Quality metrics for assessing security-critical computer programs

Existing secure software development principles tend to focus on coding vulnerabilities, such as buffer or integer overflows, that apply to individual program statements, or issues associated with the run-time environment, such as component isolation. Here we instead consider software security from the perspective of potential information flow through a program’s object-oriented module structure. In particular, we define a set of quantifiable "security metrics" which allow programmers to quickly and easily assess the overall security of a given source code program or object-oriented design. Although measuring quality attributes of object-oriented programs for properties such as maintainability and performance has been well-covered in the literature, metrics which measure the quality of information security have received little attention. Moreover, existing securityrelevant metrics assess a system either at a very high level, i.e., the whole system, or at a fine level of granularity, i.e., with respect to individual statements. These approaches make it hard and expensive to recognise a secure system from an early stage of development. Instead, our security metrics are based on well-established compositional properties of object-oriented programs (i.e., data encapsulation, cohesion, coupling, composition, extensibility, inheritance and design size), combined with data flow analysis principles that trace potential information flow between high- and low-security system variables. We first define a set of metrics to assess the security quality of a given object-oriented system based on its design artifacts, allowing defects to be detected at an early stage of development. We then extend these metrics to produce a second set applicable to object-oriented program source code. The resulting metrics make it easy to compare the relative security of functionallyequivalent system designs or source code programs so that, for instance, the security of two different revisions of the same system can be compared directly. This capability is further used to study the impact of specific refactoring rules on system security more generally, at both the design and code levels. By measuring the relative security of various programs refactored using different rules, we thus provide guidelines for the safe application of refactoring steps to security-critical programs. Finally, to make it easy and efficient to measure a system design or program’s security, we have also developed a stand-alone software tool which automatically analyses and measures the security of UML designs and Java program code. The tool’s capabilities are demonstrated by applying it to a number of security-critical system designs and Java programs. Notably, the validity of the metrics is demonstrated empirically through measurements that confirm our expectation that program security typically improves as bugs are fixed, but worsens as new functionality is added.

Using image quality metrics to detect smoke-affected laser data

Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.

Image, Video and 3D Data Registration: Medical, Satellite and Video Processing Applications with Quality Metrics

Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods.

This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state-of-the-art registration methodologies used in a variety of targeted applications.

Key features:
- Provides a state-of-the-art review of image and video registration techniques, allowing readers to develop an understanding of how well the techniques perform by using specific quality assessment criteria
- Addresses a range of applications from familiar image and video processing domains to satellite and medical imaging among others, enabling readers to discover novel methodologies with utility in their own research
- Discusses quality evaluation metrics for each application domain with an interdisciplinary approach from different research perspectives

Image, video and 3D data registration: medical, satellite and video processing applications with quality metrics

Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods. This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state–of–the–art registration methodologies used in a variety of targeted applications.

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our data analytics tool based on k-means clustering and on SSE, silhouette, Dunn index and Xi-Beni index quality metrics. Experiments performed on a dataset obtained from our online e-learning platform show that the extracted interaction patterns were representative in classifying learners. Furthermore, the performed monitoring activities created a strong basis for generating automatic feedback to learners in terms of their course participation, while relying on their previous performance. In addition, our analysis introduces automatic triggers that highlight learners who will potentially fail the course, enabling tutors to take timely actions.

Visual metrics for the evaluation of sensor data quality in outdoor perception

This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke. An example of application is given with monocular SLAM estimating the pose of the UGV while smoke is present in the environment. It is shown that the proposed novel quality metric can be used to anticipate situations where the quality of the pose estimate will be significantly degraded due to the input image data. This leads to decisions of advantageously switching between data sources (e.g. using infrared images instead of visual images).

Visual metrics for the evaluation of sensor data quality in outdoor perception

This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke.

Measuring and tracking quality factors in Free and Open Source Software projects

Free and Open Source Software (FOSS) has gained increased interest in the computer software industry, but assessing its quality remains a challenge. FOSS development is frequently carried out by globally distributed development teams, and all stages of development are publicly visible. Several product and process-level quality factors can be measured using the public data. This thesis presents a theoretical background for software quality and metrics and their application in a FOSS environment. Information available from FOSS projects in three information spaces are presented, and a quality model suitable for use in a FOSS context is constructed. The model includes both process and product quality metrics, and takes into account the tools and working methods commonly used in FOSS projects. A subset of the constructed quality model is applied to three FOSS projects, highlighting both theoretical and practical concerns in implementing automatic metric collection and analysis. The experiment shows that useful quality information can be extracted from the vast amount of data available. In particular, projects vary in their growth rate, complexity, modularity and team structure.

The role of complexity metrics in a multi-institutional dosimetry audit of VMAT

OBJECTIVE: To demonstrate the benefit of complexity metrics such as the modulation complexity score (MCS) and monitor units (MUs) in multi-institutional audits of volumetric-modulated arc therapy (VMAT) delivery.

METHODS: 39 VMAT treatment plans were analysed using MCS and MU. A virtual phantom planning exercise was planned and independently measured using the PTW Octavius(®) phantom and seven29(®) 2D array (PTW-Freiburg GmbH, Freiburg, Germany). MCS and MU were compared with the median gamma index pass rates (2%/2 and 3%/3 mm) and plan quality. The treatment planning systems (TPS) were grouped by VMAT modelling being specifically designed for the linear accelerator manufacturer's own treatment delivery system (Type 1) or independent of vendor for VMAT delivery (Type 2). Differences in plan complexity (MCS and MU) between TPS types were compared.

RESULTS: For Varian(®) linear accelerators (Varian(®) Medical Systems, Inc., Palo Alto, CA), MCS and MU were significantly correlated with gamma pass rates. Type 2 TPS created poorer quality, more complex plans with significantly higher MUs and MCS than Type 1 TPS. Plan quality was significantly correlated with MU for Type 2 plans. A statistically significant correlation was observed between MU and MCS for all plans (R = -0.84, p < 0.01).

CONCLUSION: MU and MCS have a role in assessing plan complexity in audits along with plan quality metrics. Plan complexity metrics give some indication of plan deliverability but should be analysed with plan quality.

ADVANCES IN KNOWLEDGE: Complexity metrics were investigated for a national rotational audit involving 34 institutions and they showed value. The metrics found that more complex plans were created for planning systems which were independent of vendor for VMAT delivery.

Using Provenance for Quality Assessment and Repair in Linked Open Data

As the number of data sources publishing their data on the Web of Data is growing, we are experiencing an immense growth of the Linked Open Data cloud. The lack of control on the published sources, which could be untrustworthy or unreliable, along with their dynamic nature that often invalidates links and causes conflicts or other discrepancies, could lead to poor quality data. In order to judge data quality, a number of quality indicators have been proposed, coupled with quality metrics that quantify the “quality level” of a dataset. In addition to the above, some approaches address how to improve the quality of the datasets through a repair process that focuses on how to correct invalidities caused by constraint violations by either removing or adding triples. In this paper we argue that provenance is a critical factor that should be taken into account during repairs to ensure that the most reliable data is kept. Based on this idea, we propose quality metrics that take into account provenance and evaluate their applicability as repair guidelines in a particular data fusion setting.

High definition video quality assessment metric built upon full reference ratios

Métrica de calidad de video de alta definición construida a partir de ratios de referencia completa. La medida de calidad de video, en inglés Visual Quality Assessment (VQA), es uno de los mayores retos por solucionar en el entorno multimedia. La calidad de vídeo tiene un impacto altísimo en la percepción del usuario final (consumidor) de los servicios sustentados en la provisión de contenidos multimedia y, por tanto, factor clave en la valoración del nuevo paradigma denominado Calidad de la Experiencia, en inglés Quality of Experience (QoE). Los modelos de medida de calidad de vídeo se pueden agrupar en varias ramas según la base técnica que sustenta el sistema de medida, destacando en importancia los que emplean modelos psicovisuales orientados a reproducir las características del sistema visual humano, en inglés Human Visual System, del que toman sus siglas HVS, y los que, por el contrario, optan por una aproximación ingenieril en la que el cálculo de calidad está basado en la extracción de parámetros intrínsecos de la imagen y su comparación. A pesar de los avances recogidos en este campo en los últimos años, la investigación en métricas de calidad de vídeo, tanto en presencia de referencia (los modelos denominados de referencia completa), como en presencia de parte de ella (modelos de referencia reducida) e incluso los que trabajan en ausencia de la misma (denominados sin referencia), tiene un amplio camino de mejora y objetivos por alcanzar. Dentro de ellos, la medida de señales de alta definición, especialmente las utilizadas en las primeras etapas de la cadena de valor que son de muy alta calidad, son de especial interés por su influencia en la calidad final del servicio y no existen modelos fiables de medida en la actualidad. Esta tesis doctoral presenta un modelo de medida de calidad de referencia completa que hemos llamado PARMENIA (PArallel Ratios MEtric from iNtrInsic features Analysis), basado en la ponderación de cuatro ratios de calidad calculados a partir de características intrínsecas de la imagen. Son: El Ratio de Fidelidad, calculado mediante el gradiente morfológico o gradiente de Beucher. El Ratio de Similitud Visual, calculado mediante los puntos visualmente significativos de la imagen a través de filtrados locales de contraste. El Ratio de Nitidez, que procede de la extracción del estadístico de textura de Haralick contraste. El Ratio de Complejidad, obtenido de la definición de homogeneidad del conjunto de estadísticos de textura de Haralick PARMENIA presenta como novedad la utilización de la morfología matemática y estadísticos de Haralick como base de una métrica de medida de calidad, pues esas técnicas han estado tradicionalmente más ligadas a la teledetección y la segmentación de objetos. Además, la aproximación de la métrica como un conjunto ponderado de ratios es igualmente novedosa debido a que se alimenta de modelos de similitud estructural y otros más clásicos, basados en la perceptibilidad del error generado por la degradación de la señal asociada a la compresión. PARMENIA presenta resultados con una altísima correlación con las valoraciones MOS procedentes de las pruebas subjetivas a usuarios que se han realizado para la validación de la misma. El corpus de trabajo seleccionado procede de conjuntos de secuencias validados internacionalmente, de modo que los resultados aportados sean de la máxima calidad y el máximo rigor posible. La metodología de trabajo seguida ha consistido en la generación de un conjunto de secuencias de prueba de distintas calidades a través de la codificación con distintos escalones de cuantificación, la obtención de las valoraciones subjetivas de las mismas a través de pruebas subjetivas de calidad (basadas en la recomendación de la Unión Internacional de Telecomunicaciones BT.500), y la validación mediante el cálculo de la correlación de PARMENIA con estos valores subjetivos, cuantificada a través del coeficiente de correlación de Pearson. Una vez realizada la validación de los ratios y optimizada su influencia en la medida final y su alta correlación con la percepción, se ha realizado una segunda revisión sobre secuencias del hdtv test dataset 1 del Grupo de Expertos de Calidad de Vídeo (VQEG, Video Quality Expert Group) mostrando los resultados obtenidos sus claras ventajas. Abstract Visual Quality Assessment has been so far one of the most intriguing challenges on the media environment. Progressive evolution towards higher resolutions while increasing the quality needed (e.g. high definition and better image quality) aims to redefine models for quality measuring. Given the growing interest in multimedia services delivery, perceptual quality measurement has become a very active area of research. First, in this work, a classification of objective video quality metrics based on their underlying methodologies and approaches for measuring video quality has been introduced to sum up the state of the art. Then, this doctoral thesis describes an enhanced solution for full reference objective quality measurement based on mathematical morphology, texture features and visual similarity information that provides a normalized metric that we have called PARMENIA (PArallel Ratios MEtric from iNtrInsic features Analysis), with a high correlated MOS score. The PARMENIA metric is based on the pooling of different quality ratios that are obtained from three different approaches: Beucher’s gradient, local contrast filtering, and contrast and homogeneity Haralick’s texture features. The metric performance is excellent, and improves the current state of the art by providing a wide dynamic range that make easier to discriminate between very close quality coded sequences, especially for very high bit rates whose quality, currently, is transparent for quality metrics. PARMENIA introduces a degree of novelty against other working metrics: on the one hand, exploits the structural information variation to build the metric’s kernel, but complements the measure with texture information and a ratio of visual meaningful points that is closer to typical error sensitivity based approaches. We would like to point out that PARMENIA approach is the only metric built upon full reference ratios, and using mathematical morphology and texture features (typically used in segmentation) for quality assessment. On the other hand, it gets results with a wide dynamic range that allows measuring the quality of high definition sequences from bit rates of hundreds of Megabits (Mbps) down to typical distribution rates (5-6 Mbps), even streaming rates (1- 2 Mbps). Thus, a direct correlation between PARMENIA and MOS scores are easily constructed. PARMENIA may further enhance the number of available choices in objective quality measurement, especially for very high quality HD materials. All this results come from validation that has been achieved through internationally validated datasets on which subjective tests based on ITU-T BT.500 methodology have been carried out. Pearson correlation coefficient has been calculated to verify the accuracy of PARMENIA and its reliability.

Towards a Learning Object pedagogical quality metric based on the LORI evaluation model

Evaluating and measuring the pedagogical quality of Learning Objects is essential for achieving a successful web-based education. On one hand, teachers need some assurance of quality of the teaching resources before making them part of the curriculum. On the other hand, Learning Object Repositories need to include quality information into the ranking metrics used by the search engines in order to save users time when searching. For these reasons, several models such as LORI (Learning Object Review Instrument) have been proposed to evaluate Learning Object quality from a pedagogical perspective. However, no much effort has been put in defining and evaluating quality metrics based on those models. This paper proposes and evaluates a set of pedagogical quality metrics based on LORI. The work exposed in this paper shows that these metrics can be effectively and reliably used to provide quality-based sorting of search results. Besides, it strongly evidences that the evaluation of Learning Objects from a pedagogical perspective can notably enhance Learning Object search if suitable evaluations models and quality metrics are used. An evaluation of the LORI model is also described. Finally, all the presented metrics are compared and a discussion on their weaknesses and strengths is provided.

3D video quality of experience - influence of scale and crosstalk

This paper gives an overview of three recent studies by the authors on the topic of 3D video Quality of Experience (QoE). Two of studies [1,2] investigated different psychological dimension that may be needed for describing 3D video QoE and the third the visibility and annoyance of crosstalk[3]. The results shows that the video quality scale could be sufficient for evaluating S3D video experience for coding and spatial resolution reduction distortions. It was also confirmed that with a more complex mixture of degradations more than one scale should be used to capture the QoE in these cases. The study found a linear relationship between the perceived crosstalk and the amount of crosstalk.