Reviewerly/exHarmony huggingface dataset:
Financial Transaction Risk Identification Method Based on Boosting-SVM Algorithm

The development of the network economy has brought a great impact on the society. In the process of online transactions, if the transaction risks are not prevented and improved in time, it will directly affect the normal development of the social economy. Therefore, it is necessary to synchronize transaction subjects and objects, and at the same time control transaction risks, and promote the rapid development of e-commerce, which is an important problem to be solved urgently in current online transactions. This paper first summarizes the characteristics and risk performance of new transactions and identifies and controls them according to their characteristics and different risk characteristics. The cybercriminal industry uses illegal means to seek benefits in online transactions. The continuous improvement of big data technology and data mining technology makes it possible to identify transaction risks in the consumption process. In particular, the research and identification of black industries can not only ensure the normal operation of merchants and reduce the loss of economic interests of merchants but also make the transaction experience of natural users smoother. Based on the existing consumption data, this paper adopts the Boosting-SVM model to identify natural users and black-produced users in the transaction process. The results show that the model achieves good recognition results. The overall prediction accuracy of the model is over 95%, the identification accuracy rate of high-risk transactions is over 98%, and other indicators are also over 96%. Compared with other risk identification, the overall accuracy of the algorithm is increased by 1%, and the risk identification accuracy is increased by more than 8%. Generally speaking, the method in this paper provides technical support to related industries to a certain extent.
Hybrid deep learning controller for nonlinear systems based on adaptive learning rates

In this paper, a hybrid deep learning neural network controller (HDLNNC) for nonlinear systems is proposed. The proposed controller structure consists of a multi-layer feed-forward neural network, which can be trained based on the hybrid deep learning. The Lyapunov stability criterion is used to develop an adaptive learning rate due to the learning rate of the updating parameters plays a worthy role in achieving the stability of a system. To show the robustness of the proposed controller and its performance, several tests such as disturbance signals and parameter variations are carried on a numerical example. In this concern, the practical implementation of the proposed HDLNNC is executed on a real system. The results indicate that the proposed controller is able to improve the system performance compared with other existing controllers.
Mouth Gesture Classification using Computational Intelligence

Computational intelligence works as a recent advancement in many fields of classifications and predictions. The mouth detection approach classifies images based on the value of simple visual attributes and has been shown to be reliable under a variety of lighting situations. The method uses a cataract of enhanced classifiers with simple Haar-wavelet-like features on various sizes and positions. Mouth images are recognised using the Haar-cascaded technique, HOG features are extracted from the detected regions, modelled with Support Vector Machine, and LSTM classified into mouth shut, mouth open, tongue left, and tongue right in the suggested work. According to the findings, HOG characteristics combined with SVM and LSTM accurately locate the mouth regions. The models are compared to see how well they perform. SVM and LSTM gives the best results, with a 88 percent accuracy rate.
Emotion Analysis of Cross-Media Writing Text in the Context of Big Data

Since the beginning of the 21st century, sentiment analysis has been one of the most active research fields in natural language processing. Now sentiment analysis technology has not only achieved significant results in academia, but also has been widely used in practice. From business services to political campaigns, sentiment analysis is used in more and more fields. Sentiment analysis is essentially to dig out the user's emotional attitude from the massive emotional natural language text data, and analyze the emotional dynamics of the text author through certain technical means. At present, there is almost no sentiment analysis in cross-media writing content, and it can rarely help cross-media writing vision to advance with the times and comprehensive improvement of writing ability to adapt to the current rapidly developing information society; the commonly used text media in the digital age are not. Then there is the only composition tool. Various new media appearing with the development of the ages continue to intervene in writing, and it is the general trend to cultivate media literacy in writing. The main content of this paper is the research on the emotional intensity of students' Internet public Aiming at the shortcomings of the topic feature word selection in the sentiment tendency analysis of the students' Internet public opinion, improvements have been made to facilitate the research on the sentiment intensity of the sentiment analysis of the students' Internet public opinion. Sentiment analysis of students' cross-media written text through an improved MapReduce combinator model.
A Clustering-based Feature Selection for Automatic Labeling in Human Activity Recognition

Due to the popularity of mobile devices and wearable sensors, improving the accuracy of human activity recognition has become a research topic. However, to train an actual model based on machine learning in HAR, it is necessary to collect sensor information of a large number of samples and mark each sample as belonging to a specific activity. Therefore, designing an automatic labeling method is important in HAR research. In this study, we applied the feature selection based on fuzzy C-means particle swarm optimization (FS-FCPSO) to improve the accuracy of the automatic labeling results. Through the FS-FCPSO method, six human activities are automatically marked according to the selected important features, including walking, sitting, standing, upstairs, downstairs, and lying down. The results of these six activities are automatically marked by the FS-FCPSO method. Our results show that the FS-FCPSO method is more suitable for automatic marking in HAR than K-means and fuzzy C-means algorithms.
Posture Corrector: Incorrect Posture and Repetition Identifier using OpenPose and Deep Neural Network

This paper suggests a software that can analyze a video that is recording a physical exercise and give a feedback to user according to their posture. OpenPose and user recorded video is used to extract body key points and this information are used to detect incorrect posture and detect start and end of the repetition. The software will use two different deep neural networks to identify whether the posture was correct or not for each repetition and gave feedback according to user&#x2019;s posture at the end of analysis.
Robotics and Artificial Intelligence in Hospitality Sector: A Descriptive Bibliometric Analysis and Future Research Directions

In the mid-twentieth century, the idea of artificial intelligence (AI) and robots was first explored. Since then, AI has driven the interests of researchers in every sector worldwide. Automated distribution, conveyor restaurants, chat bots, mechanical concierges, and other innovations in the hospitality business are all instances of how the industry has begun to integrate artificial intelligence and robots. Despite the enormous advances in science and technology, the hotel / hospitality business has a limited amount of robotics and AI research. Using a bibliographic approach, this study analyzes research on AI technologies and robotics in the hotel trade. VOSViewer, a bibliographic mapping tool, is used to extract the information from Scopus. Authorship, citations, publication volumes, organizational origins, and other indicators are studied via the VOSViewer tool.
Building Dynamic permutation based Privacy Preservation Model with Block Chain Technology for IoT Healthcare Sector

Blockchain technology has the potential to address present interoperability issues in health information systems by establishing a technological standard that ensures safe electronic health data exchange amongst people, healthcare suppliers and medicinal up keep organizations including medical professionals. Patients&#x0027; sensory data can be fed into Internet of Things (IoT) devices in real timewhich can be evaluated and managed in the healthcare industry. The privacy and security of health data pertaining topatients&#x0027; is now also a major concern with IoT devices across a wide variety of product sectors. According to previous research, blockchain technology has been determined to be a substantial answer to the data security concerns that present in IoT. The Dynamic Permutation with Multi-Modal Safe Data (DPMMSD) based Hyper Elliptic Curve Cryptography (HECC) Framework (DPMMSD-HECC) is suggested in this research for safeadmission and regulator topatient&#x0027;s data in IoT. The suggested framework of healthcare data management in IoT devices is successfully utilized for fulfilling the optimum confidentiality and safety requirements. Blockchain approach has beenused in this study to develop a dependable and safe data sharing stage that connects several information sources and encrypts and records IoT data in a distributed ledger. A research on security revealed that a particular information secures the parameters related to DPMMSD-HECC model for data analysts and maintains the secrecy of important data from each data source. The recommended strategy is evaluated compared to two benchmark datasets from the UCI AI repository: Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD). The. Simulation results showed that the proposed DPMMSD-HECC model has outperformed all of the other techniques in a number of ways.
Wearable Sensors and Real-Time System for Detecting violence using Artificial Intelligence

Aggressive activity in public spaces is a significant threat to personal safety and social cohesion. Cameras and other security devices have been mounted in various locations for public safety in recent years. Thousands of pieces of equipment are installed in public spaces, putting immense strain on security personnel. To classify incidents, almost all systems today need human review of the images, which is inefficient. Our proposed system is to develop an algorithm which detects violence in a given video frame. It first learns features and then trains on those learned features. It detects violence in given video and if violence is detected in frames, it will send an alert message. YOLOv5algorithmisfoundtobeabletoidentifyapersoninagi venvideo. Alongshort-termmemorynetwork (LSTM) is used to capture long-term dependency in the time domain.
Dynamic Hand Gesture Recognition with Self-co-articulation removal in Indoor and Outdoor Environment

In a continuous video sequence, recognition of dynamic hand gestures is a challenging task and more difficulties arise when the recording environment is also dynamic in nature. This paper proposes a system which solves the problem of the complex environment. It also tries to solve the problem of presence of unwanted strokes during the formation of the gesticulation pattern. Gestures used in this paper are recorded in both indoor and outdoor environment. Detection is done by a two-level algorithm using skin color and motion information. Hand tracking is done using modified tracker which overcomes the difficulties arising in existing KLT due to movement of hand. Incremental feature selection is utilized to use only those features for recognition which provides best results in terms of accuracy. SVM is utilized for recognition of the gestures and the recognition accuracy has been compared with deep learning algorithm also. It has been observed that 96.67% recognition accuracy is being achieved using deep learning which is better than existing machine learning algorithm.
Assessing reading comprehension in CET-4: a cross-sectional case study

Testing reading has always been indispensable in language tests. The present study aims to evaluate three parallel reading tests from the National College English Test Band Four (CET-4) in June 2021 in terms of reading ability orientations, text readability and its correlations with reading performances. The quantitative analysis reveals: (1) in Banked Cloze (BC), lexical items designed and examined were largely confined to an intrasentential level. Meanwhile, an either-or situation was found in each test on adverbs; (2) Matching prioritized the skill of scanning for explicit details; (3) Reading in Depth (RD) focused on comprehension of facts and details while seldom involved testing gists at textual levels; (4) text readability in the same question type among the three target tests was short of an expected internal consistency and (5) the correlation between test results and text readability in CET-4 reading comprehension (RC) was not absolute but dependent on question types. Based on findings derived, this study would hopefully offer some vital clues to a more scientific design and assessment of CET reading tests in both theory and practice. And it is also expected to shed some light on evaluating text readability from new perspectives.
Discovery of Potential High Utility Itemset from Uncertain Database using Multi Objective Particle Swarm Optimization Algorithm

In recent decades, Internet of Things devices have grown in popularity across a wide range of industries and uses. As a result, vast amounts of data are created and generated. Despite the fact that the collected data contains a great quantity of crucial information, most current and general pattern mining algorithms simply analyses a single item and exact information to identify the needed data. Because the amount of data gathered is so huge, it is vital to identify meaningful and updated data in a short period of time. In this paper, we use a multi-objective evolutionary framework to effectively mine the interesting Potential High Utility Itemset (PHUI) in a limited period, with the majority of items being PHUI utility and uncertainty. In an unpredictable context, the benefits of the proposed model (dubbed MOPSO-PHUIM) can identify lucrative PHUIs without pre-defined threshold values (i.e., minimal utility and minimum uncertainty). To illustrate the efficiency of the created MOPSO-PHUIM, two encoding techniques are also taken into account. Using the developed MOPSO-PHUIM model for decision-making, a set of non-dominated PHUIs may be found in a short amount of time. Studies are then carried out to demonstrate the utility and performance of the built MOPSO-PHUIM model in terms of velocity, hyper volume, and the different result discovered when compared to generic techniques.
Performance Analysis on Advances in Frequent Pattern Growth Algorithm

In data mining, association-rules mining are a crucial technology. The Frequent-Growth (FP) algorithm is a well-known algorithm for association mining. However, FP-Growth method in mining requires two scans of the database which affects the process's efficiency. We have compared two different FP-Growth algorithms namely Painting-Growth algorithm as well as not Painting-Growth algorithm via the study of association mining and FP-Growth algorithm. With the FP-Growth algorithm, we have compared two different types of enhanced algorithms. Performance of these enhanced algorithms is absolutely better than FP-Growth algorithm, according to the experimental results. Painting-Growth algorithm has a data volume of greater than 1050 and also, N Painting-Growth algorithm has a data volume of less than 10000.
Analysing the Twitter Sentiments in COVID-19 Using Machine Learning Algorithms

The coronavirus emanated in Wuhan city of China, in the last month of 2019 and was even announced as a global threat. Social media could be an utterly noteworthy supply of facts during a time of crisis. User-generated texts yield perception into users' minds withinside the direction of such times, giving us insights into their critiques in addition to moods. This venture examines Twitter messages (tweets) regarding people's sentiment on the unconventional coronavirus. The essential aim of sentiment evaluation is the origin of human emotion from messages or tweets. This venture is geared toward using numerous gadgets studying type algorithms to expect the people's reception of the worldwide pandemic by reading their tweets on Twitter. In the course of this paper, we are testing our dataset on five different classifiers, namely Random Forest, Logistic regression, Multinomial naive Bayes, K-nearest neighbor, and Support vector machines classifiers. Together with precision rankings and balanced accuracy rankings, metrics are offered to gauge the fulfilment of the numerous algorithms implemented. The K-Nearest Neighbor classifier has given the highest precision score while the Logistic Regression classifier gives the highest recall, F1, accuracy and balanced accuracy scores.
Transformer-Convolution Network for Arbitrary Shape Text Detection

Arbitrary shape text detection is a prevalent topic in computer vision. Text instances in natural scenes may involve different sizes, different shapes, and complex background textures. Therefore, the ability to extract accurate text features becomes extremely significant for subsequent detection work. This paper proposes a novel Transformer-Convolution Network(TCNet) to participate in scene text detection task. TCNet contains two major modules named CNN module and Transformer module. CNN module is used to extract local features from the input images, while Transformer module establish connections among various local features. The two structures are complementary to each other, in particular, such combination between local features and relative locations contributes to a more precise detection, promoting the convergence and reducing the amount of parameters. Numerous experiments based on public datasets have demonstrated the excellent performance under the condition of sufficient data. specifically, in the case of small data, our method achieves the state-of-the-art performance whether in quadrilateral text or arbitrary shape text datasets.
Vision‐oriented algorithm for fast decision in 3D video coding

This paper designs a novel method to reduce the coding complexity of 3D-HEVC encoder by utilizing the properties of human visual perception. Two vision-oriented edge detections are proposed: for colour texture detection, the authors adopt the Just-Noticeable Distortion (JND); for depth map, the authors combine the Sample Adaptive Offset (SAO) and the Just Noticeable Depth Difference (JNDD) model. The authors also analyse the properties of colour texture and depth map to classify the coding tree unit (CTU) into various kinds of types, including complex-edge CTU, moderate-edge CTU and homogeneous CTU. Besides, fast mode decisions and early termination criteria are performed individually on each type of CTUs according to their characteristics. Especially for those CTUs with more edge information, the proposed projection-based fast mode decision and residual-based early termination preserve important colour texture while speeding up the coding at the same time. The proposed vision-oriented algorithm reduces 31.981% of the overall average coding time with only 1.580% BD-Bitrate increase. Experimental results show that the proposed algorithm can provide considerable time-saving while still maintain the video quality, which outperforms the previous researches.
A Fuzzy Strategy to Eliminate Uncertainty in Grading Positive Tuberculosis

Sputum smear microscopic examination is an effective, fast, and low-cost technique that is highly specific in areas with a high prevalence of pulmonary tuberculosis. Since manual screening needs trained pathologist in high prevalence zones, the possibility of deploying adequate technicians during the epidemic sessions would be impractical. This condition can cause overburdening and fatigue of working technicians which may tend to reduce the potential efficiency of Tuberculosis (TB) diagnosis. Hence, automation of sputum inspection is the most appropriate aspect in TB outbreak zones to maximize the detection ability. Sputum collection, smear preparing, staining, interpreting smears, and reporting of TB severity are all part of the diagnosis of tuberculosis. This study has analyzed the risk of automating TB severity grading. According to the findings of the analysis, numerous TB-positive cases do not fit into the standard TB severity grade while applying direct rule-driven strategy. The manual investigation, on the other hand, arbitrarily labels the TB grade on those cases. To counter the risk of automation, a fuzzy-based Tuberculosis Severity Level Categorizing Algorithm (TSLCA) is introduced to eliminate uncertainty in classifying the level of TB infection. TSLCA introduces the weight factors, which are dependent on the existence of maximum number of Acid-Fast Bacilli (AFB) per microscopic Field of View (FOV). The fuzzification and defuzzification operations are carried out using the triangular membership function. In addition, the [Formula: see text]-cut approach is used to eliminate the ambiguity in TB severity grading. Several uncertain TB microscopy screening reports are tested using the proposed TSLCA. Based on the experimental results, it is observed that the TB grading by TSLCA is consistent, error-free, significant and fits exactly into the standard criterion. As a result of the proposed TSLCA, the uncertainty of grading is eliminated and the reliability of tuberculosis diagnosis is ensured when adapting automatic diagnosis.
A Deep Convolutional Neural Network Based Risk Identification Method for E-Commerce Supply Chain Finance

With the popularity of the Internet, the rise of e-commerce platforms has led to the rapid development of supply chain (SC) financial services in China, and the competitiveness of commercial banks and core enterprises in the supply chain is now gradually increasing, rapidly expanding into an important area of competition between the two. As an emerging force rebounding from the economic downturn, e-commerce platform transactions, with their unique characteristics of informatization, diversification, and convenience, have provided a broad space for Internet SC finance. The article mainly analyzes the risk identification method of e-commerce SC finance, analyzes its risk from the financing process, gives corresponding data support for the matters or processes that may cause financing risk based on DCNN model, and takes Jingdong SC finance as an example and analyzes its main financing methods and risk identification process; based on different experimental comparisons, a multigroup experimental study shows that the accuracy of supply chain finance risk identification using deep convolutional neural network models can reach 95.36%, which demonstrates the effectiveness of the proposed method by providing better performance compared to traditional BP and SVM networks.