Reviewerly/exHarmony huggingface dataset:
Methods of Data Collection: A Fundamental Tool of Research

For conducting research, it is a must to collect data. Data is basically the information that is required for investigating a research problem after proper designing. The importance of data collection lies in the fact that without gathering the particular information the research could not be carried out. The data may be primary or secondary. Usually, the methods of primary data collection in behavioural sciences include observation methods, interviews, questionnaires, and through database. The sources of secondary data include the previously published books, magazines, journals, etc. and unpublished autobiographies and biographies, etc. Thus, data collection is mandatory to accomplish the research process and therefore, it is the fundamental tool of research. This paper reviews, in detail, the various methods and different ways of gathering the information for undertaking research.
The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China

Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (p &lt; 0.01), and of Hg was 0.318 (p &lt; 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check

Chinese Spelling Check (CSC) is to detect and correct Chinese spelling errors.Many models utilize a predefined confusion set to learn a mapping between correct characters and its visually similar or phonetically similar misuses but the mapping may be out-of-domain.To that end, we propose SpellBERT, a pretrained model with graph-based extra features and independent on confusion set.To explicitly capture the two erroneous patterns, we employ a graph neural network to introduce radical and pinyin information as visual and phonetic features.For better fusing these features with character representations, we devise masked language model alike pre-training tasks.With this feature-rich pre-training, SpellBERT with only half size of BERT can show competitive performance and make a state-of-the-art result on the OCR dataset where most of the errors are not covered by the existing confusion set *
Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models

The key challenge of question answering over knowledge bases (KBQA) is the inconsistency between the natural language questions and the reasoning paths in the knowledge base (KB).Recent graph-based KBQA methods are good at grasping the topological structure of the graph but often ignore the textual information carried by the nodes and edges.Meanwhile, pre-trained language models learn massive open-world knowledge from the large corpus, but it is in the natural language form and not structured.To bridge the gap between the natural language and the structured KB, we propose three relation learning tasks for BERTbased KBQA, including relation extraction, relation matching, and relation reasoning.By relation-augmented training, the model learns to align the natural language expressions to the relations in the KB as well as reason over the missing connections in the KB.Experiments on WebQSP show that our method consistently outperforms other baselines, especially when the KB is incomplete.
Uncovering Main Causalities for Long-tailed Information Extraction

Information Extraction (IE) aims to extract structural information from unstructured texts.In practice, long-tailed distributions caused by the selection bias of a dataset, may lead to incorrect correlations, also known as spurious correlations, between entities and labels in the conventional likelihood models.This motivates us to propose counterfactual IE (CFIE), a novel framework that aims to uncover the main causalities behind data in the view of causal inference.Specifically, 1) we first introduce a unified structural causal model (SCM) for various IE tasks, describing the relationships among variables; 2) with our SCM, we then generate counterfactuals based on an explicit language structure to better calculate the direct causal effect during the inference stage; 3) we further propose a novel debiasing approach to yield more robust predictions.Experiments on three IE tasks across five public datasets show the effectiveness of our CFIE model in mitigating the spurious correlation issues.
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering

Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on Question Answering tasks.However, most of those datasets are in English, and the performances of state-of-theart multilingual models are significantly lower when evaluated on non-English data.Due to high data collection costs, it is not realistic to obtain annotated data for each language one desires to support.We propose a method to improve Crosslingual Question Answering performance without requiring additional annotated data, leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion.We show that the proposed method allows to significantly outperform the baselines trained on English data only, establishing thus a new state-of-the-art on four multilingual datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr).* * : equal contribution.The work of Arij Riabi was partly carried out while she was working at reciTAL. 1 https://rajpurkar.github.io/SQuAD-explorer/ et al. (2020) and Lewis et al. ( 2020a) concurrently proposed two different evaluation sets which are comparable to the SQuAD development set.Both reach the same conclusion: due to the lack of non-English training data, models do not achieve the same performance in Non-English languages than they do in English.To the best of our knowledge, no method has been proposed to fill this gap.
Dilated Filters for Edge-Detection Algorithms

Edges are a basic and fundamental feature in image processing, that are used directly or indirectly in huge amount of applications. Inspired by the expansion of image resolution and processing power dilated convolution techniques appeared. Dilated convolution have impressive results in machine learning, we discuss here the idea of dilating the standard filters which are used in edge detection algorithms. In this work we try to put together all our previous and current results by using instead of the classical convolution filters a dilated one. We compare the results of the edge detection algorithms using the proposed dilation filters with original filters or custom variants. Experimental results confirm our statement that dilation of filters have positive impact for edge detection algorithms form simple to rather complex algorithms.
Slide Over

Conventional analysis using clinical histopathology is based on bright-field microscopy of thinly sliced tissue specimens. Although bright-field microscopy is a simple and robust method of examining microscope slides, the preparation of the slides needed is a lengthy and labor-intensive process. Slide-free histopathology, however, uses direct imaging of intact, minimally processed tissue samples using advanced optical-imaging systems, bypassing the extended workflow now required for the preparation of tissue sections. This article explains the technical basis of slide-free microscopy, reviews common slide-free optical microscopy techniques, and discusses the opportunities and challenges involved in clinical implementation.
Towards Building ASR Systems for the Next Billion Users

Recent methods in speech and language technology pretrain very large models which are fine-tuned for specific tasks. However, the benefits of such large models are often limited to a few resource rich languages of the world. In this work, we make multiple contributions towards building ASR systems for low resource languages from the Indian subcontinent. First, we curate 17,000 hours of raw speech data for 40 Indian languages from a wide variety of domains including education, news, technology, and finance. Second, using this raw speech data we pretrain several variants of wav2vec style models for 40 Indian languages. Third, we analyze the pretrained models to find key features: codebook vectors of similar sounding phonemes are shared across languages, representations across layers are discriminative of the language family, and attention heads often pay attention within small local windows. Fourth, we fine-tune this model for downstream ASR for 9 languages and obtain state-of-the-art results on 3 public datasets, including on very low-resource languages such as Sinhala and Nepali. Our work establishes that multilingual pretraining is an effective strategy for building ASR systems for the linguistically diverse speakers of the Indian subcontinent.
Deepfake Detection Using the Rate of Change between Frames Based on Computer Vision

Recently, artificial intelligence has been successfully used in fields, such as computer vision, voice, and big data analysis. However, various problems, such as security, privacy, and ethics, also occur owing to the development of artificial intelligence. One such problem are deepfakes. Deepfake is a compound word for deep learning and fake. It refers to a fake video created using artificial intelligence technology or the production process itself. Deepfakes can be exploited for political abuse, pornography, and fake information. This paper proposes a method to determine integrity by analyzing the computer vision features of digital content. The proposed method extracts the rate of change in the computer vision features of adjacent frames and then checks whether the video is manipulated. The test demonstrated the highest detection rate of 97% compared to the existing method or machine learning method. It also maintained the highest detection rate of 96%, even for the test that manipulates the matrix of the image to avoid the convolutional neural network detection method.
Blockchain Innovation: Current and Future Viewpoints for the Travel Industry

Disintermediation of the travel industry in the modern area has the most powerful and successful innovation, obviously with the assistance of Blockchain innovation. The market structure has changed since the appearance of online travel planners. Determined to improve the system for the travel industry that as of now depends on this blockchain innovation, for which the reason for this paper was made, a subjective strategy with semi-organized meetings appears to be appropriate to decide the ideal use to draw out the fundamental capability of this blockchain innovation for the business. the travel industry as per specialists. From the consequences of meetings led just as from the aftereffects of considering the impact of blockchain on business, it tends to be inferred that blockchain innovation is a fitting new advance as far as restricting and forbidding new go betweens in the entrance of the travel industry, subsequently it will prompt the vanishing of go-betweens from the travel industry market and an increment in the framework straightforward that everybody knows.
Contextualized Query Embeddings for Conversational Search

This paper describes a compact and effective model for low-latency passage retrieval in conversational search based on learned dense representations.Prior to our work, the state-ofthe-art approach uses a multi-stage pipeline comprising conversational query reformulation and information retrieval modules.Despite its effectiveness, such a pipeline often includes multiple neural models that require long inference times.In addition, independently optimizing each module ignores dependencies among them.To address these shortcomings, we propose to integrate conversational query reformulation directly into a dense retrieval model.To aid in this goal, we create a dataset with pseudo-relevance labels for conversational search to overcome the lack of training data and to explore different training strategies.We demonstrate that our model effectively rewrites conversational queries as dense representations in conversational search and open-domain question answering datasets.Finally, after observing that our model learns to adjust the L 2 norm of query token embeddings, we leverage this property for hybrid retrieval and to support error analysis.
Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction

Emotion cause extraction (ECE) aims to extract the causes behind the certain emotion in text.Some works related to the ECE task have been published and attracted lots of attention in recent years.However, these methods neglect two major issues: 1) pay few attentions to the effect of document-level context information on ECE, and 2) lack of sufficient exploration for how to effectively use the annotated emotion clause.For the first issue, we propose a bidirectional hierarchical attention network (BHA) corresponding to the specified candidate cause clause to capture the document-level context in a structured and dynamic manner.For the second issue, we design an emotional filtering module (EF) for each layer of the graph attention network, which calculates a gate score based on the emotion clause to filter the irrelevant information.Combining the BHA and EF, the EF-BHA can dynamically aggregate the contextual information from two directions and filters irrelevant information.The experimental results demonstrate that EF-BHA achieves the competitive performances on two public datasets in different languages (Chinese and English).Moreover, we quantify the effect of context on emotion cause extraction and provide the visualization of the interactions between candidate cause clauses and contexts.
Verifiable Computing Applications in Blockchain

From weak clients outsourcing computational tasks to more powerful machines, to distributed blockchain nodes needing to agree on the state of the ledger in the presence of adversarial nodes, there is a growing need to efficiently verify the results of computations delegated to untrusted third parties. Verifiable computing is a new and interesting research area that addresses this problem. Recently, new applications of verifiable computing techniques have emerged in blockchain technology for secure key management, sybil-resistance and distributed consensus, and smart contracts, while providing desired performance and privacy guarantees. In this paper, we provide an overview of common methods for verifying computation and present how they are applied to blockchain technology. We group the presented verifiable computing applications into five main application areas, i.e., multiparty approval for secure key management, sybil-resistance and consensus, smart contracts and oracles, scalability, and privacy. The main contribution of this survey is to answer two research questions: 1) what are the main application areas of verifiable computing in blockchain technology, and 2) how are verifiable computing techniques used in major blockchain projects today.
Sharing Classical Secrets with Continuous-Variable Entanglement: Composable Security and Network Coding Advantage

Secret sharing is a multi-party cryptographic primitive that can be applied to a network of partially distrustful parties for encrypting data that is both sensitive (it must remain secure) and important (it must not be lost or destroyed). When sharing classical secrets (as opposed to quantum states), one can distinguish between protocols that leverage bi-partite quantum key distribution (QKD) and those that exploit multi-partite entanglement. The latter class are known to be vulnerable to so-called participant attacks and, while progress has been made recently, there is currently no analysis that quantifies their performance in the composable, finite-size regime which has become the gold standard for QKD security. Given this - and the fact that distributing multi-partite entanglement is typically challenging - one might well ask: Is there any virtue in pursuing multi-partite entanglement based schemes? Here, we answer this question in the affirmative for a class of secret sharing protocols based on continuous variable graph states. We establish security in a composable framework and identify a network topology, specifically a bottleneck network of lossy channels, and parameter regimes within the reach of present day experiments for which a multi-partite scheme outperforms the corresponding QKD based method in the asymptotic and finite-size setting. Finally, we establish experimental parameters where the multi-partite schemes outperform any possible QKD based protocol. This one of the first concrete compelling examples of multi-partite entangled resources achieving a genuine advantage over point-to-point protocols for quantum communication and represents a rigorous, operational benchmark to assess the usefulness of such resources.
A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.
Analysis of Local Trajectory Planners for Mobile Robot with Robot Operating System

The goal of this work is to analyze and compare trajectory planners for a mobile robot in Robot Operating System (ROS), focusing on the performance of local planners on symmetric and asymmetric environments. In addition, two global planners, Dijkstra and A-star, are implemented in order to have a complete analysis and comprehension of the navigation architecture. Two local planning algorithms, Dynamic Window Approach and Timed Elastic Bands, are analyzed and compared more in depth using the mobile robot TurtleBot 3 Burger, an open-source and low-cost platform. The analyzed criteria were geometric and angular precision of the final position and orientation, time and distance of the complete trajectory, and usage of computational power. Experiments were carried out in two environments with different spatial arrangement of obstacles, with the intention of analyzing the behavior both in simulation with the Gazebo software and in the real robot. Both local planning algorithms enabled the robot to reach the target destination without any collisions, presenting the main difference in the usage of processing power.
A Tent Marine Predators Algorithm with Estimation Distribution Algorithm and Gaussian Random Walk for Continuous Optimization Problems

The marine predators algorithm (MPA) is a novel population-based optimization method that has been widely used in real-world optimization applications. However, MPA can easily fall into a local optimum because of the lack of population diversity in the late stage of optimization. To overcome this shortcoming, this paper proposes an MPA variant with a hybrid estimation distribution algorithm (EDA) and a Gaussian random walk strategy, namely, HEGMPA. The initial population is constructed using cubic mapping to enhance the diversity of individuals in the population. Then, EDA is adapted into MPA to modify the evolutionary direction using the population distribution information, thus improving the convergence performance of the algorithm. In addition, a Gaussian random walk strategy with medium solution is used to help the algorithm get rid of stagnation. The proposed algorithm is verified by simulation using the CEC2014 test suite. Simulation results show that the performance of HEGMPA is more competitive than other comparative algorithms, with significant improvements in terms of convergence accuracy and convergence speed.
Object Detection in Autonomous Driving Scenarios Based on an Improved Faster-RCNN

Object detection is one of the key algorithms in automatic driving systems. Aiming at addressing the problem of false detection and the missed detection of both small and occluded objects in automatic driving scenarios, an improved Faster-RCNN object detection algorithm is proposed. First, deformable convolution and a spatial attention mechanism are used to improve the ResNet-50 backbone network to enhance the feature extraction of small objects; then, an improved feature pyramid structure is introduced to reduce the loss of features in the fusion process. Three cascade detectors are introduced to solve the problem of IOU (Intersection-Over-Union) threshold mismatch, and side-aware boundary localization is applied for frame regression. Finally, Soft-NMS (Soft Non-maximum Suppression) is used to remove bounding boxes to obtain the best results. The experimental results show that the improved Faster-RCNN can better detect small objects and occluded objects, and its accuracy is 7.7% and 4.1% respectively higher than that of the baseline in the eight categories selected from the COCO2017 and BDD100k data sets.