Reviewerly/exHarmony huggingface dataset:
Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover

Authors:
raw_author_name: Juhun Lee
raw_affiliation_string: Department of Radiology, The University of Pittsburgh, 200 Lothrop Street, Pittsburgh, PA, 15237, USA
raw_author_name: Robert M. Nishikawa
raw_affiliation_string: Department of Radiology, The University of Pittsburgh, 200 Lothrop Street, Pittsburgh, PA, 15237, USA

Abstract: Abstract Background The wide heterogeneity in the appearance of breast lesions and normal breast structures can confuse computerized detection algorithms. Our purpose was therefore to develop a Lesion Highlighter (LH) that can improve the performance of computer-aided detection algorithms for detecting breast cancer on screening mammograms. Methods We hypothesized that a Cycle-GAN based Lesion Remover (LR) could act as an LH , which can improve the performance of lesion detection algorithms. We used 10,310 screening mammograms from 4,832 women that included 4,942 recalled lesions (BI-RADS 0) and 5,368 normal results (BI-RADS 1). We divided the dataset into Train:Validate:Test folds with the ratios of 0.64:0.16:0.2. We segmented image patches (400 × 400 pixels) from either lesions marked by MQSA radiologists or normal tissue in mammograms. We trained a Cycle-GAN to develop two GANs, where each GAN transferred the style of one image to another. We refer to the GAN transferring the style of a lesion to normal breast tissue as the LR. We then highlighted the lesion by color-fusing the mammogram after applying the LR to its original. Using ResNet18, DenseNet201, EfficientNetV2, and Vision Transformer as backbone architectures, we trained three deep networks for each architecture, one trained on lesion highlighted mammograms (Highlighted), another trained on the original mammograms (Baseline), and Highlighted and Baseline combined (Combined). We conducted ROC analysis for the three versions of each deep network on the test set. Results The Combined version of all networks achieved AUCs ranging from 0.963 to 0.974 for identifying the image with a recalled lesion from a normal breast tissue image, which was statistically improved (p-value &lt; 0.001) over their Baseline versions with AUCs that ranged from 0.914 to 0.967. Conclusions Our results showed that a Cycle-GAN based LR is effective for enhancing lesion conspicuity and this can improve the performance of a detection algorithm.
Elicitation and aggregation of multimodal estimates improve wisdom of crowd effects on ordering tasks

Authors:
raw_author_name: Erin Chiou
raw_affiliation_string: The Polytechnic School, Arizona State University, 7271 E Sonoran Arroyo Mall, Mesa, AZ, 85212, USA

Abstract: We present a wisdom of crowds study where participants are asked to order a small set of images based on the number of dots they contain and then to guess the respective number of dots in each image. We test two input elicitation interfaces-one elicits the two modalities of estimates jointly and the other independently. We show that the latter interface yields higher quality estimates, even though the multimodal estimates tend to be more self-contradictory. The inputs are aggregated via optimization and voting-rule based methods to estimate the true ordering of a larger universal set of images. We demonstrate that the quality of collective estimates from the simpler yet more computationally-efficient voting methods is comparable to that achieved by the more complex optimization model. Lastly, we find that using multiple modalities of estimates from one group yields better collective estimates compared to mixing numerical estimates from one group with the ordinal estimates from a different group.
Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

Authors:
raw_author_name: Zhelong Jiang
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Gang Chen
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Ruixiu Qiao
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Pengcheng Feng
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Yihao Chen
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Junjia Su
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhiyuan Zhao
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Min Jin
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Xu Chen
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhigang Li
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Huaxiang Lu
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China

Abstract: The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.
Application of Deep Learning and Intelligent Sensing Analysis in Smart Home

Authors:
raw_author_name: Yi Lu
raw_affiliation_string: College of Art and Design, Beijing University of Technology, Beijing 100124, China
raw_author_name: Lejia Zhou
raw_affiliation_string: College of Art and Design, Beijing University of Technology, Beijing 100124, China
raw_author_name: Siyu Zha
raw_affiliation_string: The Future Laboratory, Tsinghua University, Beijing 100084, China
raw_author_name: Sen Ge
raw_affiliation_string: College of Art and Design, Beijing University of Technology, Beijing 100124, China

Abstract: Deep learning technology can improve sensing efficiency and has the ability to discover potential patterns in data; the efficiency of user behavior recognition in the field of smart homes has been further improved, making the recognition process more intelligent and humanized. This paper analyzes the optical sensors commonly used in smart homes and their working principles through case studies and explores the technical framework of user behavior recognition based on optical sensors. At the same time, CiteSpace (Basic version 6.2.R6) software is used to visualize and analyze the related literature, elaborate the main research hotspots and evolutionary changes of optical sensor-based smart home user behavior recognition, and summarize the future research trends. Finally, fully utilizing the advantages of cloud computing technology, such as scalability and on-demand services, combining typical life situations and the requirements of smart home users, a smart home data collection and processing technology framework based on elderly fall monitoring scenarios is designed. Based on the comprehensive research results, the application and positive impact of optical sensors in smart home user behavior recognition were analyzed, and inspiration was provided for future smart home user experience research.
TdDS-UNet: top-down deeply supervised U-Net for the delineation of 3D colorectal cancer

Authors:
raw_author_name: Shuchao Chen
raw_affiliation_string: Guilin University of Electronic Technology, Guilin, Guangxi, China, Guilin, 541004, CHINA
raw_author_name: Fei Xie
raw_affiliation_string: Sun Yat-sen University Cancer Center, Guangdong, Guangzhou, Guangdong, 510060, CHINA
raw_author_name: Shanshan Liu
raw_affiliation_string: Guilin University of Electronic Technology, Guilin, Guilin, 541004, CHINA
raw_author_name: Haojiang Li
raw_affiliation_string: Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, People's Republic of China, Guangzhou, Guangdong, 510060, CHINA
raw_author_name: Qiong Gong
raw_affiliation_string: Guilin University of Electronic Technology, Guilin, Guilin, 541004, CHINA
raw_author_name: Guangying Ruan
raw_affiliation_string: Sun Yat-sen University Cancer Center, guangdong, Guangzhou, Guangdong, 510060, CHINA
raw_author_name: Hong-Bo Chen
raw_affiliation_string: Guilin University of Electronic Technology, Guilin, Guilin, 541004, CHINA

Abstract: Automatically delineating colorectal cancers with fuzzy boundaries from 3D images is a challenging task, but the problem of fuzzy boundary delineation in existing deep learning-based methods have not been investigated in depth. Here, an encoder-decoder-based U-shaped network (U-Net) based on top-down deep supervision (TdDS) was designed to accurately and automatically delineate the fuzzy boundaries of colorectal cancer. TdDS refines the semantic targets of the upper and lower stages by mapping ground truths that are more consistent with the stage properties than upsampling deep supervision. This stage-specific approach can guide the model to learn a coarse-to-fine delineation process and improve the delineation accuracy of fuzzy boundaries by gradually shrinking the boundaries. Experimental results showed that TdDS is more customizable and plays a role similar to the attentional mechanism, and it can further improve the capability of the model to delineate colorectal cancer contours. A total of 103, 12, and 29 3D pelvic magnetic resonance imaging volumes were used for training, validation, and testing, respectively. The comparative results indicate that the proposed method exhibits the best comprehensive performance, with a dice similarity coefficient (DSC) of 0.805 ± 0.053 and a hausdorff distance (HD) of 9.28 ± 5.14 voxels. In the delineation performance analysis section also showed that 44.49% of the delineation results are satisfactory and do not require revisions. This study can provide new technical support for the delineation of 3D colorectal cancer. Our method is open source, and the code is available athttps://github.com/odindis/TdDS/tree/main.
Quantum-walk search in motion

Authors:
raw_author_name: Kallol Sen
raw_affiliation_string: ICTP South American Institute for Fundamental Research, IFT-UNESP (1° andar), Rua Dr. Bento Teobaldo Ferraz 271, Bloco 2-Barra Funda, São Paulo, SP, 01140-070, Brazil

Abstract: Abstract In quantum computing, the quantum walk search algorithm is designed for locating fixed marked nodes within a graph. However, when multiple marked nodes exist, the conventional search algorithm lacks the capacity to simultaneously amplify the marked nodes as well as identify the correct chronological ordering between the marked nodes, if any. To address this limitation, we explore a potential extension of the algorithm by introducing additional quantum states to label the marked nodes. The labels resolve the ambiguity of simultaneous amplification of the marked nodes. Additionally, by associating the label states with a chronological ordering, we can extend the algorithm to track a moving particle on a two-dimensional surface. Our algorithm efficiently searches for the trajectory of the particle and is supported by a proposed quantum circuit. This concept holds promise for a range of applications, from real-time object tracking to network management and routing.
A systematic literature review on the applications of recurrent neural networks in code clone research

Authors:
raw_author_name: Fahmi H. Quradaa
raw_affiliation_string: Department of Computer Science, Aden Community College, Aden, Yemen; Department of Computer Science, University of Peshawar, Peshawar, Pakistan
raw_author_name: Sara Shahzad
raw_affiliation_string: Department of Computer Science, University of Peshawar, Peshawar, Pakistan
raw_author_name: Rashad S. Almoqbily
raw_affiliation_string: Department of Computer Science, Aden Community College, Aden, Yemen; Department of Computer Science, University of Peshawar, Peshawar, Pakistan

Abstract: Code clones, referring to code fragments that are either similar or identical and are copied and pasted within software systems, have negative effects on both software quality and maintenance. The objective of this work is to systematically review and analyze recurrent neural network techniques used to detect code clones to shed light on the current techniques and offer valuable knowledge to the research community. Upon applying the review protocol, we have successfully identified 20 primary studies within this field from a total of 2099 studies. A deep investigation of these studies reveals that nine recurrent neural network techniques have been utilized for code clone detection, with a notable preference for LSTM techniques. These techniques have demonstrated their efficacy in detecting both syntactic and semantic clones, often utilizing abstract syntax trees for source code representation. Moreover, we observed that most studies applied evaluation metrics like F-score, precision, and recall. Additionally, these studies frequently utilized datasets extracted from open-source systems coded in Java and C programming languages. Notably, the Graph-LSTM technique exhibited superior performance. PyTorch and TensorFlow emerged as popular tools for implementing RNN models. To advance code clone detection research, further exploration of techniques like parallel LSTM, sentence-level LSTM, and Tree-Structured GRU is imperative. In addition, more research is needed to investigate the capabilities of the recurrent neural network techniques for identifying semantic clones across different programming languages and binary codes. The development of standardized benchmarks for languages like Python, Scratch, and C#, along with cross-language comparisons, is essential. Therefore, the utilization of recurrent neural network techniques for clone identification is a promising area that demands further research.
Say farewell to bland regression reporting: Three forest plot variations for visualizing linear models

Authors:
raw_author_name: Jonathan Fries
raw_affiliation_string: Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria

Abstract: Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.
Highly secured and effective management of app-based online voting system using RSA encryption and decryption

Authors:
raw_author_name: Md Mizanur Rahman
raw_affiliation_string: Chittagong University of Engineering & Technology, CUET, 4349, Chittagong, Bangladesh

Abstract: This study pioneers an innovative approach to fortifying online voting systems, leveraging RSA (Rivest-Shamir-Adleman) encryption and decryption techniques for robust data protection. Through a comprehensive amalgamation of advanced security layers, including MobileFaceNet-driven face verification, device fingerprint matching, and multi-factor authentication, this system engenders a resilient shield against cyber vulnerabilities. By harnessing a Firebase database, user information is securely stored and authenticated, affirming their pivotal role in the democratic process. The symphony of RSA encryption and decryption orchestrates a formidable fortress around data transmission and storage, ensuring impregnable security against digital threats. This paradigm shift in voting technology strives to not only elevate security but also enhance accessibility and convenience, ultimately contributing to the evolution of online voting systems and fostering greater participation rates and reducing associated costs in the digital era.
A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

Authors:
raw_author_name: Zhongliang Deng
raw_affiliation_string: State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Qasim Ali
raw_affiliation_string: Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
raw_author_name: Asadullah Shaikh
raw_affiliation_string: Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
raw_author_name: Mana Saleh Reshan
raw_affiliation_string: Scientific and Engineering Research Centre, Najran University, Najran, 61441, Saudi Arabia
raw_author_name: Adel Sulaiman
raw_affiliation_string: Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
raw_author_name: Hani Alshahrani
raw_affiliation_string: Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia

Abstract: In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care.
CeCR: Cross-entropy contrastive replay for online class-incremental continual learning

Authors:
raw_author_name: Guanglu Sun
raw_affiliation_string: School of Computer Science and Tecnology, Harbin University of Science and Technology, Harbin 150080, China
raw_author_name: Baolun Ji
raw_affiliation_string: School of Computer Science and Tecnology, Harbin University of Science and Technology, Harbin 150080, China
raw_author_name: Minghui Chen
raw_affiliation_string: School of Computer Science and Tecnology, Harbin University of Science and Technology, Harbin 150080, China

Abstract: Aiming at the realization of learning continually from an online data stream, replay-based methods have shown superior potential. The main challenge of replay-based methods is the selection of representative samples which are stored in the buffer and replayed. In this paper, we propose the Cross-entropy Contrastive Replay (CeCR) method in the online class-incremental setting. First, we present the Class-focused Memory Retrieval method that proceeds the class-level sampling without replacement. Second, we put forward the class-mean approximation memory update method that selectively replaces the mistakenly classified training samples with samples of current input batch. In addition, the Cross-entropy Contrastive Loss is proposed to implement the model training with obtaining more solid knowledge to achieve effective learning. Experiments show that the CeCR method has comparable or improved performance in two benchmark datasets in comparison with the state-of-the-art methods.
Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview

Authors:
raw_author_name: Xiaobing Feng
raw_affiliation_string: College of Electrical and Information Engineering, Hunan University, Changsha, China
raw_author_name: Wen Shu
raw_affiliation_string: College of Electrical and Information Engineering, Hunan University, Changsha, China
raw_author_name: Junyu Li
raw_affiliation_string: College of Electrical and Information Engineering, Hunan University, Changsha, China
raw_author_name: Junyao Xu
raw_affiliation_string: Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China

Abstract: Abstract The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.