Editorial: Language Representation and Learning in Cognitive and Artificial Intelligence Systems

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Conceptual development from the perspective of a brain-inspired robotic architecture

Concepts are central to reasoning and intelligent behaviour. Scientific evidence shows that conceptual development is fundamental for the emergence of high-cognitive phenomena. Here, we model such phenomena in a brain-inspired cognitive robotic model and examine how the robot can learn, categorise, and abstract concepts to voluntary control behaviour. The paper argues that such competence arises with sufficient conceptual content from physical and social experience. Hence, senses, motor abilities and language, all contribute to a robot's intelligent behaviour. To this aim, we devised a method for attaining concepts, which computationally reproduces the steps of the inductive thinking strategy of the Concept Attainment Model (CAM). Initially, the robot is tutor-guided through socio-centric cues to attain concepts and is then tested consistently to use these concepts to solve complex tasks. We demonstrate how the robot uses language to create new categories by abstraction in response to human language-directed instructions. Linguistic stimuli also change the representations of the robot's experiences and generate more complex representations for further concepts. Most notably, this work shows that this competence emerges by the robot's ability to understand the concepts similarly to human understanding. Such understanding was also maintained when concepts were expressed in multilingual lexicalisations showing that labels represent concepts that allowed the model to adapt to unfamiliar contingencies in which it did not have directly related experiences. The work concludes that language is an essential component of conceptual development, which scaffolds the cognitive continuum of a robot from low-to-high cognitive skills, including its skill to understand

Medical Systems Data Security and Biometric Authentication in Public Cloud Servers

Advances in distributed computing and virtualization allowed cloud computing to establish itself as a popular data management and storage option for organizations. However, unclear safeguards, practices, as well as the evolution of legislation around privacy and data protection, contribute to data security being one of the main concerns in adopting this paradigm. Another important aspect hindering the absolute success of cloud computing is the ability to ensure the digital identity of users and protect the virtual environment through logical access controls while avoiding the compromise of its authentication mechanism or storage medium. Therefore, this paper proposes a system that addresses data security wherein unauthorized access to data stored in a public cloud is prevented by applying a fragmentation technique and a NoSQL database. Moreover, a system for managing and authenticating users with multimodal biometrics is also suggested along with a mechanism to ensure the protection of biometric features. When compared with encryption, the proposed fragmentation method indicates better latency performance, highlighting its strong potential use-case in environments with lower latency requirements such as the healthcare IT infrastructure

A New automatic system of cell colony counting

The counting process of cell colonies is always a long and laborious process that is dependent on the judgment and ability of the operator. The judgment of the operator in counting can vary in relation to fatigue. Moreover, since this activity is time consuming it can limit the usable number of dishes for each experiment. For these purposes, it is necessary that an automatic system of cell colony counting is used. This article introduces a new automatic system of counting based on the elaboration of the digital images of cellular colonies grown on petri dishes. This system is mainly based on the algorithms of region-growing for the recognition of the regions of interest (ROI) in the image and a Sanger neural net for the characterization of such regions. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and confronted with the K-Nearest Neighbour (K-NN) and a Linear Discriminative Function (LDF). The preliminary results are shown

New Techniques in Diagnostic X-ray Imaging: A Simulation Tool and Experimental Findings

Abstract Absorption X-ray imaging is a well-established technique. However it is still a challenging task in its search for a compromise between the need for high spatial resolution and high contrast and the request to keep the dose delivered to the patient within acceptable values. New imaging techniques are under investigation, like the use of new X-ray sources, phase contrast imaging or K-edge imaging. Monte Carlo or analytic simulations are often the best way to test and predict the effectiveness of these techniques. A new simulation tool for X-ray imaging will be presented together with some applications to the characterization of new X-ray sources, in-line phase contrast effect and angiographic K-edge imaging. Simulation results will be compared also with experimental dat

Friendly but Faulty: A Pilot Study on the Perceived Trust of Older Adults in a Social Robot

The efforts to promote ageing-in-place of healthy older adults via cybernetic support are fundamental to avoid possible consequences associated with relocation to facilities, including the loss of social ties and autonomy, and feelings of loneliness. This requires an understanding of key factors that affect the involvement of robots in eldercare and the elderly willingness to embrace the robots’ domestic use. Trust is argued to be the main foundation of an effective adult-care provider, which might be more significant if such providers are robots. Establishing, and maintaining trust usually involves two main dimensions: 1) the robot’s reliability (i.e., performance) and 2) the robot’s intrinsic attributes, including its degree of anthropomorphism and benevolence. We conducted a pilot study using a mixed methods approach to explore the extent to which these dimensions and their interaction influenced elderly trust in a humanoid social robot. Using two independent variables, type of attitude (warm, cold) and type of conduct (error, no-error), we aimed to investigate if the older adult participants would trust a purposefully faulty robot when the robot exerted a warm behaviour enhanced with non-functional touch more than a robot that did not, and in what way the robot error affected trust. Lastly, we also investigated the relationship between trust and a proxy variable of actual use of robots (i.e., intention to use robots at home ). Given the volatile and context-dependent nature of trust, our close-to real-world scenario of elder-robot interaction involved the administration of health supplements, in which the severity of robot error might have a greater implication on the perceived trust

Vertebral Augmentation: Is It Time to Get Past the Pain? A Consensus Statement from the Sardinia Spine and Stroke Congress

Vertebral augmentation has been used to treat painful vertebral compression fractures and metastatic lesions in millions of patients around the world. An international group of subject matter experts have considered the evidence, including but not limited to mortality. These considerations led them to ask whether it is appropriate to allow the subjective measure of pain to so dominate the clinical decision of whether to proceed with augmentation. The discussions that ensued are related below

Pattern Recognition Techniques Applied To Biomedical Patterns

Abstract—Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects. Keywords—Computer Aided Detection, mammary tumor, pattern recognition, dissimilarit