Reviewerly/exHarmony huggingface dataset:
SPART: Optimizing CNNs by Utilizing Both Sparsity of Weights and Feature Maps

Intense convolution computation and great memory requirement in CNNs constraint their wider deployments and applications. Although both the weights and feature maps in CNNs can be sparse, directly mapping sparse convolution to spGEMM in HPC domain fails to improve the actual performance. Besides, existing sparse formats like CSR are not suitable for encoding the sparse feature maps because convolution operates across rows. In this work, we propose a new format and a novel sparse convolution algorithm to optimize sparse CNNs on GPUs. First, we design the Compressed Feature Map (CFM) format to store the sparse feature maps. Second, we propose an efficient sparse convolution algorithm called SPART with sparse weights and sparse feature maps. Finally, we optimize this algorithm on GPUs. Our experiments show that our SPART algorithm has good performance. Compared with dense convolution, the speedup of SPART is up to $$\mathbf {2.62 \times } $$ ( $$\mathbf {1.77 \times }$$ in average) on V100 and up to $$\mathbf {1.84 \times }$$ ( $$\mathbf {1.24 \times }$$ in average) on Titan X.
Evolution processes of coupled thermal qubits

We investigate the quantum speed limit time (QSLT) of quantum evolution before thermal equilibrium of two coupled qubits each of which is coupled to a separate thermal bath at the same temperature within the Born-Markov approximation. The evolution process in one particular initial state can change between speed-up and speed-down two times before reaching equilibrium. We call this double cusp behaviour. This behaviour is an anomalous phenomenon in evolution processes in the weak-coupling Markovian regime. We study QSLT corresponding to all pure initial energy eigenstates and categorise them. In addition, we also display the conditions for double cusp behaviour in terms of temperature, qubit interaction and frequency.
Risk Assessment Method for Smart Substation Secondary System Based on Deep Neural Network

In the risk assessment for the smart substations, the traditional Monte Carlo-based methods rely on prior distribution knowledge and cannot cover all the potential failure scenarios. In this paper, a risk assessment method based on the deep neural network for the smart substation secondary system is proposed. Firstly, a deep neural network established by the deep auto-encoders is proposed to quantitatively evaluate the operational risk of the smart substation. Secondly, the key indicators that affect the substation operations are hierarchically combed and refined, and are used as the inputs of the deep neural network. Finally, numerical simulation results from an actual smart substation show that compared with the traditional Monte Carlo-based assessment methods, the accuracy of the proposed method for assessing the operation states of the smart substation can be improved by 48.03% under the same iterations. In addition, the running time of the proposed method is less by 12.3% than the time of traditional method in the case with the same iterations. Hence, effectiveness and feasibility of the proposed method can be verified.
An Autoencoder-Based Dimensionality Reduction Algorithm for Intelligent Clustering of Mineral Deposit Data

Currently, there has been a dramatic growth of data size and data dimension in geophysics, while achieving the rapid advancements of geological big data technologies with the support of various detection methods. Then, high-dimensional and massive geological data impose very challenging obstacles to traditional data analysis approaches. Given the success of deep learning methods and techniques in big data analysis applications, it is expected that they are also able to achieve the satisfactory performance in dealing with high-dimensional complex geological data. Hence, through the combination of one of the effective implementations of deep learning, i.e., autoencoder, and a clustering algorithm, i.e., K-means, in this paper we achieve the dimensionality reduction for complex data, so as to extract useful data features from mineral deposit data, with the purpose of improving computational efficiency. The experimental results demonstrate the effectiveness our developed method.
Effect of temperature sintering on physical properties, hardness and crystal structures of ceramic TiO2 doped with 2 % Fe2O3

Abstract The ceramic TiO 2 doped with 2 % weight of Fe 2 O 3 has been prepared by using powder metallurgy method. The experimental research begins with the preparation of TiO 2 powder mixed according with Fe 2 O 3 powder then calcined at 1050°C for one hour. Then the calcined powder is crushed and mixed with celuna binder WE-518 as much 3% weight and compacted by using hydraulic press machine with 8 tons force. The sintering temperatures were varied starting from 1150 °C, 1200 °C and 1250 °C. The characterization of sintered samples includes: testing of its physical properties (density and porosity), hardness and microstructure analysis (X-Ray Diffraction). The results of the characterization show that the optimum of sintering temperature was achieved at the temperature 1250 °C, the results were obtained such as : density value = 2.60 g/cm3 and porosity value = 17.8 %. The result of hardness testing shows that the highest value of hardness is 292 Hv at sintering temperature 1250 °C. The result of XRD shows that it was found TiO 2 phase as dominant and minor phase that is TiO 2 rutile.
Transmission Line Fault Analysis Method Based on Fuzzy Knn

The improvement of the quality of life of the masses has led to more stringent requirements on the safety of the power system. Aiming at the problem that knn has low accuracy when dealing with the unconstrained data of the category and the complexity of the power system and the diversification of the structure, the Spark+Hadoop+Hive is used as the technical framework to establish a real-time fault analysis model of the transmission line based on the Spark computing platform. The problem of low timeliness in transmission line analysis further improves the accuracy of the algorithm in fault analysis of transmission lines.
NeuMorph

This article deals with morphological tagging for low-resource languages. For this purpose, five Indic languages are taken as reference. In addition, two severely resource-poor languages, Coptic and Kurmanji, are also considered. The task entails prediction of the morphological tag (case, degree, gender, etc.) of an in-context word. We hypothesize that to predict the tag of a word, considering its longer context such as the entire sentence is not always necessary. In this light, the usefulness of convolution operation is studied resulting in a convolutional neural network (CNN) based morphological tagger. Our proposed model (BLSTM-CNN) achieves insightful results in comparison to the present state-of-the-art. Following the recent trend, the task is carried out under three different settings: single language, across languages, and across keys. Whereas the previous models used only character-level features, we show that the addition of word vectors along with character-level embedding significantly improves the performance of all the models. Since obtaining high-quality word vectors for resource-poor languages remains a challenge, in that scenario, the proposed character-level BLSTM-CNN proves to be most effective. 1
An Automatic Text Summary Extraction Method Based on Improved TextRank and TF-IDF

In the era of information explosion, more and more people advocate "fast reading". Compared with some texts longer texts can express vivid images but it will waste time. Therefore, automatic text abstraction has become one of the research hotspots in natural language processing. In order to ensure the accuracy of automatic text abstraction by computer, a weighting method based on comment factor, position factor and weighting of part-of-speech factor is designed which mainly combines and improves TextRank and TF-IDF algorithm. This method focuses on introducing the reader's comments as a comment factor, supplementing the shortcomings of paying too much attention to the original text and ignoring the reader's comments. Secondly, the frequency factor and part-of-speech factor are introduced to ensure the readability of the generated abstracts. The experimental results show that the method has good improvement in accuracy, consistency and connectivity.
Performance Benchmarking of Infrastructure-as-a-Service (IaaS) Clouds with Cloud WorkBench (Tutorial)

The continuing growth of the cloud computing market has led to an unprecedented diversity of cloud services with different performance characteristics. To support service selection, researchers and practitioners conduct cloud performance benchmarking by measuring and objectively comparing the performance of different providers and configurations (e.g., instance types in different data center regions). In this tutorial, we demonstrate how to write performance tests for IaaS clouds using the Web-based benchmarking tool Cloud WorkBench (CWB). We will motivate and introduce benchmarking of IaaS cloud in general, demonstrate the execution of a simple benchmark in a public cloud environment, summarize the CWB tool architecture, and interactively develop and deploy a more advanced benchmark together with the participants.
A Continuous Media Data Broadcasting Model for Base Stations Moving Straight

Due to the recent development of robotics technology, movable robots work in some shopping malls or plants. They can generally communicate with other robots or computers and have a large possibility to serve as communications base stations. However, it is difficult to keep communications with them for long time since they move for their main services. Especially for the communications for receiving continuous media data such as video or audio, this is a large problem because the communication times tend to be long and the clients cannot receive the whole data in the cases that the robots move away in the middle of the communications. Slowing down their moving speeds shrinks a merit of using robots. Hence, in this paper, we create a communication model for movable base stations and investigate the possibility for robots to serve as base stations.
Semiparametric regression based on three forms of trigonometric function in fourier series estimator

Abstract The semiparametric regression is one of the three forms of regression analysis which is made up of parametric and nonparametric. While the parametric is based on linear estimator, this nonparametric component is an innovation. This research proposes all the possible trigonometric basis usually used in Fourier series as nonparametric component estimator, its advantage, which includes its ability to overcome data with oscillation patterns. This study discusses nonparametric regression based on complete and sine Fourier series. Both estimators are developed using the cosine Fourier series concept. The outputs are two estimators which are used for parametric and nonparametric components with the corresponding form in semiparametric regression. In addition, all of these can be applied in real problems, and the best estimator is determined based on the smallest GCV and MSE for an oscillation parameter which gives the highest coefficient of determination for the selected one.
Brain Storm Optimization Algorithm based on Competition Mechanism

Brain storm optimization algorithm (BSO) is a new but excellent algorithm who based on brainstorming process. However, BSO has some disadvantages like premature convergence and high time complexity. In order to solve these problems, a new BSO based on competition mechanism (BSOCM) is proposed in this paper. BSOCM replaces the k-means cluster by sorting the population, and adopts the mutation strategy of competition mechanism to stimulate the population to generate more individuals. In this way, BSOCM reduces time complexity. At the same time, the strategy of competition mechanism is added to ensure the optimization ability of the algorithm, and the precocity of the algorithm is reduced. The experimental results showed that BSOCM could avoid premature local optimization. The benchmark function of CEC 2013 was used in a comprehensive experiment, the test results of BSO, DE, MBSO, PSO and BSOCM were compared respectively. Relevant experiments in this paper prove that BSOCM has good search efficiency.
Internal and external similarity aggregation stereo match algorithm

When extracting disparity map, for the high non-occlusion region error rate and low efficiency of stereo matching algorithms, we defined internal similarity (IS) of neighbor pixels in reference color image, and in the color and sub-pixel space, we defined external similarity (ES) of candidate matching pixels between the reference and target images, and given the calculation methods of the internal similarity and external similarity. We discussed the relationship between the internal similarity and external similarity, and taken the internal similarity as the aggregation degree of the external similarity, and the internal and external similarity aggregation (IESA) algorithm was proposed to aggregate the external similarity by the internal similarity along eight directions, after that, the disparity map was calculated by the winner take all (WTA) algorithm, which was used to search the disparity corresponding to the maximum aggregation similarity. Finally, the box plot filter (BPF) was proposed to smooth the disparity map. Experiments on the Middlebury stereo datasets and the extend datasets show that the proposed algorithms achieve the state-of-the-art results.
Lightweight Contrast Modeling for Attention-Aware Visual Localization

Salient object detection, which aims at localizing the attention-aware visual objects, is the indispensable technology for intelligent robots to understand and interact with the complicated environments. Existing salient object detection approaches mainly focus on the optimization of detection performance, while ignoring the considerations for computational resource consumption and algorithm efficiency. Contrarily, we build a superior lightweight network architecture to simultaneously improve performance on both accuracy and efficiency for salient object detection. Specifically, our proposed approach adopts the lightweight bottleneck as its primary building block to significantly reduce the number of parameters and to speed up the process of training and inference. In practice, the visual contrast is insufficiently discovered with the limitation of the small empirical receptive field of CNN. To alleviate this issue, we design a multi-scale convolution module to rapidly discover high-level visual contrast. Moreover, a lightweight refinement module is utilized to restore object saliency details with negligible extra cost. Extensive experiments on efficiency and accuracy trade-offs show that our model is more competitive than the state-of-the-art works on salient object detection task and has prominent potentials for robots applications in real time.
Layered Embeddings for Amodal Instance Segmentation

The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches. Source code available at https://github.com/yanfengliu/layered_embeddings .
Difficulties in Writing Recount Text Faced by Senior High School Students in Indonesia

Writing an English for EFL learner is a difficult skill because English is a foreign language. Besides that, sometimes EFL learner is confused with organizing the idea, translating the language from Indonesia to English, follow the grammatical rule, find and write the proper vocabulary. In this research focus on the students' writing difficulties in recount text, the factors which influence the students' writing difficulties in recount text, and the way of the teacher solve the students' writing difficulties. This study investigates the writing difficulties of students in SMA Kemala Bhayangkari 3 Porong. In this study, the data served as a qualitative descriptive which took the students' exercise of recount text and interviewed the subject. The result shows that there are different difficulties for each students' ability. Internal and external factors also turned out to be one of the causes of difficulties faced by students in writing recount text. The results of interviews with teachers are known that teacher’s have used metacognitive strategies as solving problems faced by students in writing skills.
Sequential Modeling for Polyps Identification from the Vocal Data

Given the revival of neural networks and its recent impact in other disciplines and record-breaking performances in a variety of applications, in this paper, we employed a deep sequential model for polyps detection from the vocal data. Previous research of acoustic signal recognition (ASR) has focused on hand-crafted machine learning fashion, such as Mel-frequency cepstral coefficients with hidden Markov model and Gaussian mixture model. The deep model demonstrates its flexibility and potential to outperform the traditional methods, and we expand its scope on medical symptom identification. The mapping between the raw vocal signal and the symptom recognition is established, and we show that we can achieve a good recognition accuracy, which may appear to clinical diagnosis in the near future.
Photontorch: Simulation and Optimization of Large Photonic Circuits Using the Deep Learning Framework PyTorch

We propose a new framework for simulating and optimizing large photonic circuits. The framework utilizes gpu-acceleration to efficiently simulate the circuits in a highly parallel manner, while optimization is achieved through backpropagation by essentially viewing the network as a sparsely connected recurrent neural network.
Application of Bayesian super-resolution imaging algorithm to micro–nano satellite images

The need for high-resolution imaging becomes particularly important in remote sensing image applications, such as ground-object identification. We introduce a Bayesian multiframe super-resolution algorithm that efficiently improves the imaging resolution of our micro–nano carbon satellite images. We begin by presenting a theoretical overview of the algorithm, and subsequently we conduct experiments in two phases. In the first phase, we compare the performance of our algorithm with those of other similar algorithms using a set of reference images. In the second practical application phase, we apply all the algorithms considered to panchromatic images obtained from our civilian micro–nano carbon satellite. Our results indicate that the proposed algorithm significantly outperforms the other algorithms, affording higher image resolution and greater image detail. We believe that our approach can contribute to the further development of satellite imaging systems.
Motivation to Select Point of View in Cinematic Virtual Reality

This paper examines the effects of participants' preferred point of view of two protagonists, and their motivation for this preference, on two viewings of a cinematic 360-degree video filmed from the first person perspective. Before watching the film, which dramatized gender bias in a STEM workplace, participants were asked to state whether they preferred to view the film from the point of view (POV) of a male protagonist, a female protagonist, or make no selection. They were then asked why they held this preference. Their answers were predictive. Participants' tracked head movements, and the events participants recalled from the film, differed according to their pre-stated preference and motivation.
FlexSight - A Flexible and Accurate System for Object Detection and Localization for Industrial Robots

We present a novel smart camera - the FlexSight C1 - designed to enable an industrial robot to detect and localize several types of objects and parts in an accurate and reliable way. The C1 integrates all the sensors and a powerful mini computer with a complete Operating System running robust 3D reconstruction and object localization algorithms on-board, so it can be directly connected to the robot that is guided directly by the device during the production cycle without any external computers in the loop. In this paper, we describe the FlexSight C1 hardware configuration along with the algorithms designed to face the model based localization problem of textureless objects, namely: (1) an improved version of the PatchMatch Stereo matching algorithm for depth estimation; (2) an object detection pipeline based on deep transfer learning with synthetic data. All the presented algorithms have been tested on publicly available datasets, showing effective results and improved runtime performance.
End-to-End Deep Imitation Learning: Robot Soccer Case Study

In imitation learning, behavior learning is generally done using the features extracted from the demonstration data. Recent deep learning algorithms enable the development of machine learning methods that can get high dimensional data as an input. In this work, we use imitation learning to teach the robot to dribble the ball to the goal. We use B-Human robot software to collect demonstration data and a deep convolutional network to represent the policies. We use top and bottom camera images of the robot as input and speed commands as outputs. The CNN policy learns the mapping between the series of images and speed commands. In 3D realistic robotics simulator experiments, we show that the robot is able to learn to search the ball and dribble the ball, but it struggles to align to the goal. The best-proposed policy model learns to score 4 goals out of 20 test episodes.
Ensemble of Strategies and Perturbation Parameter Based SOMA for Constrained Technological Design Optimization Problem

In this paper, we are introducing a novel ensemble based adaptive strategy for the Self Organizing Migrating Algorithm (SOMA), namely the "Ensemble of Strategies and Perturbation Parameter in SOMA" (ESP-SOMA). The proposed algorithm as well as several other state of the art selected metaheuristic algorithms are utilized in the task of optimization of waste processing batch reactor geometry and control. Since there is a growing demand for intelligent and fast problem solution or optimal utilization of resources in modern industrial field, especially in the Industry 4.0 era, this paper represents an insight into the applicability and effectivity of modern adaptive state of the art metaheuristic optimization algorithms in the task of highly constrained industrial design optimization problem. The simple statistical comparison of the results given by three different metaheuristic algorithms is also reported here.