Call muting-unexpected silences during voice calls due to extended RTP packet loss is a major challenge in high-mobility 5G environments, severely degrading Mean Opinion Score (MOS) and user experience. We propose DRLCQ, a Deep Reinforcement Learning-based framework that dynamically tunes Cell Individual Offset (CIO) in real time to reduce mute events and enhance voice quality. Integrated as an xApp within the O-RAN Near-RT RIC, DRLCQ leverages live network KPIs (e.g., SINR, jitter, packet loss) to learn optimal handover decisions. Evaluated against static and heuristic baselines, DRLCQ achieves over 20% fewer call mute incidents and up to 85% higher MOS, demonstrating a scalable and intelligent solution for AI-native RAN control.

This paper proposes a Generative AI-based Digital Twin (GDT) platform for automated network feature performance evaluation, designed for Beyond 5G (B5G) networks. The platform addresses the inefficiencies of manual evaluation by utilizing a conditional Generative Adversarial Network (cGAN) to simulate network performance based on historical data and new AI/ML features. The Network GDT integrates a novel Digital Twin Augmenting Condition (DTAC) framework, allowing for real-time simulation and performance evaluation of network features. This system significantly reduces the time and cost associated with manual evaluations, improves decision-making, and optimizes Quality of Service (QoS) and Quality of Experience (QoE). The cGAN-based model dynamically generates synthetic data, enabling comprehensive performance insights and proactive AI solution testing under various network scenarios. Experimental results demonstrate high prediction accuracy for congestion use case, validating the robustness of the proposed system. The platform's dual-phase strategy ensures that AI-based solutions are rigorously tested in simulated environments before deployment in real networks, minimizing risks and enhancing stability. This approach provides a scalable and efficient solution for future B5G networks, paving the way for more reliable and optimized wireless communication systems.

The evolution of telecommunications technology is at the cusp of a major transition from 5G to Beyond 5G networks. With this imminent shift, the demand for robust and efficient mitigation solutions has become increasingly vital. AI-based mitigation solutions for solving B5G network problems are directly pushed into the actual field or manually evaluated by the operator first. With Big Data involved in 5G and Beyond, evaluating them manually or without evaluation, pushing them into the real field might have severe consequences in the actual network. There is no intelligent and proactive platform to test the implications of ML models on the networks. In this paper, we propose a pioneering approach that involves the development of an AI-based 5G network clone to serve as a performance evaluation ground for AI-based mitigation solutions tailored for B5G networks. Our methodology outlines the initial phase of evaluating these mitigation solutions within the simulated environment of the AI-based 5G network clone, followed by their subsequent deployment in real-world network infrastructures. This strategy aims to ascertain the efficacy, reliability, and adaptability of the proposed solutions before their integration into the next-generation B5G networks.

This research paper presents a novel framework designed to automate the provisioning of ML services, intelligently tailoring the ML package based on various factors such as service profiles, regional resource usage patterns, operator-defined KPIs, and current ML resource utilization in the network. Our proposed framework employs dynamic and automatic cell grouping techniques using similarity metric correlation algorithms across Base Stations (BS). It selectively trains a representative cell within each group using the best available Machine Learning (ML) model automatically determined. The trained model of the representative cell is subsequently applied to the remaining BS within the same group. To evaluate the effectiveness of our solution, we conducted extensive evaluations using real-world operator data from 5G networks, encompassing a wide range of network KPIs. The results demonstrate the remarkable impact of our framework, showcasing substantial resource savings in terms of ML Server Processing time, memory consumed, and server util percentage. Furthermore, our approach significantly reduces the number of ML trainings required, all while maintaining high ML prediction accuracies. On average, our solution achieves an impressive 39.94% reduction in ML server processing time, a substantial 60.46% reduction in ML server memory, a remarkable 75.11% reduction in server util percentages, and a total of 649 fewer ML trainings for 5G operator data across various network KPIs. These achievements highlight the efficacy of our framework in optimizing resource allocation without compromising the accuracy of ML predictions.

SUMMARY: RNA-Seq data analysis results in lists of genes that may have a similar function, based on differential gene expression analysis or co-expression network analysis. While tools have been developed to identify biological processes that are enriched in the genes sets, there remains a need for tools that identify enrichment of tissue-specific genes. Therefore, we developed TissueEnrich, a tool that calculates tissue-specific gene enrichment in an input gene set. We demonstrated that TissueEnrich can assign tissue identities to single cell clusters and differentiated embryonic stem cells. AVAILABILITY AND IMPLEMENTATION: The TissueEnrich web application is freely available at http://tissueenrich.gdcb.iastate.edu/. The R package is available through Bioconductor at https://bioconductor.org/packages/TissueEnrich. Both the web application and R package are for non-profit academic use under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Complexity is a fundamental attribute of life. Complex systems are made of parts that together perform functions that a single component, or subsets of components, cannot. Examples of complex molecular systems include protein structures such as the F1Fo-ATPase, the ribosome, or the flagellar motor: each one of these structures requires most or all of its components to function properly. Given the ubiquity of complex systems in the biosphere, understanding the evolution of complexity is central to biology. At the molecular level, operons are classic examples of a complex system. An operon's genes are co-transcribed under the control of a single promoter to a polycistronic mRNA molecule, and the operon's gene products often form molecular complexes or metabolic pathways. With the large number of complete bacterial genomes available, we now have the opportunity to explore the evolution of these complex entities, by identifying possible intermediate states of operons. RESULTS: In this work, we developed a maximum parsimony algorithm to reconstruct ancestral operon states, and show a simple vertical evolution model of how operons may evolve from the individual component genes. We describe several ancestral states that are plausible functional intermediate forms leading to the full operon. We also offer Reconstruction of Ancestral Gene blocks Using Events or ROAGUE as a software tool for those interested in exploring gene block and operon evolution. AVAILABILITY AND IMPLEMENTATION: The software accompanying this paper is available under GPLv3 license on: https://github.com/nguyenngochuy91/Ancestral-Blocks-Reconstruction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Islamic State of Iraq and al-Sham (ISIS) is a dominant insurgent group operating in Iraq and Syria that rose to prominence when it took over Mosul in June, 2014. In this paper, we present a data-driven approach to analyzing this group using a dataset consisting of 2200 incidents of military activity surrounding ISIS and the forces that oppose it (including Iraqi, Syrian, and the American-led coalition). We combine ideas from logic programming and causal reasoning to mine for association rules for which we present evidence of causality. We present relationships that link ISIS vehicle-bourne improvised explosive device (VBIED) activity in Syria with military operations in Iraq, coalition air strikes, and ISIS IED activity, as well as rules that may serve as indicators of spikes in indirect fire, suicide attacks, and arrests.

Learning complex behaviour is a difficult task for any artificial agent. Decomposing a task into multiple sub-tasks, learning the sub-tasks separately, and then learning to use them as a whole is a natural way to reduce the dimensionality and complexity of the task function. This approach is demonstrated on a predator agent in the predator-prey-hunter domain. This extended domain has a new agent, a ‘hunter’, that chases the predators. The evading and chasing behaviours are learnt as separate sub-tasks by separate networks using the NEAT neuro-evolution method. A separate network is then evolved to use these networks based on the situation. Task decomposition using this approach performs significantly better in the predator-prey-hunter domain compared to a monolithic network evolved directly on the whole task.

Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Cephalometric analysis is divided in two categories, manual and automatic approaches. The manual approach is limited in accuracy and repeatability due to differences in inter- and intra-personal marking. In this paper, we have attempted to develop and test a novel method for automatic localization of craniofacial structures based on the detected edges in the region of interest. Before edge detection of the particular region, the region was filtered by adaptive non local filter for noise removal by keeping the edge information undisturbed. According to the gray-scale feature at the different regions of the cephalograms, modified Canny edge detection algorithm for obtaining tissue contour was proposed. With the application of morphological opening and edge linking approaches, an improved bidirectional contour tracing methodology was proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method.

We describe an approach for automatically generating application aware graphical modeling environments from the meta-model specification of an application domain. A generated graphical modeling environment: a) provides domain-specific graphical metaphors in the modeling palette, b) imposes domain-specific modeling constraints to prevent semantically incorrect models, and c) provides domain-specific operators and languages to capture application domain constraints. The domain meta-model is specified using a meta-model which is an extension of the UML meta-model. One of the major advantage of using application domain-specific modeling environment is to make it easy for business analyst to create semantically correct models. We use UML2.0 to specify the domain metamodel. The advantage of using UML2.0 representation is the reuse of vendor-supported technologies including MDA tools. An implementation using the Eclipse framework is also discussed

Trends in model-based development which are enabling model-based testing of complex enterprise software solutions are outlined. However, a fundamental factor contributing to lifecycle costs continues to be the inability to effectively handle planned or unplanned changes. With respect to lifecycle costs, the current model-based approaches are off-target, and an integrated model-based development and testing approach to get us back on track is outlined.

We seek to develop vision-based autonomy for small-scale aircraft known as Micro Air Vehicles (MAVs). Development of such autonomy presents significant challenges, in no small measure because of the inherent instability of these flight vehicles. Therefore, we propose a virtual flight testbed that seeks to mitigate these challenges by facilitating the rapid development of new vision-based control algorithms that would have been, in its absence, substantially more difficult to transition to successful flight testing. The proposed virtual testbed is a precursor to a more complex Hardware-In-the-Loop (HILS) facility currently being constructed at the University of Florida. These systems allow us to experiment with vision-based algorithms in controlled laboratory settings, thereby minimizing loss-of-vehicle risks associated with actual flight testing. In this paper, we first discuss our testbed system, both virtual and real. Second, we present our vision-based approaches to MAV stabilization, object tracking and autonomous landing. Finally, report experimental flight results for both the virtual testbed as well as for flight tests in the field, and discuss how algorithms developed in the virtual testbed were seamlessly transitioned to real flight testing.

The paper summarizes the themes and goals of the Workshop on Advances in Model-Based Software Testing.

A new measure of performance, which uses both application integrity and traditional network response time, is proposed. Modern networked application services rely on a stack of network protocols and a host of other services many of which cross-organizational and corporate boundaries. We point out that traditional software quality assurance techniques don't scale up for post-deployment integrity checks for such applications and services. A new methodology to do non-stop post-production monitoring of networked application services for transactional integrity and time delay measurement is proposed. Specifically we describe the Telcordia/spl trade/ Application Assurance System, which we have created for measuring real-time performance of web-based applications used in commercial settings. The system measures both post-production application integrity and time delay. The measurements are carried out by sending synthetic end-user transactions and analyzing the responses. Statistical models for analyzing the data using single monitoring site as well as multiple monitoring sites are described. Creating synthetic end-user transactions is crucial for our method. The paper presents a method for generation of 'highly efficient' end-user transactions from a graphical model of the functionality of the system. Highly efficient transactions are generated using combinatorial designs. The graphical model is incrementally created using a recorder. We give several empirical examples of efficacy of this system and uses for finding performance problems.

Ad hoc network protocols are often developed, tested and evaluated using simulators. However, when the time comes to deploy those protocols for use or testing on real systems the protocol must be reimplemented for the target platform. This usually results in two, completely separate code-bases that must be maintained. Bugs which are found and fixed under simulated conditions must also be fixed separately in the deployed implementation, and vice versa. There is ample opportunity for the two implementations to drift apart, possibly to the point where the deployed and simulated version have little actual resemblance to each other. Testing the deployed version may also require construction of a testbed, a potentially time-consuming and expensive endeavor. Even if constructing an actual testbed is feasible, simulators are very useful for running large, repeatable scenarios for tasks such as protocol evaluation and regression testing. Furthermore, since the implementation may require modification of the kernel network stack, there's a good chance that a particular implementation may only run on specific versions of specific operating systems. To address these issues, we constructed the nsclick simulation environment by embedding the Click Modular Router inside of the popular \ns~network simulator. Routing protocols may be implemented as Click graphs and easily moved between simulation and any operating system supported by Click. This paper describes the design, use, validation and performance of nsclick.

Model-based testing is a new and evolving technique for generating a suite of test cases from requirements.Testers using this approach concentrate on a data model and generation infrastructure instead of hand-crafting individual tests.Several relatively small studies have demonstrated how combinatorial test generation techniques allow testers to achieve broad coverage of the input domain with a small number of tests.We have conducted several relatively large projects in which we applied these techniques to systems with millions of lines of code.Given the complexity of testing, the modelbased testing approach was used in conjunction with test automation harnesses.Since no large empirical study has been conducted to measure efficacy of this new approach, we report on our experience with developing tools and methods in support of model-based testing.The four case studies presented here offer details and results of applying combinatorial test-generation techniques on a large scale to diverse applications.Based on the four projects, we offer our insights into what works in practice and our thoughts about obstacles to transferring this technology into testing organizations.

The paradigm of model based testing shifts the focus of testing from writing individual test cases to developing a model from which a test suite can be generated automatically. We report on our experience with model based testing of a highly programmable system that implements intelligent telephony services in the US telephone network. Our approach used automatic test case generation technology to develop sets of self checking test cases based on a machine readable specification of the messages in the protocol under test. The AETG/sup TM/ software system selected a minimal number of test data tuples that covered pairwise combinations of tuple elements. We found the combinatorial approach of covering pairwise interactions between input fields to be highly effective. Our tests revealed failures that would have been difficult to detect using traditional test designs. However, transferring this technology to the testing organization was difficult. Automatic generation of cases represents a significant departure from conventional testing practice due to the large number of tests and the amount of software development involved.