Reviewerly/exHarmony huggingface dataset:
Efficiently Approaching Groups of People in a Socially Acceptable Manner in Environments with Obstacles

Advancements in mobile robotics have allowed humans and robots to interact in different environments and ways. A problem of great interest in Human-Robot Interaction is how to approach individuals, e.g., to gather information, in a socially acceptable manner. We present a new method for planning sequential visits to various groups of people in cluttered environments. The problem is formulated as a Set Orienteering Problem, where each group denotes a cluster with a set of possible approaching points considering different F-formations. We use the concept of a social probabilistic roadmap to determine safe paths between groups. Simulations considering different cases show that methodology produces efficient tours that maximize the number of approached individuals while respecting social norms of distance and a limited budget.
TVG-ReID: Transformer-Based Vehicle-Graph Re-Identification

Vehicle re-identification is the task of identifying the same vehicle in different environments and from different angles and cameras. It is more challenging than re-identification of humans: 1)small differences between vehicles of the same model make it difficult to capture their subtle characteristics; 2)vehicles of different types and colors may have similar characteristics from different viewpoints or external conditions. To address these challenges, we propose a TVG-ReID network, using a Transformer network to enhance features extracted from a CNN backbone network. A vehicle knowledge graph transfer method(Vehicle-Graph) is proposed, which treats each vehicle as a node in a graph, where simple information is transmitted through edges to constrain the distance of the nodes in a metric learning manner. Experiments on two vehicle re-identification datasets demonstrate the good performance of our proposed model.
The Advantages and Disadvantages of Using Artificial Intelligence in Education

This study aimed to identify the advantages and disadvantages of using artificial intelligence in education from the perspective of the students of Al Ain University. The researcher used the descriptive approach due to its compatibility to the study objectives. The study sample consisted of (184) students from Al Ain University in Al Ain City, who were selected using the random stratified sampling method. The questionnaire was developed and distributed by using Google Drive software. The results showed that most students think that artificial intelligence can improve learning personal experiences, process a considerable amount of data and improve task management. However, there have been different opinions about the ability of artificial intelligence to control students' behavior and direct learning, improve the efficiency of educational system, provide notes and reviews, reduce dependency on teachers, and enhance social interaction. Furthermore, several students expressed their concerns about the possible missing of traditional educational jobs, the costs of implementing artificial intelligence systems, the errors of programming and error-processing, and missing the human relationships in the classroom.&#x0D; &#x0D; Received: 10 May 2023 / Accepted: 22 June 2023 / Published: 5 July 2023
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative sampling

Contrastive learning has made breakthrough advancements in graph representation learning, which encourages the representation of positive samples to be close and those of negative samples to be far away. However, existing graph contrastive learning (GCL) frameworks have made great efforts toward designing different augmentation strategies for positive samples, while randomly utilizing all other nodes as negative samples and treating them equally, completely ignoring the differences between negative samples. Moreover, almost every GCL framework replaces original graph with different augmented views, which may lead to unexpected information missing caused by randomly perturbing edges and features. To address these issues, we propose a self-supervised graph Contrastive learning framework with Curriculum negative sampling, called ConCur, which feeds negative samples in an easy-to-hard fashion for contrastive learning by performing our proposed curriculum negative sampling strategy. More specifically, ConCur consists of two phases: Graph Augmentations and Curriculum Contrastive Training. Graph Augmentations aim at constructing positive and negative samples through different graph augmentation strategies. In the Curriculum Contrastive Training, we first utilize a triplet network to learn node representations by receiving original graph and different augmented views as input. Then, we propose a curriculum negative sampling strategy to enumerate negative samples from easy to hard for contrastive training. Finally, we utilize a unified contrastive loss to optimize node representations. Comprehensive experiments on five real-world datasets reveal that ConCur yields substantial relative encouraging results on the node classification task.
Reversible and colorable deep image steganography with large capacity

Image steganography is a technique for hiding secret data within a cover image and thus making the data immune to typical steganalysis attacks. Recently, there is a great concern about color distortion in most commonly used color-based image steganography methods. To address this issue, we first propose a steganography method that hides secret images in grayscale image and creates a color image. Specifically, we design a colorable information hiding mechanism to convert the image steganography task to gray-to-color conversion. In addition, we exploit the invertible neural network to realize image hiding and recovering in the forward and backward propagation, respectively. Meanwhile, we develop a perceptual loss function to effectively improve the quality of the stego image for increasing steganography security. Experiments indicate that the proposed method outperforms other state-of-the-art image steganography on secret image recovery, embedding capacity, and steganography security over different datasets.
A novel deep CNN model for improved breast cancer detection using ultrasound images

The biggest cause of cancer related fatalities in women globally is breast cancer. So, the early detection of this disease is beneficial to the patient since it allows for the adaptation of appropriate care. Breast ultrasonography images with complex artifacts and high noise levels make diagnosis extremely difficult for radiologists. Automatic breast cancer classification is therefore our top objective. In this article, a variety of cutting-edge deep convolutional neural networks (CNNs) are used for breast cancer classification, including AlexNet, VGG-16, and ResNet-18 etc. A novel CNN model named as BCI-Net has been proposed for classifying breast cancer. As activation function plays a very important role for the performance and training dynamics in neural network, in this model the Mish activation function is applied. For this experiment, a publicly available ultrasound images dataset named Breast Ultrasonography Images (BUSI) is used. Throughout the experiments, the proposed model has gained 98.70% average accuracy using hold-out validation; where it has acquired 97.49% overall average accuracy with a standard deviation (sd) of 1.14% after doing five fold cross-validation. In the clinical diagnosis, this analysis can be used as a secondary strategy of breast cancer detection.
FASTSync: A FAST Delta Sync Scheme for Encrypted Cloud Storage in High-bandwidth Network Environments

More and more data are stored in cloud storage, which brings two major challenges. First, the modified files in the cloud should be quickly synchronized to ensure data consistency, e.g., delta synchronization (sync) achieves efficient cloud sync by synchronizing only the updated part of the file. Second, the huge data in the cloud needs to be deduplicated and encrypted, e.g., Message-Locked Encryption (MLE) implements data deduplication by encrypting the content among different users. However, when combined, a few updates in the content can cause large sync traffic amplification for both keys and ciphertext in the MLE-based cloud storage, significantly degrading the cloud sync efficiency. A feature-based encryption sync scheme, FeatureSync, is proposed to address the delta amplification problem. However, with further improvement of the network bandwidth, the performance of FeatureSync stagnates. In our preliminary experimental evaluations, we find that the bottleneck of the computational overhead in the high-bandwidth network environments is the main bottleneck in FeatureSync. In this article, we propose an enhanced feature-based encryption sync scheme FASTSync to optimize the performance of FeatureSync in high-bandwidth network environments. The performance evaluations on a lightweight prototype implementation of FASTSync show that FASTSync reduces the cloud sync time by 70.3% and the encryption time by 37.3%, on average, compared with FeatureSync.
A comparison of machine learning techniques in building an intelligent tutoring system

An Intelligent Teaching System (ITS) integrates Artificial Intelligence (AI) to the field of education, in order to dynamically adapts to users with different background and provide optimal teaching methods. Recent advancements in intelligent tutoring have proved its effectiveness in enhancing the achievement and abilities of learners. At the same time, with the rapid development of AI technology, various AI and machine learning algorithms have been applied to the design of ITS, optimizing their performance to varying degrees. This paper provides an overall review of previous ITS research using various techniques of artificial intelligence and machine learning (ML) and provides an overview of ITS and its architecture. In addition, it discusses and summarizes current research efforts and barriers to ITS using AI, as well as some future opportunities. This paper provides an overall comparison of various machine learning techniques that have previously been applied to ITS and an overview of ITS and its architecture. In addition, it discusses and summarizes the current barriers to ITS using AI, as well as an expectation of its future development.
Pemrosesan Query dan Pemeringkatan Hasil dalam Information Retrieval: Sebuah Kajian Literatur

In this study, we reviewed the literature on information retrieval starting from the basics of Information Retrieval (IR), components and future challenges. The purpose of this study is to observe techniques that have been used by previous researchers in IR, especially query processing and ranking of search results. We used a literature review method by identifying, reviewing, and observing techniques in IR based on the results of several previous studies. We collected more literature sources from the ACM Digital Library, Researchgate and MDPI. From this research, we found several IR models for searching relevant documents (information) and ranking accurate results, including EXplanaTion RAnking (EXTRA), Deep-QPP, ColBERT-PRF and UQSCM-RFD.
Output regeneration defense against membership inference attacks for protecting data privacy

Purpose Recently, deep learning (DL) has been widely applied in various aspects of human endeavors. However, studies have shown that DL models may also be a primary cause of data leakage, which raises new data privacy concerns. Membership inference attacks (MIAs) are prominent threats to user privacy from DL model training data, as attackers investigate whether specific data samples exist in the training data of a target model. Therefore, the aim of this study is to develop a method for defending against MIAs and protecting data privacy. Design/methodology/approach One possible solution is to propose an MIA defense method that involves adjusting the model’s output by mapping the output to a distribution with equal probability density. This approach effectively preserves the accuracy of classification predictions while simultaneously preventing attackers from identifying the training data. Findings Experiments demonstrate that the proposed defense method is effective in reducing the classification accuracy of MIAs to below 50%. Because MIAs are viewed as a binary classification model, the proposed method effectively prevents privacy leakage and improves data privacy protection. Research limitations/implications The method is only designed to defend against MIA in black-box classification models. Originality/value The proposed MIA defense method is effective and has a low cost. Therefore, the method enables us to protect data privacy without incurring significant additional expenses.
Digitalization and Virtual Assistive Systems in Tourist Mobility: Evolution, an Experience (with Observed Mistakes), Appropriate Orientations and Recommendations

Digitalization and virtualization are extremely active and important approaches in a large scope of activities (marketing, selling, enterprise management, logistics). Tourism management is also highly concerned by this evolution. In this paper we try to present today’s situation based on a 7-week trip showing appropriate and shame situations. After this case study, we give a list of appropriate practices and orientations and confirm the fundamental role of User Experience in validating the proposed assistive system and the User Interfaces needed for client/user satisfaction. We also outline the expected role of Metaverse in the future of the evolution of this domain.
Not All Pain Leads to Gain: The Role of Learner Engagement in Adaptive Flashcard Training

Adaptive training methods have been designed to enhance students' learning outcomes by tailoring the educational content based on the learner's performance during training. In the present study, we examined different adaptive sequencing methods for a flashcard-based trainer. One sequencing method, the Adaptive Response Time-based Sequencing (ARTS) algorithm presents cards based on an individual learner's accuracy and reaction time, such that incorrectly identified cards are prioritized over correctly identified cards. Although previous research has suggested that ARTS is more efficient and effective than other forms of flashcard sequencing, recent research was unable to replicate these findings. To that end, the current experiment compared ARTS to an adaptive control condition that reversed the ARTS algorithm and investigated if learner engagement plays a role in adaptive flashcard-based training. A sample of 50 college students learned to identify African countries in one of two adaptive flashcard sequencing conditions – ARTS and control. Engagement was measured using the flow state scale for occupational tasks and training effectiveness was determined by calculating immediate and delayed learning gains. Results revealed no statistically significant differences between ARTS and the control on immediate and delayed gains. Further, the ARTS group reported significantly lower engagement levels than the control group. A mediation analysis revealed that the relationship between the training and the learning gains was significantly mediated by engagement in an inverse format, suggesting that training reduced the levels of engagement which in turn canceled out the learning gains. Based on these findings, we present the theoretical and practical implications.
THE QUANTUM FOR ALL PROJECT: RATIONALE AND OVERVIEW

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 3311-3316ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.0913Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
READING DESCRIPTIVE TEXT COMPREHENSION IMPROVEMENT BY JOGSAW TEACHING TECHNIQUE

The goal of this research was to decide how effective jigsaw improved the comprehension ability of reading descriptive text of class X students of Sultan Agung 1 Semarang Islamic High School in 2018/2019 academic year in understanding descriptive text. This research type was on a quasi-experimental design. The Senior High School students gade tenth as the population of this study which divided into two groups, X IPS 1 class developed as group of class control and X IPS 2 served as the group of experimental. The way to gather some data, Pre-test and post were used to evaluate the students’ competence. Grounded on the results of the study, the mean score of pretest done in the control class was in the average of 52.93, while on the experimental class was happened 53.06. Treatment was implemented on the experiment class in about four times while the control class was educated by conservative teaching. After implementing four times of treatment, the mean score of the post-test of the control class rose up by 60.17. Accordingly, the post-test mean of the experimental class in the average score of 70.32. Based on the results of data analysis showed that there was a significant difference because of the T-test indicating that sig (2-tailed) showed in the number 0.000. Briefly, Ho was rejected but H1 was accepted. This means that jigsaw was operative to enhance reading comprehension ability of the students. Hence, Using jigsaw in understanding descriptive text could give better comprehension in students' reading skills Keywords: Jigsaw, Reading Comprehension, Descriptive Text
A Moving Object Detection Method Based on Graph Neural Network

Moving object detection means segmenting objects that exhibit motion in image sequences. Several deep neural network-based methods have been proposed and have almost perfect performance. However, when these algorithms are used on unseen videos, their performance drops dramatically. In this paper, we propose a moving object detection method based on Graph Neural Network(GNN) for unseen videos called IGMOD. Specifically, we use Mask-RCNN for segmenting instances, use the temporal median filter to initialize the background and extract optical flow, texture, and intensity features for representing each instance. Then, we use k-Nearest Neighbors(K-NN) to construct a graph for all instances. Finally, we use the improved GraphSAGE to solve a problem of node classification, to predict whether the instance is foreground or background. We conducted experiments on the CDNet2014 dataset to evaluate our proposed algorithm , the results indicate that our method surpasses state-of-the-art algorithms.
Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

Aiming at the problems resulting from the fact that the existing point of interest (POI) recommendation methods cannot effectively consider the personalized differences of users' mobile behavior in space and time, the author proposes a personalized POI recommendation method using attention-based time sequence and distance contexts gated recurrent unit (ATSD-GRU). First, the author combined the time sequence and distance context with the GRU to extract useful information from users, effectively alleviating the data sparsity. Second, inspired by the attention mechanism, the author introduced the attention model further into the neural network to capture the user's main mobile behavior intention. Finally, the author studied the ATSD-GRU and trained through Bayesian personalized sorting framework and back propagation algorithm. Experiments imply that the proposed method outperforms the comparison method in terms of the F1 index for any recommended number. When the recommendation list length is 15, the proposed algorithm exhibits an accuracy of 9.23% and a recall rate of 14.65%, both higher than the compared algorithm.
Multi-objective sustainable supply chain network optimization based on chaotic particle—Ant colony algorithm

For the optimal design of the sustainable supply chain network, considering the comprehensiveness of the problem factors, considering the three aspects of economy, environment and society, the goal is to minimize the establishment cost, minimize the emission of environ-mental pollution and maximize the number of labor. A mixed integer programming model is established to maximize the efficiency of the supply chain network. The innovation of this paper, first, is to consider the impact of economic, environmental and social benefits in a continuous supply chain, where the environmental benefits not only consider carbon emissions but also include the emissions of plant wastewater, waste and solid waste as influencing factors. Second, a multi-objective fuzzy affiliation function is constructed to measure the quality of the model solution in terms of the overall satisfaction value. Finally, the chaotic particle ant colony algorithm is proposed, and the problem of premature convergence in the operation of the particle swarm algorithm is solved. Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.
Teaching reform and development of mechanical major in secondary vocational schools based on "Internet +" and big data

In the rapid development of Internet technology, the development mode of "Internet + education" has gradually entered the field of vocational education, and new information education technologies continue to emerge. According to the practical investigation and research, while getting rid of the traditional teaching mode, the mechanical major teaching in secondary vocational schools has mastered more technical theories through various types of educational practice activities, and has now formed an education system with "Internet +" as the theme. In this paper, on the basis of understanding the current situation of education reform of mechanical major in secondary vocational schools in the new era, according to the development mode and main content of "Internet + education", in-depth discussion on teaching reform measures of mechanical major in secondary vocational schools based on "Internet +" and big data, in order to provide outstanding talents for economic construction and development in the new era.
Ensemble DeBERTa Models on USMLE Patient Notes Automatic Scoring using Note-based and Character-based approaches

To maximize the accuracy and efficiency of the USMLE Step 2 clinical skills examination evaluation process, we proposed an ensemble model that helps automatically score patient notes written by test takers instead of physician raters manually scoring them by appropriate features. This research used DeBERTa-base, DeBERTa-large, and DeBERTa-v3-large as three base models and ensembled them with two different approaches: Note-based and Character-based. We concluded that LSTM Note-based ensemble topped the overall performance with an F1-score of 0.81747 on the validation data, 48% higher than the F1-score of the most effective base model (DeBERTa-v3-large). Furthermore, the performance is robust when breakdown by clinical cases and folds and applied to the testing set (0.88737 accuracy). Finally, the ensemble approach to different base models (BERT-base-uncased and BERT-large-uncased) achieved a 32% F-1 score boost. We demonstrated the ensemble model has excellent potential to improve performance in general Natural Language Understanding tasks.