Reviewerly/exHarmony huggingface dataset:
Survey and comparative analysis of machine learning algorithms for breast cancer diagnosis: A comprehensive review

Breast cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. Machine learning algorithms have emerged as powerful tools for analyzing complex medical data and aiding in the diagnosis of breast cancer. This paper provides an overview of the application of machine learning algorithms in breast cancer diagnosis. The findings indicate that machine learning algorithms, such as support vector machines (SVM), random forests, artificial neural networks (ANN), and deep learning models, have been extensively explored for breast cancer diagnosis. These algorithms leverage the vast amounts of available data, including patient demographics, medical history, imaging data (mammography, ultrasound, MRI), and genetic profiles, to identify patterns and make predictions. One of the primary applications of machine learning algorithms in breast cancer diagnosis is the classification of tumors as malignant or benign. By training on labeled datasets, these algorithms can learn to differentiate between cancerous and non-cancerous cases, thus assisting in accurate tumor diagnosis. Additionally, machine learning algorithms can be used to predict the likelihood of cancer recurrence, which helps guide treatment decisions and post-treatment monitoring. Feature selection and extraction techniques also play a vital role in breast cancer diagnosis using machine learning algorithms. These techniques aim to identify the most relevant features or biomarkers associated with breast cancer, reducing the dimensionality of the data and enhancing the performance of the models. Feature selection algorithms, such as recursive feature elimination and correlation-based feature selection, contribute to the identification of critical indicators for accurate diagnosis. Furthermore, the integration of different data sources and modalities, such as combining clinical data with imaging data or genetic data, has shown promise in improving breast cancer diagnosis accuracy. By fusing multiple types of information, machine learning algorithms can leverage the complementary nature of these data sources to enhance diagnostic capabilities. Despite the advancements made, challenges remain in the field of breast cancer diagnosis using machine learning algorithms. Issues such as data quality, interpretability of models, and generalizability to diverse populations need to be addressed to ensure the reliable and equitable application of these algorithms in clinical practice.
Sensor Equivariance by LiDAR Projection Images

In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution and field of view. To that end, we propose an architecture for processing this data in an instance segmentation framework. We focus specifically on LiDAR as a key sensor modality for machine vision tasks and highly automated driving (HAD). Through an experimental setup in a controlled synthetic environment, we identify a bias on sensor resolution and field of view and demonstrate that our proposed method can reduce said bias for the task of LiDAR instance segmentation. Furthermore, we define our method such that it can be applied to other projection-based sensors, such as cameras. To promote transparency, we make our code and dataset publicly available. This method shows the potential to improve performance and robustness in various machine vision tasks that utilize projection-based sensors.
Automated Design Space Exploration for Resource Allocation in Software-Defined Vehicles

Modern vehicles include an increasing amount of software, e.g., for autonomous driving capabilities, connectivity, and personalized user experience. The capabilities in current vehicles are still mostly provided by multiple separated embedded systems, while the current trend goes toward purely software-defined vehicles (SDV). Traditional distributed electrical/electronic (E/E) architectures have tightly coupled hardware/software, and the computational power is optimized for the included feature set. For SDVs, a centralized E/E architecture utilizing high-performance computers has been proposed. In contrast to individual embedded systems with limited and fixed functionality, combing a large set of individual software components in a single system leads to a high complexity in the proper allocation of resources. Model-based system engineering (MBSE) has been promoted in the automotive industry to handle complex system design. However, existing MBSE approaches focus mainly on traditional E/E architectures. In this work, we propose an automated and model-based approach that can address the resource allocation problem in SDVs. Users can formally describe the vehicle’s resources, safety/non-safety requirements, and optimization objectives based on existing software engineering standards. The proposed method is not restricted to specific system models, requirements, or optimization goals and is, therefore, compatible with other E/E architectures. By introducing a model-independent transformation from the model information to solver-independent optimization formulas, the resource allocation problem can be solved automatically by a wide range of state-of-the-art solvers. We demonstrate the applicability of this approach in a SDV scenario with a high-performance computer and multiple applications.
Idiolect: A Reconfigurable Voice Coding Assistant

This paper presents Idiolect, an open source <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> IDE plugin for voice coding and a novel approach to building bots that allows for users to define custom commands on-the-fly. Unlike traditional chatbots, Idiolect does not pretend to be an omniscient virtual assistant but rather a reconfigurable voice programming system that empowers users to create their own commands and actions dynamically, without rebuilding or restarting the application. We offer an experience report describing the tool itself, illustrate some example use cases, and reflect on several lessons learned during the tool’s development.
Domain Adaptation in LiDAR Semantic Segmentation via Hybrid Learning with Alternating Skip Connections

In this paper we address the challenging problem of domain adaptation in LiDAR semantic segmentation. We consider the setting where we have a fully-labeled data set from source domain and a target domain with a few labeled and many unlabeled examples. We propose a domain adaptation framework that mitigates the issue of domain shift and produces appealing performance on the target domain. To this end, we develop a GAN-based image-to-image translation engine that has generators with alternating connections, and couple it with a LiDAR semantic segmentation network. Our framework is hybrid in nature in the sense that our model learning is composed of self-supervision, semi-supervision and unsupervised learning. Extensive experiments on benchmark LiDAR semantic segmentation data sets demonstrate that our method achieves superior performance in comparison to state-of-the-art baselines and prior arts.
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework

The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications. Especially for detecting humans, which is often a fundamental task in safety-critical applications, it is vital to prevent errors. To address this challenge, we propose a self-monitoring framework that allows for the perception system to perform plausibility checks at runtime. We show that by incorporating an additional component for detecting human body parts, we are able to significantly reduce the number of missed human detections by factors of up to 9 when compared to a baseline setup, which was trained only on holistic person objects. Additionally, we found that training a model jointly on humans and their body parts leads to a substantial reduction in false positive detections by up to 50% compared to training on humans alone. We performed comprehensive experiments on the publicly available datasets DensePose and Pascal VOC in order to demonstrate the effectiveness of our framework. Code is available at https://github.com/FraunhoferIKS/smf-object-detection.
Domain Adaption Object Detection with Global - Local Contrastive Learning and Co-training Network

With the continuous enrichment and development of technical means, object detection has developed rapidly. However, object detection relies on a plenty of number of labeled datasets, and when applied to unlabeled or less-labeled datasets, the performance suffers greatly due to the effect of domain transfer. To solve this problem, we propose a contrastive learning domain adaptation method, which transfers from the labeled source domain to the unlabeled target domain. In our paper, we first propose a contrastive learning network for global and local matching, where the global contrastive module is used to learn image-level representations globally, while the contrastive learning module for local matching is beneficial for semantic segmentation. Local region representation can be better learned. Then, both domain samples are fed into the co-training network at the same time, so that they can share weights with each other. The design of the two modules promotes global and local consistent representation while improving detection accuracy. Experimental results show that our proposed model framework improves on datasets, and existing methods are more efficient and competitive.
An Ant Colony Approach with Subjective-Objective Weight Optimization for Service Selection

To solve the problem that current service selection algorithms converge slowly and cannot simultaneously consider user requirements as well as service performance, an ant colony approach with subject-objective weight optimization for service selection is proposed. First, we use the subjective and objective weight analysis method to preprocess services and convert each service’s multiple Quality of Service attribute into a normalized value. Then, three optimizations are proposed for the original ant colony optimization algorithm. The initial pheromone concentration is unevenly distributed according to the integrated value, the influencing factors of the algorithm are changed from fixed values to dynamic adaptive adjustment values, the pheromone update rule is improved. Finally, an enhanced ant colony optimization algorithm is applied to service selection. Experimental results show that the proposed method has a faster convergence rate than other referencing algorithms in dealing with the service selection problem, and the selection result satisfies the users better.
BRCNN: Chinese medical question answer selection based on BERT

Nowadays, automatic question answer systems are beneficial to improve the efficiency of computer-aided diagnosis. It is difficult for current methods to select best matched answers for patients’ questions since they are hard to dynamically generate word vectors or to provide rich sentence representations. This paper presents an BRCNN model that combines BERT, bidirectional GRU(BiGRU) and multi-scale CNNs for the selection of Chinese medical question answer. In detail, the proposed model works in four steps. First, we input the question or answer into BERT to generate dynamic word vectors. Next, we input word vectors into BiGRU to obtain bidirectional sequential information. And then, we input the bidirectional information into multi-scale CNNs to capture features carried by different n-grams. Finally, we measure the similarity between the question and the answers for selecting a best matched answer by using cosine similarity. In particular, we combine the advantages of different models to obtain more accurate and rich representations. We conduct some experiments to verify the proposed model by comparing with a series of models on cMedQA V1.0 dataset. The results demonstrate that our proposed model achieves 10.6% higher accuracy than other models.
Optimization of the Pedestrian and Vehicle Detection Model based on Cloud-Edge Collaboration

With the increase of delay-sensitive applications, it is a trend to deploy deep neural networks on the edge. However, to improve the accuracy of pedestrian and vehicle detection models, various types of detection models such as YOLOv4 have complex structures, resulting in a large number of computing resources required for model training, which in turn makes it challenging to deploy pedestrian and vehicle detection models on resource-constrained edge devices. A common method is channel pruning. On the one hand, the configuration requirements for the running environment are too high to be done by the edge alone. To solve this problem, a pedestrian and vehicle detection framework based on cloud-edge collaboration is proposed to facilitate the deployment of pedestrian and vehicle detection model at the edge. On the other hand, the pruning proportion needs to be set manually and the evaluation criteria are mostly one-sided, which leads to easy to delete channels by mistake. Therefore, an automatic pruning method combining the convolution layer and BN layer is proposed to compress the model size. Experimental results show that the proposed framework enables the pruned model to be deployed on Jetson TX2, and the FPS is improved by 6.72 times. Channel pruning reduces the volume and parameter number to 96.77%, and the computational amount to 81.37%.
Sensitive Region-Based Metamorphic Testing Framework using Explainable AI

Deep Learning (DL) is one of the most popular research topics in machine learning and DL-driven image recognition systems have developed rapidly. Recent research has employed metamorphic testing (MT) to detect misclassified images. Most of them discuss metamorphic relations (MR), with limited attention given to which regions should be transformed. We focus on the fact that there are sensitive regions where even small transformations can easily change the prediction results and propose an MT framework that efficiently tests for regions prone to misclassification by transforming these sensitive regions. Our evaluation demonstrated that the sensitive regions can be specified by Explainable AI (XAI) and our framework effectively detects faults.
ConFormer: Convolutional Transformer Exploiting Spatial and Temporal Information for 3D Human Pose Estimation

Transformer has recently been introduced into 3D human pose estimation by exploiting spatial and temporal information. Though Transformer-based methods have made some considerable progress, they still leave some potential improvements on the table, such as poor locality, ignoring channel adaptability, and computational overhead. In this paper, we propose a spatial-temporal convolutional Transformer architecture, which lifts a long continuous 2D pose sequence to one 3D pose. Specifically, a spatial interaction module is adopted to model spatial relations between body joints within each frame and a temporal interaction module is applied to capture temporal dependencies across frames. Besides, we apply a special kernel attention mechanism in the spatial interaction module to explicitly encode the local and global relations between the body joints. In the temporal domain, the convolutional position embedding and the convolutional projection are utilized to enhance the ability to model local relationships and decrease computational complexity. The evaluation experiments of our method are conducted on two popular 3D human pose estimation datasets, Human3.6M and MPI-INF-3DHP. Results show that our proposed approach achieves better performance.
Optimal selection of resampling methods for imbalanced data with high complexity

Class imbalance is a major problem in classification, wherein the decision boundary is easily biased toward the majority class. A data-level solution (resampling) is one possible solution to this problem. However, several studies have shown that resampling methods can deteriorate the classification performance. This is because of the overgeneralization problem, which occurs when samples produced by the oversampling technique that should be represented in the minority class domain are introduced into the majority-class domain. This study shows that the overgeneralization problem is aggravated in complex data settings and introduces two alternate approaches to mitigate it. The first approach involves incorporating a filtering method into oversampling. The second approach is to apply undersampling. The main objective of this study is to provide guidance on selecting optimal resampling methods in imbalanced and complex datasets to improve classification performance. Simulation studies and real data analyses were performed to compare the resampling results in various scenarios with different complexities, imbalances, and sample sizes. In the case of noncomplex datasets, undersampling was found to be optimal. However, in the case of complex datasets, applying a filtering method to delete misallocated examples was optimal. In conclusion, this study can aid researchers in selecting the optimal method for resampling complex datasets.
Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making

Current research on pedestrian behavior understanding focuses on the dynamics of pedestrians and often implicitly assumes that their perceptual abilities are omnidirectional. In practice, human visual system has a number of limitations, such as restricted field of view (FoV) and sensing range, which consequently affect decision-making and the overall behavior of the pedestrians. By explicitly modeling pedestrian perception, we can better understand its effect on their decision-making. To this end, we propose an agent-based pedestrian behavior model Intend-Wait-Perceive-Cross with three novel elements: field of vision, working memory, and scanning strategy, all motivated by findings from behavioral literature. Through extensive experimentation we investigate the effects of perceptual limitations on crossing decisions and demonstrate how they contribute to detectable changes in pedestrian behaviors, e.g. increased wait times and chance of making riskier decisions.
Denoising Sequence Embeddings via Contrastive Learning for Micro-video Recommendation

In recent years, micro-video apps such as TikTok, Kwai, etc. have become widely popular, but recommendation models dedicated for micro-videos are relatively few in number. The paper analyses the characteristics of micro-video apps and sequences of interactions between users and micro-videos, and then presents a model CLASRec for micro-video recommendation. CLASRec follows the typical method of sequential recommendation, i.e., learns embeddings of user interests from historical interaction sequences. On this basis, for an interaction sequence of the same user, CLASRec adopts contrastive learning to reduce the noise implicit in interaction embeddings. In particular, two contrastive encoders are presented to align the embeddings of a pair of semantically-equivalent sequences. The extensive experiments are conducted on two real-world datasets. The experimental results show that CLASRec outperforms existing multiple models and achieves significant performance improvements in terms of Recall and NDCG.
Intent-based Service Management to Improve Resource Design Efficiency for Cloud Services

As cloud services have expanded, an increasing number of service providers are implementing various kinds of services and functions such as web services and machine learning in the cloud environment.To provide the cloud service promptly and improve customer satisfaction, a cloud service provider needs to efficiently design the resources in accordance with the service requirements.This article presents an intent-based service management framework that enables the needed cloud resources to be derived in accordance with the service provider's service requirements, cloud environmental conditions, and operation policies.
Firmware Integrity Protection: A Survey

Over the last years, firmware integrity protection has significantly been addressed in various contexts such as internet of things, vehicles, wireless sensor networks and other systems. However, due to variety of studied systems, the proposed approaches often make divergent and sometimes even conflicting security assumptions. In this regard, we propose in this article a complete survey of the most relevant approaches addressing the firmware integrity protection regardless the considered system or field of application. We first organize the approaches in three main categories, depending on the protection type: 1. secure update, 2. attestation, and 3. secure boot. We then propose two new taxonomies, the first one concerns secure update mechanisms and the second one considers secure boot. Moreover, we extend the scope of an existing taxonomy for attestation mechanisms by studying new approaches and discussing limitations. Finally, we identify open research challenges and then give suggestions and guidelines on how to address them.
Osteosarcoma detection in histopathology images using ensemble machine learning techniques

Osteosarcoma is a bone malignancy that severely affects the long bones of a human arm or leg. Histopathological analysis with ML (Machine Learning) algorithm offers a gainful way to examine the difference in the osteosarcoma texture. This work presents a new ensemble learning (EL) model with severity analysis in classifying bone tumor (cancer). Initially, pre-processing includes noise removal with Weighted Bilateral (WB) filtering, image quality enhancement using Parabolic Balance Contrast Enhancement (PBCE), and unwanted regions are discarded using erosion and dilation operations. The SIKC (Superpixel Improved K-means Clustering) approach characterizes the segmentation process. The extraction of valuable information is done with color and texture features, namely RGB histogram and Spatial Gray Level Dependence Matrix (Spatial GLDM). Next, the appropriate features are selected using Max_Relevance Min_Redundancy (MRMR) technique. Finally, classification is performed using EL models such as Kernel-Support Vector Machine (KSVM), Improved ANN with Beetle Swarm Optimization (IANN_BSO), and Kernel Extreme Learning Machine Optimized with Chaotic Salp Swarm (OKELM), which categorizes the images into Viable, Non-tumor and Non-viable type tumor along with severity analysis. The proposed study used a PYTHON tool for implementation, and the simulation is done by utilizing publicly accessible histopathological images obtained from TCIA (The Cancer Imaging Archive). The performance of a proposed EL model is evaluated using accuracy, specificity, sensitivity, kappa, F-measure, ROC (AUC) etc. Thus, the proposed EL model achieved the highest results in terms of accuracy (98.505%) compared to other classifiers such as AdaBoost (91.70%), ELM (96.41%), KNN (86.90%), SVM (83.80%), and ANN (90.13%) respectively.
Project Co-Art: Improving Children’s Imagination Through AI-based Human-Computer Co-creation

This research proposes a new AI-based human-computer co-creation system, Co-Art, which helps enhance children’s imagination through drawing. In contrast to existing approaches, we use a more efficient and child-friendly form of voice and brush interaction, where the machine understands the sentence described by the child and visualizes it in an abstract style. The machine’s artwork can inspire the child to create a second version. In a comparative experiment, we found that the aesthetic and richness of the images that the Co-Art-trained children drew showed a significant difference compared to the non-trained group $(p\lt 0.01)$, with a significant increase in imagination and creativity $(p\lt 0.05)$.