Reviewerly/exHarmony huggingface dataset:
Joint Gaussian Distribution and Attention for Time-Aware Recommendation Systems

Authors:
raw_author_name: Runqiang Zang
raw_affiliation_string: School of Information, Renmin University of China, Beijing, China
raw_author_name: Meiyun Zuo
raw_affiliation_string: School of Information, Renmin University of China, Beijing, China
raw_author_name: Rong Ma
raw_affiliation_string: School of Information Science and Engineering, Lanzhou University, Lanzhou, China

Abstract: Sequential models have achieved admirable success in recommendation systems. However, most sequential models typically only consider the chronological order of items through timestamps and ignore the relative distances in the sequence, which weakens the temporal relationships between items. To address this issue, we propose a temporal recommendation system using the Gaussian distribution and attention mechanism, which considers the sequentiality and interaction among items. Technically, we first deploy the word vector space along the time dimension as sequence features. Then, we use the Gaussian process to effectively represent the duration influence of items and the context interaction between items as high-level features. Finally, an innovative attention mechanism is used to capture the hidden correlation relationships between representation subspaces of different levels of features. Experiments conducted on two widely used real public datasets show that our model outperforms the state-of-the-art recommendation systems.
Progressive Learning of 3D Reconstruction Network From 2D GAN Data

Authors:
raw_author_name: Aysegul Dundar
raw_affiliation_string: Department of Computer Science, Bilkent University, Ankara, Turkey; NVIDIA, CA, USA
raw_author_name: Jun Gao
raw_affiliation_string: Department of Computer Science, University of Toronto, Canada; NVIDIA, CA, USA
raw_author_name: Andrew Tao
raw_affiliation_string: NVIDIA, CA, USA
raw_author_name: Bryan Catanzaro
raw_affiliation_string: NVIDIA, CA, USA

Abstract: This paper presents a method to reconstruct high-quality textured 3D models from single images. Current methods rely on datasets with expensive annotations; multi-view images and their camera parameters. Our method relies on GAN generated multi-view image datasets which have a negligible annotation cost. However, they are not strictly multi-view consistent and sometimes GANs output distorted images. This results in degraded reconstruction qualities. In this work, to overcome these limitations of generated datasets, we have two main contributions which lead us to achieve state-of-the-art results on challenging objects: 1) A robust multi-stage learning scheme that gradually relies more on the models own predictions when calculating losses, 2) A novel adversarial learning pipeline with online pseudo-ground truth generations to achieve fine details. Our work provides a bridge from 2D supervisions of GAN models to 3D reconstruction models and removes the expensive annotation efforts. We show significant improvements over previous methods whether they were trained on GAN generated multi-view images or on real images with expensive annotations. Please visit our web-page for 3D visuals: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://research.nvidia.com/labs/adlr/progressive-3d-learning</uri> .
Fuzzy Representation Learning on Dynamic Graphs

Authors:
raw_author_name: Hong-Yu Yao
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, China
raw_author_name: Yuan-Long Yu
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, China
raw_author_name: Chun-Yang Zhang
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, China
raw_author_name: Yue-Na Lin
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, China

Abstract: Exploring dynamic patterns from complex and large-scale networks is a significant and challenging task in graph analysis. One of the most advanced solutions is dynamic graph representation learning, which embeds structural and temporal correlations into a representative vector for each node or subgraph. Existing models have made some successes, such as overcoming the problems of induction for unseen nodes and scalability for large-scale evolving networks. However, these models usually rely on crisp representation learning that is incapable of modeling feature fuzziness and capturing uncertainties in dynamic graphs. While real-world dynamic networks as complex systems always contain non-negligible but inestimable uncertainties in node/link attributes and network topology. These uncertainties may cause the learned representations from crisp models hard to precisely reflect network evolution. To address the issues, we propose a new dynamic graph representation learning model, called FuzzyDGL, which first incorporates fuzzy representation learning to handle the uncertainties in dynamic graphs. Through combining CDGRL with fuzzy logic, the FuzzyDGL digests both of their advantages. On the one hand, it has flexible model scalability and brilliant inductive capability. On the other hand, it can model feature fuzziness to reduce the impact of uncertainties in dynamic graphs, improving the quality of learned representations. To demonstrate its effectiveness, we conduct two important tasks of network analysis, including link prediction and node classification, over eight real-world datasets. The experimental results show the strong competitiveness and generalization of the FuzzyDGL against a number of baseline models.
An improved Arabic text classification method using word embedding

Authors:
raw_author_name: Tarik Sabri
raw_author_name: Said Bahassine
raw_author_name: Omar El Beggar
raw_author_name: Mohamed Kissi

Abstract: Feature selection (FS) is a widely used method for removing redundant or irrelevant features to improve classification accuracy and decrease the model’s computational cost. In this paper, we present an improved method (referred to hereafter as RARF) for Arabic text classification (ATC) that employs the term frequency-inverse document frequency (TF-IDF) and Word2Vec embedding technique to identify words that have a particular semantic relationship. In addition, we have compared our method with four benchmark FS methods namely principal component analysis (PCA), linear discriminant analysis (LDA), chi-square, and mutual information (MI). Support vector machine (SVM), k-nearest neighbors (K-NN), and naive Bayes (NB) are three machine learning based algorithms used in this work. Two different Arabic datasets are utilized to perform a comparative analysis of these algorithms. This paper also evaluates the efficiency of our method for ATC on the basis of performance metrics viz accuracy, precision, recall, and F-measure. Results revealed that the highest accuracy achieved for the SVM classifier applied to the Khaleej-2004 Arabic dataset with 94.75%, while the same classifier recorded an accuracy of 94.01% for the Watan-2004 Arabic dataset.
Predicting automobile insurance fraud using classical and machine learning models

Authors:
raw_author_name: Yap Bee Wah
raw_affiliation_string: UNITAR International University
raw_author_name: Ng Kok Haur
raw_affiliation_string: Universiti Malaya

Abstract: Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases.
Data integrity aware system for executing dynamic operations on outsourced cloud data

Authors:
raw_author_name: K. Roslin Dayana
raw_affiliation_string: Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, India
raw_author_name: P. Shobha Rani
raw_affiliation_string: Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, India

Abstract: The cloud computing platform of today handles a greater amount of real-time data, which requires dynamic data operations. The majority of the currently available cloud techniques, however, do not concentrate on dynamic data operations. Hence, this work provides a system that enables dynamic data operations to be carried out on a cloud computing platform, such as the insertion, deletion, and updating of data. The performance of the work is tested in terms of time consumption with respect to data storage, retrieval, data block insertion and deletion operations. The analysis is carried out by altering the data volume and the performance of the work is satisfactory with reasonable time consumption.
Exploiting Physical Referent Features as Input for Multidimensional Data Selection in Augmented Reality

Authors:
raw_author_name: Gary Perelman
raw_affiliation_string: Institut de recherche en informatique de Toulouse; Université Toulouse III - Paul Sabatier
raw_author_name: Marcos Serrano
raw_affiliation_string: Institut de recherche en informatique de Toulouse; Université Toulouse III - Paul Sabatier
raw_author_name: Emmanuel Dubois
raw_affiliation_string: Etude de L’Interaction Personne SystèmE; Institut de recherche en informatique de Toulouse; Université Toulouse III - Paul Sabatier

Abstract: Embedding data into the physical environment using augmented reality (AR) is a practical approach for data visualization as it offers a large and flexible display space on or around the physical referent, i.e. the physical object to which the data is related. Yet current interaction in such context is often performed using cumbersome dedicated devices, tiring mid-air gestures or awkward on-body input. In this paper, we investigate the use of the physical referent itself as a support for input interaction with an embedded space-time cube (STC) representation. Hence, we first identify the most promising mappings between the physical features of the referent (edges, faces, corners) and the STC dimensions. Then, we design three data selection techniques using the physical referent and compare them to mid-air gestures when performing selection tasks on the STC. Overall, our work demonstrates that using the physical referent to support input interaction with embedded data representations is an efficient and comfortable approach for data selection in standing and sitting situations.
Deep learning enabled blockchain based electronic heathcare data attack detection for smart health systems

Authors:
raw_author_name: S. Chidambaranathan
raw_affiliation_string: Department of Computer Applications, St. Xavier's College, Palayamkottai, Tamil Nadu, India
raw_author_name: R. Geetha
raw_affiliation_string: Department of Computer Applications, St. Xavier's College, Palayamkottai, Tamil Nadu, India

Abstract: The idea of networked personal medical devices is a component of contemporary Smart Health Systems (SHS). These gadgets offer remote observing and the exchange of wellbeing information, which enormously further develop the patient's personal satisfaction while at the same time reducing treatment expenses for both the patient and the medical care suppliers (telemedicine). For individuals' wellbeing, a cutting edge individual wellbeing record framework is fundamental. Here, there are still difficulties with information mix from different EHRs, information interoperability, and guaranteeing that admittance to information is totally under the power of the patient. Some security issues are caused by the Network. To settle these issues, we propose a novel profound learning-based framework that circuits state of the art decentralized innovations like IPFS and blockchain with wellbeing information interoperability principles and advances like FHIR's APIs. In this review, we show that correspondence between private clinical gadgets is as a matter of fact powerless to different cyber attacks. We show how an outer assailant could involve man-in-the-center, replay, bogus information infusion, and refusal of-administration assaults to block delicate wellbeing information stream by capturing the correspondence of the individual clinical gadget. We likewise suggest an Interruption Recognition Framework (IDS), GAN, to additional screen traffic on private clinical gear and spot attacks against them. Our extensive investigation shows that GAN, with an F1-score of 98 % and an accuracy of 98.7 %, can successfully and accurately recognise numerous assaults on personal medical equipment.
Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

Authors:
raw_author_name: Hapnes Toba
raw_affiliation_string: Maranatha Christian University
raw_author_name: Mewati Ayub
raw_affiliation_string: Maranatha Christian University
raw_author_name: Maresha Caroline Wijanto
raw_affiliation_string: Maranatha Christian University

Abstract: Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom\'s epistemic taxonomy based on textual comments in educational discussion forums. Our proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom\'s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.
Professional and personal traits of the teacher and the relationship with didactic strategies

Authors:
raw_author_name: Omar Chamorro-Atalaya
raw_affiliation_string: Universidad Nacional Tecnológica de Lima Sur
raw_author_name: Anthony Gonzáles-Pacheco
raw_affiliation_string: Universidad Nacional de Trujillo
raw_author_name: Manuel Quipuscoa-Silvestre
raw_affiliation_string: Universidad Nacional de Trujillo
raw_author_name: Raul Suarez-Bazalar
raw_affiliation_string: Universidad Nacional del Callao
raw_author_name: Ademar Vargas-Díaz
raw_affiliation_string: Universidad César Vallejo
raw_author_name: Víctor Villanueva-Acosta
raw_affiliation_string: Universidad de San Martín de Porres

Abstract: &lt;span lang="EN-US"&gt;During the context of virtual classes due to the pandemic, it was evidenced that many teachers had to face accelerated adaptation stages, which is why teachers presented difficulties in designing their teaching strategies and students showed certain difficulties in their learning. In this sense, this article aims to determine how the professional and personal traits of the teacher are related to their teaching strategies, in the context of COVID-19, from the perspective of the students. For which an investigation was developed with a quantitative approach, correlational level and non-experimental design. It was determined that there is a moderate direct relationship between the professional and personal traits of the teacher with his didactic strategies in the context of virtual teaching. Thus, it was also identified that teachers do not pour their professional experiences into the development of the subject, additionally it does not favor the solution of problems related to real life applications; this leads teacher to reflect on the importance of the construction of student knowledge, especially if it is about subjects that are taught in the last cycles, since the student is waiting for how and in which processes their knowledge will be applied theoretical knowledge.&lt;/span&gt;
DGTRL: Deep graph transfer reinforcement learning method based on fusion of knowledge and data

Authors:
raw_author_name: Genxin Chen
raw_affiliation_string: School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu Province, China
raw_author_name: Jin Qi
raw_affiliation_string: School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu Province, China
raw_author_name: Yu Gao
raw_affiliation_string: School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu Province, China
raw_author_name: Zhenjiang Dong
raw_affiliation_string: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu Province, China
raw_author_name: Yanfei Sun
raw_affiliation_string: School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu Province, China

Abstract: Deep reinforcement learning has shown promising application effects in many fields. However, issues such as low sample efficiency and weak knowledge transfer and generalization capabilities constrain its in-depth development. This paper proposes a deep graph transfer reinforcement learning method based on knowledge and data fusion. Through knowledge transfer between tasks and data training in exploration, efficient learning driven by knowledge and data fusion is realized. First, we map the trajectories of agents in the source and target domains into graph structures to represent knowledge and environmental models and use a graph matching method to match the knowledge adaptation domain based on similarity. Second, real-time transfer of source domain knowledge is carried out in a similar mapping domain, and policy mapping, verification, and adaptive transfer mechanisms based on knowledge reuse frequency are designed to effectively improve the transfer effect. Finally, we evaluate and validate the proposed algorithm, mechanism, and their application effectiveness in diverse and multitask knowledge transfer scenarios using three indicators in environments such as maze. The results show that the method improves the target task time indicators by 84.7% and 70% under the conditions of no verification exploration and a standard transfer setting, respectively, achieving significant knowledge reuse effects.
Teaching speaking in Kampung Inggris: the tutors’ challenges and solutions

Authors:
raw_author_name: Agis Andriani
raw_affiliation_string: Universitas Siliwangi
raw_author_name: Fuad Abdullah
raw_affiliation_string: Universitas Siliwangi
raw_author_name: Arini Nurul Hidayati
raw_affiliation_string: Universitas Siliwangi

Abstract: Teaching English speaking has become a centre of attention among scholars and teaching English to speakers of other languages (TESOL) practitioners for a few decades. However, the issue of teaching English speaking in Kampung Inggris/English Village remains under-researched, notably viewed from the tutors’ challenges and solutions in the Indonesian English as a foreign language (EFL) milieu. This study addressed this gap. Two English tutors from Kampung Inggris/English Village got involved as the participants. The data were collected through semi-structured interviews and analyzed with thematic analysis. The findings reported that there were five challenges encountered by tutors when teaching English speaking, namely tutors' inability of exploring English language teaching materials, tutors' insufficient English vocabulary, tutors' ineffective time management during language teaching practices, demotivated tutees to learn English, and perceived teaching anxiety. In addition, the tutors stipulated four solutions to cope with such challenges, namely building a good rapport between tutor and tutees, tutors' self-motivation to English vocabulary enrichment, selecting appropriate and providing updated English language teaching materials, and motivating English language tutees. Therefore, teaching English speaking should not only rely on immersing students in fluency-based activities but also manage tutees' psychological factors to attain more effective teaching English speaking objectives.
Multimodal Detection and Classification of Robot Manipulation Failures

Authors:
raw_author_name: Arda Inceoglu
raw_affiliation_string: Artificial Intelligence and Robotics Laboratory, Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Turkey
raw_author_name: Eren Erdal Aksoy
raw_affiliation_string: School of Information Technology, Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden
raw_author_name: Sanem Sariel
raw_affiliation_string: Artificial Intelligence and Robotics Laboratory, Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Turkey

Abstract: An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of the physical robot-object interaction may lead to failures in object manipulation. In this work, we focus on detecting and classifying both manipulation and post-manipulation phase failures using the same exteroception setup. We cover a diverse set of failure types for primary tabletop manipulation actions. In order to detect these failures, we propose FINO-Net [1], a deep multimodal sensor fusion-based classifier network architecture. FINO-Net accurately detects and classifies failures from raw sensory data without any additional information on task description and scene state. In this work, we use our extended FAILURE dataset [1] with 99 new multimodal manipulation recordings and annotate them with their corresponding failure types. FINO-Net achieves 0.87 failure detection and 0.80 failure classification F1 scores. Experimental results show that FINO-Net is also appropriate for real-time use.