The Scheduling of Meetings of multiple users is a real world problem that was studied intensively in recent years. The present paper proposes a realistic model for representing and solving meetings scheduling problems (MSPs) and the use of constraints optimization algorithms to solve MSPs. A central component of the proposed model of MSPs is a mechanism to balance the trade-off between competitive and cooperative environments. Agents solve the problem by balancing the global (e.g., cooperative) optimum against typical self-interests of users. These are represented in the model by the quality of the resulting personal schedule. The experimental evaluation of the features of the proposed model uses a Local Search Algorithm which produces a high quality solution in a reasonable time.
No abstract is provided for this article.
A decision tree is a predictive model that recursively partitions the covariate’s space into subspaces such that each subspace constitutes a basis for a different prediction function. Decision trees can be used for various learning tasks including classification, regression and survival analysis. Due to their unique benefits, decision trees have become one of the most powerful and popular approaches in data science. Decision forest aims to improve the predictive performance of a single decision tree by training multiple trees and combining their predictions. This paper provides an introduction to the subject by explaining how a decision forest can be created and when it is most valuable. In addition, we are reviewing some popular methods for generating the forest, fusion the individual trees’ outputs and thinning large decision forests.
A concise overview of the fundamentals and the main types of machine ensembles serves to propose a structured perspective for the papers that are included in this special session. The subsequent brief discussion of the works, emphasizing their principal contributions, permits an extraction of a series of suggestions for further research in the fruitful area of ensemble learning.
The WSDM Cup 2017 Triple scoring challenge is aimed at calculating and assigning relevance scores for triples from type-like relations. Such scores are a fundamental ingredient for ranking results in entity search. In this paper, we propose a method that uses neural embedding techniques to accurately calculate an entity score for a triple based on its nearest neighbor. We strive to develop a new latent semantic model with a deep structure that captures the semantic and syntactic relations between words. Our method has been ranked among the top performers with accuracy - 0.74, average score difference - 1.74, and average Kendall's Tau - 0.35.
No abstract is provided for this article.
No abstract is provided for this article.
An intelligent model for campaign management was developed as a collaborative research effort between Deutsche Telekom and Ben-Gurion University. The model segments and filters potential customers for solicitation according to a novel algorithm. However, a mathematical model, however cleverly designed, doesn’t encompass the human knowledge, experience and specific requests of an expert campaign manager. Therefore campaign managers might be reluctant to use the model as it stands. The 'Interactive Audience Selection' solution proposed here attempts to bridge the gap between the campaign managers and the intelligent model by steering to combine and benefit from both the expert knowledge of the managers as well as the model’s mathematical capabilities. The suggested information system tool enables the campaign manager to view the models' choice of potential customers for solicitation, understand it and improve it for the next audience sample. We propose that the transparency of the models' logic and the control the user has over the model's output will lead to the successful execution of a profitable campaign, without neglecting customer value.
Children with Autism Spectrum Disorder (ASD) often face unique risks during sports activities due to challenges such as motor coordination difficulties, sensory sensitivities, and communication impairments. This paper provides a comprehensive review of the use of wearable sensor technologies to enhance the safety and participation of children with ASD in sports. Utilizing a systematic approach, we analyze 144 papers identified through advanced search methodology. Our findings reveal that wearable sensors can monitor physiological signals like heart rate variability and electrodermal activity and biomechanical signals such as movement patterns to detect early signs of distress, anxiety, or potential injury. The integration of these technologies into sports settings for children with ASD presents significant potential for improving safety, reducing participation barriers, and enhancing overall well-being. Key findings indicate that while the application of wearable sensors in this context is still emerging, early results are promising. However, challenges remain regarding device usability, data privacy, and the need for further research to validate the effectiveness of these technologies in real-world sports environments. This review highlights the importance of interdisciplinary collaboration among researchers, technology developers, educators, and caregivers to develop and implement wearable sensor solutions that are tailored to the unique needs of children with ASD, thereby promoting safer and more inclusive sports participation.
Based on our experience, and supported by the literature in the field, we sincerely doubt that the surgical approach to sperm retrieval as described by the authors resulted in such a high SSR that was reported.The authors performed only a small equatorial horizontal incision on the albuginea, with 8-10 tiny scissors biopsies from the extruded parenchyma, eventually followed by contralateral multiple conventional superficial biopsies: due to the high heterogeneity of testicular spermatogenesis in patients with NOA, in most cases, the testes hide only isolated, focal areas of residual spermatogenesis that could be hardly identified with superficial biopsies.Even the more invasive approach used by the authors by means of multiple testicular biopsies may fail to retrieve sperm in patients with NOA: however, such an approach might result in the loss of significant amounts of testicular tissue, with risks of testicular devascularization and subsequent testicular hypo-atrophy (Schlegel and Su, 1997) and/or hypogonadism.Management of NOA patients cannot prescind from the use of the best surgical treatment available: given the existent Level 1 evidence in well-performed meta-analyses (Deruyver et al., 2014;Bernie et al., 2015) as previously highlighted (Esteves et al., 2020) is it undoubtful that microdissection testicular sperm extraction provides optimized sperm retrieval results, especially in the hands of well-trained and experienced urologists, since it enables the identification at high magnification of 'dilated' tubules with preserved spermatogenesis, at least in 90% of cases (Caroppo et al., 2019).When a less-effective surgical treatment is used, even sophisticated machine-learning models cannot add to the counselling of patients with NOA.
No abstract is provided for this article.
Although many studies have examined adversarial examples in the real world, most of them relied on 2D photos of the attack scene; thus, the attacks proposed cannot address realistic environments with 3D objects or varied conditions. Studies that use 3D objects are limited, and in many cases, the real-world evaluation process is not replicable by other researchers, preventing others from reproducing the results. In this study, we present a framework that crafts an adversarial patch for an existing real-world scene. Our approach uses a 3D digital approximation of the scene as a simulation of the real world. With the ability to add and manipulate any element in the digital scene, our framework enables the attacker to improve the patch's robustness in real-world settings. We use the framework to create a patch for an everyday scene and evaluate its performance using a novel evaluation process that ensures that our results are reproducible in both the digital space and the real world. Our evaluation results show that the framework can generate adversarial patches that are robust to different settings in the real world.
Autonomous systems are usually equipped with sensors to sense the surrounding environment. The sensor readings are interpreted into beliefs upon which the robot decides how to act. Unfortunately, sensors are susceptible to faults. These faults might lead to task failure. Detecting these faults and diagnosing a fault's origin is an important task that should be performed quickly online. While other methods require a high fidelity model that describes the behavior of each component, we present a method that uses a structural model to successfully detect and diagnose sensor faults online. We experiment our method with a laboratory robot Robotican1 and a flight simulator FlightGear. We show that our method outperforms previous methods in terms of fault detection and provides an accurate diagnosis.
