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Generative systems & the sense of vision

Generative systems & the sense of vision

The in-and-of temporal the The artist sequential is sense of to its is personal more placing, of vision to painting that paint any sense to the quality pf the important with what or in p[r]

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Vision does not always help stroke survivors compensate for impaired limb position sense

Vision does not always help stroke survivors compensate for impaired limb position sense

To examine the extent to which stroke survivors used vision to compensate for impaired position sense, we ini- tially identified stroke survivors with impaired position sense. To do this we established normative reference ranges from our controls. First, we ensured there were no differences in control subject in performance be- tween the first and second hand performing the task in the occluded vision condition (which could potentially arise from learning effects) on any of the parameters using a paired t-test (Varp = 0.29, C/Ep = 0.75, Shiftp = 0.64). Next we used regression models from the control data to establish normative reference ranges specific to age, sex and test-hand of each stroke survivor [32]. We were able to use the average measures from both hands of the controls for our normal range, except for C/E in the Normal Vision condition, where we noted a signifi- cant difference in the performance of the dominant and non-dominant arm of controls and made our compari- sons accordingly. The normative reference ranges were used to identify stroke survivors who were Normal (inside 95% normative reference range) or Impaired (outside 95% normative reference range) on each measure. We then ex- amined relationships between visual conditions using Pear- son correlations (Occluded versus Normal) and Fisher’s tests of independence categorized by visual condition (Oc- cluded or Normal) and performance (Normal or Impaired).
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From vision to sense: When TV becomes a game

From vision to sense: When TV becomes a game

“A word that describes the technological, industrial, cultural and social changes in the ways media circulates within our culture… Perhaps most broadly, media convergence refers to a situation in which multiple media systems coexist and where media content flows fluidly across them. Convergence is understood here as an ongoing process or series of

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Vision Systems with the Human in the Loop

Vision Systems with the Human in the Loop

active process. Instead of just monitoring its surroundings, a cognitive vision system is able to communicate or interact with its environment. This underlines that the acquisition, storage, retrieval, and use of knowledge is no end in itself but guides the system’s perception and (re)action. Simulta- neously, the capabilities to perceive and act guide cognitive processes. Without perception and the possibility to manipu- late or communicate perceived entities or events, knowledge cannot be acquired. Memory, however, is a limited resource. Besides mechanisms for learning, cognitive vision thus also implies attention control and a sense for relevance which comes along with the capability to forget irrelevant infor- mation. This requires flexible knowledge representation and techniques for top-down and bottom-up processing as well as functionalities for contextual reasoning and categorisa- tion. Together with the biologically motivated principle of multiple computations [4], categorisation yields adaptabil- ity, flexibility, and robustness.
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Community Sense and Response Systems

Community Sense and Response Systems

The Community Seismic Network project at Caltech is a collaboration between civil engineers, geophysicists, and computer scientists that seeks to rapidly detect earthquakes and provide real- time estimates of their impact using community-operated sensors. Given that large earthquakes are among the few scenarios that can threaten an entire city, the CSN project is built upon a vision of people sharing accelerometer data from their personal devices to collectively produce the information needed for effective real-time and post-event responses to dangerous earthquakes. To that end, CSN has partnered with more than a thousand volunteers in the Los Angeles area and cities around the world who contribute real-time acceleration data from their Android smartphones and low-cost USB-connected sensors. This chapter, based on [37], describes the project goals and challenges, as well as the client-server architecture implemented on Google App Engine, Android phones, and desktop computers. The main contribution is the design and implementation of the CSN-Droid and CrowdShake Android client.
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AutoMAKE : generative systems, digital manufacture and craft production

AutoMAKE : generative systems, digital manufacture and craft production

In relation to established craft practices, it could be argued that the digital systems developed within this project propose a new way of creating objects which can be related to the older tradition of bespoke commissioning, but potentially in a more democratic and widely available way. Therefore this type of system has the potential to rekindle and expand a craft tradition in which maker and client work together to develop a design that is unique to the individual. However, as Emily Campbell argues, craft contains the idea of personal meaning, which she feels has been lost in much recent product design [4]. This personal meaning for the owner of a craft object is created through a complex range of psychological associations. There is a question whether the new design and production systems described here have the potential to produce objects which have enough ‘craft’ characteristics to retain the ability to create personal meaning. On the one hand they produce unique objects, but on the other hand, they are not ‘handmade’. There is a range of skills employed within the development of the systems that allow the creation of new artefacts, however they are not the traditional skills associated with craft practice. Furthermore, the aesthetic characteristics of the objects produced are inherently a balance between the generative system, the software designer and the user, rather than solely the vision of the maker. As users begin to try the software and the systems tested, the significance of these issues can be reviewed and the hybrid nature of the project assessed.
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Generative Adversarial  Networks for Online Visual  Object Tracking Systems

Generative Adversarial Networks for Online Visual Object Tracking Systems

Lately, deep learning techniques have been used to make significant progress in artificial intelligence overall and in computer vision in particular, which aims to simulate the functionality of the human eye and the components in the brain responsible for the sense of sight. Deep Learning uses deep neural networks, which are artificial neural networks with several hidden layers between the input and output layers [17]. The term “deep” usually refers to the high number of hidden layers included in the neural network. Usually deep networks can have up to 150 layers. Neural networks are motivated by the goal of building systems that learn and think like humans’ brains. Deep Learning models can learn the representation of data and their features by being trained using large sets of labeled data [18].
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On Panini and the Generative Capacity of Contextualized Replacement Systems

On Panini and the Generative Capacity of Contextualized Replacement Systems

The underlying formalism to P¯an.inian grammar, while our knowledge of it is incomplete, presents enough evidence to conclusively demonstrate that it is far greater in its expressive power than either RL or CFL. P¯an.ini has nevertheless anticipated modern generative- syntactic practice in defining for himself a very versatile tool which he then applies very thriftily to advance his own objectives of grammatical brevity and elegance. As a result, his As.t.¯adhy¯ay¯ı may even be amenable to an RL-style analysis, as Hyman (2007) has claimed. But in light of this investigation, the result of this analysis certainly could not be a grammar in P¯an.ini’s own style, but rather P¯an.ini’s grammar recast into someone else’s style. No proof is presented here, however, that the P¯an.inian framework is complete in the sense that it can generate any context-sensitive language. This remains an open question. We have not even touched upon perhaps the greatest difference between P¯an.ini’s own formalism and the standard string-rewriting systems concomitant with Chomsky’s hierarchy, which is its built-in capacity for disambiguation. P¯an.ini’s grammar, through its use of rule precedence and other meta-conventions, generates a single derivation for every grammatical sentence of Sanskrit.
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CHANGE MANAGEMENT IN COMPLEX SYSTEMS: USING APPRECIATIVE INQUIRY FOR GENERATIVE LEARNING

CHANGE MANAGEMENT IN COMPLEX SYSTEMS: USING APPRECIATIVE INQUIRY FOR GENERATIVE LEARNING

The use of AI in this change management project allowed an organization in crisis to Discover its core values, Dream of a better communication process, Design a new vision, and rearticulate its Destiny. AI as a change management tool used a specific model of change that created a transition team, Champions of Change, that would be the impetus for the newly defined organization dreams, vision, and values.

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Generative Manufacturing of Ceramic Parts Vision Rapid Prototyping Andreas Gebhardt 1

Generative Manufacturing of Ceramic Parts Vision Rapid Prototyping Andreas Gebhardt 1

Foundry sand systems using polymeric binders open up a more easy solution. Already in 1992 the plastic laser sintering process was used to process foundry sand. Therefore the sand particles were coated with polymeric binder. The process was mostly used to make kernels which were equivalent to the ones made in the classic kernel making machines but showed a much more complex geometry. The results are similar to the ones received from the 3D printing process mentioned above This technology opened up new possibilities to the foundry people worldwide. As an example complex arrangements structures can be made. (Figure 6, middle and right).
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Structured Generative Models of Continuous Features for Word Sense Induction

Structured Generative Models of Continuous Features for Word Sense Induction

probability of a sense being applicable, since it is the variable most directly associated with the am- biguous word. It is possible however, to assign senses to joint configurations of two or all three of the latent variables, in order to better use the information provided by our model. Since the number of pos- sible configurations grows exponentially with the number of latent variables, this approach can lead to very fine-grained partitions of the data. In practice, we observe that only a small number of all these configurations are given a high probability mass. Evaluation results for our model MultiContextContin- uous (MCC) and state-of-the-art systems are reported in Table 2. We report results with both the single variable approach (MCC-S) and joint variable assignments (MCC-L,S and MCC-G,L,S).
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AI for a Generative Economy: The Role of Intelligent Systems in Sustaining Unalienated Labor, Environment, and Society

AI for a Generative Economy: The Role of Intelligent Systems in Sustaining Unalienated Labor, Environment, and Society

In this paper we will briefly review the problems created by mass production, and the principles of generative economies in their traditional form. We then present some initial experiments with what we might term “artisanal cyborgs” -- a synthesis between traditional work practices and contemporary automation technologies. We conclude with a vision for how this research

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The Recursively Generative Nature of Complex Agri-
Eco-Socio-Technical Systems

The Recursively Generative Nature of Complex Agri- Eco-Socio-Technical Systems

It has also been noted that with complex problem spaces it is better to consider possibility and diversity rather than probability and uniformity [17], to value effectiveness over efficiency [2] and to aim to make wise decisions based on several factors. Such factors include societal perspectives, emerging technologies and natural systems that may be impacted by the system or impact it in turn. To this end a new approach is required that transcends disciplinary research [18] and design, an approach that encourages wide collaboration across disciplines to bring stakeholders with multiple, perhaps conflicting objectives and constraints, to a common understanding of the essence of the problem while allowing a resolution to the problem to emerge iteratively within an acceptable timescale.
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A Contrastive Evaluation of Word Sense Disambiguation Systems for Finnish

A Contrastive Evaluation of Word Sense Disambiguation Systems for Finnish

Since this data is potentially needed even by knowledge based systems, which should not have access to a training corpus, it is estimated here based on the fre- quency data in PWN. Unlike most PWN aligned WordNets, which are aligned at the synset level, FinnWordNet is aligned with PWN at the lemma level. An example of when this distinction takes effect is when lemmas are structurally similar. For ex- ample, in the synset ”singer, vocalist, vocalizer, vocaliser”, the Finnish lemma laulaja is mapped only to singer rather than to every lemma in the synset. When there is no clear distinction to be made, whole synsets are mapped. This reasoning fits with the existing structure of PWN: Relations between synsets encode purely semantic concerns, whereas relations between lemmas encode so-called morpho-semantic re- lationships, such as morphological derivation.
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A framework for incorporating sense of place into the management of marine systems

A framework for incorporating sense of place into the management of marine systems

According to (Lewicka 2011), the person aspect of SoP has attracted more research attention than place and process. Numerous studies across disciplines have shown that strong predictors of SoP are attributes of the person whose SoP is being measured (Jorgensen and Stedman 2006). For instance, length of residence, an easily observable personal attribute, is one of the strongest predictors of SoP. Potentially related to residence length are the experience and familiarity a person has with a place, which also relate to greater SoP (Carter et al. 2007). Similarly, historical connection with a place, including family and religious ancestry and personal investments, e.g., ownership, influence SoP (Brehm et al. 2006). A recent Australian study (Brown et al. 2015) demonstrated that people from coastal areas tend to be attached to smaller defined areas, i.e., one specific beach, than noncoastal residents, who are more likely to be attached to a larger geographical space, i.e., the ocean, likely due to deeper connection to these coastal areas influenced by the factors described above. Similarly, the place attributes of a landscape can influence a person’s SoP. Place attributes can be estimated or measured relatively easily, although difficulties do arise, given that the potential number of physical features that may affect SoP is infinite (Lewicka 2011). For example, landscapes can be characterized by features such as vegetation, color, texture, and slope (Carter et al. 2007) amongst others, and these may all impact upon SoP. In addition, there are many intangible physical features that facilitate attachment (Kaplan 1984), for example, the turning of the seasons, an enjoyable weather event, or the movement of light across a body of water. Place aspects are multiple, and their inclusion should be based on local circumstance and common sense to ensure they are useful predictors of SoP
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Generative designing with biological systems   searching for production tools aimed at individualization

Generative designing with biological systems searching for production tools aimed at individualization

Generative or semi-generative biological systems are presented as tools which enable one to design and produce for personalization, where variables like randomness, the production time and especially the natural functions of the biological actuators are cherished and allow us to grow unique objects. The intention is that these systems function as study models of issues that arise when analyzing the consequences for design as a discipline in the presented context: can the authorship of the designer be called into question since he does not define the final product but the system to obtain it? Are the barriers that separate design from the fine arts being dazed in these production systems that do not follow traditional manufacturing processes? And if they are, why is there a need for the separation of these two realities? Given that the production of these artifacts is dependent on the close relationship between user and system, and that in order to obtain results time and dedication is needed, will this hence engender a closer relationship between object and user?…
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Towards Implicit Content Introducing for Generative Short Text Conversation Systems

Towards Implicit Content Introducing for Generative Short Text Conversation Systems

In open domains, Xing et al. (2016) incorpo- rate topic information into Seq2Seq framework to generate informative and interesting responses. To provide informative clues for content introducing, Li et al. (2016b) detect entities from previous ut- terances and search for more related entities in a large knowledge graph. A very recent study similar to ours is Mou et al. (2016), where the predicted word explicitly occurs in the generated utterance. Unlike the existing work, we explore an implicit content-introducing method for neural conversation systems, which utilizes the addition- al cue word in a “soft” manner to generate a more meaningful response given a user-issued query. 6 Conclusion
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Ultra-Low-Power Vision Systems for Wireless Applications

Ultra-Low-Power Vision Systems for Wireless Applications

performance computing platforms. The natural way obtaining information of the world around us is visual. We obtain more than 90% of information about the world surrounding us with our eyes, and about half of our brain is busy with its interpretation. Even small animals, birds and insects can easily interpret the visual world surrounding them - this with a fraction of the computational power of an ordinary computer. On the other hand, an artificial vision system is a system that observes the visual world around it and interprets it to provide information about the scene. Such systems are currently bulky, expensive, power hungry, and instead of having cognitive capabilities are often limited to image recording. They are widely used in surveillance and security systems, traffic and pedestrian monitoring, etc., which require infrastructures for the power supply and data communication. They all are based on standard electronic hardware, which is not specifically optimized for energy-aware operations. For example, a commercial imager continuously delivers sequences of images with large redundancy becouse only a small amount of the available information is used to perform a visual task. The processor is required to execute visual processing even though no relevant information occurs in the scene, turning into a large waste of power and of computing resources. These aspects are of main importance in case of a long lasting autonomous system, which has to operate with a limited available energy budget. Although microelectronic technology has brought significant improvements in system performance and energy efficiency, vision computation did not make over the years significant progress in energy-autonomous applications in recent years. While the power consumption of a standard vision system can range from a few Watts to tens of Watts, a wireless sensor node burns typically mW on average. This means that there is a gap of 2 to 3 orders of magnitude in power consumption between these two technologies.
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Review of vision-based steel surface inspection systems

Review of vision-based steel surface inspection systems

This is particularly true when occurrence of pseudo defects is very limited as can be observed for many hot- and cold-rolled strips. In such cases, detection itself serves a very useful purpose of identifying a particular product as defective in a continuous production line. Appropriate action of segregating the defective product follows naturally. However, without defect classification, corrective actions cannot be taken to stop occurrence of defects in future. Defect detection loses its significances without classification in situations where pseudo defect percentage is significant and/or variable. This is mostly the case with slab, billet and rod/bar. Here, vision-based inspection systems are useful only when features are extracted from RoIs, and classification is achieved be- tween defects and pseudo defects and then between the defects themselves.
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Generative Encoder Decoder Models for Task Oriented Spoken Dialog Systems with Chatting Capability

Generative Encoder Decoder Models for Task Oriented Spoken Dialog Systems with Chatting Capability

to-speech (TTS) modules. Turn-taking was done via the built-in Chrome voice activity detection (VAD) plus a finite state machine-based end-of- turn detector (Zhao et al., 2015). Lastly, a hybrid named entity recognizer (NER) was trained using Conditional Random Field (CRF) (McCallum and Li, 2003) and rules to extract 4 types of entities (location, hour, minute, pm/am) for the EI process. The experiment setup is as follows: when a user logs into the website, the system prompts the user with a goal, which is a randomly chosen combina- tion of departure place, arrival place and time (e.g. leave from CMU and go to the airport at 10:30 AM). The system also instructs the user to say goodbye if the he/she thinks the goal is achieved or wants to give up. The user begins a conversa- tion with one of the two evaluated systems, with a 50/50 chance of choosing either system (not vis- ible to the user). After the user’s session is fin- ished, the system asks the him/her to give two scores between 1 and 5 for correctness and nat- uralness of the system respectively. The subjects in this study consist of undergraduate and grad- uate students. However, many subjects did not follow the prompted goal, but rather asked about bus routes of their own. Therefore, the dialog was manually labeled for dialog success. A dia- log is successful if and only if the systems give at least one bus schedule that matches with all three slots expressed by the users. Table 2 shows the
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