Reviewerly/exHarmony huggingface dataset:
Geometrical Interpretation and Design of Multilayer Perceptrons

Authors:
raw_author_name: Ruiyuan Lin
raw_affiliation_string: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
raw_author_name: Zhiruo Zhou
raw_affiliation_string: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
raw_author_name: Suya You
raw_affiliation_string: U.S. Army Research Laboratory, Adelphi, MD, USA
raw_author_name: Raghuveer Rao
raw_affiliation_string: U.S. Army Research Laboratory, Adelphi, MD, USA
raw_author_name: C.-C. Jay Kuo
raw_affiliation_string: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

Abstract: The multilayer perceptron (MLP) neural network is interpreted from the geometrical viewpoint in this work, that is, an MLP partition an input feature space into multiple nonoverlapping subspaces using a set of hyperplanes, where the great majority of samples in a subspace belongs to one object class. Based on this high-level idea, we propose a three-layer feedforward MLP (FF-MLP) architecture for its implementation. In the first layer, the input feature space is split into multiple subspaces by a set of partitioning hyperplanes and rectified linear unit (ReLU) activation, which is implemented by the classical two-class linear discriminant analysis (LDA). In the second layer, each neuron activates one of the subspaces formed by the partitioning hyperplanes with specially designed weights. In the third layer, all subspaces of the same class are connected to an output node that represents the object class. The proposed design determines all MLP parameters in a feedforward one-pass fashion analytically without backpropagation. Experiments are conducted to compare the performance of the traditional backpropagation-based MLP (BP-MLP) and the new FF-MLP. It is observed that the FF-MLP outperforms the BP-MLP in terms of design time, training time, and classification performance in several benchmarking datasets. Our source code is available at https://colab.research.google.com/drive/1Gz0L8AnT4ijrUchrhEXXsnaacrFdenn?usp = sharing.
Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation

Authors:
raw_author_name: Yichi Zhang
raw_affiliation_string: School of Data Science, Fudan University, Shanghai, 200433, China; Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, 200433, China. Electronic address: zhangyichi23@m.fudan.edu.cn.
raw_author_name: Le Ding
raw_affiliation_string: School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China. Electronic address: 17376120@buaa.edu.cn.
raw_author_name: Bingsen Xue
raw_affiliation_string: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. Electronic address: bingsenxue@sjtu.edu.cn.
raw_author_name: Jicong Zhang
raw_affiliation_string: School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Hefei Innovation Research Institute, Beihang University, Hefei, 230012, China. Electronic address: jicongzhang@buaa.edu.cn.
raw_author_name: Rong Cai
raw_affiliation_string: School of Engineering Medicine, Beihang University, Beijing, 100191, China; Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, 100191, China. Electronic address: cairong@buaa.edu.cn.
raw_author_name: Cheng Jin
raw_affiliation_string: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China; Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China. Electronic address: chengjin520@sjtu.edu.cn.

Abstract: Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarize both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review can inspire the research community to explore solutions to this challenge and further advance the field of medical image segmentation.
Advancing the College of Engineering Strategic Goal of Becoming a National Model of Inclusivity and Collaboration

Authors:
raw_author_name: Michelle Kay Bothwell
raw_affiliation_string: Oregon State University

Abstract: Abstract The College of Engineering at a public research institution, with Land Grant and Sea Grant designations, has committed to becoming a community of faculty, students, and staff that is increasingly more inclusive, collaborative, diverse, and centered on student success. We are meeting this commitment in part through design and implementation of new and revised practices for recruitment, professional development, mentoring, and advancement. But changing organizational culture is a large-scale undertaking. In order to build an organizational conscience for the college and secure its transformation into a community where all members feel welcome and engaged, "top-down" policy change must be complemented by enlistment of change agents from every employment sector of the college. For this purpose, a 20-member Change Team — including a balance of tenure-track and professional faculty and staff — was assembled in AY18 and empowered to work with college leadership to envision, develop, and resource infrastructure and communication needs to engage all college employees in our culture change process. The work of the Change Team has emerged as essential to advancing our goals in relation to community. We will discuss inception and evolution of the Team, and profile five projects launched in AY18: • Increasing competency and building an organizational conscience (this project foregrounds the symbolic level of oppression through bringing two events to campus, featuring an internationally acclaimed social justice scholar, and a play that explores the dangers of racism and political correctness). • Design of modules to enhance graduate students' capacities to engage issues of inclusivity, equity and social justice. • Development of a scaffolded approach to enhancing undergraduate students' knowledge and skills supporting inclusive and socially just teaming practices. • Alignment of 'Action Plans' developed by engineering faculty and administrators who completed the NSF-supported ADVANCE seminar. • Revision of the manufacturing engineering undergraduate curriculum in order to enhance its flexibility and accessibility.
A Blockchain-Based Scheme for Secure Data Offloading in Healthcare With Deep Reinforcement Learning

Authors:
raw_author_name: Qiang He
raw_affiliation_string: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
raw_author_name: Zheng Feng
raw_affiliation_string: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
raw_author_name: Hui Fang
raw_affiliation_string: Research Institute for Interdisciplinary Science and the School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China
raw_author_name: Xingwei Wang
raw_affiliation_string: College of Computer Science and Engineering, Northeastern University, Shenyang, China
raw_author_name: Liang Zhao
raw_affiliation_string: College of Computer Science and Engineering, Shenyang Aerospace University, Shenyang, China
raw_author_name: Yudong Yao
raw_affiliation_string: Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
raw_author_name: Keping Yu
raw_affiliation_string: Graduate School of Science and Engineering, Hosei University, Tokyo, Japan

Abstract: With the widespread popularity of the Internet of Things and various intelligent medical devices, the amount of medical data is rising sharply, and thus medical data processing has become increasingly challenging. Mobile edge computing technology allows computing power to be allocated at the edge closer to users, which enables efficient data offloading for healthcare systems. However, existing studies on medical data offloading seldom guarantee effective data privacy and security. Moreover, the research equipping data offloading architectures with Blockchain neglect the delay and energy consumption costs incurred in using Blockchain technology for medical data offloading. Therefore, in this paper, we propose a data offloading scheme for healthcare based on Blockchain technology, which achieves optimal medical resource allocation and simultaneously minimizes the cost of offloading tasks. Specifically, we design a smart contract to ensure secure data offloading. And, we formulate the cost problem as a Markov Decision Process, solved by a policy search-based deep reinforcement learning (Asynchronous Advantage Actor-Critic) scheme, where we jointly consider offloading decisions, allocation of computing resources and radio transmission bandwidth, and Blockchain data security audits. The security of our smart-contract-based mechanism is theoretically and empirically proved, while extensive experimental results also show that our solution can obtain superior performance gains with lower cost than other baselines.
A Training-Free, Lightweight Global Image Descriptor for Long-Term Visual Place Recognition Toward Autonomous Vehicles

Authors:
raw_author_name: Dingyu Xue
raw_affiliation_string: College of Information Science and Engineering, Northeastern University, Shenyang, China
raw_author_name: Feng Pan
raw_affiliation_string: College of Information Science and Engineering, Northeastern University, Shenyang, China
raw_author_name: Wei Liu
raw_affiliation_string: Neusoft Reach Automotive Technology Company, Shenyang, China
raw_author_name: Jun Hu
raw_affiliation_string: Neusoft Reach Automotive Technology Company, Shenyang, China
raw_author_name: Shuai Cheng
raw_affiliation_string: Neusoft Reach Automotive Technology Company, Shenyang, China

Abstract: Long-term visual place recognition (VPR) has recently become a popular research topic in the field of autonomous driving. In urban scenarios, variations in scene appearance due to the change in seasons and illumination bring great challenges for scene description. Several learning-based VPR techniques can learn latent invariant descriptors for appearance variations and show excellent performance in long-term VPR tasks. However, these methods require huge datasets and computational resources (e.g., GPUs) for training and inference. Mobile platforms such as autonomous vehicles often cannot provide sufficient computing power. To address this issue, in this paper, a training-free lightweight global image descriptor named SSR-VLAD is proposed for VPR. This descriptor is able to work accurately in real-time without GPUs, even on embedded platforms. The contribution of this work has two aspects. (1) A novel semantic skeleton representation (SSR) is proposed to describe the semantic spatial distribution of scenes by using the semantic spatial context; (2) Inspired by the Vector of Locally Aggregated Descriptors (VLAD), a spatial-temporal aggregation framework for SSR features is constructed to aggregate all SSR features into one SSR-VLAD descriptor, which encodes the spatial and temporal information into a fixed-size global descriptor. SSR-VLAD shows robust performance towards the appearance variations of scenes. Specifically, on three public datasets with challenging urban scenes, experimental results show that SSR-VLAD has competitive VPR performance compared to several state-of-the-art (SoTA) VPR methods. Additionally, SSR-VLAD achieves SoTA real-time computational performance with lower RAM consumption in computationally constrained scenarios.
Deep Autoencoder-Based Z-Interference Channels With Perfect and Imperfect CSI

Authors:
raw_author_name: Xinliang Zhang
raw_affiliation_string: Department of Electrical and Computer Engineering, Villanova University, Villanova, PA, USA
raw_author_name: Mojtaba Vaezi
raw_affiliation_string: Department of Electrical and Computer Engineering, Villanova University, Villanova, PA, USA

Abstract: A deep autoencoder (DAE)-based structure for end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The proposed structure jointly optimizes the two encoder/decoder pairs and generates interference-aware constellations that dynamically adapt their shape based on interference intensity to minimize the bit error rate (BER). An in-phase/quadrature-phase (I/Q) power allocation layer is introduced in the DAE to guarantee an average power constraint and enable the architecture to generate constellations with nonuniform shapes. This brings further gain compared to standard uniform constellations such as quadrature amplitude modulation. The proposed structure is then extended to work with imperfect channel state information (CSI). The CSI imperfection due to both the estimation and quantization errors are examined. The performance of the DAE-ZIC is compared with two baseline methods, i.e., standard and rotated constellations. The proposed structure significantly enhances the performance of the ZIC both for the perfect and imperfect CSI. Simulation results show that the improvement is achieved in all interference regimes (weak, moderate, and strong) and consistently increases with the signal-to-noise ratio (SNR). For instance, more than an order of magnitude BER reduction is obtained with respect to the most competitive conventional method at weak interference when SNR > 15dB and two bits per symbol are transmitted. The improvements reach about two orders of magnitude when quantization error exists, indicating that the DAE-ZIC is more robust to the interference compared to the conventional methods.
UTDNet: A unified triplet decoder network for multimodal salient object detection

Authors:
raw_author_name: Fushuo Huo
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
raw_author_name: Ziming Liu
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
raw_author_name: Jingcai Guo
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
raw_author_name: Wenchao Xu
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
raw_author_name: Song Guo
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China

Abstract: Image Salient Object Detection (SOD) is a fundamental research topic in the area of computer vision. Recently, the multimodal information in RGB, Depth (D), and Thermal (T) modalities has been proven to be beneficial to the SOD. However, existing methods are only designed for RGB-D or RGB-T SOD, which may limit the utilization in various modalities, or just finetuned on specific datasets, which may bring about extra computation overhead. These defects can hinder the practical deployment of SOD in real-world applications. In this paper, we propose an end-to-end Unified Triplet Decoder Network, dubbed UTDNet, for both RGB-T and RGB-D SOD tasks. The intractable challenges for the unified multimodal SOD are mainly two-fold, i.e., 1) accurately detecting and segmenting salient objects, and 2) preferably via a single network that fits both RGB-T and RGB-D SOD. First, to deal with the former challenge, we propose the multi-scale feature extraction unit to enrich the discriminative contextual information, and the efficient fusion module to explore cross-modality complementary information. Then, the multimodal features are fed to the triplet decoder, where the hierarchical deep supervision loss further enable the network to capture distinctive saliency cues. Second, as to the latter challenge, we propose a simple yet effective continual learning method to unify multimodal SOD. Concretely, we sequentially train multimodal SOD tasks by applying Elastic Weight Consolidation (EWC) regularization with the hierarchical loss function to avoid catastrophic forgetting without inducing more parameters. Critically, the triplet decoder separates task-specific and task-invariant information, making the network easily adaptable to multimodal SOD tasks. Extensive comparisons with 26 recently proposed RGB-T and RGB-D SOD methods demonstrate the superiority of the proposed UTDNet.
Indonesian Presidential Election Sentiment: Dataset of Response Public Before 2024.

Authors:
raw_author_name: Asno Azzawagama Firdaus
raw_affiliation_string: Master Program of Informatics, Universitas Ahmad Dahlan, Yogyakarta 55166, Indonesia
raw_author_name: Anton Yudhana
raw_affiliation_string: Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta 55166, Indonesia
raw_author_name: Imam Riadi
raw_affiliation_string: Department of Information System, Universitas Ahmad Dahlan, Yogyakarta 55166, Indonesia
raw_author_name: None Mahsun
raw_affiliation_string: Department of Indonesian Language and Literature Education, Universitas Mataram, Mataram 83125, Indonesia

Abstract: Indonesia is one of the countries that is currently entering the political year for the election of President, Regional Heads, and Members of the Legislative in 2024. This has become a hot topic on social media, especially about the Presidential Election. Twitter is one of the platforms with the largest users in Indonesia. It is interesting to see the alignment of Twitter users towards presidential candidates who already have a carrying party, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan based on a sentiment analysis approach. User feedback data about Indonesian Presidential candidates are obtained from the Twitter platform using Twitter API with Python programming language. The data obtained was 30,000 data with each candidate as many as 10,000 data. Data is pulled in April 2023 with specific keywords. The time for data withdrawal is chosen based on the announcement of Presidential Candidates carried by political parties before the schedule for determining or campaigning for Presidential candidates. Current data can potentially be used again as a comparison of analysis of presidential candidates on campaign time spans and after campaigns or actual calculation results. The data that can be accessed is in CSV format and has gone through several stages such as labelling using Language experts, removing spam Tweets & empty cells and preprocessing.
A cohesive structure of Bi-directional long-short-term memory (BiLSTM) -GRU for predicting hourly solar radiation

Authors:
raw_author_name: Ramesh C. Bansal
raw_affiliation_string: Department of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates; Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa
raw_author_name: Ali A. Ismail
raw_affiliation_string: Department of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates
raw_author_name: Shazia Hasan
raw_affiliation_string: Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, United Arab Emirates

Abstract: Uncertain weather scenarios have an impact on the output of solar farms and therefore affect the security of the grid. It is advantageous for power system operators to forecast solar energy to balance the load generation and for optimal power scheduling. The most promising deep-learning techniques to combine weather variables with precise measurements of solar irradiance are not widely discussed. To close this research gap and produce better prediction results, this article aims to formulate and compare two distinctive deep learning algorithms for using time series forecasting approaches to predict solar irradiance. For multivariate data, the forecasting technique Bi-Directional Long Short-Term Memory (BiLSTM), and BiLSTM-GRU (Gated Recurrent Unit) Dropout, are examined in this study. The output results from the proposed model are compared with other benchmark models based on performance error measurements, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Mean Absolute Percentage Error (MAPE). It was found that the proposed hybrid method, BiLSTM-GRU with dropout outperformed the other methods in terms of solar irradiance predicting accuracy. The analysis presented the best RMSE of 1.55 and MAE of 1.13 for BiLSTM and RMSE of 1.40 and MAE of 0.91 for BiLSTM-GRU architecture using hyperparameter tuning. The comparison results show that the prediction accuracy is improved by tuning the hyperparameters.
DSCA: A Dual Semantic Correlation Alignment Method for domain adaptation object detection

Authors:
raw_author_name: Yinsai Guo
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, China
raw_author_name: Hang Yu
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, China
raw_author_name: Shaorong Xie
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, China
raw_author_name: Liyan Ma
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, China
raw_author_name: Xinzhi Cao
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, China
raw_author_name: Xiangfeng Luo
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, China

Abstract: In self-driving cars, adverse weather (e.g., fog, rain, snow, and cloud) or occlusion scenarios result in domain shift being unavoidable in object detection. Researchers have recently proposed Domain Adaptive Object Detection (DAOD), i.e., aligning the source and target domains at the image and instance levels distribution by utilizing the Unsupervised Domain Adaptation (UDA) method. However, the semantic correlation information is ignored leading to the effect of aligning not good, and low detection accuracy of objects in adverse weather or occlusion scenarios. Here, we propose a Dual Semantic Correlation Alignment (DSCA) method for DAOD to address the problem. The core idea behind DSCA is to make full use of semantic correlation information including context correlation semantic information and class correlation semantic information to align object semantic information in source and target domains, which supplement and enhance the missing information for target domains. It consists of a two-level semantic alignment: (1) context correlation semantic alignment is developed to obtain the context correlation semantic information of the object to align context semantic information at the image level; (2) class correlation semantic alignment is proposed to obtain the class correlation semantic information of the object to align class semantic information at the instance level. The two-level semantic alignment can effectively decrease negative transfer and complete object information to improve the detection accuracy of objects in different domains. Experiments on four challenging benchmarks show that our proposed DSCA method outperforms state-of-the-art DAOD methods.