Reviewerly/exHarmony huggingface dataset:
MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks

Authors:
raw_author_name: Xinyu Fu
raw_affiliation_string: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
raw_author_name: Irwin King
raw_affiliation_string: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

Abstract: Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degradation issue when HGNNs become deep, researchers combine metapaths into HGNNs to associate nodes closely related in semantics but far apart in the graph. However, existing metapath-based models suffer from either information loss or high computation costs. To address these problems, we present a novel Metapath Context Convolution-based Heterogeneous Graph Neural Network (MECCH). MECCH leverages metapath contexts, a new kind of graph structure that facilitates lossless node information aggregation while avoiding any redundancy. Specifically, MECCH applies three novel components after feature preprocessing to extract comprehensive information from the input graph efficiently: (1) metapath context construction, (2) metapath context encoder, and (3) convolutional metapath fusion. Experiments on five real-world heterogeneous graph datasets for node classification and link prediction show that MECCH achieves superior prediction accuracy compared with state-of-the-art baselines with improved computational efficiency. The code is available at https://github.com/cynricfu/MECCH.
An intelligent auto-response short message service categorization model using semantic index

Authors:
raw_author_name: Budi Padmaja
raw_author_name: Myneni Madhu Bala
raw_affiliation_string: VNR Vignana Jyothi Institute of Engineering and Technology
raw_author_name: Vytla Avinash

Abstract: Short message service (SMS) is one of the quickest and easiest ways used for communication, used by businesses, government organizations, and banks to send short messages to large groups of people. Categorization of SMS under different message types in their inboxes will provide a concise view for receivers. Former studies on the said problem are at the binary level as ham or spam which triggered the masking of specific messages that were useful to the end user but were treated as spam. Further, it is extended with multi labels such as ham, spam, and others which is not sufficient to meet all the necessities of end users. Hence, a multi-class SMS categorization is needed based on the semantics (information) embedded in it. This paper introduces an intelligent auto-response model using a semantic index for categorizing SMS messages into 5 categories: ham, spam, info, transactions, and one time password’s, using the multi-layer perceptron (MLP) algorithm. In this approach, each SMS is classified into one of the predefined categories. This experiment was conducted on the “multi-class SMS dataset” with 7,398 messages, which are differentiated into 5 classes. The accuracy obtained from the experiment was 97%.
Convolutional Transformer network for fine-grained action recognition

Authors:
raw_author_name: Yujun Ma
raw_affiliation_string: School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
raw_author_name: Ruili Wang
raw_affiliation_string: School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
raw_author_name: Ming Zong
raw_affiliation_string: School of Electronics Engineering and Computer Science, Peking University, China
raw_author_name: Wanting Ji
raw_affiliation_string: School of Information, Liaoning University, Shenyang, China
raw_author_name: Yi Wang
raw_affiliation_string: DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, China
raw_author_name: Baoliu Ye
raw_affiliation_string: Department of Computer Science and Technology, Nanjing University, China

Abstract: Fine-grained action recognition is one of the critical problems in video processing, which aims to recognize similar actions of subtle interactions between humans and objects. Inspired by the remarkable performance of the Transformer in natural language processing, Transformer has been applied to the fine-grained action recognition task. However, Transformer needs abundant training data and extra supervision to achieve comparable results with convolutional neural networks (CNNs). To address these issues, we propose a Convolutional Transformer Network (CTN), which integrates the merits of CNN (e.g., sharing weights, capturing low-level features in videos and locality) and the benefits of Transformer (e.g., dynamic attention and learning long-range dependencies). In this paper, we propose two modifications to the original Transformer: (i) we propose a video-to-tokens module that can extract tokens from extracted spatial-temporal features in videos by 3D convolutions instead of the direct token embedding from raw input video clips; (ii) we completely replace the linear mapping in multi-head self-attention layer with depth-wise convolutional mapping, which applies a depth-wise separable convolution operation on embedded token maps. With these two modifications, our approach can extract the effective spatial-temporal features from videos and process the long sequences of tokens encountered in videos. Experimental results demonstrate that our proposed CTN can achieve state-of-the-art accuracy on two fine-grained action recognition datasets (i.e., Epic-Kitchens and Diving 48) with a small computational increase.
Ten Meter Walk Test with Mobile Devices: A dataset with accelerometer, magnetometer, and gyroscope

Authors:
raw_author_name: Cristiana Lopes Gabriel
raw_affiliation_string: Research Unit for Inland Development, Polytechnic of Guarda, Guarda, Portugal
raw_author_name: Ivan Miguel Pires
raw_affiliation_string: Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda, Portugal
raw_author_name: Norberto Jorge Gonçalves
raw_affiliation_string: Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
raw_author_name: Paulo Jorge Coelho
raw_affiliation_string: INESC Coimbra, University of Coimbra, Department of Electrical and Computer Engineering, Pólo 2, 3030-290 Coimbra, Portugal; Polytechnic of Leiria, Leiria, Portugal
raw_author_name: Eftim Zdravevski
raw_affiliation_string: Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
raw_author_name: Petre Lameski
raw_affiliation_string: Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
raw_author_name: Nuno M. Garcia
raw_affiliation_string: Instituto de Telecomunicações, 6201-001 Covilhã, Portugal; Universidade de Lisboa, 1749-016 Lisboa, Portugal

Abstract: This paper presents a dataset related to the performance of the Ten Meter Walking Test, a test to allow locomotor capacity in different research and clinical settings. One of the most important parameters to measure is the gait speed during a path of ten meters. The data available in this dataset consists of accelerometer, magnetometer, and gyroscope data acquired with a mobile device in a waistband. The experiments were performed two times by 109 individuals (30 males and 79 females) in different senior residences in the Fundão municipality (Portugal). The dataset includes 208 samples because the sensors reported some failures. The acquisition of the sensors data allows the creation of a technological method for the automatic measurement of features related to the Ten Meter Walk Test, promoting patient independence in measuring their physical health status.
SOQCS: A Stochastic Optical Quantum Circuit Simulator

Authors:
raw_author_name: Javier Osca
raw_affiliation_string: Department of Theoretical Physics, Maynooth University, Ireland; Tyndall National Institute, University College Cork, “Lee Maltings”, Dyke Parade, Cork, Ireland

Abstract: Stochastic Optical Quantum Circuit Simulator (SOQCS) is a C++ and Python library which offers a framework to define, simulate and study quantum linear optical circuits in presence of various imperfections typically encountered in experiments. Quantum circuits can be defined from basic components, including emitters, linear optical elements, delays and detectors. The imperfections come from partial distinguishability of photons, lossy propagation media, unbalanced beamsplitters and non-ideal emitters and detectors for example. SOQCS also provides various simulator cores and tools to analyze the output. Furthermore, the configuration of detectors also includes postselection. SOQCS is developed using a modular approach in which different modules are applied in an automated easy to use manner. Furthermore, the modular approach allows for further extensions of the SOQCS capabilities in future.
A fitting model with optimal Multiple Image Hiding effect

Authors:
raw_author_name: Lin Huo
raw_affiliation_string: International College, Guangxi University, Nanning 530004, China
raw_author_name: Lang Huang
raw_affiliation_string: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
raw_author_name: Zheng Gan
raw_affiliation_string: Guangxi Zhuang Autonomous Region Information Center, Nanning 530201, China
raw_author_name: Rui Pei Chen
raw_affiliation_string: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China

Abstract: Image hiding is a field of research that focuses on covert storage and transmission techniques.It involves embedding a secret image within a container image to create a classified-carrying image that resembles a normal image.Nevertheless,the current image hiding methods based on Invertible neural networks suffer from a critical issue of information loss during the hiding process.This leads to a substantial degradation in the quality of the secret image extracted,thereby preventing the simultaneous achievement of secure transmission,high-capacity transmission,and high fidelity of the secret image in an insecure network environment.To address this issue,we introduce a novel image hiding architecture called FMIN (Fitting Models Based on Invertible Network).FMIN incorporates our innovative fitting model,which fits the loss information and generates a variable z simulating the loss information at the receiver side.This variable z is used as an input to the revealing process,enabling the high-quality extraction of multiple secret images from a single classified loaded image.Additionally,we introduce a novel decoupled training strategy aimed at enhancing the stability of image hiding model during training.Experimental results demonstrate that the image hiding method based on the proposed FMIN architecture in this paper significantly outperforms other SOTA image hiding methods for single-image and multiple-images hiding.
Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms

Authors:
raw_author_name: Maisa J. Al-Khazaleh
raw_affiliation_string: The Hashemite University
raw_author_name: Marwah Alian
raw_affiliation_string: The Hashemite University

Abstract: Sentiment analysis is a popular natural language processing task that recognizes the opinions or feelings of a piece of text. Microblogging platforms such as Twitter are a valuable resource for finding such people’s opinions. The majority of Arabic sentiment analysis studies indicated that the data utilized to train machine learning algorithms is balanced. In this paper, we investigated the impact of sampling techniques and classification algorithms on an imbalanced Arabic dataset about people’s perceptions of COVID-19, with the majority of opinions reflecting people’s fear and stress about the pandemic, and the minority reflecting the belief that the pandemic was a hoax. The experiments concentrated on analyzing the imbalanced learning of Arabic sentiments using over-sampling and under-sampling techniques on seven single machine learning algorithms and two common ensemble algorithms from the bagging and boosting families, respectively. Results show that resampling-based approaches can overcome the difficulty of an imbalanced dataset, and the use of over-sampled data leads to better performance than that of under-sampled data. The results also reveal that using oversampled data from synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, or adaptive synthetic sampling with random forest classifier is the most effective in addressing this classification problem, with F1-score value of 0.99.
Developing a model for unmanned aerial vehicle with fixed-wing using 3D-map exploring rapidly random tree technique

Authors:
raw_author_name: Omar I. Dallal Bashi
raw_affiliation_string: Northern Technical University
raw_author_name: Husamuldeen K. Hameed
raw_affiliation_string: High Institute of Telecommunications and Post
raw_author_name: Yasir Mahmood Al Kubaisi
raw_affiliation_string: Dubai Academic Health Corporation
raw_author_name: Ahmad H. Sabry
raw_affiliation_string: University of Al-Nahrain

Abstract: While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.
Customizing the minimum number of replicas for achieving fault tolerance in a cloud/grid environment

Authors:
raw_author_name: Mahdi S. Almhanna
raw_affiliation_string: University of Babylon
raw_author_name: Firas Sabah Al-Turaihi
raw_affiliation_string: University of Babylon
raw_author_name: Rafah M. Almuttairi
raw_affiliation_string: Al-Zahraa University for Women

Abstract: Networks consist of numerous resources; it is crucial not to overlook fault tolerance and consider it during planning. This is because errors during implementation can result in wasted time and effort, thereby squandering these resources. One solution to address this issue effectively is to implement the task on multiple resources to minimize the occurrence of failed tasks. However, employing an unspecified or fixed number of resources can lead to the depletion of network resources and the overall failure of the network. Replication plays a pivotal role in enhancing data availability in distributed systems. By storing data in multiple locations, users can still access it even if some copies are unavailable due to site failure. Many replication-based algorithms utilize a predetermined number of iterations per function, which may consume excessive network resources, even if the ongoing task does not require such abundant resources. This paper proposes task replication as a viable mechanism for an efficient and fault-tolerant scheduling system. We introduce an algorithm that dynamically selects the optimal and minimal number of replicas based on the network's failure history. This approach aims to minimize the failure rate during task execution.
Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

Authors:
raw_author_name: Firman Tempola
raw_affiliation_string: Universitas Khairun
raw_author_name: Retantyo Wardoyo
raw_affiliation_string: Universitas Gadjah Mada
raw_author_name: Aina Musdholifah
raw_affiliation_string: Universitas Gadjah Mada
raw_author_name: Lilik Sumaryanti
raw_affiliation_string: Universitas Musamus

Abstract: This study uses clove imagery by classifying it according to ISO 2254-2004 standards: whole, headless, and mother clove. This type of clove will affect the quality and economic value when it has been dried. For this reason, it is necessary to take a first step to control cloves' quality. One way is to classify it from the start. This research will utilize the convolution neural network algorithm and compare it with model transfer learning and modified VGG16 architecture on clove images. In addition, research is also looking for the most optimal hyperparameter. The results of this study indicate that the application of convolution neural network (CNN) to clove images obtains an accuracy value of 84% using a hyperparameter of 50 epochs, a learning rate of 0.001, and a batch size of 16. Meanwhile, for the application of transfer learning VGG16, Resnet50, MobileNetV2, InceptionV3, DensetNet151, and modified VGG16 have respectively each of the highest accuracy including 95.70%, 76.15%, 96.89%, 98.07%, 98.96%, and 99.11%.
Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy

Authors:
raw_author_name: Pirapong Singsathid
raw_affiliation_string: Khon Kaen University
raw_author_name: Jeerayut Wetweerapong
raw_affiliation_string: Khon Kaen University
raw_author_name: Pikul Puphasuk
raw_affiliation_string: Khon Kaen University

Abstract: Differential evolution (DE) is a population-based optimization algorithm widely used to solve a variety of continuous optimization problems. The self-adaptive DE algorithm improves the DE by encoding individual parameters to produce and propagate better solutions. This paper proposes a self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy (SDE-FMP). The algorithm introduces the dynamical mutation operation using the fitness rank of the individuals to divide the population into three groups and then select groups and their vectors with adaptive probabilities to create a mutant vector. Mutation and crossover operations use the encoded scaling factor and the crossover rate values in a target vector to generate the corresponding trial vector. The values are changed according to the pheromone when the trial vector is inferior in the selection, whereas the pheromone is increased when the trial vector is superior. In addition, the algorithm also employs the resetting operation to unlearn and relearn the dominant pheromone values in the progressing search. The proposed SDE-FMP algorithm using the suitable resetting periods is compared with the well-known adaptive DE algorithms on several test problems. The results show that SDE-FMP can give high-precision solutions and outperforms the compared methods.
Diverse Structure-Aware Relation Representation in Cross-Lingual Entity Alignment

Authors:
raw_author_name: Yuhong Zhang
raw_affiliation_string: Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China and School of Computer Science and Information Engineering, Hefei University of Technology, China
raw_author_name: Jianqing Wu
raw_affiliation_string: Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, China and School of Computer Science and Information Engineering, Hefei University of Technology, China
raw_author_name: Kui Yu
raw_affiliation_string: Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, China and School of Computer Science and Information Engineering, Hefei University of Technology, China
raw_author_name: Xindong Wu
raw_affiliation_string: Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, China and School of Computer Science and Information Engineering, Hefei University of Technology, China

Abstract: Cross-lingual entity alignment (CLEA) aims to find equivalent entity pairs between knowledge graphs (KGs) in different languages. It is an important way to connect heterogeneous KGs and facilitate knowledge completion. Existing methods have found that incorporating relations into entities can effectively improve KG representation and benefit entity alignment, and these methods learn relation representation depending on entities, which cannot capture the diverse structures of relations. However, multiple relations in KG form diverse structures, such as adjacency structure and ring structure. This diversity of relation structures makes the relation representation challenging. Therefore, we propose to construct the weighted line graphs to model the diverse structures of relations and learn relation representation independently from entities. Especially, owing to the diversity of adjacency structures and ring structures, we propose to construct adjacency line graph and ring line graph, respectively, to model the structures of relations and to further improve entity representation. In addition, to alleviate the hubness problem in alignment, we introduce the optimal transport into alignment and compute the distance matrix in a different way. From a global perspective, we calculate the optimal 1-to-1 alignment bi-directionally to improve the alignment accuracy. Experimental results on two benchmark datasets show that our proposed method significantly outperforms state-of-the-art CLEA methods in both supervised and unsupervised manners.
The reality of backdoored S-Boxes—An eye opener

Authors:
raw_author_name: Shah Fahd
raw_affiliation_string: Department of Information Security, National University of Sciences and Technology, H-12, Islamabad, 44000, Pakistan
raw_author_name: Mehreen Afzal
raw_affiliation_string: Department of Information Security, National University of Sciences and Technology, H-12, Islamabad, 44000, Pakistan
raw_author_name: Waseem Iqbal
raw_affiliation_string: Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, 123, Oman; Department of Information Security, National University of Sciences and Technology, H-12, Islamabad, 44000, Pakistan
raw_author_name: Dawood Shah
raw_affiliation_string: Department of Mathematics, Quaid-i-Azam University, Islamabad, 45320, Pakistan
raw_author_name: Ijaz Khalid
raw_affiliation_string: Department of Mathematics, Quaid-i-Azam University, Islamabad, 45320, Pakistan

Abstract: The real-life incidents researched in academia have revealed that (possibly) state-level efforts are made to camouflage the intentional flaws in the mathematical layer of an S-Box for exploiting the information-theoretic properties, i.e., Kuznyechik. To investigate the common features in the intentionally weakened mappings, this research thoroughly examines the backdoored structures from the perspective of 24 cryptanalytic attack vectors available in the open literature. We have debunked the earlier claims by the backdoor engineers that the proposed designs are stealthy against statistical distinguishers. Although a backdoored architecture fulfils the notions of randomness, it lacks the strength to resist sophisticated cryptanalytic attacks. Our analysis has revealed that during the backdoor insertion phase, a malicious designer compromises vital cryptographic properties, prominently the algebraic degree, differential trails, avalanche characteristics and leaving the open ground for hybrid cryptanalytic attacks. It is also observed that these mappings attain the upper bound of BCT, FBCT and DLCT, paving the way for hybrid attacks with high probability and lower data complexity.