Reviewerly/exHarmony huggingface dataset:
Recovery of sparse linear classifiers from mixture of responses

In the problem of learning a mixture of linear classifiers, the aim is to learn a collection of hyperplanes from a sequence of binary responses. Each response is a result of querying with a vector and indicates the side of a randomly chosen hyperplane from the collection the query vector belongs to. This model provides a rich representation of heterogeneous data with categorical labels and has only been studied in some special settings. We look at a hitherto unstudied problem of query complexity upper bound of recovering all the hyperplanes, especially for the case when the hyperplanes are sparse. This setting is a natural generalization of the extreme quantization problem known as 1-bit compressed sensing. Suppose we have a set of $\ell$ unknown $k$-sparse vectors. We can query the set with another vector $\boldsymbol{a}$, to obtain the sign of the inner product of $\boldsymbol{a}$ and a randomly chosen vector from the $\ell$-set. How many queries are sufficient to identify all the $\ell$ unknown vectors? This question is significantly more challenging than both the basic 1-bit compressed sensing problem (i.e., $\ell=1$ case) and the analogous regression problem (where the value instead of the sign is provided). We provide rigorous query complexity results (with efficient algorithms) for this problem.
Joint Optic Disc and Optic Cup Segmentation Based on New Skip-Link Attention Guidance Network and Polar Transformation

The challenge faced by the joint optic disc and optic cup segmentation is how to learn an efficient segmentation model with good performance. This paper proposes a method based on a new type of Skip-Link attention guidance network and polar transformation, called Skip-Link Attention Guidance Network (SLAG-CNN) model, which implements the simultaneous segmentation of the optic disc and optic cup. In SLAG-CNN, the Skip-Link Attention Gate (SLAG) module was first introduced, which was used as a sensitive extension path to transfer the semantic information and location information of the previous feature map. Each SLAG module of the SLAG-CNN model combines channel attention and spatial attention, and adds Skip-Link to form a new attention module to enhance the segmentation results. Secondly, multi-scale input images are constructed by spatial pyramid pooling. Finally, a weighted cross-entropy loss function is used at each side output layer to sum up to calculate the total model loss. On the DRISHTI-GS1 dataset, the joint optic disc and optic cup segmentation task proves the effectiveness of our proposed method.
Deep Learning Algorithms Design and Implementation Based on Differential Privacy

Deep learning models bear the risks of privacy leakage. Attackers can obtain sensitive information contained in training data with some techniques. However, existing differentially private methods such as Differential Privacy-Stochastic Gradient Descent (DP-SGD) and Differential Privacy-Generative Adversarial Network (DP-GAN) are not very efficient as they require to perform sampling multiple times. More importantly, DP-GAN algorithm need public data to set gradient clipping threshold. In this paper, we introduce our refined algorithms to tackle these problems. First, we employ random shuffling instead of random sampling to improve training efficiency. We also test Gaussian and Laplace Mechanisms for clipping gradients and injecting noise. Second, we employ zero Concentrated Differential Privacy (zCDP) to compute overall privacy budget. Finally, we adopt dynamical gradient clipping in DP-GAN algorithm. During each iteration, we random sample training examples and set the average gradients norm as the new threshold. This not only makes the algorithm more robust but also doesn’t increase the overall privacy budget. We experiment with our algorithms on MNIST data sets and demonstrate the accuracies. In our refined DP-SGD algorithm, we achieve test accuracy of 96.58%. In our refined DP-GAN algorithm, we adopt the synthetic data to train models and reach test accuracy of 91.64%. The results show that our approach ensures model usability and provides the capability of privacy protection.
Generating Multi-hops Entangled Network Via Spin Dipolar Interaction

The possibility of generating a multi-hops network between different entangled nodes (qubits) via spin Dipolar interaction is examined. The negativity, tangle and the non-local coherent advantage are used as quantifiers of the generated quantum correlations. The phenomena of the sudden death/birth is displayed for the entangled two nodes, while the sudden changes phenomena (increasing/ decreasing) is depicted for all entangled three nodes. The amount of correlations between the different nodes depend on the initial network settings, where the largest amount is predicted if the network is initially conducted via maximum entangled nodes. The generated quantum correlations between each three nodes are more robust than those generated between two nodes. For the generated entangled two nodes, the direction of the interaction and its strength have a remarkable effect on the correlation behavior, while they has a slightly effect on the correlation of the three nodes.
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models

Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph. Although such approaches have shown advances in downstream node classification tasks, they are ineffective in jointly representing larger $k$-node sets, $k{>}2$. We propose MHM-GNN, an inductive unsupervised graph representation approach that combines joint $k$-node representations with energy-based models (hypergraph Markov networks) and GNNs. To address the intractability of the loss that arises from this combination, we endow our optimization with a loss upper bound using a finite-sample unbiased Markov Chain Monte Carlo estimator. Our experiments show that the unsupervised MHM-GNN representations of MHM-GNN produce better unsupervised representations than existing approaches from the literature.
Desain Instalasi Listrik Bangunan Bertingkat (Studi Kasus: Pesantren Khoiru Ummah Sumedang)

The electrical installation is a circuit of electrical equipment which is interconnected with one another and it is within the scope of the electrical power system. This article is to present the result of social community service (PkM) activity at Boarding School of STP SMP/SMA KU Sumedang, that shows an electrical installation design of multi-storey building. In construction of multi-storey building of this boarding school, they do not have an electrical installation design. So, the design in this article is to provide design drawings of the electrical installation, so it could be guidance of setting up of electrical installation for the new boarding school building. So that, they got the electrical installation safely for human and the equipment, as well reliable and comfort during using electricity. The design drawings that resulted from the social community service are drawings of front look, beside look, wiring, and single line diagram (SLD). Also, resulted total power calculation, protection, and capacity (rating) of MCB, and calculated cable rating (KHA), choosing type and cross-sectional area of cables that will be used in electrical installation design for the boarding school building.
CognitOS Board: A Wall-Sized Board to Support Presentations in Intelligent Environments

The proliferation of Internet of Things devices and services and their integration in everyday environments led to the emergence of intelligent offices, classrooms, conference, and meeting rooms that adhere to the paradigm of Ambient Intelligence. Usually, the type of activities performed in such environments (i.e., presentations and lectures) can be enhanced by the use of large Interactive Boards that—among others—allow access to digital content, promote collaboration, enhance the process of exchanging ideas, and increase the engagement of the audience. Additionally, the board contents are expected to be plenty, in terms of quantity, and diverse, in terms of type (e.g., textual data, pictorial data, multimedia, figures, and charts), which unavoidably makes their manipulation over a large display tiring and cumbersome, especially when the interaction lasts for a considerable amount of time (e.g., during a class hour). Acknowledging both the shortcomings and potentials of Interactive Boards in intelligent conference rooms, meeting rooms, and classrooms, this work introduces a sophisticated framework named CognitOS Board, which takes advantage of (i) the intelligent facilities offered by the environment and (ii) the amenities offered by wall-to-wall displays, in order to enhance presentation-related activities. In this article, we describe the design process of CognitOS Board, elaborate on the available functionality, and discuss the results of a user-based evaluation study.
Causal network learning with non-invertible functional relationships

Discovery of causal relationships from observational data is an important problem in many areas. Several recent results have established the identifiability of causal directed acyclic graphs (DAGs) with non-Gaussian and/or nonlinear structural equation models (SEMs). Focusing on nonlinear SEMs defined by non-invertible functions, which exist in many data domains, a novel test is proposed for non-invertible bivariate causal models. Algorithms are further developed to incorporate this test in structure learning of DAGs that contain both linear and nonlinear causal relations. Extensive numerical comparisons show that the proposed algorithms outperform existing DAG learning methods in identifying causal graphical structures. The practical application of the methods is illustrated by learning causal networks for combinatorial binding of transcription factors from ChIP-Seq data.
Noise Reduction in SAR Images with Variable Mode CT

Multiplicative noise of an image destructs the pixel association and leads to entropy loss. Somehow filters managed to reduce the noise but were not able to reform the image this leads to blurring of the image. To avoid this transform were measured, this results in a satisfactory image reconstruction with less part of information recovery. For this reason, enhancing image was considered, enhancing the image leads to improve entropy value. So, previously redundant and fusing methods were applied to Satellite Aperture Radar (SAR) images. Here we are providing a novel approach of fusing decomposition techniques i.e., Redundant curvelet transform (RFDCT) with variational Mode Decomposition (VMD). This results in the improvement of 11 parametric values and comparing with existing simulations of RFDCT, RFDCT with Empirical Mode Decomposition (EMD).
Sentiment analysis using tweets data from Twitter of Indonesian's Capital City changes using classification method support vector machine

Capital city in every country has an important role. One of them is for the central of government. A capital city is selected by considering many aspects. These include location which is in the middle of the country, and history which was happened before. On the middle of 2019, Indonesian's President announced that Indonesian's capital city will be shifted from Jakarta to East Kalimantan. The statement makes a lot of public opinions especially in social media Twitter. The opinions can be used for sentiment analysis. This research aims to classify the information of public sentiments about Indonesian's capital city changes. The classification divided by two class which are positive sentiments and negative sentiments. The classification method that used in this sentiment analysis research is Support Vector Machine (SVM) with three function method, which are linear function, Radial Basis Function (RBF), and sigmoid function. The process include text mining, text preprocessing, analyze using SVM, data classification, and visualization. From the result of classification, linear function is the best function on this research with the accuracy value is 96,93% with 97,81% specificity value and 95% sensitivity value.
Multi-Stage Influence Function

Multi-stage training and knowledge transfer, from a large-scale pretraining task to various finetuning tasks, have revolutionized natural language processing and computer vision resulting in state-of-the-art performance improvements. In this paper, we develop a multi-stage influence function score to track predictions from a finetuned model all the way back to the pretraining data. With this score, we can identify the pretraining examples in the pretraining task that contribute most to a prediction in the finetuning task. The proposed multi-stage influence function generalizes the original influence function for a single model in (Koh & Liang, 2017), thereby enabling influence computation through both pretrained and finetuned models. We study two different scenarios with the pretrained embeddings fixed or updated in the finetuning tasks. We test our proposed method in various experiments to show its effectiveness and potential applications.
Spherical object recognition based on the number of contour edges extracted by fitting and convex hull processing

The detection of spherical targets in workpiece shape clustering and fruit classification tasks is challenging. Spherical targets produce low detection accuracy in complex fields, and single-feature processing cannot accurately recognize spheres. Therefore, a novel spherical descriptor (SD) for contour fitting and convex hull processing is proposed. The SD achieves image de-noising by combining flooding processing and morphological operations. The number of polygon-fitted edges is obtained by convex hull processing based on contour extraction and fitting, and two RGB images of the same group of objects are obtained from different directions. The two fitted edges of the same target object obtained at two RGB images are extracted to form a two-dimensional array. The target object is defined as a sphere if the two values of the array are greater than a custom threshold. The first classification result is obtained by an improved K-NN algorithm. Circle detection is then performed on the results using improved Hough circle detection. We abbreviate it as a new Hough transform sphere descriptor (HSD). Experiments demonstrate that recognition of spherical objects is obtained with 98.8% accuracy. Therefore, experimental results show that our method is compared with other latest methods, HSD has higher identification accuracy than other methods.
Analysis and Synthesis of Traffic Scenes from Road Image Sequences

Traffic scene construction and simulation has been a hot topic in the community of intelligent transportation systems. In this paper, we propose a novel framework for the analysis and synthesis of traffic elements from road image sequences. The proposed framework is composed of three stages: traffic elements detection, road scene inpainting, and road scene reconstruction. First, a new bidirectional single shot multi-box detector (BiSSD) method is designed with a global context attention mechanism for traffic elements detection. After the detection of traffic elements, an unsupervised CycleGAN is applied to inpaint the occlusion regions with optical flow. The high-quality inpainting images are then obtained by the proposed image inpainting algorithm. Finally, a traffic scene simulation method is developed by integrating the foreground and background elements of traffic scenes. The extensive experiments and comparisons demonstrate the effectiveness of the proposed framework.
Single-Image Super-Resolution based on a Self-Attention Deep Neural Network

How to reconstruct a high-resolution image from a single low-resolution image with plenty of details and rational hierarchy is the important problem for single image super-resolution. In this paper, we propose a novel deep neural network for single image super-resolution, called self-attention deep neural network (SADNN), where the self-attention mechanism is utilized to obtain the relationships between widely separated spatial regions. Thus, the global dependences from the features of an image are captured to promote the hierarchy of the image. Moreover, a new defined loss function, including a pixel-wise loss and a perceptual loss, is proposed to improve the image-detail reconstruction ability of the deep neural network during the training. Extensive experimental results demonstrate that the proposed method can improve texture details and the visual impression of the reconstructed high-resolution image significantly.
A novel feature selection technique based on Roach Infestation Optimization for Internet Traffic Classification

In this paper, we propose a novel feature selection technique based on the Roach Infestation Optimization (RIO) meta heuristic for Internet traffic classification. The RIO based feature selection technique is a pre-processing step before launching the classification task where the aim is to identify the set of significant features to be used in the classification task. The proposed technique is combined with both random forest and Bayes network classifiers and evaluated on the well known NIMS dataset. The numerical results show the effectiveness of the proposed technique for Internet traffic classification.
Towards a Holistic Schema Matching Approach Designed for Large-Scale Schemas

Holistic schema matching is a fundamental challenge in the big data integration domain. Ideally, clusters of semantically corresponding elements are created and are updated as more schemas are matched. Developing a high-quality holistic schema matching approach is critical for two main reasons. First, identifying as many accurate and holistic semantic correspondences as possible right from the beginning. Second, reducing considerably the search space. Nevertheless, this problem is challenging since overlapping schema elements are not available. Identifying schema overlaps is further complicated for two main reasons: (1) there is a large number of schemas; and (2) overlaps vary for different schemas. In this paper we present HMO, a Holistic schema Matching approach based on schema Overlaps and designed for large-scale schemas. HMO can balance the search space and the quality of the holistic semantic correspondences. To narrow down the search space, HMO matches schemas based on their overlaps. To obtain high-accuracy, HMO uses an existing high-quality semantic similarity measure. Experimental results on four real-world domains show effectiveness and scalability of our matching approach.
A Cross-Cultural Study of Mobile Instant Messaging (MIM): Does Culture Shape Young Peoples Communication Styles on MIM

The goal of the study was to identify culture specific communication patterns in mobile instant messaging (MIM). Differences in communication style between Germans and Japanese based on the followi...
Testbed for control synthesis for multiple mobile robot systems: General concept and current research

The paper deals with a testbed for control development for a system of multiple mobile robots sharing a common motion space. We present a general concept of a hybrid (discrete-event/continuous-time) control system that formally ensures correct concurrent robot movement, and describe a specific solution that follows the general concept and is underway its implementation for a team of TurtleBot robots operating under ROS.
A novel hybrid augmented loss discriminator for text‐to‐image synthesis

For the text-to-image synthesis task, most discriminators in existing generative adversarial networks based methods tend to fall into a local suboptimal state too early in the training process, resulting in the poor quality of generated images. To address the above problems, a hybrid augmented loss discriminator is designed. In this designed discriminator, to reduce the sensitivity of the discriminator classification recognition, make it pay attention to the semantic and structural changes, we add the loss value of the fake sample to the loss value of the real sample. Moreover, to indirectly guide the generator to generate samples, the loss value of the real sample is added to the fake sample. The loss value mixed with real and fake samples actually augments signal transmission. It perturbs parameter update of the discriminator during optimization and prevents the discriminator from falling into the local suboptimal state prematurely. Whereafter, we apply the proposed discriminator to two kinds of text-to-image synthesis tasks. Experimental results show that the proposed method can help the baseline models to improve performance.
Geometric Modeling of a Topographic Surface Based on a Fractal Coordinate System

Traditionally, euclidean lines served as the basis for intuitive understanding of coordinates. Everyone must have noticed that the approximate estimate of the distance in the mountains or in highly rugged terrain does not match the actual distance traveled. If we estimate that we will overcome the site in half an hour, then it really turns out that we spent almost an hour. This is due to the fact that we usually think of lines as smooth, but in fact, in nature, almost all lines are strongly cut and curved. B. B. Mandelbrot called such lines fractal. Recent research has shown that an effective way to model undifferentiated surfaces for solving problems is to use fractal geometry methods. Undifferentiated surfaces include the earth’s surface, which in the theory of fractal geometry is represented by a stochastic fractal. The article considers the problem of constructing a digital geometric elevation model based on fractal theory. The author presents a method for calculating the fractal dimension of self-similar structures of natural origin, describes the differences in approaches to fractal analysis of abstract mathematical fractals and real fractals, for calculating the fractal dimension of which it is necessary to use a numerical algorithm. The method of finding the dimension that allows to avoid uncertainties in the selection phase approximation based on the number of cells covering the size of the cell. This method can be used for both deterministic and natural (stochastic) fractals. This method is verified by the fractal coordinates of the earth’s surface.
Hybridizing UFO with Other ML Tools to Locate Faults by Just Knowing Relay Operating Times

Universal functions originator (UFO) is a new machine learning (ML) tool that can find relationships between responses and predictors and then automatically formulate them as mathematical equations using the required number of analytic functions and arithmetic operators. Since it was introduced in the literature there is still an urgent question about whether it is worthwhile to hybridize it with other ML tools, such as linear regression (LR), nonlinear regression (NLR), support vector machine (SVM), and artificial neural network (ANN). This study is the first attempt to hybridize UFO, as a universal transformation unit (UTU), with the preceding ML tools. The goal here is to let UTU take care of the non-linearity issue of the dataset before being sent to other ML tools. These new hybrid computing systems are applied to locate three-phase (3φ) faults in an electric power network by utilizing the operating times measured from the two-end numerical directional overcurrent relays (DOCRs) of a faulty line. The results show that the hybrid approaches are viable where their estimations are much better than those obtained by the classical ML tools. This study proves that the strong side of UFO can be integrated with others to have superior computing systems.