Pattern-based methods for vulnerability discovery

As technology has advanced, the cyber threats are more advanced and common, thus it is difficult for the conventional methods to detect it. This paper explores the role of Machine Learning (ML) in the improvement of cybersecurity strategies in relation to threat detection and management. ML's pattern recognition ability is used to enhance threat recognition performance and decrease false positives. Study of past research indicates that several ML algorithms like decision tree, random forest, and convolution neural networks (CNN's), have been useful in interpreting the anomalies and distinguishing various sorts of attacks. In this paper the impact of ML algorithms on the improvement of cybersecurity especially in detecting, assessing and addressing innumerable threats present in the network traffic is reviewed. In consideration of these challenges, a system with the aim of threat classification and features extraction through use of some ML capabilities is presented. This would help in making the cyber security defences flexible and quick, encouraging timely and effective assessment and management of the threats. Some prominent areas of application of ML in the protection of cyber security are in-detecting intrusions in the networks, malware classification, preventing phishing attacks, as well as automating incident response.

This study investigated the effects of video-based perceptual training on pattern recognition and pattern prediction ability in elite field sport athletes and determined whether enhanced perceptual skills influenced the physiological demands of game-based activities. Sixteen elite women soccer players (mean +/- SD age, 18.3 +/- 2.8 years) were allocated to either a video-based perceptual training group (N = 8) or a control group (N = 8). The video-based perceptual training group watched video footage of international women's soccer matches. Twelve training sessions, each 15 minutes in duration, were conducted during a 4-week period. Players performed assessments of speed (5-, 10-, and 20-m sprint), repeated-sprint ability (6 x 20-m sprints, with active recovery on a 15-second cycle), estimated maximal aerobic power (V O2 max, multistage fitness test), and a game-specific video-based perceptual test of pattern recognition and pattern prediction before and after the 4 weeks of video-based perceptual training. The on-field assessments included time-motion analysis completed on all players during a standardized 45-minute small-sided training game, and assessments of passing, shooting, and dribbling decision-making ability. No significant changes were detected in speed, repeated-sprint ability, or estimated V O2 max during the training period. However, video-based perceptual training improved decision accuracy and reduced the number of recall errors, indicating improved game awareness and decision-making ability. Importantly, the improvements in pattern recognition and prediction ability transferred to on-field improvements in passing, shooting, and dribbling decision-making skills. No differences were detected between groups for the time spent standing, walking, jogging, striding, and sprinting during the small-sided training game. These findings demonstrate that video-based perceptual training can be used effectively to enhance the decision-making ability of field sport athletes; however, it has no effect on the physiological demands of game-based activities.

Prediction and prevention of cyber-attacks using machine learning techniques are imperative, as the complexity and frequency of cyber threats continue to challenge individuals, institutions. With the increasing sophistication of cyber-attacks, conventional detection and prevention techniques are typically limited to recognizing novel or morphed threats. Machine learning can be helpful in this respect due to its capacity to deal with massive data, ability for pattern recognition and capability of adaptation to new situations. In this paper the literature is reviewed and methods applied are studied and analyzed. Given the importance of the topic and the large amount of data breached or exposed, we present a system for analyzing and classifying the most important data in this field. Several types of algorithms are used to recommend the best ones. A special system is presented that classifies cyber-attacks using different algorithms. The results of our experiments are presented, compared, and the best methods that can be developed are recommended

Multiprocessor systems for connectionist diagnosis of technical processes

Using information technology such as search engine for collecting and monitoring of network public opinion is a practical and effective method. This paper puts forward an improved algorithm of SVM classifier based on incremental learning online, and then implements a topic crawler system for network public opinion and to grab the public opinion.

BackgroundThere is extensive evidence for volumetric reductions in the hippocampus in patients with anorexia nervosa (AN), however the impact on function is unclear. Pattern separation and recognition are hippocampus-dependent forms of learning thought to underlie stimulus discrimination.MethodsThe present study used the Mnemonic Similarity Task to investigate pattern separation and recognition for the first time in patients with AN (N = 46) and healthy controls (N = 56). An Analysis of Covariance examined between-group differences, controlling for age, antidepressant use and method of task delivery (remote vs. in person).ResultsWhen controlling for covariates, pattern recognition memory scores were lower in the AN group with a medium effect size (d = 0.51). In contrast, there was a small effect whereby patients with AN had a greater pattern separation score than controls (d = 0.34), albeit this difference was not significant at the p = 0.05 threshold (p = 0.133). Furthermore, pattern separation and recognition memory abilities were not related to age, body mass index, eating disorder psychopathology or trait anxiety levels.ConclusionsThis preliminary study provides initial evidence for an imbalance in pattern separation and recognition abilities in AN, a hippocampus-dependent cognitive ability. Further studies should endeavour to investigate pattern separation and recognition performance further in AN, as well as investigate other hippocampus-dependent functions.

Sparrow search mechanism-based effective feature mining algorithm for the broken wire signal detection of prestressed concrete cylinder pipe

Due to the development of technology and computer science, especially in the field of artificial intelligence and machine learning, today information technologies have become the most important element in most fields. The developments in these technologies included building models and systems that depend mainly on machine learning. One of the most important areas that we need to develop is pattern recognition and classification. The term patterns include giving the computer the ability to recognize and classify images, shapes, sounds, and digital and textual data into different classes. These systems depend on building a model based on artificial intelligence and a trained neural network to analyze and classify patterns. In this article, we develop neural network algorithms based on five different datasets to build an intelligent pattern classification and recognition system by providing a neural network method for pattern recognition (NNM - PR). Using these algorithms, a high accuracy scale was obtained compared to previous studies.

This study aims to explore one preservice teacher’s interpretation of students’ mathematical thinking using pattern recognition and linear function tasks. Specifically, the research question for this study was as follows: How does a preservice middle grade mathematics teacher interpret students’ mathematical thinking using tasks designed to test students’ pattern recognition and linear function abilities? The preservice teacher, Amy, was a middle school preservice teacher from the Mathematics and Science Education Department of a major land-grant university in the southeastern Unites States. The data consists of Amy’s responses during a face-to-face, semi-structured interview, written work she produced during the interview, and a careful analysis of a transcript of the interview. With regards to interpreting important mathematical ideas in students’ work in the National Council of Teachers of Mathematics [NCTM] videos, this study found that the preservice teacher provided explanations of specific important mathematical ideas and their meaning. When the preservice teacher interpreted students’ mistakes or misconceptions, she explained the misconceptions mathematically rather than providing specific examples from the video. In terms of interpreting the similarities between students’ reasoning, the preservice teacher’s description of the similarities between students’ reasoning was broad. Depending on the task and how many times she watched the videos, either one or two times, the extent to which she provided evidence varied when she interpreted the differences between students’ reasoning. This study revealed that the strength of the preservice teacher’s interpretation strategies varied depending on the tasks. The preservice teacher did not provide detailed explanations of students’ reasonings when watching NCTM videos even though she solved all the tasks in multiple ways and explained the key concepts while solving the problems.

This study introduces a new method for optimizing evolutionary algorithms to learn characteristics of unknown solar energy systems or subsystems. When used together, Genetic Algorithm (GA) and Deep Learning methods allow for precise PV system calibration. By combining GA's optimization powers with deep learning models' pattern recognition abilities, this technique achieves precise system calibration by fine-tuning model parameters. One way to create a connection between variables like irradiance, temperature, and voltage, which are input parameters, and variables like power production or defect detection, is to employ a model that uses deep learning, and brain networks in particular. Feature extraction is a strong suit of CNNs, making them a promising tool for image analysis in the context of spatial pattern defect detection. Using GA, one may optimize the settings of either the PV system or the deep learning model. To do this, one must define a fitness function that represents the target performance metric. This metric might be anything from maximizing power production to reducing prediction error. In order to decrease the discrepancy between the expected and actual electrical output, GA may optimize the biases and weights of a neural network. To sum up, optimizing and calibrating PV systems has never been easier than with the help of a genetic algorithm and deep learning. Together, these things have the potential to make solar energy systems more efficient, provide better results, and enhance model accuracy. This method optimizes seven parameters of a PV system based on observed PV power: nominal power, tilt, azimuth, albedo, irradiance, temperature dependence, and the DC/AC ratio, which is the proportion of nominal module power to nominal inverter power. Through the optimization of these parameters, a digital twin is generated that faithfully mimics the real characteristics and actions of the unidentified solar power plants or subsystems. In order to create this method, we used data on the PV system's on-site power output, surrounding temperature, and irradiance as collected by satellite in southwest Germany. Here, we provide a method that, when applied to nominal power, produces a mean bias error of around 10%, 3° for azimuth with tilt angles, a temperature coefficient ranging from 0.01% to 0.09%, and now-casts having an accuracy of about 6%. As a conclusion, we provide a novel approach to properly parametrize and model PV systems with little to no prior information about their characteristics and elements.

In this paper, an efficient approach to pattern recognition (classification) is suggested. It is based on minimization of misclassification probability and uses transition from high dimensional problem (dimension p≥2) to one dimensional problem (dimension p=1) in the case of the two classes as well as in the case of several classes with separation of classes as much as possible. The probability of misclassification, which is known as the error rate, is also used to judge the ability of various pattern recognition (classification) procedures to predict group membership. The approach does not require the arbitrary selection of priors as in the Bayesian classifier and represents the novel pattern recognition (classification) procedure that allows one to take into account the cases, which are not adequate for Fisher’s classification rule (i.e., the distributions of the classes are not multivariate normal or covariance matrices of those are different or there are strong multi-nonlinearities). Moreover, it also allows one to classify a set of multivariate observations, where each of the observations belongs to the same unknown class. For the cases, which are adequate for Fisher’s classification rule, the proposed approach gives the results similar to that of Fisher’s classification rule. For illustration, practical examples are given.

In today’s digital environment, the pattern is everywhere. It is present in many aspects of our daily life. Algorithms can be used to detect or mathematically observe a pattern physically. Vector feature values represent a pattern in the digital world. With the advent of artificial intelligence (AI) techniques in the recent era, there are so many machine learning (ML) and deep learning (DL) models that have been developed. ML is the branch of AI, which can perform tasks like data analysis, analytical model building, and classification without being explicitly programmed. DL is the subset of ML in AI. In DL, mostly artificial neural networks (ANNs) are utilized. It has the capability of work based upon unsupervised learning from the data that is unlabeled and unstructured. Using these DL and ML models, extract the meaningful features from the given image or video is known as pattern recognition (PR). PR is used in many engineering applications such as computer vision, natural language processing, character recognition, robotics, speech recognition, and so on. It is also used in many medical image processing applications and telemedicine. The present work discussed the pattern recognition problem and its various stages in detail. In addition to that, the application of deep learning and machine learning in pattern recognition is also explained briefly.

Eliminating vulnerabilities from low-level code is vital for securing software. Static analysis is a promising approach for discovering vulnerabilities since it can provide developers early feedback on the code they write. But, it presents multiple challenges not the least of which is understanding what makes a bug exploitable and conveying this information to the developer. In this paper, we present the design and implementation of a practical vulnerability assessment framework, called [InlineEquation not available: see fulltext.]. Melange performs data and control flow analysis to diagnose potential security bugs, and outputs well-formatted bug reports that help developers understand and fix security bugs. Based on the intuition that real-world vulnerabilities manifest themselves across multiple parts of a program, Melange performs both local and global analyses in stages. To scale upi?źto large programs, global analysis is demand-driven. Our prototype detects multiple vulnerability classes in C and C++ code including type confusion, and garbage memory reads. We have evaluated Melange extensively. Our case studies show that Melange scales upi?źto large codebases such as Chromium, is easy-to-use, and most importantly, capable of discovering vulnerabilities in real-world code. Our findings indicate that static analysis is a viable reinforcement to the software testing tooli?źset.

Modern software systems evolve under tight delivery cycles, heterogeneous cloud deployments, and increasingly stringent security and compliance requirements. While machine learning (ML) has demonstrated promise for predicting defect-prone and vulnerability-prone components, many deployments remain bounded by data sparsity, limited execution context, and constrained throughput of dynamic testing. Meanwhile, high-performance computing (HPC) infrastructures provide abundant parallelism, yet they are often underutilized for software quality engineering workflows that require large-scale simulation, test amplification, and telemetry-driven analytics. This manuscript proposes an integrated ML + HPC methodology for accelerating defect and vulnerability discovery through high-throughput simulation analytics. The approach treats quality discovery as a throughput-optimized, data-centric pipeline: (i) generate execution diversity via scalable simulation and test amplification, (ii) capture and normalize multi-level telemetry from builds, tests, runtime traces, and security checks, (iii) learn ranking and classification models that prioritize code regions and change sets for deeper analysis, and (iv) close the loop with root-cause triage and remediation signals. We formalize the research gap as the missing coupling between predictive models and scalable execution diversity, and we outline an evaluation framework spanning prediction quality, defect and vulnerability yield, and end-to-end cost-per-finding. The resulting methodology enables software teams to scale discovery beyond conventional CI constraints, improving early warning capability and accelerating remediation in complex enterprise-grade systems.

Happiness prediction based on large-scale online data and machine learning models is an emerging research topic that underpins a range of issues, from personal growth to social stability. Many advanced machine learning (ML) models with explanations are used for happiness online assessment while maintaining high accuracy of results. However, expert feedback and sociological theory may be absent from these models, which limits the association between prediction results and the right reasons for why they occurred. Sociological studies have shown that primary and secondary relations are inherent in happiness factors, which can be used as domain knowledge to guide model training. Inspired by such insights, this article attempts to provide new insights into the explanation consistency from an empirical study perspective. Then this article studies how to represent and introduce domain knowledge constraints to make ML models more trustworthy. We achieve this by 1) proving that multiple prediction models with additive factor attributions will have the desirable property of primary and secondary relations consistency; and 2) showing that factor relations with quantity can be represented as an importance distribution for encoding domain knowledge. Factor explanation difference is penalized by the Wasserstein distance among prediction models. Experimental results using two online datasets show that domain knowledge of stable factor relations exists. Using this knowledge not only improves happiness prediction accuracy but also reveals more significant happiness factors for assisting decisions.