Reviewerly/exHarmony huggingface dataset:
A pattern-first pipeline approach for entity and relation extraction

Entity-relation extraction is the task of extracting entities and their semantic relations from a piece of unstructured text. In recent studies, Machine Reading Comprehension (MRC) based methods have been applied to this task and achieved significant results. As a pipelined approach, these methods always extract head entities first, and then identify related tail entities by enumerating each relationship. These entity-first methods will lead to the entity redundancy problem. They also suffer from the error propagation issue, which is an inherent issue of the multi-step inference process. Moreover, most existing MRC-based models, which use tagging-based methods for entity recognition, could not deal with overlapping entities. To address these, we propose Patti, a Pattern-First Pipeline Approach for Entity and Relation Extraction. Firstly, Patti leverages a novel MRC-based pattern classifier to identify relation patterns. Next, a span-based method was introduced to extract entities under the guidance of questions parameterized by the patterns yield in the first step. Finally, to alleviate the error propagation issue, Patti employs an additional MRC-based classifier to remove falsely extracted candidate entity-relation triples. Experiment results show that our approach significantly outperforms the entity-first baseline models on CoNLL04 and ACE05 datasets.
Rapid -YOLO: A novel YOLO based architecture for shadow detection

Shadow detection is widely considered as segmentation problem that happens to be time consuming process with lots of duplication of results. The alternative to segmentation can be the applicability of single stage detectors like YOLO (You Only Look Once) that can run on the input image once and perform the detections during single pass. The study proposes a novel deep learning-based architecture namely, Rapid-YOLO which is an extended form of YOLOv3 architecture for detection of shadows. The proposed model is an extension of YOLOv3 architecture with addition of extra YOLO detection layers and convolution layers for aiding detection accuracy. The model exploits use of spatial pyramid pooling for scaling variations, a novel non-maximum suppression using chaotic whale optimization (CW-NMS) for overpowering false bounding box detections. We further propose tangent loss function for confidence and classification loss to improve training time and reduce errors. Drop block regularization is also used to reduce over-fitting and optimize results for shadow detection. Further, lack of proper dataset for single shot detectors encouraged us to create a novel dataset for the use of YOLO based detectors to boost further work in this direction. The model has been verified by rich experiments and results prove that the proposed methodology is superior to the state-of-the-art methods in terms of objective quantization and in subjective vision.
Multi‐scale spatial‐spectral attention network for multispectral image compression based on variational autoencoder

Based upon the fact that multispectral image compression needs to remove both spatial and spectral redundancy, recent learnt models via end-to-end manners have shown promising performance. However, most of them ignore the characteristics of multispectral image, i.e., the non-stationarity of spectral correlation and the scale-diversity of spatial features. Meanwhile, they directly utilize fully factorized entropy model, rendering compression performance suboptimal. This paper proposes a Multi-Scale Spatial-Spectral Attention Network (MSSSA-Net) based on variational autoencoder (VAE). Our MSSSA-Net (1) incorporates a simple neuroscience-based non-local attention module into attention mechanism to capture the tiny features in adjacent pixels and large-scale features in spatial domain simultaneously, (2) proposes a multi-scale spectral attention block to extract non-stationary correlation of adjacent spectra at different scales. We demonstrate that our MSSSA-Net offers the state-of-the-art performance in comparison with classical algorithms, including JPEG2000 and 3D-SPIHT, and recent learnt image compression models, on 7-band and 8-band datasets from Landsat-8 and WorldView-3 satellites, when measured by PSNR, MS-SSIM and Mean Spectral Angle. Extensive ablation experiments have verified the effectiveness of each component, and have demonstrated that, for multispectral image compression, Scale-only Hyperprior can make a better trade-off between compression performance and complexity compared with Mean & Scale Hyperprior and Joint Autoregressive model.
Analysis of Learner’s Sentiments to Evaluate Sustainability of Online Education System during COVID-19 Pandemic

Education is an important domain that may be improved by analyzing the sentiments of learners and educators. Evaluating the sustainability of the education system is critical for the continuous improvement and satisfaction of the learner’s community. This research work focused on the evaluation of the effectiveness of the online education system that has been adopted during the COVID-19 pandemic. For this purpose, sentiments/reviews of learners were collected from the Twitter website regarding the education domain during COVID-19. To automate the process of evaluation, a hybrid approach was applied that used a knowledgebase of opinion words along with machine learning and boosting algorithms with n-grams (unigram, bigram, trigram and combination of all these n-grams). This automated approach helped to evaluate the transition of the education system in different circumstances. An ensemble classifier was created in combination with a customized knowledgebase using classifiers that individually performed best with each of the n-grams. Due to the imbalanced nature of the data (tweets), these operations were performed by applying the synthetic minority oversampling technique (SMOTE). The obtained results show that the use of a customized knowledgebase not only improved the performance of the individual classifiers but also produced quality results with the ensemble model. As per the observed results, the online education system was not found sustainable as the majority of the learners were badly affected due to some important aspects (health issues, lack of training and resources).
Intuitive, Efficient and Ergonomic Tele-Nursing Robot Interfaces: Design Evaluation and Evolution

Tele-nursing robots provide a safe approach for patient-caring in quarantine areas. For effective nurse–robot collaboration, ergonomic teleoperation and intuitive interfaces with low physical and cognitive workload must be developed. We propose a framework to evaluate the control interfaces to iteratively develop an intuitive, efficient, and ergonomic teleoperation interface. The framework is a hierarchical procedure that incorporates general to specific assessment and its role in design evolution. We first present pre-defined objective and subjective metrics used to evaluate three representative contemporary teleoperation interfaces. The results indicate that teleoperation via human motion mapping outperforms the gamepad and stylus interfaces. The tradeoff with using motion mapping as a teleoperation interface is the non-trivial physical fatigue. To understand the impact of heavy physical demand during motion mapping teleoperation, we propose an objective assessment of physical workload in teleoperation using electromyography. We find that physical fatigue happens in the actions that involve precise manipulation and steady posture maintenance. We further implemented teleoperation assistance in the form of shared autonomy to eliminate the fatigue-causing component in robot teleoperation via motion mapping. The experimental results show that the autonomous feature effectively reduces the physical effort while improving the efficiency and accuracy of the teleoperation interface.
Moving a Robot In Unknown Areas Without Collision Using Robot Operating System

Nowadays, robots have become a most crucial role. With technology development, we can do so many things using robotic technology. There are lots of projects in which robots move in a known area. This study proposes a mechanism to move a robot in an unknown area. We can use this kind of robot in hazardous environments, and we can use this robot in several ways. The proposed system is based on the Robotic Operation System (ROS) and the simulator Gazebo. The obstacle avoidance part is done using a laser sensor. After that, there should be a direction-changing mechanism in the developing algorithm. That implemented using loops. Because after the robot changes direction, it again needs to check whether another object is there in the navigated location. The proposed algorithm was developed with the autonomous navigation mechanism. Map generation is another functionality of this project. It is done using Simultaneous Localization And Mapping (SLAM). Map visualization was done using the Rviz application. With the robot&#x2019;s movement, the robot&#x2019;s current position is calculated using x, y, and z coordinates. Also, this project has included reverse navigation functionality. Reverse navigation is a novel section in this research work. The objective of this study and the outcome is to move the robot without having any crashes. Also, we can use this to evaluate dangerous areas. Experimental results of the direction and velocity changes have been mentioned in the results and discussion section.
Exploration of Sentiment Analysis Techniques on a Multilingual Dataset Dealing with Tamil-English reviews

Multilingualism is the ability of an individual or a group to communicate effectively in three or more languages. On the other hand, monolinguals refer to the ability to converse in only one language. Multilingualism is defined as the use of two or more languages by a single speaker or a group of speakers. Multilingual Sentiment analysis refers to a group of languages that can be categorized as positive, negative, or neutral. It istough to capture something so demanding. The sentiments of what the user intended to say have to be created using Multilingual Sentiment Analysis. Due to the tremendous increase in user-generated multilingual information on social media, NLP (Natural Language Processing) is a field of study that focuses on the analysis of natural language. It has attempted to develop new methods for managing several languages in order to extract hidden emotion. The use of multiple languages in regular conversations has created a slew of problems. In reality, most multilingual people prefer to communicate in their own language rather than English. This study's focus is on comparing different multilingual sentiment analysis methodologies. This article discusses many strategies that have been used so far in the field of multilingual sentiment analysis.
Parallel complete gradient clustering algorithm and its properties

Clustering is one of the most important tasks in the field known as ‘Exploratory Data Analysis’ (EDA). It explores the dependencies hidden in individual data attributes, dividing them from one set into smaller subsets. In this paper, a Parallel Complete Gradient Clustering Algorithm (PCGCA) is proposed. The Complete Gradient Clustering Algorithm (CGCA) provides a natural interpretation combined with no need for assumptions regarding the number of clusters, making it an appealing choice. Moreover, in CGCA, internal optimization procedures point out the parameters influencing the size of clusters. Algorithms based on kernel density estimation can, therefore, be applied for diverse practical scenarios. Another very useful usage is outlier detection – which is especially important in the currently fast-growing data industry. The described algorithm has been validated in terms of both the speed of calculation and the quality of the obtained solution. The quality of the solution was evaluated with the use of eleven clustering indexes calculated on six data sets. In addition, the obtained result was compared with several classical well-known methods of clustering.
Improving Cross-Modal Understanding in Visual Dialog Via Contrastive Learning

Visual Dialog is a challenging vision-language task since the visual dialog agent needs to answer a series of questions after reasoning over both the image content and dialog history. Though existing methods try to deal with the cross-modal understanding in visual dialog, they are still not enough in ranking candidate answers based on their understanding of visual and textual contexts. In this paper, we analyze the cross-modal understanding in visual dialog based on the vision-language pre-training model VD-BERT and propose a novel approach to improve the cross-modal understanding for visual dialog, named ICMU. ICMU enhances cross-modal understanding by distinguishing different pulled inputs (i.e. pulled images, questions or answers) based on four-way contrastive learning. In addition, ICMU exploits the single-turn visual question answering to enhance the visual dialog model’s cross-modal understanding to handle a multi-turn visually-grounded conversation. Experiments show that the proposed approach improves the visual dialog model’s cross-modal understanding and brings satisfactory gain to the Vis-Dial dataset.
Secure Authentication scheme using CEEK technique for Trusted Environment

Distributed computing is imperative to move the IT area forward, yet cloud information security is essential. Unfortunately, the veracity of cloud information is at serious risk. Regardless of the way that cloud-related frameworks are undeniably more remarkable and trustworthy than standard PC equipment, they, in any case, face an assortment of interior and outer information trustworthiness challenges. This work presents a solid distributed storage framework that advances security insurance. The information proprietor transfers the information to the cloud and is then approved to utilize the private key. For the guard of distributed computing conditions, RSA based method uses a recently proposed cryptographic calculation dependent on Client End produced Encrypted Keycode (CEEK). It decreases the obligation of the outsider and further develops security checks via robotizing the logging of dubious gadgets to create key-code utilizing Device motherboard numbers, Device HDD numbers, and client passwords.
Regulatory information transformation ruleset expansion to support automated building code compliance checking

The traditional building code compliance checking process mainly relies on design reviewers to review design documents or models manually. The intensive manual effort needed makes this process time-consuming, costly, and error-prone. Automated compliance checking (ACC) could be a promising upgrade of the traditional manual code compliance checking. With a reduced workload on design reviewers, ACC is cheaper, faster, and immune to human errors. To support ACC, building code requirements need to be represented in a computer-processable format to enable automated reasoning, which will in turn allow an automated assessment of the building design's compliance status with building codes. A major limitation of many existing ACC systems/methods is their limited range of checkable building code requirements. To address that, the state of the art uses pattern matching-based rules to transform building code requirements into computable formats automatically, but the ruleset was developed and tested only on a few chapters of building code requirements. An efficient ruleset expansion method is needed to increase its range of checkable building code requirements at a low cost, to bring ACC systems closer to full deployment. In this paper, the authors proposed a new regulatory information transformation ruleset expansion method for expanding an existing ruleset. This method can expand the range of checkable code requirements of ACC systems without significant manual effort. The proposed ruleset expansion method takes an iterative approach to ensure the generality and validity of new pattern matching-based rules and the quality of information transformation results. The expanded ruleset was tested on generating logic clauses from Chapter 5 of the International Building Code 2015. Compared to the baseline ruleset, the expanded ruleset increased the predicate-level precision, recall, and F1-score of the logic clause generation by 10.44%, 25.72%, and 18.02%, to 95.17%, 96.60%, and 95.88%, respectively.
A Comprehensive Analysis of Mixed Reality Visual Displays in Context of its Applicability in IoT

Recent technological advancements have enabled the development of a variety of apps and programmes which can be used to produce decision-making resources for resolving day-to-day problems. It is a fusion of the physical and virtual worlds that enables people to engage with technology and the surroundings in novel ways. To allow this new reality, computer vision, screen technology, interface technologies, and graphics processing capabilities have all progressed. Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) are just a few of the computing ideas that have emerged to achieve these objectives. This study investigates a review of previous research studies that have been authored. This evaluation was conducted to investigate the architecture which has already been suggested. MR implementations require appropriate ways to handle dynamic phenomena such as fast changes in the external environment and object mobility. More research into setting up a new computer aided design approach capable of handling independent landscape creation and massive data representation, with a concentration on virtually actual simulation inside an important scenario, is necessary to improve the MR mobile applications.
Game-based Learning Sustainability During Social Distance: The Role of Gamification Quality

&lt;p style="text-align: justify;"&gt;Online learning is an obligation in teaching and learning activities during the Coronavirus disease (COVID-19). Game-based learning is a solution in improving student learning outcomes. This research aims to determine the level of acceptance of gamification in terms of Gamification quality (GQ), instructor characteristic (IC), and technology anxiety (TA). The target respondents were students taking information systems courses based on enterprises resources planning (ERP) Gamification. The sample used is a census. That is, the entire population is taken as a sample. A total of 93 students filled out the online questionnaire. Then, data analysis using Structural Equation Model - Partial Least Square (SEM-PLS). Student satisfaction (SS) and perceived ease of use (PEOUG) are the most influences. PEOUG is also the construct that has the most significant relationship impact, especially with the perceived usefulness (PUG). Meanwhile, two constructs do not significantly impact TA on PUG and PUG on Intention to use gamification (INTG). The obligation of students requires students to ignore the impact and function of gamification. The results of this research also show that technology acceptance model (TAM), the constructs IC, TA, and GQ have a positive effect on PEOUG. Then PUG and PEOUG can positively affect SS.&lt;/p&gt;
Fuzzy Information Discrimination Measures and Their Application to Low Dimensional Embedding Construction in the UMAP Algorithm

Dimensionality reduction techniques are often used by researchers in order to make high dimensional data easier to interpret visually, as data visualization is only possible in low dimensional spaces. Recent research in nonlinear dimensionality reduction introduced many effective algorithms, including t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), dimensionality reduction technique based on triplet constraints (TriMAP), and pairwise controlled manifold approximation (PaCMAP), aimed to preserve both the local and global structure of high dimensional data while reducing the dimensionality. The UMAP algorithm has found its application in bioinformatics, genetics, genomics, and has been widely used to improve the accuracy of other machine learning algorithms. In this research, we compare the performance of different fuzzy information discrimination measures used as loss functions in the UMAP algorithm while constructing low dimensional embeddings. In order to achieve this, we derive the gradients of the considered losses analytically and employ the Adam algorithm during the loss function optimization process. From the conducted experimental studies we conclude that the use of either the logarithmic fuzzy cross entropy loss without reduced repulsion or the symmetric logarithmic fuzzy cross entropy loss with sufficiently large neighbor count leads to better global structure preservation of the original multidimensional data when compared to the loss function used in the original UMAP algorithm implementation.
Using Blockchain for Data Collection in the Automotive Industry Sector: A Literature Review

Today’s cars can share data with other cars, automakers, and service providers. Shared data can help improve the driving experience, the performance of the car, and the traffic situations. Among all data-collection techniques, blockchain technology offers an immutable and secure solution to support data collection in the automotive industry. Despite its advantages, collecting auto data with blockchain still faces several challenges. Thus, the purpose of this study was to conduct a review of published articles that have addressed the challenges of adopting blockchain for data collection in the automotive industry. This paper allowed us to answer the predefined research question: “What are the challenges of using blockchain for data collection in the automotive industry as presented in the published literature?” The review included articles published from 2017 to January 2022, and from the screened records, 13 articles were analyzed in full-text form. The founded challenges were categorized into seven categories: connectivity, privacy, security attacks, scalability, performance, costs, and monetizing. This review will help researchers, car manufacturers, and third-party suppliers to assess the applicability of the blockchain for data collection.
Secured Secret Sharing of QR Codes Based on Nonnegative Matrix Factorization and Regularized Super Resolution Convolutional Neural Network

Advances in information technology have harnessed the application of Quick Response (QR) codes in day-to-day activities, simplifying information exchange. QR codes are witnessed almost everywhere, on consumables, newspapers, information bulletins, etc. The simplicity of QR code creation and ease of scanning with free software have tremendously influenced their wide usage, and since QR codes place information on an object they are a tool for the IoT. Many healthcare IoT applications are deployed with QR codes for data-labeling and quick transfer of clinical data for rapid diagnosis. However, these codes can be duplicated and tampered with easily, attributed to open- source QR code generators and scanners. This paper presents a novel (n,n) secret-sharing scheme based on Nonnegative Matrix Factorization (NMF) for secured transfer of QR codes as multiple shares and their reconstruction with a regularized Super Resolution Convolutional Neural Network (SRCNN). This scheme is an alternative to the existing polynomial and visual cryptography-based schemes, exploiting NMF in part-based data representation and structural regularized SRCNN to capture the structural elements of the QR code in the super-resolved image. The experimental results and theoretical analyses show that the proposed method is a potential solution for secured exchange of QR codes with different error correction levels. The security of the proposed approach is evaluated with the difficulty in launching security attacks to recover and decode the secret QR code. The experimental results show that an adversary must try 258 additional combinations of shares and perform 3 × 288 additional computations, compared to a representative approach, to compromise the proposed system.