The increasing incidence of cancer has spurred research across various fields. The digitization of tissue samples stained with hematoxylin-eosin (H&E) enables the acquisition of histological images, which are used to develop computational systems to support pathologists in cancer diagnosis. However, the limited availability of public images has been a well-documented obstacle to advancing these systems. This work presents a systematic review of published studies on the preprocessing of H&E images, focusing on identifying public databases. Through this review, we mapped the countries of origin of these images, the most common lesions, preprocessing techniques, and evaluation metrics. This study provides valuable insights for future research in Biomedicine and Computer Science.
Android malware has emerged as a major threat to mobile device security. While machine learning techniques have proven effective in detecting malicious applications, they remain vulnerable to attacks based on adversarial examples. This study investigates the robustness of five widely-used classifiers (Random Forest, Logistic Regression, Support Vector Machines, Naïve Bayes, and K-Nearest Neighbors) against adversarial machine learning in malware classification. Through systematic evaluation using a gradient-based perturbation method, the analysis reveals critical vulnerabilities where adversarial samples cause misclassifications, particularly converting malware into benign classifications. Random Forest and K-Nearest Neighbors exhibited greater robustness compared to other classifiers, but their accuracy still degraded under high-severity attacks. Linear models exhibit more severe degradation, with Logistic Regression showing consistent vulnerability to adversarial perturbations across all malware families. The results demonstrate that current detection systems can be dangerously deceived by adversarial examples, potentially allowing malicious applications to evade detection.
Mental health problems are becoming more common. Unfortunately, there are still not enough health specialists able to assist them. As a way to provide accessible and immediate support to those in need, digital interventions are receiving increased attention. Conversational agents based on large language models (LLMs) are increasingly used in mental health support. To help psychiatric patients, we have developed a therapeutic dialogue system called Terabot. Earlier experiments revealed several situations that paused the natural flow of the conversation with the patients. We created a new LLM-based dialogue system with an eye tracker as an additional input signal. We propose a feedback loop where the eye tracker gives the patient’s real-time gaze data into the dialogue system. Thanks to this, the patient can receive a response from Terabot, which is more suitable for the current dialogue situation. The dialogue agent’s responses can be improved, resulting in a more human-like flow of conversation.
Robot-human interaction is rapidly increasing in work environments and patient/elderly care centers. Human-robot collaboration is becoming increasingly prevalent in modern production facilities. In addition, robotic systems have been actively used to enable patients and people needing assistance to continue their daily lives. This study aims to determine the estimated position of the human's hand so that the mobile manipulator carrying or grasping an object can deliver the object from hand to hand to the designated human. For this, the humans in the environment and their body poses are determined using deep learning on the image taken live from the depth sensor camera on the mobile manipulator. Then, the hand positions of the humans are determined using the determined poses. The distances between the mobile manipulator and each detected person are calculated using the signals received from the depth sensor camera on the mobile manipulator. Thus, the mobile manipulator can create a position vector for each detected human. Using these position vectors, it will be possible for the mobile robot to deliver the object it carries to the desired human more quickly by using different shortest-path algorithms. Moreover, it will be easier for robots that use the proposed approach to assist humans in work environments or patient/elderly care centers.
With the rapid growth of video streaming services, particularly under the advent of 5G, ensuring optimal enduser service quality has become a pressing challenge for mobile network operators (MNOs). The increasing demand for high-bandwidth, low-latency video applications, such as live streaming and ultra-high-definition content, requires new approaches to detect and mitigate service quality degradation. This paper investigates the use of machine learning models for estimating end-user service quality in 5G networks for adaptive video streaming performance. We analyze real-world data collected from a 5G standalone network, combining in-network metrics, client-side data, and radio environment measures. A key aspect of this study is comparing the effectiveness of the currently available mobile core network data from the User Plane Function with high-resolution (1-2 second) client-side data. The client-side measurements were obtained by the GoDASH framework and was used for proposing improvements on core network user plane probing function. Although client side measured service quality measurement are the ideal, our results demonstrate that core network transport probing data can be used for estimating the end user service quality or detecting video stall but the probing metrics should be synchronized with the video chunks. Including radio environment data in the input dataset of the model only slightly improves the performance. Higher resolution core network data improves the model accuracy but they would be needed for covering mobility scenarios and changing traffic conditions. This research provides actionable insights for MNOs to enhance network analytics and address potential service quality issues in 5G environments.
