Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary Bags of Space-Time Features for Human Analysis

The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. In computer vision, a very common representation has adopted the form of the well-known Bag of Visual Words. This representation appears implicitly in most approaches where images are described, and is also present in a huge number of areas and domains: image content retrieval, pedestrian detection, human-computer interaction, surveillance, e-health, and social computing, amongst others. The early stages of this dissertation provide an approach for learning visual representations inside evolutionary algorithms, which consists of evolving weighting schemes to improve the BoVW representations for the task of recognizing categories of videos and images. Thus, we demonstrate the applicability of the most common weighting schemes, which are often used in text mining but are less frequently found in computer vision tasks. Beyond learning these visual representations, we provide an approach based on fusion strategies for learning spatiotemporal representations, from multimodal data obtained by depth sensors. Besides, we specially aim at the evolutionary and dynamic modelling, where the temporal factor is present in the nature of the data, such as video sequences of gestures and actions. Indeed, we explore the effects of probabilistic modelling for those approaches based on dynamic programming, so as to handle the temporal deformation and variance amongst video sequences of different categories. Finally, we integrate dynamic programming and generative models into an evolutionary computation framework, with the aim of learning Bags of SubGestures (BoSG) representations and hence to improve the generalization capability of standard gesture recognition approaches. The results obtained in the experimentation demonstrate, first, that evolutionary algorithms are useful for improving the representation of BoVW approaches in several datasets for recognizing categories in still images and video sequences. On the other hand, our experimentation reveals that both, the use of dynamic programming and generative models to align video sequences, and the representations obtained from applying fusion strategies in multimodal data, entail an enhancement on the performance when recognizing some gesture categories. Furthermore, the combination of evolutionary algorithms with models based on dynamic programming and generative approaches results, when aiming at the classification of video categories on large video datasets, in a considerable improvement over standard gesture and action recognition approaches. Finally, we demonstrate the applications of these representations in several domains for human analysis: classification of images where humans may be present, action and gesture recognition for general applications, and in particular for conversational settings within the field of restorative justice.
Efficient Brain Tumor Detection Using Wavelet Transform

Brain tumor detection is a challenging task and its very important to analyze the structure of the tumor correctly so a automatic method is used now a days for the detection of the tumor. This method saves time as well as it reduces the error which occurs in the method of manual detection. In this paper the tumor is detected using wavelet transform. MRI is an important tool used in many fields of medicine and is capable of generating a detailed image of any part of the human body. The tumor is segmented from the MRI images, features are extracted and then the area of the tumor is determined. PNN can successfully handle the process of brain tumor classification.
Sequence Synchronisation in Chaos-based Direct Sequence Spread Spectrum Communication Systems

This thesis presents an investigation into the modelling and performance of sequence synchronisation in Chaos-based Direct Sequence Spread Spectrum (CDS-SS) communication systems with the aims of identifying the existing gaps in research and suggesting a new and accurate modelling approach. The CDS-SS systems offer physical layer security without the need for a significant increase in computation or power requirements. This is significant when conventional encryption techniques cannot be used on secure communication systems because of their high power requirements as well as computational intensity. The main focus of this thesis is the problem of synchronising chaos-based spreading sequences. The modelling approaches previously used for chaos-based spreading codes do not take their non-binary nature into account and are not accurate as a result. This thesis extends the existing analysis in the literature to include CDS-SS synchronisation in presence of channel fading. Subsequently, an accurate approach is developed for modelling the synchronisation block of a CDS-SS system. This approach is based on the statistical description of chaos-based spreading codes crossand auto-correlation functions and is termed chaos correlation statistics (CCS). To verify the validity of this approach, the acquisition stage of a CDS-SS synchronisation block is modelled using three scenarios: noise, fading and interleaving. It has been shown that the CCS method makes an accurate prediction of the acquisition phase performance for all three scenarios considered. The CCS method is also used to model a CDS-SS tracking loop statistically with high accuracy as verified by comparison with numerical analysis results. The effects of tracking loop errors and timing jitter on the probability of error have been derived for the same three scenarios mentioned above. These newly derived probability of error equations are verified by extensive simulation results. Overall it is concluded that sequence synchronisation for CDS-SS systems is possible and the CCS method is a suitable tool for accurate prediction of synchronisation performance in CDS-SS systems. Also, the CCS approach can be used in any scenario in which accurate modelling of the correlation function of non-binary spreading sequences is needed.
Methodological Guidelines for Modelling and Design of Embedded Systems

The development of heterogeneous embedded systems is a demanding discipline. Technical challenges arise from the need to develop complex, featurerich products that take the constraints of the physical world into account. This thesis shows how a modelling approach to embedded systems development can address some of these challenges. Methodological guidelines supporting various levels of modelling fidelity are presented. These range from: mono-disciplinary modelling, where the embedded controller as well as its environment are modelled in a single formalism, to multi-disciplinary modelling where separate formalisms are used to describe the controller and environment. The use of the guidelines is demonstrated by means of several industrial case studies from the electronic warfare domain. To support the project management aspect of using a modelling approach to embedded systems development, the integration of formal modelling and agile methods is described. The result is a collection of lightweight methodological guidelines which can be integrated into industry strength development processes.
Ensuring Uniformity in Random Peer Sampling Services

The peer sampling service is a core building block for gossip protocols in peer-to-peer networks. Ideally, a peer sampling service continuously provides each peer with a sample of peers picked uniformly at random in the network. While empirical studies have shown that uniformity was achieved, analysis proposed so far assume strong restrictions on the topology of the overlay network it continuously generates. In this work, we analyze a Generic Random Peer Sampling Service (GRPS) that satisfies the desirable properties for any peer sampling service –small views, uniform sample, load balancing, and independence– and relieve strong degree connections in the nodes assumed in previous works. The main result we prove is: starting from any simple (without loops and parallel edges) directed graph with out-degree equal to c for all nodes, and recursively applying GRPS, eventually results in a random simple directed graph with out-degree equal to c for all nodes. We test empirically convergence time and independence time for GRPS. We use this empirical evaluation to show that GRPS performs better than previously presented peer sampling services. We also present a variant of GRPS that ensures that the in and out-degrees of nodes in the initial network are maintained in the resulting graph. Finally, we discuss on how to deal with new nodes in both settings.
Implementation and Evaluation of Dynamic Level of Audio Detail

Sound synthesis involves creating a desired sound using software or algorithms and analysing the digital signal processing involved in the creation of the sound, rather than recording it. However, synthesis techniques are often too computationally complex for use in many scenarios. This project aims to implement and assess sound synthesis models with dynamic Level of Audio Detail (LOAD). The manipulations consist of modifying existing models to achieve a more or less complex implementation while still retaining the perceptual characteristics of the sound. The models implemented consist of sine waves, noise sources and filters that reproduce the desired sound, which could then be enhanced or reduced to provide dynamic LOAD. These different levels were then analysed in the time-frequency domain, and computational time and floating point operations were assessed as a function of the LOAD.
Qualitative assessment of knowledge transfer regarding preterm birth in Malawi following the implementation of targeted health messages over 3 years

Background In 2012, we performed a needs assessment and gap analysis to qualitatively assess providers’ and patients’ knowledge and perceptions regarding preterm birth (PTB). During the study, we identified knowledge gaps surrounding methods to reduce the risk of occurrence of PTB and management options if preterm labor/birth occur. We targeted health messages toward these gaps. The objective of the present study was to assess the impact of our community health worker-based patient education program 3 years after it was implemented. Methods Fifteen focus groups including 70 participants were included in the study. The groups comprised either patients/patient couples or health providers. A minimum of two facilitators led each group using 22 a priori designed and standardized lead-in prompts for participants with four additional prompts for providers only. A single researcher recorded responses, and transcript notes were reviewed by the facilitators and interpreters immediately following each group discussion to ensure accuracy. Results The understanding of term vs preterm gestation was generally accurate. Every participant knew of women who had experienced PTB, and the general perception was that two to three women out of every ten had this experience. The majority of respondents thought that women should present to their local health clinic if they experience preterm contractions; few were aware of the use of antenatal steroids for promoting fetal lung maturity, but many acknowledged that the neonate may be able to receive life-sustaining treatment if born at a higher level of care facility. The majority of participants were aware that PTB could recur in subsequent pregnancies. All respondents were able to list ways that women could potentially reduce the risk of PTB. Conclusion After employing targeted health messages, the majority of participants expressed improved understanding of the definition of PTB, methods to prevent risk of PTB, and management options for preterm labor or PTB.
Study on Method of Single Disease Cost Prediction Based on Clinical Pathway and CBR

Single disease cost estimation is one of the most important aspects in hospital cost management.This article analyzes the existing problems in the cost estimation of single disease,presents the method of single disease cost prediction based on clinical pathway and CBR(case-based reasoning). A CBR system is used for estimating the single disease cost of new case based on the most similar cased from the case database.It focuses on the organization model of the case database based on hospital information system(HIS)and the optimization of the feature weight based on simulating stealing algorithm,which can improve the efficiency and accuracy of case retrieval.Finally,an example is applied to verify the validity of the method,which can contribute a lot to the hospital cost management.
BUI: BROWSER USER INTERFACE AND EXTENSIONS WITH CLOUD STORAGE

BUI: Browser User Interface. BUI is a technology which unifies the desktop environment inside the browser. Implementing such an interface will radically alter the desktop environment. First and foremost, the concept of a separate browser running on a desktop is eliminated. In fact, the browser will be the desktop. The need for having browser as a separate application running on the desktop is eliminated and the browser is made the desktop itself. Such a technology will provide a better access to the internet and better management of work on and off-line. The Browser User Interface will be easier to implement and provides easy use to all its users. This technology basks on the growth of the browsers. The BUI based operating system is perfect for organizations which involves cloud computing for storage. BUI eradicates the explicit connection to the internet and automatically connects to the net while the OS program is loaded. BUI marks the future of the operating systems using browser as desktop interface.
Shared Memory Based RDD Data Sharing on Spark

Apache Spark is an increasingly popular fast big data analytics engine, which focuses on a large-scale data processing. Currently, Spark's memory management mainly oriented to single application, and do not directly support the typical scenarios of multiple data processing applications. In this paper, we propose an extension of Apache Spark, called Shared Memory Spark (SMSpark). SMSpark introduces shared memory based on RDD data sharing between applications. We conducted experiment in a cluster using two typical applications. Experimental results show that compared to Spark, SMSpark gains better performance.
The set of test tasks assessing special physical fitness of 17-year-old soccer players

Background: The objective of the research was to work out the set of test tasks assessing special phy- sical fitness of 17-year-old soccer players. Material/Methods: At first, on the basis of the analysis of the said issue in literature and the authors’ own coaching experience, 13 test tasks were selected. Next, reliability and accuracy of the chosen test tasks were examined on the group of 16 athletes from the Centre of Sports training for youth in Gdansk. The reliability was assessed by the test-re-test method and the accuracy according to a criterion of internal accuracy tests. Except for one task, all efficiency tests met scientific requirements for motor tests. Finally, with the use of statistical analysis (linear regression equation, Hellwig’s algorithm of the optimal choice of explaining variables), four tests implementing most information for the assessment of game-play were chosen. Results: A four-task set of tests assessing selected components of physical fitness reflected 86.7% of the game-play of young competitors. Conclusions: Results of special physical fitness tests should be treated as important information about young athletes’ special physical efficiency, closely interdependent with the effectiveness of their activity in a classified game-play, but only when the tests themselves have previo - usly been scientifically proved. words:
Reconstruction-free Inference from Compressive Measurements by Suhas

As a promising solution to the problem of acquiring and storing large amounts of image and video data, spatial-multiplexing camera architectures have received lot of attention in the recent past. Such architectures have the attractive feature of combining a two-step process of acquisition and compression of pixel measurements in a conventional camera, into a single step. A popular variant is the single-pixel camera that obtains measurements of the scene using a pseudo-random measurement matrix. Advances in compressive sensing (CS) theory in the past decade have supplied the tools that, in theory, allow near-perfect reconstruction of an image from these measurements even for sub-Nyquist sampling rates. However, current state-of-the-art reconstruction algorithms suffer from two drawbacks – They are (1) computationally very expensive and (2) incapable of yielding high fidelity reconstructions for high compression ratios. In computer vision, the final goal is usually to perform an inference task using the images acquired and not signal recovery. With this motivation, this thesis considers the possibility of inference directly from compressed measurements, thereby obviating the need to use expensive reconstruction algorithms. It is often the case that non-linear features are used for inference tasks in computer vision. However, currently, it is unclear how to extract such features from compressed measurements. Instead, using the theoretical basis provided by the Johnson-Lindenstrauss lemma, discriminative features using smashed correlation filters are derived and it is shown that it is indeed possible to perform reconstruction-free inference at high compression ratios with only a marginal loss in accuracy. As a specific inference problem in computer vision, face recognition is considered, mainly beyond the visible spectrum such as in the short wave infra-red region (SWIR), where sensors are expensive.
EFFECTS OF SOLID STATE FERMENTATION BY Monascus purpureus ON PHENOLIC CONTENT AND BIOLOGICAL ACTIVITIES OF COCONUT TESTA AND RICE BRAN

Solid-state fermentation (SSF) is an alternative low cost useful process that has many important applications in the field of biotechnology. In this study, SSF has been employed as a process for the production of value-added agricultural by-product using coconut testa (CT), rice bran (RB) and the combination of both substrates (CT-RB). The effect of SSF by Monascus purpureus on total phenolic content (TPC), antioxidant, anti-tyrosinase and anti-elastase of the substrates were studied and compared with its non-fermented counterparts. The results showed that the SSF has improved the TPC up to three-fold higher in the studied substrates. Antioxidant potential evaluated using FRAP analysis also exhibited an enhancement in fermented substrates with the values ranging from 23.70 to 63.15 mg AAE/g sample. On the other hand, the radical scavenging activity evaluated using DPPH assay showed a different trend in comparison to the TPC and FRAP analyses. In another two analyses, tyrosinase and elastase inhibition activities were also enhanced in most substrates upon the fermentation. The changes in free phenolic acids content (p-coumaric, caffeic, ferulic, sinapic, vanillic, protocatechuic, gallic and 4-hydroxybenzoic and syringic acid) of the substrates after fungal fermentation was also examined through high performance liquid chromatography (HPLC) analysis. In summary, SSF offers a tool to further increase the bioactive potential of the studied substrate.
Evolutionary Learning of Goal-Oriented Communication Strategies in Multi-Agent Systems

obtain the corresponding communication strategy. Therefore, the system designer does not need to know or analyze the properties of each information instance and its effect on the performance goal of the system. This can eliminate a significant design task in developing a multi-agent system. Moreover, the proposed approach can assist system designers to find Abstract: Previous studies in multi-agent systems have observed that varying the type of information that agents communicate, such as goals and beliefs, has a significant impact on the performance of the system with respect to different, usually conflicting, performance metrics, such as speed of solution, communication efficiency, and travel distance/cost. Therefore, when designing a communication strategy for a multi-agent system, it is un-likely that one strategy can perform well with respect to all of performance metrics. Yet, it is not clear in advance, which strategy will be the best with respect to each metric. With multi-agent systems being a common paradigm for building distributed systems in different domains, performance goals can vary from one application to the other according to the domain’s specifications and re-quirements. To address this issue, this work proposes a genetic algorithm-based approach for learning a goal-oriented communication strategy. The approach enables learning an effective communication strategy with respect to flexible, user-defined measurable performance goals. The learned strategy will determine what, when, and to whom information should be communicated during the course of task execution in order to improve the performance of the system with respect to the stated goal. Our preliminary evaluation shows that the proposed approach has promising results and the learned strategies have significant usefulness in improving the performance of the system with respect to the goals.
A Welfare Model of Street Vendors: Cases from Denpasar, Bali, Indonesia

This study determines the socio-economic characteristics of street vendors in Denpasar, Bali, Indonesia and proposes a welfare model to examine their well-being. The results showed that street vendors in Denpasar are mostly male, married, with an average age of 39. Most are recent migrants who rent their housing. The majority sell food and beverages from carts and work almost 8 hours per day. Most are self-employed and may be assisted by family labor. Most do not do bookkeeping for their business activities and many are not registered businesses. Most have little access to capital and do not participate in empowerment programs in the form of education/training in business skills. Binary logistic regression analysis shows that the incomes of street vendors are related positively to marital status, age, employment status, physical facilities, and presence of bookkeeping.