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The Onion Router (Tor) is used by adversaries and warfighters alike to encrypt session information and gain anonymity on the internet. Since its creation in 2002, Tor has gained popularity by terrorist organizations, human traffickers, and illegal drug distributors who wish to use Tor services to mask their identity while engaging in illegal activities. Fingerprinting attacks assist in thwarting these attempts. Website fingerprinting (WF) attacks have been proven successful at linking a user to the website they have viewed over an encrypted Tor connection. With consumer video streaming traffic making up a large majority of internet traffic and sites like YouTube remaining in the top visited sites in the world, it is just as likely that adversaries are using videos to spread misinformation, illegal content, and terrorist propaganda. Video fingerprinting (VF) attacks look to use encrypted network traffic to predict the content of encrypted video sessions in closed- and open-world scenarios. This research builds upon an existing dataset of encrypted video session data and use statistical analysis to train a machine-learning classifier, using deep fingerprinting (DF), to predict videos viewed over Tor. DF is a machine learning technique that relies on the use of convolutional neural networks (CNN) and can be used to conduct VF attacks against Tor. By analyzing the results of these experiments, we can more accurately identify malicious video streaming activity over Tor., Civilian, Approved for public release. Distribution is unlimited.

In the natural language processing (NLP) research community, disentangled representation learning hasbecome commonplace in text style transfer and sentiment analysis. Previous studies have demonstrated the utility of extracting style from text corpora in order to augment context-dependent downstream tasks such as text generation. Within sentiment analysis specifically, disentangled representation learning has been shown to produce latent representations that can be used to improve downstream classification tasks. In this study, we build upon this existing framework by (1) investigating disentangled representation learning in the multidimensional task of emotion detection, (2) testing the robustness of this methodology over varying datasets, and (3) exploring the interpretability of the produced latent representations. We discover that closely following existing disentangled representation learning methods for sentiment analysis in a multi- class setting, performance decreases significantly, and we are unable to effectively distinguish content and style in our learned latent representations. Further work is necessary to determine the effectiveness of style disentanglement for text in multi-class settings using adversarial training.

In the natural language processing (NLP) research community, disentangled representation learning hasbecome commonplace in text style transfer and sentiment analysis. Previous studies have demonstrated the utility of extracting style from text corpora in order to augment context-dependent downstream tasks such as text generation. Within sentiment analysis specifically, disentangled representation learning has been shown to produce latent representations that can be used to improve downstream classification tasks. In this study, we build upon this existing framework by (1) investigating disentangled representation learning in the multidimensional task of emotion detection, (2) testing the robustness of this methodology over varying datasets, and (3) exploring the interpretability of the produced latent representations. We discover that closely following existing disentangled representation learning methods for sentiment analysis in a multi- class setting, performance decreases significantly, and we are unable to effectively distinguish content and style in our learned latent representations. Further work is necessary to determine the effectiveness of style disentanglement for text in multi-class settings using adversarial training.

In this article we briefly review current research in adversarial attacks and defenses and form a basis for a theoretical explanation as to why a generative energy model is the solution to the defense problem as it exists for securing naturally trained classifiers. We further expand on this topic and discuss future efforts toward the use of a generalized adversarial defense framework based on Stochastic Security to defend against the strongest known adversarial attacks. We further expand on this idea and demonstrate that Energy-based models can be extended towards multiple tasks and datasets. Furthermore, we discuss some architectural improvements to the framework that lead to improvements in synthesis and defense (The Hat-EBM and the Fixer). This work lies at the intersection of generative modeling, adversarial defense, and chaotic dynamics.

The Contestation Manifesto and its associated paraphernalia are artefacts from a speculative, near-future community action known as the Contestation Café. Being one in a series of research through design projects on contestation, the Contestation Café is a critical, yet also practical, design intervention rooted in how the act of repair has moved through the physical into the digital, and the shared values therein. Tracing the history of electronic repair - from the early valve radios, the invention of the transistor, microchips, programmable devices and through to IoT, connected devices and the "fluid assemblages"that emerge - we can see the shared values of the physical act of repair with the more intangible act of contestation in the digital world of algorithmic systems. Overlaying the values and tactics of the Right to Repair movement with the emerging concerns around data-driven systems we find ourselves examining the Repair Café as a potential model for community contestation and the construction of publics. The Repair Café started in Amsterdam in 2009 and has since spread to 35 countries with over 1700 instances of these cafés. The Repair Café is not intended to be a place where you bring your broken appliances for someone else to repair - rather, it is a place where you learn how to repair and recycle your own products and devices, and - more importantly - a place where you simply learn that things can be fixed rather than thrown out. In a similar spirit, the Contestation Café would be a place for those who feel mistreated by automated decision systems, AIs and algorithms, to bring their broken interactions and their unfair decisions to learn how to contest, push back and reclaim their agency and autonomy. Rather than Repairers, the Contestation Café has a panel of Fixers - people who inhabit the space between designers and users, with a particular knowledge of these systems and how to map and navigate them - who are there to share their experience and knowl

Adversarial attacks pose a risk to machine learning (ML)-based network intrusion detection systems (NIDS). In this manner, it is of great significance to explore to what degree these methods can be viably utilized by potential adversaries. The majority of adversarial techniques are designed for unconstrained domains such as the image recognition domain, where these methods apply alterations to the pixels in a picture. Therefore, the applicability of these techniques to the NIDS domain is very limited. Related work on adversarial techniques for NIDS generally considers feature-space techniques, which cannot be applied in a practical situation since only the extracted network traffic features are modified and not the actual network traffic. To solve these limitations, a traffic-space approach for creating adversarial examples for evading ML-based NIDS is proposed and assessed with several classification models. The proposed constrained adversarial crafting method is based on the Iterative Fast Gradient Sign Method (IFGSM) and is called the Constrained Iterative Fast Gradient Sign Method (CIFGSM). A constraint set is added as a penalty term to the loss function of the optimization to ensure that the adversarial values remain within the valid space. Additionally, an L2 regularization term is used to minimize the distance between the original and adversarial network traffic samples. The proposed method is evaluated and shown to be an effective way for generating realistic and practical adversarial evasion packets. To achieve this, network packet components and their characteristics are defined as a constraint set which can be used for the optimization task and a custom adversarial loss function is created that encapsulates the different elements of this optimization problem. Furthermore, multiple models are evaluated to test the transferability of this method. Conclusively, the proposed method is evaluated in a realistic scenario, where adversarial packet captures are cr, Computer Science | Cyber Security

Deep learning has proven to be a major leap in machine learning, allowing completely new problems to be solved. While flexible and powerful, neural networks have the disadvantage of being large and demanding high performance from the devices on which they are run. In order to deploy neural networks on more, and simpler, devices, techniques such as quantization, sparsification and tensor decomposition have been developed. These techniques have shown promising results, but their effects on model robustness against attacks remain largely unexplored. In this thesis, Universal Adversarial Perturbations (UAP) and the Fast Gradient Sign Method (FGSM) are tested against VGG-19 as well as versions of it compressed using 8-bit quantization, TensorFlow’s float16 quantization, and 8-bit and 4-bit single layer quantization as introduced in this thesis. The results show that UAP transfers well to all quantized models, while the transferability of FGSM is high to the float16 quantized model, lower to the 8-bit models, and high to the 4-bit SLQ model. We suggest that this disparity arises from the universal adversarial perturbations’ having been trained on multiple examples rather than just one, which has previously been shown to increase transferability. The results also show that quantizing a single layer, the first layer in this case, can have a disproportionate impact on transferability., Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet

Deep learning has proven to be a major leap in machine learning, allowing completely new problems to be solved. While flexible and powerful, neural networks have the disadvantage of being large and demanding high performance from the devices on which they are run. In order to deploy neural networks on more, and simpler, devices, techniques such as quantization, sparsification and tensor decomposition have been developed. These techniques have shown promising results, but their effects on model robustness against attacks remain largely unexplored. In this thesis, Universal Adversarial Perturbations (UAP) and the Fast Gradient Sign Method (FGSM) are tested against VGG-19 as well as versions of it compressed using 8-bit quantization, TensorFlow’s float16 quantization, and 8-bit and 4-bit single layer quantization as introduced in this thesis. The results show that UAP transfers well to all quantized models, while the transferability of FGSM is high to the float16 quantized model, lower to the 8-bit models, and high to the 4-bit SLQ model. We suggest that this disparity arises from the universal adversarial perturbations’ having been trained on multiple examples rather than just one, which has previously been shown to increase transferability. The results also show that quantizing a single layer, the first layer in this case, can have a disproportionate impact on transferability., Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet

Adversarial attacks pose a risk to machine learning (ML)-based network intrusion detection systems (NIDS). In this manner, it is of great significance to explore to what degree these methods can be viably utilized by potential adversaries. The majority of adversarial techniques are designed for unconstrained domains such as the image recognition domain, where these methods apply alterations to the pixels in a picture. Therefore, the applicability of these techniques to the NIDS domain is very limited. Related work on adversarial techniques for NIDS generally considers feature-space techniques, which cannot be applied in a practical situation since only the extracted network traffic features are modified and not the actual network traffic. To solve these limitations, a traffic-space approach for creating adversarial examples for evading ML-based NIDS is proposed and assessed with several classification models. The proposed constrained adversarial crafting method is based on the Iterative Fast Gradient Sign Method (IFGSM) and is called the Constrained Iterative Fast Gradient Sign Method (CIFGSM). A constraint set is added as a penalty term to the loss function of the optimization to ensure that the adversarial values remain within the valid space. Additionally, an L2 regularization term is used to minimize the distance between the original and adversarial network traffic samples. The proposed method is evaluated and shown to be an effective way for generating realistic and practical adversarial evasion packets. To achieve this, network packet components and their characteristics are defined as a constraint set which can be used for the optimization task and a custom adversarial loss function is created that encapsulates the different elements of this optimization problem. Furthermore, multiple models are evaluated to test the transferability of this method. Conclusively, the proposed method is evaluated in a realistic scenario, where adversarial packet captures are cr, Computer Science | Cyber Security

This paper serves as an investigation in the use of energy-based models for adversarial defense via purification and training. Convergent and non-convergent energy-based models are tasked to remove white-box adversarial signals embedded into images from the CIFAR-10 dataset so that they may be classified correctly. This work presents an analysis behind the stochastic behavior of MCMC sampling for adversarial noise reduction in meta-stable energy basins and the benefits and challenges associated with different regimes of energy-based learning for this task.

With the method of conversation analysis, this study examines the level of adverseness in questions between members of parliament from different parties. The data consists of question – response sequences derived from a ministerial statement from the prime minister in the House of Commons. This study finds that, in question – response sequences between oppositional members of parliament and the prime minister, adversarial presuppositions in questions can be used as a strategy to project negative traits upon the respondent. Adversarial dimensions of hostility, assertiveness and directness can also be found in adversarial questions. In these instances, the respondent may adjust their answer to match the level of adverseness from the questioner through the use of certain lexis, creating counter sequences. Adversarial questions are the most common type of question from members of the oppositional party, and there are different adversarial strategies being used. Questions from members of the government party do not make use of adversarial strategies, and should not be described as adversarial.

This paper serves as an investigation in the use of energy-based models for adversarial defense via purification and training. Convergent and non-convergent energy-based models are tasked to remove white-box adversarial signals embedded into images from the CIFAR-10 dataset so that they may be classified correctly. This work presents an analysis behind the stochastic behavior of MCMC sampling for adversarial noise reduction in meta-stable energy basins and the benefits and challenges associated with different regimes of energy-based learning for this task.

Modern-day machine learning algorithms are vulnerable to adversarial perturbations. Before machine learning is employed in operational environments it is important for the Department of Defense to understand the vulnerabilities in adversarial domains. With an emphasis on applications to image classification, this thesis intends to investigate methods for constructing adversarial manipulations intended to fool a statistical classifier in order to establish best practices prior to employment in a mission-critical environment. The state-of-the-art method for generating adversarial perturbations utilizes an easy to obtain linear approximation of the defender’s loss function and applies change to each pixel’s value in the direction of the gradient by a pre-determined constant in order to maximize the defender’s loss function. This results in misclassification of the image. This thesis aims to improve existing methods for developing adversarial inputs to image classifiers by attempting to find new ways to make perturbations less perceptible to human vision. We develop two new perturbation methods. Color Aware attempts to constrain perturbation changes to match human perception by mapping to CIELAB color space, a color space better suited to represent human vision, while Color & Edge Aware constrains perturbation changes in visually smooth regions in images., Outstanding Thesis, Captain, United States Marine Corps, Approved for public release. distribution is unlimited

With the method of conversation analysis, this study examines the level of adverseness in questions between members of parliament from different parties. The data consists of question – response sequences derived from a ministerial statement from the prime minister in the House of Commons. This study finds that, in question – response sequences between oppositional members of parliament and the prime minister, adversarial presuppositions in questions can be used as a strategy to project negative traits upon the respondent. Adversarial dimensions of hostility, assertiveness and directness can also be found in adversarial questions. In these instances, the respondent may adjust their answer to match the level of adverseness from the questioner through the use of certain lexis, creating counter sequences. Adversarial questions are the most common type of question from members of the oppositional party, and there are different adversarial strategies being used. Questions from members of the government party do not make use of adversarial strategies, and should not be described as adversarial.

The principle of equality of arms is an essential optimization require­ment, which requires that “each party must be afforded a reasonable opportunity to present his case under conditions that do not place him at a disadvantage vis-à-vis his opponent”. This procedural figure, notwithstanding, has not been studied deeply and seriously in Colombian doctrine and jurisprudence despite its evident importance. The aforementioned situation has allowed an irrespon­sible and mistaken use of this concept and that is the reason why this article arises, which aims, without pretending to be exhaustive, to give a greater clarity about this optimization requirement, starting with a study about its antecedents, going through the analysis of its essential features, the reference to different positions about its basis, definition, content and scope, its differentiation with other rights and principles and the reference to some categories developed at the international jurisdiction that are relevant to achieve a full understanding of it. Subsequently, it will be possible to open an organized debate on this procedural figure in order to make viable an optimized and responsible use of this concept., El principio de igualdad de armas es un mandato esencial que consiste en que “cada parte debe tener una oportunidad razonable para presentar su caso en condiciones que no la pongan en desventaja con respecto a su oponente”1. Esta figura, a pesar de su connotada y evidente importancia, no ha sido objeto de estudio profundo y serio en la doctrina y la jurisprudencia colombianas. Lo anterior ha permitido un uso irresponsable y equivocado de este concepto, y es de ahí que nace la necesidad de este artículo, el cual tiene por objeto, sin pretender ser exhaustivo, dar una mayor claridad sobre este mandato de op­timización, empezando por revisar sus antecedentes; pasando por el análisis de cada uno de sus elementos esenciales; la referencia a algunas categorías desarrolladas en la jurisdicción internacional que son de suma importancia para alcanzar un entendimiento íntegro de dicho principio; el señalamiento de las diferentes posiciones sobre su fundamento, definición y alcance; y una diferenciación con otros derechos y principios con los que suele confundirse. De esta manera, se sentarán las bases para que, posteriormente, sea posible abrir un debate organizado acerca de esta figura que haga viable un ejercicio optimizado y responsable de ella.

This dissertation presents novel approaches and applications of machine learning architectures. In particular, these approaches are based on tools from topological data analysis and are used in conjunction with conventional machine learning methods. Topological data analysis, which is based on algebraic topology, can identify significant global mathematical structures which are out of reach of many other approaches. When we use topology we benefit from generality, and when we use conventional methods we benefit from specificity.This dissertation contains a broad overview of data science and topological data analysis, then transitions to three distinct machine learning applications of these methods. The first application uses linear methods to discover the inherent dimensionality of the manifold given by congressional roll call votes. The second uses persistent homology to identify extremely noisy images in both supervised and unsupervised tasks. The last application uses mapper objects to produce robust classification algorithms. Two additional projects are presented later in the appendix, and are related to the three main applications. The first of these constructs a method to choose optimal optimizers, and the second places mathematical constraints on the structure of renormalization group flows.

This dissertation presents novel approaches and applications of machine learning architectures. In particular, these approaches are based on tools from topological data analysis and are used in conjunction with conventional machine learning methods. Topological data analysis, which is based on algebraic topology, can identify significant global mathematical structures which are out of reach of many other approaches. When we use topology we benefit from generality, and when we use conventional methods we benefit from specificity.This dissertation contains a broad overview of data science and topological data analysis, then transitions to three distinct machine learning applications of these methods. The first application uses linear methods to discover the inherent dimensionality of the manifold given by congressional roll call votes. The second uses persistent homology to identify extremely noisy images in both supervised and unsupervised tasks. The last application uses mapper objects to produce robust classification algorithms. Two additional projects are presented later in the appendix, and are related to the three main applications. The first of these constructs a method to choose optimal optimizers, and the second places mathematical constraints on the structure of renormalization group flows.

Classification techniques are widely used in security settings in which data can be deliberately manipulated by an adversary trying to evade detection and achieve some benefit. However, traditional classification systems are not robust to such data modifications. Most attempts to enhance classification algorithms in adversarial environments have focused on game theoretical ideas under strong underlying common knowledge assumptions, which are not actually realistic in security domains. We provide an alternative framework to such problems based on adversarial risk analysis which we illustrate with examples. Computational, implementation and robustness issues are discussed.

Risk analysis is an essential methodology for cybersecurity as it allows organizations to deal with cyber threats potentially affecting them, prioritize the defense of their assets, and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks, and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both intentional and nonintentional threats and the use of insurance as part of the security portfolio. A simplified case study illustrates the proposed framework, serving as template for more complex problems.

Agents that perform intelligent tasks interacting with humans in a seamless manner are becoming a reality. In contexts in which interactions among agents repeat over time, they might evolve from a cooperative to a competitive attitude, and vice versa, depending on environmental factors and other contextual circumstances. We provide a framework to model transitions between competition and cooperation in a community of agents. Competition is dealt with through the paradigm of adversarial risk analysis, which provides a disagreement solution; implicitly, we minimize the distance to such solution. Cooperation is handled through a concept of maximal separation from the disagreement solution. Mixtures of both problems are used to refer to in-between behaviour. We illustrate the ideas with several simulations in relation with a group of robots. Our motivation is the constitution of communities of robotic agents that interact among them and with one or more users.

Generative Adversarial Neural Networks are neural networks which participate in a zero- sum game, competing against each other to maximize an objective. One network, the generator, hopes to generate data that lies in a similar distribution to given data. The discriminator aims to separate data generated by the generator and ground truth data. This allows us to generate and replicate data given a dataset.These networks have been increasingly popular in generating images from text. By leveraging the Introspective Learning framework, we are able to take image classification networks and synthesize images. We show that our results are competitive on many-to-many mappings against Conditional Generative Adversarial Neural Networks.

Generative Adversarial Neural Networks are neural networks which participate in a zero- sum game, competing against each other to maximize an objective. One network, the generator, hopes to generate data that lies in a similar distribution to given data. The discriminator aims to separate data generated by the generator and ground truth data. This allows us to generate and replicate data given a dataset.These networks have been increasingly popular in generating images from text. By leveraging the Introspective Learning framework, we are able to take image classification networks and synthesize images. We show that our results are competitive on many-to-many mappings against Conditional Generative Adversarial Neural Networks.

In this work it is examined how Bidirectional Conditional GANs (BiCoGANs) perform as a method of dimensionality reduction, compared to UMAP and t-SNE, when applied to mammograms from the Dutch Breast Cancer Screen Dataset in the context of cancer risk as- sessment. Before reducing dimensions, the mammograms have been cropped to 72 x 88 tex- ture samples, mostly containing breast tissue. The latent dimensions to be used have been decided on with the use of intrinsic dimensionality estimators. The quality of resulting em- beddings has been measured with AUROC, which has been calculated based on classification from a fully-connected classifier trained several times on each embedding. It has been found that in this case UMAP outperforms both t-SNE and BiCoGANs, with the best mean result for texture samples being AUROC = 0.547 +/- 0.018. For the BiCoGAN, the best mean result was AUROC = 0.541 +/- 0.021.

In this work it is examined how Bidirectional Conditional GANs (BiCoGANs) perform as a method of dimensionality reduction, compared to UMAP and t-SNE, when applied to mammograms from the Dutch Breast Cancer Screen Dataset in the context of cancer risk as- sessment. Before reducing dimensions, the mammograms have been cropped to 72 x 88 tex- ture samples, mostly containing breast tissue. The latent dimensions to be used have been decided on with the use of intrinsic dimensionality estimators. The quality of resulting em- beddings has been measured with AUROC, which has been calculated based on classification from a fully-connected classifier trained several times on each embedding. It has been found that in this case UMAP outperforms both t-SNE and BiCoGANs, with the best mean result for texture samples being AUROC = 0.547 +/- 0.018. For the BiCoGAN, the best mean result was AUROC = 0.541 +/- 0.021.

Children's Courts in Australia are important parts of the systems which protect children and deal with young people who offend. They make decisions about the best interests of these vulnerable children and young people and require that children are represented in court. The paper reports on views of 46 stakeholders about quality of and access to representation in the care jurisdiction of the Childrens Court in Australian Capital Territory. Most stakeholders were adamant that for all parties to receive as fair a hearing as possible, they needed representation. There were a range of views about the quality of representation and about what quality representation looks like in an adversarial system of decision-making.

We consider the classical problem of constrained queueing (or switched networks): There is a set of N queues to which unit sized packets arrive. The queues are interdependent, so that at any time step, only a subset of the queues can be activated. One packet from each activated queue can be transmitted, and leaves the system. The set of feasible subsets that can be activated, denoted S, is downward closed and is known in advance. The goal is to find a scheduling policy that minimizes average delay (or flow time) of the packets. The constrained queueing problem models several practical settings including packet transmission in wireless networks and scheduling cross-bar switches. In this paper, we study this problem using the the competitive analysis: The packet arrivals can be adversarial and the scheduling policy only uses information about packets currently queued in the system. We present an online algorithm, that for any epsilon > 0, has average flow time at most O(R^2/epsilon^3*OPT+NR) when given (1+epsilon) speed, i.e., the ability to schedule (1+epsilon) packets on average per time step. Here, R is the maximum number of queues that can be simultaneously scheduled, and OPT is the average flow time of the optimal policy. This asymptotic competitive ratio O(R^3/epsilon^3) improves upon the previous O(N/epsilon^2) which was obtained in the context of multi-dimensional scheduling [Im/Kulkarni/Munagala, FOCS 2015]. In the full general model where N can be exponentially larger than R, this is an exponential improvement. The algorithm presented in this paper is based on Makespan estimates which is very different from that in [Im/Kulkarni/Munagala, FOCS 2015], a variation of the Max-Weight algorithm. Further, our policy is myopic, meaning that scheduling decisions at any step are based only on the current composition of the queues. We finally show that speed augmentation is necessary to achieve any bounded competitive ratio.

Este artículo se propone examinar los modelos de procesos penales de los movimientos de reforma que se establecieron en las últimas décadas del siglo XX, en los países iberoamericanos. Para ello, primero se enfatiza las características históricas de cada sistema, poniendo de relieve la importancia de no confundir el acusatorio con el adversarial, rechazando la coincidencia entre los sistemas actuales y mixto inquisitivo histórico. Apoya la importancia de la igualdad de armas y e del contradictoria en la búsqueda de un equilibrio entre los modelos de procedimiento.

This article aims without hesitation, generate a discussion on the issue of the purpose of the process in our country, it has always been a dilemma, if the end of the process is the solution of the conflict or the search for truth to substantiate the decision. Quantitative methodological approach was used, with a type of documentary or bibliographic research, descriptive to procedurally define the concept of truth in the context of social rule of law, to refer to the refined truth that at the time Rene Descartes, defined as the product of reasoning with the construction of syllogisms that do not have any errors. And experimental analyzing judicial congestion from the cultural point of view and what it has meant for the country type, because even though the Judicial Branch is autonomous, in recent years it has seen a production of sentences, which effectively creates a distortion of the judicial function, where the end of the process is the solution of the conflict, regardless of the effective protection of the rights of citizens, making a very skewed axis of our cultural change., El presente artículo pretende generar una discusión sobre el tema de la finalidad del proceso en nuestro país, pues siempre ha existido la disyuntiva, si el fin del proceso es la solución del conflicto o la búsqueda de la verdad para fundamentar la decisión. Se utilizó el enfoque metodológico cuantitativo, con un tipo de investigación documental o bibliográfica, de tipo descriptivo para delimitar procesalmente el concepto de verdad en el contexto del Estado social de derecho, para tomar como referencia la verdad depurada de la que en su momento Rene Descartes definió como el producto del razonamiento con la construcción de silogismos que no posean errores de ningún tipo. Y de tipo experimental para analizar la congestión judicial desde el punto de vista cultural y lo que ha representado para el país, pues a pesar de que la rama judicial es autónoma, en los últimos años se ha visto como una empresa de producción de sentencias, que en la práctica genera una desnaturalización de la función judicial, en donde el fin del proceso sea la solución del conflicto, sin importar la tutela efectiva de los derechos de los ciudadanos, haciendo un cambio muy sesgado de nuestro eje cultural

This article designs a first-order road map for modeling research to bridge the scientific gap between observations from physical sensor networks at 3-50 m on the one hand and determination of covert tactical adversarial intent of individuals with deception and in extensive clutter on the other. To be successful, the research needs to integrate kinesiological neurophysiological, psychological, and cognitive science and sociocultural anthropology and information science components. Research and development (R&D) issues that need to be considered include metrics for cognitive phenomena and how well detection systems work, data sets, determining whether actors can provide sufficient verisimilitude to create data sets, and relevant sensing technologies and information fusion techniques. Successful procedures may need to include actively (but unobtrusively) perturbing the situation in which the sensing takes place in order to elicit specific responses. Comprehensive government R&D programs are required to promote rapid progress., Presented at the 13th International Conference on Information Fusion held in Edinburgh, UK on 26-29 July 2010. Sponsored in part by Office of Naval Research, Office of Naval Research Global, and U.S. Army Research Laboratory's Army Research Office (ARO).

Child custody decisions on separated families seldom acknowledge the impact that such decisions have on subsequent partners of the biological parents. New partners are thus “invisible” in litigation in the Family Court. This project utilised a qualitative research methodology to explore this experience with a purposive experiential sample of 12 second partners. The primary themes identified in thematic content analysis were exclusion and invisibility in a system over which they had little control. The findings suggest that the negative psychological impact would be lessened if a policy of inclusion was adopted in the Family Court process.

in Criminal Procedure Stories, Carol Steiker, ed., 2005