Reviewerly/exHarmony huggingface dataset:
Mitigating Membership Inference in Deep Survival Analyses with Differential Privacy

Deep neural networks have been increasingly integrated in healthcare applications to enable accurate predicative analyses. Sharing trained deep models not only facilitates knowledge integration in collaborative research efforts but also enables equitable access to computational intelligence. However, recent studies have shown that an adversary may leverage a shared model to learn the participation of a target individual in the training set. In this work, we investigate privacy-protecting model sharing for survival studies. Specifically, we pose three research questions. (1) Do deep survival models leak membership information? (2) How effective is differential privacy in defending against membership inference in deep survival analyses? (3) Are there other effects of differential privacy on deep survival analyses? Our study assesses the membership leakage in emerging deep survival models and develops differentially private training procedures to provide rigorous privacy protection. The experimental results show that deep survival models leak membership information and our approach effectively reduces membership inference risks. The results also show that differential privacy introduces a limited performance loss, and may improve the model robustness in the presence of noisy data, compared to non-private models.
Beyond Labels: Visual Representations for Bone Marrow Cell Morphology Recognition

Analyzing and inspecting bone marrow cell cytomorphology is a critical but highly complex and time-consuming component of hematopathology diagnosis. Recent advancements in artificial intelligence have paved the way for the application of deep learning algorithms to complex medical tasks. Nevertheless, there are many challenges in applying effective learning algorithms to medical image analysis, such as the lack of sufficient and reliably annotated training datasets and the highly class-imbalanced nature of most medical data. Here, we improve on the state-of-the-art methodologies of bone marrow cell recognition by deviating from sole reliance on labeled data and leveraging self-supervision in training our learning models. We investigate our approach’s effectiveness in identifying bone marrow cell types. Our experiments demonstrate significant performance improvements in conducting different bone marrow cell recognition tasks compared to the current state-of-the-art methodologies <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Globally Time-Optimal Path Planning for Unmanned Underwater Vehicles in Three-Dimensional Current Fields Using Hamilton-Jacobi Partial Differential Equations

A new approach to underwater vehicle path planning, based on solving the Hamilton-Jacobi partial differential equation characterizing a vehicle's minimum travel time, is presented. The travel-time equation is derived as a natural consequence of the dynamic programming principle. In addition, the underlying Hamilton-Jacobi equation is shown to govern the geometric motion by which the reachability front evolves, which results in a straightforward derivation of the level set approach [14]. The viscosity solution of the travel-time equation is computed using the fast sweeping method with the Lax-Friedrichs flux; the resulting algorithm is computationally efficient and straightforward to implement. The method is validated through several examples for which optimal trajectories are derived using the calculus of variations. Additional examples in two and three spatial dimensions are also included.
Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region

CCTVs are commonly used for traffic monitoring and accident detection, but their images suffer from severe perspective distortion causing object size reduction with distance. This issue is exacerbated in tunnel CCTVs, positioned low due to space constraints, leading to challenging object detection, especially for distant small objects, due to perspective effects. To address this, this study proposes a solution involving a region of interest setup and an inverse perspective transformation technique. The transformed images, achieved through this technique, enlarge distant objects, maintaining object detection performance and appearance velocity across distances. To validate this, artificial CCTV images were generated in a virtual tunnel environment, creating original and transformed image datasets under identical conditions. Comparisons were made between the appearance velocity and object size of individual vehicles and for deep learning model performance with multiple moving vehicles. The evaluation was conducted across four distance intervals (50 m to 200 m) from the tunnel CCTV location. The results reveal that the model using original images experiences a significant decline in object detection performance beyond 100 m, while the transformed image-based model maintains a consistent performance up to the distance of 200 m.
Enhancing Code Language Models for Program Repair by Curricular Fine-tuning Framework

Automated program repair (APR) is a key technique for enhancing software maintenance productivity by fixing buggy code automatically. Recently, large code language models (CLMs) have exhibited impressive capabilities in code generation. However, for complex programming tasks, especially program repair, the success rate of CLMs is still low. One of the reasons is that CLMs are typically developed for general purpose and their potential for APR applications has yet to be fully explored. In this paper, we propose APRFiT, a general curricular fine-tuning framework that improves the success rate of CLMs for APR. Firstly, APRFiT generates syntactically diverse but semantically equivalent bug-fixing programs via code augmentation operators to enrich the diversity of bug-fixing dataset automatically. Secondly, APRFiT designs a curriculum learning-based mechanism to help CLMs develop deep understanding of program semantics from these augmented bug-fixing code variants and improve the effectiveness of fine-tuning for APR tasks. We implement APRFiT on different CLMs and evaluate them on Bugs2Fix small and medium datasets. The extensive experiments demonstrate that, the existing CLMs implemented with APRFiT substantially outperform original models and generate 2.5 to 14.5 percent more correct patches than baselines both effectively and efficiently.
Identifying Defect-Inducing Changes in Visual Code

Defects, or bugs, often form during software development. Identifying the root cause of defects is essential to improve code quality, evaluate testing methods, and support defect prediction. Examples of defect-inducing changes can be found using the SZZ algorithm to trace the textual history of defect-fixing changes back to the defect-inducing changes that they fix in line-based code. The line-based approach of the SZZ method is ineffective for visual code that represents source code graphically rather than textually. In this paper we adapt SZZ for visual code and present the SZZ Visual Code (SZZ-VC) algorithm, that finds changes in visual code based on the differences of graphical elements rather than differences of lines to detect defect-inducing changes. We validated the algorithm for an industry-made AAA video game and 20 music visual programming defects across 12 open source projects. Our results show that SZZ-VC is feasible for detecting defects in visual code for 3 different visual programming languages.
The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework

Over the years, deep reinforcement learning (DRL) has shown great potential in mapless autonomous robot navigation and path planning. These DRL methods rely on robots equipped with different light detection and range (LiDAR) sensors with a wide field of view (FOV) configuration to perceive their environment. These types of LiDAR sensors are expensive and are not suitable for small-scale applications. In this paper, we address the performance effect of the LiDAR sensor configuration in DRL models. Our focus is on avoiding static obstacles ahead. We propose a novel approach that determines an initial FOV by calculating an angle of view using the sensor's width and the minimum safe distance required between the robot and the obstacle. The beams returned within the FOV, the robot's velocities, the robot's orientation to the goal point, and the distance to the goal point are used as the input state to generate new velocity values as the output action of the DRL. The cost function of collision avoidance and path planning is defined as the reward of the DRL model. To verify the performance of the proposed method, we adjusted the proposed FOV by ±10° giving a narrower and wider FOV. These new FOVs are trained to obtain collision avoidance and path planning DRL models to validate the proposed method. Our experimental setup shows that the LiDAR configuration with the computed angle of view as its FOV performs best with a success rate of 98% and a lower time complexity of 0.25 m/s. Additionally, using a Husky Robot, we demonstrate the model's good performance and applicability in the real world.
Natural Language Processing Techniques in Digital Diagnostic System for Lung Cancer Detection

Digital diagnostic system (DDS) for lung cancer (LC) discovery can advantage from the utilize of Natural Language Processing (NLP) procedures. In arrange to create organized data that will be utilized for assurance, NLP can offer help remove information from unstructured data sources, such as electronic prosperity records and helpful reports. In this paper, advanced symptomatic devices for LC location are talked about that can increment the exactness and speed of analyze whereas diminishing the workload of specialists and therapeutic experts by utilizing NLP methods. Free-text information, such as persistent depictions of their side effects, can be processed utilizing NLP calculations to supply organized information that can be analyzed. The data from therapeutic reports, such as radiology reports, can be extricated utilizing NLP and changed into organized information for think about. In expansion to information preparing, NLP methods can too be utilized for data recovery and choice bolster. NLP can be utilized to look huge databases of restorative information, such as PubMed and Medline, to recover relevant information for conclusion. NLP can moreover be utilized to supply choice back to specialists, by displaying pertinent data and cases from the case base when diagnosing patients with LC. NLP procedures have the potential to revolutionize the way computerized demonstrative frameworks for LC location work. These frameworks can decrease the workload of specialists and restorative experts while increasing the exactness and speed of diagnostics by utilizing NLP.
Optimal Configuration of Multi-Task Learning for Autonomous Driving

For autonomous driving, it is imperative to perform various high-computation image recognition tasks with high accuracy, utilizing diverse sensors to perceive the surrounding environment. Specifically, cameras are used to perform lane detection, object detection, and segmentation, and, in the absence of lidar, tasks extend to inferring 3D information through depth estimation, 3D object detection, 3D reconstruction, and SLAM. However, accurately processing all these image recognition operations in real-time for autonomous driving under constrained hardware conditions is practically unfeasible. In this study, considering the characteristics of image recognition tasks performed by these sensors and the given hardware conditions, we investigated MTL (multi-task learning), which enables parallel execution of various image recognition tasks to maximize their processing speed, accuracy, and memory efficiency. Particularly, this study analyzes the combinations of image recognition tasks for autonomous driving and proposes the MDO (multi-task decision and optimization) algorithm, consisting of three steps, as a means for optimization. In the initial step, a MTS (multi-task set) is selected to minimize overall latency while meeting minimum accuracy requirements. Subsequently, additional training of the shared backbone and individual subnets is conducted to enhance accuracy with the predefined MTS. Finally, both the shared backbone and each subnet undergo compression while maintaining the already secured accuracy and latency performance. The experimental results indicate that integrated accuracy performance is critically important in the configuration and optimization of MTL, and this integrated accuracy is determined by the ITC (inter-task correlation). The MDO algorithm was designed to consider these characteristics and construct multi-task sets with tasks that exhibit high ITC. Furthermore, the implementation of the proposed MDO algorithm, coupled with additional SSL (semi-supervised learning) based training, resulted in a significant performance enhancement. This advancement manifested as approximately a 12% increase in object detection mAP performance, a 15% improvement in lane detection accuracy, and a 27% reduction in latency, surpassing the results of previous three-task learning techniques like YOLOP and HybridNet.
A Novel CNN-Based FSK Demodulator With Efficient FPGA Implementation

Nowadays, neural networks have become a new approach to achieving the destinations of researchers due to their accuracy. Neural networks are widely used in machine vision, image classification, and sound detection, but in the field of signal processing, it still has no content of the extent in the field of image processing. This is not due to the inability of deep learning to meet the needs of signal processing, but because that is an ultra-modern solution. This paper proposed a novel CNN-Based FSK demodulator (CFD) that uses a compact convolutional neural network as a special method to demodulate the FSK signal. The proposed CNN network has a minimal computational load and is optimized in terms of hardware implementation complexity. The designed CNN network has three layers: a convolutional layer, a Max-Pooling layer, and a fully connected layer. The CFD is designed to demodulate an FSK signal of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$20\pm 1\text{MHz}$</tex> with a bit rate of 1Mbps. The BER of lower than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–6</sup> has been achieved at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{E}_{\mathrm{b}}/\mathrm{N}_{0}=15\text{dB}$</tex> . The resource utilization of implemented proposed demodulator on FPGA only shows 970 and 751, for LUT and FF respectively, which is minimal and indicates the efficiency of our method.
Deep Wavelet Convolutional Neural Networks for Multimodal Human Activity Recognition Using Wearable Inertial Sensors

Recent advances in wearable systems have made inertial sensors, such as accelerometers and gyroscopes, compact, lightweight, multimodal, low-cost, and highly accurate. Wearable inertial sensor-based multimodal human activity recognition (HAR) methods utilize the rich sensing data from embedded multimodal sensors to infer human activities. However, existing HAR approaches either rely on domain knowledge or fail to address the time-frequency dependencies of multimodal sensor signals. In this paper, we propose a novel method called deep wavelet convolutional neural networks (DWCNN) designed to learn features from the time-frequency domain and improve accuracy for multimodal HAR. DWCNN introduces a framework that combines continuous wavelet transforms (CWT) with enhanced deep convolutional neural networks (DCNN) to capture the dependencies of sensing signals in the time-frequency domain, thereby enhancing the feature representation ability for multiple wearable inertial sensor-based HAR tasks. Within the CWT, we further propose an algorithm to estimate the wavelet scale parameter. This helps enhance the performance of CWT when computing the time-frequency representation of the input signals. The output of the CWT then serves as input for the proposed DCNN, which consists of residual blocks for extracting features from different modalities and attention blocks for fusing these features of multimodal signals. We conducted extensive experiments on five benchmark HAR datasets: WISDM, UCI-HAR, Heterogeneous, PAMAP2, and UniMiB SHAR. The experimental results demonstrate the superior performance of the proposed model over existing competitors.
Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses. This study reshapes the parameters of blood cell classification, harnessing the capabilities of AI and broadening the scope from 5 to 11 specific blood cell categories with the challenging 11-class PBC dataset. This transformation facilitates a more profound exploration of blood cell diversity, surpassing prior constraints in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking “Naturalize” augmentation technique produces remarkable results. The 2K-PBC dataset generated with “Naturalize” boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The “Naturalize” technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.
Information Technology Innovativeness and Data-Breach Risk: A Longitudinal Study

The adoption of new Information Technology (IT) innovations has led to increased uncertainty among employees, a greater demand for security measures, and more entry points for cyber-attacks, which all increase the risk of data breaches for firms. Despite the prevalence of discussions around this issue, there has been a lack of empirical research examining the data breach risk associated with IT innovations. To address this gap, we have developed arguments based on an organizational learning theoretical framework that explains how IT innovativeness can exacerbate data breach risk. Through our analysis of a sample of data breaches that occurred between 2013 and 2021, we have discovered that there is a positive association between firm IT innovativeness and the risk of data breaches. We also find that the effects of IT innovativeness can vary under certain conditions. For example, we find that the positive relationship between IT innovativeness and data breach risk is mitigated when managers possess IT expertise or when firms have established extensive board connections with cybersecurity managers. Moreover, we find that the relationship between IT innovativeness and data breach risk is amplified in complex environments but not in dynamic or munificent ones. This study takes the lead in advancing the theoretical understanding and empirical validation of security-related risks associated with IT innovations. Moreover, our findings serve as a timely reminder for research and practice to carefully consider the implications of introducing novel technologies into firms and the potential dark side consequences that may arise. Additionally, this study underscores the importance of understanding organizational learning in risk assessment and change management, as well as the critical role of contextual factors in moderating the unintended security-related consequences linked to IT innovations.
Examining the Differential Effectiveness of Fear Appeals in Information Security Management Using Two-Stage Meta-Analysis

Most of the information security management research involving fear appeals is guided by either protection motivation theory or the extended parallel processing model. Over time, extant research has extended these theories, as well as their derivative theories, in a variety of ways, leading to several theoretical and empirical inconsistencies. The large body of fragmented, and sometimes conflicting, research has muddied the broader understanding of what drives protection- and defensive motivation. We provide guidance to the security discourse by offering the first study in the literature to employ two-stage meta-analytic structural equation modeling (TSSEM), which combines covariance-based structural equation modeling and meta-analysis. Information systems (IS) researchers have traditionally used meta-analysis for structural equation modeling for such purposes—an approach that has several serious statistical flaws. Using 341 systematically selected empirical security articles (representing 383 unique studies) and TSSEM, we pool a large series of five datasets to test six models, from which we examine the effects of constructs and paths in the security fear-appeals literature. We compare and test six versions of models inspired by issues in the broader fear-appeals literature. We confirm the importance of both the threat- and coping-appraisal processes; establish the central role of fear and that it has greater importance than threat; show that efficacy is a stronger predictor of protection motivation than is threat; demonstrate that response costs as currently measured are ineffective but that maladaptive rewards have a strong negative effect on protection motivation and a positive effect on defensive motivation; and provide evidence that dual models of danger control and fear control should be used.