Reviewerly/exHarmony huggingface dataset:
MTBR: Multi-Target Boosting for Regression

Gradient boosting method has been successfully used for single target prediction problems. In real world applications, however, problems involving the prediction of multiple target attributes are often of interest. In this paper, a multi-target boosting method for regression problems, named as MTBR, is proposed. Although MTBR builds one model for each target attribute separately, all the target attributes are utilized when building each model. In each boosting iteration, the base learner, the regression tree in particular, is learned by selecting the best models from all the target attributes. We also introduce a novel knowledge transfer approach. That is, the tree structure learned from one target attribute, representing a way to partition the feature space, is used to predict another target attribute. Experiments with six data sets compare MTBR to other ensemble regression methods, and prove the effectiveness of MTBR in leveraging the knowledge of multiple target attributes and improving the model accuracy.
Pattern mining for knowledge discovery

Pattern mining aims to discover from data the implicit, previously unknown and potentially useful information and knowledge in the form of patterns. Over the past 20 years, numerous pattern mining algorithms have been proposed. They focused on algorithmic efficiency, functionalities, and other aspects. These algorithms have been applied to various real-life applications running in serial, parallel, and/or high-performing computing environments. In this paper, we review many existing pattern mining algorithms and suggest some pattern mining algorithms---especially, hybrid vertical frequent pattern mining running in serial, parallel, high-performing computing, and/or edge/fog environments---to discover knowledge from dataset.
CGPA: Coarse-Grained Pruning of Activations for Energy-Efficient RNN Inference

Recurrent neural networks (RNNs) perform element-wise multiplications across the activations of gates. We show that a significant percentage of activations are saturated and propose coarse-grained pruning of activations (CGPA) to avoid the computation of entire neurons, based on the activation values of the gates. We show that CGPA can be easily implemented on top of a TPU-like architecture with negligible area overhead, resulting in 12% speedup and 12% energy savings on average for a set of widely used RNNs.
Comparative Analysis of Backpropagation With Learning Vector Quantization (LVQ) to Predict Rainfall in Medan City

Abstract Rainfall is a very important factor in agriculture and development planning. This study aims to predict rainfall and rainfall properties in Medan using a multilayer neural network with Backpropagation and Learning Vector Quantization (LVQ) algorithms. Rainfall data used for training is rainfall data for the last 30 years. The parameters used in this study consisted of two inputs namely the month and the volume of rainfall. The training process produces the best architecture with 3 layer three for Backpropagation and two layers for Learning Vector Quantization with learning rate 0.5. From the prediction results obtained Backpropagation algorithm more accurate in predicting rainfall data last 30 years compared with LVQ with an average difference of 21.99%. Backpropagation and LVQ algorithms have better accuracy in the dry season, with accuracy for Backpropagation algorithm between 75 - 99% and LVQ algorithm of 60 - 82%. For both algorithms, the influence of El-Nino and La-Nina phenomena is not so significant.
Key recovery attacks on reduced-round Joltik-BC in the single-key setting

Joltik-BC is a tweakable block cipher used in the CAESAR candidate Joltik. In this paper, we propose meet-in-the-middle attacks on Joltik-BC in the single-key setting. Our attacks are constructed utilizing the freedom of the tweak and differential enumeration technique. As a result, we get an attack on 8-round Joltik-BC-64-64 with a data complexity of 253.5, a memory complexity of 253 and a time complexity of 253.6. We also present an attack on 10-round Joltik-BC-128-64 with a data complexity of 256.1, a memory complexity of 2123.5 and a time complexity of 2126.5. As far as we know, these are the best single-key attacks on Joltik-BC.
Constant composition codes as subcodes of linear codes

In this paper, on one hand, by a kind of defining set we construct a class of optimal linear codes, whose weight distribution is determined. Furthermore, we propose a class of constant composition codes (CCCs) from optimal linear codes. some of these CCCs are optimal depending on LFVC bound. On the other hand, a class of constant composition codes are constructed from known linear codes and the parameters of these CCCs are completely determined.
PENYULUHAN GIZI DENGAN MEDIA FLASHCARD TERHADAP PENGETAHUAN PESAN UMUM GIZI SEIMBANG PADA SISWA SEKOLAH

School children are the most rapid growth period after toddlers. Knowledge of nutrition plays an important role in determining the degree of public health. So to increase knowledge, nutrition counseling is needed with interesting media, one of them is flashcard. The purpose of this study was to determine the effectiveness of nutritional counseling using flashcard media on the knowledge of the general message of balanced nutrition in fifth grade students of SDN Cikoneng 1 and SDN H. Agus Salim. The research design used was quasi experiment with a pre-test and post-test control group design. The study was conducted on fifth grade students of Cikoneng 1 Elementary School as a treatment group, amounting to 23 people and SDN H. Agus Salim as a control group of 46 people. The sampling technique is total sampling. Counseling was carried out for 30 minutes then given flashcard games in the treatment group and the control group using leaflets. Analysis of the data used is the t-Dependent test and Man Whitney test. The results of the study in the treatment and control groups showed significant differences in the value of knowledge before and after counseling with each p value (p = 0,000). Flashcard media is more effective in increasing knowledge in students (p = 0,000). Schools are expected to be able to forward the flashcard media and disseminate information about the General Message of Balanced Nutrition.&#x0D; Keywords: Extension, Flashcard, General Message Balanced Nutrition
Angular Accuracy of Steerable Feature Detectors

The detection of landmarks or patterns is of interest for extracting features in biological images. Hence, algorithms for finding these keypoints have been extensively investigated in the literature, and their localization and detection properties are well known. In this paper, we study the complementary topic of local orientation estimation, which has not received similar attention. Simply stated, the problem that we address is the following: estimate the angle of rotation of a pattern with steerable filters centered at the same location, where the image is corrupted by colored isotropic Gaussian noise. For this problem, we propose an estimator formulated as linear combinations of circular harmonics with given radial profiles. We prove that the proposed estimator is unbiased. This property allows us to use a statistical framework based on the Cramér--Rao lower bound (CRLB) to study the limits on the accuracy of the corresponding class of estimators. We aim at evaluating the performance of detection methods based on steerable filters in terms of angular accuracy (as a lower bound), while considering the connection to maximum likelihood estimation. Beyond the general results, we analyze the asymptotic behavior of the lower bound in terms of the order of steerablility and propose an optimal subset of components that minimizes the bound. We define a mechanism for selecting optimal subspaces of the span of the detectors. These are characterized by the most relevant angular frequencies. Finally, we project our template to the span of circular harmonics with given radial profiles and experimentally show that the prediction accuracy achieves the predicted CRLB. As an extension, we also consider steerable wavelet detectors.
Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles

In this paper, we present iDVO (inertia-embedded deep visual odometry), a self-supervised learning based monocular visual odometry (VO) for road vehicles. When modelling the geometric consistency within adjacent frames, most deep VO methods ignore the temporal continuity of the camera pose, which results in a very severe jagged fluctuation in the velocity curves. With the observation that road vehicles tend to perform smooth dynamic characteristics in most of the time, we design the inertia loss function to describe the abnormal motion variation, which assists the model to learn the consecutiveness from long-term camera ego-motion. Based on the recurrent convolutional neural network (RCNN) architecture, our method implicitly models the dynamics of road vehicles and the temporal consecutiveness by the extended Long Short-Term Memory (LSTM) block. Furthermore, we develop the dynamic hard-edge mask to handle the non-consistency in fast camera motion by blocking the boundary part and which generates more efficiency in the whole non-consistency mask. The proposed method is evaluated on the KITTI dataset, and the results demonstrate state-of-the-art performance with respect to other monocular deep VO and SLAM approaches.
A fast solution for bi-objective traffic minimization in geo-distributed data flows

Geo-distributed analytics is becoming an increasingly common-place as IoT, fog computing and big data processing platforms are nowadays integrating with each other. In this work, we deal with a problem encountered when complex Spark workflows run on top of geographically dispersed nodes, either data centers or individual machines. There have been proposals that optimize the execution of such workflows in terms of the aggregate traffic generated or the latency (which is due to data transmission), or both metrics. However, the state-of-the-art solutions that target both objectives are either significantly sub-optimal or suffer from high optimization overhead. In this work, we address this limitation. The main solutions that we propose are both efficient and effective; based on either the extremal optimization or the greedy algorithm design paradigm, they can yield significant improvements having an optimization overhead of a few tens of seconds even for Spark workflows of 15 stages running on 15 distributed nodes. We also show the inadequacy of evolutionary optimization solutions, such as genetic algorithms, for our problem.
3D Fluid Flow Estimation with Integrated Particle Reconstruction

The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. Almost all existing work processes the acquired multi-view video in two separate steps: first, a per-frame reconstruction of the particles, usually in the form of soft occupancy likelihoods in a voxel representation; followed by 3D motion estimation, with some form of dense matching between the precomputed voxel grids from different time steps. In this sequential procedure, the first step cannot use temporal consistency considerations to support the reconstruction, while the second step has no access to the original, high-resolution image data. We show, for the first time, how to jointly reconstruct both the individual tracer particles and a dense 3D fluid motion field from the image data, using an integrated energy minimization. Our hybrid Lagrangian/Eulerian model explicitly reconstructs individual particles, and at the same time recovers a dense 3D motion field in the entire domain. Making particles explicit greatly reduces the memory consumption and allows one to use the high-resolution input images for matching. Whereas the dense motion field makes it possible to include physical a-priori constraints and account for the incompressibility and viscosity of the fluid. The method exhibits greatly ( $${\approx }70\%$$ ) improved results over a recent baseline with two separate steps for 3D reconstruction and motion estimation. Our results with only two time steps are comparable to those of state-of-the-art tracking-based methods that require much longer sequences.
Scientific Documents Classification Using Support Vector Machine Algorithm

Abstract One of the ways to publish scientific paper is to present it in a conference. Before being presented in the conference, the registered scientific paper has to be grouped into categories. Because there are so many scientific papers that have been registered, it is an urgent need to establish a system that can automatically identify the topic of scientific papers and classify them according to recognized topics. One method that can be used to organize documents according to their categories is classification. In this study, Support Vector Machine algorithm is used as a method to classify documents into 5 categories of computer science. System test is done by taking 150 documents as data sample and 50 documents as data testing. This study produced a classification system of scientific documents and showed the method used was able to classify documents with 90% accuracy results.
You Shall Know the Most Frequent Sense by the Company it Keeps

Identification of the most frequent sense of a polysemous word is an important semantic task. We introduce two concepts that can benefit MFS detection: companions, which are the most frequently co-occurring words, and the most frequent translation in a bitext. We present two novel methods that incorporate these new concepts, and show that they advance the state of the art on MFS detection.
Exploiting multi-layer graph factorization for multi-attributed graph matching

Multi-layer graph matching methods effectively solve multi-attributed graph matching problems based on the multi-layer structure adopted to address the ambiguity and uncertainty arisen from the attribute integration. However, despite of its effectiveness for matching multi-attributed graphs, the approach has a long way to apply in the practical environment due to the scalability problem caused by a huge matrix for describing the multi-layer structure. In this paper, we propose a novel multi-layer graph matching algorithm based on the multi-layer graph factorization to address the issue. The main contribution of this research is three-fold. First, we propose a factorization method that decomposes the huge multi-layer matrix into several small matrices for efficiency. Second, we reformulate the original multi-layer matching problem into two relaxed problems by using the factorized matrices. Third, based on the relaxed problems, we propose a multi-layer graph matching algorithm inspired from the convex-concave relaxation procedure. In our extensive experiments on the synthetic and real-image datasets, the proposed method exhibits better performance than state-of-the-art algorithms based on the single-layer structure.
End-to-End Learning of Deterministic Decision Trees

Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable. Kontschieder 2015 has addressed this deficit, but at the cost of losing a main attractive trait of decision trees: the fact that each sample is routed along a small subset of tree nodes only. We here propose a model and Expectation-Maximization training scheme for decision trees that are fully probabilistic at train time, but after an annealing process become deterministic at test time. We analyze the learned oblique split parameters on image datasets and show that Neural Networks can be trained at each split. In summary, we present an end-to-end learning scheme for deterministic decision trees and present results on par or superior to published standard oblique decision tree algorithms.
Distant Supervision for Relation Extraction in The Persian Language using Piecewise Convolutional Neural Networks

In this paper we use distant supervision for the task of relation extraction from a large corpus in the Persian language. There are supervised and unsupervised methods for relation extraction from text. In supervised relation extraction we use hand labeled corpora. This method suffers from domain dependencies and the difficulties of labeling the text. In unsupervised method, we use large corpora without having to label them but relations extracted using this method cannot be used to populate knowledge bases. Distant supervision takes advantage of large corpora without suffering from domain dependencies and can populate knowledge bases. For our experiment we use FarsBase, a knowledge base containing millions of relation instances, and align entities in 630000 Persian Wikipedia articles to these relation instances and create a distantly supervised dataset. We then extract new relation instances using piecewise convolutional neural networks and compare the results with the baseline model that uses manually extracted features.
Discriminating Topical Influencers Based on the User Relative Emotion

Topical influencers are experts on a specific topic, who always play an important role in the opinion dissemination.From the perspective of influence polarity, topical influencers can be classified into opinion leaders who gain a great deal of support, trolls who are widely condemned, and controversial figures who trigger debate.In this paper, discriminating topical influencers of social networks are addressed.First, the trained BiLSTM-CRF model is constructed to extract emotional elements, and, then, the proposed emotional matching and emotional transforming algorithms are leveraged to obtain the relative emotion of users.Second, the rank of influencers is calculated by the quantified user behavior characteristics and the multi-centrality algorithm.Finally, according to relative emotion of users, the topical influencers are divided into three categories-opinion leaders, trolls, and controversial figures.Experimental results show that the user relative sentiment analysis method proposed in this paper has higher accuracy, and the empirical analysis manifests that the three kinds of influencers affect the opinion distribution in various degrees.
An eye-tracking attention based model for abstractive text headline

Online network platforms provide great convenience for users to obtain information. However, it’s challenging to select the required information from enormous texts. Automatic text headline generation methods not only guide users to select the information they are interested in, but also solve the problem of information overload. Nevertheless, the existing works mainly utilize the grammar rules to obtain the key information of the source text, while ignoring the dwell time of user’s attention on different text contents. To address this issue, this paper proposes an abstractive text headline generation model based on the eye-tracking attention mechanism. Specifically, this model first relies on the eye-tracking data to establish the mapping relationship between text words and the words’ reading time. Then, an eye-tracking attention mechanism is constructed to judge the importance of different words. Finally, this attention mechanism is integrated into the encoder-decoder framework to generate a high-quality headline. Experimental results obtained from different datasets demonstrate that the headline generated by our model is more concise. Moreover, our proposed model outperforms significantly the classical headline generation models on ROUGE-1, ROUGE-2 and ROUGE-L.
Fragile Watermarking for Image Authentication Using QR factorization and Fourier Transform

Image authentication technique is one of the important methods for a large number of multimedia applications. When a digital image is passed over non-secure channels such as the Internet, it may be changed and manipulated. For some important images such as military and medical images, these manipulations are very harmful and such images should be protected against them. There are several ways such as fragile and semi-fragile watermarking to authenticate images from malicious attacks. This paper presents a fragile watermarking algorithm for image authentication by using QR factorization and Fourier Transform (FT). By applying Fourier transform to host image, frequency domain which causes visual quality in watermarking is achieved. After applying FT, it is factorized by QR decomposition. QR factorization is also applied to watermark image. After factorizing both images, a coefficient of the upper triangular matrix R from watermark image is embedded to the upper triangular matrix R from host image. So a sign of the watermark image is hidden in the host image. This method is a fragile watermarking and it is sensitive to a little attack. So if an image is attacked over the Internet, the watermark image can not be extracted and it means that it has been attacked and it helps us to recognize if an image is changed after being transmitted over the Internet. The experimental results show that this method is sensitive to every weak attack and extraction part can not extract watermark image if it has been attacked.
A scale adaptive network for crowd counting

Scale variations occur frequently and present a great challenge for crowd counting in practical applications. In this paper, we propose a scale adaptive network to address the scale variation problem for crowd counting. We design a scale expansion unit which uses normal and dilated convolution to expand the receptive field size range of its input and connect several such units densely to cover a large range of densely distributed receptive field sizes so as to fit objects of different sizes in images. To alleviate competition among different scales, especially the negative effect of inappropriate scales, we also design a residual channel-wise re-weighting unit which is inserted after each scale expansion unit to enhance informative feature channels. We evaluate the effectiveness of the proposed scale adaptive network on ShanghaiTech-B and WorldExpo’10 datasets.
Last-Position Elimination-Based Fireworks Algorithm for Function Optimization

As rising swarm intelligence, fireworks algorithm (FWA) is designed to search the global optimum by the cooperation between the firework with the best fitness named as core firework (CF) and the other non-CFs. Loser-out tournament based fireworks algorithm (LoTFWA) is the most pioneering variant characterized by using competition as a new manner of interaction. However, its independent selection operator may prevent non-CFs from aggregating to CF in the late evolutionary phase if they fall into different local optima. This work proposes a last-position elimination-based fireworks algorithm which allocates more fireworks in the initial process of the optimization to search and locate the scattered local optima. Then for every fixed number of generations, the firework with the worst performance is eliminated and its budget of sparks is reallocated to other fireworks. In the final stage of optimization, only CF survives with all the budget of sparks and thus the aggregation of non-CFs to CF is ensured. Extensive experimental results performed on both CEC2013 and CEC2015 benchmarks covering 43 functions show that the proposed algorithm significantly outperforms most of the state-of-the-art FWA variants.