Reviewerly/exHarmony huggingface dataset:
Construction of power projects knowledge graph based on graph database Neo4j

In The Belt and Road, China’s overseas power projects grow more and more, which is associated with a large number of dispersing project information. Construction of power project knowledge graph based on graph database Neo4j can facilitate the management of overseas power projects and to have an intuitive understanding of the relationships between projects for further overall planning. Meanwhile, enterprises can describe the spatial distribution characteristics of countries along the The Belt and Road according to the knowledge graph of overseas power projects, so as to understand the space and potential of future power investment development in different countries.
TASO: Time and Space Optimization for Memory-Constrained DNN Inference

Convolutional neural networks (CNNs) are used in many embedded applications, from industrial robotics and automation systems to biometric identification on mobile devices. State-of-the-art classification is typically achieved by large networks, which are prohibitively expensive to run on mobile and embedded devices with tightly constrained memory and energy budgets. We propose an approach for ahead-of-time domain specific optimization of CNN models, based on an integer linear programming (ILP) for selecting primitive operations to implement convolutional layers. We optimize the trade-off between execution time and memory consumption by: 1) attempting to minimize execution time across the whole network by selecting data layouts and primitive operations to implement each layer; and 2) allocating an appropriate work space that reflects the upper bound of memory footprint per layer. These two optimization strategies can be used to run any CNN on any platform with a C compiler. Our evaluation with a range of popular ImageNet neural architectures (GoogleNet, AlexNet, VGG, ResNetand SqueezeNet) on the ARM Cortex-A15 yields speedups of 8× compared to a greedy algorithm based primitive selection, reduces memory requirement by 2.2× while sacrificing only 15% of inference time compared to a solver that considers inference time only. In addition, our optimization approach exposes a range of optimal points for different configurations across the Pareto frontier of memory and latency trade-off, which can be used under arbitrary system constraints.
Multimodal Clustering via Deep Commonness and Uniqueness Mining

Deep multimodal clustering have shown their competitiveness among different multimodal clustering algorithms. Existing algorithms usually boost the multimodal clustering by exploring the common knowledge among multiple modalities, which underutilizes the uniqueness of multiple modalities. In this paper, we enhance the mining of modality-common knowledge by extracting the modality-unique knowledge of each modality simultaneously. Specifically, we first utilize autoencoders to extract the modality-common and modality-unique features of each modality respectively. Meanwhile, the cross reconstruction is used to build latent connections among different modalities, i.e., maintain the consistency of modality-common features of each modality as well as heightening the diversity of modality-unique features of each modality. After that, modality-common features are fused to cluster the multimodal data. Experimental results on several benchmark datasets demonstrate that the proposed method outperforms state-of-art works obviously.
Classifying CT scan images based on contrast material and age of a person: ConvNets approach

A computed tomography scan is a process that uses computers and X-ray machines, which rotate to create cross-sectional images of the body. The images provided by the CT scans are more accurate and detailed in comparison to standard X-ray images. The detail in the images can be increased by administering a chemical dye called contrast. A medical practitioner can administer the dye with prior permission from the patient as there is a risk of having side effects such as cancerous disease and headaches. Our study aims at providing a classification model using a CNN learning algorithm to classify images based on the age of a person and if contrast material is used. A practitioner hence can get prior knowledge about trends in CT scan images and if administering contrast material would affect the image in any way. Our proposed model provides an accuracy of 89.99% on any test CT image.
Classification of Ovarian Cysts on Ultrasound Images Using Watershed Segmentation and Contour Analysis

Ovarian cyst is a disease that occurs in the uterus of a woman, the method of detection and analysis is carried out by experts by looking at and observing the size of the cyst and the characteristics of the cyst on an ultrasound device. The accuracy of manual ovarian cyst measurement analysis on ultrasound examination results often results in errors, therefore a tool is needed to calculate the size of the cyst and detect the characteristics of the cyst based on the papillary growth in the cyst. Ultrasound image from the hospital as input from the system, then a preprocessing process is carried out to remove noise in the image, the next step is the segmentation process using the watershed method, the segmentation results will be used for feature extraction by detecting cysts and papillary and their sizes using contour analysis with the bounding box method. The extraction feature will be used for cyst classification. This system has an accuracy rate of 97.8%.
Gesture Enhanced Comprehension of Ambiguous Human-to-Robot Instructions

This work demonstrates the feasibility and benefits of using pointing gestures, a naturally-generated additional input modality, to improve the multi-modal comprehension accuracy of human instructions to robotic agents for collaborative tasks.We present M2Gestic, a system that combines neural-based text parsing with a novel knowledge-graph traversal mechanism, over a multi-modal input of vision, natural language text and pointing. Via multiple studies related to a benchmark table top manipulation task, we show that (a) M2Gestic can achieve close-to-human performance in reasoning over unambiguous verbal instructions, and (b) incorporating pointing input (even with its inherent location uncertainty) in M2Gestic results in a significant (30%) accuracy improvement when verbal instructions are ambiguous.
Descriptive richness of free‐text metadata: A comparative analysis of three language archives

Abstract As archiving became a priority in documentary linguistics only in the 1990's, existing research indicates that language archives are not yet up to date on best practices in information organization. As a result, metadata in language archives varies substantially, depending on the depositor, self‐upload procedures, and metadata creation guidelines. Focusing on free‐text metadata, known to provide rich information, this study analyzes item‐level metadata in three language archives: the Endangered Language Archive, Pacific Regional Archive for Digital Sources in Endangered Cultures, and the Archive of the Indigenous Languages of Latin America. The study identified categories of information included in Description metadata fields and the relative distribution of these categories. The most commonly occurring categories of information observed in this study can serve as a basis for the development of best practice guidelines for item‐level metadata in language archives.
Toward an Enhanced Bengali Text Classification Using Saint and Common Form

Language processing tool has been strengthened by the measurements of text classification. Due to this concern many approaches investigate through the text documentation problem. Behalf of this conception we have focused on our bengali text written format. A long period of time bengali people are familiar with two bengali accents about saint and common form. With concern text document processing becomes easier to translate. Most well-known supervised six classifiers we have used to classify these two bengali forms of saint and common. Classifiers prediction will determine whether it is saint or common form. Collection of text documents more than 1200 mix sentences grabbed from bengali written sources. Each text needs preprocess to classify the text into a solid form of output. Before applying algorithms there has been some prerequisite ability to split the sentences, stemming, remove stop words, construct contraction. Ending with preprocess, the processed bengali text had been taken as input on machine learning classifiers that have raised very spontaneous outcomes over the accent of bengali data. Foremost output produced by NB classifier to identify the actual form about 77% on bengali text of saint and common form. Apart from that, other ML classifiers XGB, RB, DT, SVC, KNN showed nearly prediction upto 77% -64% accuracy which we have proposed in different segments of this paper.
GNNVis: Visualize Large-Scale Data by Learning a Graph Neural Network Representation

Many achievements have been made by studying how to visualize large-scale and high-dimensional data in typically 2D or 3D space. Normally, such a process is performed through a non-parametric (unsupervised) approach which is limited in handling the unseen data. In this work, we study the parametric (supervised) model which is capable to learn a mapping between high-dimensional data space Rd and low-dimensional latent space Rs with similarity structure in Rd preserved where s l d. The GNNVis is proposed, a framework that applies the idea of Graph Neural Networks (GNNs) to the parametric learning process and the learned mapping serves as a Visualizer (Vis) to compute the low-dimensional embeddings of unseen data online. In our framework, the features of data nodes, as well as the (hidden) information of their neighbors are fused to conduct Dimension Reduction. To the best of our knowledge, none of the existing visualization works have studied how to combine such information into the learning representation. Moreover, the learning process of GNNVis is designed as an end-to-end manner and can easily be extended to arbitrary Dimension Reduction methods if the corresponding objective function is given. Based on GNNVis, several typical dimension reduction methods t-SNE, LargeVis, and UMAP are investigated. As a parametric framework, GNNVis is an inherently efficient Visualizer capable of computing the embeddings of large-scale unseen data. To guarantee its scalability in the Training Stage, a novel training strategy with Subgraph Negative Sampling (SNS) is conducted to reduce the corresponding cost. Experimental results in real datasets demonstrate the advantages of GNNVis. The visualization quality of GNNVis outperforms the state-of-the-art parametric models, and is comparable to that of the non-parametric models.
Fusing Global Domain Information and Local Semantic Information to Classify Financial Documents

Many institutions are devoted to providing investment advising services to stock investors to help them make sound investment decisions. Industry analysts at these institutions need to analyze huge amounts of financial news documents, and yield investment advising reports to the service subscribers. Automatic document classification is required to organize collected financial news documents into pre-defined fine-grained categories, before the document analysis tasks. It is challenging to implement accurate fine-grained classification over massive financial documents, because documents from close fine-grained categories are highly semantically similar, while existing classification methods may fail to differentiate the subtle differences for documents from close fine-grained categories. In this paper, we implement a document classification framework, named GraphSEAT, to classify financial documents for a leading financial information service provider in China. Specifically, we build a heterogeneous graph to model the global structure of our targeting financial documents, where documents and financial named entities are deemed as nodes, and a document is connected to a contained named entity with an edge, and we then train a graph convolutional network (GCN) with attention mechanisms, to learn an embedding representation containing domain information for a document. We also extract semantic information from a document's word sequence with a neural sequence encoder, and finally form an overall embedding representation for a document and make the prediction, via fusing the two learned representations of the document with attention mechanisms. We perform extensive experiments on our real-world financial news dataset and three public datasets, to evaluate the performance of the document classification framework, and the experimental results demonstrate that GraphSEAT outperforms all compared eight baseline models, especially on our dataset.
A Survey on the Use of Computer Vision to Improve Software Engineering Tasks

Software engineering (SE) research has traditionally revolved around engineering the source code. However, novel approaches that analyze software through computer vision have been increasingly adopted in SE. These approaches allow analyzing the software from a different complementary perspective other than the source code, and they are used to either complement existing source code-based methods, or to overcome their limitations. The goal of this manuscript is to survey the use of computer vision techniques in SE with the aim of assessing their potential in advancing the field of SE research. We examined an extensive body of literature from top-tier SE venues, as well as venues from closely related fields (machine learning, computer vision, and human-computer interaction). Our inclusion criteria targeted papers applying computer vision techniques that address problems related to any area of SE. We collected an initial pool of 2,716 papers, from which we obtained 66 final relevant papers covering a variety of SE areas. We analyzed what computer vision techniques have been adopted or designed, for what reasons, how they are used, what benefits they provide, and how they are evaluated. Our findings highlight that visual approaches have been adopted in a wide variety of SE tasks, predominantly for effectively tackling software analysis and testing challenges in the web and mobile domains. The results also show a rapid growth trend of the use of computer vision techniques in SE research.
Boosting prior knowledge in streaming variational Bayes

Exploiting prior/human knowledge is an effective way to enhance Bayesian models, especially in cases of sparse or noisy data, for which building an entirely new model is not always possible. There is a lack of studies on the effect of external prior knowledge in streaming environments, where the data come sequentially and infinitely. In this work, we show the problem of vanishing prior knowledge in streaming variational Bayes. This is a serious drawback in various applications. We then develop a simple framework to boost the external prior when learning a Bayesian model from data streams. By boosting, the prior knowledge can be maintained and efficiently exploited through each minibatch of streaming data. We evaluate the performance of our framework in four scenarios: streaming in synthetic data, streaming sentiment analysis, streaming learning for latent Dirichlet allocation, and streaming text classification, in comparison with the methods that do not keep priors. From extensive experiments, we find that when provided good external knowledge, our framework can improve the performance of a Bayesian model, often by a significant margin for noisy and short text streams.
Low cost thermal sensor array for wide area monitoring

Thermal sensors have come into light with the rise of the COVID-19 global pandemic as one of the tools used for screening. There are multiple sensors available on the market ranging from low cost to very high cost. This paper presents how smaller and cheaper sensors can be combined in a larger array of sensors that give better coverage and can be used for shape detection and object tracking. The sensors used are AMG8833 FeatherWing and the platform is Raspberry Pi.
Fast Task Adaptation Based on the Combination of Model-Based and Gradient-Based Meta Learning

Deep reinforcement learning (DRL) recently has attained remarkable results in various domains, including games, robotics, and recommender system. Nevertheless, an urgent problem in the practical application of DRL is fast adaptation. To this end, this article proposes a new and versatile metalearning approach called fast task adaptation via metalearning (FTAML), which leverages the strengths of the model-based methods and gradient-based metalearning methods for training the initial parameters of the model, such that the model is able to efficiently master unseen tasks with a little amount of data from the tasks. The proposed algorithm makes it possible to separate task optimization and task identification, specifically, the model-based learner helps to identify the pattern of a task, while the gradient-based metalearner is capable of consistently improving the performance with only a few gradient update steps through making use of the task embedding produced by the model-based learner. In addition, the choice of network for the model-based learner in the proposed method is also discussed, and the performance of networks with different depths is explored. Finally, the simulation results on reinforcement learning problems demonstrate that the proposed approach outperforms compared metalearning algorithms and delivers a new state-of-the-art performance on a variety of challenging control tasks.
Features of non-classical light during its interaction with atoms and semiconductor nano-systems

Abstract The interactions of a three-level atomic system with a single mode of quantized field is investigated analytically. The effect of coherent population trapping is demonstrated for an arbitrary initial quantum field state and the peculiarities of this regime in squeezed vacuum light are found. Mutual influence between atomic and field subsystems and their entanglement arising during the interaction is demonstrated. A significant change of the field state during the interaction is shown. The possibility of photon bunching and anti-bunching and the formation of a quantum field with non-Gaussian properties is found.
Exploiting Raw Images for Real-Scene Super-Resolution

Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single image super-resolution, most algorithms only perform well on synthetic data, which limits their applications in real scenarios. In this paper, we study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images. We focus on two issues of existing super-resolution algorithms: lack of realistic training data and insufficient utilization of visual information obtained from cameras. To address the first issue, we propose a method to generate more realistic training data by mimicking the imaging process of digital cameras. For the second issue, we develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images. In addition, we propose a dense channel-attention block for better image restoration as well as a learning-based guided filter network for effective color correction. Our model is able to generalize to different cameras without deliberately training on images from specific camera types. Extensive experiments demonstrate that the proposed algorithm can recover fine details and clear structures, and achieve high-quality results for single image super-resolution in real scenes.
Continuous Experiment Definition Characteristics

The definition of an experiment is a fundamental artifact of continuous experiments. It is used to formalize ideas as a plan of action and to document attained knowledge. Although there is a large body of research on continuous experimentation, the definition of experiments is not researched sufficiently. In this paper, we present a taxonomy of characteristics of continuous experiment definitions. The taxonomy is synthesized from a systematic literature review and a tool review of experimentation platforms. Our findings show amongst others that there are 17 characteristics of experiment definitions. Moreover, that platforms primarily focus on the execution of experiments and that experimentation is more than the execution of an experiment.
Two-Stage Audience Expansion for Financial Targeting in Marketing

With the revolution of mobile internet, online finance has grown explosively. In this new area, one challenge of significant importance is how to effectively deliver the financial products or services to a set of target users by marketing. Given a product or service to be promoted and a set of users as seeds, audience expansion is such a targeting technique, which aims to find potential audience among a large number of users. However, in the context of finance, financial products and services are dynamic in nature as they co-vary with the socio-economic environment. Moreover, marketing campaigns for promoting products or services always consist of different rules of play, even for the same type of products or services. As a result, there is a strong demand for the timeliness of seeds in financial targeting. Conventional one-stage audience expansion methods, which generate expanded users by expanding over seeds, would encounter two problems under this setting: (1) the seeds would inevitably involve a number of users that are not representative for expansion, and direct expansion over these noisy seeds would dramatically deteriorate the performance; (2) one-stage expansion over fixed seeds cannot timely and accurately capture users' preferences over the currently running campaign due to the lack of timeliness of seeds.
RenewLedger : Renewable energy management powered by Hyperledger Fabric

Trading and storage of renewable energy offers a way for the prosumer to extract value from the surplus energy that they produce, while also mitigating energy shortfall. Power companies can enlist prosumers in demand response strategies for grid stability and cost savings. We present RenewLedger, a blockchain-based framework for renewable energy transaction, storage management and direct-to-consumer demand response incentivization and gamification for peak shaving. We design and implement this system using Hyperledger Fabric and report on performance benchmarking experiments conducted using Hyperledger Caliper.
Who Touched My Browser Fingerprint?

Browser fingerprints are dynamic, evolving with feature values changed over time. Previous fingerprinting datasets are either small-scale with only thousands of browser instances or without considering fingerprint dynamics. Thus, it remains unclear how an evolution-aware fingerprinting tool behaves in a real-world setting, e.g., on a website with millions of browser instances, let alone how fingerprint dynamics implicate privacy and security.