks, so enhancing the robustness of such models remains a significant challenge. In this paper, we propose a subgraph based adversarial sample detection against adversarial perturbations. To the best of our knowledge, this is the first work on the adversarial detection in the deep-learning graph classification models, using the Subgraph Networks (SGN) to restructure the graph's features. Moreover, we develop the joint adversarial detector to cope with the more complicated and unknown attacks. Specifically, we first explain how adversarial attacks can easily fool the models and then show that the SGN can facilitate the distinction of adversarial examples generated by state-of-the-art attacks. We experiment on five real-world graph datasets using three different kinds of attack strategies on graph classification. Our empirical results show the effectiveness of our detection method and further explain the SGN's capacity to tell apart malicious graphs.
KrigHedge: Gaussian Process Surrogates for Delta Hedging

We investigate a machine learning approach to option Greeks approximation based on Gaussian Process (GP) surrogates. Our motivation is to implement Delta hedging in cases where direct computation is expensive, such as in local volatility models, or can only ever be done approximately. The proposed method takes in noisily observed option prices, fits a non-parametric input-output map and then analytically differentiates the latter to obtain the various price sensitivities. Thus, a single surrogate yields multiple self-consistent Greeks. We provide a detailed analysis of numerous aspects of GP surrogates, including choice of kernel family, simulation design, choice of trend function and impact of noise. We moreover connect the quality of the Delta approximation to the resulting discrete-time hedging loss. Results are illustrated with two extensive case studies that consider estimation of Delta, Theta and Gamma and benchmark approximation quality and uncertainty quantification using a variety of statistical metrics. Among our key take-aways are the recommendation to use Matérn kernels, the benefit of including virtual training points to capture boundary conditions, and the significant loss of fidelity when training on stock-path-based datasets.
Improving query performance on dynamic graphs

Querying large models efficiently often imposes high demands on system resources such as memory, processing time, disk access or network latency. The situation becomes more complicated when data are highly interconnected, e.g. in the form of graph structures, and when data sources are heterogeneous, partly coming from dynamic systems and partly stored in databases. These situations are now common in many existing social networking applications and geo-location systems, which require specialized and efficient query algorithms in order to make informed decisions on time. In this paper, we propose an algorithm to improve the memory consumption and time performance of this type of queries by reducing the amount of elements to be processed, focusing only on the information that is relevant to the query but without compromising the accuracy of its results. To this end, the reduced subset of data is selected depending on the type of query and its constituent filters. Three case studies are used to evaluate the performance of our proposal, obtaining significant speedups in all cases.
Mapping and Visualization of Fuzzy Control Research

Fuzzy control is an important control algorithm and is applied in many areas. In this paper, scientometric analysis is applied to present a systematic overview of the research topics and current status of the fuzzy control. 13651 articles and 91885 relevant references from 2010 to 2019 are investigated from the Web of Science Core Collection. The most influential authors and journals are presented. The disciplines and topics of fuzzy control research are identified. The visual network of knowledge structure of fuzzy control is established. The deeper researches of citation bursts, centrality and sigma are made. The results of this research provide the practitioners a better understanding of fuzzy control and help the researchers do relevant further study.
Analysis of temperature-sensitive on short-term electricity load forecasting

Short-term electricity load forecasting is essential for the operating energy of a nation. Accuracy of forecasting is important for power operation however it is dependent on weather. This paper focuses on model sensitive analysis temperature on short-term electricity load forecasting. The new models have proposed to analyze the effect of temperature on the electricity load forecasting. In detail, three methods (linear regression, local method and Monte Carlo) are analysis for temperature-sensitive on short-term electricity load forecasting. Then, the proposed models are applied to Vietnam electricity load forecasting.
Measuring the Past to Guide the Future: Takeaways from a Retrospective Disciplinary Analysis on Print Books and Ebooks

This study investigates the relationship between librarians’ purchases and the actual preferences of library patrons based on usage. Using ten years of purchase history, we reviewed how print and ebook preferences have changed over time and what role, if any, discipline plays in format preferences. This information will be used to help subject librarians across various disciplines to make informed print and ebook purchases, relying on actual patron preferences rather than guesswork.
Asymptotic analysis of model selection criteria for general hidden Markov models

The paper obtains analytical results for the asymptotic properties of Model Selection Criteria – widely used in practice – for a general family of hidden Markov models (HMMs), thereby substantially extending the related theory beyond typical 'i.i.d.-like' model structures and filling in an important gap in the relevant literature. In particular, we look at the Bayesian and Akaike Information Criteria (BIC and AIC) and the model evidence. In the setting of nested classes of models, we prove that BIC and the evidence are strongly consistent for HMMs (under regularity conditions), whereas AIC is not weakly consistent. Numerical experiments support our theoretical results.
Privacy-Aware Image Authentication from Cryptographic Primitives

Abstract Image authentication is the process of verifying image origin, integrity and authenticity. In many situations, image authentication should allow reasonable image editing, which does not introduce any wrong information against the original one. While it has been studied both extensively and intensively with considerable efforts, there is no satisfactory method supporting region extraction. This paper presents a solution to address the issue of privacy protection in authenticated images. Our scheme allows anyone to extract sub-image blocks from an original image (authenticated by the image producer) and generate a proof tag to prove the credibility of the extracted image blocks. The process of proof tag generation does not require any interaction with the image producer. In addition, the image producer is able to define must-be-preserved image blocks (e.g. producer logo) during the extraction. We define the security property for the authenticated sub-images and give a generic design with two core primitives: an ordinary digital signature scheme and a cryptographic accumulator. The security of our design can be reduced to the underlying cryptographic primitives and its practical performance is demonstrated by a bunch of evaluations. We believe the proposed design, together with other image authentication methods, will further facilitate image relevant services and applications.
Runtime analysis of evolutionary algorithms via symmetry arguments

We use an elementary argument building on group actions to prove that the selection-free steady state genetic algorithm analyzed by Sutton and Witt (GECCO 2019) takes an expected number of $\Omega(2^n / \sqrt n)$ iterations to find any particular target search point. This bound is valid for all population sizes $\mu$. Our result improves over the previous lower bound of $\Omega(\exp(n^{\delta/2}))$ valid for population sizes $\mu = O(n^{1/2 - \delta})$, $0 < \delta < 1/2$.
Mask2LFP: Mask-constrained Adversarial Latent Fingerprint Synthesis

Latent fingerprints are one of the most valuable and unique biometric attributes that are extensively used in forensic and law enforcement applications. Compared to rolled/plain fingerprint, latent fingerprint is of poor quality in term of friction ridge patterns, hence a more challenging for automatic fingerprint recognition systems. Considering the difficulties of dusting, lifting, and recovery of latent fingerprint, this type of fingerprints remain expensive to develop and collect. In this paper, we present a novel approach for synthetic latent fingerprint generation using Generative Adversarial Network (GAN). Our proposed framework, named mask to latent fingerprint (Mask2LFP), uses binary mask of distorted fingerprint-like shapes as input, and outputs a realistic latent fingerprint. This work focuses on the generation of synthetic latent fingerprints. The aim is to alleviate the scarcity issue of latent fingerprint data and serve the increasing need for developing, evaluating, and enhancing fingerprint-based identification systems, especially in forensic applications.
Design and Implementation of an Over Temperature Protection Circuit

Based on the theory of thermal characteristic of diode and use reference voltage compare with diode temperature to realize a thermal shutdown circuit within thermal hysteresis. Bandgap circuit was used to provide reference voltage. Detailed theory derivation and design method were gave by this paper, the corresponding solution of the difficult part was also gave. By the comparison of simulation and test result we can know that this kind of thermal shutdown circuit can realize accurate thermal shutdown and hysteresis.
Smart Contracts as a diploma anti-forgery system in higher education - a pilot project

The problem of counterfeiting diplomas in education with the advancement of digital technology is increasingly pronounced. The process of forging documents is almost always accompanied by reduced transparency in issuing the documents with no possibility to easily check the validity of the document. One of the currently very attractive and challenging technology in digitized society is the blockchain technology and all the sequential systems that have emerged based on it. One such system is the Ethereum platform, which uses blockchain technology and enables the creation of decentralized applications programmed to run on the Ethereum network. One of the Ethereum use is a Smart Contract, which allows applications to be executed online, completely autonomously without the influence of a third party, once a previously defined condition is satisfied. The objective of this research is to explore the possibility of using a Smart Contract in the process of the creation and issuance of diplomas at a higher education institution. At the end of the analysis, we provide an overview of the advantages and disadvantages of this procedure, as well as potential possibilities for its improvements. The possibilities of automation and the cost of such a process were also considered.
A New Approach Item Rating Data Mining on the Recommendation System

Collaborative filtering (CF) in the recommendation system using user habits, behaviors, and item rating to recommend the products which suit customer’s needs. Therefore, analyzing user rating data is one of the factors that improve the efficiency of the recommendation system. This paper proposes a new approach to analyze rating item and input the implicit effect of items rating to the recommendation system based on the TrustSVD model and matrix factorization (MF) techniques. The experimental results showed that our proposed solution achieves 18% better than the matrix factorization method and 15% the Multi-Relational Matrix Factorization method, respectively.
A Novel, Efficient Implementation of a Local Binary Convolutional Neural Network

In order to reduce the computational complexity of convolutional neural networks (CNNs), the local binary convolutional neural network (LBCNN) has been proposed. In the LBCNN, a convolutional layer is divided into two sublayers. Sublayer 1 is a sparse ternary-weighted convolutional layer, and Sublayer 2 is a 1 ×1 convolutional layer. With the use of two sublayers, the LBCNN has lower computational complexity and uses less memory than the CNN. In this brief, we propose a platform that includes a weight preprocessor and layer accelerator for the LBCNN. The proposed weight preprocessor takes advantage of the sparsity in the LBCNN and encodes the weight offline. The layer accelerator effectively uses the encoded data to reduce computational complexity and memory accesses for an inference. When compared to the state-of-the-art design, the experimental results show that the number of clock cycles are reduced by 76.32%, and memory usage is reduced by 39.41%. The synthesized results show that the clock period is reduced by 4.76%; the cell area is reduced by 46.48%, and the power consumption is reduced by 40.87%. The inference accuracy is the same as that of the state-of-the-art design.
Locality/Fairness-Aware Job Scheduling in Distributed Stream Processing Engines

Distributed stream processing engines (DSPEs) deploy multiple tasks on distributed servers to process data streams in real time. Many DSPEs have provided locality-aware stream partitioning (LSP) methods to reduce network communication costs. However, an even job scheduler provided by DSPEs deploys tasks far away from each other on the distributed servers, which cannot use the LSP properly. In this paper, we propose a Locality/Fairness-aware job scheduler (L/F job scheduler) that considers locality together to solve problems of the even job scheduler that only considers fairness. First, the L/F job scheduler increases cohesion of contiguous tasks that require message transmissions for the locality. At the same time, it reduces coupling of parallel tasks that do not require message transmissions for the fairness. Next, we connect the contiguous tasks into a stream pipeline and evenly deploy stream pipelines to the distributed servers so that the L/F job scheduler achieves high cohesion and low coupling. Finally, we implement the proposed L/F job scheduler in Apache Storm, a representative DSPE, and evaluate it in both synthetic and real-world workloads. Experimental results show that the L/F job scheduler is similar in throughput compared to the even job scheduler, but latency is significantly improved by up to 139.2% for the LSP applications and by up to 140.7% even for the non-LSP applications. The L/F job scheduler also improves latency by 19.58% and 12.13%, respectively, in two real-world workloads. These results indicate that our L/F job scheduler provides superior processing performance for the DSPE applications.
Automatic Data Generation for Deep Learning Model Training of Image Classification used for Augmented Reality on Pre-school Books

Nowadays, Augmented Reality (AR) has rightfully been taking as one of the leading position. Still, there are many different AR markers with different encryption and decryption methods which provide the users with an excellent capability to augment computer graphics generated virtual information onto real-world objects (e.g. text-book pictures or diagrams). However, the users need to choose which marker provider that matches their needs and create suitable markers based on the chosen provider's requirements. “Is it worth to re-print the entire existing books in-order to add AR functions?”. In this paper, we describe a new architecture to set up and present AR experiences by applying the benefit of deep learning (DL), the power of smart devices, and the flexibility of the Client-Server Architecture of the Internet. To set up, photos of pages in a textbook (but not limited to all pages) are uploaded to our server. For each page, the server will automatically generate different 3D views (thousands with different light conditions and perspectives) of the pages to form a sufficiently large enough dataset. They are then trained with a chosen convolutional neural network such as Alexnet, GoogleNet, VGG, GoogLeNet, or ResNet. The obtained model is then stored and can be loaded back to the client to serve as a classification process on a web browser using TensorFlow.JS, to recognise pages of the book. TensorFlow.JS is capable of running on smart devices with their built-in cameras; the recognised page will be used to specify which 3D graphics is displaying on top the page. This novel AR marker generating method is not only capable of keeping the original images of the books but also believed to achieve a higher detection accuracy. Thus, it is a promising, low-cost AR approach to be used in many areas, including education and training.
Application of the scrum framework to optimize time in construction projects

Currently, the construction industry is characterized by a high competition between its companies. Due to this, companies must guarantee that the performance of their projects is possible by using an appropriate management system which allows an adequate planning of activities, since selecting an inadequate system can cause serious inconveniences such as having rework and increase cost and time in projects. Therefore, the construction industry is constantly searching for management models that can minimize the previously mentioned inconveniences in order to differentiate themselves and take advantage of their competitors. Precisely, the Scrum framework, conceived in the software engineering industry, is an alternative to traditional projects management, offering an exponential improvement in communication for project teams and improving the planning of activities, through an iterative framework that allows flexibility for changing requirements, reduces uncertainty and creates reliability for what was planned. Thus, this research adapts and applies the Scrum framework within a construction project, optimizing considerably the execution time.
Single-to-Multi-theorem Transformations for Non-interactive Statistical Zero-Knowledge

Non-interactive zero-knowledge proofs or arguments allow a prover to show validity of a statement without further interaction. For non-trivial statements such protocols require a setup assumption in form of a common random or reference string (CRS). Generally, the CRS can only be used for one statement (single-theorem zero-knowledge) such that a fresh CRS would need to be generated for each proof. Fortunately, Feige, Lapidot and Shamir (FOCS 1990) presented a transformation for any non-interactive zero-knowledge proof system that allows the CRS to be reused any polynomial number of times (multi-theorem zero-knowledge). This FLS transformation, however, is only known to work for either computational zero-knowledge or requires a structured, non-uniform common reference string.In this paper we present FLS-like transformations that work for non-interactive statistical zero-knowledge arguments in the common random string model. They allow to go from single-theorem to multi-theorem zero-knowledge and also preserve soundness, for both properties in the adaptive and non-adaptive case. Our first transformation is based on the general assumption that one-way permutations exist, while our second transformation uses lattice-based assumptions. Additionally, we define different possible soundness notions for non-interactive arguments and discuss their relationships.