Ecosystems of Virtual Orientalism and Entrepreneurial Vision: Creativity and Liability of Newness in Digital Age with reference to India and Australia

As neural networks are increasingly included as core components of safety–critical systems, developing effective testing techniques specialized for them becomes crucial. The bulk of the research has focused on testing neural-network models; but these models are defined by writing programs, and there is growing evidence that these neural-network programs often have bugs too.This paper presents aNNoTest: an approach to generating test inputs for neural-network programs. A fundamental challenge is that the dynamically-typed languages (e.g., Python) commonly used to program neural networks cannot express detailed constraints about valid function inputs (e.g., matrices with certain dimensions). Without knowing these constraints, automated test-case generation is prone to producing invalid inputs, which trigger spurious failures and are useless for identifying real bugs. To address this problem, we introduce a simple annotation language tailored for concisely expressing valid function inputs in neural-network programs. aNNoTest takes as input an annotated program, and uses property-based testing to generate random inputs that satisfy the validity constraints. In the paper, we also outline guidelines that simplify writing aNNoTest annotations.We evaluated aNNoTest on 19 neural-network programs from Islam et al’s survey. Islam et al. (2019), which we manually annotated following our guidelines — producing 6 annotations per tested function on average. aNNoTest automatically generated test inputs that revealed 94 bugs, including 63 bugs that the survey reported for these projects. These results suggest that aNNoTest can be a valuable approach to finding widespread bugs in real-world neural-network programs.

Determining a feasible safety factor for space trusses is an important phase in structural analysis that could have economic benefits. We know there are many kinds of imperfections in structural elements, which include both material and geometric flaws. Predicting factual behavior of structures is very difficult and occasionally impossible. Elements with initial geometric imperfections in space trusses are a common phenomenon, in addition, equivalent initial geometric imperfections can be applied for modeling of residual stresses or eccentric loading effect. The number of members in the space structures is usually high as is the diversity in the kind of initial imperfection. Therefore, there is a high likelihood that models must be analyzed. The structure must be analyzed with non-linear methods, making these approaches time consuming, and potentially uneconomical. In this study, we selected 30 cases for random analysis based on Monte Carlo methods to find the bearing capacity of the space truss. We attained results from the LUSAS program LUSAS Modeller, Version 13, UK program and these were then exported as input data to the Artificial Neural Network (ANN) program. A reasonable neural network has been found of predicting another 30 cases for load bearing capacity without any analysis and only based on the neural network program. Finally, a new approach for determining the load capacity of the space trusses was extracted and we predicted the occurrence possibility of the convenience load bearing capacity in 60 cases.

Wika sa Pampublikong Espasyo: Isang Pag-aaral sa Tanawing Pangwika ng Maynila

Temporal window system: a new approach for dynamic detection — application to surface acoustic wave gas sensors

Chiang Mai, a renowned tourist destination in northern Thailand, offers a fascinating linguistic landscape (LL) that reflects the intersection of cultural heritage, globalization, and tourism dynamics. This study explores the distribution and functions of languages in Chiang Mai's public signage, focusing on the dominance of Thai, the auxiliary roles of English and Chinese, and the implications for cultural preservation and global accessibility. Thai signage reinforces cultural identity and heritage, while English and Chinese cater to the linguistic needs of international tourists, enhancing navigation and cross-cultural communication. The research highlights the dual functions of LL: its informational role facilitates practical communication, and its symbolic role reflects the city’s inclusivity and cultural richness. However, challenges such as inconsistent translations and limited representation of minority languages remain. Addressing these issues through standardized multilingual policies and the integration of technology could further improve Chiang Mai’s LL, positioning it as a global model for balancing cultural preservation and tourism. This study contributes to the growing discourse on multilingualism and linguistic landscapes, offering insights into the role of language in fostering sustainable and inclusive tourism.

This study aimed to answer the questions 'What are the neural networks and mechanisms involved in visual and kinesthetic motor imagery?', and 'Is part of cognitive processing included during visual and kinesthetic motor imagery?' by investigating the neurophysiological networks and activations during visual and kinesthetic motor imagery using motor imagery tasks (golf putting). The experiment was conducted with 19 healthy adults. Functional magnetic resonance imaging (fMRI) was used to examine neural activations and networks during visual and kinesthetic motor imagery using golf putting tasks. The findings of the analysis on cerebral activation patterns based on the two distinct types of motor imagery indicate that the posterior lobe, occipital lobe, and limbic lobe exhibited activation, and the right hemisphere was activated during the process of visual motor imagery. The activation of the temporal lobe and the parietal lobe were observed during the process of kinesthetic motor imagery. This study revealed that visual motor imagery elicited stronger activation in the right frontal lobe, whereas kinesthetic motor imagery resulted in greater activation in the left frontal lobe. It seems that kinesthetic motor imagery activates the primary somatosensory cortex (BA 2), the secondary somatosensory cortex (BA 5 and 7), and the temporal lobe areas and induces human sensibility. The present investigation evinced that the neural network and the regions of the brain that are activated exhibit variability contingent on the category of motor imagery.

Application of backpropagation neural networks to diagnosis of breast and ovarian cancer

– Linguistic Landscape (LL) is defined as the study of languages in their written form in the public space. One of the most recent and interesting developments in the field sees LL related to educational contexts. The aim of the study presented here was to observe, document and analyse the linguistic occurrences in written form on signs in a sample of Italian secondary schools. The research seeks to give a full overview of the functions of the so-called Linguistic Schoolscape, paying particular attention to the presence of immigrant languages, the characteristics of multilingual signs and the differences between top-down and bottom-up signs. The data presented here are intended to contribute to research in a number of respects: to the development of LL studies in school structures, towards a greater understanding of language dynamics within Italian schools and as a contribution to educational research. The Italian school is in a moment of great linguistic turmoil due to many factors, such as the high number of first and second generation immigrant pupils and the pressure from the Council of Europe, which is pushing for plurilingual and intercultural education to be implemented. The observation of the LL of classrooms and corridors appears to be a useful tool in order to obtain a first picture of how these factors influence and find space within the school walls.

In this communication we discuss two issues: 1) the (public) awareness of the centrality of meaning in contemporaneous society; 2) the fact that this awareness looks for a specific know-how we call the semiotic expertise - a know-how which is of central relevancy for a global meaning economy and market. The topics of this communication are: 1) Some general remarks on the centrality of meaning in the to-days communication society and knowledge economy. 2) A first concrete example from cross-cultural communication (advertising). 3) A second concrete example (for topic 2) from the field of digital media and digital (audiovisual) archives

The physical distancing, a consequence of the current pandemic, has contributed to highlight a number of problems related to digital archives on the web, which have been debated for about 20 years. In particular, when faced with the cultural need to aggregate different data in the field of research and studies, it emphasised how the integration of cultural heritage in the digital domain has not yet been achieved. In this regard, the issue is not so much and not only technological but above all of a conceptual, historiographical and methodological nature. Starting from the assessment that the technological tool is the answer and not the question, this essay presents some reflections on fundamental issues, still under debate, related to the cultural heritage of fashion, especially fashion archives. This is in order to propose a hypothesis that is historiographical rather than technological, for potential integration of digital fashion archives into the web, as the founding pillar of the intercultural system of national heritage.

Many digital collections of cultural heritage resources, commonly referred to as digital archives, have been developed in Japan. This paper covers the multiple facets of digital archiving for a comprehensive understanding of Japanese activities and issues learnt since the 1990s, starting from the development of digital archives and related activities. It then discusses some issues for sustainable development of digital archives: social, usability, technological and longevity. As building and maintaining digital archives is expensive, it is important to demonstrate their value to ensure their sustainability. Continuous effort must be made to build really usable digital archives, and to develop appropriate measures to evaluate usability. Such effort must include human resource development and community networking.

Neural network programming in bioprocess variable estimation and state prediction

A new method has been developed to predict the undrained shear strength from geophysical logs and core measurements using neural networks. A limited number of undrained shear strength measurements are performed on cores, resulting in a limited, discontinuous data set. A series of neural networks were developed to predict the undrained shear strength of the sediments where measurements were not available. The prediction is based on a learned relationship between the suite of geophysical logs and the measurements of undrained shear strength. The use of neural networks to predict the undrained shear strength in cores requires that a statistically significant number of shear strength measurements are available to train and test the neural network. Geophysical logs are necessary to develop a relationship with the shear strength measurements. Shear strengths were measured on core samples in intervals ranging from 15 cm to greater than 200 cm. The neural network was able to resolve the shear strength measurements to a depth resolution of approximately 8 cm. The predicted undrained shear strength values, when tested against actual shear strength measurements, show good correlation. Introduction The expense involved in drilling and coring marine sediment in the world's oceans for scientific research is high. Testing and sampling of each core section must be prioritized and shared within the scientific community. Non-destructive testing of sediment core complements sediment sampling, accessing more information from each core section than from sampling alone. The multi-sensor core logger (MSCL) is an instrument that non-destructively measures the physical properties of sediments, such as bulk density, resistivity, compressional wave velocity and magnetic susceptibility at very high depth resolution. Each core section from the Integrated Ocean Drilling Program (IODP) and the Ocean Drilling Program (ODP) is run through the MSCL. In addition to the MSCL, in-situ non-destructive measurements are made through downhole wireline logging. Wireline logs give a continuous record of several geophysical properties. Traditionally, however, measurements of the undrained shear strength (Su) of a sediment require that the test disturbs the sediment. To maximize the amount of undrained shear strength data available from a borehole, a neural network computer program was used to estimate Su values based on a learned relationship between Su measurements and the MSCL and downhole wireline log data available. If only a limited number of Su measurements are possible, the neural network program is used to fill in the gaps of Su data where measurements were not possible. Neural Network Overview A neural network is a pattern recognition or function approximation computer program that emulates the learning and predictive behavior of the human brain, in which future responses or actions are dictated by the outcome of previous experiences (Haykin, 1994). A typical neural network consists of a series of input data, one or more hidden layers of neurons that give a weight to the data inputs, gauging their importance, and an output layer.

Cartesian Genetic Programming of Artificial Neural Networks is a NeuroEvolutionary method based on Cartesian Genetic Programming. Cartesian Genetic Programming has recently been extended to allow recurrent connections. This work investigates applying the same recurrent extension to Cartesian Genetic Programming of Artificial Neural Networks in order to allow the evolution of recurrent neural networks. The new Recurrent Cartesian Genetic Programming of Artificial Neural Networks method is applied to the domain of series forecasting where it is shown to significantly outperform all standard forecasting techniques used for comparison including autoregressive integrated moving average and multilayer perceptrons. An ablation study is also performed isolating which specific aspects of Recurrent Cartesian Genetic Programming of Artificial Neural Networks contribute to it’s effectiveness for series forecasting.

Abstract. When referring to the tangible cultural heritage, people tend to concern more about the conservation and research of the entity of the tangible heritage than the cross-cultural communication of the cultural heritage which is also one of the most important components of the preservation of the cultural heritage. As an exotic new born of the cultural heritage, the entity born from the cross-cultural communication inherits the properties of the cultural heritage on the one hand, and on the other hand generates diversities as a result of the differences based on social, cultural and environment. And the business model is one of the most important reasons for the production of diversities. There’s no doubt that a good form of business model makes great significance to the cross-cultural communication. Therefore, the study of the business model of cultural heritage in the process of cross-cultural communication will not only contributes to the deeper understanding towards the phenomenon of the cultural heritage’s cross-cultural communication, but also leads to the introspection to the tangible cultural heritage itself. In this way, a new kind of conservative notion could take form, and the goal of protecting cultural heritage could be achieved. Thus the Chinese Garden is a typical representation of the cultural heritage which makes great sense in the cross-cultural communication. As a kind of tangible cultural heritage, the Chinese gardens are well preserved in different regions in China. While the spirits of the Chinese garden carry forward through the construction of the Chinese gardens abroad during the cross-cultural communication. As a new kind of form of the cross-cultural communication of the cultural heritage, on the one hand, the Chinese gardens overseas built ever since China's Reform and Opening express creatively of the materialist and the spirituality of the traditional Chinese Garden, and on the other hand, those Chinese gardens overseas face all kinds of tough issued such as investment, business model and management. The exploration of the reasons for these tough issues makes a great sense of the study towards the cross-cultural communication and preservation of the cultural heritage. In this paper, the development of the whole overseas gardens and the cultural consumption of the Chinese gardens in Europe is generalized, then two typical cases are selected from those two categories mentioned above. By way of field study and interviews, it shows different strategies towards cultural consumption and provides constructive advice for the survival and development of overseas Chinese gardens.