Reviewerly/exHarmony huggingface dataset:
Object Detection in Video Streams

Most of the available how-to guides for working with OpenCV in Java require you to have an insane amount of knowledge before getting started. The good news for you is that with what you have learned up to now with this book, you can get started with OpenCV in Java in seconds.
Weakly supervised pancreas segmentation based on classactivation maps

The quality of the segmentation of organs and pathological tissues has significantly improved in recent years by using deep learning approaches, which are typically trained with fully supervised learning. For these fully supervised learning methods, an immense amount of fully labeled training data is required, which are, especially in medicine, costly to generate. To overcome this issue, weakly supervised training methods are used, because they do not need fully labeled ground truth data. For the localization of objects weakly supervised learning has already become more important. Recently, weakly supervised learning also became increasingly important in the area of segmentation of pathological tissues. However, these currently available approaches still require additional anatomical information. In this paper, we present a weakly supervised segmentation method that does not need ground truth segmentations as input or additional anatomical information. Our method consists of three classification networks in sagittal, axial, and coronal direction that decide whether a slice contains the structure to be segmented. Then, we use the class activation maps of the classification output to generate a combined segmentation. Our network was trained for the challenging task of pancreas segmentation with the publicly available TCIA pancreas dataset and we reached Dice scores for slices of up to 0.86 and an overall Dice score of up to 0.53.
PETA PUZZLE 3D BERBASIS MOBILE AUGMENTED REALITY SEBAGAI PROTOTIPE MEDIA PEMBELAJARAN GEOGRAFI

Presentation of maps in paper form is still often done. The advancement of mobile devices and the support of augmented reality technology are alternatives in making map media presentations more interesting and interactive. This research aims to develop media prototypes in the form of educational games based on mobile augmented reality. Media development is carried out using a prototype approach. The developed media is able to display 3D map objects properly based on the results of black box testing and beta testing. Black box testing shows that the media functions as needed. Beta testing states that this application can be accepted with the percentage of survey results is 89.7%.
Multi-step segmentation for prostate MR image based on reinforcement learning

Medical image segmentation is a complex and critical step in the field of medical image processing and analysis. Manual annotation of the medical image requires a lot of effort by professionals, which is a subjective task. In recent years, researchers have proposed a number of models for automatic medical image segmentation. In this paper, we formulate the medical image segmentation problem as a Markov Decision Process (MDP) and optimize it by reinforcement learning method. The proposed medical image segmentation method mimics a professional delineating the foreground of medical images in a multi-step manner. The proposed model get notable accuracy compared to popular methods on prostate MR data sets. Meanwhile, we adopted a deep reinforcement learning (DRL) algorithm called deep deterministic policy gradient (DDPG) to learn the segmentation model, which provides an insight on medical image segmentation problem.
What is Normal, What is Strange, and What is Missing in an Knowledge Graph

Knowledge graphs (KGs) store highly heterogeneous information about the world in the structure of a graph, and are useful for tasks such as question answering and reasoning. However, they often contain errors and are missing information. Vibrant research in KG refinement has worked to resolve these issues, tailoring techniques to either detect specific types of errors or complete a KG.In this work, we introduce a \textit{unified solution} to KG characterization by formulating the problem as \emph{unsupervised KG summarization} with a set of inductive, \textit{soft rules}, which describe what is \emph{normal} in a KG, and thus can be used to identify what is \emph{abnormal}, whether it be strange or missing. Unlike first-order logic rules, our rules are labeled, rooted graphs, i.e., patterns that describe the expected neighborhood around a (seen or unseen) node, based on its type and information in the KG. Stepping away from the traditional support/confidence-based rule mining techniques, we propose \method, \emph{Knowledge Graph Inductive SummarizaTion}, which learns a summary of inductive rules that best compress the KG according to the Minimum Description Length principle---a formulation that we are the first to use in the context of KG rule mining. We apply our rules to three large KGs (\NELL{}, \DBpedia{}, and \Yago{}), and tasks such as compression, various types of error detection, and identification of incomplete information. We show that \method outperforms task-specific, supervised and unsupervised baselines in error detection and incompleteness identification, (identifying the location of up to 93\% of missing entities---over 10\% more than baselines), while also being efficient for large knowledge graphs.
Data-Driven Elections in Canada: What We Might Expect in the 2019 Federal Election Campaign?

This paper reviews the role that voter analytics played in the 2019 federal election campaign in Canada. It reviews the database campaign and the operation of the federal parties' voter relationship management systems. It analyses the use of mobile devices for smart campaigning. It surveys the various companies which operated for the major parties. And it analyzes the role played by Facebook as a platform for political advertising.
Cross-lingual Automatic Speech Recognition Exploiting Articulatory Features

Articulatory features (AFs) provide language-independent attribute by exploiting the speech production knowledge. This paper proposes a cross-lingual automatic speech recognition (ASR) based on AF methods. Various neural network (NN) architectures are explored to extract cross-lingual AFs and their performance is studied. The architectures include mutilayer perception(MLP), convolutional NN (CNN) and long short-term memory recurrent NN (LSTM). In our cross-lingual setup, only the source language (English, representing a well-resourced language) is used to train the AF extractors. AFs are then generated for the target language (Mandarin, representing an under-resourced language) using the trained extractors. The frame-classification accuracy indicates that the LSTM has an ability to perform a knowledge transfer through the robust cross-lingual AFs from well-resourced to under-resourced language. The final ASR system is built using traditional approaches (e.g. hybrid models), combining AFs with conventional MFCCs. The results demonstrate that the cross-lingual AFs improve the performance in under-resourced ASR task even though the source and target languages come from different language family. Overall, the proposed cross-lingual ASR approach provides slight improvement over the monolingual LF-MMI and cross-lingual (acoustic model adaptation-based) ASR systems.
Normal-Gamma IMM Filter for Linear Systems with Non-Gaussian Measurement Noise

In this paper, we consider state estimation of systems with non-Gaussian measurement noise. The non-Gaussian measurement noise and an auxiliary variable (to quantify the uncertainty in measurement-noise covariance) are modeled by a mixture of normal-gamma distributions. The normal-gamma distribution has been used to model heavy-tailed measurement noise in our previous work and proved effective. In the normal-gamma mixture, the auxiliary variable can quantify different levels of the uncertainty. Compared with traditional Gaussian mixture models, this normal-gamma mixture model is more flexible and powerful. We propose a filter in the interacting multiple model (IMM) framework with each filter being a normal-gamma filter. The proposed filter takes advantage of the IMM approach better than many existing methods. It also inherits properties of the normal-gamma filter in effectiveness and efficiency. Performance of the proposed filter is evaluated for estimation in several cases. Simulation results show that the proposed method outperforms the traditional Gaussian IMM filter, by much in some cases.
Allocating MapReduce workflows with deadlines to heterogeneous servers in a cloud data center

Total profit is one of the most important factors to be considered from the perspective of resource providers. In this paper, an original MapReduce workflow scheduling with deadline and data locality is proposed to maximize total profit of resource providers. A new workflow conversion based on dynamic programming and ChainMap/ChainReduce is designed to decrease transmission times among MapReduce jobs of workflows. A new deadline division considering execution time, float time and job level is proposed to obtain better deadlines of MapReduce jobs in workflows. With the adapted replica strategy in MapReduce workflow, a new task scheduling is proposed to improve data locality which assigns tasks to servers with the earliest completion time in order to ensure resource providers obtain more profit. Experimental results show that the proposed heuristic results in larger total profit than other adopted algorithms.
Configure Your Database for Better Performance

In the last chapter, we talked about information that needed to be gathered for PostgreSQL installation, and planning installation based on that information. Also, we covered types of PostgreSQL installations, pre/post-installation tuning, troubleshooting procedures, and some general recommendations to consider during installation to avoid performance issues in future. In this chapter, we will cover some initial postinstallation steps for beginners, all important configuration files and their uses and their default settings, recommendations to tune configuration files, and OS parameters tuning for performance improvement.
Types And Levels Of Writing Anxiety Among Indonesian Secondary School And University Students

This preliminary study attempts to examine the types and levels of writing anxiety experienced by Indonesian students studying at secondary schools and universities.Using a survey method, 219 Indonesian EFL students from secondary schools and universities were asked to complete a Second Language Writing Anxiety Inventory (SLWAI).Statistical analysis was performed using SPSS and found that many students across education levels experienced second language writing anxiety at moderate level.Results of SPSS calculation showed that there were significant differences between male and female students towards the types of anxiety.According to the table, it is clearly asserted that cognitive anxiety is significantly influenced by gender (t= 2.661, Sig.=0.008), nor avoidance behavior (t= -0.345, Sig.= 0.730) and somatic anxiety (t= 1.251, Sig.= 0.212).Moreover, male students (M= 3.30) are more likely to be anxious at the type of cognitive anxiety than female students (M= 2.99).
Electric power transaction of electric vehicle based on smart contract and double auction

The increase of the number of electric vehicles leads to serious valley imbalance in the power grid. In order to achieve peak cutting and valley filling, according to the energy storage characteristics of electric vehicles, this paper proposes an electric vehicle group (EVG) energy trading method based on smart contract and double auction matching mechanism, and constructs the electric energy transaction process of the electric vehicle and electric vehicle in the electric vehicle group under the unbalanced load of the power grid. Through deploying the double auction matching algorithm to the smart contract, the automatic execution of matching transaction and automatic clearing of transaction cost are realized, which saves the economic cost of manual matching mode. In addition, the multi-stage quotation method proposed greatly improves the number of transactions. The simulation experiment based on Monte Carlo simulation shows that the power transaction method can not only effectively alleviate the valley problem of power grid load, achieve the effect of reducing and raising Valley, but also can improve the transaction efficiency.
Backup and Restore Best Practices

In the last chapter, we talked about user management in PostgreSQL using roles by assigning proper privileges and secure data by encrypting using the pgcrypto module. It helps to improve security by controlling object and data access. In this chapter, we are going to talk about backup/restore strategies and procedures. Building these strategies needs a lot of information. This chapter walks you through the stages in which information will be gathered to set up backup/restore for a database, based on the criticality of the data. This chapter also talks about when to/what to/ how to backup/restore.
Hiding Voice Message using Both Cryptography and Steganography

The goal of encryption voice is to ensure the preservation of the conversation details and not to allow anyone to tamper with or see it. This conversation is either secret or very private; no one can understand the substance of that information or messages only authorized persons who have own secret key. The best method to protect the voice message from unauthorized persons is to use both cryptography and steganography. This paper reviews the Advanced Encryption Standard (AES) algorithm used for encrypting voice message and the Least Signification Bit steganography (LSB) used for embedding encrypted voice message with related key in color image without impacting the content and quality of it. After hiding a secret voice in an image, Stego image is created then is sent to the recipient. The mean square error (MSE) and the signal-to-noise ratio (PSNR) is calculated to measure the quality of the sent image. The findings of the research are that the stego image cannot be distinguished by the naked eye from the original cover image when the bit value is 1 or 2 and thus we reach the goal to cover the presence of a hidden sound inside.
Optical reservoir computing for high-dimensional spatio-temporal chaotic systems prediction (Conference Presentation)

There have been a number of rapid advances in the prediction of the dynamics of chaotic systems using a technique known as Reservoir Computing. These techniques are mostly not effective for large networks, as the complexity of the task increases quadratically both in time and memory. We report new advances in Optical Reservoir Computing using multiple light scattering to accelerate the recursive computation of the reservoir states. Different approaches to information encoding based on phase or amplitude spatial light modulations are compared. We demonstrate the scalability and the good prediction performance of our approach using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.
Benchmarking with Sysbench

It is very important to verify the impact of changes before you apply them to your production systems. This applies to both small changes like modifying a query and large changes such as refactoring the application and schema as well as MySQL upgrades. You may think that the optimal performance test is based on your production schema and data using the same queries that the application executes. However, it is not always as simple as it sounds to recreate the right workload, so sometimes it is necessary to use standard benchmark suites.
Sublinear Cost Low Rank Approximation via Subspace Sampling

Low Rank Approximation (LRA) of a matrix is a hot research subject, fundamental for Matrix and Tensor Computations and Big Data Mining and Analysis. Computations with LRA can be performed at sublinear cost, that is, by using much fewer memory cells and arithmetic operations than an input matrix has entries. Although every sublinear cost algorithm for LRA fails to approximate the worst case inputs, we prove that our sublinear cost variations of a popular subspace sampling algorithm output accurate LRA of a large class of inputs. Namely, they do so with a high probability (whp) for a random input matrix that admits its LRA. In other papers we propose and analyze other sublinear cost algorithms for LRA and Linear Least Sqaures Regression. Our numerical tests are in good accordance with our formal results.
Joint Makespan-aware and Load Balance-aware optimization of Task Scheduling in Cloud

Task scheduling plays an important role in cloud computing. This paper develops an efficient task scheduling method using biogeography-based optimization (ETS-BBO). Experimental results show that ETS-BBO outperforms ant colony optimization (ACO), Genetic algorithm (GA) and round-robin (RR) algorithms and can solve the joint optimization problem effectively.
CYBER CRIME AND SECURITY

Cyber-attacks ask those attacks begun on unsuspecting online users either employing a computer because the thing of the crime (hacking, phishing, spamming etc.), or as a tool to advance other criminal activities (cyber stalking, fraud, child pornography etc.). Cyber-attacks are increasing exponentially hence making cyber security to be a challenge during this digital age. Cyber security is to supply determent opposed the cybercrime, while cybercrime is that group of movements made by the people by creating explosion in network, stealing others important and private ,documents ,data, hack bank details and accounts and transferring money to their own.
Prediksi dan Analisis Data Gempa Bumi di Provinsi Bengkulu dengan Metode Artificial Neural Network

Many things that have experienced technological advancements are one of which is the continued development of software. One example of software development today is the Matlab software. Matlab is currently able to be used to predict and analyze an existing problem. A lot of research has been done with the use of matlab software. Artificial Neural Network (ANN) is one of the command languages ​​used in the matlab program which is used to predict the data entered. Besides that, Bengkulu Province is one of the earthquake and tsunami prone areas in Indonesia. This is because Bengkulu is in the subduction zone (collision) of active plate encounters in Indonesia Australia and Eurasia. Recording earthquake data must be recorded properly because this earthquake data will often be used, both for development and for the environment. The ANN method uses trial epoch until the smallest error is obtained. The smallest error results stopped at the Epoch 2000 trial. The Epoch 2000 trial produced the biggest error of 59.1% and the smallest error of 2.93%. The results of this study, obtained ANN is quite capable of calculating earthquake data predictions in the province of Bengkulu in 2018.
On the methods of testing the homogeneity of two independent samples

Methods for testing the homogeneity of two independent samples refer to a classic area of mathematical statistics. Various criteria for testing the statistical hypothesis of homogeneity in different statements have been developed and their properties have been studied for more than 110 years since publication of the fundamental Student’s article. Nowadays, the streamlining of the totality of gained scientific results has become an urgent problem. It is necessary to analyze the whole variety of problem statements for testing the statistical hypotheses of the homogeneity of two independent samples, as well as the corresponding statistical criteria. Such an analysis is the goal of the article. We summarize the main results regarding the methods for testing the homogeneity of two independent samples and their comparative study which allows system analysis of the diversity of such methods in order to select the most appropriate for processing specific data. The main statements of the problem of testing the homogeneity of two independent samples are formulated using the basic probabilistic-statistical model. A comparative analysis of the Student and Cramer — Welch criteria designed to test the homogeneity of mathematical expectations is presented along with substantiation of the recommendation on the widespread use of the Cramer – Welch criterion. The criteria of Wilcoxon, Smirnov, Lehmann – Rosenblatt are considered among nonparametric methods for testing homogeneity. Two myths about the Wilcoxon criteria are dismantled. Analysis of the publications of the founders revealed the incorrectness of the term «Kolmogorov – Smirnov criterion». To verify the absolute homogeneity, i.e. coincidence of the distribution functions of samples, it is recommended to use the Lehmann – Rosenblatt criterion. The current problems of the development and application of nonparametric criteria are discussed, including the difference between nominal and real significance levels, which complicates comparison of the criteria in power. The necessity of taking into account the coincidence of the sample values (from the view point of the classical theory of mathematical statistics, the probability of coincidences is 0) is marked.
Complex Portfolio as one of the Means of the Future Primary School Teachers Training of the New Ukrainian School

The article deals with the problem of improving of the future primary school teachers training by means of a complex subject portfolio in a particular course of study in the context of education system reforming in Ukraine. The aim of this study is to track and prove the value of the subject portfolio in the “Methods of Teaching Subjects of Language and Literary Education” course. The article substantiates and validates the hypothesis that a complex portfolio with the proposed structure and developed assignments is an effective tool for the future primary school teachers training. Analyzing the results of the questionnaires, observing the process of students' fulfilling of problem-solving and creative assignments (mind maps, reflective essays, pedagogical diaries, their own media products and audio books for schoolchildren, lesson plans and visual aids design) confirmed that a complex portfolio is an relevant means for development of critical thinking, intrinsic motivation, ensures the autonomy and self-sufficiency of each student, forms confidence in their abilities and readiness to work in New Ukrainian School. The study involved 180 students of the first (bachelor’s) degree in specialty 013 Primary Education on the Faculty of Primary Education and Philology in Khmelnytskyi Humanitarian- Pedagogical Academy (2017–2019 academic years). The quality dynamics of the students’ knowledge and skills before and after the implementation of the complex portfolio in “Methods of Teaching Subjects of Language and Literary Education” course allows to acknowledge the expediency of its implementing.