Reviewerly/exHarmony huggingface dataset:
Using Big Data Analytics to Analyze Pre- and Post-launch Emotions: A Study of Apple’s iPhone 12

Big data, with its potential to ascertain valued insights, have recently attracted substantial interest from both academics and research practitioners. Big data analytics is increasingly becoming a trending practice in new product development (NPD) using social media. In this article, we analyze the user emotions toward a brands product in particular Apple’s iPhone. We compare users’ emotions before and after launch of the product, which serve as input for NPD. In this study, we used tweets and comments posted on Twitter and YouTube platforms, respectively, which act as a cheaper, real time, and more accurate alternative to conventional practice of conducting surveys. A total of 20,175 user tweets and comments were collected in two time periods before and after product launch. The user’s behavior during the two time periods was compared. It was observed that in pre-launch period anticipation and joy were the common emotions. During the post-launch period, joy and trust were the most used emotions. Thus, the results show that the proposed approach can help the businesses by adjusting their NPD process based upon real-time feedback of consumers expectations and experiences and replace traditional methods.
Analysis and Evaluation of Wireless Network Security with the Penetration Testing Execution Standard (PTES)

The use of computer networks in an agency aims to facilitate communication and data transfer between devices. The network that can be applied can be using wireless media or LAN cable. At SMP XYZ, most of the computers still use wireless networks. Based on the findings in the field, it was found that there was no user management problem. Therefore, an analysis and audit of the network security system is needed to ensure that the network security system at SMP XYZ is safe and running well. In conducting this analysis, a tool is needed which will be used as a benchmark to determine the security of the wireless network. The tools used are Penetration Testing Execution Standard (PTES) which is one of the tools to become a standard in analyzing or auditing network security systems in a company in this case, namely analyzing and auditing wireless network security systems. After conducting an analysis based on these tools, there are still many security holes in the XYZ wireless SMP that allow outsiders to illegally access and obtain vulnerabilities in terms of WPA2 cracking, DoS, wireless router password cracking, and access point isolation so that it can be said that network security at SMP XYZ is still not safe
Making Video Recognition Models Robust to Common Corruptions With Supervised Contrastive Learning

The video understanding capability of video recognition models has been significantly improved by the development of deep learning techniques and various video datasets available. However, video recognition models are still vulnerable to invisible perturbations, which limits the use of deep video recognition models in the real world. We present a new benchmark for the robustness of action recognition classifiers to general corruptions, and show that a supervised contrastive learning framework is effective in obtaining discriminative and stable video representations, and makes deep video recognition models robust to general input corruptions. Experiments on the action recognition task for corrupted videos show the high robustness of the proposed method on the UCF101 and HMDB51 datasets with various common corruptions.
Prototypical Network Based on Manhattan Distance

Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data, such as medical images, terrorist surveillance, and so on. The Metric Learning in the Few-shot Learning algorithm is classified by measuring the similarity between the classified samples and the unclassified samples. This paper improves the Prototypical Network in the Metric Learning, and changes its core metric function to Manhattan distance. The Convolutional Neural Network of the embedded module is changed, and mechanisms such as average pooling and Dropout are added. Through comparative experiments, it is found that this model can converge in a small number of iterations (below 15,000 episodes), and its performance exceeds algorithms such as MAML. Research shows that replacing Manhattan distance with Euclidean distance can effectively improve the classification effect of the Prototypical Network, and mechanisms such as average pooling and Dropout can also effectively improve the model.
An Effective Patient Monitoring System using IOT

The rules in the continuous improvement, Internet of Things (IOT) make every things consistent and hence been seen as the accompanying specific surprise. A part of the employments of Internet of Things are wise halting, splendid residence, insightful city, sharp condition, mechanical spots, cultivating fields and prosperity checking process. On such application be in social protection to screen the patient prosperity Internet of Things allows remedial technology dynamically proficient with allow consistent seeing of patient prosperity, for sensor get patient's data and decreases the human screw up. In Internet of Things patient's parameters get transmitted through helpful devices by methods for an entryway, where it is secured and separated. The immense test in the execution of Internet of Things for restorative administrations applications is checked like manner Internet of Things in the helpful field draws out the response for effective patient seeing at lessened cost and moreover diminishes between patient outcome and infirmity the officials. This paper involves watching patient's body temperature, breathe rate, heart beat and body improvement using Arduino UNO.
Simultaneous detection and diagnosis of mammogram mass using bilateral analysis and soft label based metric learning

In the clinics, mammogram masses appear as asymmetric structures between the left and right breasts. In this paper, we design a bilateral image analysis method based on convolutional neural network which can detect and classify breast mass regions simultaneously. It mainly consists of three parts: a feature similarity based region matching technique, mass region of interest (ROI) selection step and a deep metric learning based classifier. Firstly, discriminative score maps are calculated relied on the deep features extracted from bilateral left and right mammograms respectively in global or local spatial image domain. The contralateral correspondences are determined by minimum discriminative scores. Secondly, to select the mass candidate ROIs and further remove false positive mass-to-normal pairs, we propose a dynamic histogram weighting mechanism with three new constrains imposed on the distribution of discriminative score histogram. In addition, a novel soft label based deep metric learning regularization is designed for mass ROI classifier to tackle the large variation of masses in shape, size, texture and breast density. We apply it to the open dataset Digital Database for Screening Mammography. Compared with other state-of-the-art approaches, the proposed scheme gives competitive results in classification and localization tasks for mammographic lesions.
Correction: Liu et al. Research on Building DSM Fusion Method Based on Adaptive Spline and Target Characteristic Guidance. Information 2021, 12, 467

Missing Citation [...]
Spork: Structured Merge for Java With Formatting Preservation

The highly parallel workflows of modern software development have made merging of source code a common activity for developers. The state of the practice is based on line-based merge, which is ubiquitously used with “git merge”. Line-based merge is however a generalized technique for any text that cannot leverage the structured nature of source code, making merge conflicts a common occurrence. As a remedy, research has proposed structured merge tools, which typically operate on abstract syntax trees instead of raw text. Structured merging greatly reduces the prevalence of merge conflicts but suffers from important limitations, the main ones being a tendency to alter the formatting of the merged code and being prone to excessive running times. In this paper, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spork</small> , a novel structured merge tool for <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">java</small> . <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spork</small> is unique as it preserves formatting to a significantly greater degree than comparable state-of-the-art tools. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spork</small> is also overall faster than the state of the art, in particular significantly reducing worst-case running times in practice. We demonstrate these properties by replaying 1740 real-world file merges collected from 119 open-source projects, and further demonstrate several key differences between <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spork</small> and the state of the art with in-depth case studies.
Blockchain-Based Distributed Deep Learning Task Assignment Scheme

At present, the combination of blockchain and deep learning is an upsurge. This paper proposed a deep learning task allocation scheme based on blockchain. With the objective of minimizing energy consumption under the QoS constraint, in the proposed deep learning task allocation scheme, the reputation evaluation mechanism of participants was first proposed to reduce the possibility of malicious attacks on the system. Secondly, the task sharding based on data parallelism is to minimize the response time of different types of tasks and ensure the security of the allocation process. Finally, a task allocation algorithm based on reinforcement learning method is proposed, which allocated tasks considering user selection and dynamic resources to reduce energy consumption.
A Web-Based Application for Data Collection and Report Generation Using Django

This paper proposes an user friendly Graphical User Interface (GUI) for data collection, maintaining databases and automated report generation processes. In recent years, the use of database has become more prevalent in almost every industry. However, the lack of technical knowledge in handling databases is common among most of the end users. More importantly, key data collection is a critical task while handling huge database research. This system makes the data collection process very simple by enabling the authorized users to update the database with Django Authentication System and also reduces the administrator’s load of frequently updating the database. Data generation includes tasks such as searching, focusing, monitoring, selecting, extracting, and capturing data. The proposed reporting software is more sophisticated and efficient in the reporting process than spreadsheets, from data entry to display. This application uses HTML and CSS in the user interface, JavaScript for interactivity, Microsoft’s MySQL for backend and Django web-framework for processing. With the combination of the above mentioned tools, the proposed system is successful in collecting, storing, manipulating and generating customized reports based on the user’s request. It is intelligent enough and has flexible methods to fetch data using dynamic SQL queries in generating reports with multiple parameters.
Franco-Taiwanese Research on Extended Reality Experience

This technical note provides descriptions on a collaborative research project between Université Paris 8/École Pratique des Hautes Études-Laboratoire des Usages des Techniques d'Information Numériques (Paris8/EPHE-LUTIN) and National Taiwan Normal University-Science Education Center (NTNU-SEC). This project, entitled "Franco-Taiwanese Research on Extended Reality Experience", is about research and development of new, innovative, performant eXtended Reality (XR) contents, methods, procedures, and services in order to enhance science teaching and learning and to bring subsequent great benefices to education. The aim of the collaboration is the making of eXtended Reality eXperience (XRX) for teachers and pupils of the 12-year basic education curricula. As a result of reaching our aim, we expect that the XR systems and instructional modules developed through our project will be learning efficient for the students acquiring knowledge and the know-how of Science Education.
Boosting Perturbation-Based Iterative Algorithms to Compute the Median String

The most competitive heuristics for calculating the median string are those that use perturbation-based iterative algorithms. Given the complexity of this problem, which under many formulations is NP-hard, the computational cost involved in the exact solution is not affordable. In this work, the heuristic algorithms that solve this problem are addressed, emphasizing its initialization and the policy to order possible editing operations. Both factors have a significant weight in the solution of this problem. Initial string selection influences the algorithm’s speed of convergence, as does the criterion chosen to select the modification to be made in each iteration of the algorithm. To obtain the initial string, we use the median of a subset of the original dataset; to obtain this subset, we employ the Half Space Proximal (HSP) test to the median of the dataset. This test provides sufficient diversity within the members of the subset while at the same time fulfilling the centrality criterion. Similarly, we provide an analysis of the stop condition of the algorithm, improving its performance without substantially damaging the quality of the solution. To analyze the results of our experiments, we computed the execution time of each proposed modification of the algorithms, the number of computed editing distances, and the quality of the solution obtained. With these experiments, we empirically validated our proposal.
Optimised Informed RRTs for Mobile Robot Path Planning

Path planners based on basic rapidly-exploring random trees (RRTs) are quick and effcient, and thus favourable for real-time robot path planning, but are almost-surely suboptimal. In contrast, the optimal RRT (RRT*) converges to the optimal solution, but may be expensive in practice. Recent work has focused on accelerating the RRT*’s convergence rate. The most successful strategies are informed sampling, path optimisation, and a combination thereof. However, informed sampling and its combination with path optimisation have not been applied to the basic RRT. Moreover, while a number of path optimisers can be used to accelerate the convergence rate, a comparison of their effectiveness is lacking. This paper investigates the use of informed sampling and path optimisation to accelerate planners based on both the basic RRT and the RRT*, resulting in a family of algorithms known as optimised informed RRTs. We apply different path optimisers and compare their effectiveness. The goal is to ascertain if applying informed sampling and path optimisation can help the quick, though almost-surely suboptimal, path planners based on the basic RRT attain comparable or better performance than RRT*-based planners. Analyses show that RRT-based optimised informed RRTs do attain better performance than their RRT*-based counterparts, both when planning time is limited and when there is more planning time.
A Review of Machine Learning Approach for Twitter Sentiment Analysis

One of the active sciences or studies whose importance is rising is the science of sentiment analysis. The reason is due to the increasing sources of data that require investigation. Among the most valuable sources is Twitter, in addition to Facebook and other social media platforms. The objective of sentiment analysis is to classify sentiment/opinions of users as positive, negative, or neutral from textual data. This analysis is valuable for many applications that require understanding people's or users' opinions and emotions about a particular topic, product, or service. Several researchers tackle the problem of sentiment analysis using machine learning algorithms. In this paper, a comparative study is presented of various researches conducted a sentiment analysis on social media and especially on Tweets. The survey carried out in this paper provides an overview of preprocessing steps, machine learning algorithms, and approaches used for sentiment classification during the period 2015-2020.
The Seminar on Integration of Industry and Education and the Construction of National-level Quality Undergraduate Course Held in Haikou City

On January 10, 2020, a seminar on the integration of production and education and first-class undergraduate courses was held in Haikou, Hainan Province. 23 teachers from 16 colleges and universities including China Agricultural University, China Civil Aviation Flight Academy, and Qilu University of Technology attended the meeting.
Semi-supervised generative adversarial networks for anomaly detection

Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos. Additionally, we intend to tackle one of the most recurring difficulties of anomaly detection: illumination. For this, we propose a light augmentation algorithm based on gamma correction to help the semi-supervised generative adversarial networks on its classification task. The proposed process performs slightly better than other proposed models.
Proving and disproving confluence of context-sensitive rewriting

Context-sensitive rewriting is a restriction of term rewriting where reductions are allowed on specific arguments of function symbols only, and then in particular positions of terms. Confluence is an abstract property of reduction relations guaranteeing that two diverging reduction sequences can always be joined into a common reduct. In this paper we investigate confluence of context-sensitive rewriting and present some novel results. In particular, a characterization of local confluence of context-sensitive rewriting as the joinability of an extended class of critical pairs which we introduce here. We also show that the treatment of joinability of critical pairs using theorem proving and solving feasibility problems is useful to automatically prove and disprove confluence of context-sensitive rewriting. Our techniques have been implemented in a new tool, CONFident. We show by means of benchmarks the impact of the new techniques discussed in the paper.
The New Deep Web Drug Market Place (And the role of Bitcoin as a currency for drugs)

Drugs are a true menace to our society today. “Drug use on the rise” is an increasingly common headline in newspapers and it is well-known that this is the case. With the topic of drugs becoming more and more common in popular media, youngsters are especially influenced to try drugs. This is not a new problem, as such, and has been a relevant issue in modern society. Coupled with this, the internet plays a huge role in spreading information about emerging drugs (such as synthetic and ‘designer’ drugs). [2] This paper aims to understand the role of the internet’s Deep Web [3] and Bitcoin (and other Crypto currencies) in dealing drugs online.
Sign Language Recognition Using Convexity Defects and Hough Line

This paper presents a novel feature extraction framework for sign language recognition application to assist the deaf and speech-impaired in daily communication.Our method extracts visual features from the hand gesture using convexity defects, K-curvature and Hough line which can differentiate visually similar signs.In segmentation stage, Canny edge detection and skin color segmentation with histogram backprojection method are used to extract the hand region from the background.The features to be extracted are categorized into shape, orientation and motion.The shape features of the sign language will then be extracted using convexity defect, K-curvature and Hough line techniques.The orientation feature will be extracted by calculating the palm center to wrist angle.The trajectory motion of the dynamic gesture is extracted using the chain code method.Lastly, Decision Tree classification is employed in classification of both static and dynamic gestures.The proposed framework is carried out in smartphone platform to recognize 26 alphabets, 10 numbers and eight dynamic American Sign Language (ASL).The average accuracy of 29 static ASL achieved is 85.72% and average accuracy of 10 dynamic ASL achieved is 77%.Through this research, it is found that the proposed framework can recognize larger sign languages database as compared with previous convexity defect-based sign language recognition research.
Digital Asset Management: A Lowdown on Intricacies of Digital Rights and Permissions

The adaptability of digital asset management has prevailed enormous businesses and individuals who desire to manage information effectively. The criteria of this paper will revolve around the in-depth intricacies of digital asset management, its functions, significance, and categories of DAM. The introduction of digital rights and permissions aligned with highlighted copyright permission. Initializing from manual asset management and proceeding towards digital asset management, media asset management, and finalizing at massive content management, these terms seem to be striving to explain a unified thing. However, each domain of asset management has separate aspects of managing information. Every organization will opt for its criteria of approach for managing the assets that rely upon the environmental, current cultural, and financial state of the company.
Spatio-spectral Image Reconstruction Using Non-local Filtering

In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in high-frequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions.