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Cognitive, comparative, and developmental psychologists have long been interested in humans’ and animals’ ability to respond to abstract relations. Cross-species research has used relational matching-to-sample (RMTS) tasks in which participants try to find stimulus pairs that “match” because they express the same abstract relation (same or different). Researchers seek to understand the cognitive processes that underlie successful matching, and the cognitive constraints that create species differences in these tasks. Here we describe a dissociative framework drawn from cognitive neuroscience. It has strong potential to illuminate the area of same-different conceptualization. It has already influenced comparative research on categorization and metacognition. This dissociative framework also shows that species differences in same-different conceptualization have resonance with species differences in other comparative domains.

To explain animal learning, researchers invoke a dominant associative construct. In contrast, researchers freely acknowledge humans' explicit-declarative learning capacity. Here, we stretched animals' learning performance toward the explicit pole of cognition. We tested four macaques (Macaca mulatta) in new discrimination-learning paradigms. Monkeys learned a series of two-choice discrimination tasks. But immediate reinforcement was denied. Instead, reinforcement was lagged-monkeys received feedback for trial N only after seeing and responding to the N + 1-trial stimulus. Theory suggests that lagged reinforcement will eliminate a dominant form of implicit discrimination learning. Yet monkeys still learned successfully. Thus, monkeys may have alternative learning algorithms usable when reinforcement is displaced and reinforcement learning undermined. This learning may, as in humans, take a more explicit form. This and related methods that disable associative learning-fostering a possible transition to explicit cognition-could have empirical utility and theoretical significance within comparative psychology. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Problematic and disruptive behaviours continue to be a pervasive problem in elementary school classrooms across the globe, with recent reports indicating rising trends. The present study seeks to d...

Privacy breaches are one of the biggest concerns on Online Social Networks (OSNs), especially with an introduction of automated attacks by socialbots, which can automatically extract victims' private content by exploiting social behavior to befriend them. The key insight of this attack is that by intelligently sending friend requests to a small subset of users, called the Critical Friending Set (CFS), such a bot can evade current defense mechanisms. We study the vulnerability of OSNs to socialbot attacks. Specifically, we introduce a new optimization problem, Min-Friending, which identifies a minimum CFS to friend in order to obtain at least Q benefit, which quantifies the amount of private information the bot obtains. The two main challenges of this problem are how to cope with incomplete knowledge of network topology and how to model users' responses to friend requests. In this paper, we show that Min-Friending is inapproximable within a factor of (1-o(1)) ln Q and present an adaptive approximation algorithm using adaptive stochastic optimization. The key feature of our solution lies in the adaptive method, where partial network topology is revealed after each successful friend request. Thus the decision of whom to send a friend request to next is made with the outcomes of past decisions taken into account. Traditional tools break down when attempting to place a bound on the performance of this technique with realistic user models. Therefore, we additionally introduce a novel curvature-based technique to construct an approximation ratio of ln Q for a model of user behavior learned from empirical measurements on Facebook.

How strong are the connections between individuals? This is a fundamental question in the study of social networks. In this work, we take a topological view rooted in the idea of local sparsity to answer this question on large social networks to which we have only incomplete access. Prior approaches to measuring network structure are not applicable to this setting due to the strict limits on data availability. Therefore, we propose a new metric, the Edgecut Weight, for this task. This metric can be calculated efficiently in an online fashion, and we empirically show that it captures important elements of communities. Further, we demonstrate that the distribution of these weights characterizes connectivity on a network. Subsequently, we estimate the distribution of weights on Twitter and show both a lack of strong connections and a corresponding lack of community structure.

Previous research indicates that victimized youth are reluctant to address their friends’ victimizing behaviors and are likely to remain in a victimizing friendship despite experiencing significant distress. Research investigating the complex factors underlying this commitment to victimizing friendships is required. To this end, a qualitative research design was used as previously victimized youth asynchronously contributed to an anonymous online discussion forum for 2 months. Guided by two theories used to understand staying/leaving processes in intimate partner violence and a social-ecological systems perspective, the data were captured into descriptive categories. The results demonstrate that adolescents ( N = 25) were reluctant to address their friends’ victimizing behaviors and many persisted in their victimizing friendships. The factors underlying this reluctance were categorized as cognitive, relational, and contextual. The results indicate that persisting in a friendship despite feeling victimized can seem paradoxical to outsiders but there are clear reasons underlying adolescents’ choices.

The introduction of Smart Grid systems has raised many new security concerns. If an attacker can compromise components of the Smart Grid communication network, they can fabricate malicious messages to interfere with the grid and ultimately cause outages. One method to address this concern is to conduct network audits by logging network traffic into dedicated servers in order to detect malicious messages. This may be done by using switches’ or routers’ ability to duplicate packets they receive with minimal overhead. The question of how many and which switches/routers to select for this task naturally follows. This paper considers the problem of finding minimal set of routers/switches in a Smart Grid communication network to use for auditing traffic. Accordingly, we devise three algorithms: the first one is highly effective with an approximation ratio of $(2+\theta)(\ln |V| + 1)$ . The second method is a highly scalable algorithm with a constant performance ratio. And the last one is a dynamic algorithm which can efficiently update its solution in response to changes to critical traffic. We experimentally evaluate our solutions and compare them to an optimal Integer Programming formulation, finding that they perform near-optimally and significantly outperform a simple heuristic in all cases.

In rule-based (RB) category-learning tasks, the optimal strategy is a simple explicit rule, whereas in information-integration (II) tasks, the optimal strategy is impossible to describe verbally. Many studies have reported qualitative dissociations between training and performance in RB and II tasks. Virtually all of these studies were testing predictions of the dual-systems model of category learning called COVIS. The most prominent alternative account to COVIS is that humans have one learning system that is used in all tasks, and that the observed dissociations occur because the II task is more difficult than the RB task. This article describes the first attempt to test this difficulty hypothesis against anything more than a single set of data. First, two novel predictions are derived that discriminate between the difficulty and multiple-systems hypotheses. Next, these predictions are tested against a wide variety of published categorization data. Overall, the results overwhelmingly reject the difficulty hypothesis and instead strongly favor the multiple-systems account of the many RB versus II dissociations.

A growing body of literature has documented the contribution of teacher-student relationship quality to both persistence and reduction in peer aggression incidents in the school context. The research literature indicates that students who are involved in peer aggression also tend to experience lower levels of closeness in their relationships with their teachers. However, these study results have not yet been aggregated, and the size and direction of effects remains unclear. In the present study we quantitatively synthesized 66 individual studies (Nstudents = 352,376) in two meta-analyses by aggregating cross-sectional associations between peer aggression involvement and teacher-student relationship closeness that have been reported in the literature over the last 20 years. A small, negative, and significant association was found between perpetration and victimization and teacher-student relationship closeness, indicating that students who experience greater involvement in peer aggression also have relationships with their teachers that are lacking in closeness. Three moderator analyses were also conducted. No moderating effect was found for school level or measure type; however, a significant moderating effect was found for informant type. The results from the meta-analyses lead to direct recommendations for practice regarding how we can best support students’ psychosocial development in the school context.

Early animal-metacognition researchers singled out simultaneous metacognition paradigms for theoretical criticism, because these paradigms presented concretely rewarded perceptual responses and the metacognitive response simultaneously. This method potentially introduced associative cues into the situation that could confound the interpretation of the metacognitive response. Evaluating this possibility, we compared humans' metacognitive performances in simultaneous and nonsimultaneous (prospective, retrospective) paradigms that were otherwise identical. Results show that the metacognition response in these tasks is not prompted by associative cues arising from the simultaneous task format. To the contrary, the metacognitive response is used more robustly and accurately when it is removed from direct competition with the primary perceptual responses. Thus, early researchers were correct to judge that the nonsimultaneous paradigms tap metacognition more robustly and sensitively. However, this is probably true because the simultaneous paradigm mingles responses adjudicated on two different cognitive-processing levels. And, in that case, the metacognitive response can be outcompeted and suppressed by the salient presence of primary, concretely rewarded perceptual responses. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Categorization is an essential cognitive process useful for transferring knowledge from previous experience to novel situations. The mechanisms by which trained categorization behavior extends to novel stimuli, especially in animals, are insufficiently understood. To understand how pigeons learn and transfer category membership, seven pigeons were trained to classify controlled, bi-dimensional stimuli in a two-alternative forced-choice task. Following either dimensional, rule-based (RB) or information integration (II) training, tests were conducted focusing on the "analogical" extension of the learned discrimination to novel regions of the stimulus space (Casale, Roeder, & Ashby, 2012). The pigeons' results mirrored those from human and non-human primates evaluated using the same analogical task structure, training and testing: the pigeons transferred their discriminative behavior to the new extended values following RB training, but not after II training. Further experiments evaluating rule-based models and association-based models suggested the pigeons use dimensions and associations to learn the task and mediate transfer to stimuli within the novel region of the parametric stimulus space.

The authors consider theory in the animal-metacognition literature. Theoretical interpretation was long dominated by associative interpretations, a conservative approach well illustrated in the 2009 special issue of animal metacognition in Comparative Cognition and Behavioral Reviews. We suggest, though, that this approach risks a self-limiting understanding of animal minds, and an imprecise understanding of the cognitive requirements inherent in metacognition tasks. In fact, some tasks self-entail the need for higher-level decision-making processes, processes that—in humans—we would call explicit, declarative, and conscious. These points are illustrated using the inaugural study on dolphin metacognition. We urge researchers to turn more toward illuminating the cognitive architecture of capacities like metacognition, including illuminating the depth, and structure, the learning/memory systems, the cognitive levels, and the declarative awareness possibly present in animals’ minds. The empirical development of this literature demonstrates that researchers are now prepared to do so. This study can produce strong synergies across the allied fields of biopsychology, comparative and cognitive psychology, and neuroscience.

Utility providers are gradually deploying social networks as a useful addition to the Smart Grid in order to help engage consumers in energy management and efficient usage. Besides its benefits, is there any negative impact to the Smart Grid? In this paper, we investigate the vulnerability of Smart Grid when integrating into social networks, where attackers utilize misinformation propagation in social network to alter electricity customer's behavior with the goal of causing degradation to power infrastructure. Stand in both perspectives of power facility administrator and adversary, we model the vulnerability assessment of the system under an optimization problem, which enables us to provide theoretical analysis and behavior investigation of the system based on the complexity theory. As solving the problem is challenging, we propose heuristic solutions and show their efficiency on assessing the system's vulnerability in the presence of misinformation attacks. Therefore, we conclude that misinformation attacks must be considered when developing the security model for Socially-enabled Smart Grid technology and planning mitigation techniques.

The study of nonhumans' metacognitive judgments about trial difficulty has grown into an important comparative literature. However, the potential for associative-learning confounds in this area has left room for behaviorist interpretations that are strongly asserted and hotly debated. This article considers how researchers may be able to observe animals' strategic cognitive processes more clearly by creating temporally extended problems within which associative cues are not always immediately available. We asked humans and rhesus macaques to commit to completing spatially extended mazes or to decline completing them through a trial-decline response. The mazes could sometimes be completed successfully, but other times had a constriction that blocked completion. A deliberate, systematic scanning process could preevaluate a maze and determine the appropriate response. Latency analyses charted the time course of the evaluative process. Both humans and macaques appeared, from the pattern of their latencies, to scan the mazes through before committing to completing them. Thus monkeys, too, can base trial-decline responses on temporally extended evaluation processes, confirming that those responses have strategic cognitive-processing bases in addition to behavioral-reactive bases. The results also show the value of temporally and spatially extended problems to let researchers study the trajectory of animals' online cognitive processes. (PsycINFO Database Record

Research suggests that about a quarter of bullying incidences occur within friendships. Yet little attention is given to the underlying social processes and wider macro-system forces that shape fri...

Metacognition encompasses the processes of monitoring representational and perceptual states and controlling information-gathering behaviors. Metacognition is considered one of humans’ most sophisticated abilities, and it has been a growing area of focus in comparative cognition research. Despite the successes of some species such as the great apes and some Old World monkeys, there has been a fairly consistent lack of metacognitive responding in the New World primate species, capuchin monkeys. These failures are meaningful for what they highlight about the phylogenetic breadth of metacognition, and for what they offer to ongoing debates about the proper interpretation of data from other species that do succeed in various tests of comparative metacognition. We summarize these meaningful failures and place them in a broader context of comparative metacognition research, with a specific focus on explaining what it might mean that some monkeys seemingly do not know what they know.

Celebrating the life of Professor James (Jim) Ralph Hanson.

While adaptive cruise control (ACC) systems have been broadly few studies have investigated the user experience characteristics of recent production systems. The present study was designed for the express purpose of testing one such system. Nineteen participants drove a 2014 BMW X-5 M, equipped with adaptive cruise control, in two scenarios designed to be representative of the usage scenarios described by the vehicle manufacturer. During testing, user interactions with the system were recorded while opinions regarding the usability and operation of the system were elicited through a series of interviews and questionnaires. While participants responded favorably to the system conceptually, participants expressed considerable dissatisfaction with the ACC’s braking behavior and with specific aspects of the system’s user interface. These findings add to a growing body of research that suggests that mode confusion or insufficient awareness of system status may be a significant impediment to the use of ACC systems generally.

Comparative and cognitive psychologists interpret performance in different ways. Animal researchers invoke a dominant construct of associative learning. Human researchers acknowledge humans' capacity for explicit-declarative cognition. This article offers a way to bridge a divide that defeats productive cross-talk. We show that animals often challenge the associative-learning construct, and that it does not work to try to stretch the associative-learning construct to encompass these performances. This approach thins and impoverishes that important construct. We describe an alternative approach that restrains the construct of associative learning by giving it a clear operational definition. We apply this approach in several comparative domains to show that different task variants change-in concert-the level of awareness, the declarative nature of knowledge, the dimensional breadth of knowledge, and the brain systems that organize learning. These changes reveal dissociable learning processes that a unitary associative construct cannot explain but a neural-systems framework can explain. These changes define the limit of associative learning and the threshold of explicit cognition. The neural-systems framework can broaden empirical horizons in comparative psychology. It can offer animal models of explicit cognition to cognitive researchers and neuroscientists. It can offer simple behavioral paradigms for exploring explicit cognition to developmental researchers. It can enliven the synergy between human and animal research, promising a productive future for both.

Motivated by the dynamic resource allocation problem for device-to-device (D2D) communications, we study the online set multicover problem (OSMC). In the online set multicover, the set X of elements to be covered is unknown in advance; furthermore, the coverage requirement of each element $$x \in X$$ is initially unknown. Elements of X together with coverage requirements are presented one at a time in an online fashion; and a feasible solution must be maintained at all times. We provide the first deterministic, online algorithms for OSMC with competitive ratios. We consider two versions of OSMC; in the first, each set may be picked only once, while the second version allows each set to be picked multiple times. For both versions, we present the first deterministic, online algorithms, with competitive ratios $$O( \log n \log m )$$ and $$O( \log n (\log m + \log k) )$$ , repectively, where n is the number of elements, m is the number of sets, and k is the maximum coverage requirement. By simulation, we show the efficacy of these algorithms for resource allocation in the D2D setting by analyzing network throughput and other metrics, obtaining a large improvement in running time over offline methods.

Classroom circles have been recognized as a valuable pedagogical approach to develop students’ social-emotional learning and to establish a sense of community within a classroom. Until recently, there has been little consideration that teachers, themselves, may benefit from circling experiences. To garner a deeper understanding of circling for teachers, this study examined teacher candidates’ experiences with circling in a teacher education course. Focus groups with former teacher candidates procured three themes: circling creates safe and engaging spaces for learning, productive tensions create opportunities for connection, and, teachers create effective circles with authenticity. The results suggest that circling should be similarly used with educators, in addition to use with students, and could be embedded within current teacher-education programming., La valeur pédagogique des cercles de discussion est reconnue. Cette approche permet de favoriser l’apprentissage socio-émotionnel des étudiants et de créer un sens de la communauté au sein d’un groupe-classe. Or, jusqu’à récemment, peu d’intérêt était accordé aux bénéfices que les enseignants pouvaient tirer de telles expériences. Afin de mieux comprendre le processus des cercles de discussion chez les enseignants, ce projet de recherche analyse l’expérience vécue par de futurs enseignants expérimentant le cercle de discussion dans le cadre d’un cours de formation des maitres. Trois thèmes ont émergé des groupes de discussion menés auprès d’anciens aspirants à l’enseignement : les cercles favorisent un climat d’apprentissage sécuritaire et stimulant, les tensions productives font naître des opportunités relationnelles et les enseignants créent des cercles efficaces avec authenticité. Ces résultats indiquent que les cercles de discussion pourraient être utilisés non seulement avec les élèves, mais également avec les enseignants et même, intégrés au programme actuel de formation des maitres.

Drawing from social–ecological systems theory, the authors argue that -current research on childhood bullying would benefit from analyses that consider the -mesosystem—specifically, how teacher–student relationships can influence -children's bullying experiences. The authors provide two theoretical conceptions for how children's peer interactions are implicitly shaped by teacher–student relationship quality: attachment and social referral. Implications for practice, with an emphasis on developing teachers' social–emotional competencies to strengthen positive teacher–student relationships, are proposed.

Frozen dinners prepared by a caterer were reported to have been spoiled. Microbiological testing of samples was performed, and a survey of time-temperature exposures (during preparation, storage, delivery and reheating) was conducted of procedures duplicating those at the time the spoiled food was prepared. Growth of spoilage bacteria was not inhibited by freezing a customer's week's supply of packaged meals in cardboard boxes. This growth continued during transit in a precooled insulated truck. Meals reheated in a plywood box oven (furnished by the caterer for this purpose) failed to reach lethal temperatures for vegetative mesophilic bacteria within a practicable time (90 min). Holding the meals in wire baskets or in shallow metal trays during freezing and reheating the frozen meals to 74 C (165 F) in conventional domestic ovens prevented spoilage of the meals.

To explain learning, comparative researchers invoke an associative construct by which immediate reinforcement strengthens animal's adaptive responses. In contrast, cognitive researchers freely acknowledge humans' explicit-learning capability to test and confirm hypotheses even lacking direct reinforcement. We describe a new dissociative framework that may stretch animals' learning toward the explicit pole of cognition. We discuss the neuroscience of reinforcement-based learning and suggest the possibility of disabling a dominant form of reinforcement-based discrimination learning. In that vacuum, researchers may have an opportunity to observe animals' explicit learning strategies (i.e., hypotheses, rules, task self-construals). We review initial research using this framework showing explicit learning by humans and perhaps by monkeys. Finally, we consider why complementary explicit and reinforcement-based learning systems might promote evolutionary and ecological fitness. Illuminating the evolution of parallel learning systems may also tell part of the story of the emergence of humans' extraordinary capacity for explicit-declarative cognition.

Categorization’s great debate has weighed single-system exemplar theory against the possibility of alternative processing systems. We take an evolutionary and fitness perspective toward this debate to illuminate it in a new way. There are continuities in category processes—extending across millions of years in vertebrate evolution—that have profound theoretical implications. Thus, animals are crucial behavioral ambassadors to this area. They reveal the roots of human categorization, the basic assumptions of vertebrates entering category tasks, and the surprising weakness of exemplar memory as a category-learning strategy. These results have joined neuroscience results to prompt important changes in categorization theory. Categorization’s great debate is ending. Broad-based converging evidence now makes it clear that the unitary exemplar view is insufficient. Categorization is served by multiple systems of process and representation.

Primate Cognition is the study of cognitive processes, which represent internal mental processes involved in discriminations, decisions, and behaviors of humans and other primate species. Cognitive control involves executive and regulatory processes that allocate attention, manipulate and evaluate available information (and, when necessary, seek additional information), remember past experiences to plan future behaviors, and deal with distraction and impulsivity when they are threats to goal achievement. Areas of research that relate to cognitive control as it is assessed across species include executive attention, episodic memory, prospective memory, metacognition, and self-control. Executive attention refers to the ability to control what sensory stimuli one attends to and how one regulates responses to those stimuli, especially in cases of conflict. Episodic memory refers to memory for personally experienced, autobiographical events. Prospective memory refers to the formation and implementation of future-intended actions, such as remembering what needs to be done later. Metacognition consists of control and monitoring processes that allow individuals to assess what information they have and what information they still need, and then if necessary to seek information. Self-control is a regulatory process whereby individuals forego more immediate or easier to obtain rewards for more delayed or harder to obtain rewards that are objectively more valuable. The behavioral complexity shown by nonhuman primates when given tests to assess these capacities indicates psychological continuities with human cognitive control capacities. However, more research is needed to clarify the proper interpretation of these behaviors with regard to possible cognitive constructs that may underlie such behaviors. WIREs Cogn Sci 2016, 7:294-316. doi: 10.1002/wcs.1397 For further resources related to this article, please visit the WIREs website.

Metacognition refers to thinking about thinking, and there has been a great deal of interest in how this ability manifests across primates. Based on much of the work to date, a tentative division has been drawn with New World monkeys on one side and Old World monkeys and apes on the other. Specifically, Old World monkeys, apes and humans often show patterns reflecting metacognition, but New World monkeys typically fail to do so, or show less convincing behavioral patterns. However, recent data suggests that this difference may relate to other aspects of some experimental tasks. For example, one possibility is that risk tolerance affects how capuchin monkeys, a New World primate species, tend to perform. Specifically, it has recently been argued that on tasks in which there are two or three options, the ‘risk’ of guessing is tolerable for capuchins since there is a high probability of being correct even if they ‘know they do not know’ or feel something akin to uncertainty. The current study investigated this possibility by manipulating the degree of risk (2-choices versus 6-choices) and found that capuchin monkeys used the uncertainty response more on 6-choice trials than on 2-choice trials. We also found that rate of reward does not appear to underlie these patterns of performance, and propose that the degree of risk is modulating the use of the uncertainty response in capuchin monkeys. Thus, the apparent differences between New and Old world monkeys in metacognition may reflect differences in risk tolerance rather than access to metacognitive states.

Ongoing research explores whether animals have precursors to metacognition—that is, the capacity to monitor mental states or cognitive processes. Comparative psychologists have tested apes, monkeys, rats, pigeons, and a dolphin using perceptual, memory, foraging, and information-seeking paradigms. The consensus is that some species have a functional analog to human metacognition. Recently, though, associative modelers have used formal-mathematical models hoping to describe animals’ “metacognitive” performances in associative-behaviorist ways. We evaluate these attempts to reify formal models as proof of particular explanations of animal cognition. These attempts misunderstand the content and proper application of models. They embody mistakes of scientific reasoning. They blur fundamental distinctions in understanding animal cognition. They impede theoretical development. In contrast, an energetic empirical enterprise is achieving strong success in describing the psychology underlying animals’ metacognitive performances. We argue that this careful empirical work is the clear path to useful theoretical development. The issues raised here about formal modeling—in the domain of animal metacognition—potentially extend to biobehavioral research more broadly.

While online social networks (OSNs) have become an important platform for information exchange, the abuse of OSNs to spread misinformation has become a significant threat to our society. To restrain the propagation of misinformation in its early stages, we study the Distance-constrained Misinformation Combat under Uncertainty problem, which aims to both reduce the spread of misinformation and enhance the spread of correct information within a given propagation distance. The problem formulation considers the competitive diffusion of misinformation and correct information. It also accounts for the uncertainty in identifying initial misinformation adopters. For competitive propagation with major-threshold activation, we propose a solution based on stochastic programming and provide an upper-bound in the presence of uncertainty. We propose an efficient Combat Seed Selection algorithm to tackle general-threshold activation, in which we define a measure, "effectiveness", to evaluate the contribution of nodes to the fight against misinformation. Through extensive experiments, we validate that our algorithm outputs high-quality solution with very fast computation.

The explosive growth of Online Social Networks in recent years has led to many individuals relying on them to keep up with friends & family. This, in turn, makes them prime targets for malicious actors seeking to collect sensitive, personal data. Prior work has studied the ability of socialbots, i.e. bots which pretend to be humans on OSNs, to collect personal data by befriending real users. However, this prior work has been hampered by the assumption that the likelihood of users accepting friend requests from a bot is non-increasing -- a useful constraint for theoretical purposes but one contradicted by observational data. We address this limitation with a novel curvature based technique, showing that an adaptive greedy bot is approximately optimal within a factor of 1 - 1/e1/δ ~0.165. This theoretical contribution is supported by simulating the infiltration of the bot on OSN topologies. Counter-intuitively, we observe that when the bot is incentivized to befriend friends-of-friends of target users it out-performs a bot that focuses on befriending targets.

A variety of structures are shown in the solid state by the phosphanides MPHR of alkali metals. Whereas oligomeric and polymeric ladder or helical structures based on M-P bonds predominate for compounds of the lighter homologues (M=Li, Na, K), the 2,6-dimesitylphenylphosphanides of Rb and Cs are present as a Rb

As the existing power grid becomes increasingly complex, the deployment of Smart Grids-which can significantly improve the stability and efficiency of power infrastructure- has seen increasing interest. However, with new technology comes new security concerns. Recent work has shown that fabricating valid but malicious messages on a Smart Grid's SCADA network can cause widespread power outages. Moreover, the large scale, complexity, and tight constraints of these networks makes deploying in-line detection systems insufficient. A common approach is to instead conduct whole-network audits by temporarily duplicating & forwarding all network traffic to a server dedicated to detecting malicious content. This is usually done by taking advantage of port- mirroring to duplicate the packets received with minimal overhead. However, the operation of these audits sees a number of challenges. For instance, each router used to collect traffic demands physical set-up - thus there is a real cost to needlessly high coverage. In this work, we consider the problem of efficiently finding the minimal set of routers in the SCADA network to use for auditing traffic. This efficiency is critical for enabling timely auditing. Similar versions of this problem have seen study. However, they suffer either from a severe mismatch w.r.t. the problem domain, or from serious scalability concerns. This motivates us to devise a novel (2+\theta)(ln|V|+1) approximation algorithm for this problem with a 2- approximation for the special case of tree networks. We experimentally evaluate our solution and compare it to an optimal IP formulation, finding that it performs near-optimally on small networks and significantly outperforms heuristics in all cases.

In this work, we examine the problem of adaptively uncovering network topology in an incomplete network, to support more accurate decision making in various real-world applications, such as modeling for reconnaissance attacks and network probing. While this problem has been partially studied, we provide a novel take on it by modeling it with a set of crawlers termed “bots” which can uncover independent portions of the network in parallel. Accordingly, we develop three adaptive algorithms, which make decisions based on previous observations due to incomplete information, namely AGP, a sequential method; FastAGP, a parallel algorithm; and ALSP, an extension of FastAGP uses local search to improve guarantees. These algorithms are proven to have 1/3, 1/7, and 1/ (5 + ∊) approximation ratios, respectively. The key analysis of these algorithms is the connection between adaptive algorithms and an intersection of multiple partition matroids. We conclude with an evaluation of these algorithms to quantify the impact of both adaptivity and parallelism. We find that in practice, adaptive approaches perform significantly better, while FastAGP performs nearly as well as AGP in most cases despite operating in a massively parallel fashion. Finally, we show that a balance between the quantity and quality of bots is ideal for maximizing observation of the network.

Cognitive psychologists distinguish implicit, procedural category learning (stimulus-response associations learned outside declarative cognition) from explicit-declarative category learning (conscious category rules). These systems are dissociated by category learning tasks with either a multidimensional, information-integration (II) solution or a unidimensional, rule-based (RB) solution. In the present experiments, humans and two monkeys learned II and RB category tasks fostering implicit and explicit learning, respectively. Then they received occasional transfer trials-never directly reinforced-drawn from untrained regions of the stimulus space. We hypothesized that implicit-procedural category learning-allied to associative learning-would transfer weakly because it is yoked to the training stimuli. This result was confirmed for humans and monkeys. We hypothesized that explicit category learning-allied to abstract category rules-would transfer robustly. This result was confirmed only for humans. That is, humans displayed explicit category knowledge that transferred flawlessly. Monkeys did not. This result illuminates the distinctive abstractness, stimulus independence, and representational portability of humans' explicit category rules. (PsycINFO Database Record

The reactivity of tin and lead phosphanido complexes with chalogens is reported. The addition of sulfur to [(BDI)MPCy2] (M = Sn, Pb; BDI = CH{(CH3)CN-2,6-iPr2C6H3}2) results in the formation of phosphinodithioates [(BDI)MSP(S)Cy2] regardless of the conditions; however, when selenium is added to [(BDI)MPCy2], a selenium insertion product, phosphinoselenoite [(BDI)MSePCy2], can be isolated. This compound readily reacts with additional selenium to form the phosphinodiselenoate complex [(BDI)MSeP(Se)Cy2]. In contrast, the addition of selenium to [(BDI)SnP(SiMe3)2] results in the formation of the heavy ether [(BDI)SnSeSiMe3]. Differences in the solution and solid-state molecular species of tin phosphinoselenoite and phosphinodiselenoate complexes were probed using multinuclear solution and solid-state NMR spectroscopy.

Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit categorization.

The uncertainty response has grounded the study of metacognition in nonhuman animals. Recent research has explored the processes supporting uncertainty monitoring in monkeys. It has revealed that uncertainty responding, in contrast to perceptual responding, depends on significant working memory resources. The aim of the present study was to expand this research by examining whether uncertainty monitoring is also working memory demanding in humans. To explore this issue, human participants were tested with or without a cognitive load on a psychophysical discrimination task that included either an uncertainty response (allowing the participant to decline difficult trials) or a middle-perceptual response (labeling the same intermediate trial levels). The results demonstrated that cognitive load reduced uncertainty responding, but increased middle responding. However, this dissociation between uncertainty and middle responding was only observed when participants either lacked training or had very little training with the uncertainty response. If more training was provided, the effect of load was small. These results suggest that uncertainty responding is resource demanding, but with sufficient training, human participants can respond to uncertainty either by using minimal working memory resources or by effectively sharing resources. These results are discussed in relation to the literature on animal and human metacognition.

Throughout civilization, the power of the word has significantly influenced and shaped societies. The contributions of writers has been substantial and this is certainly true in the field of intellectual disability. The renowned author, Charles Dickens, spoke of the need for appropriate education and treatment for people with these disabilities. He is notable for his early and prophetic vision of their potential for growth. This paper reviews important examples that were included in his novels as well as in other writings on people with intellectual disabilities. The manuscript places his work in an historical perspective, highlights his contributions to the literature of disability advocacy, and references his relevance to the field of intellectual disability.