Reviewerly/exHarmony huggingface dataset:
Usage of block chain in real estate business for transparency and improved security

In recent years, block chain technology is gaining more popularity due to its unique properties like immutability and fairness. Medical, education and IT industry are the some of the fields where block chain is intensively used for the purpose of digital transformation and security. Similar way, real estate industry is moving towards digitization by replacing the pen and pencil business. For this purpose, real estate industry is embedding block chain technology for keeping records and transactions securely and provide transparency to the customers. The distributed data storage innovation, whose intention is to impart trust in the legitimacy of advanced exchanges and to make proficient answers for business and private land together. This will help them in purchasing property to directing due industriousness to empowering publicly supported ventures, and that's just the beginning. Purchasing of our dream house, renting houses for vacation, buying land property, all can be done directly from the block chain. Blockchain technology provides shareable type of record-keeping method which is intended to be hard to alter. Blockchain innovation works through decentralized shared stages, building versatility against the spread of undermined data and boosting protection from misrepresentation. Block chain can provide the daily rate of real estate transactions, a common database of leases and purchases which are frequently required by the customer. Enhancing the traditional Multiple Listing Service (MLS) database to a block chain-based would create a far more shareable ledger system where stockbrokers and mediators can utilize all the entire past business dealing of a property. This book chapter explains how block chain can help real estate businessmen and customers in selling and buying the properties respectively to improve the security of transactions performed, provide transparency to customers in property dealings and increase the service availability to customers always without any delay.
Blockchain-Based Dangerous Driving Map Data Cognitive Model in 5G-V2X for Smart City Security

The development of 5G network communication has brought technological innovation to smart city communication, making the realization of V2X (vehicle to everything) technology possible. Vehicles wirelessly communicate with other vehicles, sensors, pedestrians, and roadside units, raising data security issues while driving. In order to ensure driving safety, the risk map cognitive model is established with the help of blockchain technology. In this model, the key map data and personal privacy information are encrypted and uploaded to form a blockchain, and the smart contract technology is used for automatic script processing. Then, according to different risk scenarios, cognitive learning is carried out for different risk levels, the cognitive results and corresponding operations are fed back to the intelligent vehicle, and these operations ensure the safe operation of the vehicle according to the intelligent vehicle. Finally, the feasibility of the model was verified by comparing different dangerous scenarios. The experimental results show that this risk cognition model can cognize the data of the intelligent vehicle according to different danger scenarios, and the model can transmit acceleration, deceleration, braking, and other behaviors to the intelligent vehicle to ensure smart city driving safety.
Tubular shape aware data generation for segmentation in medical imaging

Chest X-ray is one of the most widespread examinations of the human body. In interventional radiology, its use is frequently associated with the need to visualize various tube-like objects, such as puncture needles, guiding sheaths, wires, and catheters. Detection and precise localization of these tube-like objects in the X-ray images are, therefore, of utmost value, catalyzing the development of accurate target-specific segmentation algorithms. Similar to the other medical imaging tasks, the manual pixel-wise annotation of the tubes is a resource-consuming process.In this work, we aim to alleviate the lack of annotated images by using artificial data. Specifically, we present an approach for synthetic generation of the tube-shaped objects, with a generative adversarial network being regularized with a prior-shape constraint. Namely, our model uses Frangi-based regularization to draw synthetic tubes in the predefined fake mask regions and, then, uses the adversarial component to preserve the global realistic appearance of the synthesized image.Our method eliminates the need for the paired image-mask data and requires only a weakly labeled dataset, with fine-tuning on a small paired sample (10-20 images) proving sufficient to reach the accuracy of the fully supervised models.We report the applicability of the approach for the task of segmenting tubes and catheters in the X-ray images, whereas the results should also hold for the other acquisition modalities and image computing applications that contain tubular objects.
NLP-based Automatic Answer Evaluation

Using answer scripts to evaluate students is an important part of evaluating their performance. In most cases, an answer script evaluation is done dynamically, which might head to prejudice. It depends on a number of factors, including the mood of the teacher and the interaction between the student and the teacher. Further, checking is an important part of the process an extremely time-consuming and arduous process This study demonstrates a natural language processing-based technique for evaluation of answer scripts by an algorithm. To score the answer script, text is extracted from the answer, the extracted text is compared with the stored correct answers to calculate similarity, then a weight value is assigned to each measure. Keyword-based summarizing algorithms are utilized to generate a summary from the retrieved material. For producing the final mark, four similarity measures (Cosine) are employed as parameters. These investigations have shown that automatic assessment of response scripts is quite valuable, and that the given marks are often as same as the hand marks scores.
SMS Spam Detection Using TFIDF and Voting Classifier

In today’s digital world, Mobile SMS (short message service) communication has almost become a part of every human life. Meanwhile each mobile user suffers from the harass of Spam SMS. These Spam SMS constitute veritable nuisance to mobile subscribers. Though hackers or spammers try to intrude in mobile computing devices, SMS support for mobile devices become more vulnerable as attacker tries to intrude into the system by sending unsolicited messages. An attacker can gain remote access over mobile devices. We propose a novel approach that can analyze message content and find features using the TF-IDF techniques to efficiently detect Spam Messages and Ham messages using different Machine Learning Classifiers. The Classifiers going to use in proposed work can be measured with the help of metrics such as Accuracy, Precision and Recall. In our proposed approach accuracy rate will be increased by using the Voting Classifier.
Unintentional Action Localization via Counterfactual Examples

How do humans localize unintentional action like " A boy falls down while playing skateboard "? Cognitive science shows that an 18-month-old baby understands the intention by observing the actions and comparing the feedback. Motivated by this evidence, we propose a causal inference approach that constructs a video pool containing intentional knowledge, conducts the counterfactual intervention to observe intentional action, and compares the unintentional action with intentional action to achieve localization. Specifically, we first build a video pool, where each video contains the same action content as an original unintentional action video. Then we conduct the counterfactual intervention to generate counterfactual examples. We further maximize the difference between the predictions of factual unintentional action and counterfactual intentional action to train the model. By disentangling the effects of different clues on the model prediction, we encourage the model to highlight the intention clue and alleviate the negative effect brought by the training bias of the action content clue. We evaluate our approach on a public unintentional action dataset and achieve consistent improvements on both unintentional action recognition and localization tasks.
Joint estimation of depth and motion from a monocular endoscopy image sequence using a multi-loss rebalancing network

Building an in vivo three-dimensional (3D) surface model from a monocular endoscopy is an effective technology to improve the intuitiveness and precision of clinical laparoscopic surgery. This paper proposes a multi-loss rebalancing-based method for joint estimation of depth and motion from a monocular endoscopy image sequence. The feature descriptors are used to provide monitoring signals for the depth estimation network and motion estimation network. The epipolar constraints of the sequence frame is considered in the neighborhood spatial information by depth estimation network to enhance the accuracy of depth estimation. The reprojection information of depth estimation is used to reconstruct the camera motion by motion estimation network with a multi-view relative pose fusion mechanism. The relative response loss, feature consistency loss, and epipolar consistency loss function are defined to improve the robustness and accuracy of the proposed unsupervised learning-based method. Evaluations are implemented on public datasets. The error of motion estimation in three scenes decreased by 42.1%,53.6%, and 50.2%, respectively. And the average error of 3D reconstruction is 6.456 ± 1.798mm. This demonstrates its capability to generate reliable depth estimation and trajectory reconstruction results for endoscopy images and meaningful applications in clinical.
Multimodal high-order relational network for vision-and-language tasks

Vision-and-language tasks require the understanding and learning of visual semantic relations, language syntactic relations and mutual relations between these two modalities. Existing methods only focus on intra-modality low-order relations by simply combining pairwise features while ignoring the intra-modality high-order relations and the sophisticated correlations between visual and textual relations. We thus propose the multimodal high-order relational network (MORN) to simultaneously capture the intra-modality high-order relations and the sophisticated correlations between visual and textual relations. The MORN model consists of three modules. A coarse-to-fine visual relation encoder first captures the fully-connected relations between all visual objects, and then refines the local relations between neighbor objects. Moreover, a textual relation encoder is used to capture the syntactic relations between text words. Finally, a relational multimodal transformer is designed to align the multimodal representations and model sophisticated correlations between textual and visual relations. Our proposed approach shows state-of-the-art performance on two vision-and-language tasks, including visual question answering (VQA) and visual grounding (VG).
Webcam Motion Detection in Real-Time Using Python

Webcams are an integral part of our daily life now. Through webcams, we are able to remotely monitor the surrounding. Webcams are able to capture images which need to be extracted with meaningful information. This paper presents a method to use programming to extract the information. Python’s super flexible and easy to use nature is what makes it amongst the popular programming language, especially for Data Scientists. A major point is that how simple it is to work with datasets of large size. Today, every technology company is building up some strategy regarding data. They’ve all realized that having the right kind of data: which provide great insight and should be clean and, as much of it as possible, gives them a key advantage over their competitors. Data, if used carefully and effectively, can offer deep insights that cannot be discovered anywhere else.Using image processing capabilities of python, we are able to develop a webcam motion detector. Code for this is written on Pycharm framework and OpenCV is the library used for purpose of image processing.
A robust double-parallel extreme learning machine based on an improved M-estimation algorithm

To solve the problem of improving the regression accuracy and model stability of the extreme learning machine(ELM), a new approach based on an improved M-estimation optimized double-parallel extreme learning machine is proposed in this study, namely robust double-parallel extreme learning machine(RD-ELM). Firstly, RD-ELM is constructed with a double parallel forward structure, thus the information can be received from both hidden layer neurons and input layer neurons. Secondly, we use an improved M-estimation to calculate output weights of neural network by iteratively reweighted Least-Squares Estimation(LSE), with weights assigned by the least absolute residual estimation of the samples. Finally, we establish a regression prediction model utilized to test the goodness of fit in a SinC function and verify the regression ability in eight benchmark regression problems. Then the proposed method is applied to an actual operational condition of a power plant. Experimental results show that the proposed method can efficiently process the influence of outliers and noise with strong anti-jamming ability. Compared with other methods, RD-ELM has superior performance that is stronger robustness and better generalization performance in many benchmark data and practical experiments.
An Integrated Topic Modelling and Graph Neural Network for Improving Cross-lingual Text Classification

In recent years, along with the dramatic developments of deep learning in the natural language processing (NLP) domain, notable multilingual pre-trained language techniques have been proposed. These recent multilingual text analysis and mining models have demonstrated state-of-the-art performance in several primitive NLP tasks, including cross-lingual text classification (CLC). However, these recent multilingual pre-trained language models still suffer limitations regarding their adaptation for specific task-driven fine-tuning in the context of low-resource languages. Moreover, they also encounter problems related to the capability of preserving the global semantic (e.g., topic, etc.) and long-range relationships between words to better fine-tune and effectively handle the cross-lingual text classification task. To meet these challenges, in this article, we propose a novel topic-driven multi-typed text graph attention–based representation learning method for dealing with the cross-lingual text classification problem called TG-CTC. In the proposed TG-CTC model, we utilize a novel fused topic-driven multi-typed text graph representation to jointly learn the rich-schematic structural and global semantic information of texts to effectively handle the CLC task. More specifically, we integrate the heterogeneous text graph attention network with the neural topic modelling approach to enrich the semantic information of learned textual representations in the context of multiple languages. Extensive experiments in benchmark multilingual datasets showed the effectiveness of the proposed TG-CTC model compared with the contemporary state-of-the-art baselines.
A Sophisticated Analysis of the Blockchain Technology and its Applications Domain

Two researchers “Stuart Haber and W. Scott Stornetta” who aimed to advance a system where document timestamps might not be manipulated with, initially projected blockchain technology in 1991. Aftertwenty years later, blockchain have become start in real world usage with the introduction of Bitcoin in January of 2009. Originallyit was considered as a mechanism to empower the trust-less Bitcoin cryptocurrency, since then as an increasing number of businesses and stakeholders began to see blockchain as an appealing solution to solving current business challenges and unsettled mature deals, the technology's initial purpose was broadened. This research insights into literature evaluation of the blockchain technology, comparing its usage to that of related technologies like crypto-currencies and Bitcoin. This article found that the blockchain's unique qualities, such as anonymity, immutability, decentralisation, privacy and security, help a number of different types of fields and topics. Blockchain mining is a process to verify and secure every step in the transactions during operating cryptocurrencies.
Object Detection using TensorFlow

Objects in the home that are often used tend to follow specific patterns in terms of time and location. Analyzing these trends can help us keep track of our belongings and increase efficiency by reducing the amount of time wasted forgetting or looking for them. Tensor Flow, a relatively new framework from Google, was utilised to model our neural network in our project. Multiple objects in real-time video streams are detected using the Tensor Flow Object Detection API. The system then detects trends and alerts the user if an abnormality is discovered. Finding REMO—detecting relative mobility patterns in geographic lifelines is a study reported by Laube et al. A neural network model is constructed and trained with the goal of being able to accurately identify digits from handwritten photographs. For this, the Tensor Flow syntax was employed, using Keras as the front end. The trained model can take an image of a handwritten digit as input and predict the digit's class, that is, it can predict the digit or the input picture's class. Machine vision improvements, in combination with a camera and artificial intelligence programming, may now be used by PCs to recognize images.
Sensor-based modeling of problem-solving in virtual reality manufacturing systems

Problem-solving is the process to achieve a goal when the solution path is uncertain. Recently, technological advancements have changed problems’ characteristics and their solutions in engineering fields. Strong problem-solving skills are essential to allow engineers to assess new problems and quickly implement solutions. Engineering problem-solving skills are first educated in schools and usually evaluated through written exams. However, high grades in exams do not represent sufficient problem-solving skills in real-world engineering problems. Decision making with insufficient problem-solving skills in real world may result in costly consequences. Therefore, it is imperative to evaluate and reinforce problem-solving skills of engineering students in real-world problems. With the rapid technological advancements, availability of virtual reality (VR) and eye-tracking facilitates the study of engineering problem-solving. The immersive environment created by VR enables students to better understand and solve real-world engineering problems. On the other hand, eye-tracking allows for studying fundamental cognitive processes during information processing. It is critical to integrate VR simulation with data-driven modeling of eye movements to evaluate and enhance engineering problem-solving skills. In this paper, we integrate sensing technology (i.e., eye-tracking) and virtual reality (VR) to model problem-solving in manufacturing systems. A novel data-driven model that integrates signal detection theory (SDT) with Conflict & Error (C&E) is developed to quantify engineering problem-solving skills. First, we simulate a manufacturing system in a VR game environment. Students are given an assembly problem to produce a car toy that satisfies some particular requirements in the VR manufacturing system while eye-tracking data are collected throughout the assembly process. Second, eye-tracking data are analyzed with a SDT model to quantify problem-solving skills. Third, a joint SDT-C&E model is developed to analyze eye-tracking data and benchmark with results generated from the SDT model. Experimental results show that the joint SDT-C&E model is more effective to quantify problem-solving skills of engineering students than the SDT model.
Credit Risk Early Warning of Small and Medium-Sized Enterprises Based on Blockchain Trusted Data

Small and medium-sized enterprises (SMEs) are now growing rapidly and playing an important role in the development of the national economy. As the economy grows, the contradiction between the credit risk of SMEs and the credit risk early warning mechanism of traditional supply chain financing has become increasingly important. In response to the issues of a single source of business information, the high investment cost of the existing early risk early warning mechanism, etc., from a commercial bank credit risk management perspective, this paper proposes to build an SMEs credit risk early warning system based on reliable blockchain data. The reliability of the data obtained is assessed utilising a hierarchical analysis and a vague overall judgement method. The results show that the use of blockchain technology can enhance the credibility and accuracy of the data, which provides a data guarantee for more rapid risk alert.
FreMix: Frequency-Based Mixup for Data Augmentation

Deep learning models have attracted tremendous attention in computer vision in recent years, while most of them heavily rely on massive data for training. As one of the solutions to the sparse data problem, data augmentation techniques, such as image translation and rotation, can substantially increase the model’s generalization ability and performance. However, on one hand, these approaches primarily work under the pixel domain, which is limited to fully mining and fusing picture data from the frequency viewpoint. On the other hand, the fusion weighting factors are primarily modified in a manual fashion, which increases the application costs in practice. To this end, we propose a novel method termed as frequency-based Mixup (FreMix) that allows images to be fused in the frequency domain and to improve the efficiency of data augmentation by adaptively adjusting the weighting coefficients in this paper. In FreMix, first, a fast Fourier transformation (FFT) is performed on the input image, such that the frequency information rather than raw pixel information can be extracted for further augmentation. Besides, an exploration-exploitation training paradigm is exploited, such that the FreMix can be trained periodically to facilitate learning and avoid manually hyperparameter settings. We conduct comparing experiments on three benchmark datasets including CIFAR, ImageNet, and ILSVRC2015, and the experimental results validate the effectiveness of the proposed method.
Situational Awareness for Reactive Power Management in Large-Scale Electric Grids

Situational awareness is imperative for reactive power management, particularly for interpreting the results of studies evaluating the impact of geomagnetic disturbances or high levels of renewable generation on the grid. This paper introduces a visualization technique, VAR Ready Reserves (VRRs), to provide a novel and useful tool to enhance the situational awareness of users performing and interpreting power system studies. This visualization technique can be adapted to demonstrate the dispatch, injection, and absorption capability of reactive power devices (such as generators, shunts, SVCs) in either a chart view (VRR charts) or with an integrated system view (VRR GDVs) to provide users with the awareness of reactive power capability and dispatch over the duration of a simulation or spatially. This paper reviews industry practices for reactive power management, summarizes existing visualization strategies, and demonstrates the newly-developed VRRs on a 2000-bus case study.