IDENAS: Internal Dependency Exploration for Neural Architecture Search

Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional algorithms rely on well-defined input and output variables however, there are scenarios where the distinction between the input and output variables and the underlying, associated (input and output) layers of the model, are unknown. Neural Architecture Search (NAS) and Feature Selection have emerged as promising solutions in such scenarios. This research proposes IDENAS, an Internal Dependency-based Exploration for Neural Architecture Search, integrating NAS with feature selection. The methodology explores internal dependencies in the complete parameter space for classification involving 1D sensor and 2D image data as well. IDENAS employs a modified encoder-decoder model and the Sequential Forward Search (SFS) algorithm, combining input-output configuration search with embedded feature selection. Experimental results demonstrate IDENASs superior performance in comparison to other algorithms, showcasing its effectiveness in model development pipelines and automated machine learning. On average, IDENAS achieved significant modelling improvements, underscoring its significant contribution to advancing the state-of-the-art in neural architecture search and feature selection integration.Comment: 57 pages, 19 figures + appendix, the related software code can be found under the link: https://github.com/viharoszsolt/IDENA

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Identification and recognition of animals from biometric markers using computer vision approaches: a review

Although classic methods (such as ear tagging, marking, etc.) are generally used for animal identification and recognition, biometric methods have gained popularity in recent years due to the advantages they offer. Systems utilizing biometric markers have been developed for various purposes in animal management, including more effective and accurate tracking of animals, vaccination, disease management, and prevention of theft and fraud. Animals" irises, retinas, faces, muzzle, and body patterns contain unique biometric markers. The use of these markers in computer vision approaches for animal identification and tracking systems has become a highly effective and promising research area in recent years. This review aims to provide a general overview of the latest developments in image processing approaches for animal identification and recognition applications. In this review, we examined in detail all relevant studies we could access from different electronic databases for each biometric method. Afterward, the opportunities and challenges of classical and biometric methods were compared. We anticipate that this study, which conducts a literature review on animal identification and recognition based on computer vision approaches, will shed light on future research towards developing automated systems with biometric methods

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Contour and texture for visual recognition of object categories

The recognition of categories of objects in images has become a central topic in computer vision. Automatic visual recognition systems are rapidly becoming central to applications such as image search, robotics, vehicle safety systems, and image editing. This work addresses three sub-problems of recognition: image classification, object detection, and semantic segmentation. The task of classification is to determine whether an object of a particular category is present or not. Object detection aims to localize any objects of the category. Semantic segmentation is a more complete image understanding, whereby an image is partitioned into coherent regions that are assigned meaningful class labels. This thesis proposes novel discriminative learning approaches to these problems. Our primary contributions are threefold. Firstly, we demonstrate that the contours (the outline and interior edges) of an object are, alone, sufficient for accurate visual recognition. Secondly, we propose two powerful new feature types: (i) a learned codebook of contour fragments matched with an improved oriented chamfer distance, and (ii) a set of texture-based features that simultaneously exploit local appearance, approximate shape, and appearance context. The efficacy of these new features types is evaluated on a wide variety of datasets. Thirdly, we show how, in combination, these two largely orthogonal feature types can substantially improve recognition performance above that achieved by either alone

Equine clinics

The present final report concluding the degree of the Integrated Master in Veterinary Medicine at the University of Évora is based on the curricular externship in an outpatient clinic with Dr. An Sleeckx in the Greater Lisbon / Ribatejo area. This report is divided into two parts. The first part presents the casuistic of the externship and describes clinical cases like abdominal pain, lameness, pre-purchase exam, intoxication with monensin, insect bite hypersensitivity and castration which were followed during the externship. As painful events like lameness and colic are very common in equine clinics, a literature research on pain assessment in horses was made and presented in the second part as a monography. The focus was on new composite pain scales including behavior and facial expressions which seem to be the most reliable to detect pain according to the newest publications; Resumo: Clínica e Cirurgia de Equinos O presente relatório de conclusão do curso de Mestrado Integrado em Medicina Veterinária da Universidade de Évora é baseado no estágio curricular realizado em clínica ambulatória com a Dra. An Sleeckx na zona da Grande Lisboa/ Ribatejo. O relatório é dividido em duas partes. A primeira parte apresenta a casuística do estágio e descreve casos clínicos como dor abdominal, claudicações, acto de compra, intoxicação com monensina, hipersensibilidade à picada de insectos e castração, que foram acompanhados durante o estágio. Uma vez que eventos dolorosos como as claudicações e as cólicas são muito comuns nas clínicas equinas, foi feita uma pesquisa bibliográfica sobre avaliação da dor em cavalos, que foi apresentada na segunda parte como uma monografia. O foco foi em novas escalas composta de dor, incluindo comportamento e expressões faciais que parecem ser as mais fiáveis para detectar a dor de acordo com as mais recentes publicações

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions