ON THE CONNECTION BETWEEN NORMAL DEFAULT REASONING AND CONDITIONAL LOGIC

Conditional logic plays an important role in recent attempts to investigate default reasoning. In this paper we show that normal default reasoning can be captured in the conditional logic CL: Reiter extensions of a normal default theory delta = <D, W> correspond to sets of sentences that are maximally CL-consistent with respect to Cond-E(delta) which is a set of conditional sentences constructed using defaults in D that are relevant to extensions. We also discuss Delgrande conditional approach to default reasoning and point out one of its weaknesses. In employing CL, we provide a semantic interpretation of defaults that is weaker than that of normality/typicality proposed by Delgrande and develop an approach that produces all the Reiter extensions of a normal default theory. We also show that there is a one-to-one correspondence between conditional proofs of sentences that belong to extensions and Reiter default proofs

ANONYMOUS AUTHENTICATION PROTOCOLS FOR IOT BASED HEALTHCARE: A SURVEY

Abstract - Nowadays, with the increase in chronic diseases and the aging population in all countries, it has become a huge burden on hospitals to accommodate all patients and monitor them. Applying wireless sensors network for the IoT based medical system enabled medical doctors and families to monitor patients' conditions all the time through the collected data from sensors connected to the patients. These sensitive data should be transmitted through secure channels to hospitals or medical centers. Many approaches proposed to present solutions to the security challenges of the IoT based medical systems. In this paper we review the most prominent approaches that tackle the security and Anonymous Authentication (AA)in healthcare systems. Furthermore, we compare between these approaches in term of types of security attacks, security measures, the approaches that were used to solve some of the security issues, and the network technology used such as WSN and RFID. We found that every approach has some drawbacks regarding security attacks and security measures. We believe that some of security attacks like denial of service and modification attacks need to be considered in future research. The same goes for security measures like non-traceability, and backward and forward secrecy. Moreover, 80%of authentication schemes use certificateless authentication. This type of authentication has low computation cost and saves energy which is convenient to the constrained devices. AVISPA and Ban logic are the most common tools used for validation in the surveyed approaches. A comparison between these techniques according to different features is illustrated which may help the researchers to easily identify the gaps in the surveyed approaches so as to propose solutions for these issue

A Convolutional Deep Neural Network Approach to Predict Autism Spectrum Disorder Based on Eye-Tracking Scan Paths

Autism spectrum disorder (ASD) is a developmental disorder that encompasses difficulties in communication (both verbal and non-verbal), social skills, and repetitive behaviors. The diagnosis of autism spectrum disorder typically involves specialized procedures and techniques, which can be time-consuming and expensive. The accuracy and efficiency of the diagnosis depend on the expertise of the specialists and the diagnostic methods employed. To address the growing need for early, rapid, cost-effective, and accurate diagnosis of autism spectrum disorder, there has been a search for advanced smart methods that can automatically classify the disorder. Machine learning offers sophisticated techniques for building automated classifiers that can be utilized by users and clinicians to enhance accuracy and efficiency in diagnosis. Eye-tracking scan paths have emerged as a tool increasingly used in autism spectrum disorder clinics. This methodology examines attentional processes by quantitatively measuring eye movements. Its precision, ease of use, and cost-effectiveness make it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder. The detection of autism spectrum disorder can be achieved by observing the atypical visual attention patterns of children with the disorder compared to typically developing children. This study proposes a deep learning model, known as T-CNN-Autism Spectrum Disorder (T-CNN-ASD), that utilizes eye-tracking scans to classify participants into ASD and typical development (TD) groups. The proposed model consists of two hidden layers with 300 and 150 neurons, respectively, and underwent 10 rounds of cross-validation with a dropout rate of 20%. In the testing phase, the model achieved an accuracy of 95.59%, surpassing the accuracy of other machine learning algorithms such as random forest (RF), decision tree (DT), K-Nearest Neighbors (KNN), and multi-layer perceptron (MLP). Furthermore, the proposed model demonstrated superior performance when compared to the findings reported in previous studies. The results demonstrate that the proposed model can accurately classify children with ASD from those with TD without human intervention

LONG-TERM VISUAL OUTCOMES AND CLINICAL FEATURES AFTER ANTI-VASCULAR ENDOTHELIAL GROWTH FACTOR INJECTION-RELATED ENDOPHTHALMITIS

Purpose: To determine long-term visual outcomes in patients who developed endophthalmitis after intravitreal anti-vascular endothelial growth factor injections and to correlate visual outcomes with clinical features. Methods: This is a retrospective, multicenter, consecutive case series of patients diagnosed with anti-vascular endothelial growth factor injection-related endophthalmitis who were treated at Mid Atlantic Retina, the Retina Service of Wills Eye Hospital, Philadelphia, PA, and the University of Southern California Roski Eye Institute, Los Angeles, CA. Patients were included if they had at least 1 year of follow-up. Primary outcome was to evaluate long-term visual outcomes up to 5 years of follow-up. The secondary outcome was to determine clinical features (e.g., culture results) that may predict long-term visual acuity outcomes. Results: A total of 56 cases of endophthalmitis from 168,247 anti-vascular endothelial growth factor injections were identified (0.033%, 1/3,004 injections), from which 51 eyes met inclusion criteria. Mean follow-up period was 3.3 years (median 4 years; range 1-5 years). A total of 24 patients (47%) reached a maximum final follow-up of 5 years. Mean Snellen visual acuity at the causative injection visit was 20/102 and decreased to counting fingers at diagnosis (P < 0.001). At 6-month follow-up, mean visual acuity improved to 20/644 (P < 0.001) and remained stable up to 5 years (20/480, P = 0.003) follow-up compared with diagnosis. At the final follow-up, 20 eyes had visual acuity that returned to within one line of baseline visual acuity (visual recovery group), whereas 31 patients' visual acuity was at least one line worse than initial visual acuity (visual deterioration group). The cultures for the visual recovery group were more likely to grow coagulase-negative Staphylococcus, whereas the visual deterioration group primarily grew Streptococcus species, Staphylococcus aureus, and Enterococcus faecalis (P = 0.002, comparing organisms isolated in the visual recovery and deterioration group). Conclusion: Visual outcomes after anti-vascular endothelial growth factor injection-related endophthalmitis seem to reach peak improvement by 6 months and remain stable up to a median of 4-year follow-up. Patients who develop culture-negative endophthalmitis or endophthalmitis secondary to coagulase-negative Staphylococcus are more likely to regain baseline visual acuity compared with cases secondary to Streptococcus species

Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage

Preterm birth, defined as a delivery before 37 weeks’ gestation, continues to affect 8–15% of all pregnancies and is associated with significant neonatal morbidity and mortality. Effective prediction of timing of delivery among women identified to be at significant risk for preterm birth would allow proper implementation of prophylactic therapeutic interventions. This paper aims first to develop a model that acts as a decision support system for pregnant women at high risk of delivering prematurely before having cervical cerclage. The model will predict whether the pregnancy will continue beyond 26 weeks’ gestation and the potential value of adding the cerclage in prolonging the pregnancy. The second aim is to develop a model that predicts the timing of spontaneous delivery in this high risk cohort after cerclage. The model will help treating physicians to define the chronology of management in relation to the risk of preterm birth, reducing the neonatal complications associated with it. Data from 274 pregnancies managed with cervical cerclage were included. 29 of the procedures involved multiple pregnancies. To build the first model, a data balancing technique called SMOTE was applied to overcome the problem of highly imbalanced class distribution in the dataset. After that, four classification models, namely Decision Tree, Random Forest, K-Nearest Neighbors (K-NN), and Neural Network (NN) were used to build the prediction model. The results showed that Random Forest classifier gave the best results in terms of G-mean and sensitivity with values of 0.96 and 1.00, respectively. These results were achieved at an oversampling ratio of 200%. For the second prediction model, five classification models were used to predict the time of spontaneous delivery; linear regression, Gaussian process, Random Forest, K-star, and LWL classifier. The Random Forest classifier performed best, with 0.752 correlation value. In conclusion, computational models can be developed to predict the need for cerclage and the gestation of delivery after this procedure. These models have moderate/high sensitivity for clinical application

An Investigation of Microsoft Azure and Amazon Web Services from Users’ Perspectives

Cloud computing is one of the paradigms that have undertaken to deliver the utility computing concept. It views computing as a utility similar to water and electricity. We aim in this paper to make an investigation of two highly efficacious Cloud platforms: Microsoft Azure (Azure) and Amazon Web Services (AWS) from users’ perspectives the point of view of users. We highlight and compare in depth the features of Azure and AWS from users’ perspectives. The features which we shall focus on include (1) Pricing, (2) Availability, (3) Confidentiality, (4) Secrecy, (5) Tier Account and (6) Service Level Agreement (SLA). The study shows that Azure is more appropriate when considering Pricing and Availability (Error Rate) while AWS is more appropriate when considering Tier account. Our user survey study and its statistical analysis agreed with the arguments made for each of the six comparisons factors