A large part of technological advances, especially in the field of industry, have been focused on the optimization of productive processes. However, the detection of anomalies has turned out to be a great challenge in fields like industry, medicine or stock markets. The present work addresses anomaly detection on a control level plant. We propose the application of different intelligent techniques, which allow to obtain one-class classifiers using real data taken from the correct plant operation. The performance (...) of each classifier is assessed and validated with real created faults, achieving successful overall results. (shrink)

Aquaculture plays a significant role in both economic development and food production. Maintaining an ecological environment with good water quality is essential to ensure the production efficiency and quality of aquaculture. Effective management of water quality can prevent abnormal conditions and contribute significantly to food security. Detecting anomalies in the aquaculture environment is crucial to ensure that the environment is maintained correctly to meet healthy and proper requirements for fish farming. This article focuses on the use of deep learning techniques (...) to detect anomalies in water quality data in the aquaculture environment. Four deep learning anomaly detection techniques, including Autoencoder, Variational Autoencoder, Long-Short Term Memory Autoencoder, and Spectral-Residual Convolutional Neural Network, were analysed using multiple real-world sensor datasets collected from IoT aquaculture systems. Extensive experiments were conducted for temperature, dissolved oxygen, and pH parameters, and the evaluation analysis revealed that the Long-Short Term Memory Autoencoder anomaly detection method showed promising results in detecting anomalies for the temperature and oxygen datasets, while the Spectral-Residual Convolutional Neural Network demonstrated the best performance on the pH datasets. (shrink)

This proposal introduces a novel decision-making framework to advance safe economic activities in cyberspace. We focus on identifying anomalies within crypto-asset trading, recognized as potential sources of criminal activity, severely undermining the credibility of such assets. Detecting and mitigating such anomalies holds significant societal implications, particularly in fostering trust within blockchain networks. We aim to bolster the “social trust” inherent to blockchain technology by facilitating informed economic activities in cyberspace. To achieve this, we propose integrating two artificial intelligence (AI) systems (...) into a blockchain-based decentralized autonomous organization (DAO). The first AI application involves amalgamating various anomaly indicators, spanning from cluster coefficient, entropy, triangular motif analysis, correlation tensor analysis, loop component by Hodge decomposition, loop causality detection, network classification using graph Laplacian, and persistent homology analysis, into a comprehensive indicator using a Boltzmann machine. The second AI application entails deploying conversational AI to guide and support traders, aiding them in making informed trading decisions. This system is designed to alert DAO members to anomalies based on the integrated indicators, especially during massive price fluctuations. We operate under the assumption of close collaboration between governments, experts, traders, system developers, and operators to effectively organize DAOs. The primary technical challenge in our proposal lies in developing a wallet assisted by an intelligent software agent capable of safe interactions with traders within a unified DAO. With this organization, we envision fostering a global economic ecosystem where physical and cyber worlds converge, allowing democratic economic participation. (shrink)

In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis (...) anomaly detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high. (shrink)

The gradual increase in the density of highway vehicles and traffic flow makes the abnormal driving state of vehicles an indispensable tool for assisting traffic dispatch. Intelligent transportation systems can detect and track vehicles in real time, acquire characteristics such as vehicle traffic, vehicle speed, vehicle flow density, and vehicle trajectory, and further perform advanced tasks such as vehicle trajectory. The detection of abnormal vehicle trajectory is an important content of vehicle trajectory understanding. And the development of the Internet (...) of Things and 5G technology has led to a continuous increase in the rate of data information circulation. The “Internet of Vehicles” generated based on the practice of 5G communication technology constitutes a vehicle abnormal trajectory detection system, which has very high feasibility and safety and stability. Therefore, this research is aimed at the needs of preventing major accidents and forensic analysis during highway vehicles. Based on the integration of the Internet of Things 5G communication technology, a trajectorial anomaly detection of highway vehicle trajectory based on the integration of the Internet of Things 5G is proposed. By accurately sensing unsafe events at the perception layer, network layer, and application layer, the vehicle driving trajectory state is divided into several simple semantic representations. The semantic representation is analyzed, and then the moving target detection and moving target tracking algorithms needed to extract the vehicle trajectory are introduced. Through video detection and tracking of moving vehicle targets, the driving trajectory of the vehicle is obtained, and the movement characteristics of the vehicle in each frame of image are extracted. According to the relationship between the trajectory of the vehicle and the lane line, the vehicle trajectory analysis is realized, and then it is judged whether the vehicle has abnormal trajectory. Compared with the traditional method of manually detecting the driving condition of the vehicle, the abnormal trajectory detection of the vehicle based on the integration of the Internet of Things and 5G can quickly detect the abnormal trajectory of the vehicle in the traffic monitoring video. (shrink)

The application of machine learning algorithms to healthcare data can enhance patient care while also reducing healthcare worker cognitive load. These algorithms can be used to detect anomalous physiological readings, potentially leading to expedited emergency response or new knowledge about the development of a health condition. However, while there has been much research conducted in assessing the performance of anomaly detection algorithms on well-known public datasets, there is less conceptual comparison across unsupervised and supervised performance on physiological data. (...) Moreover, while heart rate data are both ubiquitous and noninvasive, there has been little research specifically for anomaly detection of this type of data. Considering that heart rate data are indicative of both potential health complications and an individual’s physical activity, this is a rich source of largely overlooked data. To this end, we employed and evaluated five machine learning algorithms, two of which are unsupervised and the remaining three supervised, in their ability to detect anomalies in heart rate data. These algorithms were then evaluated on real heart rate data. Findings supported the effectiveness of local outlier factor and random forests algorithms in the task of heart rate anomaly detection, as each model generalized well from their training on simulated heart rate data to real world heart rate data. Furthermore, results support that simulated data can help configure algorithms to a degree of performance when real labeled data are not available and that this type of learning might be especially helpful in initial deployment of a system without prior data. (shrink)

Contemporary Artificial Intelligence methods, especially their subset-deep learning, are finding their way to successful implementations in the detection and classification of intrusions at the network level. This paper presents an intrusion detection mechanism that leverages Deep AutoEncoder and several Deep Decoders for unsupervised classification. This work incorporates multiple network topology setups for comparative studies. The efficiency of the proposed topologies is validated on two established benchmark datasets: UNSW-NB15 and NetML-2020. The results of their analysis are discussed in terms (...) of classification accuracy, detection rate, false-positive rate, negative predictive value, Matthews correlation coefficient and F1-score. Furthermore, comparing against the state-of-the-art methods used for network intrusion detection is also disclosed. (shrink)

The present research describes a novel adaptive anomaly detection method to optimize the performance of nonlinear and time-varying systems. The proposal integrates a centroid-based approach with the real-time identification technique Recursive Least Squares. In order to find anomalies, the approach compares the present system dynamics with the average (centroid) of the dynamics found in earlier states for a given setpoint. The system labels the dynamics difference as an anomaly if it rises over a determinate threshold. To validate (...) the proposal, two different datasets obtained from a level control plant operation have been used, to which anomalies have been artificially added. The results shown have determined a satisfactory performance of the method, especially in those processes with low noise. (shrink)

In herein article an attempt of problem solution connected with anomaly detection in network traffic with the use of statistic models with long or short memory dependence was presented. In order to select the proper type of a model, the parameter describing memory on the basis of the Geweke and Porter-Hudak test was estimated. Bearing in mind that the value of statistic model depends directly on quality of data used for its creation, at the initial stage of the (...) suggested method, outliers were identified and then removed. For the implementation of this task, the criterion using the value of interquartile range was used. The data prepared in this manner were useful for automatic creation of statistic models classes, such as ARFIMA and Holt-Winters. The procedure of calculation of model parameters’ optimal values was carried out as a compromise between the models coherence and the size of error estimation. Then, relations between the estimated network model and its actual parameters were used in order to detect anomalies in the network traffic. Considering the possibility of appearance of significant real traffic network fluctuations, procedure of updating statistic models was suggested. The results obtained in the course of performed experiments proved efficacy and efficiency of the presented solution. (shrink)

A brain tumor is an abnormal mass or growth of a cell that leads to certain death, and this is still a challenging task in clinical practice. Early and correct diagnosis of this type of cancer is very important for the treatment process. For this reason, this study aimed to develop computer-aided systems for the diagnosis of brain tumors. In this research, we proposed three different end-to-end deep learning approaches for analyzing effects of local and deep features for brain MRI (...) images anomaly detection. The first proposed system is Directional Bit-Planes Deep Autoencoder which extracts and learns local and direction features. The DBP-DAE by decomposition of a local binary pattern into eight bit-planes extracts are directional and inherent local-structure features from the input image and learns robust feature for classification purposes. The second one is a Dilated Separable Residual Convolutional Network which extracts high and low-level features. The main advantage of this approach is that it is robust and shows stable results regardless to size of image database and to solve overfitting problems. To explore the effects of mixture of local and deep extracted feature on accuracy of classification of brain anomaly, a multibranch convolutional neural network approach is proposed. This approach is designed according to combination of DBP-DAE and DSRCN in an end-to-end manner. Extensive experiments conducted based on brain tumor in MRI image public access databases and achieves significant results compared to state-of-the-art algorithms. In addition, we discussed the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s. (shrink)

We examine the epistemological foundations of a leading technique in the search for evidence of life on exosolar planets. Specifically, we consider the “transit method” for spectroscopic analysis of exoplanet atmospheres, and the practice of treating anomalous chemical compositions of the atmospheres of exosolar planets as indicators of the potential presence of life. We propose a methodology for ranking the anomalousness of atmospheres that uses the mathematical apparatus of support vector machines, and which aims to be agnostic with respect to (...) the particular chemical biosignatures of life. We argue that our approach is justified by an appeal to the “hinge” model of epistemic justification first proposed by as reported by Wittgenstein. We then compare our approach to previous work due to Walker et al. :779–824, 2018) and Cleland :722–729, 2019a; Does ‘life’ have a definition?, Cambridge University Press, New York, 2019b). (shrink)

Research on implicit conflict detection suggests that people are sensitive to violations of logical principles. When they make reasoning errors, their epistemic radar presumably detects an anomaly....

The use of technologies like artificial intelligence can drive productivity growth, efficiency and innovation. The goal of this study is to develop an anomaly detection method for locating flaws on the surface of sandwich panels using YOLOv5. The proposed algorithm extracts information locally from an image through a prediction system that creates bounding boxes and determines whether the sandwich panel surface contains flaws. It attempts to reject or accept a product based on quality levels specified in the standard. (...) To evaluate the proposed method, a comparison was made with a sandwich panel damage detection method based on a convolutional neural network and methods based on thresholding. The findings show that the proposed method, which is based on an object detector, is more accurate than the alternatives. The characteristics of the model, which can reject or accept a product according to the standard and limit allowable manufacturing flaws to obtain a quality product, also enable this system to improve industrial standards for producing sandwich panels while increasing speed. (shrink)

The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show (...) that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%. (shrink)

Detecting Intrusions and anomalies is becoming much more challenging with new attacks popping out over a period of time. Achieving better accuracies by applying benchmark classifier algorithms used for identifying intrusions and anomalies have several hidden data mining challenges. Although neglected by many research findings, one of the most important and biggest challenges is the similarity or membership computation. Another challenge that cannot be simply neglected is the number of features that attributes to dimensionality. This research aims to come up (...) with a new membership function to carry similarity computation that can be helpful for addressing feature dimensionality issues. In principle, this work is aimed at introducing a novel membership function that can help to achieve better classification accuracies and eventually lead to better intrusion and anomaly detection. Experiments are performed on KDD dataset with 41 attributes and also KDD dataset with 19 attributes. Recent approaches CANN and CLAPP have showed new approaches for intrusion detection. The proposed classifier is named as UTTAMA. UTTAMA performed better to both CANN and CLAPP approaches w.r.t overall classifier accuracy. Another promising outcome achieved using UTTAMA is the U2R and R2L attack accuracies. The importance of proposed approach is that the accuracy achieved using proposed approach outperforms CLAPP, CANN, SVM, KNN and other existing classifiers. (shrink)

This work proposes an explanation of the Pioneer anomaly, the unmodelled and as yet unexplained blueshift detected in the microwave signal of the Pioneer 10 and other spaceships by Anderson et al. in 1998. What they observed is similar to the effect that would have either (i) an anomalous acceleration a P the ship towards the Sun, or (ii) an acceleration of the clocks a t =a P /c. The second alternative is investigated here, with a phenomenological model in (...) which the anomaly is an effect of the background gravitational potential Ψ (t) that pervades all the universe and is increasing because of the expansion. It is shown that 2a t =dΨ /dt=d 2τ clocks /dt 2, evaluated at present time t 0, where t and τ clocks are the coordinate time and the time measured by the atomic clocks, respectively. The result of a simple estimate gives the value a t ⋍ 1.8× 10−18 s−1, while Anderson et al. suggested a t =(2.9±0.4) × 10−18 s−1 on the basis of their observations. The calculation are performed near the Newtonian limit but in the frame of general relativity. (shrink)

Over the past hundred years, a number of scientific investigators claim to have adduced experimental evidence for phenomena information” seems to behave like a weak signal that has to compete for the information-processing resources of the organism, a reduction of ongoing sensorimotor activity may facilitate ESP detection. Such a meaningful convergence of results suggests that psi phenomena may represent a unitary, coherent process whose nature and compatibility with current physical theory have yet to be determined. The theoretical implications and (...) potential practical applications of psi could be significant, irrespective of the small magnitude of psi effects in laboratory settings. (shrink)

A fundamental tenet of Paul Feyerabend’s pluralistic view of science has it that theory proliferation, that is, the availability of theoretical alternatives, is of crucial importance for the detection of anomalies in established theories. Paul Hoyningen-Huene calls this the Anomaly Importation Thesis, according to which anomalies are imported, as it were, into well-established theories from competing alternatives. This article pursues two major objectives: (a) to work out the systematic details of Feyerabend’s ideas on theory proliferation and anomaly (...) import as they are presented in his early publications and his Against Method and (b) to compare Feyerabend’s ideas on theory proliferation and anomaly import with corresponding features in Popper’s critical rationalist philosophy of science. As it turns out, neither the Principle of Proliferation nor the Anomaly Importation Thesis are necessarily incompatible with critical rationalism. In spite of Feyerabend’s general anti-Popperian attitude, I argue that theoretical pluralism can be seen as an advancement of the critical rationalist philosophy and that critical rationalism provides good arguments for pluralism. (shrink)

Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the (...) monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance. (shrink)

The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet. Intrusion detection systems are used to protect IoT systems from the various anomalies and attacks at the network level. Intrusion Detection (...) Systems can be improved through machine learning techniques. Our work focuses on creating classification models that can feed an IDS using a dataset containing frames under attacks of an IoT system that uses the MQTT protocol. We have addressed two types of method for classifying the attacks, ensemble methods and deep learning models, more specifically recurrent networks with very satisfactory results. (shrink)

The study considers the development of methods for detecting anomalous network connections based on hybridization of computational intelligence methods. An analysis of approaches to detecting anomalies and abuses in computer networks. In the framework of this analysis, a classification of methods for detecting network attacks is proposed. The main results are reduced to the construction of multi-class models that increase the efficiency of the attack detection system, and can be used to build systems for classifying network parameters during the (...) attack. A model of an artificial immune system based on an evolutionary approach, an algorithm for genetic-competitive learning of the Kohonen network and a method of hierarchical hybridization of binary classifiers with the addition to the detection of anomalous network connections have been developed. The architecture of the network distributed attack detection system has been developed. The architecture of the attack detection system is two-tier: the first level provides the primary analysis of individual packets and network connections using signature analysis, the second level processes the processing of aggregate network data streams using adaptive classifiers. A signature analysis was performed to study network performance based on the Aho-Korasik and Boyer-Moore algorithms and their improved analogues were implemented using OpenMP and CUDA technologies. The architecture is presented and the main points of operation of the network attack generator are shown. A system for generating network attacks has been developed. This system consists of two components: an asynchronous transparent proxy server for TCP sessions and a frontend interface for a network attack generator. The results of the experiments confirmed that the functional and non-functional requirements, as well as the requirements for computing intelligent systems, are met for the developed attack detection system. (shrink)

We introduce the CUBISM system for the analysis and deep understanding of multi-participant dialogues. CUBISM brings together two typically separate forms of discourse analysis: semantic analysis and sociolinguistic analysis. In the paper proper, we describe and illustrate major components of the CUBISM system, and discuss the challenge posed by the system’s ultimate purpose, which is to automatically detect anomalous changes in participants’ expressed or implied beliefs about the world and each other, including shifts toward or away from cultural and community (...) norms. (shrink)

According to the standard interpretation of Einstein’s field equations, gravity consists of mass-energy curving spacetime, and an additional physical force or entity—denoted by Λ (the ‘cosmological constant’)—is responsible for the Universe’s metric-expansion. Although General Relativity’s direct predictions have been systematically confirmed, the dominant cosmological model thought to follow from it—the ΛCDM (Lambda cold dark matter) model of the Universe’s history and composition—faces considerable challenges, including various observational anomalies and experimental failures to detect dark matter, dark energy, or inflation-field candidates. This (...) paper shows that Einstein’s Equivalence Principle entails two possible physical interpretations of General Relativity’s field equations. Although the field equations facially appear to support the standard interpretation—that gravity consists of mass-energy curving spacetime—the field equations can be equivalently understood as holding that gravitational effects instead result from mass-energy accelerating the metric-expansion of a second-order spacetime fabric superimposed upon an absolute, first-order Euclidean space, resulting in the observational appearance of spacetime curvature. This alternative interpretation of relativity is shown to be empirically equivalent to the standard interpretation of relativity, albeit with a changing value for Λ (which is similar to how Λ is understood in the conception of Λ as ‘quintessence’, but in this case takes Λ to be gravity). The reconceptualization is then shown to potentially resolve major observational anomalies for the ΛCDM model, including recent observations conflicting with ΛCDM predictions, as well as failures to directly detect dark matter, dark energy, and inflation field/particle candidates. (shrink)

The relationship between the neurobiology of speech and music has been investigated for more than a century. There remains no widespread agreement regarding how (or to what extent) music perception utilizes the neural circuitry that is engaged in speech processing, particularly at the cortical level. Prominent models such as Patel’s Shared Syntactic Integration Resource Hypothesis (SSIRH) and Koelsch’s neurocognitive model of music perception suggest a high degree of overlap, particularly in the frontal lobe, but also perhaps more distinct representations in (...) the temporal lobe with hemispheric asymmetries. The present meta-analysis study used activation likelihood estimate analyses to identify the brain regions consistently activated for music as compared to speech across the functional neuroimaging (fMRI and PET) literature. Eighty music and 91 speech neuroimaging studies of healthy adult control subjects were analyzed. Peak activations reported in the music and speech studies were divided into four paradigm categories: passive listening, discrimination tasks, error/anomaly detection tasks and memory-related tasks. We then compared activation likelihood estimates within each category for music versus speech, and each music condition with passive listening. We found that listening to music and to speech preferentially activate distinct temporo-parietal bilateral cortical networks. We also found music and speech to have shared resources in the left pars opercularis but speech-specific resources in the left pars triangularis. The extent to which music recruited speech-activated frontal resources was modulated by task. While there are certainly limitations to meta-analysis techniques particularly regarding sensitivity, this work suggests that the extent of shared resources between speech and music may be task-dependent and highlights the need to consider how task effects may be affecting conclusions regarding the neurobiology of speech and music. (shrink)

A sustainable supply chain management strategy reduces risks and meets environmental, economic and social objectives by integrating environmental and financial practices. In an ever-changing environment, supply chains have become vulnerable at many levels. In a global supply chain, carefully tracing a product is of great importance to avoid future problems. This paper describes a self-updating smart contract, which includes data validation, for tracing global supply chains using blockchains. Our proposal uses a machine learning model to detect anomalies on traceable data, (...) which helps supply chain operators detect anomalous behavior at any point in the chain in real time. Hyperledger Caliper has been used to evaluate our proposal, and obtained a combined average throughput of 184 transactions per second and an average latency of 0.41 seconds, ensuring that our proposal does not negatively impact supply chain processes while improving supply chain management through data anomaly detection. (shrink)

The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; recommender (...) systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI. (shrink)

The rapid urbanization of modern cities necessitates innovative approaches to data collection and integration for smarter urban management. With the Internet of Things (IoT) at the core of these advancements, the ability to efficiently gather, analyze, and utilize data becomes paramount. Generative Artificial Intelligence (AI) is revolutionizing data collection by enabling intelligent synthesis, anomaly detection, and real-time decision-making across interconnected systems. This paper explores how generative AI enhances IoT-driven data collection in smart cities, focusing on applications in transportation, (...) energy, public safety, and environmental monitoring. By addressing challenges such as data privacy, scalability, and ethical considerations, the study highlights how generative AI transforms urban governance and paves the way for sustainable and citizen-centric development. Key trends, case studies, and future research directions are discussed, showcasing the potential of generative AI as a cornerstone of smart city initiatives. (shrink)

In recent years, the distributed architecture has been widely adopted by security companies with the rapid expansion of their business. A distributed system is comprised of many computing nodes of different components which are connected by high-speed communication networks. With the increasing functionality and complexity of the systems, failures of nodes are inevitable which may result in considerable loss. In order to identify anomalies of the possible failures and enable DevOps engineers to operate in advance, this paper proposes a two-layer (...) prediction architecture based on the monitoring sequences of nodes status. Generally speaking, in the first layer, we make use of EXPoSE anomaly detection technique to derive anomaly scores in constant time which are then used as input data for ensemble learning in the second layer. Experiments are conducted on the data provided by one of the largest security companies, and the results demonstrate the predictability of the proposed approach. (shrink)

This groundbreaking proceedings volume explores the integration of Artificial Intelligence (AI) across key domains—healthcare, finance, education, robotics, industrial and other engineering applications —unveiling its transformative potential and practical implications. With a multidisciplinary lens, it transcends technical aspects, fostering a comprehensive understanding while bridging theory and practice. Approaching the subject matter with depth, the book combines theoretical foundations with real-world case studies, empowering researchers, professionals, and enthusiasts with the knowledge and tools to effectively harness AI. Encompassing diverse AI topics—machine learning, natural (...) language processing, computer vision, data analytics and supervisory control — the volume showcases state-of-the-art techniques propelling AI advancements. Structured into four parts: Part 1: Artificial Intelligence (AI), explores evolving deep neural networks, reinforcement learning, and explainable AI, providing a deep dive into the technical foundations of AI advancements. Part 2: Robotics and Control Systems, delves into the integration of AI in robotics and automatic control, addressing supervisory control, automated robotic movement coordination, anomaly detection, dynamic programming, and fault tolerance, offering insights into the evolving landscape of intelligent automation. Part 3: AI and Society, examines the societal impact of AI through chapters on ethical considerations, economic growth, environmental engagements, and hazard management, providing a holistic perspective on AI's role in shaping society. Part 4: PhD Symposium, presents the future of AI through cutting-edge research, covering legal and ethical dimensions, privacy considerations, and computationally efficient solutions, offering a glimpse into the next generation of AI advancements. Catering to a diverse audience—from industry leaders to students—the volume consolidates the expertise of renowned professionals, serving as a comprehensive resource for navigating the ever-evolving AI landscape. An essential reference for those staying at the forefront of AI developments. (shrink)

The acquisition and evolution of speech production, discourse and communication can be negatively impacted by brain malformations. We describe, for the first time, a case of developmental dynamic dysphasia (DDD) in a right-handed adolescent boy (subject D) with cortical malformations involving language-eloquent regions (inferior frontal gyrus) in both the left and the right hemispheres. Language evaluation revealed a markedly reduced verbal output affecting phonemic and semantic fluency, phrase and sentence generation and verbal communication in everyday life. Auditory comprehension, repetition, naming, (...) reading and spelling were relatively preserved, but executive function was impaired. Multimodal neuroimaging showed a malformed cerebral cortex with atypical configuration and placement of white matter tracts bilaterally and abnormal callosal fibers. Dichotic listening showed right hemisphere dominance for language and functional magnetic resonance imaging (fMRI) additionally revealed dissociated hemispheric language representation with right frontal activation for phonology and bilateral dominance for semantic processing. Moreover, subject D also had congenital mirror movements (CMM), defined as involuntary movements of one side of the body that mirror intentional movements of the other side. Transcranial magnetic stimulation and fMRI during voluntary unimanual (left and right) hand movements showed bilateral motor cortex recruitment and tractography revealed a lack of decussation of bilateral corticospinal tracts. Genetic testing aimed to detect mutations that disrupt the development of commissural tracts correlating with CMM (e.g., Germline DCC mutations) was negative. Overall, our findings suggest that DDD in subject D resulted from the underdevelopment of the left inferior frontal gyrus with limited capacity for plastic reorganization by its homologous counterpart in the right hemisphere. Corpus callosum anomalies probably contributed to hinder interhemispheric connectivity necessary to compensate language and communication deficits after left frontal involvement. (shrink)

The success of particle detection in high energy physics colliders critically depends on the criteria for selecting a small number of interactions from an overwhelming number that occur in the detector. It also depends on the selection of the exact data to be analyzed and the techniques of analysis. The introduction of automation into the detection process has traded the direct involvement of the physicist at each stage of selection and analysis for the efficient handling of vast amounts (...) of data. This tradeoff, in combination with the organizational changes in laboratories of increasing size and complexity, has resulted in automated and semi-automated systems of detection. Various aspects of the semi-automated regime were greatly diminished in more generic automated systems, but turned out to be essential to a number of surprising discoveries of anomalous processes that led to theoretical breakthroughs, notably the establishment of the Standard Model of particle physics. The automated systems are much more efficient in confirming specific hypothesis in narrow energy domains than in performing broad exploratory searches. Thus, in the main, detection processes relying excessively on automation are more likely to miss potential anomalies and impede potential theoretical advances. I suggest that putting substantially more effort into the study of electron–positron colliders and increasing its funding could minimize the likelihood of missing potential anomalies, because detection in such an environment can be handled by the semi-automated regime—unlike detection in hadron colliders. Despite virtually unavoidable excessive reliance on automated detection in hadron colliders, their development has been deemed a priority because they can operate at currently highest energy levels. I suggest, however, that a focus on collisions at the highest achievable energy levels diverts funds from searches for potential anomalies overlooked due to tradeoffs at the previous energy thresholds. I also note that even in the same collision environment, different research strategies will opt for different tradeoffs and thus achieve different experimental outcomes. Finally, I briefly discuss current searches for anomalous process in the context of the previous analysis. (shrink)

In this paper, the authors have detected a new effect in the area of geomagnetism, related to the behavior of a magnetic dipole freely floating on water surface. An experiment is described in the present paper in which a magnetic dipole fixed upon a float placed on non- magnetized water surface undergoes displacement along with reorientation caused by fine structure of the earth's magnetic field. This fact can probably be explained by secular decrease of the earth's major dipole moment. Further, (...) a detailed study of the phenomenon may create interesting premises for its practical use, particularly for the analysis of fine structure of geomagnetic field and its time-dependent anomalies. A strange behavior of some sea fish species prior to strong earthquakes may be explained if the fish are assumed as 'live magnetic dipoles'. (shrink)

On July 2021, the UK High Court of Justice heard the Case CO/2066/2020 on the application of Heidi Crowter who lives with Down’s syndrome, and Máire Lea-Wilson whose son Aidan has Down’s syndrome. Crowter and Lea-Wilson, with the support of the disability rights campaign, ‘Don’t Screen Us Out’, have been taking legal action against the Secretary of State for Health and Social Care (the UK Government) for a review of the 1967 Abortion Act: the removal of section 1(1)(d) making termination (...) of pregnancy lawful for ‘severe’ fetal indications detected after 24 weeks' gestation. On 23 September 2021, the High Court dismissed the claim. This action came at a time when non-invasive prenatal testing (NIPT) was introduced into the NHS England Fetal Anomaly Screening Programme for the trisomies 21, 13 and 18. The implementation of NIPT has been heavily criticised, in particular by ‘Don’t Screen Us Out’ campaigners, for increasing fetal selection and discrimination of people living with disabilities. The case of Crowter and Lea-Wilson echoes debates in other European countries such as in France and Germany, where the introduction of NIPT in the public healthcare system has provoked equally vehement public reactions and discussions. The comparison between these three countries allows contextualising the public discourses around NIPT and the ground for termination of pregnancy in relation to different socio-cultural and political contexts. We examine how each country, and particularly England, deals with the conflict between the principles of promoting the rights of people living with disabilities and preserving women’s reproductive autonomy. (shrink)

Prenatal screening pathways, as nowadays offered in most Western countries consist of similar tests. First, a risk-assessment test for major aneuploides is offered to pregnant women. In case of an increased risk, invasive diagnostic tests, entailing a miscarriage risk, are offered. For decades, only conventional karyotyping was used for final diagnosis. Moreover, several foetal ultrasound scans are offered to detect major congenital anomalies, but the same scans also provide relevant information for optimal support of the pregnancy and the delivery. Recent (...) developments in prenatal screening include the application of microarrays that allow for identifying a much broader range of abnomalities than karyotyping, and non-invasive prenatal testing that enables reducing the number of invasive tests for aneuploidies considerably. In the future, broad NIPT may become possible and affordable. This article will briefly address the ethical issues raised by these technological developments. First, a safe NIPT may lead to routinisation and as such challenge the central issue of informed consent and the aim of prenatal screening: to offer opportunity for autonomous reproductive choice. Widening the scope of prenatal screening also raises the question to what extent ‘reproductive autonomy’ is meant to expand. Finally, if the same test is used for two different aims, namely detection of foetal anomalies and pregnancy-related problems, non-directive counselling can no longer be taken as a standard. Our broad outline of the ethical issues is meant as an introduction into the more detailed ethical discussions about prenatal screening in the other articles of this special issue. (shrink)

In the field of cybernetic systems and more specifically in robotics, one of the fundamental objectives is the detection of anomalies in order to minimize loss of time. Following this idea, this paper proposes the implementation of a Hybrid Intelligent System in four steps to impute the missing values, by combining clustering and regression techniques, followed by balancing and classification tasks. This system applies regression models to each one of the clusters built on the instances of data set. Subsequently, (...) a variety of balancing techniques are applied to improve the classifier’s ability to discern whether it is in an error or a normal state. These techniques support to obtain better classification ratios in which a robot is close to error and allow us to bring the behavior back to a normal state. The experimentation is performed using a modern and public data set, which has been extracted from a component-based robotic system, in which different anomalies are induced by software in their components. (shrink)

Whoever compares the genomes of distantly related species might find aberrantly high sequence similarity at certain loci. Such anomaly can only be explained by genetic material being transferred through other means than reproduction, that is, a horizontal transfer. Between multicellular organisms, the transferred material will likely turn out to be a transposable element. Because TEs can move between loci and invade chromosomes by replicating themselves, HT of TEs profoundly impacts genome evolution. Yet, very few studies have quantified HTT at (...) large taxonomic scales. Indeed, this task currently faces difficulties that range from the variable quality of available genome sequences to limitations of analytical procedures, some of which have been overlooked. Here we review the many challenges that an extensive analysis of HTT must overcome, we expose biases and limits of current methods, suggest solutions or workarounds, and reflect upon approaches that could be developed to better quantify this phenomenon. With the large amount of genomic resources now available, horizontal transfer of transposable elements can be studied as an evolutionary process, rather than as isolated cases. We discuss some of the difficulties encountered in detecting and counting such transfers and propose guidelines and directions for future research in this area. (shrink)

Anomaloscope An instrument used for detecting anomalies of color vision. The test subject adjusts the ratio of two monochromatic lights to form a match with a third monochromatic light. The most common form of this procedure involves a Rayleigh match: a match between a mixture of monochromatic green and red lights, and a monochromatic yellow light. Normal subjects will choose a matching ratio of red to green light that falls within a fairly narrow range of values. Subjects with anomalous color (...) vision will choose a ratio of red to green that falls outside this range, and red-green dichromats will accept any ratio of red to green as forming a match. (shrink)

The presence of asymmetry in the human cerebral hemispheres is detectable at both the macroscopic and microscopic scales. The horizontal expansion of cortical surface during development (within individual brains), and across evolutionary time (between species), is largely due to the proliferation and spacing of the microscopic vertical columns of cells that form the cortex. In the asymmetric planum temporale (PT), minicolumn width asymmetry is associated with surface area asymmetry. Although the human minicolumn asymmetry is not large, it is estimated to (...) account for a surface area asymmetry of approximately 9% of the region’s size. Critically, this asymmetry of minicolumns is absent in the equivalent areas of the brains of other apes. The left-hemisphere dominance for processing speech is thought to depend, partly, on a bias for higher resolution processing across widely spaced minicolumns with less overlapping dendritic fields, whereas dense minicolumn spacing in the right hemisphere is associated with more overlapping, lower resolution, holistic processing. This concept refines the simple notion that a larger brain area is associated with dominance for a function and offers an alternative explanation associated with “processing type.” This account is mechanistic in the sense that it offers a mechanism whereby asymmetrical components of structure are related to specific functional biases yielding testable predictions, rather than the generalization that “bigger is better” for any given function. Face processing provides a test case – it is the opposite of language, being dominant in the right hemisphere. Consistent with the bias for holistic, configural processing of faces, the minicolumns in the right-hemisphere fusiform gyrus are thinner than in the left hemisphere, which is associated with featural processing. Again, this asymmetry is not found in chimpanzees. The difference between hemispheres may also be seen in terms of processing speed, facilitated by asymmetric myelination of white matter tracts ( Anderson et al., 1999 found that axons of the left posterior superior temporal lobe were more thickly myelinated). By cross-referencing the differences between the active fields of the two hemispheres, via tracts such as the corpus callosum, the relationship of local features to global features may be encoded. The emergent hierarchy of features within features is a recursive structure that may functionally contribute to generativity – the ability to perceive and express layers of structure and their relations to each other. The inference is that recursive generativity, an essential component of language, reflects an interaction between processing biases that may be traceable in the microstructure of the cerebral cortex. Minicolumn organization in the PT and the prefrontal cortex has been found to correlate with cognitive scores in humans. Altered minicolumn organization is also observed in neuropsychiatric disorders including autism and schizophrenia. Indeed, altered interhemispheric connections correlated with minicolumn asymmetry in schizophrenia may relate to language-processing anomalies that occur in the disorder. Schizophrenia is associated with over-interpretation of word meaning at the semantic level and over-interpretation of relevance at the level of pragmatic competence, whereas autism is associated with overly literal interpretation of word meaning and under-interpretation of social relevance at the pragmatic level. Both appear to emerge from a disruption of the ability to interpret layers of meaning and their relations to each other. This may be a consequence of disequilibrium in the processing of local and global features related to disorganization of minicolumnar units of processing. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Ethics CommentaryTamra Lysaght, Senior Research FellowThe case scenario reflects the dilemmas frequently faced by researchers who discover incidental findings in the course of their studies. Indeed, the literature suggests that the prevalence of incidental findings in neuroimaging research is common,1 and a recent meta-analysis found that such findings increase significantly with age.2 Thus, given the age of the study population and increased likelihood of co-morbidities, it is puzzling that (...) the research team did not anticipate the possibility of finding an anomaly. Even more puzzling is that the reviewing IRB did not detect this omission and require that the team build a plan into the research protocol for managing incidental findings. Following international recommendations,3 such a plan would have likely included protocols for obtaining consent in dealing with an incidental finding, a procedure for evaluating the clinical relevance of the finding, and arranging for suitable follow-up care if necessary.Nevertheless, the research team, not having obtained consent to disclose an incidental finding to the participant, or not, must decide whether to break protocol and contact the participant with information that could have serious health implications for his or her health, especially if the observed lesion turns out to be cancer or some other life-threatening neuropathology. Any decision to break the research protocol should be made in consultation with the approving IRB. The principal investigator, Dr. M, who is ultimately responsible for the ethical conduct of this study, has a number of factors to consider when making this decision. First and foremost, Dr. M will need to weigh up the risks and benefits of disclosing the findings to this 72-year-old [End Page 283] research participant who is presumably already experiencing cognitive impairments, if not early stage symptoms of dementia.Numerous international scholars agree that disclosure of incidental findings should rely on the net beneficence to an individual research participant.4 Specifically, Wolf et al. argue that there should be a strong net benefit for disclosing an incidental finding, such that the information reveals a condition that is likely to be either life-threatening or grave and can be avoided or ameliorated through an intervention. In such instances, the researchers have an ethical obligation to disclose the findings and potentially follow up with clinical care. However, if there is only a net possible benefit, such that the condition is not life-threatening but may be grave or serious and cannot be avoided or ameliorated, then investigators may consider other factors, such as the respect that is owed to research participants and their needs and preferences for information about their health and well-being. Where the condition is not likely to be serious, researchers are not compelled to disclose an incidental finding.In determining the seriousness of an incidental finding, a specialist with expertise in assessing the clinical significance of MRI scans is needed to examine the participants’ images.5 In this instance, Dr. M has a neurologist on the research team who has detected the lesion but is unable to ascertain its clinical relevance without further investigation. The reasons why follow-up information is required is not detailed in the case scenario, but may have arisen because the scans were taken for research purposes and may not be clinically validated for diagnostic purposes — a possibility given the aims of the study (i.e., to elucidate the transition of minimal cognitive impairment to dementia, not brain tumours). For whatever reason, the detected lesion is only a suspected incidental finding that may or may not be life-threatening or grave. Thus, the net benefit to the participant in disclosing this unclear finding is harder to judge.Assuming that the lesion is a serious or life-threatening condition, the team may consider whether it can be avoided or ameliorated with an intervention. The cognitively impaired, although still mentally competent, participant is 72 years old and may respond poorly to a neurosurgical intervention if the diagnosis of a tumour or malignancy is confirmed. The physical and emotional distress of such a procedure may exacerbate the existing cognitive impairments, reducing the net benefit ratio of the disclosure. Indeed, the team should consider the impact that the disclosure... (shrink)

With the reduction in diseases due to nutritional deficiencies and infection, disorders which are wholly or partly genetic are becoming relatively more important in all branches of modern medicine. Genetic counselling has developed in recent years from just explaining to an individual or a couple the risk of them producing a handicapped child, to the possibility in many cases of better diagnosis and active intervention to reduce the risks. At the same time antenatal screening programmes have been introduced to detect (...) women who may be carrying a fetus with a severe handicapping anomaly. The ethical aspects of these advances are considered in this article. A practical approach to the resolution of any dilemmas is proposed which concentrates on the duties incumbent on doctors and other health care workers involved with patients who have or may carry genetic disorders. (shrink)

Unusual atmospheric phenomena (UAPs) were observed in daylight by multiple observers on board two civil aircraft in widely separated locations. We summarise results of an investigation based on radio communications reporting events in real time to Air Traffic Control (ATC), ATC radar and weather radar recordings, Civil Aviation Authority (CAA) documents, witness interviews and statements, and other sources. We describe attempts to explain the phenomena with the help of expert specialist advisers and professional resources in the fields of meteorology, atmospheric (...) optics, oceanography and geophysics. We are able to show that widespread media stories describing enormous phenomena up to a mile wide and detected by radar were based on speculation and misunderstandings. Many news reports were grossly exaggerated and inaccurate. However, we are unable to conclusively identify the UAPs observed. It proves possible to eliminate a number of theories with a fairly high level of confidence, leaving us with two types of phenomena—a rare atmospheric-optical effect or an earthquake precursor—both of which have the potential to explain at least some, although not all, features of the reports. We highlight certain features that appear to leave open the possibility of unusual physical processes. Keywords: UAP—atmospheric optics—anomaly—mirage—earthquake lights—EQL. (shrink)

The supervisor of the activities of a system user should benefit from the knowledge contained in the event logs of the user. They allow the monitoring of the sequential and parallel user activities. To make event logs more accessible to the supervisor, we suggest a process mining approach, including first the design of an understanding model of the activities of a system user. The model design is based on the relationships between the event logs and the activities of a system (...) user. An intervention model completes the understanding model to assist the supervisor. The intervention model enables an action of the supervisor on the critical activities, and the detection of anomalies. The models are automatically designed with a model-driven engineering approach. An experiment on a smart home system illustrates this tooled design, where the supervisor is a medical or paramedical staff member. (shrink)

In his celebrated historic-epistemological work Identité et réalité, Émile Meyerson claimed that the scientific conservation principles were first suggested and accepted for philosophical reasons, and only afterwards were submitted to experimental tests. One of the instances he discussed in his book is the principle of mass conservation in chemical reactions. Meyerson pointed out that several authors, from Antiquity to Kant, accepted the idea of quantitative conservation of matter; and Lavoisier himself was strongly influenced by a priori ideas, using this principle (...) instead of attempting to test it. This paper will review Meyerson’s claim and historic evidence, focusing especially the late nineteenth and early twentieth century, when the principle of mass conservation was tested in highly accurate experiments. Instead of confirming the principle, some of those experiments led to the detection of anomalies. Hans Landolt, for instance, noticed that there were some small violations of the principle. He observed mass variations of about 10−6 in chemical reactions produced in hermetically sealed glass tubes. Since Landolt was a famous chemist, his results produced a strong response. Several researchers repeated his experiments, with different results. Landolt himself improved his experiments, with a balance that could detect mass changes of 10−7. After changes of the experimental procedure, the chemical reactions did not show significant mass changes. There was not, however, any “crucial experiment” “proving” that mass was conserved. The observed anomalies were set aside mainly by theoretical reasons, after the discovery of radioactivity and the development of the theory of relativity. (shrink)

Event‐related brain potentials were recorded while subjects listened to sentences containing a controlled infinitival complement. Subject and object control items were used, both with 2 potential antecedents in the upper clause. Half of the sentences had a gender agreement violation between the null subject of the infinitival complement and an adjective predicated of it. The rapid detection of this anomaly would indicate that the parser had established the coreference relation between the null subject and an antecedent, and that (...) the processor had rapidly consulted verb control information to select the proper antecedent of the null subject. The results showed that for both subject and object control items ungrammatical adjectives elicited a P600 effect. These data imply that the processor has coindexed the null subject with an antecedent, and that the antecedent has been selected on the basis of control information. These results are compatible with parsing models that emphasize the rapid influence of verb‐specific information on sentence processing. (shrink)

As the world population ages, the patients per physician ratio keeps on increasing. This is even more important in the domain of chronic pathologies where people are usually monitored for years and need regular consultations.To address this problem, we propose an automated system to monitor a patient population, detecting anomalies in instantaneous data and in their temporal evolution, so that it could alert physicians. By handling the population of healthy patients autonomously and by drawing the physicians' attention to the patients–at-risk, (...) the system allows physicians to spend comparatively more time with patients who need their services. In such a system, the interaction between the patients, the diagnosis module, and the physicians is very important. We have based this system on a combination of stochastic models, fuzzy filters, and strong medical semantics. (shrink)