A Semantic-Based Framework for Summarization and Page Segmentation in Web Mining

This chapter addresses two crucial issues that arise when one applies Web-mining techniques for extracting relevant information. The first one is the acquisition of useful knowledge from textual data; the second issue stems from the fact that a web page often proposes a considerable amount of \u2018noise\u2019 with respect to the sections that are truly informative for the user's purposes. The novelty contribution of this work lies in a framework that can tackle both these tasks at the same time, supporting text summarization and page segmentation. The approach achieves this goal by exploiting semantic networks to map natural language into an abstract representation, which eventually supports the identification of the topics addressed in a text source. A heuristic algorithm uses the abstract representation to highlight the relevant segments of text in the original document. The verification of the approach effectiveness involved a publicly available benchmark, the DUC 2002 dataset, and satisfactory results confirmed the method effectiveness

An approximate randomization-based neural network with dedicated digital architecture for energy-constrained devices

Variable energy constraints affect the implementations of neural networks on battery-operated embedded systems. This paper describes a learning algorithm for randomization-based neural networks with hard-limit activation functions. The approach adopts a novel cost function that balances accuracy and network complexity during training. From an energyspecific perspective, the new learning strategy allows to adjust, dynamically and in real time, the number of operations during the network’s forward phase. The proposed learning scheme leads to efficient predictors supported by digital architectures. The resulting digital architecture can switch to approximate computing at run time, in compliance with the available energy budget. Experiments on 10 real-world prediction testbeds confirmed the effectiveness of the learning scheme. Additional tests on limited-resource devices supported the implementation efficiency of the overall design approac

Hardware-Aware Affordance Detection for Application in Portable Embedded Systems

Affordance detection in computer vision allows segmenting an object into parts according to functions that those parts afford. Most solutions for affordance detection are developed in robotics using deep learning architectures that require substantial computing power. Therefore, these approaches are not convenient for application in embedded systems with limited resources. For instance, computer vision is used in smart prosthetic limbs, and in this context, affordance detection could be employed to determine the graspable segments of an object, which is a critical information for selecting a grasping strategy. This work proposes an affordance detection strategy based on hardware-aware deep learning solutions. Experimental results confirmed that the proposed solution achieves comparable accuracy with respect to the state-of-the-art approaches. In addition, the model was implemented on real-time embedded devices obtaining a high FPS rate, with limited power consumption. Finally, the experimental assessment in realistic conditions demonstrated that the developed method is robust and reliable. As a major outcome, the paper proposes and characterizes the first complete embedded solution for affordance detection in embedded devices. Such a solution could be used to substantially improve computer vision based prosthesis control but it is also highly relevant for other applications (e.g., resource-constrained robotic systems)

Vibration Monitoring in the Compressed Domain with Energy-Efficient Sensor Networks

Structural Health Monitoring (SHM) is crucial for the development of safe infrastructures. Onboard vibration diagnostics implemented by means of smart embedded sensors is a suitable approach to achieve accurate prediction supported by low-cost systems. Networks of sensors can be installed in isolated infrastructures allowing periodic monitoring even in the absence of stable power sources and connections. To fulfill this goal, the present paper proposes an effective solution based on intelligent extreme edge nodes that can sense and compress vibration data onboard, and extract from it a reduced set of statistical descriptors that serve as input features for a machine learning classifier, hosted by a central aggregating unit. Accordingly, only a small batch of meaningful scalars needs to be outsourced in place of long time series, hence paving the way to a considerable decrement in terms of transmission time and energy expenditure. The proposed approach has been validated using a real-world SHM dataset for the task of damage identification from vibration signals. Results demonstrate that the proposed sensing scheme combining data compression and feature estimation at the sensor level can attain classification scores always above 94%, with a sensor life cycle extension up to 350x and 1510x if compared with compression-only and processing-free implementations, respectively

Embedded Electronic System Based on Dedicated Hardware DSPs for Electronic Skin Implementation

The effort to develop an electronic skin is highly motivated by many application domains namely robotics, biomedical instrumentations, and replacement prosthetic devices. Several e-skin systems have been proposed recently and have demonstrated the need of an embedded electronic system for tactile data processing either to mimic the human skin or to respond to the application demands. Processing tactile data requires efficient methods to extract meaningful information from raw sensors data. In this framework, our goal is the development of a dedicated embedded electronic system for electronic skin. The embedded electronic system has to acquire the tactile data, process and extract structured information. Machine Learning (ML) represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensors data. This paper presents an embedded electronic system based on dedicated hardware implementation for electronic skin systems. It provides a Tensorial kernel function implementation for machine learning based on Tensorial kernel approach. Results assess the time latency and the hardware complexity for real time functionality. The implementation results highlight the high amount of power consumption needed for the input touch modalities classification task. Conclusions and future perspectives are also presented

Growth of room temperature ferromagnetic Ge1-xMnx quantum dots on hydrogen passivated Si (100) surfaces

A method for the synthesis of room-temperature ferromagnetic dilute semiconductor Ge1-xMnx (5 % < x < 8 %) quantum dots by molecular beam epitaxy by selective growth on hydrogen terminated silicon (100) surface is presented. The functionalized substrates, as well as the nanostructures, were characterized in situ by reflection high-energy electron diffraction. The quantum dots density and equivalent radius were extracted from field emission scanning electron microscope pictures, obtained ex-situ. Magnetic characterizations were performed by superconducting quantum interference device vibrating sample magnetometry revealing that ferromagnetic order is maintained up to room temperature: two different ferromagnetic phases were identified by the analysis of the field cooled – zero field cooled measurements

Affordance segmentation of hand-occluded containers from exocentric images

Visual affordance segmentation identifies the surfaces of an object an agent can interact with. Common challenges for the identification of affordances are the variety of the geometry and physical properties of these surfaces as well as occlusions. In this paper, we focus on occlusions of an object that is hand-held by a person manipulating it. To address this challenge, we propose an affordance segmentation model that uses auxiliary branches to process the object and hand regions separately. The proposed model learns affordance features under hand-occlusion by weighting the feature map through hand and object segmentation. To train the model, we annotated the visual affordances of an existing dataset with mixed-reality images of hand-held containers in third-person (exocentric) images. Experiments on both real and mixed-reality images show that our model achieves better affordance segmentation and generalisation than existing models.Comment: Paper accepted to Workshop on Assistive Computer Vision and Robotics (ACVR) in International Conference on Computer Vision (ICCV) 2023; 10 pages, 4 figures, 2 tables. Data, code, and trained models are available at https://apicis.github.io/projects/acanet.htm

Enhancing Cyber Security of LoRaWAN Gateways under Adversarial Attacks

The Internet of Things (IoT) has disrupted the IT landscape drastically, and Long Range Wide Area Network (LoRaWAN) is one specification that enables these IoT devices to have access to the Internet. Former security analyses have suggested that the gateways in LoRaWAN in their current state are susceptible to a wide variety of malicious attacks, which can be notoriously difficult to mitigate since gateways are seen as obedient relays by design. These attacks, if not addressed, can cause malfunctions and loss of efficiency in the network traffic. As a solution to this unique problem, this paper presents a novel certificate authentication technique that enhances the cyber security of gateways in the LoRaWAN network. The proposed technique considers a public key infrastructure (PKI) solution that considers a two-tier certificate authority (CA) setup, such as a root-CA and intermediate-CA. This solution is promising, as the simulation results validate that about 66.67% of the packets that are arriving from an illegitimate gateway (GW) are discarded in our implemented secure and reliable solution

Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems CISIS’08

The International Workshop on Computational Intelligence for Security in Information Systems (CISIS) proposes a meeting ground to the various communities involved in building intelligent systems for security, namely: information security, data mining, adaptive learning methods and soft computing among others. The main goal is to allow experts and researchers to assess the benefits of learning methods in the data-mining area for information-security applications. The Workshop offers the opportunity to interact with the leading industries actively involved in the critical area of security, and have a picture of the current solutions adopted in practical domains. This volume of Advances in Soft Computing contains accepted papers presented at CISIS’08, which was held in Genova, Italy, on October 23rd-24th, 2008. The selection process to set up the Workshop program yielded a collection of about 40 papers. This allowed the Scientific Committee to verify the vital and crucial nature of the topics involved in the event, and resulted in an acceptance rate of about 60% of the originally submitted manuscripts

Neural projection techniques for the visual inspection of network traffic

A crucial aspect in network monitoring for security purposes is the visual inspection of the traffic pattern, mainly aimed to provide the network manager with a synthetic and intuitive representation of the current situation. Towards that end, neural projection techniques can map high-dimensional data into a low-dimensional space adaptively, for the user-friendly visualization of monitored network traffic. This work proposes two projection methods, namely, cooperative maximum likelihood Hebbian learning and auto-associative back-propagation networks, for the visual inspection of network traffic. This set of methods may be seen as a complementary tool in network security as it allows the visual inspection and comprehension of the traffic data internal structure. The proposed methods have been evaluated in two complementary and practical network-security scenarios: the on-line processing of network traffic at packet level, and the off-line processing of connection records, e.g. for post-mortem analysis or batch investigation. The empirical verification of the projection methods involved two experimental domains derived from the standard corpora for evaluation of computer network intrusion detection: the MIT Lincoln Laboratory DARPA dataset