STUDENT NUMBERS AND SUSTAINING COURSES AND FIELDS IN PH.D. PROGRAMS

Many agricultural economics departments are concerned about the vitality of their Ph.D. programs. A particular problem is insufficient student numbers to justify teaching certain courses or fields. As a consequence, much faculty time can be spent debating alternative program structures without any real idea of the likelihood that a proposed program structure will succeed. This article presents a framework for deriving some analytical and empirical results for alternative Ph.D. program structures. A downloadable program is used to generate some representative results that will hopefully help others minimize speculations and time spent in committee or departmental meetings.Ph.D. programs, student numbers, Teaching/Communication/Extension/Profession, A2, Q1,

Bayesian Models Applied to Cyber Security Anomaly Detection Problems

Cyber security is an important concern for all individuals, organisations and governments globally. Cyber attacks have become more sophisticated, frequent and dangerous than ever, and traditional anomaly detection methods have been proved to be less effective when dealing with these new classes of cyber threats. In order to address this, both classical and Bayesian models offer a valid and innovative alternative to the traditional signature-based methods, motivating the increasing interest in statistical research that it has been observed in recent years. In this review, we provide a description of some typical cyber security challenges, typical types of data and statistical methods, paying special attention to Bayesian approaches for these problems

Estudio experimental de plasmas binarios de alcohol etílico con gases inertes

En la actualidad el estudio de plasmas generados a partir de VOC, han sido tema de gran interés por numerosos Laboratorios alrededor del mundo, la descarga producida con esta atmósfera, se analizará por medio de espectroscopia óptica de emisión y usando sondas de Langmuir. El alcohol etílico es un VOC que se aplica en innumerables productos como disolvente y diluyente de pintura, sino que también se emite a la atmósfera a partir de una variedad de fuentes antropogénicas y naturales. En este estudio, una alta concentración de alcohol etílico, que se utiliza como un modelo de VOC, se descompondrá mediante un plasma de alcohol etílico mezclado con gases inertes. Se estudiará el comportamiento de la presión, el flujo de gas. El efecto de la corriente sobre la descomposición de etanol y la influencia de las especies activas, tales como O2, CO, CO+, Hα, Hβ, y en la formación de subproductos del plasma se examinarán mediante el análisis de los productos generados.Beca para estudios de posgrado otorgada por CONACyT Proyecto DGAPA IN101613 CONACyT 225991y PIFI 201

Household income pooling and the demand for food: does family financial structure matter?

Research on food consumption and expenditures usually employs the use of unitary models that do not account for type of family financial structure. This research presents two collective models of household behavior, conditional and unconditional models, which were used in the analysis of the household data that came from the “Parental Time, Role Strain and Children’s Fat Intake and Obesity Related Outcomes”. This research utilized the Generalized Method of Moments in the estimation of the system of expenditures on food at and away from home to test the validity of the unitary model by testing one of the implications of this model, the income pooling hypothesis, as well as family financial structure. It was found that the omission of family financial structure and not the income pooling hypothesis would lead to the incorrect assertion that the unitary model is the correct model for the analysis of intrahousehold allocation. The collective models proposed in this research were found to be preferred to those of the unitary models. These two models, conditional and unconditional, not only allow for the effect of earned and unearned incomes of fathers and mothers to be different, but also incorporate family financial structure into the analysis of expenditures on food at and away from home. This research shows that the parameters of the unitary models are reduced form parameters that do not represent the effect that the variable of interest has on the household expenditures category of interest. This research finds that these reduced form parameters show the total effect which is composed of three parts. First, the change in the expenditure category of interest that comes about from a change in the variable of interest when we hold family financial structure constant. Second, the change in the expenditure category of interest that comes about from a change in the family financial structure. Third, the change in family financial structure that comes about from a change in the variable of interest

Bayesian Nonparametric Methods for Cyber Security with Applications to Malware Detection and Classification

The statistical approach to cyber security has become an active and important area of research due to the growth in number and threat of cyber attacks perpetrated nowadays. In this thesis, we centre our attention on the Bayesian approach to cyber security, which provides several modelling advantages such as the flexibility achieved through the probabilistic quantification of uncertainty. In particular, we have found that Bayesian models have been mainly used to detect volume-traffic anomalies, network anomalies and malicious software. To provide a unifying view of these ideas, we first present a thorough review on Bayesian methods applied to cyber security. Bayesian models applied to detecting malware and classifying them into known malicious classes is one of the cyber security areas discussed in our review. However, and contrary to detecting traffic and network anomalies, this area has not been widely developed from a Bayesian perspective. That is why we have centred our attention on developing novel supervised learning Bayesian nonparametric models to detect and classify malware using binary features built directly from the executables’ binary code. For these methods, important theoretical properties and simulation techniques are fully developed and for real malware data, we have compared their performance against well-known machine learning models which have been widely applied in this area. With respect to our methodologies, we first present a new discrete nonparametric prior specifically designed for binary data that builds on an elegant nonparametric hierarchical structure, which allows us to study the importance of each individual feature across the groups found in the data. Moreover, and due to the large, and possibly redundant, number of features, we have developed a generalised version of the model that allows the introduction of a feature selection step within the inferential learning. Finally, for a more complex modelling where there is a need to introduce dependence across the features, we have extended the capabilities of this new class of nonparametric priors by using it as the building block of a latent feature model

Application of facial neuromuscular electrical stimulation (fNMES) in psychophysiological research: Practical recommendations based on a systematic review of the literature.

Facial neuromuscular electrical stimulation (fNMES), which allows for the non-invasive and physiologically sound activation of facial muscles, has great potential for investigating fundamental questions in psychology and neuroscience, such as the role of proprioceptive facial feedback in emotion induction and emotion recognition, and may serve for clinical applications, such as alleviating symptoms of depression. However, despite illustrious origins in the 19th-century work of Duchenne de Boulogne, the practical application of fNMES remains largely unknown to today's researchers in psychology. In addition, published studies vary dramatically in the stimulation parameters used, such as stimulation frequency, amplitude, duration, and electrode size, and in the way they reported them. Because fNMES parameters impact the comfort and safety of volunteers, as well as its physiological (and psychological) effects, it is of paramount importance to establish recommendations of good practice and to ensure studies can be better compared and integrated. Here, we provide an introduction to fNMES, systematically review the existing literature focusing on the stimulation parameters used, and offer recommendations on how to safely and reliably deliver fNMES and on how to report the fNMES parameters to allow better cross-study comparison. In addition, we provide a free webpage, to easily visualise fNMES parameters and verify their safety based on current density. As an example of a potential application, we focus on the use of fNMES for the investigation of the facial feedback hypothesis

Gestión de conocimiento en la competitividad del sector de TI. El caso de la región Tijuana – San Diego

This article shows the role of knowledge management and some additional factors as triggers for competitive advantage in companies in the TI sector in the Tijuana - San Diego region, in the border region of Baja California and California. The knowledge management through information technologies tries to generate organizational success. The implementation of knowledge management through the development of processes and systems with these factors creates a competitive advantage. One of the limitations on competitiveness is that companies in the it sector have limited use of knowledge management, this creates disadvantages and causes lack of control of intangible assets in the organization. Project management, quality systems and process control are activities that seek continuous improvement and therefore its implementation is necessary. In this first approach, knowledge management and competitiveness are considered, and it is also proposed to continue the study to corroborate this relevance.Este artículo muestra el rol de la gestión de conocimiento y de algunos factores adicionales como detonadores de la ventaja competitiva en empresas del sector de ti en la región Tijuana– San Diego, en la zona fronteriza de Baja California y California. La gestión del conocimiento mediante tecnologías de la información intenta generar el éxito organizacional. La implementación de la gestión del conocimiento mediante el desarrollo de procesos y sistemas con estos factores crea una ventaja competitiva. Una de las limitantes sobre la competitividad es que las empresas del sector de ti tienen uso limitado de la gestión del conocimiento, lo que genera desventajas y provoca falta de control de los activos intangibles en la organización. La administración de proyectos, los sistemas de calidad y el control de procesos son actividades que buscan la mejora continua y, por consiguiente, es necesaria su implementación. En esta primera aproximación, se consideran la gestión del conocimiento y la competitividad; además, se propone continuar el estudio para corroborar esta relevancia

SampleHST-X: A Point and Collective Anomaly-Aware Trace Sampling Pipeline with Approximate Half Space Trees

The storage requirement for distributed tracing can be reduced significantly by sampling only the anomalous or interesting traces that occur rarely at runtime. In this paper, we introduce an unsupervised sampling pipeline for distributed tracing that ensures high sampling accuracy while reducing the storage requirement. The proposed method, SampleHST-X, extends our recent work SampleHST. It operates based on a budget which limits the percentage of traces to be sampled while adjusting the storage quota of normal and anomalous traces depending on the size of this budget. The sampling process relies on accurately defining clusters of normal and anomalous traces by leveraging the distribution of mass scores, which characterize the probability of observing different traces, obtained from a forest of Half Space Trees (HST). In our experiments, using traces from a cloud data center, SampleHST yields 2.3 to 9.5 better sampling performance. SampleHST-X further extends the SampleHST approach by incorporating a novel class of Half Space Trees, namely Approximate HST, that uses approximate counters to update the mass scores. These counters significantly reduces the space requirement for HST while the sampling performance remains similar. In addition to this extension, SampleHST-X includes a Family of Graph Spectral Distances (FGSD) based trace characterization component, which, in addition to point anomalies, enables it to sample traces with collective anomalies. For such traces, we observe that the SampleHST-X approach can yield 1.2 to 19 better sampling performance

Gestión de conocimiento en la competitividad del sector de TI. El caso de la región Tijuana – San Diego

Este artículo muestra el rol de la gestión de conocimiento y de algunos factores adicionales como detonadores de la ventaja competitiva en empresas del sector de ti en la región Tijuana– San Diego, en la zona fronteriza de Baja California y California. La gestión del conocimiento mediante tecnologías de la información intenta generar el éxito organizacional. La implementación de la gestión del conocimiento mediante el desarrollo de procesos y sistemas con estos factores crea una ventaja competitiva. Una de las limitantes sobre la competitividad es que las empresas del sector de ti tienen uso limitado de la gestión del conocimiento, lo que genera desventajas y provoca falta de control de los activos intangibles en la organización. La administración de proyectos, los sistemas de calidad y el control de procesos son actividades que buscan la mejora continua y, por consiguiente, es necesaria su implementación. En esta primera aproximación, se consideran la gestión del conocimiento y la competitividad; además, se propone continuar el estudio para corroborar esta relevancia