Reviewerly/exHarmony huggingface dataset:
The Effects of Observer Expectations on Judgments of Anti-Asian Hate Tweets and Online Activism Response

The rise of racial hate speech on social media has raised critical questions for scholars to explore. It is necessary to understand how outside observers passively evaluate (a) online racial hate speech posts on social media and (b) whether those evaluations are related to observers’ subsequent behavior. This study explored how observers evaluate acts of majority-on-minority and minority-on-minority anti-Asian hate tweets on Twitter. In an experiment ( n = 196) informed by expectancy violations theory, we tested how White observers evaluated anti-Asian tweets ostensibly posted by either a White or Black source. Analysis revealed a moderated-mediation pathway in which observers’ political partisanship (Democrat/Republican) affected how they judged the ethnic prototypicality of White and Black sources of racial hate speech; these source prototypicality judgments were in turn associated with observers’ judgments of tweet offensiveness and self-reported intentions to engage in online activism (i.e., signing an online petition). These results contribute to our understanding of outside observers’ differential expectancies regarding online hate speech, and how those expectancies can affect perceptions of and reactions to acts of racism.
Convolutional Recurrent Neural Networks for Text Lecture Summarization

Text summarization can be utilized for variety type of purposes; one of them for summary lecture file. A long document expended long time and large capacity. Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information. Summarization is a technique which provides the primary points of the whole document, and in the same time it will indicates the majority of the information in a small amount of time. For this reason it can save user time, decrease storage, and increase transfer speed to transmit through the internet. The summarization process will eliminate duplicated data, unimportant information, and also replace complex expression with simpler expression. The proposed method is using convolutional recurrent neural network deep model as a method for abstractive text summarization of lecture file that will be great helpful to students to address lecture notes. This method proposes a novel encoder-decoder deep model including two deep model networks which are convolutional and recurrent. The encoder part which consists of two convolutional layers followed by three recurrent layers of type bidirectional long short term memory. The decoder part which consists of one recurrent layer of type long short term memory. And also using attention mechanism layer. The proposed method training using standard CNN/Daily Mail dataset that achieved 92.90% accuracy. Index Terms— Text Summarization, Deep Learning, Convolutional Neural Network, Long Short Term Memory, Convolutional Recurrent Neural Network.
A Self-Adaptive Gallery Construction Method for Open-World Person Re-Identification

Person re-identification, or simply re-id, is the task of identifying again a person who has been seen in the past by a perception system. Multiple robotic applications, such as tracking or navigate-and-seek, use re-identification systems to perform their tasks. To solve the re-id problem, a common practice consists in using a gallery with relevant information about the people already observed. The construction of this gallery is a costly process, typically performed offline and only once because of the problems associated with labeling and storing new data as they arrive in the system. The resulting galleries from this process are static and do not acquire new knowledge from the scene, which is a limitation of the current re-id systems to work for open-world applications. Different from previous work, we overcome this limitation by presenting an unsupervised approach to automatically identify new people and incrementally build a gallery for open-world re-id that adapts prior knowledge with new information on a continuous basis. Our approach performs a comparison between the current person models and new unlabeled data to dynamically expand the gallery with new identities. We process the incoming information to maintain a small representative model of each person by exploiting concepts of information theory. The uncertainty and diversity of the new samples are analyzed to define which ones should be incorporated into the gallery. Experimental evaluation in challenging benchmarks includes an ablation study of the proposed framework, the assessment of different data selection algorithms that demonstrate the benefits of our approach, and a comparative analysis of the obtained results with other unsupervised and semi-supervised re-id methods.
Designing an Interactive Chatbot for Educational Assistance using Rasa Framework

The AI Chatbots is becoming very popular these days. Many companies and organizations implement chatbots on their server and websites nowadays. These chatbots are very useful in improving their business. Whatever organization the chatbot is a part of, gets better user experience for the customers and the organization itself as well. The customers who initially could get stuck on a query, might have to make enquiries physically or e-mailing. Instead Chatbot can answer their query within seconds. CRCE bot is also like one of these above stated bots. It is a chatbot made for our Institute website. Students, teachers as well as student’s parents can highly benefit from this system. The users can easily navigate through the website using the bot with simple queries like the existing system discussed later. Additionally, this bot has the capability to answer the asked queries too. For example, you can ask ‘What was the cut-off for this year for admission in a certain field or department’ and the bot will answer you the exact cutoff you asked in a natural language instead of just redirecting us to the cutoffs list page. This system proves very useful for all the students, teachers and all other people associated with the college.
Quantum hybrid algorithm for solving SAT problem

Combinatorial problems usually have a large search space, and almost all classical algorithms for solving this class of problems are inefficient for real-life input sizes. Quantum algorithms have been introduced with the aim of reducing computational time. In addition, to speed up the computation while solving combinatorial problems, another approach is to reduce the search space and focus on a restricted area. In this work, we study a parameter that helps reduce the search space for the solution to the satisfiability problem (SAT). We propose an approach based on Grover’s algorithm that considers the defined parameter to reduce the search space for the solution of the SAT problem. Our algorithm has a complexity of the order O(N/S), or O(N/lS) if there are l good solutions among the initial solutions, with N the number of potential solutions and S the reduction or subdivision factor of the space.
Collaborative Filtering Recommender System Based on Memory Based in Twitter Using Decision Tree Learning Classification (Case Study: Movie on Netflix)

In this modern era, watching movie becomes the activity that most people like to do. Netflix is one of the platforms that provides an easily accessible movie streaming services because the user doesn’t have to go to the theater. Because of the large number of movies that can be watched on Netflix, this research will develop a Recommender System that will ease people to watch the movies based on their preferences. The method used in this research is Memory-Based Collaborative Filtering (CF) Method. This method consists of two kinds which are User-Based CF Method and Item Based CF Method. Both of those methods will be combined with the Decision Tree Learning Classification Type C4.5 to determine which method suit better for the dataset. RMSE (Root Mean Squared Error) method, precision and recall will be used to evaluate the models. Dataset used is dataset which Dataset taken fresh from Twitter which will be merged with ratings from several websites (IMDB, Rotten Tomatoes, Metacritic). Website data rating will be added to decrease the possibility of sparsity. Result in this research shows that User-Based CF is better suit for the dataset by having 89% average precision and 88% average recall. Those result proves that MemoryBased CF can be combined with Decision Tree Learning Classification because it has high precision and recall value.
Application of Multivariate Video Analysis in English Teaching Effect Evaluation Based on Computational Neural Model Simulation

Video observation and content analysis are used to make a "quantitative-qualitative" analysis of English teachers' teaching behavior reflected in English classroom teaching videos, and to accurately describe, analyze, and summarize the characteristics of English teachers' teaching behavior from various aspects. Based on this, this study uses video analysis methods and NVivo 11 qualitative analysis tools to import quantitative data obtained from teaching video content into Excel tables for statistical analysis, objectively describe the rules and characteristics of junior high school English teachers' teaching effects, and then put forward suggestions to optimize teaching effects and strategies and suggestions to promote English classroom development. This paper establishes a video analysis and evaluation model. First, calculate the weights required by the model, then calculate the relationship matrix, and then calculate the second-level video analysis and evaluation. Using the second-level weight and relationship matrix, the teacher's evaluation value will be obtained.
Subnetwork ensembling and data augmentation: Effects on calibration

Abstract Deep Learning models based on convolutional neural networks are known to be uncalibrated, that is, they are either overconfident or underconfident in their predictions. Safety‐critical applications of neural networks, however, require models to be well‐calibrated, and there are various methods in the literature to increase model performance and calibration. Subnetwork ensembling is based on the over‐parametrization of modern neural networks by fitting several subnetworks into a single network to take advantage of ensembling them without additional computational costs. Data augmentation methods have also been shown to enhance model performance in terms of accuracy and calibration. However, ensembling and data augmentation seem orthogonal to each other, and the total effect of combining these two methods is not well‐known; the literature in fact is inconsistent. Through an extensive set of empirical experiments, we show that combining subnetwork ensemble methods with data augmentation methods does not degrade model calibration.
Optical differentiation in a polar coordinate system

Optical analog computing prevails over digital computing in several aspects, such as processing speed and power consumption. Optical differentiation, as a main branch, can be particularly significant in image recognition. Various differentiators have been developed to realize the two-dimensional differentiation in Cartesian coordinates. Here, we propose the optical differentiation in a polar coordinate system, which can be factorized into the radial differentiation and the angular differentiation. Experimental results demonstrate that the variations along radial and angular directions can be, respectively, highlighted by the two kinds of differentiation, which suggests that employing polar coordinates may be more intuitive and informative in practical use. This work is probable to enrich the content of optical differentiation and extend potential applications in image recognition.
A Hybrid Labeling Strategy for Imbalanced Data Stream in Presence of Concept Drifts

Imbalanced data stream with concept drift is a tough challenge to predict correct labels. Handling both of these issues with limited labeling budgets gets more attention nowadays. Though some existing methods try to address these problems at the same time, it fails to provide appropriate outcomes for imbalanced data with a lower budget. So, to get higher performances with lower labeling budgets we propose a method is proposed. A cluster-based initialization technique is considered to train an ensemble classifier with the most representative instances. This cluster-based initialization process helps to be robust against the concept drift and reduce the overall labeling cost in active learning situation. In addition, a noble imbalance strategy is introduced that provides a special priority to the minority class data. For measuring the efficiency of the method, four synthetic datasets generator with abrupt and gradual drifting and two real-world datasets are considered. The proposed method attains higher AUC values for all the datasets with the least labeling cost. And, every dataset shows better recall values as compare to the other related methods.
Blockchain Technology: Growing Usecases and Issues to Tackle

Advantages of decentralized systems over centralized approaches are reason for increasing incorporation of blockchain technology in almost every digital task. Core benefits of blockchain include decentralization, persistency, auditability and anonymity. This paper discusses Blockchain Technology from the perspective of related attributes, applications and challenges. Specifically, we try to elaborate on the current work pertaining to application of Blockchain Technology to different domains such as Internet of Things (IoT), Artificial Intelligence (AI), Big Data and Software Defined Networking (SDN) keeping in view the challenges of optimized storage, scalability and security.
A Decoupled Multi-Task Network for Shadow Removal

Shadow removal, which aims to restore the illumination in shadow regions, is challenging due to the diversity of shadows in terms of location, intensity, shape, and size. Different from most multi-task methods, which design elaborate multi-branch or multi-stage structures for better shadow removal, we introduce feature decomposition to learn better feature representations. Specifically, we propose a single-stage and decoupled multi-task network (DMTN) to explicitly learn the decomposed features for shadow removal, shadow matte estimation, and shadow image reconstruction. First, we propose several coarse-to-fine semi-convolution (SMC) modules to capture features sufficient for joint learning of these three tasks. Second, we design a theoretically supported feature decoupling layer to explicitly decouple the learned features into shadow image features and shadow matte features via weight reassignment. Last, these features are converted to a target shadow-free image, affiliated shadow matte, and shadow image, supervised by multi-task joint loss functions. With multi-task collaboration, DMTN effectively recovers the illumination in shadow areas while ensuring the fidelity of non-shadow areas. Experimental results show that DMTN competes favorably with state-of-the-art multi-branch/multi-stage shadow removal methods, while maintaining the simplicity of single-stage methods. We have released our code to encourage future exploration in powerful feature representation for shadow removal <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nachifur/DMTN</uri>
Development of Data Labelling and Visualization Technologies for Cultural Heritage Intelligent Curation

This study aims to enable users of digitized cultural heritage artefacts to understand the correlation between artefacts and their context, discover new knowledge, and gain inspiration. We have developed data labelling and visualization technologies specific to cultural heritage so that users, such as curators, cultural heritage experts, and museum visitors, can easily and quickly access the information that they want through the correlation between artefacts. We came up with a web-based labelling tool for cultural heritage data and applied it to data on 45,692 artefacts from the National Museum of Korea. We also invented a web-based visualization tool that converts lists of attribute information in Excel spreadsheets into visualized content and edits the attributes. The meanings of thumbnails of visualized cultural heritage and that of connected lines, a search function, and visualized cultural heritage will allow users to quickly and easily access the cultural heritage information they are looking for.
A graph‐based edge attention gate medical image segmentation method

Abstract For the purpose of solving the problems of missing edges and low segmentation accuracy in medical image segmentation, a medical image segmentation network (EAGC_UNet++) based on residual graph convolution UNet++ with edge attention gate (EAG) is proposed in the study. With UNet++ as the backbone network, the idea of graph theory is introduced into the model. First, the dropout residual graph convolution block (DropRes_GCN Block) and the traditional convolution structure in UNet++ are used as encoders. Second, EAGs are adopted so that the model pays more attention to image edge features during decoding. Finally, aiming at the imbalance problem of positive and negative samples in medical image segmentation, a new weighted loss function is introduced to enhance segmentation accuracy. In the experimental part, three datasets (LiTS2017, ISIC2018, COVID‐19 CT scans) were used to evaluate the performances of various models; multiple groups of ablation experiments were designed to verify the effectiveness of each part of the model. The experimental results showed that EAGC_UNet++ had better segmentation performance than the other models under three quantitative evaluation indicators and better solved the problem of missing edges in medical image segmentation.
Efficient multi-receiver signcryption scheme based on ring signature

With the development of society, the problem of the disclosure of the identity information of whistleblowers has become more and more prominent, resulting in few people willing to become whistleblowers. Anonymous signcryption schemes can be used to solve the problems faced by whistleblowers, and the multi-receiver signcryption in the form of one-to-many communication is more suitable. Existing related schemes all use bilinear or exponential operations, which are impractical in terms of whistleblower application because of complexity. An efficient multi-receiver signcryption scheme based on ring signature is proposed in this paper. It adopts the method of hiding the sender's identity in the set of identities trusted by the receivers to realise the anonymity of the sender and the reliability of the sender. A strict security proof shows that the proposed scheme is secure. Complexity analysis and comparison show that the efficiency advantage of the proposed scheme is obvious.
Firmware Security Verification Method of Distance Learning Terminal Based on MD5 Algorithm

In order to solve the problem of high leak and error rate in the process of firmware security verification of distance learning terminal, a method of firmware security verification of distance learning terminal based on MD5 algorithm is proposed in order to solve the adaptability problem of existing security verification methods. According to the structure of distance learning terminal, the corresponding structure model is constructed, and the firmware types of distance learning terminal are divided under this model. Collect different types of firmware programs, use MD5 algorithm to verify user identity, detect firmware code security defects and malicious attacks, and identify firmware service daemon. Combined with the verification results of user identity, the final firmware security verification results are obtained through the comparison of attacks and firmware guard strength. Through the test experiment, it is concluded that the average missed acceptance rate and false acceptance rate of the design firmware safety verification method are less than 1%, and are suitable for multiple firmware samples, which proves the safety verification effect and adaptability of the design method.
Time Cluster Personalized Ranking Recommender System in Multi-Cloud

Recommender systems have become a vital tool to identify items for users based on personalized preferences. The personalized ranking or item recommendation generates a ranked list of items for the users. Clustering methods offer better scalability than collaborative filtering (CF) methods since they make predictions within small clusters. The major challenges of recommender systems are accuracy and scalability. Traditionally, recommendation systems are based on a centralized framework that restrains quick scalability for enormous data volumes. The emergence of cloud technology resolves this issue as it handles vast data and supports massive processing. This paper proposes a time cluster personalized ranking recommender system (TCPRRS) in a multi-cloud environment. TCPRRS is a five-stage system that generates recommendations based on temporal information of user consumption and clustering with personalized ranking. Particle swarm optimization (PSO) is utilized for optimizing the solution. The efficiency of TCPRRS is estimated using similarity metrics.
An active preference learning approach to aid the selection of validators in blockchain environments

We consider a real-world problem faced in some blockchain ecosystems that select their active validators—the actors that maintain the blockchain—from a larger set of candidates through an election-based mechanism. Specifically, we focus on Polkadot, a protocol that aggregates preference lists from another set of actors, nominators, that contain a limited number of trusted validators and thereby influence the election’s outcome. This process is financially incentivized but often overwhelms human decision makers due to the problem’s complexity and the multitude of available alternatives. This paper presents a decision support system (DSS) to help the nominators choose the validators in an environment with frequently changing data. The system structures the relevant multiple attribute problem and incorporates a dedicated active learning algorithm. Its goal is to find a sufficiently small set of pairwise elicitation questions to infer nominators’ preferences. We test the proposed solution in an experiment with 115 real nominators from the Polkadot ecosystem. The empirical results confirm that our approach outperforms the unaided process in terms of required interaction time, imposed cognitive effort, and offered efficacy. The developed DSS can be easily extended to other blockchain ecosystems.
Implementation of a Collaborative Recommendation System Based on Multi-Clustering

The study aims to present an architecture for a recommendation system based on user items that are transformed into narrow categories. In particular, to identify the movies a user will likely watch based on their favorite items. The recommendation system focuses on the shortest connections between item correlations. The degree of attention paid to user-group relationships provides another valuable piece of information obtained by joining the sub-groups. Various relationships have been used to reduce the data sparsity problem. We reformulate the existing data into several groups of items and users. As part of the calculations and containment of activities, we consider Pearson similarity, cosine similarity, Euclidean distance, the Gaussian distribution rule, matrix factorization, EM algorithm, and k-nearest neighbors (KNN). It is also demonstrated that the proposed methods could moderate possible recommendations from diverse perspectives.