Detecting anonymous attacks in wireless communication medium using adaptive grasshopper optimization algorithm

Intrusion Detection Systems (IDSs) is a system that monitors network traffic for suspicious activity and issues alert when such activity is revealed. Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends. For this reason, in this study we developed a new multi-swarm adaptive grasshopper optimization algorithm to utilize adaptation mechanism in a group of swarms based on fuzzy logic to protect against sophisticated attacks. The proposed (MSAGOA) technique has the capability of global optimization and rapid convergence that are used to attain optimal feature subsets to identify attack types on IDS datasets. In the MSAGOA technique, learning engine as Extreme learning Machine, Naive Bayes, Random Forest and Decision Tree is applied as a fitness function to select the highly discriminating features and to maximize classification performance. Afterward, select the best classifier which works as a fitness function in our approach to measure the performance in terms of accuracy, detection rate, and false alarm rate. The simulations are performed on three IDS datasets such as NSL-KDD, AWID-ATK-R, and NGIDS-DS. The experimental results demonstrated that MSAGOA method has performed better and obtained high detection rate of 99.86%, accuracy of 99.89% in NSL-KDD and high detection rate of 98.73%, accuracy of 99.67% in AWID-ATK-R and detection rate of 89.50%, accuracy of 90.23% in NGIDS-DS. In addition, the performance is compared with several other existing techniques to show the efficacy of the proposed approach.     

Similarity based approximate reasoning: fuzzy control

This paper presents an approach to similarity based approximate reasoning that elucidates the connection between similarity and existing approaches to inference in approximate reasoning methodology. A set of axioms is proposed to get a reasonable measure of similarity between two fuzzy sets. The similarity between the fact(s) and the antecedent of a rule is used to modify the relation between the antecedent and the consequent of the rule. An inference is drawn using the well-known projection operation on the domain of the consequent. Zadeh's compositional rule of inference and existing similarity based reasoning techniques are considered for a new similarity based approximate reasoning technique. The proposed mechanism is used to develop a modified fuzzy control system. A new defuzzification scheme is proposed. Simulation results are presented for the well-known inverted pendulum problem.     

Detecting Clusters/Communities in Social Networks

Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.     

The role of subvocalisation in reading

A series of experiments explored the role of subvocalisation in fluent reading. Experiment I showed that when subjects were required to suppress articulation while reading, their ability to detect anomalous words or errors of word order in prose was markedly impaired although speed of reading was unaffected. Experiment II showed that this decrement was not a general effect due to performing a secondary task, since a concurrent tapping task did not impair detection accuracy. A third study explored the role of acoustic interference in reading by requiring subjects to detect errors in prose while attempting to ignore irrelevant speech, with or without articulatory suppression. Once again articulatory suppression led to a clear decrement in the subject's ability to detect errors, while unattended speech had no effect on performance. None of the manipulations influenced the speed with which the subjects performed the reading task. It is concluded that subvocalisation allows the creation of a supplementary articulatory code which is produced and utilised in parallel with other aspects of reading. Such a code seems particularly suitable for monitoring order information.     

Object detection in natural scenes: Independent effects of spatial and category-based attention

Humans are remarkably efficient in detecting highly familiar object categories in natural scenes, with evidence suggesting that such object detection can be performed in the (near) absence of attention. Here we systematically explored the influences of both spatial attention and category-based attention on the accuracy of object detection in natural scenes. Manipulating both types of attention additionally allowed for addressing how these factors interact: whether the requirement for spatial attention depends on the extent to which observers are prepared to detect a specific object category—that is, on category-based attention. The results showed that the detection of targets from one category (animals or vehicles) was better than the detection of targets from two categories (animals and vehicles), demonstrating the beneficial effect of category-based attention. This effect did not depend on the semantic congruency of the target object and the background scene, indicating that observers attended to visual features diagnostic of the foreground target objects from the cued category. Importantly, in three experiments the detection of objects in scenes presented in the periphery was significantly impaired when observers simultaneously performed an attentionally demanding task at fixation, showing that spatial attention affects natural scene perception. In all experiments, the effects of category-based attention and spatial attention on object detection performance were additive rather than interactive. Finally, neither spatial nor category-based attention influenced metacognitive ability for object detection performance. These findings demonstrate that efficient object detection in natural scenes is independently facilitated by spatial and category-based attention.     

A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer Based Testing

Statistical methods for identifying aberrances on psychological and educational tests are pivotal to detect flaws in the design of a test or irregular behavior of test takers. Two approaches have been taken in the past to address the challenge of aberrant behavior detection, which are (1) modeling aberrant behavior via mixture modeling methods, and (2) flagging aberrant behavior via residual based outlier detection methods. In this paper, we propose a two-stage method that is conceived of as a combination of both approaches. In the first stage, a mixture hierarchical model is fitted to the response and response time data to distinguish normal and aberrant behaviors using Markov chain Monte Carlo (MCMC) algorithm. In the second stage, a further distinction between rapid guessing and cheating behavior is made at a person level using a Bayesian residual index. Simulation results show that the two-stage method yields accurate item and person parameter estimates, as well as high true detection rate and low false detection rate, under different manipulated conditions mimicking NAEP parameters. A real data example is given in the end to illustrate the potential application of the proposed method.     

When similarity and causality compete in category-based property generalization

Five experiments were performed to investigate the category-based generalization of nonblank properties, properties that were novel but that were attributed to existing category features with causal explanations. Experiments 1-3 tested how such explanations interact with the well-known effects of similarity on such generalizations. The results showed that when the causal explanations were used, standard effects of typicality (Experiment 1), diversity (Experiment 2), or similarity itself (Experiment 3) were almost completely eliminated. Experiments 4 and 5 demonstrated that category-based generalizations exhibit some of the standard properties of causal reasoning; for example, an effect (i.e., a novel category property) is judged to be more prevalent when its cause (i.e., an existing category feature) is also prevalent. These findings suggest that category-based property generalization is often an instance of causal inference.     

A fuzzy-based driver assistance system using human cognitive parameters and driving style information

Reducing the number of traffic accidents due to human errors is an urgent need in several countries around the world. In this scenario, the use of human-robot interaction (HRI) strategies has recently shown to be a feasible solution to compensate human limitations while driving. In this work we propose a HRI system which uses the driver’s cognitive factors and driving style information to improve safety. To achieve this, deep neural networks based approaches are used to detect human cognitive parameters such as sleepiness, driver’s age and head posture. Additionally, driving style information is also obtained through speed analysis and external traffic information. Finally, a fuzzy-based decision-making stage is proposed to manage both human cognitive information and driving style, and then limit the maximum allowed speed of a vehicle. The results showed that we were able to detect human cognitive parameters such as sleepiness –63% to 88% accuracy–, driver’s age –80% accuracy– and head posture –90.42% to 97.86% accuracy– as well as driving style –87.8% average accuracy. Based on such results, the fuzzy-based architecture was able to limit the maximum allowed speed for different scenarios, reducing it from 50 km/h to 17 km/h. Moreover, the fuzzy-based method showed to be more sensitive with respect to inputs changes than a previous published weighted-based inference method.     

Investigating the visual span in comparative search: the effects of task difficulty and divided attention

In three experiments, participants' visual span was measured in a comparative visual search task in which they had to detect a local match or mismatch between two displays presented side by side. Experiment 1 manipulated the difficulty of the comparative visual search task by contrasting a mismatch detection task with a substantially more difficult match detection task. In Experiment 2, participants were tested in a single-task condition involving only the visual task and a dual-task condition in which they concurrently performed an auditory task. Finally, in Experiment 3, participants performed two dual-task conditions, which differed in the difficulty of the concurrent auditory task. Both the comparative search task difficulty (Experiment 1) and the divided attention manipulation (Experiments 2 and 3) produced strong effects on visual span size.     

Tracking the line of primary gaze in a walking simulator: modeling and calibration.

This article describes a system for tracking the line of primary gaze (LoPG) of participants as they view a large projection screen. Using a magnetic head tracker and a tracking algorithm, we find the onscreen location at which a participant is pointing a head-mounted crosshair. The algorithm presented for tracking the LoPG uses a polynomial function to correct for distortion in magnetic tracker readings, a geometric model for computing LoPG from corrected tracker measurements, and a method for finding the intersection of the LoPG with the screen. Calibration techniques for the above methods are presented. The results of two experiments validating the algorithm and calibration methods are also reported. Experiments showed an improvement in accuracy of LoPG tracking provided by each of the two presented calibration steps, yielding errors in LoPG measurements of less than 2 degrees over a wide range of head positions. Source code for the described algorithms can be downloaded from the Psychonomic Society Web archive, http://www.psychonomic.org/archive/.     

Phonological similarity effects on detecting change in simple arrays

In two experiments, we investigated the effects on change detection and identification of the phonological similarity of the names of drawn objects shown in simple arrays. Experiment 1 examined the effects of phonological similarity on report of identity changes (in which one object was replaced by another) in a five-item array and Experiment 2 its effects on reporting switch changes (in which objects exchanged places). Phonological similarity did not impair change detection, and identification of new objects on change trials was more accurate in Experiment 1. H owever, phonological similarity significantly reduced change detection and identification of objects that switched location in Experiment 2. The results indicate different roles for a visual short-term memory system such as the visuospatial sketchpad and for phonological coding and storage in detecting and identifying replaced objects and object locations in arrays.     

Tracking the line of primary gaze in a walking simulator: Modeling and calibration

This article describes a system for tracking the line of primary gaze (LoPG) of participants as they view a large projection screen. Using a magnetic head tracker and a tracking algorithm, we find the onscreen location at which a participant is pointing a head-mounted crosshair. The algorithm presented for tracking the LoPG uses a polynomial function to correct for distortion in magnetic tracker readings, a geometric model for computing LoPG from corrected tracker measurements, and a method for finding the intersection of the LoPG with the screen. Calibration techniques for the above methods are presented. The results of two experiments validating the algorithm and calibration methods are also reported. Experiments showed an improvement in accuracy of LoPG tracking provided by each of the two presented calibration steps, yielding errors in LoPG measurements of less than 2° over a wide range of head positions. Source code for the described algorithms can be downloaded from the Psychonomic Society Web archive,http://www.psychonomic.org/archive/.     

Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling

The use of item responses from questionnaire data is ubiquitous in social science research. One side effect of using such data is that researchers must often account for item level missingness. Multiple imputation is one of the most widely used missing data handling techniques. The traditional multiple imputation approach in structural equation modeling has a number of limitations. Motivated by Lee and Cai’s approach, we propose an alternative method for conducting statistical inference from multiple imputation in categorical structural equation modeling. We examine the performance of our proposed method via a simulation study and illustrate it with one empirical data set.     

The effect of display timing on change blindness in pigeons (Columba livia)

Change blindness is a phenomenon in which even obvious changes in a visual scene may go unnoticed. Recent research has indicated that this phenomenon may not be exclusive to humans. Two experiments investigated change blindness in pigeons, using a variant of the widely‐used flicker task to investigate the influence of display timing on change blindness. Results indicate that the duration of time during which a stimulus display is visible influences change detection accuracy, with the effect due to additional search time. The results are discussed in relation to the value of comparative cognition and cross‐species investigations of behavior.     

Pedestrian detection using a moving camera: A novel framework for foreground detection

While background subtraction techniques have been widely applied to detect moving objects in a video stream captured by a static camera, detecting moving objects using a moving camera still represents a challenging task. In this context, pedestrian detection using a camera placed on the top of a vehicle’s windshield has been rarely investigated. This is mainly due to the background ego-motion. Since the scene captured by the camera seems in motion, it is very difficult to distinguish the moving pedestrians from the others that belong to the static part of the scene. For this reason, a compensation step is needed to suppress the ego-motion. This paper presents a study on the main challenges facing pedestrian detection systems as well as methods proposed to handle these challenges. A novel trajectory classification framework for detecting pedestrians even in challenging real-world environments is proposed. The proposed method models the background motion between two consecutive frames in order to compensate the camera motion. Then, it defines a classification process that differentiates between the background and the foreground in the frame. Using the defined foreground, we consequently identify the presence of pedestrians in the scene. The proposed method was validated on a public benchmark dataset: CVC-14 containing both visible and far infrared video sequences in day and night times. Experimental results confirm the effectiveness of the proposed approach in capturing the dynamic aspect between frames and therefore detecting the presence of pedestrians in the scene.     

欺骗识别的准确率：影响因素与提高途径

欺骗是一种常见的社会现象,通过观察他人的行为表现识别欺骗则是人们的一项重要能力。研究表明,人们的欺骗识别能力仅仅略微高于随机水平。本文关注基于行为线索的欺骗识别研究。首先,介绍欺骗识别的准确率;然后,结合Brunswik的透镜模型从欺骗线索的有效性和欺骗线索的利用两方面分析识别准确率的影响因素;并在此基础上探讨了提高识别准确率的途径。最后,对未来可能的研究方向进行展望。     

Color-based grouping and inhibition in visual search: evidence from a probe detection analysis of preview search

In four experiments, we examined selection processes in visual search using a probe detection task to measure the allocation of attention. Under preview search conditions, probes were harder to detect on old relative to new distractors (Experiment 1). This cannot be attributed solely to low-level sensory factors (Experiment 2). In addition, probe detection was sensitive to color-based grouping of old distractors and to color similarity between old distractors (Experiments 3 and 4). These effects were dissociated when the color of the old distractors changed but probe detection effects remained. Collectively, the data indicate both group-based suppression of distractors and the separate inhibition of distractor features in search.     

追踪研究中缺失数据处理方法及应用现状分析

追踪研究中普遍存在缺失数据, 缺失数据处理方法的选择影响统计推断的精度及研究结果的有效性。首先, 阐述缺失机制及判断方法, 比较追踪研究中主要的缺失数据处理方法的特点、及实际应用中的缺失处理方法的选择和软件实现。其次, 对国内心理学中92篇追踪研究文献进行分析, 发现有59篇(64.13%)报告不同程度缺失, 其中仅39篇报告了处理方法且均为删除法。未来研究应深入探讨现有缺失数据处理方法的有效性, 进一步规范应用研究中缺失数据的处理。     

Conversation level syntax similarity metric

The syntax and semantics of human language can illuminate many individual psychological differences and important dimensions of social interaction. Accordingly, psychological and psycholinguistic research has begun incorporating sophisticated representations of semantic content to better understand the connection between word choice and psychological processes. In this work we introduce ConversAtion level Syntax SImilarity Metric (CASSIM), a novel method for calculating conversation-level syntax similarity. CASSIM estimates the syntax similarity between conversations by automatically generating syntactical representations of the sentences in conversation, estimating the structural differences between them, and calculating an optimized estimate of the conversation-level syntax similarity. After introducing and explaining this method, we report results from two method validation experiments (Study 1) and conduct a series of analyses with CASSIM to investigate syntax accommodation in social media discourse (Study 2). We run the same experiments using two well-known existing syntactic metrics, LSM and Coh-Metrix, and compare their results to CASSIM. Overall, our results indicate that CASSIM is able to reliably measure syntax similarity and to provide robust evidence of syntax accommodation within social media discourse.     

Detecting air-gapped attacks using machine learning

A GSMem malware can attack a computer connected physically with no network. However, none of the existing techniques can detect GSMem attacks, up to now. To address this problem, this paper puts forward a new method based on Machine Learning (ML), including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Boosted Tree (BT), Back-Propagation Neural Networks (BPNN) and Naive Bayes Classifier (NBC). At first, we use a large quantity of data in terms of frequencies and amplitudes of some electromagnetic waves to train our models. And then, we use the obtained models to predict that whether a GSMem attack occurs or not, according to a given frequency and amplitude. In a word, the GSMem intrusion detection problem is induced to a ML binary classification one, while the former problem is pending and the latter one has been solved. As a result, the former problem can be solved in principle in this way. The simulated experiments show that the new method is potential to detect a GSMem attack, with low False Positive Rates (FPR) and low False Negative Rates (FNR).