A Hybrid Learning Approach for Detecting Development Defects in Object-Oriented Applications

The dissolution of metal sulfides, such as ZnS, is an important biogeochemical process affecting fate and transport of trace metals in the environment. However, current studies of in situ dissolution of metal sulfides and the effects of structural defects on dissolution are lacking. Here we have examined the dissolution behavior of ZnS nanoparticles synthesized via several abiotic and biological pathways. Specifically, we have examined biogenic ZnS nanoparticles produced by an anaerobic, metal-reducing bacterium Thermoanaerobacter sp. X513 in a Zn-amended, thiosulfate-containing growth medium in the presence or absence of silver (Ag), and abiogenic ZnS nanoparticles were produced by mixing an aqueous Zn solution with either H2S-rich gas or Na2S solution. The size distribution, crystal structure, aggregation behavior, and internal defects of the synthesized ZnS nanoparticles were examined using high-resolution transmission electron microscopy (TEM) coupled with X-ray energy dispersive spectroscopy. The characterization results show that both the biogenic and abiogenic samples were dominantly composed of sphalerite. In the absence of Ag, the biogenic ZnS nanoparticles were significantly larger (i.e., ∼10 nm) than the abiogenic ones (i.e., ∼3-5 nm) and contained structural defects (e.g., twins and stacking faults). The presence of trace Ag showed a restraining effect on the particle size of the biogenic ZnS, resulting in quantum-dot-sized nanoparticles (i.e., ∼3 nm). In situ dissolution experiments for the synthesized ZnS were conducted with a liquid-cell TEM (LCTEM), and the primary factors (i.e., the presence or absence structural defects) were evaluated for their effects on the dissolution behavior using the biogenic and abiogenic ZnS nanoparticle samples with the largest average particle size. Analysis of the dissolution results (i.e., change in particle radius with time) using the Kelvin equation shows that the defect-bearing biogenic ZnS nanoparticles (γ = 0.799 J/m2) have a significantly higher surface energy than the abiogenic ZnS nanoparticles (γ = 0.277 J/m2). Larger defect-bearing biogenic ZnS nanoparticles were thus more reactive than the smaller quantum-dot-sized ZnS nanoparticles. These findings provide new insight into the factors that affect the dissolution of metal sulfide nanoparticles in relevant natural and engineered scenarios, and have important implications for tracking the fate and transport of sulfide nanoparticles and associated metal ions in the environment. Moreover, our study exemplified the use of an in situ method (i.e., LCTEM) to investigate nanoparticle behavior (e.g., dissolution) in aqueous solutions.

Influence of magnetic field on structural defects in Si and GaAs

Advances in biochemistry, physiology, immunology, and cytochemistry, combined with a variety of new approaches for the evaluation of fine structure, have yielded new insights into the structural physiology and pathology of blood platelets. Subpopulations of platelet granules have been clearly defined; they include the catalase containing organelles referred to as peroxisomes; lysosomes enclosing hydrolytic enzymes; and the alpha-granules in which platelet factor 4, mitogenic factor, beta thromboglobulin, thrombin sensitive protein, fibrinogen, and coagulation factor V are localized. Features of platelet membrane systems have been particularly well-delineated, and recent evidence suggests that membrane complexes serve as the sarcoplasmic reticulum of platelets and the site of prostaglandin synthesis. Improved understanding of platelet biostructure resulting from these observations has made it possible to develop specific relationships between defects in structure and pathological behavior of the cells in vitro and in vivo.

ObjectivesClinical discoveries are heralded by observing unique and unusual clinical cases. The effort of identifying such cases rests on the shoulders of busy clinicians. We assess the feasibility and applicability of an augmented intelligence framework to accelerate the rate of clinical discovery in preeclampsia and hypertensive disorders of pregnancy-an area that has seen little change in its clinical management.MethodsWe conducted a retrospective exploratory outlier analysis of participants enrolled in the folic acid clinical trial (FACT, N=2,301) and the Ottawa and Kingston birth cohort (OaK, N=8,085). We applied two outlier analysis methods: extreme misclassification contextual outlier and isolation forest point outlier. The extreme misclassification contextual outlier is based on a random forest predictive model for the outcome of preeclampsia in FACT and hypertensive disorder of pregnancy in OaK. We defined outliers in the extreme misclassification approach as mislabelled observations with a confidence level of more than 90%. Within the isolation forest approach, we defined outliers as observations with an average path length z score less or equal to -3, or more or equal to 3. Content experts reviewed the identified outliers and determined if they represented a potential novelty that could conceivably lead to a clinical discovery.ResultsIn the FACT study, we identified 19 outliers using the isolation forest algorithm and 13 outliers using the random forest extreme misclassification approach. We determined that three (15.8%) and 10 (76.9%) were potential novelties, respectively. Out of 8,085 participants in the OaK study, we identified 172 outliers using the isolation forest algorithm and 98 outliers using the random forest extreme misclassification approach; four (2.3%) and 32 (32.7%), respectively, were potential novelties. Overall, the outlier analysis part of the augmented intelligence framework identified a total of 302 outliers. These were subsequently reviewed by content experts, representing the human part of the augmented intelligence framework. The clinical review determined that 49 of the 302 outliers represented potential novelties. ConclusionsAugmented intelligence using extreme misclassification outlier analysis is a feasible and applicable approach for accelerating the rate of clinical discoveries. The use of an extreme misclassification contextual outlier analysis approach has resulted in a higher proportion of potential novelties than using the more traditional point outlier isolation forest approach. This finding was consistent in both the clinical trial and real-world cohort study data. Using augmented intelligence through outlier analysis has the potential to speed up the process of identifying potential clinical discoveries. This approach can be replicated across clinical disciplines and could exist within electronic medical records systems to automatically identify outliers within clinical notes to clinical experts.

With the emergence of the massive educational data, education data mining techniques have extensively drawn considerable interest from scholars to explore the relationship between students’ achievements and other factors. In this study, the data set about the students’ achievements of Portuguese in two secondary education schools in Portugal is selected for education data mining, which involves the personal information, social and school related factors. To analyze the relationship between the students' achievements and other factors, this study proposed an ensemble model based on weighted voting for predicting the students’ achievements of Portuguese in the final period. First, the raw data is preprocessed using some basic methods, including dummy coding, correlation analysis, standardization, and normalization. Second, the isolation forest algorithm-based outlier adaption is applied to deal with the data set to enhance the robustness of the ensemble model. Finally, two base classifiers, i.e. gradient boosting decision tree and extreme gradient boosting, are integrated to form the ensemble model. The experiments are presented for verifying the superiority of the proposed model by comparing with five base classifiers, including gradient boosting decision tree, adaptive boosting, extreme gradient boosting, random forest, and decision tree. The experimental results demonstrate that the ensemble model performs better than other base classifiers in classification, and prove the validity of the outlier adaption based on isolation forest algorithm.

The features of the processes of plastic relaxation and recrystallization of steels of various types with different crystal lattices are studied. It has been established that structural signs of collective or secondary recrystallization, as well as high-speed shear or rotational plastic deformation, are observed in the laser impact zone. The conditions for the occurrence of these processes associated with the behavior of defects in the crystal structure under pulsed loading are discussed. It is shown that the conditions of laser exposure ensured the processes of restoring the structure of steels of a dynamic nature.

The ability of titanium dioixide to split water into OH − and H + species is heavily dependent on the behaviour of defects in the crystal structure at or near the surface. We present an in situ study of defect migration in rutile TiO 2 (110) conducted using X‐ray photoelectron spectroscopy (XPS). First, surface and subsurface defects were created in the crystal by argon ion sputtering. Subsequent in situ exposure of the defective crystal to liquid water healed the surface defects, whereas the subsurface remained defective. The sample was then annealed while XPS was used to monitor the concentration of titanium defects. At low annealing temperatures, Ti 3+ was observed to migrate from the subsurface to the surface. Further annealing gradually restored the surface and subsurface to the defect‐free Ti 4+ form, during which the changes in abundance of Ti 1+ , Ti 2+ and Ti 3+ defects are discussed.

Accurate prediction of the state of charge (SOC) of lead-acid batteries is the key to ensuring battery life. In this paper, a new combined SOC prediction model IF-GRU (Isolation Forest, Gated Recurrent Unit) is proposed. The model combines the Isolation Forest anomaly detection algorithm and the Gated Recurrent Network. The Isolation Forest algorithm is used to detect anomalous and missing values in the raw data. Length dependence of the GRU network can be further utilized to perform high-accuracy SOC estimation by implementing a sliding window that takes into account the data's charging and discharging details. In addition, the conventional Adam optimizer is utilized to improve the convergence speed of model training. The experimental data demonstrate that the IF-GRU model proposed in this paper has higher prediction accuracy and convergence speed with a RMSE of 1.59% compared with traditional LSTM network, GRU network, and BP network.

Behavior of implantation-induced defects in HgCdTe

The application of ensemble learning techniques is continuously increasing, since they have proven to be superior over traditional machine learning techniques in various domains. These algorithms could be employed for bug prediction purposes as well. Existing studies investigated the performance of ensemble learning techniques only for PROMISE and the NASA MDP public datasets; however, it is important to evaluate the ensemble learning techniques on additional public datasets in order to test the generalizability of the techniques. We investigated the performance of the two most widely-used ensemble learning techniques AdaBoost and Bagging on the Unified Bug Dataset, which encapsulates 3 class level public bug datasets in a uniformed format with a common set of software product metrics used as predictors. Additionally, we investigated the effect of using 3 different resampling techniques on the dataset. Finally, we studied the performance of using Decision Tree and Naive Bayes as the weak learners in the ensemble learning. We also fine tuned the parameters of the weak learners to have the best possible end results.

Internet of Things (IoT) plays an increasingly significant role in our daily activities, connecting physical objects around us into digital services. In other words, IoT is the driving force behind home automation, smart cities, modern health systems, and advanced manufacturing. This also increases the likelihood of cyber threats against IoT devices and services. Attackers may attempt to exploit vulnerabilities in application protocols, including Domain Name System (DNS), Hyper Text Transfer Protocol (HTTP) and Message Queue Telemetry Transport (MQTT) that interact directly with backend database systems and client-server applications to store data of IoT services. Successful exploitation of one or more of these protocols can result in data leakage and security breaches. In this paper, an ensemble intrusion detection technique is proposed to mitigate malicious events, in particular botnet attacks against DNS, HTTP, and MQTT protocols utilized in IoT networks. New statistical flow features are generated from the protocols based on an analysis of their potential properties. Then, an AdaBoost ensemble learning method is developed using three machine learning techniques, namely decision tree, Naive Bayes (NB), and artificial neural network, to evaluate the effect of these features and detect malicious events effectively. The UNSW-NB15 and NIMS botnet datasets with simulated IoT sensors' data are used to extract the proposed features and evaluate the ensemble technique. The experimental results show that the proposed features have the potential characteristics of normal and malicious activity using the correntropy and correlation coefficient measures. Moreover, the proposed ensemble technique provides a higher detection rate and a lower false positive rate compared with each classification technique included in the framework and three other state-of-the-art techniques.

As customers tendencies learn rapidly about online transactions due to the outbreak of the Covid-19 pandemic, fraudulent transactions that mimic legitimate transactions will continue to increase unabated. Over the years, fraudulent transactions have been committed but the supervised and unsupervised machine learning classifiers and fraud detection techniques could not stop the fraud from occurring at the onset. However, fraudulent cases are detected after post-occurrence thereby recognizing these techniques as non-smart detection models because fraud could only be detected using past data sets. Various fraud detection or classifier techniques using machine learning techniques such as decision trees, random forests, k-means, and neural networks have been studied. Also, the possibility of overfitting due to the branch growth associated with the decision trees is a classifier performance problem. The application of the probability axiomatic metric (PAM) to determine the accuracy of the models is a classification evaluation problem because of the relatively moderate error rate. To overcome the above shortfalls, we proposed a threshold Mahalanobis distance (TMD) classifier and adopted BETH as a performance evaluation metric. The result revealed that TMD is comparable to neural network (NN), k-means, isolation forest, XGBoost, and random forest based on the credit card data set. The analysis showed that TMD and NN have higher efficiency compared to other classifiers. This study demonstrated that the TMD classifier is unique and could be applied to extract legitimate and illegitimate transactions from customer transaction data sets. The study concludes that TMD is robust and comparable to NN and the classification errors associated with BETH are minimal compared to PAM for both TMD and NN.

Tropical forests are at high risk of dieback due to human-induced disturbances including forest fires, agricultural expansion, and logging. These disturbances can degrade the ecosystems, slow forest recovery, and disrupt the global carbon cycle, leading to irreversible changes or ‘tipping point’ in the Earth’s climate system – the point at which disruption to the climate potentially becomes irreversible. Early warning signals of tipping points for the Amazon rainforest and Greenland ice sheet have already been detected. Monitoring these forest ecosystems is crucial to mitigate future long-term consequences. In order to analyse the response of vegetation to disturbances, we must first identify such disturbances, ideally across the entire tropics over a long period of time. We must also carefully consider what we mean by a “disturbance” and it is not necessarily just the largest fire event. It may be that a significant disturbance is a modest fire event but in a region that does not typically experience burning or a fire event outside of the typical fire season. In both of those instances, we might expect the vegetation response to have different characteristics to those from regular, large burns.In this study, we applied Isolation Forest (IF) algorithm to detect Burned Area (BA) anomaly and apply it to ESA FireCCI51 dataset (2001-2020) over IPCC AR6 defined land regions, with Madagascar as a case study region. IF identifies anomalies by considering how easily they can be isolated from the main distribution and allows us to introduce features beyond just the burned area itself (e.g., time and location of the fire). Explainable AI (SHAP) analysis was also performed to further understand the predicted BA anomaly. A higher number of BA anomalies were mostly linked to larger values of BA over the Tropics and in Madagascar, however, anomalies in BA are also affected by temporal and geographical factors other than the magnitude of BA. IF detected a high number of anomalies (>20) in the northern region of Madagascar which comparatively had lower BA values which could indicate deviation from seasonal fire patterns. These results were further explained by SHAP analysis which showed that BA was the main factor influencing prediction of BA anomaly but that time and location could play a significant role in some anomaly detections. This suggests that deviation from the typical fire seasonality was another factor contributing to anomaly detection. The high number of anomalies in these specific areas highlights the need for targeted fire management strategies so that policymakers can anticipate the long-term effects of climate change and human activity on tropical forests, guiding sustainable land use, conservation, and climate adaptation efforts in vulnerable regions.

Short-term load forecasting is a prerequisite for achieving intra-day energy management and optimal scheduling in integrated energy systems. Its prediction accuracy directly affects the stability and economy of the system during operation. To improve the accuracy of short-term load forecasting, this paper proposes a multi-load forecasting method for integrated energy systems based on the Isolation Forest and dynamic orbit algorithm. First, a high-dimensional data matrix is constructed using the sliding window technique and the outliers in the high-dimensional data matrix are identified using Isolation Forest. Next, the hidden abnormal data within the time series are analyzed and repaired using the dynamic orbit algorithm. Then, the correlation analysis of the multivariate load and its weather data is carried out by the AR method and MIC method, and the high-dimensional feature matrix is constructed. Finally, the prediction values of the multi-load are generated based on the TCN-MMoL multi-task training network. Simulation analysis is conducted using the load data from a specific integrated energy system. The results demonstrate the proposed model’s ability to significantly improve load forecasting accuracy, thereby validating the correctness and effectiveness of this forecasting approach.

With the rapid development of large-scale complex networks and proliferation of various social network applications, the amount of network traffic data generated is increasing tremendously, and efficient anomaly detection on those massive network traffic data is crucial to many network applications, such as malware detection, load balancing, network intrusion detection. Although there are many methods around for network traffic anomaly detection, they are all designed for single machine, failing to deal with the case that the network traffic data are so large that it is prohibitive for a single computer to store and process the data. To solve these problems, we propose a parallel algorithm based on Isolation Forest and Spark for network traffic anomaly detection. We combine the advantages of Isolation Forest algorithm in network traffic anomaly detection and big data processing capability of Spark technology. Meanwhile, we apply the idea of parallelization to the process of modeling and evaluation. In the calculation process, by assigning tasks to multiple compute nodes, Isolation Forest and Spark can efficiently perform anomaly detection and evaluation process. By this way, we can also solve the problem of computation bottleneck on single machine. Extensive experiments on real world datasets show that our Isolation Forest and Spark is efficient and scales well for anomaly detection on large network traffic data.