Reviewerly/exHarmony huggingface dataset:
Natural language inference for Malayalam language using language agnostic sentence representation

Natural language inference (NLI) is an essential subtask in many natural language processing applications. It is a directional relationship from premise to hypothesis. A pair of texts is defined as entailed if a text infers its meaning from the other text. The NLI is also known as textual entailment recognition, and it recognizes entailed and contradictory sentences in various NLP systems like Question Answering, Summarization and Information retrieval systems. This paper describes the NLI problem attempted for a low resource Indian language Malayalam, the regional language of Kerala. More than 30 million people speak this language. The paper is about the Malayalam NLI dataset, named MaNLI dataset, and its application of NLI in Malayalam language using different models, namely Doc2Vec (paragraph vector), fastText, BERT (Bidirectional Encoder Representation from Transformers), and LASER (Language Agnostic Sentence Representation). Our work attempts NLI in two ways, as binary classification and as multiclass classification. For both the classifications, LASER outperformed the other techniques. For multiclass classification, NLI using LASER based sentence embedding technique outperformed the other techniques by a significant margin of 12% accuracy. There was also an accuracy improvement of 9% for LASER based NLI system for binary classification over the other techniques.
Noise Analysis of Passive Sampling Mixers Using Auto- and Cross-Correlation Functions

An analysis of noise in passive sampling mixers is presented, which is based on derivations of auto- and cross- correlation functions that capture statistical co-dependence of noise components during the tracking and hold phases of sampling mixer operation. This study accurately predicts the noise-folding due to cyclostationarity of the sampling noise of the mixer. As will be discussed, notwithstanding its simple form, the proposed approach yields an accurate noise model, which will simplify to existing noise models presented by prior work under their adopted assumptions.
Performance Evaluation of the MapReduce-based Parallel Data Preprocessing Algorithm in Web Usage Mining with Robot Detection Approaches

Data preprocessing is an essential task to prepare suitable target datasets to apply statistical and data mining algorithms. It has become one of the complex segment in Web usage mining due to the massive and unstructured nature of Web server log. The data preprocessing segment in Web usage mining is divided into several phases such as data fusion, data cleaning, user identification, session identification, path completion, and data formatting. This paper focuses on the initial phases of the Web usage mining process, such as data cleaning, user identification, and session identification. Due to the growing size of log data at terabyte and petabyte scale, traditional data preprocessing algorithms fail at scalability points and possess Big Data issues. During the previous few years, the MapReduce framework has evolved as one of the most used parallel programming frameworks for processing Big Data on a cluster of nodes. In this paper, a MapReduce-based data preprocessing algorithm is developed. This algorithm comprises data preprocessing subphases such as data cleaning, user identification, and session identification. Various efficient heuristics are incorporated into existing MapReduce-based data preprocessing algorithm to detect ethical and unethical robots. Further several experiments are performed on a cluster of nodes and found that the proposed MapReduce-based data preprocessing algorithm is efficient and scalable for larger datasets. Moreover, we have also analyzed the impact of robots’ requests on sessions generated in the session identification phase to measure the effectiveness of the proposed approach.
Path Planning of Mobile Robots Using Enhanced Particle Swarm Optimization

With the rapid growth of technology and extensive application of robots, autonomous mobile robots have gained a lot of attention in industry and research. One of the crucial issues is to find out a collision-free path through which robots can reach the destination. In this paper, evolutionary optimization based autonomous path planning approach for mobile robots is proposed. We have introduced an adaptive fitness function which takes care of three crucial aspects such as (i) avoidance of obstacles in the path (ii) selection of shorter path length and (iii) selection of smoother path of the path planning process. Particle swarm optimization (PSO) algorithm is used to optimize the fitness function. Optimization of the objective function via suggested fitness function enables to generate a smoother and collision free path from the initial position to the destination position in presence of various obstacles. Extensive simulation experiments are performed to validate the performance of our proposed work. Performance behavior of our proposed work is compared with some of the existing state-of-the-art optimization based path planning methods and found to produce superior results.
Optimum Distribution of Protective Materials for COVID−19 with a Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm

Many application problems are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. One of these practical applications is to optimally distribute protective materials for the newly emerged COVID-19. It is defined for a decision-maker who wants to choose a subset of candidate hospitals comprising the maximization of the distributed quantities of protective materials to a set of chosen hospitals within a specific time shift. A nonlinear binary mathematical programming model for the problem is introduced with a real application case study; the case study is solved using a novel discrete binary gaining-sharing knowledge-based optimization algorithm (DBGSK). The solution algorithm proposes a novel binary adaptation of a recently developed gaining-sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge through their life span. Discrete binary version of GSK named novel binary gaining-sharing knowledge-based optimization algorithm (DBGSK) depends mainly on two binary stages: binary junior gaining-sharing stage and binary senior gaining-sharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space.
An Automated Tool for Implementing Deep Neural Networks on FPGA

FPGA based Deep Neural Networks provide the advantage of high performance, highly parallel implementation with very low energy requirements. A designer must consider various configuration choices, processing components, data-flow types, local memory hierarchy, and fixed-data-precision for a DNN implementation. An exploration tool is essential for building a reconfigurable, fast, and efficient DNN hardware accelerator. We present a methodology to automatically create an optimized FPGA-based hardware accelerator given DNNs from standard machine learning frameworks. We generate a High-Level-Synthesis (HLS) code depending on the user preferences with a set of optimization pragmas. For a faster and cost-effective hardware accelerator, the tool employs a software-model to estimate the execution time and hardware utilization using trained machine-learning models. The model evaluation results show that our framework performance speed-up compares well with the state-of-the-art accelerators using Xilinx FPGA platforms.
Bibliometric research in the field of artificial intelligence

Abstract This paper analyzes the development trend of global artificial intelligence from the perspective of bibliometrics. From the publishing trend, the number of papers published in the field of artificial intelligence is growing rapidly. China and the United States are among the top countries in the output of scientific research papers, and India has the fastest cumulative growth rate. In the research direction, the research on computer vision, image, deep learning and neural network is the research hotspot in recent years.
A relation network design for visible thermal person re-identification

Visible thermal person re-identification (VTReID) is a cross-modal retrieval problem. Which is a huge challenge to match pedestrians due to the large modality differences between visible and thermal images. Recent works have shown that using local features describing body parts as well as global features of the personal image itself, can give robust feature representations, even in the case of missing body parts. However, the direct use of individual local-level features, without considering the relationship between the parts of the body part, confuses the ability to distinguish the identity of different people with similar attributes in the corresponding part. To address this issue, we propose a relation network design for visible thermal person re-identification(RN-VTReID). Besides, we used a combination of Global Average Pooling (GAP) and Global Max Pooling (GMP) to obtain the background and texture features of pedestrians. The experimental results show that the RN-VTReID model outperforms state-of-the-art techniques on the SYSU-MM01 and RegDB datasets, which demonstrates the effectiveness of the method.
An Adaptive Communication-Efficient Federated Learning to Resist Gradient-Based Reconstruction Attacks

The widely deployed devices in Internet of Things (IoT) have opened up a large amount of IoT data. Recently, federated learning emerges as a promising solution aiming to protect user privacy on IoT devices by training a globally shared model. However, the devices in the complex IoT environments pose great challenge to federate learning, which is vulnerable to gradient-based reconstruction attacks. In this paper, we discuss the relationships between the security of federated learning model and optimization technologies of decreasing communication overhead comprehensively. To promote the efficiency and security, we propose a defence strategy of federated learning which is suitable to resource-constrained IoT devices. The adaptive communication strategy is to adjust the frequency and parameter compression by analysing the training loss to ensure the security of the model. The experiments show the efficiency of our proposed method to decrease communication overhead, while preventing privacy data leakage.
A Hybrid Model for Medical Paper Summarization Based on COVID-19 Open Research Dataset

Automatic generation of summarization or key phrase has been applied in a variety of domains, such as scientific papers and news. In response to the COVID-19 pandemic, the white house and some research groups have prepared the COVID-19 article dataset. To struggle against the COVID-19, the automatic summarization or key phrase method can be useful for those wanting a quick overview of what the latest information is saying on pandemic topics. This paper introduce the COVID-19 dataset from Kaggle and propose a novel model which combine a conventional Seq2Seq model with attention mechanism and a classical keywords extraction method. Our motivation is to obtain key information and maintain the result coherence. Experiment results reveal that our model depending on the COVID-19 dataset achieves a considerable improvement over a classical Seq2Seq model with attention mechanism.
Discrete Semantic Matrix Factorization Hashing for Cross-Modal Retrieval

Hashing has been widely studied for cross-modal retrieval due to its promising efficiency and effectiveness in massive data analysis. However, most existing supervised hashing has the limitations of inefficiency for very large-scale search and intractable discrete constraint for hash codes learning. In this paper, we propose a new supervised hashing method, namely, Discrete Semantic Matrix Factorization Hashing (DSMFH), for cross-modal retrieval. First, we conduct the matrix factorization via directly utilizing the available label information to obtain a latent representation, so that both the inter-modality and intra-modality similarities are well preserved. Then, we simultaneously learn the discriminative hash codes and corresponding hash functions by deriving the matrix factorization into a discrete optimization. Finally, we adopt an alternatively iterative procedure to efficiently optimize the matrix factorization and discrete learning. Extensive experimental results on three widely used image-tag databases demonstrate the superiority of the DSMFH over state-of-the-art cross-modal hashing methods.
Multimodal Weibull Variational Autoencoder for Jointly Modeling Image-Text Data

For multimodal representation learning, traditional black-box approaches often fall short of extracting interpretable multilayer hidden structures, which contribute to visualize the connections between different modalities at multiple semantic levels. To extract interpretable multimodal latent representations and visualize the hierarchial semantic relationships between different modalities, based on deep topic models, we develop a novel multimodal Poisson gamma belief network (mPGBN) that tightly couples the observations of different modalities via imposing sparse connections between their modality-specific hidden layers. To alleviate the time-consuming Gibbs sampler adopted by traditional topic models in the testing stage, we construct a Weibull-based variational inference network (encoder) to directly map the observations to their latent representations, and further combine it with the mPGBN (decoder), resulting in a novel multimodal Weibull variational autoencoder (MWVAE), which is fast in out-of-sample prediction and can handle large-scale multimodal datasets. Qualitative evaluations on bimodal data consisting of image-text pairs show that the developed MWVAE can successfully extract expressive multimodal latent representations for downstream tasks like missing modality imputation and multimodal retrieval. Further extensive quantitative results demonstrate that both MWVAE and its supervised extension sMWVAE achieve state-of-the-art performance on various multimodal benchmarks.
Research on operation characteristics of UHV converter valve hall based on intelligent image processing and 3D modeling technology

Abstract There are many power equipments in the UHV valve hall, including the converter transformer, converter valve tower, all kinds of bushing, tubular bus. The operation condition monitoring of the above main equipment mainly includes electrical performance parameter measurement, oil and gas insulation medium performance parameter measurement and other means. This paper mainly introduces a monitoring method for the operation status of converter valve hall from the perspective of image processing technology. The technology mainly includes: 1) the application of intelligent image processing technology to identify and classify the database of infrared thermal imager and ultraviolet imager; 2) the real-time on-line measurement for the insulation distance of typical fittings through Kalman filter technology, including the measurement of the hottest temperature The insulation distance between partial discharge point and zero potential grounding point; 3) the electric field simulation model of typical main equipment in valve hall is established based on three-dimensional modeling technology of finite element method, and the surface electric field distribution of key main equipment fittings is obtained. Combined with the image database information, insulation distance information and typical main equipment electric field distribution information, the operation state parameters are effectively obtained, and the intelligent algorithm is applied to automatically evaluate its operation state, discover the latent fault and locate the positive fault, so as to provide effective data support and the protection strategy for the main equipment operation and maintenance.
Survey on Blockchain Applications for Healthcare: Reflections and Challenges

Blockchain is a decentralized, distributed, public, and digital ledger that is used to record transactions across many computers. At the top of Blockchain, Ethereum introduces smart contracts to support on-chain storage and enable Decentralized Apps (DApps) to interact with the Blockchain programmatically. In the healthcare domain, programmable Blockchains provide different solutions to resolve some key challenges, such as immutability, security, data provenance, robustness, availability, and privacy. This paper aims to categorize and evaluate different solutions in the healthcare domain that makes use of programmable Blockchain or part of it. We use a software engineering approach to organize the existing 23 papers in multiple categories such as challenges addressed and quality attributes promoted. We will further categorize solutions based on how much they have used Blockchain and show the other research which Blockchain is not recommended to use. This work is intended to provide researchers in the field with a well-defined and structured categorization, plus insights into the existing literature.
High accuracy and geometry-consistent confidence prediction network for multi-view stereo

Confidence prediction task attempts to infer the correctness of estimated depth hypotheseshich has gained popularity recently in stereo matching and boosts the accuracy of disparity estimation. However, less attention is paid on confidence prediction of multi-view stereo (MVS), where multi-view depth estimation is a key step for high-quality reconstruction. In this work, we propose a Geometry-consistent Confidence prediction Network (GeoConfNet), where the correctness of a depth hypothesis is accurately predicted via a deep neural network that explores both spatial coherence and cross-view consistency. The proposed deep network consists of a feature extraction module, a U-Net-based fusion module and a confidence refinement module. Furthermore, we demonstrate that truncated signed distance field (TSDF) is a powerful cross-view feature which can be an effective complement to spatial features, thereby remarkably boosting confidence prediction accuracy of MVS. Exhaustive experiments on a variety of MVS datasets as well as stereo matching datasets clearly demonstrate that our method achieves significantly better performance than state-of-the-art methods in terms of area under the curve (AUC).
DCache: A Distributed Cache Mechanism for HDFS based on RDMA

Hadoop Distributed File System (HDFS) is an important component of Hadoop, which provides data storage service. The performance of the IO subsystem has a great influence on data processing efficiency. In the Hadoop system, computing jobs are scheduled to the nodes where the data is located to reduce IO time. The job scheduler and data distribution have a great impact on the efficiency of data processing. HDFS provides a mechanism for users to specify files or directories to be cached. But the cached data is only accessible for the jobs running in the same data nodes. In this paper, we present a distributed cache mechanism for HDFS with RDMA-capable network. We separate the caching function from the original data nodes. We design and implement a new kind of component for the caching function. The cached data in these nodes can be accessed directly by any nodes in the cluster through Remote Direct Memory Access (RDMA). Compared with the existing caching mechanism, our approach improves the performance and stability of the IO process. We also accelerate the data writing process using our caching mechanism. The experiments show that compared with the existing caching mechanism of HDFS, our scheme improves the reading latency up to 3 times, improves the throughput by 30%~95% for large files, and the writing performance by 64%. When the task runs on different nodes, the IO fluctuation caused by different data distribution is dropped from 20.71 to 0.50.
Spatial and Channel Dimensions Attention Feature Transfer for Better Convolutional Neural Networks

Knowledge distillation is an extensively researched model compression technology, which uses a large teacher network to transmit information to a small student network. The critical point of the knowledge distillation method to improve the performance of the student network is to find an effective method to extract the information from the feature. The attention mechanism is a widely used feature processing method to process features effectively and obtain more expressive information. In this paper, we propose to use the dual attention mechanism in knowledge distillation to improve the performance of student networks, which extracts information from the spatial and channel dimensions of the feature. The channel dimension attention is search 'what' channel is more meaningful, and the spatial dimension attention is determine 'where' part of the feature is more expressive in a feature map. We have conducted extensive experiments on different datasets, shown that by implementing a dual attention mechanism to extract more expressive information for knowledge transfer, the student network can achieve performance beyond the teacher network.
Reliable Prediction Intervals of PV Generation Using Quantile Regression Averaging Approach

Probabilistic PV generation forecasts are necessary for the uncertainty management in the long-term planning of power systems with PV integrations. The weather-dependent PV generation makes it a challenging task necessitating a nonparametric approach, such as quantile regression, for obtaining probabilistic forecasts. Here, a quantile regression averaging approach is used to combine the selective point forecasts of autoregressive conditional heteroscedastic model, random forests model, and multiple linear regression model to further enhance the forecast accuracy. The selection of sensible regressors with physical relevance is necessary for the proposed framework. Hence, such theoretically formulated regressors capable of modeling real-world PV generation data collected from the USA are utilized for assessing the efficacy of the proposed quantile regression averaging model. The reliabilities of the prediction intervals of the proposed model is compared with the popular quantile regression forests, the quantile k-nearest neighbors, and the basic quantile regression approaches via widely used performance indices.
Multi-Scale and Attention Residual Network for Single Image Dehazing

Image dehazing has always been a thorny ill-posed problem in low-level vision. In this paper, we propose a new model which combines feature pyramid network and attention based resblock, which is called the pyramid attention residual feature extraction network (PARFE-Net). The architecture of PARFE-Net can be summarized as follows:(I)The feature pyramid network module is used to extract features from the input image. Multi-scale can extract different features on different scales. The extracted features have great semantic differences in different depths.(II) Innovatively integrate channel-wise attention and pixel-wise attention and apply them to residual block. Attention mechanism can make the weight of the model higher in important places, which significantly affects the quality of the output image. (III) Combine the resblocks in II to get a large residual network, which is called iterative attention resblock. The features of different dimensions extracted from I are concatenated according to the channel dimension, which is used as the input of iterative attention resblock. The experiment is based on NYU2 and RESIDE. Compared with the commonly used dehazing model, the evaluation index PSNR and SSIM of our approach have achieved considerable or even better results, with better robustness.