Reviewerly/exHarmony huggingface dataset:
Class 1 and Class 2 Underwater Image Enhancement and Restoration Under Turbidity Conditions

Poor visibility in underwater images is commonly attributed to the presence of impurities and the absorbed light being scattered while travelling through impure water. In this paper, image enhancement and restoration techniques are applied to TURBID image datasets. The TURBID dataset consists of three different types of underwater image conditions where blue solution, milk solution, or chlorophyll solution is added to water. The images undergo Histogram equalization (HE) and are filtered with a Wiener filter for image enhancement and image restoration, respectively. HE as the chosen enhancement method proved that it could enhance the image quality as the water surface can be seen clearer after enhancement by visual inspection. Three Wiener filter classes are chosen as the restoration method to reduce the Mean Square Error (MSE) value and to get high Peak Signal-to-Noise-Ratio (PSNR) with desired SNR value. Finally, these two image processing techniques, enhancement, and restoration are combined and the image quantitative values are compared to show that the image performance can be improved with combined enhancement and restoration techniques. It is found that Class 1 Wiener Filter with Enhance then Restore (ER) method has a value of 0 for MSE which is the lowest compared to other studied methods and has infinity values for PSNR and SNR.
Graph Attention Convolutional Network with Motion Tempo Enhancement for Skeleton-Based Action Recognition

Graph convolutional network (GCN) exhibits advantages in handling non-Euclidean data. Previous works using spatio-temporal graph convolution for skeleton action recognition achieve good performance. However, several limitations still exist. First, the uniform modeling of joint motion assumes that the motion tempo of different joints remains constant, which ignores the dynamic changes in the position offset of each joint during the action. In this work, we propose a robust action feature extractor, graph attention convolutional network with motion tempo enhancement (MTEA-GCN), which captures different joint motion tempos with two streams. Second, the dependencies among bone-connected and spatially separated joints cannot be adequately considered from the graph topology based on the human physical connections. For this reason, we propose a multi-neighborhood graph attention convolution module that fully considers the dependencies among each joint and different neighborhood joints while focusing on discriminative joints. This study experiments on two large-scale skeleton datasets, including Kinetics-Skeleton and NTU RGB+D. Our proposed MTEA-GCN shows good performance with comparable computational complexity and fewer parameters.
DFSNet: Dividing-fuse deep neural networks with searching strategy for distributed DNN architecture

The overwhelming parameters and computation consumption of deep neural networks limit their applicability to a single computing node with poor computing power, such as edge and mobile devices. Most previous works leverage model pruning and compression strategies to reduce DNN parameters for resource-constrained devices. However, most model compression methods may suffer from accuracy loss. Recently, we find that combine many weak computing nodes as a distributed system to run large and sophisticated DNN models is a promising solution for the issue. However, it is essential for the distributed system to design distributed DNN models and inference schemes, one of the great challenges of distributed system is how to design an efficient distributed DNN model for data parallelism and model parallelism, and communication overhead is also another critical performance bottleneck for distributed DNN model. Therefore, in this article, we propose DFSNet framework (Dividing-Fuse neural Network with Searching Strategy) for distributed DNN architecture. Firstly, the DFSNet framework includes a joint "dividing-fusing" method to convert regular DNN models into distributed models that are friendly for distributed systems. This method divides the conventional DNN model in the channel dimension, and sets a few special layers to fuse feature-map information from different channel groups for accuracy improvement. Since the fusion layers are sparse in the network, they do not increase too much extra inference time and communication overhead on the distributed nodes, but they can maintain the accuracy of distributed neural networks significantly. Secondly, considering the architecture of distributed computing nodes, we propose a parallel fusion topology to improve the utilization of different computing nodes. Lastly, the popular weight-sharing neural architecture search (NAS) technique is leveraged to search the position of fusion layers in the distributed DNN model for high accuracy and finally generate an efficient distributed DNN model. Compared with the original network, our converted distributed DNN achieves better performance (e.g. 1.88% precision boosting in ResNet56 on CIFAR-100 dataset, and 1.25% precision improving in MobileNetV2 on ImageNet dataset). In addition, most layers of DNN have been divided into different distributed nodes on channel dimension, which is particularly suitable for distributed DNN architecture with very low communication overhead.
Automated Query Graph Generation for Querying Knowledge Graphs

Natural language question answering over knowledge graphs is an important and interesting task as it enables common users to gain accurate answers in an easy and intuitive manner. However, it remains a challenge to bridge the gap between unstructured questions and structured knowledge graphs. To address the problem, a natural discipline is building a structured query to represent the input question. Searching the structured query over the knowledge graph can produce answers to the question. Distinct from the existing methods that are based on semantic parsing or templates, we propose an effective approach qaSQP powered by a novel notion, structural query pattern, in this paper. Given an input question, we first generate its query sketch that is compatible with the underlying structure of the knowledge graph. Then, we complete the query graph by labeling the nodes and edges under the guidance of the structural query pattern. Finally, answers can be retrieved by executing the constructed query graph over the knowledge graph. In order to improve the overall performance of answering questions, we propose the mutual optimization technique. Evaluations on three question-answering benchmarks show that our proposed approach outperforms state-of-the-art methods significantly.
A Multi-Populations Human Learning Optimization Algorithm

Due to the greediness of the individual learning operator and the social learning operator, the standard HLO is likely to fall into local optimum. Inspired by the fact that the multi-populations mechanism can increase the diversity, this paper proposes a multi-population human optimization algorithm (MPHLO), which divides the population into the elite subpopulation and ordinary subpopulation according to the fitness value. The elite population uses a global topology to fasten the convergence of the algorithm, while the ordinary population uses a ring topology to maintain the diversity of the algorithm. In addition, the two subpopulations both learn from the global optimal individual to guarantee the global search ability of the algorithm. The presented MPHLO is applied to solve CEC14 benchmark function and its results are compared with other state-of-art metaheuristic algorithms to evaluate its performance. The experimental results demonstrate that MPHLO has advantages and significantly outperforms the compared algorithms.
Learning Dense Entity-Aware Dialogue Intentions with Rewritten Utterance for External Knowledge Documents Retrieval

External knowledge-enhanced task-oriented dialogue systems aim to cover user requests beyond pre-defined DBs/APIs. Recently, existing dialogue systems have focused more on retrieving external knowledge sources relevant to dialogue contexts, achieving competitive results. However, due to the lack of modeling entity-aware dialogue intention, such dialogue systems are hard to accurately and efficiently link the out-of-API functions in real-world scenarios. To tackle this problem, this paper investigates learning dense entity-aware dialogue intentions for external knowledge documents retrieval in task-oriented dialogues. To this end, we propose an intention-guided two-stage training approach that includes intention-guided training and knowledge transfer stages. This approach, which leverages rewritten utterances that explicitly convey entity-aware user intentions, can improve the performance of existing Bi-Encoder retrievers such as DPR (Deep Passage Retriever). In intention-guided training stage, a posterior history encoder is initialized and guided by inputting rewritten utterances for learning discriminative dense representations. In knowledge transfer stage, these representations are transferred to a newly initialized prior encoder for inference via an extra intent consistency loss. In addition, negative sampling in test knowledge documents is used to learn more discriminative dense representations of the unseen domain. The advantages of our approach are no need for response annotations and extra response generator, additionally, it provides great scalability. The experimental results on augmented MultiWOZ 2.1 dataset show that our approach outperforms baseline models except for relevance classifiers in retrieval accuracy and has reasonably high efficiency.
Heterogeneous Graph Attention Network for User Geolocation

Identifying the geographic location of online social media users, also known as User Geolocation (UG), plays an essential part in many Internet application services. One main challenge is the scarcity of users’ public geographic information. To overcome it, most works focus on user geolocation prediction with posts and interactions on social media. However, they do not consider the distinction of variant social connections, which may impair the performance of the UG models. To address this issue, we propose a multi-view model, Heterogeneous graph Attention networks for user Geolocation (HAG), which introduces the attention mechanism to mine valuable cues in social networks and text contexts jointly. In the network module, we creatively apply a heterogeneous graph to model various social interactions and introduce a heterogeneous graph attention network to learn network structure information. In the text module, we propose a context attention network to extract geo-related text information. Extensive experiments conducted on three Twitter datasets show that HAG achieves state-of-the-art performance compared to strong baselines.
Automatic Colorization of images using Auto-encoders

Colorization of images is one of the preliminary steps of image analysis and documentation. Autocolorization is an automated process of converting a single-channeled image into a complete colorized 3 channel RGB image. There has been extensive research gap in the field since the dawn of deep learning. This document is a model for a statistical-learning driven approach to approach Autocolorization through building an Encoder-decoder model with Convolutional neural networks. Learning Models: Keras, openCV, Numpy and Tensorflow. A direct function to convert grayscale into coloured images that can be coupled with various software or sensors. The results obtained provide a visualization of autocolorization with different regularization techniques and optimizers.
A Chinese Knowledge Base Question Answering System

This paper presents a HAO-Interaction question answering system, which exploits knowledge based question answering (KBQA) technology to quickly obtain an answer path for the input question, and then a creative text generation mechanism to acquire the final answer text. The system also provides visibility of the answer path on the user interface in order to facilitate user understanding. Different from other KBQA systems, HAO-Interaction supports users to incorporate an organizational graph database while accessing all system functionalities. In addition, the answer generation solution implemented in the system does not require any training data. HAO-Interaction keeps low response latency while ensuring a high user satisfaction. The effectiveness of HAO-Interaction has been verified by analyzing thousands of user reviews collected by the system.
Sentiment Analysis of Online Learning Students Feedback for Facing New Semester: A Support Vector Machine Approach

Students often experience various feelings when facing a new semester. Feelings such as anxiety, fear, and excitement can occur when students take classes in online learning. As a result, in the new semester period, students gave various responses to their online lectures in the new semester. This study aims to classify online class student feedback on their participation in the new semester. Questionnaires in the form of essay questions were distributed to 375 students who took online lectures in the 2nd semester of the 2020/2021 academic year to find out how they felt about attending online lectures in the new semester. This study uses the sentiment analysis method to identify and extract student responses in subjective information that focuses on positive and negative polarities. The findings of this study indicate that sentiment analysis using the Support Vector Machine (SVM) method produces an accuracy of 84&#x0025;. SVM produces a positive prediction precision value of 77.61&#x0025;, while the negative predictive precision value gets 94.26&#x0025;. The experimental results show that sentiment analysis using the SVM method can classify student responses based on two polarities, namely positive and negative polarity.
Divide and Conquer: A Quick Scheme for Symbolic Regression

Symbolic regression aims to find a function that best explains the relationship between independent variables and the objective value based on a given set of sample data. Genetic programming (GP) is usually considered as an appropriate method for the problem since it can optimize functional structure and coefficients simultaneously. However, the convergence speed of GP might be too slow for large scale problems that involve a large number of variables. Fortunately, in many applications, the target function is separable or partially separable. This feature motivated us to develop a new method, divide and conquer (D&C), for symbolic regression, in which the target function is divided into a number of sub-functions and the sub-functions are then determined by any of a GP algorithm. The separability is probed by a new proposed technique, Bi-Correlation test (BiCT). D&C powered GP has been tested on some real-world applications, and the study shows that D&C can help GP to get the target function much more rapidly.
An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval

In the context of big data and the 4.0 industrial revolution era, enhancing document/information retrieval frameworks efficiency to handle the ever‐growing volume of text data in an ever more digital world is a must. This article describes a double-stage system of document/information retrieval. First, a Lucene-based document retrieval tool is implemented, and a couple of query expansion techniques using a comparable corpus (Wikipedia) and word embeddings are proposed and tested. Second, a retention-fidelity summarization protocol is performed on top of the retrieved documents to create a short, accurate, and fluent extract of a longer retrieved single document (or a set of top retrieved documents). Obtained results show that using word embeddings is an excellent way to achieve higher precision rates and retrieve more accurate documents. Also, obtained summaries satisfy the retention and fidelity criteria of relevant summaries.
Multi-Robot System Architecture Design in SysML and BPMN

Multi-Robot System (MRS) is a complex system that contains many different software and hardware components.This main problem addressed in this article is the MRS design complexity.The proposed solution provides a modular modeling and simulation technique that is based on formal system engineering method, therefore the MRS design complexity is decomposed and reduced.Modeling the MRS has been achieved via two formal Architecture Description Languages (ADLs), which are Systems Modeling Language (SysML) and Business Process Model and Notation (BPMN), to design the system blueprints.By using those abstract design ADLs, the implementation of the project becomes technology agnostic.This allows to transfer the design concept from on programming language to another.During the simulation phase, a multi-agent environment is used to simulate the MRS blueprints.The simulation has been implemented in Java Agent Development (JADE) middleware.Therefore, its results can be used to analysis and verify the proposed MRS model in form of performance evaluation matrix.
Big Data Clustering Algorithm Based on Computer Cloud Platform

The generation and development of big data has become a new, advanced and trending information processing technology. In the era of cloud computing, we can obtain massive and diverse user data of various types and orders of magnitude through computer network platforms. Internet information technology and mobile communication equipment are more and more widely used in people’s daily life. Based on this background, how to improve the effective analysis and research of these large amounts of data is the general trend and the foundation of future development. Therefore, this paper studies the clustering algorithm of the cloud computing platform in response to this situation. This article mainly uses the data analysis method, the experimental research method and the sampling survey method to carry on the algorithm research. Experimental results show that the improved K-means recall rate and precision rate can reach 76% and 80%.
Leveraging Local Temporal Information for Multimodal Scene Classification

Robust video scene classification models should capture the spatial (pixel-wise) and temporal (frame-wise) characteristics of a video effectively. Transformer models with self-attention which are designed to get contextualized representations for individual tokens given a sequence of tokens, are becoming increasingly popular in many computer vision tasks. However, the use of Transformer based models for video under-standing is still relatively unexplored. Moreover, these models fail to exploit the strong temporal relationships between the neighboring video frames to get potent frame-level representations. In this paper, we propose a novel self-attention block that leverages both local and global temporal relation-ships between the video frames to obtain better contextualized representations for the individual frames. This enables the model to understand the video at various granularities. We illustrate the performance of our models on the large-scale YoutTube-8M data set on the task of video categorization and further analyze the results to showcase improvement.
Speech Perception Model Based on Artificial Intelligence Technology

This article analyzes various factors that affect English phonological perception, points out that artificial intelligence technology can help improve English learners’ English phonological perception, and puts forward some suggestions on specific teaching modes. The teaching of English phonological perception should follow the development of artificial intelligence, promote its own teaching reform and innovation, build an education system to realize personalized learning and lifelong development.
Applicability of the Software Security Code Metrics for Ethereum Smart Contract

The Ethereum blockchain allows, through software called smart contract, to automate the contract execution between multiple parties without requiring a trusted middle party. However, smart contracts are vulnerable to attacks. Tools and programming practices are available to support the development of secure smart contracts. These approaches are effective to mitigate the smart contract vulnerabilities, but the unsophisticated ecosystem of the smart contract prevents these approaches from being foolproof. Besides, the Blockchain immutability does not allow smart contracts deployed in the Blockchain to be updated. Thus, businesses and developers would develop new contracts if vulnerabilities were detected in their smart contracts deployed in Ethereum, which would imply new costs for the business. To support developers and businesses in the smart contract security decision makings, we investigate the applicability of the security code metric from non-blockchain into the smart contract domain. We use the Goal Question Metric (GQM) approach to analyze the applicability of these metrics into the smart contract domain based on metric construct and measurement. As a result, we found 15 security code metrics that can be applied to smart contract development.
A Comprehensive Review on Text to Indian Sign Language Translation Systems

Language is the primary means of communication used by every individual. It is a tool to express greater ideas of ideas and emotions. It shapes thoughts and carries meanings. Indian Sign Language (ISL) used by the Deaf Community in India, does have linguistic constituents and structural properties. The area of computer science and linguistics, dealing with the relationship between computers and human language, is natural language processing. Through lexical analysis, syntax analysis, semantic analysis, processing discourses, pragmatic analysis, it processes the data. In determining the meaning of a sentence, it is critical to analyze the syntactic structure. In this paper, current computer sign language translators are considered and their pros and cons are identified and discussed. The general approaches followed by the systems are discussed. A new approach for construction of sign languages is proposed, thus resulting in increase in the accuracy of the system in translating the input phrases.
Toward Inter-Blockchain Communication Between Hyperledger Fabric Platforms

Blockchain technology is growing rapidly, and a large number of blockchain platforms and decentralized applications have come to play a vital role in many fields. However, the majority of blockchain networks operate in environments that are isolated and disconnected from each other. This has raised issues such as connectivity and scalability in the current blockchain platforms and has also limited blockchain adoption in some industry ecosystems. This chapter focuses on designing and implementing inter-blockchain communication between Hyperledger Fabric networks. Inter-blockchain architecture can enable many possible applications such as healthcare data sharing, supply chains, food traceability solutions, etc. Moreover, this chapter presents the performance evaluation of the proposed scheme in terms of execution time, throughput, and latency. The results show that the proposed solution reduces the overall level of performance of Hyperledger Fabric across all measured metrics. However, the overall proposed solution performance is acceptable, given the achieved interoperability and connectivity.