Reviewerly/exHarmony huggingface dataset:
Applications of Multi-view Learning Approaches for Software Comprehension

Program comprehension concerns the ability of an individual to make an understanding of an existing software system to extend or transform it. Software systems comprise of data that are noisy and missing, which makes program understanding even more difficult. A software system consists of various views including the module dependency graph, execution logs, evolutionary information and the vocabulary used in the source code, that collectively defines the software system. Each of these views contain unique and complementary information; together which can more accurately describe the data. In this paper, we investigate various techniques for combining different sources of information to improve the performance of a program comprehension task. We employ state-of-the-art techniques from learning to 1) find a suitable similarity function for each view, and 2) compare different multi-view learning techniques to decompose a software system into high-level units and give component-level recommendations for refactoring of the system, as well as cross-view source code search. The experiments conducted on 10 relatively large Java software systems show that by fusing knowledge from different views, we can guarantee a lower bound on the quality of the modularization and even improve upon it. We proceed by integrating different sources of information to give a set of high-level recommendations as to how to refactor the software system. Furthermore, we demonstrate how learning a joint subspace allows for performing cross-modal retrieval across views, yielding results that are more aligned with what the user intends by the query. The multi-view approaches outlined in this paper can be employed for addressing problems in software engineering that can be encoded in terms of a learning problem, such as software bug prediction and feature location.
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

With the development of graph convolutional networks (GCN), deep learning methods have started to be used on graph data. In additional to convolutional layers, pooling layers are another important components of deep learning. However, no effective pooling methods have been developed for graphs currently. In this work, we propose the graph pooling (gPool) layer, which employs a trainable projection vector to measure the importance of nodes in graphs. By selecting the k-most important nodes to form the new graph, gPool achieves the same objective as regular max pooling layers operating on images. Another limitation of GCN when used on graph-based text representation tasks is that, GCNs do not consider the order information of nodes in graph. To address this limitation, we propose the hybrid convolutional (hConv) layer that combines GCN and regular convolutional operations. The hConv layer is capable of increasing receptive fields quickly and computing features automatically. Based on the proposed gPool and hConv layers, we develop new deep networks for text categorization tasks. Our results show that the networks based on gPool and hConv layers achieves new state-of-the-art performance as compared to baseline methods.
Penerapan Metode Pembelajaran Kooperatif Tipe Number Head Together (NHT) Pada Siswa Kelas XI IPS 2 SMA Negeri 2 Watampone Untuk Meningkatkan Hasil Belajar Bahasa Indonesia

Abstract. The purpose of this study was to describe an increase in Indonesian language learning outcomes through cooperative learning method Number Head Together (NHT) in Class XI IPS 2 students at SMA 2 Watampone. This research approach, which is descriptive. The design or model of this research is Action Research. This research was carried out in Class XI IPS 2 of SMA Negeri 2 Watampone totaling 38 people. This research was conducted in the even semester 2016/2017. Data collection is done through observation, documentation, and test techniques. Data analysis techniques, namely quantitative descriptive analysis and qualitative descriptive analysis. The results showed that (1) the learning outcomes of students in the first cycle were categorized as medium with an average value of 64.15 and increased in the second cycle to be a high category with an average value of 77.89. Learning completeness in the first cycle was 44.73% and increased in the second cycle to 92.11%. Thus, the application of NHT cooperative learning can improve the learning outcomes of Indonesian students in Class XI IPS 2 at SMA 2 Watampone. (2) Cooperative learning NHT increases the student's effectiveness in learning. The indicator of improvement can be observed based on the learning outcomes of the first cycle and the second cycle which experience changes especially in changes in motivation, enthusiasm and values of students learning students understand the material.
DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.
3D Contouring for Breast Tumor in Sonography

Malignant and benign breast tumors present differently in their shape and size on sonography. Morphological information provided by tumor contours are important in clinical diagnosis. However, ultrasound images contain noises and tissue texture; clinical diagnosis thus highly depends on the experience of physicians. The manual way to sketch three-dimensional (3D) contours of breast tumor is a time-consuming and complicate task. If automatic contouring could provide a precise breast tumor contour that might assist physicians in making an accurate diagnosis. This study presents an efficient method for automatically contouring breast tumors in 3D sonography. The proposed method utilizes an efficient segmentation procedure, i.e. level-set method (LSM), to automatic detect contours of breast tumors. This study evaluates 20 cases comprising ten benign and ten malignant tumors. The results of computer simulation reveal that the proposed 3D segmentation method provides robust contouring for breast tumor on ultrasound images. This approach consistently obtains contours similar to those obtained by manual contouring of the breast tumor and can save much of the time required to sketch precise contours.
An Adaptive Boosting Algorithm for Image Denoising

Image denoising is an important problem in many fields of image processing. Boosting algorithm attracts extensive attention in recent years, which provides a general framework by strengthening the original noisy image. In such framework, many classical existing denoising algorithms can improve the denoising performance. However, the boosting step is fixed or nonadaptive; i.e., the noise level in iteration steps is set to be a constant. In this work, we propose a noise level estimation algorithm by combining the overestimation and underestimation results. Based on this, we further propose an adaptive boosting algorithm that excludes intricate parameter configuration. Moreover, we prove the convergence of the proposed algorithm. Experimental results that are obtained in this paper demonstrate the effectiveness of the proposed adaptive boosting algorithm. In addition, compared with the classical boosting algorithm, the proposed algorithm can get better performance in terms of visual quality and peak signal-to-noise ratio (PSNR).
Blaze: Simplified High Performance Cluster Computing

MapReduce and its variants have significantly simplified and accelerated the process of developing parallel programs. However, most MapReduce implementations focus on data-intensive tasks while many real-world tasks are compute intensive and their data can fit distributedly into the memory. For these tasks, the speed of MapReduce programs can be much slower than those hand-optimized ones. We present Blaze, a C++ library that makes it easy to develop high performance parallel programs for such compute intensive tasks. At the core of Blaze is a highly-optimized in-memory MapReduce function, which has three main improvements over conventional MapReduce implementations: eager reduction, fast serialization, and special treatment for a small fixed key range. We also offer additional conveniences that make developing parallel programs similar to developing serial programs. These improvements make Blaze an easy-to-use cluster computing library that approaches the speed of hand-optimized parallel code. We apply Blaze to some common data mining tasks, including word frequency count, PageRank, k-means, expectation maximization (Gaussian mixture model), and k-nearest neighbors. Blaze outperforms Apache Spark by more than 10 times on average for these tasks, and the speed of Blaze scales almost linearly with the number of nodes. In addition, Blaze uses only the MapReduce function and 3 utility functions in its implementation while Spark uses almost 30 different parallel primitives in its official implementation.
Bioheuristics: theory, algorithms and applications

The book examines the current state and problems of bioheuristics development, biographical and memetic algorithms, the issues of finding optimal solutions by trajectory algorithms and bioheuristics of multicriteria optimization. Ant and swarm bioheuristics, their features and modifications are presented. Hybrid formic and hypeuruistic swarm segmentation algorithms of complexly structured images are considered as applications. The monograph is interdisciplinary, it is addressed to masters and graduate students studying the theory and practice of creating intelligent information systems and technologies, as well as specialists in the theoretical foundations of computer science, software engineering, system analysis, information management and processing, information systems and processes, bioinformatics.
Optimising intra-seasonal irrigation water allocation: Comparison between mixed integer nonlinear programming and differential evolution

Optimising crop planning in conjunction with intra-seasonal water allocation necessitates the use of daily water budget calculations to determine the timing and amount of irrigation events, which complicates the solution of the problem to global optimality. The main objective of this research was to compare the intra-seasonal water allocation of a mixed integer nonlinear programming (MINLP) model with that of differential evolution (DE), to allocate a limited amount of water while considering irrigable area and the irrigation schedule that will maximise the total gross margin. Results show that both solution procedures adhere to economic theory of water allocation under limited water supply. The conclusion is that the MINLP model most likely achieved very near global optimality as the solutions of the two models were very close to each other. DE holds promise to solve more complex models involving risk and multiple crops.
Table2answer: Read the database and answer without SQL

Semantic parsing is the task of mapping natural language to logic form. In question answering, semantic parsing can be used to map the question to logic form and execute the logic form to get the answer. One key problem for semantic parsing is the hard label work. We study this problem in another way: we do not use the logic form any more. Instead we only use the schema and answer info. We think that the logic form step can be injected into the deep model. The reason why we think removing the logic form step is possible is that human can do the task without explicit logic form. We use BERT-based model and do the experiment in the WikiSQL dataset, which is a large natural language to SQL dataset. Our experimental evaluations that show that our model can achieves the baseline results in WikiSQL dataset.
Combining Machine Learning and Statistical Disclosure Control to Promote Open Data

We proposed machine learning-oriented statistical disclosure control as a novel solution for New Zealand Transport Agency to release more person-related variables in its open crash data for privacy-preserving data mining. Instead of making arbitrary decisions in variable aggregation and using perturbation to guard against reidentification attacks at the cost of data distortion, we creatively drew upon feature engineering and dimensionality reduction techniques such as correspondence analysis to make evidence-based data manipulation without distortion. In addition, we built random forest classifiers along the way to directly monitor the impact of our data manipulation on users' modelling, rather than relying on traditional utility metrics such as information loss and difference of eigenvalues that are less interpretable for users. The dataset produced using our method satisfied 10-anonymity based on 11 quasi-identifiers, with less than 3% suppression, compared with only 3-anonymity based on no more than 8 quasi-identifiers with far more than 3% suppression commonly reported in literature. Furthermore, our method enabled random forest classifier to achieve 0.996 for AUC and 0.895 for F-score in predicting crash severity.
Breaking bivariate records

Abstract We establish a fundamental property of bivariate Pareto records for independent observations uniformly distributed in the unit square. We prove that the asymptotic conditional distribution of the number of records broken by an observation given that the observation sets a record is Geometric with parameter 1/2.
Attention-based Context Aggregation Network for Monocular Depth Estimation

Depth estimation is a traditional computer vision task, which plays a crucial role in understanding 3D scene geometry. Recently, deep-convolutional-neural-networks based methods have achieved promising results in the monocular depth estimation field. Specifically, the framework that combines the multi-scale features extracted by the dilated convolution based block (atrous spatial pyramid pooling, ASPP) has gained the significant improvement in the dense labeling task. However, the discretized and predefined dilation rates cannot capture the continuous context information that differs in diverse scenes and easily introduce the grid artifacts in depth estimation. In this paper, we propose an attention-based context aggregation network (ACAN) to tackle these difficulties. Based on the self-attention model, ACAN adaptively learns the task-specific similarities between pixels to model the context information. First, we recast the monocular depth estimation as a dense labeling multi-class classification problem. Then we propose a soft ordinal inference to transform the predicted probabilities to continuous depth values, which can reduce the discretization error (about 1% decrease in RMSE). Second, the proposed ACAN aggregates both the image-level and pixel-level context information for depth estimation, where the former expresses the statistical characteristic of the whole image and the latter extracts the long-range spatial dependencies for each pixel. Third, for further reducing the inconsistency between the RGB image and depth map, we construct an attention loss to minimize their information entropy. We evaluate on public monocular depth-estimation benchmark datasets (including NYU Depth V2, KITTI). The experiments demonstrate the superiority of our proposed ACAN and achieve the competitive results with the state of the arts.
Three-qubit exact Grover within the blind oracular quantum computation scheme

Here we extend the concept of blind client-server quantum computation, in which a client with limited quantum power controls the execution of a quantum computation on a powerful server, without revealing any details of the computation. Our extension is to introduce a three-node setting in which an oracular quantum computation can be executed blindly. In this Blind Oracular Quantum Computation (BOQC), the oracle (Oscar) is another node, with limited power, who acts in cooperation with the client (Alice) to supply quantum information to the server so that the oracle part of the quantum computation can also be executed blindly. We develop tests of this protocol using two- and three-qubit versions of the exact Grover algorithm (i.e., with database sizes $4\leq N\leq 8$), obtaining optimal implementations of these algorithms within a gate array scheme and the blinded cluster-state scheme. We discuss the feasibility of executing these protocols in state-of-the-art three-node experiments using NV-diamond electronic and nuclear qubits.
PERANCANGAN E-MODULE PERAKITAN DAN INSTALASI PERSONAL KOMPUTER SEBAGAI MEDIA PEMBELAJARAN SISWA SMK

The research discusses about the design of electronic module assembly and installating computer. The module was designed for assembling and installating computer content displayed in digital way, so this modul is very flexible and easy to be used. The content was displayed by video to make it easier to understand by the learners. The method used consists of several phases; analysis, design, coding and testing. The design used UML process and then was developed by Kvisoft Flip book maker. The result of this research shows how the assembly and installation module was easier to be used for the teacher and the students.
Demonstration of teleportation-based error correction in the IBM quantum computer

Quantum error correcting codes (QECC) are the key ingredients both for fault-tolerant quantum computation and quantum communication. Teleportation-based error correction (TEC) helps in detecting and correcting operational and erasure errors by performing X and Z measurements during teleportation. Here we demonstrate the TEC protocol for the detection and correction of a single bit-flip error by proposing a new quantum circuit. A single phase-flip error can also be detected and corrected using the above protocol. For the first time, we illustrate detection and correction of erasure error in the superconducting qubit-based IBM's 14-qubit quantum computer.
Faster Genetic Programming GPquick via multicore and Advanced Vector Extensions

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. Programs with almost four hundred million instructions are created by crossover. To support unbounded Long-Term Evolution Experiment LTEE GP we use both SIMD parallel AVX 512 bit instructions and 48 threads to yield performance of up to 139 billion GP operations per second, 139 giga GPops, on a single Intel Xeon Gold 6126 2.60GHz server.
On the Convergence of EM for truncated mixtures of two Gaussians.

Motivated by a recent result of Daskalakis et al. \cite{DGTZ18}, we analyze the population version of Expectation-Maximization (EM) algorithm for the case of \textit{truncated} mixtures of two Gaussians. Truncated samples from a $d$-dimensional mixture of two Gaussians $\frac{1}{2} \mathcal{N}(\vec{\mu}, \vec{\Sigma})+ \frac{1}{2} \mathcal{N}(-\vec{\mu}, \vec{\Sigma})$ means that a sample is only revealed if it falls in some subset $S \subset \mathbb{R}^d$ of positive (Lebesgue) measure. We show that for $d=1$, EM converges almost surely (under random initialization) to the true mean (variance $\sigma^2$ is known) for any measurable set $S$. Moreover, for $d>1$ we show EM almost surely converges to the true mean $\vec{\mu}$ for any measurable set $S$, under the assumption that the map of EM has only three fixed points, namely $-\vec{\mu}, \vec{0}, \vec{\mu}$ (covariance matrix $\vec{\Sigma}$ is known). Our techniques deviate from those of Daskalakis et al. \cite{DTZ17}, which heavily depend on symmetry that the untruncated problem exhibits. The fact that the truncated set $S$ may not be symmetric around $\vec{0}$ makes it impossible to compute a closed form of the update rule of EM. We circumvent this fact by using techniques from dynamical systems, in particular implicit function theorem and stability analysis around the fixed points of the update rule of EM.
Science stars of YouTube

From rubbish robots to hijacking Bieber hits, Chris Stokel-Walker digs into a rich seam of video
Discovery of Natural Language Concepts in Individual Units of CNNs

Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers. In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns. In order to quantitatively analyze such an intriguing phenomenon, we propose a concept alignment method based on how units respond to the replicated text. We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.
ІНФОРМАЦІЙНО-ТВОРЧЕ ОСВІТНЄ СЕРЕДОВИЩЕ ЯК УМОВА ПРОФЕСІЙНО-ТВОРЧОЇ САМОРЕАЛІЗАЦІЇ ФАХІВЦЯ З МУЗИЧНОГО МИСТЕЦТВА

The article contains the analysis of the the significance of the information and creative environment for the professional-creative self-realization of a specialist in musical art. It has been emphasized that the implementation of information and communication technologies in the process of preparing future specialists in the field of music art in the system of continuous artistic education is one of the important factors in enriching their professional and personal experience. The purpose of informatization of the permanent system of artistic education is to increase the efficiency of learning through the information enlargement and the improvement of its methods of application, as well as the focus on the use of information technology in the educational process of higher education and professional activities. It is noted that the reorientation of modern artistic education from discrete to permanent requires the development and experimental testing of new forms of didactic interaction with musical and pedagogical workers, aimed at creating a positive motivation of specialists in musical art until the acquisition and improvement of informational competencies, continuous improvement of professional and personal experience and professional skill based on self-education activities. It is proved that expediency and efficiency of information technologies in art education are closely connected with the notion of information culture. The essential component of the information culture of a music teacher is the ability to use information and communication technologies effectively both in the musical and educational process and in the process of self-education and self-improvement. Particular attention is drawn to the fact that the creation of an innovative information and creative educational environment in a higher educational establishment depends largely on an innovative climate, the characteristics of which are often crucial for the realization of innovative goals.