Reviewerly/exHarmony huggingface dataset:
EGNN: Energy-efficient anomaly detection for IoT multivariate time series data using graph neural network

Authors:
raw_author_name: Hongtai Guo
raw_affiliation_string: School of Information Engineering, China University of Geosciences (Beijing), Beijing, 100083, China
raw_author_name: Zhangbing Zhou
raw_affiliation_string: Computer Science Department, TELECOM SudParis, Evry, 91011, France; School of Information Engineering, China University of Geosciences (Beijing), Beijing, 100083, China
raw_author_name: Deng Zhao
raw_affiliation_string: Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing), Beijing, 100083, China
raw_author_name: Walid Gaaloul
raw_affiliation_string: Computer Science Department, TELECOM SudParis, Evry, 91011, France

Abstract: Anomaly detection has been widely applied in Internet of Things (IoT) to guarantee the health of IoT applications. Current studies on anomaly detection focus mainly on measurement design and discovery methods on the cloud, which, however, are associated with issues of computational heaviness and capacity limitation when applied at the network edge. Thus, it becomes important to ensure that the detection is not only accurate but also energy-efficient. To fill this gap, this paper proposes an accurate and Energy-efficient Graph Neural Network based anomaly detection method (EGNN) for IoT multivariate time series data. Specifically, correlations between sensory data upon different IoT devices, which are rarely considered in the literature, are explored through a developed Subgraph Generation Algorithm (SGA) based on graph structure learning. As a result, a dependency graph with multiple subgraphs and their corresponding centers is generated. Thereafter, to reduce anomaly-irrelevant sensory data transmitted in the network, only sensory data upon subgraph centers are utilized for anomaly detection by a computational-light approach, i.e., a multi-layer perceptron based forecasting method. Once an anomaly is detected, sensory data of whole subgraph data are adopted for obtaining accurate anomaly results, by a graph attention based forecasting method. This GNN-based anomaly detection strategy with Mode Switching (GMS) can greatly reduce anomaly-irrelevant data transmission, especially when anomalies occur infrequently. To validate the effectiveness of our mechanism, extensive experiments are conducted upon real-world IoT multivariate time series datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in terms of accuracy and energy-efficiency.
Eliciting Multimodal and Collaborative Interactions for Data Exploration on Large Vertical Displays

Authors:
raw_author_name: Petra Isenberg
raw_affiliation_string: CNRS, Inria, LISN, Universit&#xe9; Paris-Saclay, Orsay, France
raw_author_name: Andreas Breiter
raw_affiliation_string: University of Bremen, Bremen, Germany

Abstract: We examined user preferences to combine multiple interaction modalities for collaborative interaction with data shown on large vertical displays. Large vertical displays facilitate visual data exploration and allow the use of diverse interaction modalities by multiple users at different distances from the screen. Yet, how to offer multiple interaction modalities is a non-trivial problem. We conducted an elicitation study with 20 participants that generated 1015 interaction proposals combining touch, speech, pen, and mid-air gestures. Given the opportunity to interact using these four modalities, participants preferred speech interaction in 10 of 15 low-level tasks and direct manipulation for straightforward tasks such as showing a tooltip or selecting. In contrast to previous work, participants most favored unimodal and personal interactions. We identified what we call collaborative synonyms among their interaction proposals and found that pairs of users collaborated either unimodally and simultaneously or multimodally and sequentially. We provide insights into how end-users associate visual exploration tasks with certain modalities and how they collaborate at different interaction distances using specific interaction modalities. The supplemental material is available at https://osf.io/m8zuh/?view only = 34bfd907d2ed43bbbe37027fdf46a3fa.
Foreground segmentation network using transposed convolutional neural networks and up sampling for multiscale feature encoding

Authors:
raw_author_name: Vishruth B. Gowda
raw_affiliation_string: Department of Computer Science and Engineering, SJB Institute of Technology, Bengaluru, Karnataka 560060, India; Visvesavaraya Technological University, Belgavi, Karnataka 590018, India
raw_author_name: M.T. Gopalakrishna
raw_affiliation_string: Department of Artificial Intelligence and Machine Learning, SJB Institute of Technology, Bengaluru, Karnataka 560060, India; Visvesavaraya Technological University, Belgavi, Karnataka 590018, India
raw_author_name: J. Megha
raw_affiliation_string: Department of Artificial Intelligence and Machine Learning, Ramaiah Institute of Technology, Bangalore 560054, India
raw_author_name: Shilpa Mohankumar
raw_affiliation_string: Department of Information Science and Engineering, Bangalore Institute of Technology, Bengaluru, Karnataka 560060, India

Abstract: Foreground segmentation algorithm aims to precisely separate moving objects from the background in various environments. However, the interference from darkness, dynamic background information, and camera jitter makes it still challenging to build a decent detection network. To solve these issues, a triplet CNN and Transposed Convolutional Neural Network (TCNN) are created by attaching a Features Pooling Module (FPM). TCNN process reduces the amount of multi-scale inputs to the network by fusing features into the Foreground Segmentation Network (FgSegNet) based FPM, which extracts multi-scale features from images and builds a strong feature pooling. Additionally, the up-sampling network is added to the proposed technique, which is used to up-sample the abstract image representation, so that its spatial dimensions match with the input image. The large context and long-range dependencies among pixels are acquired by TCNN and segmentation mask, in multiple scales using triplet CNN, to enhance the foreground segmentation of FgSegNet. The results, clearly show that FgSegNet surpasses other state-of-the-art algorithms on the CDnet2014 datasets, with an average F-Measure of 0.9804, precision of 0.9801, PWC as (0.0461), and recall as (0.9896). Moreover, the FgSegNet with up-sampling achieves the F-measure of 0.9804 which is higher when compared to the FgSegNet without up-sampling.
Unsupervised image enhancement under non-uniform illumination based on paired CNNs

Authors:
raw_author_name: Huaqing Zhang
raw_affiliation_string: College of Control Science and Engineering, China University of Petroleum, Qingdao, 266580, Shandong, China; College of Science, China University of Petroleum, Qingdao, 266580, Shandong, China
raw_author_name: Jian Wang
raw_affiliation_string: College of Science, China University of Petroleum, Qingdao, 266580, Shandong, China
raw_author_name: Jun Wang
raw_affiliation_string: Computer Science and the School of Data Science, City University of Hong Kong, 999077, Hong Kong, China

Abstract: This paper presents two CNN-based systems for unsupervised image enhancement under non-uniform illumination. The core of the systems is constituted by the difference of a pair of CNNs. Each CNN is composed of two convolutional layers of neurons with exponential activation function and logarithmic activation function. A weighted sum of the non-reference loss functions is used to train the paired CNNs. It includes an entropy enhancement function and a Bézier loss function to ensure global and local enhancement complementarily. It also includes a white balance loss function to remove color cast in raw images, and a gradient improvement loss function to compensate for the high frequency degradation . In addition, it includes an SSIM (structural similarity index) loss functions to ensure image fidelity. In addition to the basic system, CNNOD, an augmented version called CNNOD+ is developed, which features an information fusion/combination module with a power-law network for gamma correction. The experimental results on two benchmark datasets are discussed to demonstrate that the proposed systems outperform the state-of-the-art methods in terms of enhancement quality, model complexity, and convergence efficiency.
Sentimental analysis of audio based customer reviews without textual conversion

Authors:
raw_author_name: Sumana Maradithaya
raw_affiliation_string: Ramaiah Institute of Technology

Abstract: The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.
Deep learning based Arabic short answer grading in serious games

Authors:
raw_author_name: Lotfi El Achaak
raw_affiliation_string: Abdelmalek Essaâdi University
raw_author_name: Mohammed Bouhorma
raw_affiliation_string: Abdelmalek Essaâdi University

Abstract: Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research.
Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques

Authors:
raw_author_name: Abdelali Zakrani
raw_affiliation_string: Hassan II University
raw_author_name: Mostafa Jebbar
raw_affiliation_string: Hassan II University

Abstract: Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.
Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector

Authors:
raw_author_name: Raúl Jáuregui-Velarde
raw_affiliation_string: Universidad Privada Norbert Wiener
raw_author_name: Laberiano Andrade-Arenas
raw_affiliation_string: Universidad de Ciencias y Humanidades
raw_author_name: Pedro Molina-Velarde
raw_affiliation_string: Universidad Tecnológica del Perú
raw_author_name: Cesar Yactayo-Arias
raw_affiliation_string: Universidad Continental

Abstract: This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.
A framework for cloud cover prediction using machine learning with data imputation

Authors:
raw_author_name: Tanuja Sarode
raw_affiliation_string: Thadomal Shahani Engineering College, Affiliated to the University of Mumbai

Abstract: The climatic conditions of a region are affected by multiple factors. These factors are dew point temperature, humidity, wind speed, and wind direction. These factors are closely related to each other. In this paper, the correlation between these factors is studied and an approach has been proposed for data imputation. The idea is to utilize all these features to obtain the prediction of the total cloud cover of a region instead of removing the missing values. Total cloud cover prediction is significant because it affects the agriculture, aviation, and energy sectors. Based on the imputed data which is obtained as the output of the proposed method, a machine learning-based model is proposed. The foundation of this proposed model is the bi-directional approach of the long short-term memory (LSTM) model. It is trained for 8 stations for two different approaches. In the first approach, 80% of the entire data is considered for training and 20% of the data is considered for testing. In the second approach, 90% of the entire data is accounted for training and 10% of the data is accounted for testing. It is observed that in the first approach, the model gives less error for prediction.
EksPy: a new Python framework for developing graphical user interface based PyQt5

Authors:
raw_author_name: Aidil Saputra Kirsan
raw_affiliation_string: Institut Teknologi Kalimantan
raw_author_name: Kosuke Takano
raw_affiliation_string: Kanagawa Institute of Technology

Abstract: This study introduces EksPy Python framework, a novel framework designed for developing graphical user interface (GUI) applications in Python. EksPy framework is built on PyQt5, which is a collection of Python bindings for the Qt libraries, and it provides a user-friendly and intuitive interface. The comparative analysis of EksPy framework with existing frameworks such as Tkinter and PyQt highlights its notable features, including ease of use, rapid development, enhanced performance, effective database management, and the model-view-controller (MVC) concept. The experimental results illustrate that EksPy framework requires less code and enhances code readability, thereby facilitating better understanding and efficient development. Additionally, EksPy framework offers a modern and customizable appearance, surpassing Tkinter’s capabilities. Furthermore, it incorporates a built-in object-relational mapping (ORM) feature to simplify database interactions and adheres to the MVC architectural pattern. In conclusion, EksPy Python framework emerges as a powerful, user-friendly, and efficient framework for GUI application development in Python.
SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection

Authors:
raw_author_name: Yueyue Yao
raw_affiliation_string: Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China; Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China
raw_author_name: Jianghong Ma
raw_affiliation_string: Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China; Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China
raw_author_name: Shanshan Feng
raw_affiliation_string: Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China; Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China
raw_author_name: Yunming Ye
raw_affiliation_string: Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China; Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China

Abstract: Anomaly detection in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strength of both SVD and autoencoder to fully capture complex normal patterns in multivariate time series. An asymmetric autoencoder architecture is proposed, where two encoders are used to capture features in time and variable domains and a shared decoder is used to generate reconstructions based on latent representations from both domains. A new regularization based on singular value decomposition theory is designed to force each encoder to learn features in the corresponding axis with mathematical support delivered. A specific loss component is further proposed to align Fourier coefficients of inputs and reconstructions. It can preserve details of original inputs, leading to enhanced feature learning capability of the model. Extensive experiments on three real world datasets demonstrate the proposed algorithm can achieve better performance on multivariate time series anomaly detection tasks under highly unbalanced scenarios compared with baseline algorithms.
Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training

Authors:
raw_author_name: Ziyang Chen
raw_affiliation_string: Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, 20133, Italy
raw_author_name: Laura Cruciani
raw_affiliation_string: Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, 20133, Italy
raw_author_name: Elena Lievore
raw_affiliation_string: European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy
raw_author_name: Matteo Fontana
raw_affiliation_string: European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy
raw_author_name: Ottavio De Cobelli
raw_affiliation_string: European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy; University of Milan, Department of Oncology and Onco-haematology, Faculty of Medicine and Surgery, Milan, Italy
raw_author_name: Gennaro Musi
raw_affiliation_string: European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy; University of Milan, Department of Oncology and Onco-haematology, Faculty of Medicine and Surgery, Milan, Italy
raw_author_name: Giancarlo Ferrigno
raw_affiliation_string: Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, 20133, Italy
raw_author_name: Elena De Momi
raw_affiliation_string: European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy; Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, 20133, Italy

Abstract: Safety of robotic surgery can be enhanced through augmented vision or artificial constraints to the robotl motion, and intra-operative depth estimation is the cornerstone of these applications because it provides precise position information of surgical scenes in 3D space. High-quality depth estimation of endoscopic scenes has been a valuable issue, and the development of deep learning provides more possibility and potential to address this issue. In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes. To this aim, a fully 3D encoder-decoder network integrating spatio-temporal layers is designed, and it adopts hierarchical prediction and progressive learning to enhance prediction accuracy and shorten training time. Our network gets the depth estimation accuracy of MAE 2.55±1.51 (mm) and RMSE 5.23±1.40 (mm) using 8 surgical videos with a resolution of 1280×1024, which performs better compared with six other state-of-the-art methods that were trained on the same data. Our network can implement a promising depth estimation performance in intra-operative scenes using stereo images, allowing the integration in robot-assisted surgery to enhance safety.