Question: How perceiving the global versus local aspects influences the estimation of psychological distance from one's experienced self.

This paper explores the Implicit Association Test (IAT) as a tool for understanding the complexities of human cognition and emotion, particularly focusing on its application to 7-year-olds—a methodological and conceptual review. Among all living beings, humans possess a distinctive trait: the capacity for introspection, the ability to delve into the depths of our own minds and share these insights with others. This introspective ability is accompanied by a sense of "knowing," a feeling of objectivity, certainty, and mental control over our thoughts. We are aware of the sources of our thoughts, feelings, and actions, and we believe we make decisions deliberately and rationally. However, the 20th century brought about a significant challenge to this assumption of rationality. From Herbert Simon's (1955) pioneering theories to the perplexing dilemmas posed by Kahneman, Slovik, and Tversky (1982), and the revelations of Wegner (2002) regarding the illusion of control, psychologists have uncovered the limitations of introspection (Nisbett and Wilson, 1977) and the automatic nature of everyday thought (Bargh, 1997). These findings have highlighted the vulnerabilities of the human mind.

As psychologists have come to terms with these limitations, there has been a growing interest in developing measures to assess aspects of thinking and feeling that may not be easily accessible to consciousness. The goal is to scrutinize new forms of cognition and emotion that were previously unknown, and to reevaluate traditional concepts like attitude, preference, belief, stereotype, self-concept, and self-esteem based on what these new measures reveal. Unlike traditional methods, these novel measures do not require introspection from the subject. For some constructs, this is considered a valuable feature, while for others, it raises suspicion. For instance, measuring math ability can be approached by asking "how good are you at math?" or by assessing it through a math skills test. The former relies on introspection, whereas the latter does not. Yet, the latter is widely accepted.

In the context of global-versus-local perception and its effect on the estimation of psychological distance, the Implicit Association Test (IAT) offers a unique perspective. By measuring the strength of associations between mental representations, the IAT can reveal subtle biases and preferences that may not be apparent through introspection. This methodological approach, by avoiding reliance on introspective responses, provides a more objective measure of the psychological distance individuals perceive between themselves and various concepts.

Over the past few decades, dual attitude ⁄ process ⁄ system models have emerged as the dominant framework for understanding a wide range of psychological phenomena. Most of these models characterize the unconscious and conscious mind as being built from discrete processes or systems: one that is reflexive, automatic, fast, affective, associative, and primitive, and a second that is deliberative, controlled, slow, cognitive, propositional, and more uniquely human. Although these models serve as a useful heuristic for characterizing the human mind, recent developments in social and cognitive neuroscience suggest that the human evaluative system, like most of cognition, is widely distributed and highly dynamic. Integrating these advances with current attitude theories, we review how the recently proposed Iterative Reprocessing Model can account for apparent dual systems as well as discrepancies between traditional dual system models and recent research revealing the dynamic nature of evaluation. Furthermore, we describe important implications this dynamical system approach has for various social psychological domains. For nearly a century, psychologists have sought to understand the unconscious and conscious processes that allow people to evaluate their surroundings (Allport, 1935; Freud, 1930). Building on a model of the human mind rooted in classic Greek philosophy (Annas, 2001), many contemporary psychologists have characterized the mind as possessing discrete processes or systems: one that is evolutionarily older, reflexive, automatic, fast, affective, associative, and the other that is more uniquely human, deliberative, controlled, slow, cognitive, and propositional (see Figure 1). These dual process or system models have been highly influential throughout psychology for the past three decades (Chaiken & Trope, 1999). Indeed, a dual system model of the human mind permeates research in a wide range of psychological domains, such as attitudes and persuasion (Chaiken, 1980; Fazio, 1990; Gawronski & Bodenhausen, 2006; Petty & Cacioppo, 1986; Rydell & McConnell, 2006; Wilson, Samuel, & Schooler, 2000), stereotypes and prejudice (Crandall & Eshleman, 2003; Devine, 1989; Gaertner & Dovidio, 1986; Pettigrew & Meertens, 1995), person perception (Brewer, 1988; Fiske & Neuberg, 1990; Macrae & Bodenhausen, 2000), self-regulation (Baumeister & Heatherton, 1996; Freud, 1930; Hofmann, Friese, & Strack, 2009; Strack & Deutsch, 2004), moral cognition (Greene, Sommerville, Nystrom, Darley, & Cohen, 2001; Haidt, 2001), learning and memory (Smith & DeCoster, 2000; Sun, 2002), and decision-making (Kahneman, 2003; Sloman, 1996). Although dual system models provide generative frameworks for understanding a wide range of psychological phenomenon, recent developments in social and affective neuroscience suggest that the human evaluative system, like most of cognition, is widely distributed and highly dynamic (e.g., Ferguson & Wojnowicz, 2011; Freeman & Ambady, Social and Personality Psychology Compass 6/6 (2012): 438–454, 10.1111/j.1751-9004.2012.00438.x a 2012 Blackwell Publishing Ltd 2011; Scherer, 2009). Integrating these advances with current attitude theories, we review how the recently proposed Iterative Reprocessing Model (Cunningham & Zelazo, 2007; Cunningham, Zelazo, Packer, & Van Bavel, 2007) can account for apparent dual systems as well as discrepancies between traditional dual system models and recent research revealing the dynamic nature of evaluation. The model also address why the nature of evaluative processing differs across people (e.g., Cunningham, Raye, & Johnson, 2005; Park, Van Bavel, Vasey, & Thayer, forthcoming). Although we focus primarily on dual models of attitudes and evaluation due to space constraints, we believe the premises of our dynamic model can be generalized to other domains where dual system models of typically invoked (Chaiken & Trope, 1999), including social cognition, self-regulation, prejudice and stereotyping, and moral cognition. Therefore, we very briefly discuss the implications of our model for these other domains in the final section of this paper and encourage interested readers to read our more extensive treatment of these issues in the domain of stereotypes and prejudice (Cunningham & Van Bavel, 2009a; Van Bavel & Cunningham, 2011) and emotion (Cunningham & Van Bavel, 2009b; Kirkland & Cunningham, 2011, forthcoming). Attitudes and evaluation Attitudes are one of the most central constructs in social psychology, yet there has been considerable debate regarding the most fundamental aspects of attitudes (Fazio, 2007; Schwarz & Bohner, 2001). Allport (1935) defined an attitude as ‘‘a mental and neural state of readiness, organized through experience, exerting a directive or dynamic influence upon the individual’s response to all objects and situations with which it is related’’ (p. 810). Throughout the history of attitude research, theorists and researchers have attempted to provide a complete yet parsimonious definition of this construct. Wellknown examples include the one-component perspective (Thurstone, 1928), the tripartite model (Affective, Behavior, Cognition; Katz & Stotland, 1959; Rosenberg & Hovland, 1960), and more recently, a host of dual attitudes (e.g., Greenwald & Banaji, 1995; Rydell & McConnell, 2006; Wilson et al., 2000) and dual process models (e.g., Chaiken, 1980; Fazio, 1990; Gawronski & Bodenhausen, 2006; Petty & Cacioppo, 1986). It is widely assumed that attitudes are stored associations between objects and their evaluations, which can be accessed from memory very quickly with little conscious effort Figure 1 Illustrative example of the process and content of a dual system model (cited in Kahneman, 2003, p. 698). Evaluation is a Dynamic Process 439 a 2012 Blackwell Publishing Ltd Social and Personality Psychology Compass 6/6 (2012): 438–454, 10.1111/j.1751-9004.2012.00438.x (Fazio, 2001; Fazio, Sanbonmatsu, Powell, & Kardes, 1986; but see Schwarz, 2007). For example, people categorize positive and negative words more quickly when these words are preceded by a similarly valenced stimuli, suggesting that attitudes are automatically activated by the mere presence of the attitude object in the environment (Fazio et al., 1986). Moreover, people may have access to evaluative information about stimuli prior to their semantic content (Bargh, Litt, Pratto, & Spielman, 1989; but see Storbeck & Clore, 2007). Such research has led to the conclusion that the initial evaluative classification of stimuli as good or bad can be activated automatically and guide the perceiver’s interpretation of his or her environment (Houston & Fazio, 1989; Smith, Fazio, & Cejka, 1996). Dual attitudes and dual process models of attitudes The recent development of a wide variety of implicit attitude measures (Petty, Fazio, & Briñol, 2009; Wittenbrink & Schwarz, 2007), including measures of human physiology (Cunningham, Packer, Kesek, & Van Bavel, 2009), has fueled an explosion of research on dual attitude ⁄process ⁄ system models of attitudes and evaluations (see Table 1). Most of these models infer dual process architecture from observable dissociations between implicit and explicit measures of behavior (e.g., Dovidio, Kawakami, & Gaertner, 2002; McConnell & Leibold, 2001; Rydell & McConnell, 2006). Although many dual models generally share a common set of assumptions about the human mind, the specific features of each model differ. Therefore, we propose a rough taxonomy to characterize different classes of these models. ‘‘Dual attitudes models’’ tend to dichotomize the representations of attitudes into distinct automatic versus controlled constructs (Greenwald & Banaji, 1995; Rydell & McConnell, 2006; Wilson et al., 2000). In contrast, ‘‘dual process models’’ tend to dichotomize the processing of attitudinal representations into automatic versus controlled processes. There is considerable debate over whether these two types of processes are independent or communicate with one another (i.e., information from one system is available to the other system) (Fazio, 1990; Gawronski & Bodenhausen, 2006; Gilbert, Pelham, & Krull, 1988; Petty, Brinol, & DeMarree, 2007). In the latter case, interdependent dual process models have generally been proposed to operate in a corrective fashion, such that ‘‘controlled’’ processes can influence otherwise ‘‘automatic’’ responses (e.g., Wegener & Petty, 1997). Although dual attitudes models likely require dual processes to integrate different attitudinal representations into evaluations and behaviors, dual process models are less likely to require the assumption of dual attitude representations (e.g., Fazio, 1990). For the purpose of clarity, we use ‘‘dual system models’’ to capture models that assume dual attitudes and processes that do not interact (e.g., Rydell & McConnell, 2006; Wilson et al., 2000). There are, of course, many ways to hook up a dual system (see Gilbert, 1999 for a discussion). A complete discussion of all possible dual models and interconnections between these systems is beyond the scope of this article. Therefore, we focus on several core premises that many models have in common. Likewise, we focus on the core premises from our own model – rather than an exhaustive discussion (e.g., Cunningham et al., 2007) – in order to communicate key similarities and differences between dual models and our proposed dynamic model. Furthermore, we recognize that dual models and our proposed dynamic model do not exhaust all types of models of attitudes and evaluation – some extant models do include more than two processes (e.g., Beer, Knight, & D’Esposito, 2006; Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005) and many allow for interactive processes that operate in a post hoc, corrective fashion (e.g., Chen & Chaiken, 1999; Gawronski & 440 Evaluation is a Dynamic Process a 2012 Blackwell Publishing Ltd Social and Personality Psychology Compass 6/6 (2012):

Humans are an intensely social species and therefore it is essential for our interpersonal judgments to be valid enough to help us to avoid enemies, form useful alliances and find suitable mates; flawed judgments can literally be fatal. An evolutionary perspective implies that humans ought to have developed sufficient skills at solving problems of interpersonal judgment, including gauging the personalities of others, to be useful for the basic tasks of survival and reproduction. Yet, the view to be derived from the large and influential bias-and-error literature of social psychology is decidedly different—the social mind seems riddled with fundamental design flaws. We will argue in this paper that flawed design is probably the least plausible explanation for the existence of so many errors. We present an evolutionarily-based taxonomy of known bias effects that distinguishes between biases that are trivial or even artifactual and lead virtually nowhere, and those that have interesting implications and deserve further study. Finally, we present an evolutionary perspective that suggests that the ubiquity, automaticity, and success of interpersonal judgment, among other considerations, presents the possibility of a universal Personality Judgment Instinct. Archeological evidence and behavioral patterns observed in extant hunter-gatherer groups indicate that the human species has been intensely social for a long time (e.g., Chagnon, 1983, Tooby & Devore, 1987). Human offspring have a remarkably extended period of juvenile dependency, which both requires and provides the skills for surviving in a complex social world (Hrdy, 1999). Humans evolved language and universal emotional expressions which serve the social purpose of discerning and influencing the thoughts of others (e. and humans will infer social intentions on the basis of minimal cues, as Heider and Simmel (1944) demonstrated in their classic experiment involving chasing triangles and evading circles. Recent work has shown that children above age 4 and adults in disparate cultures (Germans and Amazonian Indians) can categorize intentions—chasing, fighting, following, playing, and courting (for adults)— from no more than the motion patterns of computerized v-shaped arrowheads (Barrett, Todd, Miller, & Blythe, in press). Most notably, humans have a deeply-felt need for social inclusion. Deprivation of social contact produces anxiety, loneliness, and depression (Baumeister & Leary, 1995); indeed, as William James (1890) observed: " Solitary confinement is by many regarded as a mode of torture Accuracy and Bias-3 too cruel and unnatural for civilised countries to adopt. " Participants in laboratory studies who are left out of a face-to-face …

In this article we present a review of research on core self-evaluations, a broad personality trait indicated by 4 more narrow traits: self-esteem, generalized self-efficacy, locus of control, and emotional stability. We review evidence suggesting that the 4 core traits are highly related, load on a single unitary factor, and have dubious incremental validity controlling for their common core. We more generally investigate the construct validity of core self-evaluations. We also report on the development and validation of the first direct measure of the concept, the Core Self-Evaluations Scale (CSES). Cross-cultural evidence on the CSES is provided. We conclude by offering an agenda for future research, discussing areas where future core self-evaluations research is most needed.

Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain–mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts.

Previous neuroimaging studies of spatial perspective taking have tended not to activate the brain's mentalising network. We predicted that a task that requires the use of perspective taking in a communicative context would lead to the activation of mentalising regions. In the current task, participants followed auditory instructions to move objects in a set of shelves. A 2x2 factorial design was employed. In the Director factor, two directors (one female and one male) either stood behind or next to the shelves, or were replaced by symbolic cues. In the Object factor, participants needed to use the cues (position of the directors or symbolic cues) to select one of three possible objects, or only one object could be selected. Mere presence of the Directors was associated with activity in the superior dorsal medial prefrontal cortex (MPFC) and the superior/middle temporal sulci, extending into the extrastriate body area and the posterior superior temporal sulcus (pSTS), regions previously found to be responsive to human bodies and faces respectively. The interaction between the Director and Object factors, which requires participants to take into account the perspective of the director, led to additional recruitment of the superior dorsal MPFC, a region activated when thinking about dissimilar others' mental states, and the middle temporal gyri, extending into the left temporal pole. Our results show that using perspective taking in a communicative context, which requires participants to think not only about what the other person sees but also about his/her intentions, leads to the recruitment of superior dorsal MPFC and parts of the social brain network.

Compassion has emerged as an important construct in studies of mental health and psychological therapy. Although an increasing number of studies have explored relationships between compassion and different facets of psychopathology there has as yet been no systematic review or synthesis of the empirical literature. We conducted a systematic search of the literature on compassion and mental health. We identified 20 samples from 14 eligible studies. All studies used the Neff Self Compassion Scale (Neff, 2003b). We employed meta-analysis to explore associations between self-compassion and psychopathology using random effects analyses of Fisher's Z correcting for attenuation arising from scale reliability. We found a large effect size for the relationship between compassion and psychopathology of r=-0.54 (95% CI=-0.57 to -0.51; Z=-34.02; p<.0001). Heterogeneity was significant in the analysis. There was no evidence of significant publication bias. Compassion is an important explanatory variable in understanding mental health and resilience. Future work is needed to develop the evidence base for compassion in psychopathology, and explore correlates of compassion and psychopathology.

This essay reviews theory and research regarding the “Michelangelo phenomenon,” which describes the manner in which close partners shape one another’s dispositions, values, and behavioral tendencies. Individuals are more likely to exhibit movement toward their ideal selves to the degree that their partners exhibit affirming perception and behavior, exhibiting confidence in the self ’s capacity and enacting behaviors that elicit key features of the self ’s ideal. In turn, movement towards the ideal self yields enhanced personal well-being and couple well-being. We review empirical evidence regarding this phenomenon and discuss self and partner variables that contribute to the process.

In this article we provide a unifying neural hypothesis on how individuals understand the actions and emotions of others. Our main claim is that the fundamental mechanism at the basis of the experiential understanding of others' actions is the activation of the mirror neuron system. A similar mechanism, but involving the activation of viscero-motor centers, underlies the experiential understanding of the emotions of others.

Using recent research, I argue that beliefs lie at the heart of personality and adaptive functioning and that they give us unique insight into how personality and functioning can be changed. I focus on two classes of beliefs—beliefs about the malleability of self-attributes and expectations of social acceptance versus rejection—and show how modest interventions have brought about important real-world changes. I conclude by suggesting that beliefs are central to the way in which people package their experiences and carry them forward, and that beliefs should play a more central role in the study of personality. KEYWORDS—personality; personality theories; change; self-beliefs; interventions

People's desires to see themselves as moral actors can contribute to their striving for and achievement of a sense of self-completeness. The authors use self-completion theory to predict (and show) that recalling one's own (im)moral behavior leads to compensatory rather than consistent moral action as a way of completing the moral self. In three studies, people who recalled their immoral behavior reported greater participation in moral activities (Study 1), reported stronger prosocial intentions (Study 2), and showed less cheating (Study 3) than people who recalled their moral behavior. These compensatory effects were related to the moral magnitude of the recalled event, but they did not emerge when people recalled their own positive or negative nonmoral behavior (Study 2) or others' (im)moral behavior (Study 3). Thus, the authors extend self-completion theory to the moral domain and use it to integrate the research on moral cleansing (remunerative moral strivings) and moral licensing (relaxed moral strivings).


Question: Separating from the experienced self: the impact of global versus local perception on estimating psychological distance.

Does Language Shape Cognition? Evidence from Spatial Perception
The role of language in shaping cognitive processes has been a topic of considerable debate. In the domain of spatial perception, the concept of frames of reference has been central. Traditionally, frames of reference were considered innate, hardwired into our neurocognitive systems. However, recent research indicates that the use of these frames in language, cognition, and gesture can vary significantly across cultures, suggesting a more malleable relationship between language and cognition. Children, it appears, can learn different spatial frames of reference with equal facility, challenging the notion of innate spatial concepts. We propose that language can significantly influence, or even restructure, spatial cognition, indicating a need to reconsider the interplay between the neurological foundations of spatial cognition and the spatial concepts used in everyday language. This finding has broader implications for understanding cognitive diversity across cultures in fundamental cognitive domains.

**Question**: The Impact of Social Media on Interpersonal Communication

**Documents**: The Effects of Social Media on Communication: A Comprehensive Review
Social media platforms have transformed the way we communicate, interact, and share information. These digital tools have made it easier to connect with people from different parts of the world, breaking down geographical barriers and fostering global communities. However, they have also introduced new challenges, such as the erosion of face-to-face communication skills, the spread of misinformation, and the potential for addiction. This paper reviews the impact of social media on interpersonal communication, examining both its positive and negative effects. It discusses how social media has enabled new forms of communication, facilitated the exchange of ideas, and created opportunities for social activism. On the downside, it explores how excessive use of social media can lead to social isolation, reduce the quality of interpersonal relationships, and contribute to mental health issues. The paper concludes by suggesting ways to mitigate the negative impacts of social media while harnessing its benefits for enhancing communication.

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Social Media's Influence on Communication: An In-Depth Analysis
The advent of social media has dramatically altered the landscape of interpersonal communication. These digital platforms have simplified the process of connecting with individuals across the globe, creating a sense of global connectivity and community. Despite their many advantages, social media platforms have also introduced a series of challenges. Among these are the decline in traditional face-to-face communication skills, the proliferation of misinformation, and the risk of addiction. This review delves into the multifaceted impact of social media on communication, exploring both its advantages and disadvantages. It highlights the ways in which social media has introduced innovative

Although there has been excellent research by many investigators on the topic of metamemory, here we will focus on our own research program. This article will begin with a description of a theoretical framework that has evolved out of metamemory research, followed by a few remarks about our methodology, and will end with a review of our previously unpublished findings. (Our published findings will not be systematically reviewed here; instead, they will be mentioned only when necessary for continuity .)

Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

This simple apparatus is used to investigate depth perception in different animals. All species thus far tested seem able to perceive and avoid a sharp drop as soon as they can move about. Human infants at the creeping and toddling stage are notoriously prone to falls from more or less high places. They must be kept from going over the brink by side panels on their cribs, gates on stairways and the vigilance of adults. As their muscular coordination matures they begin to avoid such accidents on their own. Common sense might suggest that the child learns to recognize falling-off places by experience–that is, by falling and hurting himself. But is experience really the teacher? Or is the ability to perceive and avoid a brink part of the child's original endowment? Answers to these questions will throw light on the genesis of space perception in general. Height perception is a special case of distance perception: information in the light reaching the eye provides stimuli that can be utilized for the discrimination both of depth and of receding distance on the level. At what stage of development can an animal respond effectively to these stimuli? Does the onset of such response vary with animals of different species and habitats? At Cornell University we have been investigating these problems by means of a simple experimental setup that we call a visual cliff. The cliff is a simulated one and hence makes it possible not only to control the optical and other stimuli (auditory and tactual, for instance) but also to protect the experimental subjects. It consists of a board laid across a large sheet of heavy glass which is supported a foot or more above the floor. On one side of the board a sheet of patterned material is placed flush against the undersurface of the glass, giving the glass the appearance as well as the substance of solidity. On the other side a sheet of the same material is laid upon the floor; this side of the board thus becomes the visual cliff (Fig. 1). The Classic Visual Cliff Experiment This young explorer has the good sense not to crawl out onto an apparently unsupported surface, even when Mother beckons from the other side. Rats, pups, kittens, and chicks also will not try to walk across to the other side. (So don't bother asking why the chicken crossed the visual cliff.) …

Emotional states might selectively modulate components of cognitive control. To test this hypothesis, the author randomly assigned 152 undergraduates (equal numbers of men and women) to watch short videos intended to induce emotional states (approach, neutral, or withdrawal). Each video was followed by a computerized 2-back working memory task (spatial or verbal, equated for difficulty and appearance). Spatial 2-back performance was enhanced by a withdrawal state and impaired by an approach state; the opposite pattern held for verbal performance. The double dissociation held more strongly for participants who made more errors than average across conditions. The results suggest that approach-withdrawal states can have selective influences on components of cognitive control, possibly on a hemispheric basis. They support and extend several frameworks for conceptualizing emotion-cognition interactions.

Frames of reference are coordinate systems used to compute and specify the location of objects with respect to other objects. These have long been thought of as innate concepts, built into our neurocognition. However, recent work shows that the use of such frames in language, cognition and gesture varies cross-culturally, and that children can acquire different systems with comparable ease. We argue that language can play a significant role in structuring, or restructuring, a domain as fundamental as spatial cognition. This suggests we need to rethink the relation between the neurocognitive underpinnings of spatial cognition and the concepts we use in everyday thinking, and, more generally, to work out how to account for cross-cultural cognitive diversity in core cognitive domains.

We investigated whether and why the use of a foreign language influences moral judgment. We studied the trolley and footbridge dilemmas, which propose an action that involves killing one individual to save five. In line with prior work, the use of a foreign language increased the endorsement of such consequentialist actions for the footbridge dilemma, but not for the trolley dilemma. But contrary to recent theorizing, this effect was not driven by an attenuation of emotions. An attenuation of emotions was found in both dilemmas, and it did not mediate the foreign language effect on moral judgment. An examination of additional scenarios revealed that foreign language influenced moral judgment when the proposed action involved a social or moral norm violation. We propose that foreign language influences moral judgment by reducing access to normative knowledge.

The most exciting hypothesis in cognitive science right now is the theory that cognition is embodied. Like all good ideas in cognitive science, however, embodiment immediately came to mean six different things. The most common definitions involve the straight-forward claim that "states of the body modify states of the mind." However, the implications of embodiment are actually much more radical than this. If cognition can span the brain, body, and the environment, then the "states of mind" of disembodied cognitive science won't exist to be modified. Cognition will instead be an extended system assembled from a broad array of resources. Taking embodiment seriously therefore requires both new methods and theory. Here we outline four key steps that research programs should follow in order to fully engage with the implications of embodiment. The first step is to conduct a task analysis, which characterizes from a first person perspective the specific task that a perceiving-acting cognitive agent is faced with. The second step is to identify the task-relevant resources the agent has access to in order to solve the task. These resources can span brain, body, and environment. The third step is to identify how the agent can assemble these resources into a system capable of solving the problem at hand. The last step is to test the agent's performance to confirm that agent is actually using the solution identified in step 3. We explore these steps in more detail with reference to two useful examples (the outfielder problem and the A-not-B error), and introduce how to apply this analysis to the thorny question of language use. Embodied cognition is more than we think it is, and we have the tools we need to realize its full potential.


Question: Understanding the influence of global and local perception on the estimation of psychological distance from one's experienced self.

This study explores the influence of high-level and low-level construals on decisions concerning near and distant future events, in line with Temporal Construal Theory. According to the theory, high-level construals, which are more abstract and central, are associated with distant future events, while low-level construals, which are more concrete and peripheral, are linked to near future events. The theory posits that the value of high-level construals increases over time, while the value of low-level construals decreases. In the context of goal-directed activities, the end state's desirability represents a high-level construal, whereas the feasibility of achieving this end state is a low-level construal. The research comprises five studies. Study 1 reveals that distant future activities are indeed construed at a higher level than near future activities. Studies 2 and 3 demonstrate that decisions about distant future activities are more heavily influenced by the desirability of the end state and less by the feasibility of reaching it, compared to decisions about near future activities. Study 4 involves students making real-life choices regarding academic assignments that vary in difficulty and interest. Students give relatively more weight to the assignment's interest when choosing a distant future assignment, whereas they consider difficulty more when selecting a near future assignment. Study 5 indicates that distant future plans are more closely related to the desirability of activities, as opposed to time constraints, compared to near future plans.

In summary, the research confirms that the perception of time affects how people construe and value future events, with distant events being more influenced by high-level construals (desirability) and near events by low-level construals (feasibility). This distinction has implications for decision-making processes, particularly in evaluating the attractiveness versus the attainability of future goals.

The nature of an information visualization can be considered to lie in the visual metaphors it uses to structure information. The process of understanding a visualization therefore involves an interaction between these external visual metaphors and the user's internal knowledge representations. To investigate this claim, we conducted an experiment to test the effects of visual metaphor and verbal metaphor on the understanding of tree visualizations. Participants answered simple data comprehension questions while viewing either a treemap or a node-link diagram. Questions were worded to reflect a verbal metaphor that was either compatible or incompatible with the visualization a participant was using. The results suggest that the visual metaphor indeed affects how a user derives information from a visualization. Additionally, we found that the degree to which a user is affected by the metaphor is strongly correlated with the user's ability to answer task questions correctly. These findings are a first step towards illuminating how visual metaphors shape user understanding, and have significant implications for the evaluation, application, and theory of visualization.

A series of experiments explored a form of object-specific priming. In all experiments a preview field containing two or more letters is followed by a target letter that is to be named. The displays are designed to produce a perceptual interpretation of the target as a new state of an object that previously contained one of the primes. The link is produced in different experiments by a shared location, by a shared relative position in a moving pattern, or by successive appearance in the same moving frame. An object-specific advantage is consistently observed: naming is facilitated by a preview of the target, if (and in some cases only if) the two appearances are linked to the same object. The amount and the object specificity of the preview benefit are not affected by extending the preview duration to 1 s, or by extending the temporal gap between fields to 590 ms. The results are interpreted in terms of a reviewing process, which is triggered by the appearance of the target and retrieves just one of the previewed items. In the absence of an object link, the reviewing item is selected at random. We develop the concept of an object file as a temporary episodic representation, within which successive states of an object are linked and integrated.

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

Does the human capacity for mathematical intuition depend on linguistic competence or on visuo-spatial representations? A series of behavioral and brain-imaging experiments provides evidence for both sources. Exact arithmetic is acquired in a language-specific format, transfers poorly to a different language or to novel facts, and recruits networks involved in word-association processes. In contrast, approximate arithmetic shows language independence, relies on a sense of numerical magnitudes, and recruits bilateral areas of the parietal lobes involved in visuo-spatial processing. Mathematical intuition may emerge from the interplay of these brain systems.

This study attempts a new conceptualization of communication “sources” by proposing a typology of sources that would apply not only to traditional media but also to new online media. Ontological rationale for the distinctions in the typology is supplemented by psychological evidence via an experiment that investigated the effects of different types of source attributions upon receivers’ perception of online news content. Participants (N = 48) in a 4-condition, between-participants experiment read 6 identical news stories each through an online service. Participants were told that the stories were selected by 1 of 4 sources: news editors, the computer terminal on which they were accessing the stories, other audience members (or users) of the online news service, or (using a pseudo-selection task) the individual user (self). After reading each online news story, all participants filled out a paperand-pencil questionnaire indicating their perceptions of the story they had just read. In confirmation of the distinctions made in the typology, attribution of identical content to 4 different types of online sources was associated with significant variation in news story perception. Theoretical implications of the results as well as the typology are discussed.

The purpose of the present research was to examine the relationships between self-compassion and women's body image. In Study 1, female undergraduates (N=142) completed three measures of body image and measures of self-esteem and self-compassion. Results showed that high self-compassion predicted fewer body concerns independently of self-esteem. Moreover, when both self-compassion and self-esteem were included as predictors, self-compassion accounted for unique variance in body preoccupation and weight concerns whereas self-esteem did not. In Study 2, this finding was partially replicated with one component (self-judgment) of self-compassion uniquely predicting body preoccupation in undergraduate women (N=187). High scores on self-compassion also predicted less eating guilt independent of self-esteem. Additionally, self-compassion was shown to partially mediate the relationship between body preoccupation and depressive symptoms. The findings highlight the possibility that a consideration of self-compassion for body image may contribute to identifying who is most at risk for body/shape concerns.

This work examines the Michelangelo phenomenon, an interpersonal model of the means by which people move closer to (vs. further from) their ideal selves. The authors propose that partner similarity--similarity to the ideal self, in particular--plays an important role in this process. Across 4 studies employing diverse designs and measurement techniques, they observed consistent evidence that when partners possess key elements of one another's ideal selves, each person affirms the other by eliciting important aspects of the other's ideals, each person moves closer to his or her ideal self, and couple well-being is enhanced. Partner similarity to the actual self also accounts for unique variance in key elements of this model. The associations of ideal similarity and actual similarity with couple well-being are fully attributable to the Michelangelo process, to partner affirmation and target movement toward the ideal self. The authors also performed auxiliary analyses to rule out several alternative interpretations of these findings.

Using recent research, I argue that beliefs lie at the heart of personality and adaptive functioning and that they give us unique insight into how personality and functioning can be changed. I focus on two classes of beliefs—beliefs about the malleability of self-attributes and expectations of social acceptance versus rejection—and show how modest interventions have brought about important real-world changes. I conclude by suggesting that beliefs are central to the way in which people package their experiences and carry them forward, and that beliefs should play a more central role in the study of personality. KEYWORDS—personality; personality theories; change; self-beliefs; interventions

Marketers often employ a variety of positive emotions to encourage consumption or promote a particular behavior (e.g., buying, donating, recycling) to benefit an organization or cause. The authors show that specific positive emotions do not universally increase prosocial behavior but, rather, encourage different types of prosocial behavior. Four studies show that whereas positive emotions (i.e., love, hope, pride, and compassion) all induce prosocial behavior toward close entities (relative to a neutral emotional state), only love induces prosocial behavior toward distant others and international organizations. Love’s effect is driven by a distinct form of broadening, characterized by extending feelings of social connection and the boundary of caring to be more inclusive of others regardless of relatedness. Love—as a trait and a momentary emotion— is unique among positive emotions in fostering connectedness that other positive emotions (hope and pride) do not and broadening behavior in a way that other connected emotions (compassion) do not. This research contributes to the broaden-and-build theory of positive emotion by demonstrating a distinct type of broadening for love and adds an important qualification to the general finding that positive emotions uniformly encourage prosocial behavior.

It can be difficult to understand physical measurements (e.g., 28 lb, 600 gallons) that appear in news stories, data reports, and other documents. We develop tools that automatically re-express unfamiliar measurements using the measurements of familiar objects. Our work makes three contributions: (1) we identify effectiveness criteria for objects used in concrete measurement re-expressions; (2) we operationalize these criteria in a scalable method for mining a large dataset of concrete familiar objects with their physical dimensions from Amazon and Wikipedia; and (3) we develop automated concrete re-expression tools that implement three common re-expression strategies (adding familiar context, reunitization and proportional analogy) as energy minimization algorithms. Crowdsourced evaluations of our tools indicate that people find news articles with re-expressions more helpful and re- expressions help them to better estimate new measurements.


Question: A supervised machine learning method for detecting phishing websites using wrapper feature selection.

This document introduces a novel hybrid model aimed at detecting phishing websites through the application of supervised learning algorithms. Over recent decades, the widespread adoption of online technologies has transformed the computing landscape, yet it has also given rise to escalating security challenges. With a significant portion of the population now conducting activities online, ranging from social interactions to financial transactions, the need for robust security mechanisms has become more critical than ever. Phishing, in particular, represents a significant threat, as attackers exploit this method to illicitly obtain sensitive information such as bank account details and passwords by masquerading as legitimate websites. Despite numerous proposed solutions in the literature, an optimal defense against phishing remains elusive, prompting ongoing research efforts.

Phishing websites often exhibit distinct features and patterns, and the identification of these characteristics is crucial for effective detection. This task falls under the purview of classification, a problem that can be addressed using data mining techniques. The paper at hand presents a hybrid model designed specifically for the classification of phishing websites. To assess the efficacy of this model, a dataset from the UCI repository, consisting of 30 attributes and 11,055 instances, was utilized. The experimental outcomes revealed that the proposed hybrid model achieved superior performance, characterized by high accuracy and a reduced error rate, thus offering a promising solution in the ongoing battle against phishing attacks.

In the Internet age, cyber-attacks occur frequently with complex types. Traffic generated by access activities can record website status and user request information, which brings a great opportunity for network attack detection. Among diverse network protocols, Hypertext Transfer Protocol (HTTP) is widely used in government, organizations and enterprises. In this work, we propose DeepHTTP, a semantics structure integration model utilizing Bidirectional Long Short-TermMemory (Bi-LSTM) with attention mechanism to model HTTP traffic as a natural language sequence. In addition to extracting traffic content information, we integrate structural information to enhance the generalization capabilities of the model. Moreover, the application of attention mechanism can assist in discovering critical parts of anomalous traffic and furthermining attack patterns. Additionally, we demonstrate how to incrementally update the data set and retrain model so that it can be adapted to new anomalous traffic. Extensive experimental evaluations over large traffic data have illustrated that DeepHTTP has outstanding performance in traffic detection and pattern discovery.

Android is arguably the most widely used mobile operating system in the world. Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers. Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign. However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible. Machine learning offers a way to tackle this issue of speed by automating the classification task. While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain. In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware. Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset. We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches. Our best model gets an F1 score of 0.986 with an AUC of 0.983 as compared to the existing best F1 score of 0.875 and AUC of 0.953.

Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.

Defending a server against Internet worms and defending a user’s email inbox against spam bear certain similarities. In both cases, a stream of samples arrives, and a classifier must automatically determine whether each sample falls into a malicious target class (e.g., worm network traffic, or spam email). A learner typically generates a classifier automatically by analyzing two labeled training pools: one of innocuous samples, and one of samples that fall in the malicious target class. Learning techniques have previously found success in settings where the content of the labeled samples used in training is either random, or even constructed by a helpful teacher, who aims to speed learning of an accurate classifier. In the case of learning classifiers for worms and spam, however, an adversary controls the content of the labeled samples to a great extent. In this paper, we describe practical attacks against learning, in which an adversary constructs labeled samples that, when used to train a learner, prevent or severely delay generation of an accurate classifier. We show that even a delusive adversary, whose samples are all correctly labeled, can obstruct learning. We simulate and implement highly effective instances of these attacks against the Polygraph [15] automatic polymorphic worm signature generation algorithms.

In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the s ndmailsystem call data and the network tcpdumpdata, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intraand interaudit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.

Cyber threat intelligence and security informatics play critical roles in the identification of the principal influencers and threats associated with criminal activity and extremism in web-based communities. This paper presents an effective, dynamic learning framework utilizing domain specific context information to design a novel classification model that can robustly identify malicious social media posts (e.g. enrollment propaganda for extremist groups) with expressions of extremism or criminal intent. Research towards automated identification of extreme online posts and their associated key suspects and threats faces numerous challenges, 1) Online data, particularly social media data, originated from numerous independent and heterogeneous sources are largely unstructured. 2) The tactics, techniques, and procedures (TTPs) of criminal activity and extremism are constantly evolving. 3) There are limited ground truth data to support the development of effective classification technologies. In this paper, we present a human-machine collaborative, semi-supervised learning system that can efficiently and effectively identify malicious social media posts in presence of these challenges. Our system and framework develops an initial classifier from limited annotated data and in an interactive manner evolves dynamically into a sophisticated model using shortlisted relevant samples, identified via a graph-based optimization method, solvable by maximum flow algorithm. This same method also may be used to refine the classifier as TTPs evolve. Under this framework, the classifier performance converges faster using roughly 1–2 orders of magnitude fewer annotated samples, as compared to fully supervised solutions, resulting in a reasonably acceptable accuracy of nearly 80%. We validate our framework using a large collection of English and non-English flagged words extracted from three web-based forums and manually verified by multiple independent annotators.

Sentiment Analysis (SA) research has increased tremendously in recent times .Sentiment analysis means to extract opinion of users from review documents. Sentiment classification using Machine learning (ML ) methods faces the problem of high dimensionality of feature vector. Therefore, a feature selection method is required to eliminate the irrelevant and noisy features from the feature vector for efficient working of ML algorithms Rough set theory provides an important concept for feature reduction called reduct. The cost of reduct set computation is highly influenced by the attribute size of the dataset where the problem of finding reducts has been proven as NPhard problems.Different feature selection are applied on different data set, Experimental results show that mRMR is better compared to IG for sentiment classification, Hybrid feature selection method based on the RST and Information Gain (IG) is better compared to the previous methods. Proposed methods are evaluated on four standard datasets viz. Movie review, Product (book, DVD, and electronics) reviewed datasets, and Experimental results show that hybrid feature selection method outperforms than feature selection methods for sentimental classification.

In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.

Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on Rough Set and Statistical Test named as RSST to extract relevant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on a dataset consisting of 3500 samples with 30 RSST derived essential system calls resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Besides, empirical analysis of RSST derived system calls outperform other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes and network traces.

Cyberbully is a misuse of technology advantage to bully a person. Cyberbully and its impact have occurred around the world and now the number of cases are increasing. Cyberbullying detection is very important because the online information is too large so it is not possible to be tracked by humans. The purpose of this research is to construct a classification model with optimal accuracy in identifying cyberbully conversation using Naive Bayes method and Support Vector Machine (SVM) then applying n-gram 1 to 5 for the number of class 2, 4, and 11 for each method. Naive Bayes yields an average accuracy of 92.81%, SVM with a poly kernel yields an average accuracy of 97.11% It can be concluded that SVM with poly kernel yields higher accuracy than SVM with other kernels, Naive Bayes, and Kelly Reynolds research method of decision tree (J48) and k-NN.


Question: Detecting phishing websites using supervised machine learning with wrapper-based feature selection.

This article highlights the ten most significant algorithms in data mining, as recognized by the IEEE International Conference on Data Mining in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These algorithms have had a profound impact on the data mining research community. For each algorithm, we offer a detailed description, explore its significance, and summarize the latest research findings and potential future directions. These ten algorithms encompass a wide range of data mining tasks, including classification, clustering, statistical learning, association rule mining, and link analysis, all of which are critical areas in the field of data mining.

**Question**: A Deep Learning Approach for Automatic Speech Recognition

**Documents**: A comparative study of deep learning models for automatic speech recognition
Automatic speech recognition (ASR) systems are essential in various applications, such as voice-controlled devices, transcription services, and hearing aids. In recent years, deep learning models have significantly improved the performance of ASR systems. This paper presents a comparative study of different deep learning models for ASR. We consider three types of models: recurrent neural networks (RNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks. We train these models on the TIMIT dataset, a standard benchmark for ASR, and evaluate their performance in terms of word error rate (WER). Our results show that LSTM networks outperform RNNs and CNNs in terms of WER. We also investigate the effect of different training strategies, such as batch normalization and dropout, on the performance of the models. Our findings indicate that batch normalization and dropout can significantly improve the performance of all three models. Finally, we discuss the challenges and future directions in the field of deep learning for ASR.

**Similar Documents**: This paper offers an in-depth comparative analysis of various deep learning architectures for enhancing automatic speech recognition (ASR) capabilities. ASR technology is pivotal in a multitude of applications, ranging from voice-controlled devices to transcription services and assistive technologies for the hearing impaired. With the advent of deep learning, ASR systems have witnessed a dramatic enhancement in accuracy. Here, we focus on three prominent deep learning models: Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. These models are rigorously tested on the TIMIT dataset, a widely recognized benchmark for ASR, with

The amount of data in the world and in our lives seems everincreasing and there’s no end to it. The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data pre-processing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up largescale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. Classification is an important data mining technique with broad applications. It classifies data of various kinds. This paper has been carried out to make a performance evaluation of REPTree, Simple Cart and RandomTree classification algorithm. The paper sets out to make comparative evaluation of classifiers REPTree, Simple Cart and RandomTree in the context of dataset of Indian news to maximize true positive rate and minimize false positive rate. For processing Weka API were used. The results in the paper on dataset of Indian news also show that the efficiency and accuracy of RandomTree is good than REPTree, and Simple Cart. Keywords— Simple Cart, RandomTree, REPTree, Weka, WWW

The popularity of smartphones usage especially Android mobile platform has increased to 80% of share in global smartphone operating systems market, as a result of which it is on the top in the attacker's target list. The fact of having more private data and low security assurance letting the attacker to write several malware programs in different ways for smartphone, and the possibility of obfuscating the malware detection applications through different coding techniques is giving more energy to attacker. Several approaches have been proposed to detect malwares through code analysis which are now severely facing the problem of code obfuscation and high computation requirement. We propose a machine learning based method to detect android malware by analyzing the visual representation of binary formatted apk file into Grayscale, RGB, CMYK and HSL. GIST feature from malware and benign image dataset were extracted and used to train machine learning algorithms. Initial experimental results are encouraging and computationally effective. Among machine learning algorithms Random Forest have achieved highest accuracy of 91% for grayscale image, which can be further improved by tuning the various parameters.

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, in this paper we develop and analyze proactive Machine Learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification based solutions for detecting unknown Android malware.

In addressing the growing problem of junk E-mail on the Internet, we examine methods for the automated construction of filters to eliminate such unwanted messages from a user’s mail stream. By casting this problem in a decision theoretic framework, we are able to make use of probabilistic learning methods in conjunction with a notion of differential misclassification cost to produce filters Which are especially appropriate for the nuances of this task. While this may appear, at first, to be a straight-forward text classification problem, we show that by considering domain-specific features of this problem in addition to the raw text of E-mail messages, we can produce much more accurate filters. Finally, we show the efficacy of such filters in a real world usage scenario, arguing that this technology is mature enough for deployment.

Insider threat is a significant security risk for organizations, and detection of insider threat is of paramount concern to organizations. In this paper, we attempt to discover insider threat by analyzing enterprise social and online activity data of employees. To this end, we process and extract relevant features that are possibly indicative of insider threat behavior. This includes features extracted from social data including email communication patterns and content, and online activity data such as web browsing patterns, email frequency, and file and machine access patterns. Subsequently, we take two approaches to detect insider threat: (i) an unsupervised approach where we identify statistically abnormal behavior with respect to these features using state-of-the-art anomaly detection methods, and (ii) a supervised approach where we use labels indicating when employees quit the company as a proxy for insider threat activity to design a classifier. We test our approach on a real world data set with artificially injected insider threat events. We obtain a ROC score of 0.77 for the unsupervised approach, and a classification accuracy of 73.4% for the supervised approach. These results indicate that our proposed approaches are fairly successful in identifying insider threat events. Finally, we build a visualization dashboard that enables managers and HR personnel to quickly identify employees with high threat risk scores which will enable them to take suitable preventive measures and limit security risk.

The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing, pattern classification, and data mining applications. This paper presents a novel optimization algorithm called catfish binary particle swarm optimization (CatfishBPSO), in which the so-called catfish effect is applied to improve the performance of binary particle swarm optimization (BPSO). This effect is the result of the introduction of new particles into the search space (“catfish particles”), which replace particles with the worst fitness by the initialized at extreme points of the search space when the fitness of the global best particle has not improved for a number of consecutive iterations. In this study, the K-nearest neighbor (K-NN) method with leave-oneout cross-validation (LOOCV) was used to evaluate the quality of the solutions. CatfishBPSO was applied and compared to six classification problems taken from the literature. Experimental results show that CatfishBPSO simplifies the feature selection process effectively, and either obtains higher classification accuracy or uses fewer features than other feature selection methods.

Malware is any computer software potentially harmful to both computers and networks. The amount of malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new malware. Supervised machine learning has been adopted to solve this issue. There are two types of features that supervised malware detectors use: (i) static features and (ii) dynamic features. Static features are extracted without executing the sample whereas dynamic ones requires an execution. Both approaches have their advantages and disadvantages. In this paper, we propose for the first time, OPEM, an hybrid unknown malware detector which combines the frequency of occurrence of operational codes (statically obtained) with the information of the execution trace of an executable (dynamically obtained). We show that this hybrid approach enhances the performance of both approaches when run separately.

With the rapidly growing development of cellular networks and powerful smartphones, botnets have invaded the mobile domain. Social media, like Twitter, Facebook, and YouTube have created a new communication channel for attackers. Recently, bot masters started to exploit social media for different malicious activity, such as sending spam, recruitment of new bots, and botnet command and control. In this paper we propose a detection technique for social mediabased mobile botnets using Twitter. The proposed method combines the correlation between tweeting and user activity, such as clicks or taps, and an Artificial Immune System detector, to detect tweets caused by bots and differentiate them from tweets generated by user or by user-approved applications. This detector creates a signature of the tweet and compares it with a dynamically updated signature library of bot behavior signatures. The proposed system has been fully implemented on Android platform and tested under several sets of generated tweets. The test results show that the proposed method has a very high accuracy in detecting bot tweets with about 95% detection ratio.

Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system. Recent substantial research focused on machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications. This study summarizes the evolution of malware detection techniques based on machine learning algorithms focused

In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the s ndmailsystem call data and the network tcpdumpdata, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intraand interaudit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.


Question: A supervised machine learning method for detecting phishing websites using wrapper feature selection.

This document introduces an innovative framework for visual object detection that excels in processing images at high speeds while maintaining high accuracy in detection. The framework stands out through three significant innovations. Firstly, it introduces the "Integral Image" representation, a technique that enables the rapid computation of the features utilized by the detector. Secondly, it employs a learning algorithm, rooted in AdaBoost, which meticulously selects a subset of essential visual features from a broader pool, resulting in highly efficient classifiers. Thirdly, the framework incorporates a cascading mechanism that combines increasingly complex classifiers, allowing for the swift elimination of non-object areas while dedicating more computational resources to regions that resemble objects. This cascading approach can be likened to an object-specific focus-of-attention system that, unlike its predecessors, offers statistical assurances that discarded regions are unlikely to contain the object of interest. In the specific domain of face detection, the system performs on par with the most advanced systems to date. When deployed in real-time scenarios, the detector operates at a rate of 15 frames per second, achieving this speed without relying on image differencing or skin color detection techniques.

**Question**: Anomaly Detection in Time Series Data using Deep Learning Techniques

**Documents**: Deep Learning for Anomaly Detection: A Survey
Anomaly detection is a classical problem in data mining, machine learning, and statistics. It aims to identify unusual patterns that do not conform to expected behavior, called “anomalies”, and that draw our attention. Anomalies are often associated with critical situations such as credit card fraud, medical emergencies, equipment failures, and security breaches. In this paper, we provide a survey on deep learning methods for anomaly detection. We categorize the existing methods into three groups: reconstruction-based, discrimination-based, and density-based methods. We also discuss the advantages and limitations of each method and provide guidance on choosing appropriate methods for different types of data and applications. In addition, we summarize the commonly used benchmark datasets and evaluation metrics for anomaly detection, and discuss the challenges and future research directions in this field.

**Similar Documents**: This comprehensive survey delves into the application of deep learning techniques for anomaly detection, a critical area in the realms of data mining, machine learning, and statistics. The primary goal is to pinpoint patterns that deviate from the norm, often indicative of significant events such as financial fraud, medical emergencies, equipment malfunctions, and security intrusions. The paper classifies existing deep learning approaches for anomaly detection into three distinct categories: reconstruction-based, discrimination-based, and density-based methodologies

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.12.015 ⇑ Corresponding author. E-mail addresses: juaner@lsi.uned.es (J. MartinezAraujo). Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody’s lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts. 2012 Elsevier Ltd. All rights reserved.

Web applications suffer from different security vulnerabilities that could be exploited by hackers to cause harm in a variety of ways. A number of approaches have been proposed to test for such vulnerabilities. However, some gaps are still to be addressed. In this paper, we address one of such gaps: the problem of automatically generating test data (i.e., possible attacks) to test for cross site scripting (XSS) type of vulnerability. The objective is to generate a set of test data to exercise candidate security-vulnerable paths in a given script. The desirable set of test data must be effective in the sense that it uncovers whether any path can indeed be exploited to launch an attack. We designed a genetic algorithm-based test data generator that uses a database of XSS attack patterns to generate possible attacks and assess whether the attack is successful. We considered different types of XSS vulnerability: stored, reflected and DOM based. We empirically validated our test data generator using case studies of Web applications developed using PHP and MySQL. Empirical results show that our test data generator is effective in generating, in one run, multiple test data to cover multiple target

Robust statistics is a branch of statistics which includes statistical methods capable of dealing adequately with the presence of outliers. In this paper, we propose an anomaly detection method that combines a feature selection algorithm and an outlier detection method, which makes extensive use of robust statistics. Feature selection is based on a mutual information metric for which we have developed a robust estimator; it also includes a novel and automatic procedure for determining the number of relevant features. Outlier detection is based on robust Principal Component Analysis (PCA) which, opposite to classical PCA, is not sensitive to outliers and precludes the necessity of training using a reliably labeled dataset, a strong advantage from the operational point of view. To evaluate our method we designed a network scenario capable of producing a perfect ground-truth under real (but controlled) traffic conditions. Results show the significant improvements of our method over the corresponding classical ones. Moreover, despite being a largely overlooked issue in the context of anomaly detection, feature selection is found to be an important preprocessing step, allowing adaption to different network conditions and inducing significant performance gains.

The popularity of smartphones usage especially Android mobile platform has increased to 80% of share in global smartphone operating systems market, as a result of which it is on the top in the attacker's target list. The fact of having more private data and low security assurance letting the attacker to write several malware programs in different ways for smartphone, and the possibility of obfuscating the malware detection applications through different coding techniques is giving more energy to attacker. Several approaches have been proposed to detect malwares through code analysis which are now severely facing the problem of code obfuscation and high computation requirement. We propose a machine learning based method to detect android malware by analyzing the visual representation of binary formatted apk file into Grayscale, RGB, CMYK and HSL. GIST feature from malware and benign image dataset were extracted and used to train machine learning algorithms. Initial experimental results are encouraging and computationally effective. Among machine learning algorithms Random Forest have achieved highest accuracy of 91% for grayscale image, which can be further improved by tuning the various parameters.

Bots are malicious software components used for generating spams, launching denial of service attacks, phishing, identity theft and information exfiltration and such other illegal activities. Bot detection is an area of active research in recent times. Here we propose a bot detection mechanism for a single host. A user traffic profile is used to filter out normal traffic generated by the host. The remaining suspicious traffic is subject to detailed analysis. The nature of detailed analysis is derived from a characterization of bot traffic. The detection system is tested using two real world bots. Performance evaluation results show that the proposed system achieves a high detection rate (100%) and a low false positive rate (0–8%). The traffic filtering yielded an average reduction in traffic by 70%.

In this paper, we propose a fuzzy local search (FLS) method for intrusion detection. The FLS system is a fuzzy classifier, whose knowledge base is modeled as a fuzzy rule such as "if-then" and improved by a local search metaheuristic. The proposed method is implemented and tested on the benchmark KDD'99 intrusion dataset. The results are encouraging and demonstrate the benefits of our approach.

Domain-specific search engines are growing in popularity because they offer increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, www.campsearch.com allows complex queries by age-group, size, location and cost over .summer camps. Unfortunately these domain-specific search engines are difficult and timeconsuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine for computer science research papers. It already contaius over 50,000 papers and is publicly available at ~w. cora.justres earch, com.

Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creating an accurate, succinct and comprehensible representation of the target concept. To overcome this problem, researchers have proposed algorithms for automatic construction of features. The majority of these algorithms use a limited predefined set of operators for building new features. In this paper we propose a generalized and flexible framework that is capable of generating features from any given set of constructor functions. These can be domain-independent functions such as arithmetic and logic operators, or domain-dependent operators that rely on partial knowledge on the part of the user. The paper describes an algorithm which receives as input a set of classified objects, a set of attributes, and a specification for a set of constructor functions that contains their domains, ranges and properties. The algorithm produces as output a set of generated features that can be used by standard concept learners to create improved classifiers. The algorithm maintains a set of its best generated features and improves this set iteratively. During each iteration, the algorithm performs a beam search over its defined feature space and constructs new features by applying constructor functions to the members of its current feature set. The search is guided by general heuristic measures that are not confined to a specific feature representation. The algorithm was applied to a variety of classification problems and was able to generate features that were strongly related to the underlying target concepts. These features also significantly improved the accuracy achieved by standard concept learners, for a variety of classification problems.

Defending a server against Internet worms and defending a user’s email inbox against spam bear certain similarities. In both cases, a stream of samples arrives, and a classifier must automatically determine whether each sample falls into a malicious target class (e.g., worm network traffic, or spam email). A learner typically generates a classifier automatically by analyzing two labeled training pools: one of innocuous samples, and one of samples that fall in the malicious target class. Learning techniques have previously found success in settings where the content of the labeled samples used in training is either random, or even constructed by a helpful teacher, who aims to speed learning of an accurate classifier. In the case of learning classifiers for worms and spam, however, an adversary controls the content of the labeled samples to a great extent. In this paper, we describe practical attacks against learning, in which an adversary constructs labeled samples that, when used to train a learner, prevent or severely delay generation of an accurate classifier. We show that even a delusive adversary, whose samples are all correctly labeled, can obstruct learning. We simulate and implement highly effective instances of these attacks against the Polygraph [15] automatic polymorphic worm signature generation algorithms.

For the Android malware detection / classification anti-malware community has relied on traditional malware detection methods as a countermeasure. However, traditional detection methods are developed for detecting the computer malware, which is different from Android malware in structure and characteristics. Thus, they may not be useful for Android malware detection. Moreover, majority of suggested detection approaches may not be generalized and are incapable of detecting zero-day malware due to different reasons such as available data set with specific set of examples. Thus, their detection accuracy may be questionable. To address this problem, this paper presents a malware classification approach with a reliable detection accuracy and evaluate the approach using artificially generated examples. The suggested approach generates the signature profiles and behavior profiles of each application in the data set, which are further used as input for the classification task. For improving the detection accuracy, feature fusion of features from filter methods and wrapper method and algorithm fusion is investigated. Without affecting the detection accuracy, the optimal balance between real world examples and synthetic examples is also investigated. The experimental results suggest that both AUC and F1 can be obtained up to 0.94 for both known and unknown malware using original examples and synthetic examples.