Journal articles: 'False-self system'

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Klímová, Helena. "The False We – the False Collective Self – and the Social Unconscious in a Totalitarian System." Group Analysis 48, no. 2_suppl (May 26, 2015): 50–54. http://dx.doi.org/10.1177/0533316415583262j.

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Sokolovsky, S. P., and A. P. Telenga. "METHODOLOGY FOR THE FORMATION OF INFORMATION SYSTEMS FALSE NETWORK TRAFFIC FOR PROTECTION AGAINST NETWORK RECONNAISSANCE." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 212 (February 2022): 40–47. http://dx.doi.org/10.14489/vkit.2022.02.pp.040-047.

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Simulation of false network traffic in order to protect the structural and functional characteristics of information systems is a difficult task in view of the self-similarity of its statistical properties in IP networks, not only in the current moment, but also retrospectively. A Hurst index based algorithm for assessing the degree of self-similarity of network traffic of information systems has been proposed. The connection between the fractal dimension of the attractor of the model of information system functioning and the Hurst index is shown. A technique has been developed to substantiate the characteristics of false network traffic to simulate the functioning of information systems in the process of reconfiguration of their structural and functional characteristics caused by an intruder conducting network reconnaissance. The methodology allows to solve the problem of improving the protection of information systems from network reconnaissance by providing the maximum likelihood of false network traffic by pseudophase reconstruction of the dynamic system attractor, approximating the time series of information traffic of the protected object. The approaches to the description of the network traffic of the information system are considered, the parameters determining the network interaction between the two nodes of the data transmission network are selected as follows: source IP-address, source port, destination IP-address, destination port, protocol, packet size, duration of connection. The process of functioning of information system in different situations is formalized and the dependences allowing to synthesize parameters of false network traffic, statistically similar to the reference ones are received.

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Fatourechi, M., R. K. Ward, and G. E. Birch. "A self-paced brain–computer interface system with a low false positive rate." Journal of Neural Engineering 5, no. 1 (December 11, 2007): 9–23. http://dx.doi.org/10.1088/1741-2560/5/1/002.

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Ambawade, Dayanand, and Dr Jagdish W. Bakal. "Alert Clustering using Self-Organizing Maps and K-Means Algorithm." International Journal of Engineering and Advanced Technology 12, no. 1 (October 30, 2022): 82–87. http://dx.doi.org/10.35940/ijeat.a3852.1012122.

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Alert correlation is a system that receives alerts from heterogeneous Intrusion Detection Systems and reduces false alerts, detects high-level patterns of attacks, increases the meaning of occurred incidents, predicts the future states of attacks, and detects root cause of attacks. This paper presents self-organizing maps and the k-means machine learning algorithms to reduce the number of alerts by clustering them.

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Valentino, Kristin, Dante Cicchetti, Fred A. Rogosch, and Sheree L. Toth. "True and false recall and dissociation among maltreated children: The role of self-schema." Development and Psychopathology 20, no. 1 (2008): 213–32. http://dx.doi.org/10.1017/s0954579408000102.

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AbstractThe current investigation addresses the manner through which trauma affects basic memory and self-system processes. True and false recall for self-referent stimuli were assessed in conjunction with dissociative symptomatology among abused (N= 76), neglected (N= 92), and nonmaltreated (N= 116) school-aged children. Abused, neglected, and nonmaltreated children did not differ in the level of processing self-schema effect or in the occurrence and frequency of false recall. Rather, differences in the affective valence of false recall emerged as a function of maltreatment subtype and age. Regarding dissociation, the abused children displayed higher levels of dissociative symptomatology than did the nonmaltreated children. Although abused, neglected, and nonmaltreated children did not exhibit differences in the valence of their self-schemas, positive and negative self-schemas were related to self-integration differently among the subgroups of maltreatment. Negative self-schemas were associated with increased dissociation among the abused children, whereas positive self-schemas were related to increased dissociation for the neglected children. Thus, positive self-schemas displayed by the younger neglected children were related to higher dissociation, suggestive of defensive self-processing. Implications for clinical intervention are underscored.

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Hofmeyr, Steven A., and Stephanie Forrest. "Architecture for an Artificial Immune System." Evolutionary Computation 8, no. 4 (December 2000): 443–73. http://dx.doi.org/10.1162/106365600568257.

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An artificial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, distributed computation, error tolerance, dynamic learning and adaptation, and self-monitoring. ARTIS is a general framework for a distributed adaptive system and could, in principle, be applied to many domains. In this paper, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be effective at detecting intrusions, while maintaining low false positive rates. Finally, similarities and differences between ARTIS and Holland's classifier systems are discussed.

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Asante, Emmanuel, and Raphael Avornyo. "Enhancing Healthcare System in Ghana through Integration of Traditional Medicine." Journal of Sociological Research 4, no. 2 (October 9, 2013): 256. http://dx.doi.org/10.5296/jsr.v4i2.4224.

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Abstract Traditional Medicine (TM) has been practised in Ghana from time immemorial and many Ghanaians still patronise the services of traditional medical practitioners (TMPs). However, TM has not been integrated into the formal healthcare delivery system of the country. This might be partly due to attitudes and perceptions towards it. The aim of the study was to find out the attitudes and perceptions of Scientific Medical Practitioners (SMPs) towards TM in Ghana and then propose measures for the full integration of TM into Ghana’s healthcare delivery system. A descriptive survey methodology was used to solicit responses from all 33 SMPs practising in the Central Region of Ghana. An in-depth interview and self administered questionnaire were the main instruments used for data collection. The main result of the study is that, although SMPs would want the full integration of TM into the formal healthcare delivery system, when confronted with possible ways of working with TMPs they showed reluctance to accepting them as equal partners since they perceived their practice as inferior to theirs. In order to reduce the mistrust and lack of understanding of the philosophy that underlie Scientific Medicine and Traditional Medicine, there must be regular consultations and dialogue between and among practitioners of the two medical systems. This may engender the needed trust and respect that the practitioners need to accord each other in order to develop and integrate TM into the national healthcare system.Keywords: attitudes, integration, perception, scientific medical practitioners, traditional medicine.

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Bashashati, Ali, Rabab K. Ward, and Gary E. Birch. "Towards Development of a 3-State Self-Paced Brain-Computer Interface." Computational Intelligence and Neuroscience 2007 (2007): 1–8. http://dx.doi.org/10.1155/2007/84386.

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Most existing brain-computer interfaces (BCIs) detect specific mental activity in a so-called synchronous paradigm. Unlike synchronous systems which are operational at specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a 2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG. Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41% at the fixed false positive rate of 1%. This paper proposes two designs for a 3-state self-paced BCI that is capable of handling idle brain state. The two proposed designs aim at detecting right- and left-hand extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement). It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1%) in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1%) in the context of a 2-state self-paced BCI.

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Mina Qaisar, Saeed, Dija Sidiya, Mohammad Akbar, and Abdulhamit Subasi. "An Event-Driven Multiple Objects Surveillance System." International journal of electrical and computer engineering systems 9, no. 1 (2018): 35–44. http://dx.doi.org/10.32985/ijeces.9.1.2.

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Traditional surveillance systems are constrained because of a fixed and preset pattern of monitoring. It can reduce the reliability of the system and cause an increased generation of false alarms. It results in an increased processing activity of the system, which causes an augmented consumption of system resources and power. Within this framework, a human surveillance system is proposed based on the event-driven awakening and self-organization principle. The proposed system overcomes these downsides up to a certain level. It is achieved by intelligently merging an assembly of sensors with two cameras, actuators, a lighting module and cost-effective embedded processors. With the exception of low-power event detectors, all other system modules remain in the sleep mode. These modules are activated only upon detection of an event and as a function of the sensing environment condition. It reduces power consumption and processing activity of the proposed system. An effective combination of a sensor assembly and a robust classifier suppresses generation of false alarms and improves system reliability. An experimental setup is realized in order to verify the functionality of the proposed system. Results confirm proper functionality of the implemented system. A 62.3-fold system memory utilization and bandwidth consumption reduction compared to traditional counterparts is achieved, i.e. a result of the proposed system self-organization and event-driven awakening features. It confirms that the proposed system outperforms its classical counterparts in terms of processing activity, power consumption and usage of resources

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Zhao, Xuemin. "Application of Data Mining Technology in Software Intrusion Detection and Information Processing." Wireless Communications and Mobile Computing 2022 (June 9, 2022): 1–8. http://dx.doi.org/10.1155/2022/3829160.

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In order to improve the efficiency of the software intrusion detection system, the author proposes an application based on data mining technology in software intrusion detection and information processing. Apply data mining technology to software intrusion detection; first, analyze and research software intrusion detection technology and data mining technology, including the basic concepts of software intrusion detection, the realization technology of software intrusion detection, the classification of software intrusion detection systems, and the typical software intrusion detection system situation. By detecting and analyzing known intrusion data and using association rules, constructing the inspection system rule base enables the system to learn independently and improve itself and has good scalability, while improving the degree of automation and complete intrusion detection. Experimental results show that under the same test sample, the accuracy of the detection system model designed in this paper is 95.67%, higher than the other three detection systems, and the false alarm rate is lower than other systems, which has certain advantages. It is proved that the system in this paper can help improve the accuracy of software intrusion detection, significantly reduce the false alarm rate and false alarm rate of software intrusion detection, and provide reference for the optimization and improvement of software intrusion detection system and information processing. The system has a certain degree of self-adaptation, which can effectively detect external intrusions.

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Wang, Jianqin, Henry Otgaar, Mark L. Howe, and Sen Cheng. "Self-referential false associations: A self-enhanced constructive effect for verbal but not pictorial stimuli." Quarterly Journal of Experimental Psychology 74, no. 9 (April 16, 2021): 1512–24. http://dx.doi.org/10.1177/17470218211009772.

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Memory is considered to be a flexible and reconstructive system. However, there is little experimental evidence demonstrating how associations are falsely constructed in memory, and even less is known about the role of the self in memory construction. We investigated whether false associations involving non-presented stimuli can be constructed in episodic memory and whether the self plays a role in such memory construction. In two experiments, we paired participants’ own names (i.e., self-reference) or the name “Adele” (i.e., other-reference) with words and pictures from Deese/Roediger–McDermott (DRM) lists. We found that (1) participants not only falsely remembered the non-presented lure words and pictures as having been presented, but also misremembered that they were paired with their own name or “Adele,” depending on the referenced person of related DRM lists; and (2) there were more critical lure–self associations constructed in the self-reference condition than critical lure–other associations in the other-reference condition for word but not for picture stimuli. These results suggest a self-enhanced constructive effect that might be driven by both relational and item-specific processing. Our results support the spreading-activation account for constructive episodic memory.

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Ma, Jian Hong, and Li Xia Ji. "Research of Network Monitoring Management System Based on Artificial Immune." Advanced Materials Research 648 (January 2013): 285–88. http://dx.doi.org/10.4028/www.scientific.net/amr.648.285.

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In order to solve the traditional network monitor management system fault detection of slow problem,adaptive differential of high false alarm rate we built network monitor system model combined with the artificial immune principle.Through artificial immune "self" and "non-self" recognition ability network monitoring algorithm simulated the matching,negative selection,memory mechanism of artificial immune system detection of network fault.Throughout the design process,we studied the biological immune characteristics,antibody affinity and concentration of concepts the antibody selection probability algorithm was proposed.The algorithm enhanced new antibody generation mechanisms,memory mechanism the and tolerance of the system to establish the network monitoring system provides the theoretical model.

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Luo, Huai Lin, Ling Yu Zhang, and Quan Yuan. "Self-Adapting Function Principle and Creative Design of BFT Type." Applied Mechanics and Materials 397-400 (September 2013): 830–32. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.830.

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The author summed up the total function of the self-adapting system of slow-footed multiple-flexible driving with overloading by making use of the creative design method of function principle, applied the mechanism of self-adapting to creative design this sort equipment of BFT,funded its false, and proposed way of creative design basing on the total functions.This way has super-performance of self-adapting .It has a good foreground

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Mehrer, Helmut. "Self-Diffusion, Solute-Diffusion and Interdiffusion in Binary Intermetallics." Diffusion Foundations 2 (September 2014): 1–72. http://dx.doi.org/10.4028/www.scientific.net/df.2.1.

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800x600 Intermetallics are compounds of two metals or of metal(s) and semimetal(s). Their structures are usually different from those of the constituents. Some intermetallics are interesting functional materials, others have attracted attention as high-temperature structural materials. We remind the reader of some fundamentals of solid-state diffusion and to the major techniques for tracer diffusion measurements, interdiffusion studies and the growth kinetics of layers in solid diffusion couples. Starting from self-diffusion, which is the most basic diffusion phenomenon in any solid, the paper covers the main features of diffusion in binary intermetallics from the systems Cu-Zn, Ni-Al, Fe-Al, Mg-Al, Ni-Ge, Ni-Ga, Fe-Si, Ti-Al, Ni-Mn, Mo-Si, Co-Nb and Ni-Nb.. We illustrate the influence of phase transitions on diffusion and point out some common features of diffusion in intermetallics. We discuss in detail diffusion in silicides of iron, molybdenum and of silicides of refractory metals. We also consider aluminides of iron, nickel, and titanium and in the aluminium-magnesium system. We consider diffusion in intermetallics of the cobalt-niobium and nickel-niobium system and in in the Nb-Sn and V-Ga systems. We finish with some remarks about grain boundary diffusion in intermetallics. Normal 0 21 false false false UK X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif";}

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Guo, Guang Feng. "The Study of the Ontology and Context Verification Based Intrusion Detection Model." Applied Mechanics and Materials 644-650 (September 2014): 3338–41. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3338.

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During the 30-year development of the Intrusion Detection System, the problems such as the high false-positive rate have always plagued the users. Therefore, the ontology and context verification based intrusion detection model (OCVIDM) was put forward to connect the description of attack’s signatures and context effectively. The OCVIDM established the knowledge base of the intrusion detection ontology that was regarded as the center of efficient filtering platform of the false alerts to realize the automatic validation of the alarm and self-acting judgment of the real attacks, so as to achieve the goal of filtering the non-relevant positives alerts and reduce false positives.

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McMahon, Sean, and Julie Cosmidis. "False biosignatures on Mars: anticipating ambiguity." Journal of the Geological Society 179, no. 2 (November 16, 2021): jgs2021–050. http://dx.doi.org/10.1144/jgs2021-050.

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It is often acknowledged that the search for life on Mars might produce false positive results, particularly via the detection of objects, patterns or substances that resemble the products of life in some way but are not biogenic. The success of major current and forthcoming rover missions now calls for significant efforts to mitigate this risk. Here, we review known processes that could have generated false biosignatures on early Mars. These examples are known largely from serendipitous discoveries rather than systematic research and remain poorly understood; they probably represent only a small subset of relevant phenomena. These phenomena tend to be driven by kinetic processes far from thermodynamic equilibrium, often in the presence of liquid water and organic matter, conditions similar to those that can actually give rise to, and support, life. We propose that strategies for assessing candidate biosignatures on Mars could be improved by new knowledge on the physics and chemistry of abiotic self-organization in geological systems. We conclude by calling for new interdisciplinary research to determine how false biosignatures may arise, focusing on geological materials, conditions and spatiotemporal scales relevant to the detection of life on Mars, as well as the early Earth and other planetary bodies.Thematic collection: This article is part of the Astrobiology: Perspectives from the Geology of Earth and the Solar System collection available at: https://www.lyellcollection.org/cc/astrobiology-perspectives-from-geology-of-earth-and-solar-system

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Mao, Jiang Kun, and Fan Zhan. "Study on Intrusion Detection System Based on Data Mining." Applied Mechanics and Materials 713-715 (January 2015): 2499–502. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2499.

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Intrusion detection system as a proactive network security technology, is necessary and reasonable to add a static defense. However, the traditional exceptions and errors detecting exist issues of leakage police, the false alarm rate or maintenance difficult. In this paper, The intrusion detection system based on data mining with statistics, machine learning techniques in the detection performance, robustness, self-adaptability has a great advantage. The system improves the K-means clustering algorithm, focus on solving two questions of the cluster center node selection and discriminating of clustering properties, the test shows that the system further enhance the detection efficiency of the system.

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WHITEN, BILL. "A SIMPLE ALGORITHM FOR DEDUCTION." ANZIAM Journal 51, no. 1 (July 2009): 102–22. http://dx.doi.org/10.1017/s1446181109000352.

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AbstractIt is shown that a simple deduction engine can be developed for a propositional logic that follows the normal rules of classical logic in symbolic form, but the description of what is known about a proposition uses two numeric state variables that conveniently describe unknown and inconsistent, as well as true and false. Partly true and partly false can be included in deductions. The multi-valued logic is easily understood as the state variables relate directly to true and false. The deduction engine provides a convenient standard method for handling multiple or complicated logical relations. It is particularly convenient when the deduction can start with different propositions being given initial values of true or false. It extends Horn clause based deduction for propositional logic to arbitrary clauses. The logic system used has potential applications in many areas. A comparison with propositional logic makes the paper self-contained.

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Perhinschi, M. G., H. Moncayo, B. Wilburn, J. Wilburn, O. Karas, and A. Bartlett. "Neurally-augmented immunity-based detection and identification of aircraft sub-system failures." Aeronautical Journal 118, no. 1205 (July 2014): 775–96. http://dx.doi.org/10.1017/s0001924000009532.

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Abstract This paper presents the development and testing through simulation of an integrated scheme for aircraft sub-system failure detection and identification (FDI) based on the artificial immune system (AIS) paradigm augmented with artificial neural networks. The features that define the self within the AIS paradigm include neural estimates of the angular accelerations produced by the abnormal conditions. The simulation environment integrates the NASA Generic Transport Model interfaced with FlightGear. A hierarchical multi-self strategy was investigated for developing FDI schemes capable of handling malfunctions of a variety of aircraft sub-systems. The performance of the FDI scheme has been evaluated in terms of false alarms and successful detection and identification over a wide flight envelope and for several actuator and aerodynamic surface failures. For all cases considered, the performance was very good, confirming the potential of the AIS paradigm augmented with the proposed neural network-based approach for feature definition to offer a comprehensive solution to the aircraft sub-system FDI problem.

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Moncayo, H., M. G. Perhinschi, and J. Davis. "Aircraft failure detection and identification over an extended flight envelope using an artificial immune system." Aeronautical Journal 115, no. 1163 (January 2011): 43–55. http://dx.doi.org/10.1017/s0001924000005352.

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AbstractAn integrated artificial immune system-based scheme that can operate over extended areas of the flight envelope is proposed in this paper for the detection and identification of a variety of aircraft sensor, actuator, propulsion, and structural failures/damages. A hierarchical multi-self strategy has been developed in which different self configurations are selected for detection and identification of specific abnormal conditions. Data collected using a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the scheme. The aircraft model represents a supersonic fighter, including model-following direct adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The proposed detection scheme achieves low false alarm rates and high detection and identification rates for all the categories of failures considered.

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Chen, Jing, Scott Mishler, and Bin Hu. "Conveying Automation Reliability and Automation Error Type An Empirical Study in the Cyber Domain." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 172–73. http://dx.doi.org/10.1177/1541931218621040.

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Background Emails have become an integral part of our daily life and work. Phishing emails are often disguised as trustworthy ones and attempt to obtain sensitive information for malicious reasons (Egelman, Cranor, Hong, 2008;). Anti-phishing tools have been designed to help users detect phishing emails or websites (Egelman, et al., 2008; Yang, Xiong, Chen, Proctor, & Li, 2017). However, like any other types of automation aids, these tools are not perfect. An anti-phishing system can make errors, such as labeling a legitimate email as phishing (i.e., a false alarm) or assuming a phishing email as legitimate (i.e., a miss). Human trust in automation has been widely studied as it affects how the human operator interacts with the automation system, which consequently influences the overall system performance (Dzindolet, Peterson, Pomranky, Pierce, & Beck, 2003; Lee & Moray, 1992; Muir, 1994; Sheridan & Parasuraman, 2006). With interacting with an automation system, the human operator should calibrate his or her trust level to trust a system that is capable but distrust a system that is incapable (i.e., trust calibration; Lee & Moray, 1994; Lee & See, 2004; McGuirl & Sarter, 2006). Among the various system capabilities, automation reliability is one of the most important factors that affect trust, and it is widely accepted that higher reliability levels lead to higher trust levels (Desai et al., 2013; Hoff & Bashir, 2015). How well these capabilities are conveyed to the operator is essential (Lee & See, 2004). There are two general ways of conveying the system capabilities: through an explicit description of the capabilities (i.e., description), or through experiencing the system (i.e., experience). These two ways of conveying information have been studied widely in human decision-making literature (Wulff, Mergenthaler-Canseco, & Hertwig, 2018). Yet, there has not been systematic investigation on these different methods of conveying information in the applied area of human-automation interaction (but see Chen, Mishler, Hu, Li, & Proctor, in press; Mishler et al., 2017). Furthermore, trust and reliance on automation is not only affected by the reliability of the automation, but also by the error types, false alarms and misses (Chancey, Bliss, Yamani, & Handley, 2017; Dixon & Wickens, 2006). False alarms and misses affect human performance in qualitatively different ways, with more serious damage being caused by false-alarmprone automation than by miss-prone automation (Dixon, Wickens, & Chang, 2004). In addition, false-alarm-prone automation reduces compliance (i.e., the operator’s reaction when the automation presents a warning); and miss-prone automation reduces reliance (i.e., the operator’s inaction when the automation remains silent; Chancey et al., 2017). Current Study The goal of the current study was to examine how the methods of conveying system reliability and automation error type affect human decision making and trust in automation. The automation system was a phishing-detection system, which provided recommendations to users as to whether an email was legitimate or phishing. The automation reliability was defined as the percentage of correct recommendations (60% vs. 90%). For each reliability level, there were a false-alarm condition, with all the automation errors being false alarms, and a miss condition, with all the errors being misses. The system reliability was conveyed through description (with an exact percentage described to the user) or experience (with immediate feedback to help the user learn; Barron, & Erev, 2003). A total of 510 participants were recruited and completed the experiment online through Amazon Mechanical Turk. The experimental task consisted of classifying 20 emails as phishing and legitimate, with a phishing-detection system providing recommendations. At the end of the experiment, participants rated their trust in this automated aid system. The measures included a performance measure (the decision accuracy made by the participants), as well as two trust measures (participants’ agreement rate with the phishing-detection system, and their self-reported trust in the system). Our results showed that higher system reliability and feedback increased accuracy significantly, but description or error type alone did not affect accuracy. In terms of the trust measures, false alarms led to lower agreement rates than did misses. With a less reliable system, though, the misses caused a problem of inappropriately higher agreement rates; this problem was reduced when feedback was provided for the unreliable system, indicating a trust-calibration role of feedback. Self-reported trust showed similar result patterns to agreement rates. Performance was improved with higher system reliability, feedback, and explicit description. Design implications of the results included that (1) both feedback and description of the system reliability should be presented in the interface of an automation aid whenever possible, provided that the aid is reliable, and (2) for systems that are unreliable, false alarms are more desirable than misses, if one has to choose between the two.

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Kernohan, Andrew. "Capitalism and Self-Ownership." Social Philosophy and Policy 6, no. 1 (1988): 60–76. http://dx.doi.org/10.1017/s0265052500002685.

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From the standpoint of libertarian ideology, capitalism is a form of liberation. In contrast to the slave, whose productive powers are wholly owned by his master, and the serf, whose productive powers are partially owned by his lord, the worker under capitalism is presented as possessing the fullest possible self-ownership. That capitalism fosters self-ownership is a false and stultifying myth. Exposing its errors from within capitalism's own conceptual framework requires a careful analysis of the concept of a person's “ownership” bodh of his or her productive powers and of the means of exercising these productive powers. This analysis will show that, in certain plausible circumstances, the capitalist economic system can make full self-ownership impossible. Since capitalism's supposed nurturing of self-ownership provides one of the major justifications for its moral legitimacy, capitalist ideology has a serious internal inconsistency.

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Epstein, Ziv, Nicolo Foppiani, Sophie Hilgard, Sanjana Sharma, Elena Glassman, and David Rand. "Do Explanations Increase the Effectiveness of AI-Crowd Generated Fake News Warnings?" Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 183–93. http://dx.doi.org/10.1609/icwsm.v16i1.19283.

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Social media platforms are increasingly deploying complex interventions to help users detect false news. Labeling false news using techniques that combine crowd-sourcing with artificial intelligence (AI) offers a promising way to inform users about potentially low-quality information without censoring content, but also can be hard for users to understand. In this study, we examine how users respond in their sharing intentions to information they are provided about a hypothetical human-AI hybrid system. We ask i) if these warnings increase discernment in social media sharing intentions and ii) if explaining how the labeling system works can boost the effectiveness of the warnings. To do so, we conduct a study (N=1473 Americans) in which participants indicated their likelihood of sharing content. Participants were randomly assigned to a control, a treatment where false content was labeled, or a treatment where the warning labels came with an explanation of how they were generated. We find clear evidence that both treatments increase sharing discernment, and directional evidence that explanations increase the warnings' effectiveness. Interestingly, we do not find that the explanations increase self-reported trust in the warning labels, although we do find some evidence that participants found the warnings with the explanations to be more informative. Together, these results have important implications for designing and deploying transparent misinformation warning labels, and AI-mediated systems more broadly.

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Aldhaheri, Sahar, Daniyal Alghazzawi, Li Cheng, Bander Alzahrani, and Abdullah Al-Barakati. "DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System." Applied Sciences 10, no. 6 (March 11, 2020): 1909. http://dx.doi.org/10.3390/app10061909.

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Recently Internet of Things (IoT) attains tremendous popularity, although this promising technology leads to a variety of security obstacles. The conventional solutions do not suit the new dilemmas brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provides an opportunity to improve IoT security. In this work, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts Dendritic Cell Algorithm (DCA) and Self Normalizing Neural Network (SNN). The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset, performs signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, we compared these results with State-of-the-art techniques, which showed that our model is capable of performing better classification tasks than SVM, NB, KNN, and MLP. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection.

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Liao, Jianfeng. "An Intrusion Detection Model Based on Improved ACGAN in Big Data Environment." Security and Communication Networks 2022 (May 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/6821174.

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With the development of big data technology, network intrusion problems against server vulnerabilities emerge one after another. To improve the accuracy of intrusion detection, this paper designs an intrusion detection platform based on the ACGAN (auxiliary classifier generative adversarial network) model in a big data environment. Firstly, by introducing a self-attention mechanism, the global characteristics of attack samples are extracted to improve the quality of generated samples. Then, by adding a gradient penalty, the model's convergence speed and training stability are improved. Finally, this method enhances and expands the attack samples and verifies the dataset. The experimental results show that compared with other comparison methods, the overall detection accuracy of this system is higher, and the false-positive rate and false-negative rate are lower.

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Chen, Yunbing, Xin Tian, Kai Fan, Yanni Zheng, Nannan Tian, and Ka Fan. "The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study." Computational and Mathematical Methods in Medicine 2022 (March 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/2864170.

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Objective. To explore the value of artificial intelligence (AI) film reading system based on deep learning in the diagnosis of non-small-cell lung cancer (NSCLC) and the significance of curative effect monitoring. Methods. We retrospectively selected 104 suspected NSCLC cases from the self-built chest CT pulmonary nodule database in our hospital, and all of them were confirmed by pathological examination. The lung CT images of the selected patients were introduced into the AI reading system of pulmonary nodules, and the recording software automatically identified the nodules, and the results were compared with the results of the original image report. The nodules detected by the AI software and film readers were evaluated by two chest experts and recorded their size and characteristics. Comparison of calculation sensitivity, false positive rate evaluation of the NSCLC software, and physician’s efficiency of nodule detection whether there was a significant difference between the two groups. Results. The sensitivity, specificity, accuracy, positive predictive rate, and false positive rate of NSCLC diagnosed by radiologists were 72.94% (62/85), 92.06% (58/63), 81.08% (62+58/148), 92.53% (62/67), and 7.93% (5/63), respectively. The sensitivity, specificity, accuracy, positive prediction rate, and false positive rate of AI film reading system in the diagnosis of NSCLC were 94.12% (80/85), 77.77% (49/63), 87.161% ( 80 + 49 /148), 85.11% (80/94), and 22.22% (14/63), respectively. Compared with radiologists, the sensitivity and false positive rate of artificial intelligence film reading system in the diagnosis of NSCLC were higher ( P < 0.05 ). The sensitivity, specificity, accuracy, positive prediction rate, and negative prediction rate of artificial intelligence film reading system in evaluating the efficacy of patients with NSCLC were 87.50% (63/72), 69.23% (9/13), 84.70% ( 63 + 9 )/85, 94.02% (63/67), and 50% (9/18), respectively. Conclusion. The AI film reading system based on deep learning has higher sensitivity for the diagnosis of NSCLC than radiologists and can be used as an auxiliary detection tool for doctors to screen for NSCLC, but its false positive rate is relatively high. Attention should be paid to identification. Meanwhile, the AI film reading system based on deep learning also has a certain guiding significance for the diagnosis and treatment monitoring of NSCLC.

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Chen, Zhen Guo, Guang Hua Zhang, Li Qin Tian, and Zi Lin Geng. "Intrusion Detection Based on Self-Organizing Map and Artificial Immunisation Algorithm." Key Engineering Materials 439-440 (June 2010): 29–34. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.29.

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The rate of false positives which caused by the variability of environment and user behavior limits the applications of intrusion detecting system in real world. Intrusion detection is an important technique in the defense-in-depth network security framework and a hot topic in computer security in recent years. To solve the intrusion detection question, we introduce the self-organizing map and artificial immunisation algorithm into intrusion detection. In this paper, we give an method of rule extraction based on self-organizing map and artificial immunisation algorithm and used in intrusion detection. After illustrating our model with a representative dataset and applying it to the real-world datasets MIT lpr system calls. The experimental result shown that We propose an idea of learning different representations for system call arguments. Results indicate that this information can be effectively used for detecting more attacks with reasonable space and time overhead. So our experiment is feasible and effective that using in intrusion detection.

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Erokhin, Victor V., and Larisa S. Pritchina. "Analysis and improvement of methods for detecting shellcodes in computer systems." Journal Of Applied Informatics 16, no. 92 (April 30, 2021): 103–22. http://dx.doi.org/10.37791/2687-0649-2021-16-2-103-122.

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The article discusses the problem of detecting and filtering shellcode – malicious executable code that contributes to the emergence of vulnerabilities in the operation of software applications with memory. The main such vulnerabilities are stack overflow, database overflow, and some other operating system service procedures. Currently, there are several dozen shellcode detection systems using both static and dynamic program analysis. Monitoring of existing systems has shown that methods with low computational complexity are characterized by a large percentage of false positives. Moreover, methods with a low percentage of false alarms are characterized by increased computational complexity. However, none of the currently existing solutions is able to detect all existing classes of shellcodes. This makes existing shellcode detection systems weakly applicable to real network links. Thus, the article discusses the problem of analyzing shellcode detection systems that provide complete detection of existing classes of shellcodes and are characterized by acceptable computational complexity and a small number of false alarms. This article introduces shellcode classifications and a comprehensive method of detecting them based on code emulation. This approach expands the detection range of shellcode classes that can be detected by concurrently evaluating several heuristics that correspond to low-level CPU operations during execution of various shellcode classes. The presented method allows efficient detection of simple and metamorphic shellcode. This is achieved regardless of the use of self-modifying code or dynamic code generation on which existing emulation-based polymorphic shellcode detectors are based.

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Jiang, Ya Ping, Jun Wei Zhao, and Yue Xia Tian. "A Model of Detector Generation Based on Immune Recognition and Redundancy Optimization." Applied Mechanics and Materials 457-458 (October 2013): 783–87. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.783.

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The theory of modern immunology provides a novel idea to study network intrusion detection and defense system. With the concepts of self, nonself, close degree and membership in an intrusion detection and prevention system presented in this paper, a model of detector generation based on immune recognition and redundancy optimization is proposed, in which detectors are generated by clone selection, genetic variation and evolutionary algorithm, as well as the improved redundancy optimization algorithm. The simulation experiments show that the model has higher detection rate and lower false detection rate.

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Parini, Sergio, Luca Maggi, Anna C. Turconi, and Giuseppe Andreoni. "A Robust and Self-Paced BCI System Based on a Four Class SSVEP Paradigm: Algorithms and Protocols for a High-Transfer-Rate Direct Brain Communication." Computational Intelligence and Neuroscience 2009 (2009): 1–11. http://dx.doi.org/10.1155/2009/864564.

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In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs) paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs) method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a screening, training, and testing phase aimed at collecting user-specific information regarding best stimulation frequencies, optimal sources identification, and overall system processing chain calibration in only a few minutes. The system, validated on 11 healthy/pathologic subjects, has proven to be reliable in terms of achievable communication speed (up to 70 bit/min) and very robust to false positive identifications.

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Ferrag, Mohamed Amine, and Leandros Maglaras. "DeliveryCoin: An IDS and Blockchain-Based Delivery Framework for Drone-Delivered Services." Computers 8, no. 3 (August 6, 2019): 58. http://dx.doi.org/10.3390/computers8030058.

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In this paper, we propose an intrusion detection system (IDS) and Blockchain-based delivery framework, called DeliveryCoin, for drone-delivered services. The DeliveryCoin framework consists of four phases, including system initialization phase, creating the block, updating the blockchain, and intrusion detection phase. To achieve privacy-preservation, the DeliveryCoin framework employs hash functions and short signatures without random oracles and the Strong Diffie–Hellman (SDH) assumption in bilinear groups. To achieve consensus inside the blockchain-based delivery platform, we introduce a UAV-aided forwarding mechanism, named pBFTF. We also propose an IDS system in each macro eNB (5G) for detecting self-driving network attacks as well as false transactions between self-driving nodes. Furthermore, extensive simulations are conducted, and results confirm the efficiency of our proposed DeliveryCoin framework in terms of latency of blockchain consensus and accuracy.

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Greer, Jasmine M., Kendall J. Burdick, Arman R. Chowdhury, and Joseph J. Schlesinger. "Dynamic Alarm Systems for Hospitals (D.A.S.H.)." Ergonomics in Design: The Quarterly of Human Factors Applications 26, no. 4 (August 7, 2018): 14–19. http://dx.doi.org/10.1177/1064804618769186.

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Hospital alarms today indicate urgent clinical need, but they are seldom “true.” False alarms are contributing to the ever-increasing issue of alarm fatigue, or desensitization, among doctors and nurses. Alarm fatigue is a high-priority health care concern because of its potential to compromise health care quality and inflict harm on patients. To address this concern, we have engineered Dynamic Alarm Systems for Hospitals (D.A.S.H.), a dynamic alarm system that self-regulates alarm loudness based on the environmental noise level and incorporates differentiable and learnable alarms. D.A.S.H., with its ability to adapt to environmental noise and encode nuanced physiological information, may improve patient safety and attenuate clinician alarm fatigue.

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Hu, H. B., W. L. Luo, S. X. Liu, and Y. M. Zhang. "Design of a New Fire Detection and Alarm System Based on Self-Organizing Wireless Sensor Networks." Applied Mechanics and Materials 52-54 (March 2011): 1142–46. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.1142.

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The system is composed of Fire center console, wireless control board, wireless multifunctional fire detector and wireless PTZ camera control system. It is based on string-like network topology self-organizing wireless sensor networks, which adopts Simplici TI network protocol that can reduce power consumption of the system. The system uses BP algorithm program for judging whether there is a fire, uses Wireless PTZ camera control system, which is convenient to install, for real-time monitoring the detect area, uses voice module for fire automatic voice alarm to the fire department and uses Micro-Fire GIS to show the location of a fire. BP algorithm program is embedded in wireless multifunctional fire detector. The samples of BP algorithm were derived from the fire detection standard room of the State Key Laboratory of Fire Science of China. The system is low false alarm rate, low cost, fast response and convenient to install.

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Lang, David J., Yaser Wafai, Ramez M. Salem, Edward A. Czinn, Ayman A. Halim, and Anis Baraka. "Efficacy of the Self-inflating Bulb in Confirming Tracheal Intubation in the Morbidly Obese." Anesthesiology 85, no. 2 (August 1, 1996): 246–53. http://dx.doi.org/10.1097/00000542-199608000-00004.

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Background This study was designed to determine the incidence of false-negative and false-positive results when the self-inflating bulb (SIB) is used to differentiate tracheal from esophageal intubation in morbidly obese patients using two techniques. In technique 1, the SIB is compressed before it is connected to the tube; in technique 2, the SIB is compressed after connection to the tube. Methods With institutional review board approval, 54 consenting adult morbidly obese patients (body mass index > 35) undergoing elective surgical procedures were included in the study. After anesthetic induction and muscle relaxation, both the trachea and esophagus were intubated under direct vision with identical cuffed tubes. The efficacy of the SIB in verifying the position of both tubes was tested by a second anesthesiologist. The speed of reinflation was graded as rapid ( < 4 s) or none ( > 4 s), using both techniques. In the case of tracheal intubation, the absence of reinflation was recorded as a false-negative, whereas in cases of esophageal intubation, rapid reinflation was recorded as a false-positive. Identification of tube location by the second anesthesiologist was based on SIB reinflation results from techniques 1 and 2, as well as the presence of a flatuslike sound elicited by technique 2 in esophageally placed tubes. All patients were retested by the SIB after receiving three breaths of 400-500 ml each. In all patients exhibiting false-negative results, six obese patients exhibiting true-positive results, and four nonobese patients exhibiting true-positive results, tracheal responses to the SIB maneuvers were observed directly by a flexible fiberoptic bronchoscope incorporating an airtight system, 15-20 min after mechanical ventilation was instituted. Results The incidence of false-negative results was initially 30% with technique 1 and 11% with technique 2, but decreased to 4% when technique 2 was used after the delivery of three breaths. The second anesthesiologist initially identified tube location in 92.5% of patients correctly. After the delivery of three breaths, tube location was correctly identified in 96.3% of patients. Fiberoptic bronchoscopic examination of the patients exhibiting false-negative results revealed exaggerated inward bulging of the posterior tracheal membrane during reinflation of the SIB when technique 1 was used. Conclusions Contrary to previous investigations in healthy patients, the current study demonstrates a high incidence of false-negative results when the SIB is used to confirm tracheal intubation in morbidly obese patients. If the SIB is used, the technique should include compression of the SIB after connection to the tube and should be used in conjunction with other clinical signs and technical aids. The mechanism of false-negative results in these patients seems to be related to reduction of caliber of airways secondary to a marked decrease in functional residual capacity, and collapse of large airways due to invagination of the posterior tracheal wall when sub-atmospheric pressure is generated by the SIB.

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Albrecht, Christian Rudolf, Jenny Behre, Eva Herrmann, Stefan Jürgens, and Uwe Stilla. "Investigation on Robustness of Vehicle Localization Using Cameras and LiDAR." Vehicles 4, no. 2 (May 12, 2022): 445–63. http://dx.doi.org/10.3390/vehicles4020027.

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Vehicle self-localization is one of the most important capabilities for automated driving. Current localization methods already provide accuracy in the centimeter range, so robustness becomes a key factor, especially in urban environments. There is no commonly used standard metric for the robustness of localization systems, but a set of different approaches. Here, we show a novel robustness score that combines different aspects of robustness and evaluate a graph-based localization method with the help of fault injections. In addition, we investigate the influence of semantic class information on robustness with a layered landmark model. By using the perturbation injections and our novel robustness score for test drives, system vulnerabilities or possible improvements are identified. Furthermore, we demonstrate that semantic class information allows early discarding of misclassified dynamic objects such as pedestrians, thus improving false-positive rates. This work provides a method for the robustness evaluation of landmark-based localization systems that are also capable of measuring the impact of semantic class information for vehicle self-localization.

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da Silva, Rafael Luiz, Boxuan Zhong, Yuhan Chen, and Edgar Lobaton. "Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks." Information 13, no. 7 (July 12, 2022): 338. http://dx.doi.org/10.3390/info13070338.

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Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject.

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Ali, Imran, Muhammad Asif, Muhammad Riaz Ur Rehman, Danial Khan, Huo Yingge, Sung Jin Kim, YoungGun Pu, Sang-Sun Yoo, and Kang-Yoon Lee. "A Highly Reliable, 5.8 GHz DSRC Wake-Up Receiver with an Intelligent Digital Controller for an ETC System." Sensors 20, no. 14 (July 19, 2020): 4012. http://dx.doi.org/10.3390/s20144012.

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In this article, a highly reliable radio frequency (RF) wake-up receiver (WuRx) is presented for electronic toll collection (ETC) applications. An intelligent digital controller (IDC) is proposed as the final stage for improving WuRx reliability and replacing complex analog blocks. With IDC, high reliability and accuracy are achieved by sensing and ensuring the successive, configurable number of wake-up signal cycles before enabling power-hungry RF transceiver. The IDC and range communication (RC) oscillator current consumption is reduced by a presented self-hibernation technique during the non-wake-up period. For accommodating wake-up signal frequency variation and enhancing WuRx accuracy, a digital hysteresis is incorporated. To avoid uncertain conditions during poor and false wake-up, a watch-dog timer for IDC self-recovery is integrated. During wake-up, the digital controller consumes 34.62 nW power and draws 38.47 nA current from a 0.9 V supply. In self-hibernation mode, its current reduces to 9.7 nA. It is fully synthesizable and needs 809 gates for its implementation in a 130 nm CMOS process with a 94 × 82 µm2 area. The WuRx measured power consumption is 2.48 µW, has −46 dBm sensitivity, and a 0.484 mm² chip area.

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Lane, Riki. "Trans as Bodily Becoming: Rethinking the Biological as Diversity, Not Dichotomy." Hypatia 24, no. 3 (December 2008): 136–57. http://dx.doi.org/10.1111/j.1527-2001.2009.01049.x.

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Feminist and trans theory challenges “the” binary sex/gender system, but can create a new binary opposition of subversive transgender versus conservative transsexual. This paper aims to shift debate concerning bodies as authentic/real versus constructed/mutable, arguing that such debate establishes a false dichotomy that may be overcome by reappraising scientific understandings of sex/gender. Much recent biology and neurology stresses nonlinearity, contingency, self-organization, and open-endedness. Engaging with this research offers ways around apparently interminable theoretical impasses.

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Salánki, Dániel, and Kornél Sarvajcz. "Development of a Gait Recognition System in NI LabVIEW Programming Language." Műszaki Tudományos Közlemények 11, no. 1 (October 1, 2019): 167–70. http://dx.doi.org/10.33894/mtk-2019.11.37.

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Abstract Nowadays, the biometric identifier’s world is one of the most rapidly developing security technology areas. Within the biometric identification, the research team worked in the area of gait recognition. The research team developed a complex walking recognition system in NI LabVIEW environment that can detect multiple simultaneous reference points using a universal camera and capable of matching a predetermined curve to the collected samples. In the first version, real-time processing was done with a single camera, while in the second, two high-resolution cameras work with post-processing. The program can compare and evaluate the functions that are matched to the reference curve and the current curve in a specific way, whether two walking images are identical. The self-developed gait recognition system was tested on several test subjects by the research team and according to the results, the False Acceptance Rate was zero.

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Rehman, Fiza Ur, Arshad Farid, Shefaat Ullah Shah, Muhammad Junaid Dar, Asim Ur Rehman, Naveed Ahmed, Sheikh Abdur Rashid, et al. "Self-Emulsifying Drug Delivery Systems (SEDDS): Measuring Energy Dynamics to Determine Thermodynamic and Kinetic Stability." Pharmaceuticals 15, no. 9 (August 26, 2022): 1064. http://dx.doi.org/10.3390/ph15091064.

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This research was designed to identify thermodynamically and kinetically stable lipidic self-emulsifying formulations through simple energy dynamics in addition to highlighting and clarifying common ambiguities in the literature in this regard. Proposing a model study, this research shows how most of the professed energetically stable systems are actually energetically unstable, subjected to indiscriminate and false characterization, leading to significant effects for their pharmaceutical applications. A self-emulsifying drug delivery system (SEDDS) was developed and then solidified (S-SEDDS) using a model drug finasteride. Physical nature of SEDDS was identified by measuring simple dynamics which showed that the developed dispersion was thermodynamically unstable. An in vivo study of albino rats showed a three-fold enhanced bioavailability of model drug with SEDDS as compared to the commercial tablets. The study concluded that measuring simple energy dynamics through inherent properties can distinguish between thermodynamically stable and unstable lipidic systems. It might lead to correct identification of a specific lipidic formulation and the application of appropriate characterization techniques accordingly. Future research strategies include improving their pharmaceutical applications and understanding the basic differences in their natures.

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Christyawan, Tomi Yahya, Ahmad Afif Supianto, and Wayan Firdaus Mahmudy. "Anomaly-based intrusion detector system using restricted growing self organizing map." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (March 1, 2019): 919. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp919-926.

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The rapid development of internet and network technology followed by malicious threats and attacks on networks and computers. Intrusion detection system (IDS) was developed to solve that problems. The development of IDS using machine learning is needed for classifying the attacks. One method of the classification is Self-Organizing Map (SOM). SOM able to perform classification and visualization in learning process to gain new knowledge. However, the SOM has less efficient in learning process when applied in Big Data. This study proposes Restricted Growing SOM method with clustering reference vector (RGSOM-CRV) and Parallel RGSOM-CRV to improve SOM efficiency in classification with accuracy consideration to solve Big Data problem. Growing process in RGSOM is restricted by maximum nodes and growing threshold, the reupdate weight process will update unused reference vector when map size already maximum, these two processes solve the consuming time of regular GSOM. From the results of this research against KDD Cup 1999 dataset, proposed method Parallel RGSOM-CRV able to give 91.86% accuracy, 20.58% false alarm rate, 95.32% recall or detection rate, and precision is 94.35% and time consuming is outperform than regular Growing SOM. This proposed method is very promising to handle big data problems compared with other methods.

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Muzammel, Muhammad, Mohd Zuki Yusoff, Mohamad Naufal Mohamad Saad, Faryal Sheikh, and Muhammad Ahsan Awais. "Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture." Sensors 22, no. 16 (August 15, 2022): 6088. http://dx.doi.org/10.3390/s22166088.

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Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing state-of-the-art vision-based object detection models rely heavily on a single feature descriptor for making decisions. In this research, the design of two convolutional neural networks (CNNs) based on high-level feature descriptors and their integration with faster R-CNN is proposed to detect blind-spot collisions for heavy vehicles. Moreover, a fusion approach is proposed to integrate two pre-trained networks (i.e., Resnet 50 and Resnet 101) for extracting high level features for blind-spot vehicle detection. The fusion of features significantly improves the performance of faster R-CNN and outperformed the existing state-of-the-art methods. Both approaches are validated on a self-recorded blind-spot vehicle detection dataset for buses and an online LISA dataset for vehicle detection. For both proposed approaches, a false detection rate (FDR) of 3.05% and 3.49% are obtained for the self recorded dataset, making these approaches suitable for real time applications.

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Ahmad, Malik Anas, Yasar Ayaz, Mohsin Jamil, Syed Omer Gillani, Muhammad Babar Rasheed, Muhammad Imran, Nadeem Ahmed Khan, Waqas Majeed, and Nadeem Javaid. "Comparative Analysis of Classifiers for Developing an Adaptive Computer-Assisted EEG Analysis System for Diagnosing Epilepsy." BioMed Research International 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/638036.

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Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system’s incorrect markings can be mentioned and the system should improve its classification by learning from them. We have developed a simple mechanism for neurologists to improve classification rate while encountering any false classification. This system is based on taking discrete wavelet transform (DWT) of the signals epochs which are then reduced using principal component analysis, and then they are fed into a classifier. After discussing our approach, we have shown the classification performance of three types of classifiers: support vector machine (SVM), quadratic discriminant analysis, and artificial neural network. We found SVM to be the best working classifier. Our work exhibits the importance and viability of a self-improving and user adapting computer-assisted EEG analysis system for diagnosing epilepsy which processes each channel exclusive to each other, along with the performance comparison of different machine learning techniques in the suggested system.

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D'AOUST, J. Y., and A. M. SEWELL. "Reliability of the Immunodiffusion 1–2 Test™ System for Detection of Salmonella in Foods." Journal of Food Protection 51, no. 11 (November 1, 1988): 853–56. http://dx.doi.org/10.4315/0362-028x-51.11.853.

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The novel 1–2 Test™ is a self-contained tetrathionate enrichment and immunodiffusion-agglutination reaction vial for the detection of Salmonella in preenrichment and direct enrichment cultures of processed and non-processed foods, respectively. Of 186 foods tested, 46 (24.7%) were found to contain salmonellae by all analytical conditions combined. The standard cultural procedure identified 43 (93.5%) contaminated samples whereas the 1–2 Test™ system detected 20 (43.5%) and 25 (54.3%) positive samples after 8 h and 24 h of incubation, respectively. Although six false-positive reactions were obtained after short (8 h) incubation of test vials, homologous reactions were not detected after 24 h incubation. System deficiency likely stems from the low selectivity of applied cultural conditions and inability of the 1–2 Test™ to detect salmonellae in the presence of large numbers of competing microflora.

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NG, E. Y. K., COLIN CHONG, and G. J. L. KAW. "CLASSIFICATION OF HUMAN FACIAL AND AURAL TEMPERATURE USING NEURAL NETWORKS AND IR FEVER SCANNER: A RESPONSIBLE SECOND LOOK." Journal of Mechanics in Medicine and Biology 05, no. 01 (March 2005): 165–90. http://dx.doi.org/10.1142/s0219519405001370.

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Severe Acute Respiratory Syndrome (SARS) is a highly infectious disease caused by a coronavirus. Screening to detect potential SARS infected subject with elevated body temperature plays an important role in preventing the spread of SARS. The use of infrared (IR) thermal imaging cameras has thus been proposed as a non-invasive, speedy, cost-effective and fairly accurate means for mass blind screening of potential SARS infected persons. Infrared thermography provides a digital image showing temperature patterns. This has been previously utilized in the detection of inflammation and nerve dysfunctions. It is believed that IR cameras may potentially be used to detect subjects with fever, the cardinal symptom of SARS and avian influenza. The accuracy of the infrared system can, however, be affected by human, environmental, and equipment variables. It is also limited by the fact that the thermal imager measures the skin temperature and not the body core temperature. Thus, the use of IR thermal systems at various checkpoints for mass screening of febrile persons is scientifically unjustified such as what is the false negative rate and most importantly not to create false sense of security. This paper aims to study the effectiveness of infrared systems for its application in mass blind screening to detect subjects with elevated body temperature. For this application, it is critical for thermal imagers to be able to identify febrile from normal subjects accurately. Minimizing the number of false positive and false negative cases improves the efficiency of the screening stations. False negative results should be avoided at all costs, as letting a SARS infected person through the screening process may result in potentially catastrophic results. Hitherto, there is lack of empirical data in correlating facial skin with body temperature. The current work evaluates the correlations (and classification) between the facial skin temperatures to the aural temperature using the artificial neural network approach to confirm the suitability of the thermal imagers for human temperature screening. We show that the Train Back Propagation and Kohonen self-organizing map (SOM) can form an opinion about the type of network that is better to complement thermogram technology in fever diagnosis to drive a better parameters for reducing the size of the neural network classifier while maintaining good classification accuracy.

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Zou, Li Kun, Shao Kun Liu, and Guo Fu Ma. "Intrusion Detection Model Based on Improved Genetic Algorithm Neural Network in Computer Integrated Process System." Applied Mechanics and Materials 380-384 (August 2013): 2708–11. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2708.

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In order to solve the problems of high false alarm rate and fail rate in intrusion detection system of Computer Integrated Process System (CIPS) network, this paper takes advantage that Genetic Algorithm (GA) possesses overall optimization seeking ability and neural network has formidable approaching ability to the non-linear mapping to propose an intrusion detection model based on Genetic Algorithm Neural Network (GANN) with self-learning and adaptive capacity, which includes data collection module, data preprocessing module, neural network analysis module and intrusion alarm module. To overcome the shortcomings that GA is easy to fall into the extreme value and searches slowly, it improves the adjusting method of GANN fitness value and optimizes the parameter settings of GA. The improved GA is used to optimize BP neural network. Simulation results show that the model makes the detection rate of the system enhance to 97.11%.

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Saleh, Abdul Jabbar, Asif Karim, Bharanidharan Shanmugam, Sami Azam, Krishnan Kannoorpatti, Mirjam Jonkman, and Friso De Boer. "An Intelligent Spam Detection Model Based on Artificial Immune System." Information 10, no. 6 (June 12, 2019): 209. http://dx.doi.org/10.3390/info10060209.

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Spam emails, also known as non-self, are unsolicited commercial or malicious emails, sent to affect either a single individual or a corporation or a group of people. Besides advertising, these may contain links to phishing or malware hosting websites set up to steal confidential information. In this paper, a study of the effectiveness of using a Negative Selection Algorithm (NSA) for anomaly detection applied to spam filtering is presented. NSA has a high performance and a low false detection rate. The designed framework intelligently works through three detection phases to finally determine an email’s legitimacy based on the knowledge gathered in the training phase. The system operates by elimination through Negative Selection similar to the functionality of T-cells’ in biological systems. It has been observed that with the inclusion of more datasets, the performance continues to improve, resulting in a 6% increase of True Positive and True Negative detection rate while achieving an actual detection rate of spam and ham of 98.5%. The model has been further compared against similar studies, and the result shows that the proposed system results in an increase of 2 to 15% in the correct detection rate of spam and ham.

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Zhang, Yi-Ying, Jing Shang, Xi Chen, and Kun Liang. "A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles." Energies 13, no. 6 (March 16, 2020): 1382. http://dx.doi.org/10.3390/en13061382.

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Electric vehicles (EVs) are the development direction of new energy vehicles in the future. As an important part of the Internet of things (IOT) communication network, the charging pile is also facing severe challenges in information security. At present, most detection methods need a lot of prophetic data and too much human intervention, so they cannot do anything about unknown attacks. In this paper, a self-learning-based attack detection method is proposed, which makes training and prediction a closed-loop system according to a large number of false information packets broadcast to the communication network. Using long short-term memory (LSTM) neural network training to obtain the characteristics of traffic data changes in the time dimension, the unknown malicious behavior characteristics are self-extracted and self-learning, improving the detection efficiency and quality. In this paper, we take the Sybil attack in the car network as an example. The simulation results show that the proposed method can detect the Sybil early attack quickly and accurately.

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Li, Miao, and Wei Xin Ren. "Negative Selection Algorithm Using Natural Frequency for Novelty Detection under Temperature Variations." Advanced Materials Research 163-167 (December 2010): 2747–50. http://dx.doi.org/10.4028/www.scientific.net/amr.163-167.2747.

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The vibration features are affected by damage in structure and environmental conditions while the bridges are in the operation. Environment effects should not be ignored in making correct diagnoses of structures. Negative selection algorithm inspired by immune system has the capability for self-nonself discrimination. Temperature effect on natural frequency is analyzed in the paper, and the algorithm based on Euclidean distance is applied to natural frequencies of structures under temperature variations. The results indicate that negative selection algorithm using natural frequency passes the false-positive tests, and effectively detect the anomalous condition of structure under varying temperature.

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Hafaifa, Ahmed, Ferhat Laaouad, and Kouider Laroussi. "Fuzzy modeling and control for detection and isolation of surge in industrial centrifugal compressors." Journal of Automatic Control 19, no. 1 (2009): 19–26. http://dx.doi.org/10.2298/jac0901019h.

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This paper provides the possible application of the fuzzy approaches in fault detection and isolation area for a class of complex industrial processes with uncertain interval parameters. The main idea of fuzzy fault detection and isolation is to build a model of a diagnosis procedures, using rules-based Fuzzy Expert System, capable to minimize false alarms enhance detectability and isolability and minimize detection time by hardware implementation to improve reliability, safety and global efficiency. This paper illustrates an alternative implementation to the compression systems supervision task using the basic principles of model-based fault detection and isolation associated with the self-tuning of surge measurements with subsequent appropriate corrective actions. Using a combination of fuzzy modeling approach makes it possible to devise a fault-isolation scheme based on the given incidence matrix. Simulation results of a fault detection and isolation for a compression system are provided, which illustrate the relevance of the proposed FDI method.

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Journal articles on the topic 'False-self system'

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