Extensive Evaluation Research Articles

Operationalisation of post‐COVID condition case definition in a comprehensive research protocol

AbstractBackground and purposePost‐COVID‐19 condition (PCC) is a prevalent and high‐burden sequela of SARS‐CoV‐2 infection. Because of the complexity of its manifestations, PCC case definition currently lacks standardisation and reproducibility. We aimed to devise a simple screening tool to boost reproducibility and comparability of PCC case definition across PCC studies, and to provide a framework in which to reliably identify suspected PCC cases. We also developed a comprehensive assessment protocol based on the most frequently reported PCC characteristics.MethodsWithin a European multi‐centre study and based on the conclusions of a previous systematic review, we devised a checklist to assess the presence of PCC‐associated symptoms and their temporal relationship with COVID‐19.ResultsThe checklist was developed to include three symptom cores (cognitive, psychiatric, and general). For each symptom, onset is assessed within 3 months after COVID‐19 resolution and persistence for at least 2 months. Any symptom fulfilling this criterion is sufficient to prompt suspicion of PCC. At least one symptom is required in the cognitive or psychiatric domains to suspect PCC with neuropsychiatric involvement. Our protocol features an extensive neuropsychological evaluation and self‐administered scales for mood, anxiety, stress‐related symptoms, sleep disorders, quality of life, disability, mental health, and personality traits; scales for quantitative assessment of fatigue and headache are also included.ConclusionsConsistent identification of PCC cases is crucial to correctly include patients in research and clinical studies. We propose a simple, reproducible, and flexible screening tool and a proposal for a comprehensive assessment that could be employed to enhance standardisation and comparability.

The Integration of Artificial Intelligence in Secure Access Service Edge: Enhancing Network Security and Performance

Integrating Artificial Intelligence (AI) with Secure Access Service Edge (SASE) architecture represents a significant advancement in enterprise network security and management. This article examines the transformative impact of AI technologies on SASE implementations, focusing on enhanced threat detection capabilities, dynamic network optimization, and automated compliance management. The article comprehensively analyzes AI-driven security features, including real-time threat detection, zero-trust implementation, and privacy-preserving security measures. Through extensive evaluation of deployment scenarios and performance metrics, our research demonstrates significant improvements in security effectiveness and operational efficiency, including a 90% reduction in threat detection time, a 45% decrease in network latency, and a 65% reduction in manual configuration tasks. The article highlights the system's capability to maintain robust security while preserving data privacy through advanced encrypted traffic analysis techniques. The article addresses current integration challenges and provides insights into successful implementation strategies. This article contributes to the growing body of knowledge on AI-enhanced network security architectures and provides valuable insights for organizations seeking to modernize their security infrastructure while maintaining operational efficiency and compliance with evolving regulatory requirements.

An Efficient Framework for Finding Similar Datasets Based on Ontology

Governments are embracing an open data philosophy and making their data freely available to the public to encourage innovation and increase transparency. However, the number of available datasets is still limited. Finding relationships between related datasets on different data portals enables users to search the relevant datasets. These datasets are generated from the training data, which need to be curated by the user query. However, relevant dataset retrieval is an expensive operation due to the preparation procedure for each dataset. Moreover, it requires a significant amount of space and time. In this study, we propose a novel framework to identify the relationships between datasets using structural information and semantic information for finding similar datasets. We propose an algorithm to generate the Concept Matrix (CM) and the Dataset Matrix (DM) from the concepts and the datasets, which is then used to curate semantically related datasets in response to the users’ submitted queries. Moreover, we employ the proposed compression, indexing, and caching algorithms in our proposed scheme to reduce the required storage and time while searching the related ranked list of the datasets. Through extensive evaluation, we conclude that the proposed scheme outperforms the existing schemes.

Intelligent multi-robot collaborative transport system

The research on multi-robot systems has been divided into various fields, such as communication, navigation, task allocation, and collaborative transport. While significant progress has been made in each area, there has been limited research integrating these fields to build a fully autonomous multi-robot collaborative transport system. Therefore, we identify the key issues and propose a multi-robot collaborative transport system founded on ROS1 and conduct validation in a simulated environment, laying a solid foundation for the system to run on real robots. The primary contributions of this study include three key areas: (1) modeling and validating robot collaborative transport, (2) developing a visual task allocation system leveraging FastDDS service, and (3) resolving path collision issues in multi-robot navigation through both traditional methods and reinforcement learning techniques. Extensive experimental evaluations demonstrate that the proposed intelligent multi-robot collaborative transport system can autonomously navigate to target points for collaborative transport task. Performance assessments, based on the error between the target point and the object’s arrival point as well as the transport trajectory error, reveal that the system effectively completes the assigned tasks.

Empagliflozin Raises Sodium in SIADH Patient, Enabling Timely Surgery

AbstractEmpagliflozin, a selective sodium-glucose cotransporter 2 (SGLT2) inhibitor, induces osmotic diuresis via glucosuria and may represent a novel therapeutic approach for managing syndrome of inappropriate antidiuretic hormone secretion (SIADH). This case report details the successful use of empagliflozin to correct persistent hyponatremia in a 78-year-old man with SIADH, awaiting elective abdominal surgery for hernia repair over several months. The patient, who had a history of hypertension and multinodular thyroid goiter with normal thyroid function tests, continued to experience low serum sodium levels despite discontinuing enalapril, previously used for hypertension management. An extensive evaluation confirmed a diagnosis of SIADH, with sodium levels initially ranging between 124 and 128 mmol/L. Traditional management, including fluid restriction, failed to normalize his sodium levels, leading to repeated cancellations of his scheduled surgery due to persistent hyponatremia. In October 2023, treatment with empagliflozin was initiated, leading to a gradual increase in sodium levels. Within 3 weeks, his serum sodium had normalized to 134 mmol/L, accompanied by a rise in serum osmolality. As a result, the elective surgery was successfully performed without complications. These findings suggest that empagliflozin, in combination with fluid restriction, offers a promising treatment option for improving sodium levels in patients with SIADH, helping avoid unnecessary delays in elective surgical interventions.

FLSAD: Defending Backdoor Attacks in Federated Learning via Self-Attention Distillation

Federated Learning (FL), as a distributed machine learning framework, can effectively learn symmetric and asymmetric patterns from large-scale participants. However, FL is susceptible to malicious backdoor attacks through attackers injecting triggers into the backdoored model, resulting in backdoor samples being misclassified as target classes. Due to the stealthy nature of backdoor attacks in FL, it is difficult for users to discover the symmetric and asymmetric backdoor properties. Currently, backdoor defense methods in FL cause model performance degradation while reducing backdoors. In addition, some methods will assume the existence of clean samples, which does not match the realistic scenarios. To address such issues, we propose FLSAD, an effective backdoor defense method in FL via self-attention distillation. FLSAD can recover the triggers using an entropy maximization estimator. Based on the recovered triggers, we leverage the self-attention distillation to eliminate the backdoor. Compared with the baseline backdoor defense methods, FLSAD can reduce the success rates of different state-of-the-art backdoor attacks to 2% on four real-world datasets through extensive evaluation.

Enhanced Tuberculosis Detection in Chest X-Rays Using Optimal Feature Selection with Hybrid Binary Particle Swarm Optimization (HBPSO) and a Dual Classifier Framework Combining LSTM and Spiking Neural Networks

Tuberculosis (TB) remains a significant global health challenge, particularly in developing countries, where rapid and accurate diagnosis is crucial for effective treatment. Recent advancements in machine learning and image processing have shown promise in enhancing the detection of TB from chest X-ray images. This research introduces a novel approach that integrates a hybrid feature selection method with a dual classifier framework to improve the accuracy of tuberculosis detection. Feature extraction is conducted using Multi-Scale Local Binary Patterns (MSLBP), Speeded-Up Robust Features (SURF) with Bag of Words (BoW), and Cartoon Texture Features, combining them into a comprehensive feature vector. The Hybrid Binary Particle Swarm Optimization (HBPSO) is employed for optimal feature selection, reducing redundancy and improving classification efficiency. Classification is performed using a dual framework involving a Long Short-Term Memory (LSTM) network and a Spiking Neural Network (SNN), which work in tandem to provide robust predictions. Majority voting is utilized to finalize predictions, ensuring high accuracy and adaptability across varying TB presentations. Extensive evaluation on publicly available datasets shows that this hybrid approach significantly outperforms existing methods, achieving a highest accuracy of 99.73%, along with superior precision, recall, and F1 scores.

The role of a Macmillan senior pharmacy technician in a regional cancer centre in Northern Ireland: an evaluation

Abstract Introduction In the UK, up to two-thirds of all cancer patients have at least one comorbidity, with around half having multiple long-term conditions (LTCs).1 Older people with cancer often require multiple medications including cancer-specific treatments, supportive care medications, and medications for pre-existing LTCs. They are therefore at higher risk of the adverse effects of polypharmacy, inappropriate prescribing, drug-drug interactions, and drug-disease interactions. Complexity of medication regimens and treatment burden can cause confusion and medicines mismanagement, thereby influencing a patient's ability to adhere to treatment with the outcome being an increased chance of treatment failure. It is thus essential that medicines be optimised in these patients.2 The Oncology Pharmacy Team based in the North West Cancer Centre, located on the Altnagelvin acute hospital site in the Western Health and Social Care Trust, Northern Ireland, secured Macmillan funding to provide a specialist medicines optimisation service to older patients about to start cancer treatment (the Medicines Optimisation in Older People with Cancer [MOOC] Service). A pharmacy technician and pharmacist were recruited and the service implemented from October 2023. The roles are new and innovative and require extensive evaluation. Aim To evaluate the introduction of the Macmillan senior pharmacy technician into the oncology pharmacy team, analysing impact on medicines optimisation and patient safety. Methods A census approach was taken to include all patients with a new cancer diagnosis and medicines reconciliation (MedRec) completed by the pharmacy technician during the study period. The technician identified and rectified any issues prior to the patient’s first scheduled treatment. Patients with significant medicines-related concerns, or aged ≥65 years and taking ≥8 drugs, were referred to the Macmillan pharmacist for holistic medication review. Data collected included number of MedRecs completed by the pharmacy technician (relative to total completed by the team weekly), cancer diagnosis, medicines-related problems identified, interventions made, other activities completed, and time directed away from the pharmacist. Analysis of patient age and number of medications taken informed whether inclusion criteria for MOOC pharmacist referral were appropriate. Results The specialist medicines optimisation service started in October 2023, however, data collection for the purpose of this study commenced on 1st January and took place over a two-month period.101 patients (aged 69.0 ± 5.7 years, range 57 -74 years)) taking an average of 12.6 medicines (range = 8-18), and recently diagnosed with cancer were reviewed by the pharmacy technician. On average, the technician completed 76.4% of medicines reconciliations each week. 120 medicines-related problems were identified, and 178 interventions made. The service yielded net cost savings of £826.34 (pharmacist time saved). Nine patients were referred to the MOOC pharmacist service; a further five patients did not consent to referral. Discussion / Conclusion Although the evaluation of the MOOC service is ongoing, this interim analysis demonstrated that integration of the pharmacy technician into the oncology team yielded cost savings and reduced and improved provision of patient-centred healthcare and safety. Inclusion criteria for the MOOC pharmacist review were confirmed via analysis of patient age and number of medicines taken.

StyleWe: Towards Style Fusion in Generative Fashion Design with Efficient Federated AI

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the design area hinders its digital transformation under the third wave of AI. In this paper, we introduce a Federated Generative Artificial Intelligence Clothing system, namely StyleWe, employing federated learning to aid in sketch design. StyleWe is committed to establishing an ecosystem wherein designers can exchange sketch styles among themselves. Through StyleWe, designers can generate sketches that incorporate various designers' styles from their peers, drawing inspiration from collaboration without the need for data disclosure or upload. Extensive performance evaluations and user studies indicate that our StyleWe system can produce multi-styled sketches of comparable quality to human-designed ones while significantly enhancing efficiency compared to hand-drawn sketches.

An Unexpected Finding in an Adolescent Rowing Athlete With Angina Pectoris. A Case Report.

Significant intraventricular gradient development during exercise is rare, usually occurring with left ventricular hypertrophy. The etiopathogenesis consists of the increase in nonobstructive physiological gradients; ventricular cavitary obliteration with consequent end-systolic obstruction; and midsystolic obstruction caused by systolic anterior movement of the mitral valve compromising flow. A correlation between intraventricular gradient development and various symptoms has been established. Chest pain is common in children and is a frequent reason for referral to pediatric cardiologists. Despite the benign nature of most pediatric chest pain, extensive and costly cardiac evaluation is common in these patients. In the case presented here, we describe an adolescent rowing athlete with excruciating effort angina only during upright exercise, which was replicated while performing an exercise stress echocardiography.

A bizarre case of recurrent hemoptysis and pneumothorax in a young male

Vascular Ehlers–Danlos syndrome (vEDS) is a rare inherited disorder affecting approximately 1 in 150,000 individuals, caused by heterozygous mutations in the COL3A1 gene. Characterized by primary spontaneous pneumothorax and severe connective tissue fragility, vEDS remains poorly understood clinically, making diagnosis challenging. We present the case of a 20-year-old male with a history of mitral valve prolapse, recurrent hemoptysis, knee joint dislocations, and easy bruising. In February 2023, he was admitted to the emergency department following a seizure, syncope, and altered sensorium, which led to an extensive evaluation of his non-specific symptoms. Ultimately, he was diagnosed with vEDS.

Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features

Cloud systems, typically comprised of various components ( e.g. , microservices), are susceptible to performance issues, which may cause service-level agreement violations and financial losses. Identifying performance issues is thus of paramount importance for cloud vendors. In current practice, crucial metrics, i.e. , key performance indicators (KPIs), are monitored periodically to provide insight into the operational status of components. Identifying performance issues is often formulated as an anomaly detection problem, which is tackled by analyzing each metric independently. However, this approach overlooks the complex dependencies existing among cloud components. Some graph neural network-based methods take both temporal and relational information into account, however, the correlation violations in the metrics that serve as indicators of underlying performance issues are difficult for them to identify. Furthermore, a large volume of components in a cloud system results in a vast array of noisy metrics. This complexity renders it impractical for engineers to fully comprehend the correlations, making it challenging to identify performance issues accurately. To address these limitations, we propose Identifying Performance Issues based on Relational-Temporal Features (ISOLATE ), a learning-based approach that leverages both the relational and temporal features of metrics to identify performance issues. In particular, it adopts a graph neural network with attention to characterizing the relations among metrics and extracts long-term and multi-scale temporal patterns using a GRU and a convolution network, respectively. The learned graph attention weights can be further used to localize the correlation-violated metrics. Moreover, to relieve the impact of noisy data, ISOLATE utilizes a positive unlabeled learning strategy that tags pseudo labels based on a small portion of confirmed negative examples. Extensive evaluation on both public and industrial datasets shows that ISOLATE outperforms all baseline models with 0.945 F1-score and 0.920 Hit rate@3. The ablation study also proves the effectiveness of the relational-temporal features and the PU-learning strategy. Furthermore, we share the success stories of leveraging ISOLATE to identify performance issues in Huawei Cloud, which demonstrates its superiority in practice.

ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction.

The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation, efficiently utilizing rich, high-dimensional information remains a significant challenge. Our work introduces ChemXTree, a novel graph-based model that integrates a Gate Modulation Feature Unit (GMFU) and neural decision tree (NDT) in the output layer to address this challenge. Extensive evaluations on benchmark data sets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree's superior performance, surpassing or matching the current state-of-the-art models. Visualization techniques clearly demonstrate that ChemXTree significantly improves the separation between substrates and nonsubstrates in the latent space. In summary, ChemXTree demonstrates a promising approach for integrating advanced feature extraction with neural decision trees, offering significant improvements in predictive accuracy for drug discovery tasks and opening new avenues for optimizing molecular properties.

Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP.

Severe acute pancreatitis (SAP) can be fatal if left unrecognized and untreated. The purpose was to develop a machine learning (ML) model for predicting the 30-day all-cause mortality risk in SAP patients and to explain the most important predictors. This research utilized six ML methods, including logistic regression (LR), k-nearest neighbors(KNN), support vector machines (SVM), naive Bayes (NB), random forests(RF), and extreme gradient boosting(XGBoost), to construct six predictive models for SAP. An extensive evaluation was conducted to determine the most effective model and then the Shapley Additive exPlanations (SHAP) method was applied to visualize key variables. Utilizing the optimized model, stratified predictions were made for patients with SAP. Further, the study employed multivariable Cox regression analysis and Kaplan-Meier survival curves, along with subgroup analysis, to explore the relationship between the machine learning-based score and 30-day mortality. Through LASSO regression and recursive feature elimination (RFE), 25 optimal feature variables are selected. The XGBoost model performed best, with an area under the curve (AUC) of 0.881, a sensitivity of 0.5714, a specificity of 0.9651 and an F1 score of 0.64. The first six most important feature variables were the use of vasopressor, high Charlson comorbidity index, low blood oxygen saturation, history of malignant tumor, hyperglycemia and high APSIII score. Based on the optimal threshold of 0.62, patients were divided into high and low-risk groups, and the 30-day survival rate in the high-risk group decreased significantly. COX regression analysis further confirmed the positive correlation between high-risk scores and 30-day mortality. In the subgroup analysis, the model showed good risk stratification ability in patients with different gender, renal replacement therapy and with or without a history of malignant tumor, but it was not effective in predicting peripheral vascular disease. the XGBoost model effectively predicts the severity of SAP, serving as a valuable tool for clinicians to identify SAP early.

Think VEXAS: a case report of Vexas syndrome

VEXAS syndrome is a rare auto-inflammatory disorder characterized by heterogeneous inflammatory and hematologic features. First identified in 2020, it predominantly affects men over the age of 50. Clinical manifestations commonly include recurrent fever, weight loss, skin lesions, and diverse inflammatory presentations across multiple organ systems. Diagnosing VEXAS syndrome can be challenging due to the overlap of symptoms with other autoimmune and inflammatory conditions, often requiring extensive medical evaluation before a definitive diagnosis is made. This case report highlights these diagnostic challenges and underscores the importance of early recognition to improve clinical management and outcomes.

Temporal-Incremental Learning for Android Malware Detection

Malware classification is a specific and refined task within the broader malware detection problem. Effective classification aids in understanding attack techniques and developing robust defenses, ensuring application security and timely mitigation of software vulnerabilities. The dynamic nature of malware demands adaptive classification techniques that can handle the continuous emergence of new families. Traditionally, this is done by retraining models on all historical samples, which requires significant resources in terms of time and storage. An alternative approach is Class-Incremental Learning (CIL), which focuses on progressively learning new classes (malware families) while preserving knowledge from previous training steps. However, CIL assumes that each class appears only once in training and is not revisited, an assumption that does not hold for malware families, which often persist across multiple time intervals. This leads to shifts in the data distribution for the same family over time, a challenge that is not addressed by traditional CIL methods. We formulate this problem as Temporal-Incremental Malware Learning (TIML), which adapts to these shifts and effectively classifies new variants. To support this, we organize the MalNet dataset, consisting of over a million entries of Android malware data collected over a decade, in chronological order. We first adapt state-of-the-art CIL approaches to meet TIML's requirements, serving as baseline methods. Then, we propose a novel multimodal TIML approach that leverages multiple malware modalities for improved performance. Extensive evaluations show that our TIML approaches outperform traditional CIL methods and demonstrate the feasibility of periodically updating malware classifiers at a low cost. This process is efficient and requires minimal storage and computational resources, with only a slight dip in performance compared to full retraining with historical data.

REDalign: accurate RNA structural alignment using residual encoder-decoder network

BackgroundRNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of O(L6)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(L^6)$$\\end{document} for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities.ResultsIn this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency.ConclusionREDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics.

A novel light GBM framework combined with gain optimization for predicting dementia severity of Alzheimer’s disease

ABSTRACT Introduction Alzheimer’s disease (AD) is a severe brain disorder, which comprises almost 75% of all types of dementia cases. As per the latest report, the global count of Alzheimer’s disease patients is 131 million, due to which the treatment of Alzheimer’s disease is becoming a major burden to the world healthcare system. However, Alzheimer’s disease still remains incurable due to its multifactorial nature of symptoms. Therefore, early-stage diagnosis of Alzheimer’s disease is essential, which helps in the treatment and recovery of patients to a greater extent. Background In the existing literature, few research attempts are carried out for Alzheimer’s disease prediction using machine learning algorithms; yet, most of the existing techniques fail to focus on aspects such as gain-optimisation, dementia severity identification and accuracy enhancement. Methodology To solve these issues, this article presents a light gradient boosting machine-based framework, which employs a gain-optimisation algorithm using multi-category factors for the early stage prediction of Alzheimer’s disease. Further, the proposed framework introduces a detailed feature analysis for identifying the key features needed for the disease prediction and thereby classifies the Alzheimer’s disease patients into different dementia severity types. Results The extensive evaluations of the proposed framework on different dataset sizes using real-world MRI datasets of patients, clearly demonstrate the performance of the proposed work in terms of gain-optimised accuracy metrics. Further, experimental evaluations clearly prove the better performance of the proposed framework when compared to that of other ML techniques including LR, RFC, SVR, SVC and existing methods in terms of Recall, F1-score and accuracy metrics.

FunFuzz : Greybox Fuzzing with Function Significance

Greybox fuzzing is dedicated to revealing software bugs by maximizing code coverage. Concentrating on code coverage, greybox fuzzing effectively exposes bugs in real-world programs by continuously executing the program under test (PUT) with the test inputs generated from initial seeds, making it a popular software testing technique. Although powerful, the effectiveness of greybox fuzzing can be restricted in some cases. Ignoring the significant degrees of executed functions, traditional greybox fuzzing usually fails to identify significant seeds that execute more significant functions, and thus may assign similar energy to significant and trivial seeds when conducting power scheduling. As a result, the effectiveness of greybox fuzzing can be degraded due to wasting too much energy on trivial seeds. In this paper, we introduce function significance (FS) to measure the significant degrees of functions. Our key insight is that the influential functions that connect to many other functions are significant to greybox fuzzing as they provide more probabilities to reach previously unexplored code regions. To quantify FS, we conduct influence analysis upon the call graphs extracted from the PUTs to obtain the centrality values of function nodes. With FS as the significance measurement, we further propose FunFuzz , an FS-aware greybox fuzzing technique, to optimize significant seeds and tackle the aforementioned restriction. To this end, FunFuzz dynamically tracks the functions executed by a seed during fuzzing, and computes the significance score for the seed by accumulating the FS values of the functions executed by it. Based on the computed FS values, FunFuzz then takes an estimation-based power scheduling to assign more (or less) energy to seeds that achieve over-estimated (or under-estimated) significance scores. Specifically, the seed energy is adjusted by multiplying with a scale factor computed regarding the ratio of the actual significance score achieved by executing the seed and the estimated significance score predicted by a linear model constructed on-the-fly. To evaluate FunFuzz , we prototype it on top of AFL++ and conduct experiments with 15 programs, of which 10 are from common real-world projects and five are from Magma, and compare it to seven popular fuzzers. The experimental results obtained through fuzzing exceeding 40,800 CPU hours show that: (1) In terms of covering code, FunFuzz outperforms AFL++ by achieving 0.1% \(\sim\) 18.4% more region coverage on 13 out of 15 targets. (2) In terms of finding bugs, FunFuzz unveils 114 unique crashes and 25 Magma bugs (which are derived from CVEs) in 20 trials of 24-hour fuzzing, which are the most compared to the competitor fuzzers and include 32 crashes and 1 Magma bug that the other fuzzers fail to discover. Besides the experiments focusing on code coverage and bug finding, we evaluate the key components of FunFuzz , namely the FS-centered estimation-based power scheduling and the lazy FS computation mechanism. The extensive evaluation not only suggests FunFuzz ’s superiority in code coverage and bug finding, but also demonstrates the effectiveness of the two components.

Advances in diagnosis and imaging of cervical dysfunction in the sports horse

Cervical imaging provides an abundance of data. It is important not to lose sight of the horse and to determine what clinical relevance these findings have. Good clinical examination, in combination with knowledge of functional anatomy and biomechanics, are essential in anticipation of more research which will allow veterinarians to work in a more evidence-based manner. The location of cervical pain can be pinpointed to a single or multiple locations with an extensive history, static observation, general palpation, motion evaluation, specific palpation and dynamic evaluation. The whole body should be evaluated. The majority of cervical pathology (80%) is located in the caudal cervical area (C5–T1). Radiography of this region can be improved with good quality equipment, collimation, correct positioning of the horse and a correct distance between generator and plate. Radiography and ultrasonography may underestimate cervical pathology. Computed tomography is the preferred method of imaging but may lead to over interpretation. The most common finding is narrowing of the intervertebral foramen because of enlargement of the articular process joint(s). More than one location with abnormalities is often identified.