Reviewerly/exHarmony huggingface dataset:
BDACD: Blockchain-based decentralized auditing supporting ciphertext deduplication

Authors:
raw_author_name: Yongliang Xu
raw_affiliation_string: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an, 233002, Jiangsu, China; School of Mathematics and Statistics, Fuzhou University, Fuzhou, 350108, Fujian, China
raw_author_name: Chunhua Jin
raw_affiliation_string: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an, 233002, Jiangsu, China
raw_author_name: Wenyu Qin
raw_affiliation_string: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an, 233002, Jiangsu, China
raw_author_name: Jie Zhao
raw_affiliation_string: School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China
raw_author_name: Guanhua Chen
raw_affiliation_string: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an, 233002, Jiangsu, China
raw_author_name: Fugeng Zeng
raw_affiliation_string: School of Data Sciences and Information Engineering, Guizhou Minzu University, Guiyang, 550025, Guizhou, China

Abstract: Public auditing enables data owners to entrust a third-party auditor (TPA) to perform auditing tasks periodically. To resist malicious TPAs, a plethora of blockchain-based public auditing mechanisms have been proposed. However, existing schemes cannot effectively mitigate single point of failure and collusion between TPAs and miners. These schemes are burdened by significant wastage of storage resources due to the presence of redundant data. In this paper, we propose a blockchain-based decentralized auditing scheme supporting ciphertext deduplication (BDACD). BDACD leverages a decentralized blockchain, which takes over the role traditionally played by a centralized TPA. A decentralized autonomous organization is incorporated to enhance resistance against collusion attacks. By integrating advanced ciphertext deduplication techniques, BDACD effectively mitigates storage overhead. Furthermore, we introduce a novel concept, T-Merkle hash tree, along with a corresponding search algorithm. This innovation significantly enhances blockchain storage utilization and enables efficient half-interval searches within a block. To ensure accountability and fairness, a smart contract is employed to establish an arbitration mechanism. This mechanism serves a dual purpose: penalizing any malevolent actions by cloud servers and providing adequate compensation to data owners whose data integrity has been compromised. Detailed security analysis and performance evaluation demonstrate BDACD has desirable security and efficiency.
T2FM: A novel hashtable based type-2 fuzzy frequent itemsets mining

Authors:
raw_author_name: M. Jeya Sutha
raw_affiliation_string: Department of Computer Applications, St. Xavier’s Catholic College of Engineering, Chunkankadai, Tamil Nadu, India
raw_author_name: F. Ramesh Dhanaseelan
raw_affiliation_string: Department of Computer Applications, St. Xavier’s Catholic College of Engineering, Chunkankadai, Tamil Nadu, India

Abstract: Association rule mining (ARM) is an important research issue in the field of data mining that aims to find relations among different items in binary databases. The conventional ARM algorithms consider the frequency of the items in binary databases, which is not sufficient for real time applications. In this paper, a novel hash table based Type-2 fuzzy mining algorithm (T2FM) with an efficient pruning strategy is presented for discovering multiple fuzzy frequent itemsets from quantitative databases. The algorithm employs a hash table based structure for efficient storage and retrieval of item/itemset which reduces the search efficiency to O(1) or constant time. Previously, type-2 based Apriori and FP-growth based fuzzy frequent itemsets mining were proposed, which required large amounts of computation and a greater number of candidate generation and processing. Meanwhile, the proposed approach reduces a huge amount of computation by finding the common keys before the actual intersection operation takes place. An efficient pruning strategy is proposed to avoid unpromising candidates in order to speed up the computations. Several experiments are carried out to verify the efficiency of the approach in terms of runtime and memory for different minimum support threshold and the results show that the designed approach provides better performance compared to the state-of-the-art algorithms.
Constraint-based high utility mobile trajectory pattern mining for security systems

Authors:
raw_author_name: Aiguo Li
raw_affiliation_string: College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, China
raw_author_name: Rongrong Feng
raw_affiliation_string: College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, China

Abstract: In security system, high utility pattern mining of a large number of users mobile trajectories is helpful to analyze user behavior patterns, and enhance the internal prevention of the security system.Currently, the frequent pattern mining for mobile trajectory in security systems do not take into account the differences in the safety levels between staying points and the frequency of their occurrence. Existing high utility mobile trajectory pattern mining methods are unable to discover staying points that meet specific user requirements. To solve these problems, a constraint-based high utility mobile trajectory mining algorithm is proposed (names as HUIM-ILC-ACO). It takes into account the user’s stay time, stay frequency, and the safety level of each stopping point and calculates the utility value of each staying point by incorporating these factors through weighted computation. Based on this, the algorithm combines ant colony optimization with length constraints and item constraints to construct a method for mining high utility mobile trajectory patterns that better align with user interests. Experimental results on real datasets and a target mobile trajectory RFID dataset show that proposed algorithm is efficient in terms of runtime and pattern quantity, and it can effectively mine a pattern set that is closer to user interests.
Multi-scale cross-layer feature interaction GAN for underwater image enhancement

Authors:
raw_author_name: Rong Lan
raw_affiliation_string: School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: YuHao Sun
raw_affiliation_string: School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Zhao Feng
raw_affiliation_string: School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Haiyan Yu
raw_affiliation_string: School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Lu Zhang
raw_affiliation_string: School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Abstract: Due to the intricate underwater environment, underwater images frequently exhibit issues such as low contrast, detail loss, and color distortion, which significantly affect underwater operations. To alleviate these concerns and improve image quality, multi-scale cross-layer feature interaction GAN for underwater image enhancement model (MCFIGAN) is proposed in this paper. In the proposed model, we have designed a cross-layer feature interaction module (CLFIM) that fosters the fusion and transmission of features at various levels. This approach aims to extract more comprehensive feature information and enhance the robustness of the network model. Additionally, we have constructed a multi-scale feature module (MSFEM) to extract features at different levels and scales, enabling the model to gain a more comprehensive understanding of the object. By utilizing CLFIM and MSFEM, the generator is capable of producing a generated image that contains a wealth of feature information. Then, an enhanced discriminator module (EDM) is designed to improve the model's ability to identify generated images, thereby encouraging the generator network to generate more realistic underwater images. Finally, several training sessions utilizing multiple joint losses yield underwater images with characteristics of high contrast and rich detail. Through simulation experiments conducted on EUVP dataset and UIEB dataset, our proposed model is found to be superior to related models in terms of visual effect and quantitative index, particularly in contrast enhancement, detail recovery, and color restoration.
A Relation Embedding Assistance Networks for Multi-hop Question Answering

Authors:
raw_author_name: Zhenfang Zhu
raw_affiliation_string: Shandong Jiao Tong University, China
raw_author_name: Jiangtao Qi
raw_affiliation_string: Shandong Jiao Tong University, China
raw_author_name: Fuyong Xu
raw_affiliation_string: Shandong Normal University, China
raw_author_name: Hongli Pei
raw_affiliation_string: Shandong Jiao Tong University, China
raw_author_name: Wenling Wang
raw_affiliation_string: Lu Dong University, China
raw_author_name: Ze Song
raw_affiliation_string: Shandong Jiao Tong University, China
raw_author_name: Peiyu Liu
raw_affiliation_string: Shandong Normal University, China

Abstract: Multi-hop Knowledge Graph Question Answering aims at finding an entity to answer natural language questions from knowledge graphs. When humans perform multi-hop reasoning, people tend to focus on specific relations across different hops and confirm the next entity. Therefore, most algorithms choose the wrong specific relation, which makes the system deviate from the correct reasoning path. The specific relation at each hop plays an important role in multi-hop question answering. Existing work mainly relies on the question representation as relation information, which cannot accurately calculate the specific relation distribution. In this article, we propose an interpretable assistance framework that fully utilizes the relation embeddings to assist in calculating relation distributions at each hop. Moreover, we employ the fusion attention mechanism to ensure the integrity of relation information and hence to enrich the relation embeddings. The experimental results on three English datasets and one Chinese dataset demonstrate that our method significantly outperforms all baselines. The source code of REAN will be available at https://github.com/2399240664/REAN
Transfer learning approach to reduce similar IOT sensor data for industrial applications

Authors:
raw_author_name: K. Ramesh
raw_affiliation_string: PG & Research Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women, Tiruchengode, Namakkal, Tamilnadu, India
raw_author_name: B. Rajarao
raw_affiliation_string: Department of Computer Science and Engineering, Mallareddy Engineering College, Hyderabad, 500014, India
raw_author_name: S. Angel Latha Mary
raw_affiliation_string: Department of Computer Science and Business Systems, Sri Eshwar College of Engineering, Coimbatore, 641 202, Tamilnadu, India

Abstract: The Industrial Internet of Things (IIoT) is a developing technology that has the potential to advance industrial intelligence, achieve targeted effectiveness, and lower manufacturing costs. Data fusion, a procedure that enables the collection, analysis, and processing of the enormous amounts of Internet of Things (IoT) information created by industrialized machinery and applications, is essential to IIoT's advancement and improvement of industrial products and applications. A real-time, efficient, and private information image fusion mechanism is required by the IIoT. Moreover, the current efforts call for such training of multiple systems in data analysis that also fall short of meeting real-time IIoT needs. The ineffectiveness of internal defenses and the challenge of finding the right balance between system stability and data confidentiality, however, undermine the efficiency and privacy preservation of a data fusion process. In this paper, we propose a Transfer learning-based Secure Data Fusion (TSDF) for the IIoT to address the aforementioned issues. In addition to the proposed network, we developed and deployed a defect estimation method that makes use of sensor data fusion to evaluate the manufacturing plant's operational state. The efficiency outcomes of evaluating the conceptual model are included in the original study, including a comparison to a conventional deep neural network structure. The study's findings demonstrate that TSDF may provide privacy, preserving information fusion in diverse IIoT application environments while reaching higher system capacity and low latency.
Sea-Pix-GAN: Underwater image enhancement using adversarial neural network

Authors:
raw_author_name: Prateek Chhikara
raw_affiliation_string: University of Southern California, Los Angeles, United States of America

Abstract: In the last decade, the exploration of deep-sea ecosystems has surged, offering exciting prospects for discovering untapped resources such as medical drugs, food and energy sources, and renewable energy products. Consequently, research in underwater image processing has witnessed substantial growth. However, underwater imaging poses significant challenges, particularly without sophisticated, specialized cameras. Traditional cameras are impacted by absorption and scattering in the aquatic environment, producing hazy images with a blue–green tint. This phenomenon holds implications for marine research and other disciplines that rely on underwater imaging. While hardware advancements have been made over the years, image processing remains a valuable, cost-effective, and practical approach for underwater enhancement. Despite the existence of state-of-the-art techniques for underwater enhancement and restoration, their performance is often inconsistent. While some methods excel in contrast restoration, color restoration remains a pervasive challenge. In this paper, we introduce Sea-Pix-GAN, a Generative Adversarial Network (GAN)-based model that addresses these issues in underwater image enhancement. We redefine the problem as an image-to-image translation task and tailor the objective and loss functions to achieve color, content, and style transfer. The model is trained on a large dataset of underwater scenes, encompassing the diverse color dynamics of underwater subjects. Sea-Pix-GAN demonstrates promising results in restoring color, contrast, texture, and saturation. To validate its effectiveness, we compare the performance of Sea-Pix-GAN quantitatively based on metrics like PSNR, SSIM, and UIQM and qualitatively against several existing techniques.
Efficient Syndrome Decoder for Heavy Hexagonal QECC via Machine Learning

Authors:
raw_author_name: Debasmita Bhoumik
raw_affiliation_string: Advanced Computing &amp; Microelectronics Unit, Indian Statistical Institute, Baranagar, West Bengal
raw_author_name: Ritajit Majumdar
raw_affiliation_string: IBM Quantum, IBM India Research Lab, Thanisandra, Bengaluru, Karnataka
raw_author_name: Dhiraj Madan
raw_affiliation_string: IBM Quantum, IBM India Research Lab, Thanisandra, Bengaluru, Karnataka
raw_author_name: Dhinakaran Vinayagamurthy
raw_affiliation_string: IBM Quantum, IBM India Research Lab, Thanisandra, Bengaluru, Karnataka
raw_author_name: Shesha Raghunathan
raw_affiliation_string: IBM Quantum, IBM India Research Lab, Thanisandra, Bengaluru, Karnataka
raw_author_name: Susmita Sur-Kolay
raw_affiliation_string: Advanced Computing &amp; Microelectronics Unit, Indian Statistical Institute, Baranagar, West Bengal

Abstract: Error syndromes for heavy hexagonal code and other topological codes such as surface code have typically been decoded by using Minimum Weight Perfect Matching– (MWPM) based methods. Recent advances have shown that topological codes can be efficiently decoded by deploying machine learning (ML) techniques, in particular with neural networks. In this work, we first propose an ML-based decoder for heavy hexagonal code and establish its efficiency in terms of the values of threshold and pseudo-threshold for various noise models. We show that the proposed ML-based decoding method achieves ~ 5 × higher values of threshold than that for MWPM. Next, exploiting the property of subsystem codes, we define gauge equivalence for heavy hexagonal code, by which two distinct errors can belong to the same error class. A linear search-based method is proposed for determining the equivalent error classes. This provides a quadratic reduction in the number of error classes to be considered for both bit flip and phase flip errors and thus a further improvement of ~ 14% in the threshold over the basic ML decoder. Last, a novel technique based on rank to determine the equivalent error classes is presented, which is empirically faster than the one based on linear search.
Residual networks for text-independent speaker identification: Unleashing the power of residual learning

Authors:
raw_author_name: Pooja Gambhir
raw_affiliation_string: Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India
raw_author_name: Amita Dev
raw_affiliation_string: Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India
raw_author_name: Poonam Bansal
raw_affiliation_string: Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India
raw_author_name: Deepak Kumar Sharma
raw_affiliation_string: Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India
raw_author_name: Deepak Gupta
raw_affiliation_string: Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India

Abstract: The human voice, a dynamic signal, conveys valuable information for speaker identification, encompassing gender, age, emotions, and language. In the biometrics industry, identifying voices in real-time amidst diverse accents, tones, and noisy backgrounds is a challenging task. Voice biometry, a complex aspect of speaker identification, is gaining importance in various applications, such as user authentication, attendance systems, forensics, and banking operations, as it eliminates the need for traditional credentials like cards or passwords. Recent advancements in Human–Computer Interaction technology have made conversational tasks technically feasible. Deep Neural Learning approaches, especially Convolutional Deep Neural Networks (CDNN), have emerged as a powerful tool in the field of speech processing, surpassing traditional Speaker Identification methods. This paper introduces a novel approach using 1-Dimensional Convolutional Residual Blocks for audio classification and Speaker Identification, specifically focusing on speaker recognition from spoken Hindi language. The proposed Residual architecture significantly enhances speaker identification, even in low Signal Noise Ratio environments, achieving an impressive accuracy rate of 86.02%. This outperforms traditional Gaussian Mixture Model (GMM) and Feed Forward Back-propagation Network (FFBN) model for the same set of speakers. Future research directions may explore the classification of audio and speaker identification using various acoustic features derived from speech signals.
Investigation on evaluation of education effect based on deep learning algorithm

Authors:
raw_author_name: Dong Hao
raw_affiliation_string: Faculty of Business, Yangzhou Polytechnic Institute, Yangzhou 225000, China
raw_author_name: Wang Guohua
raw_affiliation_string: Faculty of Innovation and Entrepreneurship, Yangzhou Polytechnic Institute, Yangzhou 225000, China

Abstract: With the rapid development of society today, in the context of the impact of education on personal future development, people have increasingly attached importance to education. Various types of online and offline education institutions have mushroomed. However, the factors that affect the effectiveness of education are numerous and complex. As a very important part of early childhood learning, preschool education should receive more attention. The deep learning algorithm can reasonably classify and plan the factors affecting preschool education through massive data calculations to ensure the reasonable allocation of resources and save the time and energy costs wasted in allocating educational resources. The educational effectiveness of preschool education institutions and organizations can be reasonably evaluated. This article applied deep learning algorithms to the evaluation of preschool education effectiveness. Data related to the effectiveness of preschool education in preschool education institutions using convolutional neural networks and not using algorithms were compared. The experimental results showed that the expert recognition rates of the convolutional neural network algorithm and traditional manual measurement data were 98.8% and 92.55%, respectively. At the time level of teaching quality estimation, five algorithms and 100 sets of relevant experimental data were compared. In terms of the accuracy of feature search, the average accuracy of the convolutional neural network algorithm was 97.96%, while the accuracy of the traditional manual search was 53.9%. Therefore, the application of the convolutional neural network algorithm in deep learning to the evaluation of preschool education effectiveness was more efficient and efficient and could improve the evaluation effect from various aspects.
Exploring the user guidance for more accurate building segmentation from high-resolution remote sensing images

Authors:
raw_author_name: Dinghao Yang
raw_affiliation_string: Shanghai Artificial Intelligence Laboratory, China
raw_author_name: Bin Wang
raw_affiliation_string: Shanghai Artificial Intelligence Laboratory, China
raw_author_name: Conghui He
raw_affiliation_string: Shanghai Artificial Intelligence Laboratory, China

Abstract: In recent years, the computer vision domain has witnessed a surge of interest in interactive object segmentation, an area of study that seeks to expedite the annotation process for pixel-wise segmentation tasks through user guidance. Despite this growing focus, existing methods mainly focus on a single type of pre-annotation and neglect the quality of boundary prediction, which significantly influences subsequent manual adjustments to segmentation boundaries. To address these limitations, we introduce a novel end-to-end network to facilitate more precise building segmentation using diverse types of user guidance. In our proposed method, a centroid map is generated to provide foreground prior information crucial to the subsequent segmentation procedure, and the boundary correction module automatically refines the segmentation mask from existing segmentation networks. Extensive experiments on two popular building extraction datasets demonstrate that our method outperforms all existing approaches given various user guidance (bounding boxes, inside-outside points, or extreme points), achieving the IoU scores of over 95% on SpaceNet-Vegas dataset and over 93% on Inria-building dataset. The remarkable performance of our method further demonstrates its immense potential to alleviate the labor-intensive annotation process associated with remote sensing datasets. The code of our proposed method is available at https://github.com/StephenDHYang/UGBS-pytorch.