Quasilocal Smarr relation for an asymptotically flat spacetime

A quasilocal Smarr relation is obtained from Euler's theorem for Einstein-Maxwell(-Dilaton) theory for an asymptotically flat spacetime, and its associated first law is studied. To check both, we calculate quasilocal variables by employing Brown-York quasilocal method along with Mann-Marolf counterterms, which are consistent with Tolman temperature. We also derive entropy by constructing a quasilocal thermodynamic potential via Euclidean method. Here we found that the Euclidean action value in a quasilocal frame just yields a usual thermodynamic potential form, which do not include a $PA$ term, and entropy just becomes the Bekenstein-Hawking one. Through the examples, we confirmed that our quasilocal Smarr relation is satisfied with all cases, and its first law is also exactly satisfied except the dyonic black hole with the dilaton coupling constant $a=\sqrt{3}$. In that case when making a large $R$ expansion, the first law is satisfied up to $1/R$ order but it does not hold for higher sub-leading order of $R$. This issue should be resolved in future.Comment: 24 page

Neural malware detection

At the heart of today’s malware problem lies theoretically infinite diversity created by metamorphism. The majority of conventional machine learning techniques tackle the problem with the assumptions that a sufficiently large number of training samples exist and that the training set is independent and identically distributed. However, the lack of semantic features combined with the models under these wrong assumptions result largely in overfitting with many false positives against real world samples, resulting in systems being left vulnerable to various adversarial attacks. A key observation is that modern malware authors write a script that automatically generates an arbitrarily large number of diverse samples that share similar characteristics in program logic, which is a very cost-effective way to evade detection with minimum effort. Given that many malware campaigns follow this paradigm of economic malware manufacturing model, the samples within a campaign are likely to share coherent semantic characteristics. This opens up a possibility of one-to-many detection. Therefore, it is crucial to capture this non-linear metamorphic pattern unique to the campaign in order to detect these seemingly diverse but identically rooted variants. To address these issues, this dissertation proposes novel deep learning models, including generative static malware outbreak detection model, generative dynamic malware detection model using spatio-temporal isomorphic dynamic features, and instruction cognitive malware detection. A comparative study on metamorphic threats is also conducted as part of the thesis. Generative adversarial autoencoder (AAE) over convolutional network with global average pooling is introduced as a fundamental deep learning framework for malware detection, which captures highly complex non-linear metamorphism through translation invariancy and local variation insensitivity. Generative Adversarial Network (GAN) used as a part of the framework enables oneshot training where semantically isomorphic malware campaigns are identified by a single malware instance sampled from the very initial outbreak. This is a major innovation because, to the best of our knowledge, no approach has been found to this challenging training objective against the malware distribution that consists of a large number of very sparse groups artificially driven by arms race between attackers and defenders. In addition, we propose a novel method that extracts instruction cognitive representation from uninterpreted raw binary executables, which can be used for oneto- many malware detection via one-shot training against frequency spectrum of the Transformer’s encoded latent representation. The method works regardless of the presence of diverse malware variations while remaining resilient to adversarial attacks that mostly use random perturbation against raw binaries. Comprehensive performance analyses including mathematical formulations and experimental evaluations are provided, with the proposed deep learning framework for malware detection exhibiting a superior performance over conventional machine learning methods. The methods proposed in this thesis are applicable to a variety of threat environments here artificially formed sparse distributions arise at the cyber battle fronts.Doctor of Philosoph

An integrated model of social impacts and resident’s perceptions: From a film tourism destination

Minimal research has been carried out regarding the host community’s perceptions of and reactions to film tourism impacts, utilizing a mainstream tourism destination such as Bali. This article aims to identify and explain residents’ perceptions of and attitudes toward the social impacts of film tourism, proposing an integrated theoretical model of social exchange theory, social representations theory and place change theory. Results indicate that the integrated model is particularly robust in explaining what caused a condition or event to be perceived as negative, positive or neutral place change, and why such changes are interpreted and evaluated in the social and cultural contexts. It also suggests that the locals do not perceive or necessarily respond to tourism impacts uniformly. As such, it contributes to a more wholesome understanding of the underlying dynamics and complexities involved in identifying and explaining the perceived impacts of tourism on the residents of a community in a theoretically rigorous, nuanced manner

Classroom as Dojo: Contemplative Teaching and Learning as Martial Art

This paper identifies assumptions about education behind the mainstream North American schooling: that the primary educational goal is to teach subject matter and deliver knowledge and skills, most often divorced from the immediacy of students’ lifeworld, in the service of consumerism driven industrial civilization. Moreover, student behavior defined as unproductive and disruptive in terms of reaching such instrumentalist goal is seen as in need of control and management, which then becomes central concern and operation of schooling. This paper challenges these assumptions and offers a larger educational vision and practice in alignment with world wisdom traditions, namely becoming more fully human. We describe becoming human in terms of becoming increasingly whole, integrated, attuned, and in alignment in the three-fold relationality of self-other-nature. We then propose contemplative education as a way to cultivate becoming human, and offer an example of martial art practice and an alternative paradigm of classroom-as-dojo as a guiding metaphor. Contemplative learning in the dojo aims at embodied, intersubjective, and self-authoring practices

Does diversity in team members’ agreeableness benefit creative teams?

Although deep-level diversity among team members are often discussed as important catalysts of team creativity, little is currently understood about the impact of diversity in team members’ personality on team creativity and team satisfaction. We propose that diversity in team members' agreeableness would reduce the effectiveness of creative teams through its impact on team conflict experienced. To test our hypotheses, we recruited 93 student teams to participate in a laboratory study where each member had their personality traits assessed before engaging in a team creativity task. We found that diversity in team members' agreeableness was positively associated with team task conflict experienced which, in turn, was negatively associated with team creativity. Additionally, we found that diversity in team members' agreeableness was positively associated with team relationship conflict, which, in turn, was negatively associated with team satisfaction. Implications and future directions are discussed

A Potential New Way to Reduce Bed Bug Infestations: Arthroshield 880

https://scholarworks.moreheadstate.edu/student_scholarship_posters/1086/thumbnail.jp

BRST Quantization of the Proca Model based on the BFT and the BFV Formalism

The BRST quantization of the Abelian Proca model is performed using the Batalin-Fradkin-Tyutin and the Batalin-Fradkin-Vilkovisky formalism. First, the BFT Hamiltonian method is applied in order to systematically convert a second class constraint system of the model into an effectively first class one by introducing new fields. In finding the involutive Hamiltonian we adopt a new approach which is more simpler than the usual one. We also show that in our model the Dirac brackets of the phase space variables in the original second class constraint system are exactly the same as the Poisson brackets of the corresponding modified fields in the extended phase space due to the linear character of the constraints comparing the Dirac or Faddeev-Jackiw formalisms. Then, according to the BFV formalism we obtain that the desired resulting Lagrangian preserving BRST symmetry in the standard local gauge fixing procedure naturally includes the St\"uckelberg scalar related to the explicit gauge symmetry breaking effect due to the presence of the mass term. We also analyze the nonstandard nonlocal gauge fixing procedure.Comment: 29 pages, plain Latex, To be published in Int. J. Mod. Phys.