Critical feature analysis of a radiotherapy machine

The software implementation of the emergency shutdown feature in a major radiotherapy system was analyzed, using a directed form of code review based on module dependences. Dependences between modules are labelled by particular assumptions; this allows one to trace through the code, and identify those fragments responsible for critical features. An `assumption tree' is constructed in parallel, showing the assumptions which each module makes about others. The root of the assumption tree is the critical feature of interest, and its leaves represent assumptions which, if not valid, might cause the critical feature to fail. The analysis revealed some unexpected assumptions that motivated improvements to the code.

A novel silicon membrane-based biosensing platform using distributive sensing strategy and artificial neural networks for feature analysis

A novel biosensing system based on a micromachined rectangular silicon membrane is proposed and investigated in this paper. A distributive sensing scheme is designed to monitor the dynamics of the sensing structure. An artificial neural network is used to process the measured data and to identify cell presence and density. Without specifying any particular bio-application, the investigation is mainly concentrated on the performance testing of this kind of biosensor as a general biosensing platform. The biosensing experiments on the microfabricated membranes involve seeding different cell densities onto the sensing surface of membrane, and measuring the corresponding dynamics information of each tested silicon membrane in the form of a series of frequency response functions (FRFs). All of those experiments are carried out in cell culture medium to simulate a practical working environment. The EA.hy 926 endothelial cell lines are chosen in this paper for the bio-experiments. The EA.hy 926 endothelial cell lines represent a particular class of biological particles that have irregular shapes, non-uniform density and uncertain growth behaviour, which are difficult to monitor using the traditional biosensors. The final predicted results reveal that the methodology of a neural-network based algorithm to perform the feature identification of cells from distributive sensory measurement has great potential in biosensing applications.

Feature analysis of spammers in social networks with active honeypots:a case study of Chinese microblogging networks

In this poster we presented our preliminary work on the study of spammer detection and analysis with 50 active honeypot profiles implemented on Weibo.com and QQ.com microblogging networks. We picked out spammers from legitimate users by manually checking every captured user's microblogs content. We built a spammer dataset for each social network community using these spammer accounts and a legitimate user dataset as well. We analyzed several features of the two user classes and made a comparison on these features, which were found to be useful to distinguish spammers from legitimate users. The followings are several initial observations from our analysis on the features of spammers captured on Weibo.com and QQ.com. ¦The following/follower ratio of spammers is usually higher than legitimate users. They tend to follow a large amount of users in order to gain popularity but always have relatively few followers. ¦There exists a big gap between the average numbers of microblogs posted per day from these two classes. On Weibo.com, spammers post quite a lot microblogs every day, which is much more than legitimate users do; while on QQ.com spammers post far less microblogs than legitimate users. This is mainly due to the different strategies taken by spammers on these two platforms. ¦More spammers choose a cautious spam posting pattern. They mix spam microblogs with ordinary ones so that they can avoid the anti-spam mechanisms taken by the service providers. ¦Aggressive spammers are more likely to be detected so they tend to have a shorter life while cautious spammers can live much longer and have a deeper influence on the network. The latter kind of spammers may become the trend of social network spammer. © 2012 IEEE.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

Intestinal content detection in capsule endoscopy using robust features

This work covers two aspects. First, it generally compares and summarizes the similarities and differences of state of the art feature detector and descriptor and second it presents a novel approach of detecting intestinal content (in particular bubbles) in capsule endoscopy images. Feature detectors and descriptors providing invariance to change of perspective, scale, signal-noise-ratio and lighting conditions are important and interesting topics in current research and the number of possible applications seems to be numberless. After analysing a selection of in the literature presented approaches, this work investigates in their suitability for applications information extraction in capsule endoscopy images. Eventually, a very good performing detector of intestinal content in capsule endoscopy images is presented. A accurate detection of intestinal content is crucial for all kinds of machine learning approaches and other analysis on capsule endoscopy studies because they occlude the field of view of the capsule camera and therefore those frames need to be excluded from analysis. As a so called “byproduct” of this investigation a graphical user interface supported Feature Analysis Tool is presented to execute and compare the discussed feature detectors and descriptor on arbitrary images, with configurable parameters and visualized their output. As well the presented bubble classifier is part of this tool and if a ground truth is available (or can also be generated using this tool) a detailed visualization of the validation result will be performed.

Organisaation tiedonhallinta - Kodifiointi ja henkilöltä henkilölle -strategia

Työssä tarkastellaan kahta vaihtoehtoista tiedonhallinta-strategiaa; kodifiointia ja henkilöltä henkilölle -strategiaa. Molempia strategioita analysoidaan, ja pohditaan organisaation päätöksentekoperusteita strategian valinnalle. Kodifiointi-strategiaa tutkitaan henkilöltä henkilölle -strategiaa perusteellisemmin. Strategioita tutkitaan muun muassa SWOT -analyysin avulla, sekä organisaation että käsiteltävän tiedon erityispiirteitä silmällä pitäen. Työssä tutustutaan myös tiedonhallinta-strategian suunnitteluun ja toteutukseen, sekä hahmotellaan tiedonhallinta-strategian evoluutiota. Myös tietoa ja sen luonnetta tarkastellaan yhtenä organisaation tärkeimmistä resursseista.

Intervention orthophonique et neurobiologie du cerveau : apports de la neuroimagerie à la prise en charge de l’aphasie chronique

L’aphasie est un trouble acquis du langage entraînant des problèmes de communication pouvant toucher la compréhension et/ou l’expression. Lorsque l’aphasie fait suite à un accident vasculaire cérébral, une régression des déficits communicatifs s'observe initialement, mais elle peut demeurer sévère pour certains et est considérée chronique après un an. Par ailleurs, l’aphasie peut aussi être observée dans l’aphasie progressive primaire, une maladie dégénérative affectant uniquement le langage dans les premières années. Un nombre grandissant d’études s’intéressent à l’impact de la thérapie dans l’aphasie chronique et ont démontré des améliorations langagières après plusieurs années. L’hémisphère gauche semble avoir un rôle crucial et est associé à de meilleures améliorations langagières, mais la compréhension des mécanismes de plasticité cérébrale est encore embryonnaire. Or, l’efficacité de la thérapie dans l’aphasie progressive primaire est peu étudiée. À l’aide de la résonance magnétique fonctionnelle, le but des présentes études consiste à examiner les mécanismes de plasticité cérébrale induits par la thérapie Semantic Feature Analysis auprès de dix personnes souffrant d’aphasie chronique et d’une personne souffrant d’aphasie progressive primaire. Les résultats suggèrent que le cerveau peut se réorganiser plusieurs années après une lésion cérébrale ainsi que dans une maladie dégénérative. Au niveau individuel, une meilleure amélioration langagière est associée au recrutement de l’hémisphère gauche ainsi qu’une concentration des activations. Les analyses de groupe mettent en évidence le recrutement du lobule pariétal inférieur gauche, alors que l’activation du gyrus précentral gauche prédit l’amélioration suite à la thérapie. D’autre part, les analyses de connectivité fonctionnelle ont permis d’identifier pour la première fois le réseau par défaut dans l’aphasie. Suite à la thérapie, l’intégration de ce réseau bien connu est comparable à celle des contrôles et les analyses de corrélation suggèrent que l’intégration du réseau par défaut a une valeur prédictive d’amélioration. Donc, les résultats de ces études appuient l’idée que l’hémisphère gauche a un rôle prépondérant dans la récupération de l’aphasie et fournissent des données probantes sur la neuroplasticité induite par une thérapie spécifique du langage dans l’aphasie. De plus, l’identification d’aires clés et de réseaux guideront de futures recherches afin d’éventuellement maximiser la récupération de l’aphasie et permettre de mieux prédire le pronostic.

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

An Experimental Study of Submerged Entry Nozzles (SEN) Focusing on Decarburization and Clogging

The submerged entry nozzle (SEN) is used to transport the molten steel from a tundish to a mould. The main purpose of its usage is to prevent oxygen and nitrogen pick-up by molten steel from the gas. Furthermore, to achieve the desired flow conditions in the mould. Therefore, the SEN can be considered as a vital factor for a stable casting process and the steel quality. In addition, the steelmaking processes occur at high temperatures around 1873 K, so the interaction between the refractory materials of the SEN and molten steel is unavoidable. Therefore, the knowledge of the SEN behaviors during preheating and casting processes is necessary for the design of the steelmaking processes  The internal surfaces of modern SENs are coated with a glass/silicon powder layer to prevent the SEN graphite oxidation during preheating. The effects of the interaction between the coating layer and the SEN base refractory materials on clogging were studied. A large number of accretion samples formed inside alumina-graphite clogged SENs were examined using FEG-SEM-EDS and Feature analysis. The internal coated SENs were used for continuous casting of stainless steel grades alloyed with Rare Earth Metals (REM). The post-mortem study results clearly revealed the formation of a multi-layer accretion. A harmful effect of the SENs decarburization on the accretion thickness was also indicated. In addition, the results indicated a penetration of the formed alkaline-rich glaze into the alumina-graphite base refractory. More specifically, the alkaline-rich glaze reacts with graphite to form a carbon monoxide gas. Thereafter, dissociation of CO at the interface between SEN and molten metal takes place. This leads to reoxidation of dissolved alloying elements such as REM (Rare Earth Metal). This reoxidation forms the “In Situ” REM oxides at the interface between the SEN and the REM alloyed molten steel. Also, the interaction of the penetrated glaze with alumina in the SEN base refractory materials leads to the formation of a high-viscous alumina-rich glaze during the SEN preheating process. This, in turn, creates a very uneven surface at the SEN internal surface. Furthermore, these uneven areas react with dissolved REM in molten steel to form REM aluminates, REM silicates and REM alumina-silicates. The formation of the large “in-situ” REM oxides and the reaction of the REM alloying elements with the previously mentioned SEN´s uneven areas may provide a large REM-rich surface in contact with the primary inclusions in molten steel. This may facilitate the attraction and agglomeration of the primary REM oxide inclusions on the SEN internal surface and thereafter the clogging. The study revealed the disadvantages of the glass/silicon powder coating applications and the SEN decarburization. The decarburization behaviors of Al2O3-C, ZrO2-C and MgO-C refractory materials from a commercial Submerged Entry Nozzle (SEN), were also investigated for different gas atmospheres consisting of CO2, O2 and Ar. The gas ratio values were kept the same as it is in a propane combustion flue gas at different Air-Fuel-Ratio (AFR) values for both Air-Fuel and Oxygen-Fuel combustion systems. Laboratory experiments were carried out under nonisothermal conditions followed by isothermal heating. The decarburization ratio (α) values of all three refractory types were determined by measuring the real time weight losses of the samples. The results showed the higher decarburization ratio (α) values increasing for MgO-C refractory when changing the Air-Fuel combustion to Oxygen-Fuel combustion at the same AFR value. It substantiates the SEN preheating advantage at higher temperatures for shorter holding times compared to heating at lower temperatures during longer holding times for Al2O3-C samples. Diffusion models were proposed for estimation of the decarburization rate of an Al2O3-C refractory in the SEN. Two different methods were studied to prevent the SEN decarburization during preheating: The effect of an ZrSi2 antioxidant and the coexistence of an antioxidant additive and a (4B2O3 ·BaO) glass powder on carbon oxidation for non-isothermal and isothermal heating conditions in a controlled atmosphere. The coexistence of 8 wt% ZrSi2 and 15 wt% (4B2O3 ·BaO) glass powder of the total alumina-graphite refractory base materials, presented the most effective resistance to carbon oxidation. The 121% volume expansion due to the Zircon formation during heating and filling up the open pores by a (4B2O3 ·BaO) glaze during the green body sintering led to an excellent carbon oxidation resistance. The effects of the plasma spray-PVD coating of the Yttria Stabilized Zirconia (YSZ) powder on the carbon oxidation of the Al2O3-C coated samples were investigated. Trials were performed at non-isothermal heating conditions in a controlled atmosphere. Also, the applied temperature profile for the laboratory trials were defined based on the industrial preheating trials. The controlled atmospheres consisted of CO2, O2 and Ar. The thicknesses of the decarburized layers were measured and examined using light optic microscopy, FEG-SEM and EDS. A 250-290 μm YSZ coating is suggested to be an appropriate coating, as it provides both an even surface as well as prevention of the decarburization even during heating in air. In addition, the interactions between the YSZ coated alumina-graphite refractory base materials in contact with a cerium alloyed molten stainless steel were surveyed. The YSZ coating provided a total prevention of the alumina reduction by cerium. Therefore, the prevention of the first clogging product formed on the surface of the SEN refractory base materials. Therefore, the YSZ plasma-PVD coating can be recommended for coating of the hot surface of the commercial SENs.

Modeling Features at Runtime

A feature represents a functional requirement fulfilled by a system. Since many maintenance tasks are expressed in terms of features, it is important to establish the correspondence between a feature and its implementation in source code. Traditional approaches to establish this correspondence exercise features to generate a trace of runtime events, which is then processed by post-mortem analysis. These approaches typically generate large amounts of data to analyze. Due to their static nature, these approaches do not support incremental and interactive analysis of features. We propose a radically different approach called live feature analysis, which provides a model at runtime of features. Our approach analyzes features on a running system and also makes it possible to grow feature representations by exercising different scenarios of the same feature, and identifies execution elements even to the sub-method level. We describe how live feature analysis is implemented effectively by annotating structural representations of code based on abstract syntax trees. We illustrate our live analysis with a case study where we achieve a more complete feature representation by exercising and merging variants of feature behavior and demonstrate the efficiency or our technique with benchmarks.