Method-Level Bug Severity Prediction using Source Code Metrics and LLMs

In the past couple of decades, significant research efforts are devoted to the prediction of software bugs. However, most existing work in this domain treats all bugs the same, which is not the case in practice. It is important for a defect prediction method to estimate the severity of the identified bugs so that the higher-severity ones get immediate attention. In this study, we investigate source code metrics, source code representation using large language models (LLMs), and their combination in predicting bug severity labels of two prominent datasets. We leverage several source metrics at method-level granularity to train eight different machine-learning models. Our results suggest that Decision Tree and Random Forest models outperform other models regarding our several evaluation metrics. We then use the pre-trained CodeBERT LLM to study the source code representations' effectiveness in predicting bug severity. CodeBERT finetuning improves the bug severity prediction results significantly in the range of 29%-140% for several evaluation metrics, compared to the best classic prediction model on source code metric. Finally, we integrate source code metrics into CodeBERT as an additional input, using our two proposed architectures, which both enhance the CodeBERT model effectiveness

Predicting Candidate Epitopes on Ebola Virus for Possible Vaccine Development

Zaire ebolavirus, a member of family Filoviridae is the cause of hemorrhagic fever. Due to lack of appropriate antiviral or vaccine, this disease is very lethal. In this study, we tried to find epitopes for superficial glycoprotein and nucleoprotein of Zaire ebolavirus (that have high antigenicity for MHC I, II and B cells) by using in silico methods and immunoinformatics approach. By using CTLPred, SYFPEITHI and ProPred web applications for MHC class I and SYFPEITHI and ProPred1 web applications for MHC class II, we had been able to find epitopes (peptides) that have the highest score. Also ElliPro, IgPred and DiscoTope web tools had been performed to predict B cells conformational epitopes. Linear epitope prediction for B cell was performed with six methods from IEDB. All of the results that including candidate epitopes for T cells and B cells were reported. It was expected that these peptides could be stimulated immune response and used for designing the multipeptide vaccine against ZEV but these results should be reliable with experimental analysis

GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications

Active queue control aims to improve the overall communication network throughput while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in TCP communication networks. The structure of these controllers is optimized using genetic algorithm (GA) and the output weights of ANNs are optimized using particle swarm optimization (PSO) algorithm. The controllers are radial bias function (RBF)-based, but to improve the robustness of RBF controller, an error-integral term is added to RBF equation in the second scheme. Experimental results show that GA- PSO-optimized improved RBF (I-RBF) model controls network congestion effectively in terms of link utilization with a low packet loss rate and outperform Drop Tail, proportional-integral (PI), random exponential marking (REM), and adaptive random early detection (ARED) controllers.Comment: arXiv admin note: text overlap with arXiv:1711.0635

Modeling viscosity of crude oil using k-nearest neighbor algorithm

Oil viscosity is an important factor in every project of the petroleum industry. These processes can range from gas injection to oil reservoirs to comprehensive reservoir simulation studies. Different experimental approaches have been proposed for measuring oil viscosity. However, these methods are often time taking, cumbersome and at some physical conditions, impossible. Therefore, development of predictive models for estimating this parameter is crucial. In this study, three new machine learning based models are developed to estimate the oil viscosity. These approaches are genetic programing, k-nearest neighbor (KNN) and linear discriminant analysis. Oil gravity and temperature were the input parameters of the models. Various graphical and statistical error analyses were used to measure the performance of the developed models. Also, comparison study between the developed models and the well-known previously published models was conducted. Moreover, trend analysis was performed to compare the predictions of the models with the trend of experimental data. The results indicated that the developed models outperform all of the previously published models by showing negligible prediction errors. Among the developed models, the KNN model has the highest accuracy by showing an overall mean absolute error of 8.54%. The results show that the new developed models in this study can be potentially utilized in reservoir simulation packages of the petroleum industry.Cited as: Mahdiani, M.R., Khamehchi, E., Hajirezaie, S., Hemmati-Sarapardeh, A. Modeling viscosity of crude oil using k-nearest neighbor algorithm. Advances in Geo-Energy Research, 2020, 4(4): 435-447, doi: 10.46690/ager.2020.04.0

Determination of gas-diffusion and interface-mass-transfer coefficients in fracture-heavy oil saturated porous matrix system

Za modeliranje i simulaciju pridobivanja nafte iz prirodno raspucalih ležišta za vrijeme injektiranja plina ključna je točna vrijednost koeficijenta molekularne dizufije (MDC - molecular diffusion coefficient) plinova iz pukotine zapunjene plinom u matriks nafte. Za vrijeme injektiranja miscibilnih fluida s naftom, transport injektirane supstance i nafte je pod preovladavajučim utjecajem svojstava pukotina i matriksa. Difuzija između matriksa i fraktura je važan mehanizam pridobivanja nafte. Međutim, eksperimentalno utvrđeni podaci koji se tiču prijenosa plina između sustava fraktura-matriks mehanizmom difuzije relativno su rijetki. U ovoj studiji je primjenjena metoda opadanja tlaka kako bi se dobio stvarni koeficijent molekularne difuzije CO2 i CH4 u poroznom mediju zasičenom teškom naftom pri različitim temperaturama. Koeficijenti difuzije plina i transfer mase po površini razdjela određeni su primjenom modela ravnoteže prijelaznog stanja difuzije. Također je ispitano djelovanje postojanja poroznoga medija na te koeficijente. Očekuje se da će eksperimentalni rezultati biti korisni u određivanju djelovanja difuzije na transfer matriks-fraktura, što je potrebno za simulaciju pridobivanja u prirodno raspucanim ležištima.For the modeling and simulation of oil recovery from naturally fractured reservoirs during the gas injection process, an accurate value of the molecular diffusion coefficient (MDC) of gases from a gas filled fracture into the oil matrix is essential. During the injection of miscible fluids with oil, transport of the injectant and oil are controlled by fracture and matrix properties. Diffusion is a significant-oil recovery mechanism between matrix and fracture. However, experimentally determined data concerning to gas transfer between fracture-matrix systems by diffusion mechanism are relatively rare. In this study the pressure-decay method is applied to obtain effective molecular diffusion coefficient of CO2 and CH4 in heavy oil saturated porous matrix media at different conditions of temperatures. Gas-diffusion and interface-mass-transfer coefficients are determined by applying a transient-state equilibrium diffusion model. The effect of porous media presence on these coefficients has also been investigated. It is expected that the experimental results will be useful in deriving the matrix-fracture transfer function by diffusion, which is required for simulation of recovery in naturally fractured reservoirs

CORE FLOOD STUDIES TO EVALUATE EFFICIENCY OF OIL RECOVERY BY LOW SALINITY WATER FLOODING AS A SECONDARY RECOVERY PROCESS

Various researches on laboratory and field scale illustrate that manipulating the ionic composition and the ion concentration of injected water can affect the efficiency of water flooding and the interaction of injected water with rock and other fluids present in porous media. The objectives of this paper are to investigate parameters that affect low salinity water flooding; mainly the effect of injecting water salinity on and the potential of low salinity water flooding for oil recovery in secondary recovery mode are studied. The effect of pH and differential pressure across the core are used to explain the mechanism of fine migration phenomena. The recovery results of formation water injection were compared for the seawater, formation water with a salinity of 0.1 and 0.01, and when divalent ions were removed from the formation water with a salinity of 0.01 to investigate the effect of divalent ions on oil recovery. Different types of crude oil were used for investigating the effect crude oil properties on oil recovery. Seawater injection resulted in lowest oil recovery of 2.6% and the reduction of water salinity of formation water from 0.1 to 0.01 resulted in an improvement of 4% and 7.7% in oil recovery respectively. Removing divalent ions from the injected water decreased the improving effect of low salinity water flooding. In addition, both types of crude oil responded to low salinity flooding and no straight correlation was seen between acid number and the improving effect of low salinity water flooding. </span

The Relationship between Working Memory and Confrontation Naming Following Traumatic Brain Injury

Background: The prefrontal cortex is very susceptible to traumatic brain injury (TBI), upon which many cognitive and executive functions including planning, information processing, language, memory, attention, and perception will be impaired. Working memory (WM) is associated with high levels of cognitive processes such as language and naming process communication. In the present study, the correlation between WM and confrontation naming was investigated following TBI. Methods: The current research was a prescriptive-analytic cross-sectional study examining 20 TBI patients within the age range 18-45 years. The samples were selected from Iran, the city of Mashhad, between 2013 and 2016. The participants with a score 23 or higher in Mini-Mental State Examination (MMSE) were assessed through Persian naming test and sub-tests from the Wechsler Memory Scale. The collected data were analyzed by SPSS16 software. Results: There was a significant association between subtests of confrontation naming involving ‘Correct answers without cue’ and WM (P<0.05), ‘Wrong answers’ and WM (P<0.05), as well as ‘Total correct answers’ and WM (P<0.05). Conclusion: The present study indicated modest significant correlations between measures of confrontation naming and WM. These findings provide direction for future studies on the nature of naming deficits following brain injury

Mental health disorders in child and adolescent survivors of post-war landmine explosions.

BACKGROUND: To describe the mental health status of 78 child and adolescent survivors of post-war landmine explosions. METHODS: Child and adolescent survivors of landmine explosions who were younger than 18 years old at the time of the study were identified and enrolled in this study. The mental health status of the participants was assessed by general health assessment and psychiatric examinations. Psychiatric assessment and diagnosis were undertaken using the Diagnostic and Statistical Manual for mental disorders (DSM-IV) criteria. A psychiatrist visited and interviewed each survivor and identified psychiatric disorders. RESULTS: Seventy-eight child and adolescent survivors with a mean age of 16.11 ± 2 years old were identified and agreed to participate in the study. The mean age of the victims at the time of injury was 8.2 ± 3.12 years old (range 2-15). Thirty-seven (47.4 %) of the adolescent survivors suffered from at least one psychiatric disorder. Twenty-nine survivors (37.1 %) were newly diagnosed and needed to start medication and psychiatric treatment. The most common findings were anxiety disorders (34.6 %), including posttraumatic stress disorder (PTSD) in 20 (25.6 %), and generalized anxiety disorder (GAD) in 7 (9 %) subjects. Mild-Moderate depression was found in 5 (6.4 %) subjects. No personality disorders were observed, and two patients suffered from mental retardation. The study results revealed a significant association between age of casualty, duration of injury and limb amputation, and types of psychological disorders. CONCLUSION: Child and adolescent survivors of landmine explosions had a high prevalence of psychiatric disorders

Efficient Routing In Wireless Networks And Information-Centric Networks

The current Internet architecture was designed at the 1960s and 70s, to address communication needs of that time: sharing limited, expensive, and static computer resources. Since then, the Internet usage pattern has been shifted from conventional host-centric model to a flexible content-oriented model, in which users and contents are distributed and mobile. Internet of Things is becoming a new paradigm to describe global access to services and information offered by billions of heterogeneous devices, "things", ranging from resource-constrained to powerful devices in an interoperable way.The first part of this dissertation studies the routing in Information-Centric Networks (ICN). ICN has been recently proposed and is inspiring the design of the future Internet architecture. The goal in these architectures is to provide a cost-efficient, scalable and mobile content distribution networking by adopting a content-based model of communication. ICN not only addresses the change in the Internet usage pattern but also matches the IoT applications, since they target data regardless of the identity of the object that stores or originates them.In this dissertation, Named-data network (NDN) and Content-Centric Networking (CCNx) are presented and routing strategies for them are evaluated. A comprehensive performance evaluation is done through the simulation experiments. We enhanced the performance of link-state routing by introducing a new protocol called LSCR, link-state content routing protocol, a loop-free name-based routing algorithm that propagates link-state information selectively and provides multi-path routing to content that may be replicated in different locations. We also introduced the first content routing protocol based on the diffusing computation, DNRP. DNRP provides multiple loop-free routes to the nearest instances of a data using only distance information and without requiring periodic updates, knowledge of the network topology, or the exchange of path information.MANET paves the way for the development of brand new IoT communication platforms with a high potential for a wide range of applications in different domains. Each layer in the design model require redefinition or modifications to function efficiently in MANETs every is mobile and usually has limited resources on computation, storage, power, etc.Routing in Mobile Ad-Hoc Networks is studied in the second part of this dissertation. We introduce ODVR (Ordered Distance Vector Routing), that provides loop-free routes at every instant based solely on distances to destinations maintained by nodes and reference distances included in route requests. Routing state is established on demand by means of route requests stating the reference distance and replies from nodes that satisfy this distance. To make the routing more efficient in an IoT environment with a base or gateway nodes, we introduce ADRP, a hybrid routing algorithm that takes advantage of the strengths of reactive routing algorithms as well as the benefits of proactive ones. ADRP uses the same signaling for both reactive and proactive routing

Efficient Routing In Wireless Networks And Information-Centric Networks

The current Internet architecture was designed at the 1960s and 70s, to address communication needs of that time: sharing limited, expensive, and static computer resources. Since then, the Internet usage pattern has been shifted from conventional host-centric model to a flexible content-oriented model, in which users and contents are distributed and mobile. Internet of Things is becoming a new paradigm to describe global access to services and information offered by billions of heterogeneous devices, "things", ranging from resource-constrained to powerful devices in an interoperable way.The first part of this dissertation studies the routing in Information-Centric Networks (ICN). ICN has been recently proposed and is inspiring the design of the future Internet architecture. The goal in these architectures is to provide a cost-efficient, scalable and mobile content distribution networking by adopting a content-based model of communication. ICN not only addresses the change in the Internet usage pattern but also matches the IoT applications, since they target data regardless of the identity of the object that stores or originates them.In this dissertation, Named-data network (NDN) and Content-Centric Networking (CCNx) are presented and routing strategies for them are evaluated. A comprehensive performance evaluation is done through the simulation experiments. We enhanced the performance of link-state routing by introducing a new protocol called LSCR, link-state content routing protocol, a loop-free name-based routing algorithm that propagates link-state information selectively and provides multi-path routing to content that may be replicated in different locations. We also introduced the first content routing protocol based on the diffusing computation, DNRP. DNRP provides multiple loop-free routes to the nearest instances of a data using only distance information and without requiring periodic updates, knowledge of the network topology, or the exchange of path information.MANET paves the way for the development of brand new IoT communication platforms with a high potential for a wide range of applications in different domains. Each layer in the design model require redefinition or modifications to function efficiently in MANETs every is mobile and usually has limited resources on computation, storage, power, etc.Routing in Mobile Ad-Hoc Networks is studied in the second part of this dissertation. We introduce ODVR (Ordered Distance Vector Routing), that provides loop-free routes at every instant based solely on distances to destinations maintained by nodes and reference distances included in route requests. Routing state is established on demand by means of route requests stating the reference distance and replies from nodes that satisfy this distance. To make the routing more efficient in an IoT environment with a base or gateway nodes, we introduce ADRP, a hybrid routing algorithm that takes advantage of the strengths of reactive routing algorithms as well as the benefits of proactive ones. ADRP uses the same signaling for both reactive and proactive routing