A static analysis for quantifying information flow in a simple imperative language

We propose an approach to quantify interference in a simple imperative language that includes a looping construct. In this paper we focus on a particular case of this definition of interference: leakage of information from private variables to public ones via a Trojan Horse attack. We quantify leakage in terms of Shannon's information theory and we motivate our definition by proving a result relating this definition of leakage and the classical notion of programming language interference. The major contribution of the paper is a quantitative static analysis based on this definition for such a language. The analysis uses some non-trivial information theory results like Fano's inequality and L1 inequalities to provide reasonable bounds for conditional statements. While-loops are handled by integrating a qualitative flow-sensitive dependency analysis into the quantitative analysis

Estado crítico de la investigación en la psicología ecuatoriana: el abandono de la estadística como base de la producción científica

This article reports a study that analyzed the role of statistics in the scientific production of Ecuadorian psychology based on a mixed methodology study. The sample included 410 participants (students and professionals of different cities of Ecuador). The findings on the quantitative phase included that liking and mastering statistical processes increase the probability of publishing a scientific article. Moreover, when negative belief variables and anxiety related to statistics have a higher score, the mastery of statistical processes and its use for the job of the psychologist decrease. On the qualitative phase, a significance emerged that allowed to better understand quantitative data, and to develop categories on statistics erroneous' believes, procedures, a negative predisposition towards learning statistics and others. The paper concludes by analyzing the scientific reality of Ecuadorian psychology and the need to carry out longitudinal research where it will be possible to restructure subjective constructions on the role of statistics in psychology

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells

Adherent cells exert traction forces on to their environment, which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures. This traction can be measured in a perturbation-free manner with traction force microscopy (TFM). In TFM, traction is usually calculated via the solution of a linear system, which is complicated by undersampled input data, acquisition noise, and large condition numbers for some methods. Therefore, standard TFM algorithms either employ data filtering or regularization. However, these approaches require a manual selection of filter- or regularization parameters and consequently exhibit a substantial degree of subjectiveness. This shortcoming is particularly serious when cells in different conditions are to be compared because optimal noise suppression needs to be adapted for every situation, which invariably results in systematic errors. Here, we systematically test the performance of new methods from computer vision and Bayesian inference for solving the inverse problem in TFM. We compare two classical schemes, L1- and L2-regularization, with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. Overall, we find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. Using artificial data and experimental data, we show that these methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Thus, Bayesian methods can mitigate the considerable uncertainty inherent in comparing cellular traction forces

LeakWatch: Estimating Information Leakage from Java Programs

Abstract. Programs that process secret data may inadvertently reveal information about those secrets in their publicly-observable output. This paper presents LeakWatch, a quantitative information leakage analysis tool for the Java programming language; it is based on a flexible “point-to-point ” information leakage model, where secret and publiclyobservable data may occur at any time during a program’s execution. LeakWatch repeatedly executes a Java program containing both secret and publicly-observable data and uses robust statistical techniques to provide estimates, with confidence intervals, for min-entropy leakage (using a new theoretical result presented in this paper) and mutual information. We demonstrate how LeakWatch can be used to estimate the size of information leaks in a range of real-world Java programs

Salient Attributes to Employee Compliance with Mobile Operation Ethics in Tanzania: A Case of Vodacom Tanzania Plc

This paper on determinants of employees’ compliance with mobile operation ethics in Tanzania was motivated by reports of numerous unmitigated accesses to customer information and transactions on the mobile money platform, and the need to control these transactions using effective and practical measures to sustain the service. Specifically, it sets out to establish the employees’ working environment, determine the employees’ wants and examine the employer/employee relations vis-à-vis their influence on employees’ compliance with ethics. This explanatory study collected quantitative data using a questionnaire survey that contained structured questions. Though largely quantitative, the study findings were complemented by information from interviews held with 3 key informants. Descriptive statistics was used to present the profiles of the respondents whereas multiple regression analysis was used to ascertainthe pattern of relationship between study variables. Findings indicate that the two independent variables— ‘working environment’ and ‘employer-employee relations’—were positively and statistically significant on employees’ compliance with ethics. Though, the employees’ wants were found to be positive, they were statistically insignificant in influencing employees’ compliance with ethics. This implies that employees’ compliance with ethics on mobile operations is materially influenced by the work environment and employer-employee relations. Managements must therefore play their roles pertaining to the provision of amenable work environment and enhance good relations with their employees

Economism and its Limits

Articl

Ten steps to conducting a large, multi-site, longitudinal investigation of language and reading in young children

Purpose: This paper describes methodological procedures involving execution of a large-scale, multi-site longitudinal study of language and reading comprehension in young children. Researchers in the Language and Reading Research Consortium (LARRC) developed and implemented these procedures to ensure data integrity across multiple sites, schools, and grades. Specifically, major features of our approach, as well as lessons learned, are summarized in 10 steps essential for successful completion of a large-scale longitudinal investigation in early grades. Method: Over 5 years, children in preschool through third grade were administered a battery of 35 higher- and lower-level language, listening, and reading comprehension measures (RCM). Data were collected from children, their teachers, and their parents/guardians at four sites across the United States. Substantial and rigorous effort was aimed toward maintaining consistency in processes and data management across sites for children, assessors, and staff. Conclusion: With appropriate planning, flexibility, and communication strategies in place, LARRC developed and executed a successful multi-site longitudinal research study that will meet its goal of investigating the contribution and role of language skills in the development of children’s listening and reading comprehension. Through dissemination of our design strategies and lessons learned, research teams embarking on similar endeavors can be better equipped to anticipate the challenges