Reviewerly/exHarmony huggingface dataset:
Conflict-Based Belief Revision Operators in Possibilistic Logic

In this paper, we investigate belief revision in possibilistic logic, which is a weighted logic proposed to deal with incomplete and uncertain information. Existing revision operators in possibilistic logic are restricted in the sense that the input information can only be a formula instead of a possibilistic knowledge base which is a set of weighted formulas. To break this restriction, we consider weighted prime implicants of a possibilistic knowledge base and use them to define novel revision operators in possibilistic logic. Intuitively, a weighted prime implicant of a possibilistic knowledge base is a logically weakest possibilistic term (i.e., a set of weighted literals) that can entail the knowledge base. We first show that the existing definition of a weighted prime implicant is problematic and need a modification. To define a revision operator using weighted prime implicants, we face two problems. The first problem is that we need to define the notion of a conflict set between two weighted prime implicants of two possibilistic knowledge bases to achieve minimal change. The second problem is that we need to define the disjunction of possibilistic terms. We solve these problems and define two conflict-based revision operators in possibilistic logic. We then adapt the well-known postulates for revision proposed by Katsuno and Mendelzon and show that our revision operators satisfy four of the basic adapted postulates and satisfy two others in some special cases.
Teaching Quality Assessment of College English Department Based on Factor Analysis

The English departments in colleges and universities are the training center of professional English talents. Their teaching quality bears on the international exchanges and economic development. Through questionnaire survey, interview, and literature review, this paper designed an evaluation index system (EIS) for teaching quality assessment of English departments in colleges and universities, which consists of 19 indexes. Specifically, the collected data was computed and sorted out through factor analysis, and the common factors were extracted, forming an EIS containing 19 secondary indices and 5 primary indices. Next, a weight was assigned to each index in the EIS. Taking the English departments of 3 higher educational schools in a province for example, an empirical analysis was carried out to verify the application effect of our EIS on the teaching quality of college English education. The results demonstrated the scientific nature, reasonability, and feasibility of the proposed EIS. The research enriched the theoretical and practical evidences for the teaching quality assessment of college English education, and promotes the teaching quality assessment of college English department.
SPDNet: A Real-Time Passenger Detection Method Based on Attention Mechanism in Subway Station Scenes

In order to implement real-time detection of passengers in subway stations, this paper proposes the SPDNet based on YOLOv4. Aiming at the low detection accuracy of passengers in the subway station due to uneven light conditions, we introduce the attention mechanism CBAM to recalibrate the extracted features and improve the robustness of the network. For the crowded areas in the subway station, we use the K-means++ algorithm to generate anchors that are more consistent with the passenger aspect ratio based on the dataset KITTI, which mitigates the missing caused by the incorrect suppression of true positive boxes by the Nonmaximum Suppression algorithm. We train and test our SPDNet on the KITTI dataset and prove the superiority of our method. Then, we carry out transfer learning based on the subway surveillance video dataset collected by ourselves to make it conform to the distorted passenger targets under the angle of the surveillance camera. Finally, we apply our network in a Beijing subway station and achieve satisfactory results.
Feature Selection for Human Trafficking Detection Models

Recently, social media has been used increasingly for human trafficking businesses, especially in the sex trade industry. Detecting online advertisement of sex-trade is crucial for law enforcement parties, but it is a challenging task. Applications of machine learning algorithms have been investigated to solve the problem. However, one of major obstacles is the lack of techniques of selecting effective features for model induction. This paper proposes a new method based on explainable artificial intelligence (XAI) to extract those important features for model creation. We carried out an experimentation of our approach with three classification models, Naive Bayes, Decision Tree and Neural Network, using the trafficking-10k data set. The result has shown that our proposed technique can improve the detection accuracies of all three models significantly.
Correcting Imprecise Object Locations for Training Object Detectors in Remote Sensing Applications

Object detection on aerial and satellite imagery is an important tool for image analysis in remote sensing and has many areas of application. As modern object detectors require accurate annotations for training, manual and labor-intensive labeling is necessary. In situations where GPS coordinates for the objects of interest are already available, there is potential to avoid the cumbersome annotation process. Unfortunately, GPS coordinates are often not well-aligned with georectified imagery. These spatial errors can be seen as noise regarding the object locations, which may critically harm the training of object detectors and, ultimately, limit their practical applicability. To overcome this issue, we propose a co-correction technique that allows us to robustly train a neural network with noisy object locations and to transform them toward the true locations. When applied as a preprocessing step on noisy annotations, our method greatly improves the performance of existing object detectors. Our method is applicable in scenarios where the images are only annotated with points roughly indicating object locations, instead of entire bounding boxes providing precise information on the object locations and extents. We test our method on three datasets and achieve a substantial improvement (e.g., 29.6% mAP on the COWC dataset) over existing methods for noise-robust object detection.
Biometric technology in banking institutions: ‘The customers’ perspectives’

Background: Over the years, attention has been focused on digital banking and financial technology with little or no attention being paid to biometric banking technology. Objective: The study aimed to investigate the need for security and simplicity in the authentication of retail payments, digital banking and financial technology through the application of biometric systems. Method: The study employed quantitative research methodology and a response rate of 52% was achieved. A set of questionnaires was distributed for data collection. Results: The study’s findings indicated it is imperative for all businesses that participate in financial businesses to fully implement the best possible security measures or systems to ensure or enhance security for financial business activities. Conclusion: Based on the findings of the study, it is recommended that businesses must adopt the new innovative and secured mechanisms of financial dealings to enhance innovation, security and flexibility.
Accuracy measurement of HoloLens2 IMUs in medical environments

Abstract As augmented reality devices become more available, collaborative work in the augmented space is expected to increase. Knowing the position of participants allows for realistic interaction rather than passive participation. The HoloLens 2’s camera and IMUs fuse data to locate the device. In this paper, accuracy and repeatability of the HoloLens2 position finding was analysed to provide a quantitative measure of pose repeatability and deviation from a path while in motion. Deviation from a circular path was found to be below 5 mm per 628 mm travelled.
Dynamical Analysis of a Navigation Algorithm

There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.
Toxic Comment Classification on Social Media Using Support Vector Machine and Chi Square Feature Selection

The use of social media in society continues to increase over time and the ease of access and familiarity of social media then make it easier for an irresponsible user to do unethical things such as spreading hatred, defamation, radicalism, pornography so on. Although there are regulations that govern all the activities on social media. However, the regulations are still not working effectively. In this study, we conducted a classification of toxic comments containing unethical matters using the SVM method with TF-IDF as the feature extraction and Chi Square as the feature selection. The best performance result based on the experiment that has been carried out is by using the SVM model with a linear kernel, without implementing Chi Square, and using stemming and stopwords removal with the F1 − Score equal to 76.57%.
5G Multimedia Precision Marketing Based on the Improved Multisensor Node Collaborative Filtering Recommendation Algorithm

The rapid development of web technology has brought new problems and challenges to the recommendation system: on the one hand, the traditional collaborative filtering recommendation algorithm has been difficult to meet the personalized recommendation needs of users; on the other hand, the massive data brought by web technology provides more useful information for recommendation algorithms. How to extract features from this information, alleviate sparsity and dynamic timeliness, and effectively improve recommendation quality is a hot issue in the research of recommendation system algorithms. In view of the lack of an effective multisource information fusion mechanism in the existing research, an improved 5G multimedia precision marketing based on an improved multisensor node collaborative filtering recommendation algorithm is proposed. By expanding the input vector field, the features of users’ social relations and comment information are extracted and fused, and the problem of collaborative modelling of these two kinds of important auxiliary information is solved. The objective function is improved, the social regularization term and the internal regularization term in the vector domain are analysed and added from the perspective of practical significance and vector structure, which alleviates the overfitting problem. Experiments on a large number of real datasets show that the proposed method has higher recommendation quality than the classical and mainstream baseline algorithm.
Linear Regression Algorithm in Machine Learning through MATLAB

Abstract: In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error
Game-Based Didactic Resources as a Strategy in Foreign Language Pedagogy

Gamification is a method widely used in recent years in the educational field to facilitate the teaching and learning process using different game elements and mechanics. The objective of this study was to analyze the use of non-digital game-based (NDGB) didactic resources as a strategy to facilitate the learning of Haitian Creole and its culture by native Chilean adult professionals. The study followed a qualitative approach, the design of which was a case study corresponding to the implementation of a Haitian Creole language learning program. Sixty Chilean adult professionals participated, of which thirty-one belonged to the police department and twenty-nine to the local health department. These professionals have to help Haitian migrants who arrive in the country without the capacity to communicate in the target language, Spanish. The instruments used in this case study were participant classroom observation and a semi structured interview, both implemented by the researchers. The results show that NDGB didactic resources facilitate language learning, favor situated work, and motivate learners to learn. In addition, it enables the co-construction of knowledge, based on collaborative work where all learners play a participatory role in the game. Participants showed greater commitment to language learning, those they have to attend to, and the needs of those they have to serve in their professional life.
Mobile Apps Based on Keller Personalized System of Instruction to Promote English Vocabulary Acquisition

Mobile learning (or m-learning) is the ability to learn anywhere and at any time using a portable electronic device. Mobile technologies gradually integrate into learnedness. Mobile devices and apps are changing the way learners acquires English. This study aims to develop a mobile Apps based on the Keller Plan Personalized system of instruction (PSI) theory called JuJu English Vocabulary to improve learners’ vocabulary acquisition. The mobile Apps was developed using ADDIE instructional design model. The developed Apps was investigated using a quasi-experimental research design among 60 students separated into two(2) groups: control and experimental groups. Data were analysed using SPSS software. The outcome shows a significant difference between Post-test experimental (using the mobile app) and the control group (traditional learning). This Mobile Apps design allows personalised growth among the experimental group of students in terms of their vocabulary acquisition. The design mimicking a game strategy did help increase their interest to enjoy the learning process.
Effects of Mediation of Learning Interest in Improving Student Learning Achievement

This study aims; to analyze and examine the effects of education level, years of service, work motivation, and work discipline on learning interest; to analyze and examine the effects of education level, years of service, work motivation, work discipline, and learning interest on learning achievement; to analyze and explore the indirect effects of education level, years of service, work motivation, and work discipline on learning achievement mediated by learning interest.This research is an explanatory study, which involved samples of tutors of Packet C Study Group, Jember Regency, in total of 200 respondents.The research data were primary data obtained by online questionnaires, observation, and documentation.The analytical tool used was path analysis to determine the direct effect and indirect effect using SEM method.The results of this study found that education level, years of service, work motivation, and work discipline had a significant positive effect on learning interest.Education level, years of service, work motivation, work discipline, and learning interest had a significant positive effect on learning achievement.Learning interest was able to mediate the effects of education level, years of service, work motivation, and work discipline on learning achievement.
Self-supervised Image-to-Text and Text-to-Image Synthesis

A comprehensive understanding of vision and language and their interrelation are crucial to realize the underlying similarities and differences between these modalities and to learn more generalized, meaningful representations. In recent years, most of the works related to Text-to-Image synthesis and Image-to-Text generation, focused on supervised generative deep architectures to solve the problems, where very little interest was placed on learning the similarities between the embedding spaces across modalities. In this paper, we propose a novel self-supervised deep learning based approach towards learning the cross-modal embedding spaces; for both image to text and text to image generations. In our approach, we first obtain dense vector representations of images using StackGAN-based autoencoder model and also dense vector representations on sentence-level utilizing LSTM based text-autoencoder; then we study the mapping from embedding space of one modality to embedding space of the other modality utilizing GAN and maximum mean discrepancy based generative networks. We, also demonstrate that our model learns to generate textual description from image data as well as images from textual data both qualitatively and quantitatively.
Blockchain Ecosystem for Credit Transfer in Education

Data is the key to measuring educational effectiveness promptly. But data and education are trapped and siloed across centralized systems, causing information discrepancies and inaccuracies. This has caused countless delayed opportunities, academic credential disagreements, and never-ending confusion around learning potency. This lack of transparency, despite having its fair share of usefulness, has also been quite burdensome. To alleviate this issue, our team has developed a blockchain protocol that verifies professional certifications that have been earned both locally and through another well-established online educational portal. This system allows accuracy, reliability, and immutability that has never been implemented. This foundation of clear, verified data will then be used further to power blockchain-based applications. The result is that our attempt at a versatile, holistic, and decentralized view of educational performance ensures the best e-learning outcome for students and teachers alike.
A Novel Adaptive Feature Fusion Strategy for Image Retrieval

In the era of big data, it is challenging to efficiently retrieve the required images from the vast amount of data. Therefore, a content-based image retrieval system is an important research direction to address this problem. Furthermore, a multi-feature-based image retrieval system can compensate for the shortage of a single feature to a certain extent, which is essential for improving retrieval system performance. Feature selection and feature fusion strategies are critical in the study of multi-feature fusion image retrieval. This paper proposes a multi-feature fusion image retrieval strategy with adaptive features based on information entropy theory. Firstly, we extract the image features, construct the distance function to calculate the similarity using the information entropy proposed in this paper, and obtain the initial retrieval results. Then, we obtain the precision of single feature retrieval based on the correlation feedback as the retrieval trust and use the retrieval trust to select the effective features automatically. After that, we initialize the weights of selected features using the average weights, construct the probability transfer matrix, and use the PageRank algorithm to update the initialized feature weights to obtain the final weights. Finally, we calculate the comprehensive similarity based on the final weights and output the detection results. This has two advantages: (1) the proposed strategy uses multiple features for image retrieval, which has better performance and more substantial generalization than the retrieval strategy based on a single feature; (2) compared with the fixed-feature retrieval strategy, our method selects the best features for fusion in each query, which takes full advantages of each feature. The experimental results show that our proposed method outperforms other methods. In the datasets of Corel1k, UC Merced Land-Use, and RSSCN7, the top10 retrieval precision is 99.55%, 88.02%, and 88.28%, respectively. In the Holidays dataset, the mean average precision (mAP) was 92.46%.
Rationale Discovery and Explainable AI

The justification of an algorithm’s outcomes is important in many domains, and in particular in the law. However, previous research has shown that machine learning systems can make the right decisions for the wrong reasons: despite high accuracies, not all of the conditions that define the domain of the training data are learned. In this study, we investigate what the system does learn, using state-of-the-art explainable AI techniques. With the use of SHAP and LIME, we are able to show which features impact the decision making process and how the impact changes with different distributions of the training data. However, our results also show that even high accuracy and good relevant feature detection are no guarantee for a sound rationale. Hence these state-of-the-art explainable AI techniques cannot be used to fully expose unsound rationales, further advocating the need for a separate method for rationale evaluation.
Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.