A deep representation for depth images from synthetic data

Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach

From source to target and back: symmetric bi-directional adaptive GAN

The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem. It has been shown that source labeled images can be modified to mimic target samples making it possible to train directly a classifier in the target domain, despite the original lack of annotated data. Inverse mappings from the target to the source domain have also been evaluated but only passing through adapted feature spaces, thus without new image generation. In this paper we propose to better exploit the potential of generative adversarial networks for adaptation by introducing a novel symmetric mapping among domains. We jointly optimize bi-directional image transformations combining them with target self-labeling. Moreover we define a new class consistency loss that aligns the generators in the two directions imposing to conserve the class identity of an image passing through both domain mappings. A detailed qualitative and quantitative analysis of the reconstructed images confirm the power of our approach. By integrating the two domain specific classifiers obtained with our bi-directional network we exceed previous state-of-the-art unsupervised adaptation results on four different benchmark datasets

Modeling Performance of Microservices Systems with Growth Theory

Context The microservices architectural style is gaining momentum in the IT industry. This style does not guarantee that a target system can continuously meet acceptable performance levels. The ability to study the violations of performance requirements and eventually predict them would help practitioners to tune techniques like dynamic load balancing or horizontal scaling to achieve the resilience property. Objective The goal of this work is to study the violations of performance requirements of microservices through time series analysis and provide practical instruments that can detect resilient and non-resilient microservices and possibly predict their performance behavior. Method We introduce a new method based on growth theory to model the occurrences of violations of performance requirements as a stochastic process. We applied our method to an in-vitro e-commerce benchmark and an in-production real-world telecommunication system. We interpreted the resulting growth models to characterize the microservices in terms of their transient performance behavior. Results Our empirical evaluation shows that, in most of the cases, the non-linear S-shaped growth models capture the occurrences of performance violations of resilient microservices with high accuracy. The bounded nature associated with this models tell that the performance degradation is limited and thus the microservice is able to come back to an acceptable performance level even under changes in the nominal number of concurrent users. We also detect cases where linear models represent a better description. These microservices are not resilient and exhibit constant growth and unbounded performance violations over time. The application of our methodology to a real in-production system identified additional resilience profiles that were not observed in the in-vitro experiments. These profiles show the ability of services to react differently to the same solicitation. We found that when a service is resilient it can either decrease the rate of the violations occurrences in a continuous manner or with repeated attempts (periodical or not). Conclusions We showed that growth theory can be successfully applied to study the occurences of performance violations of in-vitro and in-production real-world systems. Furthermore, the cost of our model calibration heuristics, based on the mathematical expression of the selected non-linear growth models, is limited. We discussed how the resulting models can shed some light on the trend of performance violations and help engineers to spot problematic microservice operations that exhibit performance issues. Thus, meaningful insights from the application of growth theory have been derived to characterize the behavior of (non) resilient microservices operations

The BET project: Behavior-enabled IoT

IoT is changing the way Internet is used due to the availability of a large amount of data timely collected from every-day life objects. Designing applications in this new scenario poses new challenges. This extended abstract discusses them and presents the objective of the BeT project whose main aim is to introduce a reference architecture, a conceptual framework, and related techniques to design behavior-enabled IoT systems and applications.Comment: 2 page

Leveraging over depth in egocentric activity recognition

Activity recognition from first person videos is a growing research area. The increasing diffusion of egocentric sensors in various devices makes it timely to develop approaches able to recognize fine grained first person actions like picking up, putting down, pouring and so forth. While most of previous work focused on RGB data, some authors pointed out the importance of leveraging over depth information in this domain. In this paper we follow this trend and we propose the first deep architecture that uses depth maps as an attention mechanism for first person activity recognition. Specifically, we blend together the RGB and depth data, so to obtain an enriched input for the network. This blending puts more or less emphasis on different parts of the image based on their distance from the observer, hence acting as an attention mechanism. To further strengthen the proposed activity recognition protocol, we opt for a self labeling approach. This, combined with a Conv-LSTM block for extracting temporal information from the various frames, leads to the new state of the art on two publicly available benchmark databases. An ablation study completes our experimental findings, confirming the effectiveness of our approac

But we made the easy cuts last year

Objective: After dealing with three major budget cuts in FY03, The Lamar Soutter Library faced additional reductions for FY04. After brainstorming, the management team realized that drastic journal and staffing cuts were on the horizon. Concerns for the collection and for continued customer service called for creative solutions. The poster will examine the measures taken to preserve a quality journals collection. Methods: After compiling and sorting print and online usage statistics gathered over an eighteen-month period, low-use titles were identified for possible cancellation in order to meet the revised budget guidelines. A spreadsheet was prepared including information concerning annual subscription/ licensing costs, publisher, publication frequency, appearance on core titles lists, availability via databases, inclusion in aggregation packages, subscription requirements for electronic packages, availability in nearby affiliate institutions, and usage and cost-per-use statistics. The list was refined over several months, and distributed to the faculty for input. The director of library services attended various committee meetings to answer questions and gather feedback. A final list of titles, with total projected cost savings, was compiled. There were elements of both art and science in this process. Results: The results of this process were unexpected. The faculty had been involved in the process, and understood the ramifications of massive journal cuts. As a group, the faculty Council protested to the School Administration about the planned cuts--and the administration gave the needed funds to the library to pay for the journals slated to be cut. Conclusions: Although a happy ending is not always possible, a carefully planned review process, utilizing as much hard data as possible, and keeping the users informed at each stage, can benefit the library as it seeks to provide quality resources in support of the school\u27s mission

Historic tuff masonry in Naples: different approaches to its conservation

[EN] Tuff, a sedimentary rock made of volcanic ash, is a traditional building material in the Campania region. Since its foundation Naples’ architecture, whether monumental or vernacular, has been erected in tuff masonry and only the arrival of concrete and steel has meant its downfall. Due to the soft nature of tuff, traditionally the building material was designed to be covered by plaster and very few and monumental architectures, by selecting and sculpting to the purpose the rock, were designed to be fair-faced. In years the exposition to natural and artificial degradation agents has brought a wide variety of deterioration phenomena both on the fair-faced tuff masonry and the ones that had lost plaster. In approaching the restoration of these architectures, the conservator is faced with a challenging task. This is due to the difficulty of balancing the pursue of minimum intervention and authenticity respect, the conservation of the historic consolidated image of the architecture and the necessity of using the best restoration techniques that guarantee the highest conservation of the material in future years, with particular regard to bio-compatible and sustainable materials both for operators and the environment. By analyzing the restoration of various architectures, both archaeological and modern, the paper will address this difficult task and the different decisions made by the conservators in relation to the monuments’ nature, identity, history and status of conservation. The paper is based on a multidisciplinary approach due to the contribution of the expertise of an architect, a restorer and an archaeologist.Balbi, B.; Bosso, R.; Russo Krauss, G. (2022). Historic tuff masonry in Naples: different approaches to its conservation. Editorial Universitat Politècnica de València. 963-970. https://doi.org/10.4995/HERITAGE2022.2022.1505396397