Miracle-dz/UniFAR-FTUs huggingface dataset:
Incorporating Design Knowledge into Genetic Algorithm-based White-Box Software Test Case Generators

This thesis shows how to incorporate Unified Modeling Language sequence diagrams into genetic algorithm-based automated test case generators to increase the code coverage of their resulting test cases. Automated generation of test data through evolutionary testing was proven feasible in prior research studies. In those previous investigations, the metrics used for determining the test generation method effectiveness were the percentages of testing statement and branch code coverage achieved. However, the code coverage realized in those preceding studies often converged at suboptimal percentages due to a lack of guidance in conditional statements. This study compares the coverage percentages of 16 different Java programs when test cases are automatically generated with and without incorporating associated UML sequence diagrams. It introduces a tool known as the Evolutionary Test Case Generator, or ETCG, an automatic test case generator based on genetic algorithms that provides the ability to incorporate sequence diagrams to direct the heuristic search process and facilitate evolutionary testing. When the generator uses sequence diagrams, the resulting test cases showed an average improvement of 21% in branch coverage and 8% in statement coverage over test cases produced without using sequence diagrams.
Investigation of the Relationship between Mood and Divergent Thinking

This study investigated the link between mood which was induced with music and fa- cial expression, and divergent thinking. A total of 377 participants from two different univer- sities and one company in Germany were allocated to either the control group or to one of the two experimental groups “happy” or “sad”. Participants in the happy and sad group watched a power-point presentation including pictures of facial expressions while listening to music and doing a facial feedback exercise to induce happy or sad mood. Afterwards participants were asked to complete the Unusual Uses Test (UUT) for a brick and a paperclip. Participants from the control group only answered the UUT which measured divergent thinking and can be seen as a useful estimator to predict creativity. The results underlined that the mood manipulation was successful. In addition, the study found evidence that an increasing mood score leads to a higher creativity score, indicating that a pleasant mood fosters creativity, whereas an unpleas- ant mood decreases creativity. Overall the findings imply that there is a weak relationship be- tween mood and creativity, measured with the UUT. (Less)
An ambient intelligence platform for physical play

This paper describes an ambient intelligent prototype known as socio-ec(h)o. socio-ec(h)o explores the design and implementation of a system for sensing and display, user modeling, and interaction models based on a game structure. The game structure includes, word puzzles, levels, body states, goals and game skills. Body states are body movements and positions that players must discover in order to complete a level and in turn represent a learned game skill. The paper provides an overview of background concepts and related research. We describe the prototype and game structure, provide a technical description of the prototype and discuss technical issues related to sensing, reasoning and display. The paper contributes by providing a method for constructing group parameters from individual parameters with real-time motion capture data; and a model for mapping the trajectory of participant's actions in order to determine an intensity level used to manage the experience flow of the game and its representation in audio and visual display. We conclude with a discussion of known and outstanding technical issues, and future research.
Machine Learning Methods for Regression in Astronomical Imaging

The accurate determination of the shape of galaxies is of paramount importance to astronomers in order to determine the amount of dark matter and dark energy of our universe. However, galaxy images are blurred, or convolved under the influence of various effects which translates into galaxy images being convolved by a convolution kernel. This convolution kernel or Point Spread Function (PSF) is sampled at star positions. This study attempts to build a machine learning model which can account for the phenomenon of spatial variation of the PSF within an image of stars. Four different prediction methods have been implemented to this goal. A Gaussian Process performing on an augmented input space was found to achieve the highest performance.
Machine Learning, Reasoning, and Intelligence in Daily Life: Directions and Challenges

Technical developments and trends are providing a fertile substrate for creating and integrating machine learning and reasoning into multiple applications and services. I will review several illustrative research efforts on our team, and focus on challenges , opportunities, and directions with the streaming of machine intelligence into daily life. Over the last decade, technical and infrastructural developments have come together to create a nurturing environment for developing and fielding applications of machine learning and reasoning—and for harnessing automated intelligence to provide value to people in the course of their daily lives. These developments include (1) technical advancements in machine learning and reasoning, (2) the growth in CPU and memory capabilities within commonly available devices and platforms, (3) the connectivity, content, and services provided by the evolving Web, and (4) the increasing availability of data resources, including corpora of behavioral data collected via inexpensive sensors, and through ongoing interaction with software and services. Opportunities for integrating applications of machine intelligence into the daily lives of people are growing with the increasing popularity of computing systems, the widening diversity of web services, the growing popularity of portable devices that contain general-purpose operating systems, and ongoing innovations in human-computer interaction— including the increasing prowess of speech recognition, handwriting, and sketch-understanding interfaces. Various examples of the integration of automated learning and reasoning into daily life have been appearing as implicit and explicit extensions to traditional systems and services, and also in prototypes and systems that provide qualitatively new kinds of experiences. I will review several projects and efforts undertaken by our team that highlight directions and approaches to introducing potentially valuable machine learning and reasoning into the daily lives of people. An example of an implicit integration of ambient learning and reasoning is the effort by our team to create a probabil-istic action prediction and prefetching subsystem that is embedded deeply in the kernel of Microsoft's Windows Vista operating system. The predictive component, operating within a component in the Vista operating system called Superfetch, learns by watching sequences of application launches over time to predict a computer user's application launches. These predictions, coupled with a utility model that captures preferences about the cost of waiting, are used in an ongoing optimization to prefetch unlaunched applications into memory ahead of their manual launching. The implicit service seeks to minimize the average wait for applications to be ready to use after launching actions. Moving from the …
Multi-hop cellular networks: the architecture and routing protocols

Multi-hop cellular network (MCN) is an architecture proposed by Lin and Hsu (see INFOCOM 2000. IEEE, 2000) for wireless communication & MCNs combine the benefits of having a fixed infrastructure of base stations and the flexibility of ad-hoc networks. They are capable of achieving much higher throughput than current cellular systems, which can be classified as single-hop cellular networks (SCNs). This work concentrates on MCNs and SCNs using the IEEE 802.11 standard for wireless LANs. We provide a general overview of the architecture and the issues involved in the design of MCNs, in particular the challenges to be met in the design of a routing protocol. We extend the work of Lin and Hsu to enhance the throughput of such networks further. We propose a routing protocol for use in such networks. We conduct extensive experimental studies on the performance of MCNs and SCNs under various load conditions (both TCP and UDP). Then studies clearly indicate that MCNs with the proposed routing protocol are a viable alternative for SCNs, in fact they provide much higher throughput.
A Mobile Architecture For Childhood Vaccine Preventable Illnesses Expert System

Globally, vaccination against childhood communicable diseases through the Expanded Program on Immunization (EPI) is one of the most cost-effective public health interventions put in place by various governments to eradicate and remedy the menace caused by to childhood illnesses and diseases. Despite government continuously efforts to provide these vaccines free, most pregnant and nursing mothers especially in developing countries are ill-informed of the gains and needs of participating in effect vaccination of the young child. Also, the limited number of qualified health care providers against the increasing population, poor access to quality health care facilities, poor sanitary culture and lack of electronic medical records of patients that have received any form of vaccinations had hampered the smooth delivery and administration of these vaccines in developing countries. To this end, searching for innovative technologies and methodologies for the administration and universal coverage of child immunization will among other processes improve the quality of services given by health care provider to both mother and child. This research blends child vaccination processes with the innovative power of telemedicine to provide a software system framework that will both provide an alert mechanism on available child vaccine for a specific period and advisory services to nursing mothers. It will help to furnish health care provider with medical decisions and data for effective vaccination planning. It will also assist the government to budget properly towards sustaining effective child vaccination based on the evidence available in the centralized Electronic Medical Records.
A compiler for analyzing cryptographic protocols using noninterference

The Security Process Algebra (SPA) is a CCS-like specification languag e where actions belong to two different levels of confidentiality. It has been used to define several noninterference-like security properties whose verification has been automated by the tool CoSeC. In recent years, a method for analyzing security protocols using SPA and CoSeC has been developed. Even if it has been useful in analyzing small security protocols, this method has shown to be error-prone, as it requires the protocol description and its environment to be written by hand. This problem has been solved by defining a protocol specification language more abstract than SPA, called VSP, and a compiler CVS that automatically generates the SPA specification for a given protocol described in VSP. The VSP/CVS technology is very powerful, and its usefulness is shown with some case studies: the Woo-Lam one-way authentication protocol, for which a new attack to authentication is found, and the Wide Mouthed Frog protocol, where different kinds of attack are detected and analyzed.
A hybrid training framework oriented to computer engineering educators

This work presents the successful realization of a versatile blended learning model oriented to computer engineering and computer science educators. The training course potentially enables educators to immerse in real world situation (i.e., school computer lab, synchronous and asynchronous e-learning etc). Long-term statistical results based on electronic questionnaire surveys are presented. Furthermore, critical hints that potential drive the evolution of the blended learning model as a multi training framework for state of the art technologies with considerable education impact, are briefly discussed.
Efficient Reconciliation of Correlated Continuous Random Variables using LDPC Codes

This paper investigates an efficient and practical information reconciliation method in the case where two parties have access to correlated continuous random variables. We show that reconciliation is a special case of channel coding and that existing coded modulation techniques can be adapted for reconciliation. We describe an explicit reconciliation method based on LDPC codes in the case of correlated Gaussian variables. We believe that the proposed method can improve the efficiency of quantum key distribution protocols based on continuous-spectrum quantum states.
Feedback Redundancy: A Power Efficient SEU-Tolerant Latch Design for Deep Sub-Micron Technologies

The continuous decrease in CMOS technology feature size increases the susceptibility of such circuits to single event upsets (SEU) caused by the impact of particle strikes on system flip flops. This paper presents a novel SEU-tolerant latch where redundant feedback lines are used to mask the effects of SEUs. The power dissipation, area, reliability, and propagation delay of the presented SEU-tolerant latch are analyzed by SPICE simulations. The results show that this latch consumes about 50% less power and occupies 42% less area than a TMR-latch. However, the reliability and the propagation delay of the proposed latch are still the same as the TMR-latch. the reliability of the proposed latch is also compared with other SEU-tolerant latches.
Experiments in combining boosting and deep stacked networks

Both boosting and deep stacking sequentially train their units taking into account the outputs of the previously trained learners. This parallelism suggests that it exists the possibility of getting some advantages by combining these techniques, i.e., emphasis and injection, in appropiate manners. In this paper, we propose a first mode for such a combination by simultaneously applying a general and flexible enough emphasis function and injecting the aggregated previous outputs to the learner which is being designed. We call this kind of classification mechanism Boosted and Aggregated Deep Stacked Networks (B-ADSNs). A series of experiments with some selected benchmark databases reveal that, if carefully designed, B-ADSNs never perform worse than the ADSNs (DSNs which work with aggregated output injection), and that in some cases their performance is better. We analyze and discuss the conditions to get these favourable results, and, finally, we explain that there are other combination possibilities that merit to be studied.
Structural Network Properties and Their Relation to Cognitive Flexibility and Neurological Risk Factors in Adult Survivors of Pediatric Brain Tumors

Neuroimaging techniques have been used to investigate the neurobiological mechanisms of cognitive deficits in survivors of childhood brain tumors. Graph theory is a quantitative method that characterizes brains as a complex system. By modeling brain regions as ‘nodes’ and white matter tracts between each brain region pair as ‘edges,’ graph theory provides metrics that quantify the topological properties of networks. Given that brain tumor survivorship is associated with focal and diffuse impairments, a network analysis can provide complementary information to previous neuroimaging studies in this clinical group. This study used diffusion-weighted imaging and deterministic tractography to examine the properties of the structural networks in 38 adult survivors of pediatric brain tumors (Mean age=22.5, 55% female, mean years post diagnosis=14.1 (6.2), Range post diagnosis = 4.5-30 years). Results of this study suggest that long term survivorship is associated with altered structural networks with respect to measures of integration, segregation, and centrality. Further, properties of the network mediate differences in cognitive flexibility performance between survivors and healthy peers, and mediate the relationship between cumulative neurological risk and cognitive flexibility performance. INDEX WORDS: Diffusion-weighted imaging, Deterministic tractography, Graph theory, Executive functioning, Long-term outcomes, Brain tumor survivorship, Cognitive
A multi-layered software architecture model for building software solutions in an urbanized information system

The concept of Information Systems urbanization has been proposed since the late 1990’s in order to help organizations building agile information systems. Nevertheless, despite the advantages of this concept, it remains too descriptive and presents many weaknesses. In particular, there is a lack of useful architecture models dedicated to defining software solutions compliant with information systems urbanization principles and rules. Moreover, well-known software architecture models do not provide sufficient resources to address the requirements and constraints of urbanized information systems. In this paper, we draw on the “information city” framework to propose a model of software architecture - called the 5+1 Software Architecture Model - which is compliant with information systems urbanization principles and helps organizations building urbanized software solutions. This framework improves the well-established software architecture models and allows the integration of new architectural paradigms. Furthermore, the proposed model contributes to the implementation of information systems urbanization in several ways. On the one hand, this model devotes a specific layer to applications integration and software reuse. On the other hand, it contributes to the information system agility and scalability due to its conformity to the separation of concerns principle.
Software Watermarking Through Obfuscated Interpretation: Implementation and Analysis

A robust software watermarking scheme under which the watermark can resist against various attacks including collusion attacks is proposed based on obfuscated interpretation. The idea is to spread the watermark over the entire program by modifying instruction frequencies. The obfuscated interpretation technique is introduced into the scheme to not only hide the functionality of a given program but also provide an alternative simple way to manipulate the instruction frequencies so that embeds the watermark. A series of experimental results show that the proposed watermark can resist against various semantics-preserving transformations and collusion attacks as well.
Reliability experience with Chi/OS

The Chi/OS operating system, the latest large-scale software effort of Chi Corporation, has had an excellent reliability record since its installation in November 1973. Although the system design is vital to the reliability of Chi/OS, several environmental factors are equally vital. After a brief presentation of the substantial work load supported by Chi/OS, this paper deals with those environmental factors which contribute to the reliability of the software.
Fault and Failure Tolerance

One of the many remarkable properties of the brain is the degree to which it is fault and failure tolerant. In many cases even the loss of substantial amounts of brain cells or tissue does not totally abolish brain function, a property known as "graceful degradation". It is therefore not surprising that the problems of constructing fault and failure tolerant neural networks have been studied almost since the earliest days of Neural Networks.
Intrabulbar Projecting External Tufted Cells Mediate a Timing-Based Mechanism That Dynamically Gates Olfactory Bulb Output

In the mammalian olfactory system, intrabulbar projections (IBPs) mediated by a class of external tufted cells (ET cells) specifically link isofunctional odor columns within the same olfactory bulb. To study the function of these ET cells within the glomerular network, we developed a “hemibulb” preparation that maintains IBPs intact enabling the select activation of ET cells associated with specific glomeruli. Using P2-GFP mice, a line in which the P2 glomeruli are labeled with green fluorescent protein, we recorded from P2 mitral cells (MT cells) while selectively stimulating P2 ET cells. Here, we show that ET-cell activity evokes a slow modulatory (SM) potential within MT cells, which is mediated by the glomerular network and consists of both excitatory and inhibitory components. Interestingly, the timing of the SM potential with respect to olfactory nerve (ON) stimulation can produce converse effects on MT-cell output. When ET-cell activity precedes ON stimulation, the MT-cell response is potentiated; however, when ET-cell activity follows ON stimulation, the MT-cell response is inhibited. Thus, intrabulbar projecting ET cells can shape olfactory bulb output through intraglomerular modulation of MT cells.
Academic Games in Validation Events: A Study of Academic Roles and Practices

This paper presents the results of a three-year study that examined academics’ espoused and actual practices in validation or approval events of UK degree courses. The study used narrative inquiry to explore academics’ accounts. The paper provides a literature review and then presents the findings which indicate that often procedural processes interrupt the process of curriculum making. The paper uses scenarios to illustrate the ways in which procedural processes can result in subverting and subversive practices during the validation process. It is, therefore, argued that academics take up particular stances, defined here as positional identities, which may help or hinder the validation process. The paper argues that by ignoring staff experiences, the risk is that dominant discourses of regulation become accepted without question and the spaces available for dialogue about professional futures, alongside creation of flexible curricula to address these needs, are crowded out by the performative requirements of the process.
Correlation between graphs with an application to brain networks analysis

The global functional brain network (graph) is more suitable for characterizing brain states than local analysis of the connectivity of brain regions. Therefore, graph-theoretic approaches are the natural methods to study the brain. However, conventional graph theoretical analyses are limited due to the lack of formal statistical methods for estimation and inference for random graphs. For example, the concept of correlation between two vectors of graphs is yet not defined. The aim of this article to introduce a notion of correlation between graphs. In order to develop a framework to infer correlation between graphs, we assume that they are generated by mathematical models and that the parameters of the models are our random variables. Then, we define that two vectors of graphs are independent whether their parameters are independent. The problem is that, in real world, the model is rarely known, and consequently, the parameters cannot be estimated. By analyzing the graph spectrum, we showed that the spectral radius is highly associated with the parameters of the graph model. Based on it, we constructed a framework for correlation inference between graphs and illustrate our approach in a functional magnetic resonance imaging data composed of 814 subjects comprising 529 controls and 285 individuals diagnosed with autism spectrum disorder (ASD). Results show that correlations between default-mode and control, default-mode and somatomotor, and default-mode and visual sub-networks are higher ($p<0.05$) in ASD than in controls.