Reviewerly/exHarmony huggingface dataset:
Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source

Authors:
raw_author_name: Ariane J. Marelli
raw_affiliation_string: McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada
raw_author_name: Chao Li
raw_affiliation_string: McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada
raw_author_name: Aihua Liu
raw_affiliation_string: McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada
raw_author_name: Liming Guo
raw_affiliation_string: McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada
raw_author_name: David L. Buckeridge
raw_affiliation_string: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada
raw_author_name: Jian Tang
raw_affiliation_string: Department of Decision Sciences HEC, Université de Montréal, Montreal, Québec, Canada
raw_author_name: Yue Li
raw_affiliation_string: School of Computer Science, McGill University, Montreal, Québec, Canada

Abstract: With an increasing interest in using large claims databases in medical practice and research, it is a meaningful and essential step to efficiently identify patients with the disease of interest. This study aims to establish a machine learning (ML) approach to identify patients with congenital heart disease (CHD) in large claims databases. We harnessed data from the Quebec claims and hospitalization databases from 1983 to 2000. The study included 19,187 patients. Of them, 3,784 were labeled as true CHD patients using a clinician developed algorithm with manual audits considered as the gold standards. To establish an accurate ML-empowered automated CHD classification system, we evaluated ML methods including Gradient Boosting Decision Tree, Support Vector Machine, Decision tree, and compared them to regularized logistic regression. The Area Under the Precision Recall Curve was used as the evaluation metric. External validation was conducted with an updated data set to 2010 with different subjects. Among the ML methods we evaluated, Gradient Boosting Decision Tree led the performance in identifying true CHD patients with 99.3% Area Under the Precision Recall Curve, 98.0% for sensitivity, and 99.7% for specificity. External validation returned similar statistics on model performance. This study shows that a tedious and time-consuming clinical inspection for CHD patient identification can be replaced by an extremely efficient ML algorithm in large claims database. Our findings demonstrate that ML methods can be used to automate complicated algorithms to identify patients with complex diseases.
An attachment recognition method based on semi-supervised video segmentation for tidal stream turbines

Authors:
raw_author_name: Lisu Chen
raw_affiliation_string: Shanghai Maritime University, Shanghai, 201306, China
raw_author_name: Haiyang Peng
raw_affiliation_string: Shanghai Maritime University, Shanghai, 201306, China
raw_author_name: Dingding Yang
raw_affiliation_string: Shanghai Maritime University, Shanghai, 201306, China
raw_author_name: Tianzhen Wang
raw_affiliation_string: Shanghai Maritime University, Shanghai, 201306, China

Abstract: Tidal stream turbine (TST) is a reliable machine for converting tidal stream energy to electric energy. However, the efficiency of power generation will be severely affected by the increasing marine attachment on TST blades. Accurate identification of attachments is very important for their removal. This paper proposes a semi-supervised video segmentation network (SVSN) to recognize attachments in underwater video sources. To alleviate the labeling burden, only a small number of video frames need manual annotation, and sufficient labeled data are collected by a data augmentation algorithm. For utilizing the remaining video frames, these unlabeled data and the labeled data are used to train the SVSN based on semi-supervised adversarial learning. In this way, these two kinds of data benefit from each other, making the network to learn general features for better generalization ability. The SVSN includes a generator and a discriminator. The generator is a modified SegNet to obtain initial segmentation maps. The discriminator, a simple fully convolutional network, provides pixel-wise confidence maps for the semi-supervised adversarial learning. Besides, it enhances the generator segmentation quality via considering object boundary areas. Experimental results demonstrate that precise attachment recognition and fast uncertainty estimation can be accomplished under harsh submerged conditions.
Image encryption algorithm based on a new 3D chaotic system using cellular automata

Authors:
raw_author_name: H. Pakmanesh
raw_affiliation_string: Department of Mathematics, University of Mohaghegh Ardabili, 56199-11367, Ardabil, Iran

Abstract: Cryptography and steganography are usual methods for safe transfer of information, which encryption algorithms turn original images into unreadable noise-like images. This paper presents a novel symmetric cryptosystem designed for the transmission of RGB color images through open channels and our goal is to provide a secure cryptography algorithm against cropping and noise attacks. The encryption algorithm is based on a suitable 3D hybrid chaotic system, with high Lyapunov exponent value, leveraging the advantages of common chaos maps while addressing their inherent limitations. To further enhance security, a novel pixel shuffle operator is employed to eliminate any potential neighborhood relations between image pixels. The encryption process incorporates reversible second-order cellular automata, which are applied to the outputs of the chaotic system. Key generation is achieved through the utilization of irreversible cellular automata. The resulting key space is large enough to resist brute-force attacks and performs a high level of sensitivity. Experimental results, including analysis of histograms, entropies, and pixel correlations, confirm the effectiveness of the proposed image encryption scheme, and prove its resilient against statistical attacks and a remarkable resistance against data loss attacks.
Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods

Authors:
raw_author_name: Vito Paolo Pastore
raw_affiliation_string: University of Genoa, DIBRIS, Via Dodecaneso 35, Genoa 16143, Italy
raw_author_name: Lorenzo Rosasco
raw_affiliation_string: Italian Insitute of Technology, Via Morego 30, Genoa 16163, Italy; Massachusetts Institute of Technology(MIT), Massachusetts Ave 73, Cambridge, 02139, MA, United States of America; University of Genoa, DIBRIS, Via Dodecaneso 35, Genoa 16143, Italy
raw_author_name: Francesca Odone
raw_affiliation_string: University of Genoa, DIBRIS, Via Dodecaneso 35, Genoa 16143, Italy

Abstract: The impressive performance of deep learning architectures is associated with a massive increase in model complexity. Millions of parameters need to be tuned, with training and inference time scaling accordingly, together with energy consumption. But is massive fine-tuning always necessary? In this paper, focusing on image classification, we consider a simple transfer learning approach exploiting pre-trained convolutional features as input for a fast-to-train kernel method. We refer to this approach as top-tuning since only the kernel classifier is trained on the target dataset. In our study, we perform more than 3000 training processes focusing on 32 small to medium-sized target datasets, a typical situation where transfer learning is necessary. We show that the top-tuning approach provides comparable accuracy with respect to fine-tuning, with a training time between one and two orders of magnitude smaller. These results suggest that top-tuning is an effective alternative to fine-tuning in small/medium datasets, being especially useful when training time efficiency and computational resources saving are crucial.
Motion-to-Matching: A Mixed Paradigm for 3D Single Object Tracking

Authors:
raw_author_name: Yubo Cui
raw_affiliation_string: Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China
raw_author_name: Shuo Li
raw_affiliation_string: Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China
raw_author_name: Zheng Fang
raw_affiliation_string: Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China

Abstract: 3D single object tracking with LiDAR points is an important task in the computer vision field. Previous methods usually adopt the matching-based or motion-centric paradigms to estimate the current target status. However, the former is sensitive to the similar distractors and the sparseness of point clouds due to relying on appearance matching, while the latter usually focuses on short-term motion clues (eg. two frames) and ignores the long-term motion pattern of target. To address these issues, we propose a mixed paradigm with two stages, named MTM-Tracker, which combines motion modeling with feature matching into a single network. Specifically, in the first stage, we exploit the continuous historical boxes as motion prior and propose an encoder-decoder structure to locate target coarsely. Then, in the second stage, we introduce a feature interaction module to extract motion-aware features from consecutive point clouds and match them to refine target movement as well as regress other target states. Extensive experiments validate that our paradigm achieves competitive performance on large-scale datasets (70.9% in KITTI and 51.70% in NuScenes). The code will be open soon at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LeoZhiheng/MTM-Tracker.git</uri> .
Dual-Branch Multitask Fusion Network for Offline Chinese Writer Identification

Authors:
raw_author_name: Yingyu Mao
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University of Technology, China

Abstract: Chinese characters are complex and contain discriminative information, meaning that their writers have the potential to be recognized using less text. In this study, offline Chinese writer identification based on a single character was investigated. To extract comprehensive features to model Chinese characters, explicit and implicit information as well as global and local features are of interest. A dual-branch multitask fusion network is proposed that contains two branches for global and local feature extraction simultaneously, and introduces auxiliary tasks to help the main task. Content recognition, stroke number estimation, and stroke recognition are considered as three auxiliary tasks for explicit information. The main task extracts implicit information of writer identity. The experimental results validated the positive influences of auxiliary tasks on the writer identification task, with the stroke number estimation task being most helpful. In-depth research was conducted to investigate the influencing factors in Chinese writer identification, with respect to character complexity, stroke importance, and character number, which provides a systematic reference for the actual application of neural networks in Chinese writer identification.
High-performance montgomery modular multiplier with NTT and negative wrapped convolution

Authors:
raw_author_name: Hongfei Ke
raw_affiliation_string: School of Micro-Nano Electronics, Zhejiang University, Hangzhou 310058, China
raw_author_name: Peiyong Zhang
raw_affiliation_string: School of Micro-Nano Electronics, Zhejiang University, Hangzhou 310058, China

Abstract: Modular multiplication plays a crucial role in modern cryptography. Montgomery modular multiplication(MMM), one of the most classic and practical modular multiplication algorithms, has been widely used in cryptographic algorithms such as RSA, Diffie–Hellman algorithm, and Elliptic Curve Cryptography. In this paper, we incorporate negative wrapped convolution (NWC) into the FFT-based Montgomery modular multiplication to avoid the issue of zero-padding and use carry-save arithmetics for parallel computation. By utilizing coefficient pairs (pos_part and neg_part), we reconstruct the final result and eliminate the restrictions imposed by nega-cyclic parts. Moreover, Karatsuba-like algorithm is introduced for building fine-grained large integer multipliers. We have modified the parameter specifications for our design to meet requirements from diverse application scenarios. We implement the design on Xilinx Virtex-7 FPGA under different conditions and compare the results with the state-of-the-art MMM designs. The comparisons confirm that our design has the following characteristics: low latency for process, competitive area-latency-product(ALP), efficient DSP usage, and constant delay, which enhances security against timing attacks.
Learning Global and Multi-granularity Local Representation with MLP for Sequential Recommendation

Authors:
raw_author_name: Chao Long
raw_affiliation_string: Soochow University, China
raw_author_name: Huanhuan Yuan
raw_affiliation_string: Soochow University, China
raw_author_name: Junhua Fang
raw_affiliation_string: Soochow University, China
raw_author_name: Xuefeng Xian
raw_affiliation_string: Suzhou Vocational University, China
raw_author_name: Guanfeng Liu
raw_affiliation_string: Macquarie University, Australia
raw_author_name: Victor S. Sheng
raw_affiliation_string: Texas Tech University, USA
raw_author_name: Pengpeng Zhao
raw_affiliation_string: Soochow University, China

Abstract: Sequential recommendation aims to predict the next item of interest to users based on their historical behavior data. Usually, users’ global and local preferences jointly affect the final recommendation result in different ways. Most existing works use transformers to globally model sequences, which makes them face the dilemma of quadratic computational complexity when dealing with long sequences. Moreover, the scope setting of the user’s local preference is usually static and single, and cannot cover richer multi-level local semantics. To this end, we proposed a parallel architecture for capturing global representation and M ulti-granularity L ocal dependencies with M LP for sequential Rec ommendation ( MLM4Rec ). For global representation, we utilize modified MLP-Mixer to capture global information of user sequences due to its simplicity and efficiency. For local representation, we incorporate convolution into MLP and propose a multi-granularity local awareness mechanism for capturing richer local semantic information. Moreover, we introduced a weight pooling method to adaptively fuse local-global representations instead of directly concatenation. Our model has the advantages of low complexity and high efficiency thanks to its simple MLP structure. Experimental results on three public datasets demonstrate the effectiveness of our proposed model. Our code is available here 1 .
SCTrans: Self-align and cross-align transformer for few-shot segmentation

Authors:
raw_author_name: Jun Ding
raw_affiliation_string: School of Information, Hohai University, Nanjing 210044, China
raw_author_name: Zhen Zhang
raw_affiliation_string: School of Information, Hohai University, Nanjing 210044, China
raw_author_name: HuiBin Wang
raw_affiliation_string: School of Information, Hohai University, Nanjing 210044, China

Abstract: Few-shot Semantic Segmentation (FSS) refers to train a segmentation model that can be generalized to novel categories with limited labeled images. One challenge of FSS is spatial inconsistency between support and query images, e.g., appearance and texture. Most existing methods are only committed to utilizing the semantic-level prototypes of support images to guide mask predictions. These methods, nevertheless, only focus on the most discriminate regions of the object rather than holonomic feature representations. Besides, another question exists that the lack of interaction between paired support and query images. In this paper, we propose a self-align and cross-align transformer (SCTrans) to remedy the above limitations. Specifically, we design a feature fusion module (FFM) to incorporate low-level information from the query branch into mid-level semantic features, boosting the semantic representations of query images. In addition, a feature alignment module is designed to bidirectionally propagate semantic information from support to query images conditioned on more representative support and query features, increasing both intra-class similarities and inter-class differences. Extensive experiments on PASCAL-5i and COCO-20i show that our SCTrans significantly advances the state-of-the-art methods.
Sequential auction for cloud manufacturing resource trading: A deep reinforcement learning approach to the lot-sizing problem

Authors:
raw_author_name: Kaize Yu
raw_affiliation_string: School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, PR China

Abstract: Cloud manufacturing is a rapidly growing trend in modern manufacturing, which has transformed the traditional operations and value chain structure of enterprises. It is crucial to develop a rational and effective trading mechanism of cloud manufacturing resources to meet the ever-increasing demands in this new environment. This paper proposes a sequential auction-based paradigm for the trading of manufacturing resources. The main challenge of the design of the paradigm is to determine the dynamic lot size of resources allocated to each auction considering the uncertainty of arriving demands. To achieve this, we first develop a competitive game model to identify optimal bidding strategies of arrived customers and estimate the expected revenue for each round with the given lot size. Secondly, we construct a Markov decision process (MDP) model to characterize the dynamics of the arrival of stochastic demand and the inventory transition of the manufacturing resources in sequential auctions. Lastly, we leverage a data-driven approach by integrating machine learning with an offline deep reinforcement learning (RL) approach. Specifically, we employ a long short-term memory (LSTM) model to predict forthcoming demands in the environment and develop the deep Q-network (DQN) learning algorithm to optimize lot-sizing policy by interacting with the well-learned LSTM environment model. Our simulation experiments validate the effectiveness of our approach and some management insights are given.
EFDCNet: Encoding fusion and decoding correction network for RGB-D indoor semantic segmentation

Authors:
raw_author_name: Jianlin Chen
raw_affiliation_string: Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
raw_author_name: Gongyang Li
raw_affiliation_string: Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
raw_author_name: Zhijiang Zhang
raw_affiliation_string: Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
raw_author_name: Dan Zeng
raw_affiliation_string: Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China

Abstract: Semantic segmentation is a crucial task in vision measurement systems that involves understanding and segmenting different objects and regions within an image. Over the years, numerous RGB-D semantic segmentation methods have been developed, leveraging the encoder-decoder architecture to achieve outstanding performance. However, existing methods have two main problems that constrain further performance improvement. Firstly, in the encoding stage, existing methods have a weak ability to fuse cross-modal information, and low-quality depth maps can easily lead to poor feature representation. Secondly, in the decoding stage, the upsampling of high-level semantic information may cause the loss of contextual information, and low-level features from the encoder may bring noises to the decoder through skip connections. To solve these issues, we propose a novel Encoding Fusion and Decoding Correction Network (EFDCNet) for RGB-D indoor semantic segmentation. First, in the encoding stage of EFDCNet, we focus on extracting valuable information from low-quality depth maps, and employ a channel-wise filter to select informative depth features. Additionally, we establish the global dependencies between RGB and depth features via the self-attention mechanism to enhance the cross-modal feature interactions, extracting discriminant and powerful features. Then, in the decoding stage of EFDCNet, we use the highest-level information as semantic guidance to compensate for the upsampling information and filter out noise from the low-level encoder features propagated through the skip connections to the decoder. Extensive experiments conducted on two widely-used RGB-D indoor semantic segmentation datasets demonstrate that the proposed EFDCNet surpasses the performance of relevant state-of-the-art methods. The code is available at https://github.com/ Mark9010/EFDCNet
The Computational Method for Supporting Thai VerbNet Construction

Authors:
raw_author_name: Rachada Kongkachandra
raw_affiliation_string: College of Interdisciplinary Studies, Thammasat University, Thailand
raw_author_name: Sarun Gulyanon
raw_affiliation_string: College of Interdisciplinary Studies, Thammasat University, Thailand

Abstract: VerbNet is a lexical resource for verbs that has many applications in natural language processing tasks, especially ones that require information about both the syntactic behavior and the semantics of verbs. This article presents an attempt to construct the first version of a Thai VerbNet corpus via data enrichment of the existing lexical resource. This corpus contains the annotation at both the syntactic and semantic levels, where verbs are tagged with frames within the verb class hierarchy and their arguments are labeled with the semantic role. We discuss the technical aspect of the construction process of Thai VerbNet and survey different semantic role labeling methods to make this process fully automatic. We also investigate the linguistic aspect of the computed verb classes and the results show the potential in assisting semantic classification and analysis. At the current stage, we have built the verb class hierarchy consisting of 28 verb classes from 112 unique concept frames over 490 unique verbs using our association rule learning method on Thai verbs.