Correlating contexts and NFR conflicts from event logs

In the design of autonomous systems, it is important to consider the preferences of the interested parties to improve the user experience. These preferences are often associated with the contexts in which each system is likely to operate. The operational behavior of a system must also meet various non-functional requirements (NFRs), which can present different levels of conflict depending on the operational context. This work aims to model correlations between the individual contexts and the consequent conflicts between NFRs. The proposed approach is based on analyzing the system event logs, tracing them back to the leaf elements at the specification level and providing a contextual explanation of the system’s behavior. The traced contexts and NFR conflicts are then mined to produce Context-Context and Context-NFR conflict sequential rules. The proposed Contextual Explainability (ConE) framework uses BERT-based pre-trained language models and sequential rule mining libraries for deriving the above correlations. Extensive evaluations are performed to compare the existing state-of-the-art approaches. The best-fit solutions are chosen to integrate within the ConE framework. Based on experiments, an accuracy of 80%, a precision of 90%, a recall of 97%, and an F1-score of 88% are recorded for the ConE framework on the sequential rules that were mined

Weakly Z symmetric manifolds

We introduce a new kind of Riemannian manifold that includes weakly-, pseudo- and pseudo projective- Ricci symmetric manifolds. The manifold is defined through a generalization of the so called Z tensor; it is named "weakly Z symmetric" and denoted by (WZS)_n. If the Z tensor is singular we give conditions for the existence of a proper concircular vector. For non singular Z tensor, we study the closedness property of the associated covectors and give sufficient conditions for the existence of a proper concircular vector in the conformally harmonic case, and the general form of the Ricci tensor. For conformally flat (WZS)_n manifolds, we derive the local form of the metric tensor.Comment: 13 page

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

CARET analysis of multithreaded programs

Dynamic Pushdown Networks (DPNs) are a natural model for multithreaded programs with (recursive) procedure calls and thread creation. On the other hand, CARET is a temporal logic that allows to write linear temporal formulas while taking into account the matching between calls and returns. We consider in this paper the model-checking problem of DPNs against CARET formulas. We show that this problem can be effectively solved by a reduction to the emptiness problem of B\"uchi Dynamic Pushdown Systems. We then show that CARET model checking is also decidable for DPNs communicating with locks. Our results can, in particular, be used for the detection of concurrent malware.Comment: Pre-proceedings paper presented at the 27th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur, Belgium, 10-12 October 2017 (arXiv:1708.07854

Conservation tillage and residue management improve soil health and crop productivity-Evidence from a rice-maize cropping system in Bangladesh.

The rice-maize (R-M) system is rapidly expanding in Bangladesh due to its greater suitability for diverse soil types and environments. The present conventional method of cultivating puddled transplanted rice and maize is input-intensive, decreases soil health through intense ploughing, and ultimately reduces farm profitability. There is a need to investigate alternatives. Accordingly, we conducted a replicated 2-year (2020–2021) field study to investigate the effects of conservation agriculture (CA) based tillage and crop establishment (TCE) techniques and residue management practices on the physical, chemical, and biological properties of soil along with crop productivity and the profitability of rice-maize systems in the sandy loam soil of Northwest Bangladesh. Two TCE techniques Puddled transplanted rice (PTR) followed by Conventional tillage maize (CTM) and strip tillage direct-seeded rice (STDSR) followed by strip-tilled maize (STM) were assigned to the main plots and different percentages of crop residue retention (0, 25, and 50% by height) were allocated to the subplots. Results showed that a reduction in bulk density (BD), soil penetration resistance (SPR), and increased soil porosity were associated with STDSR/STM-based scenarios (strip tillage coupled with 25 and 50% residue retention). The soil organic carbon (SOC) fractions, such as dissolved organic C (DOC), light and heavy particulate organic matter C (POM-C), MAOM, and microbial biomass C (MBC) levels in the 0–10 cm layer under ST based treatments were 95, 8, 6, 2 and 45% greater, respectively, compared to CT with no residue treatment. When compared to the CT treatment, the DOC, light POM-C, heavy POM-C, and MAOM in the 10–20 cm layer with ST treatment were 8, 34, 25, 4 and 37% higher, respectively. Residue retention in ST increased average rice, maize, and system yields by 9.2, 14.0, and 14.12%, respectively, when compared to CT. The system gross margin and benefit-cost ratio (BCR) were $1,515 ha−1 and 1.90 under conventional tillage to$1,696 ha−1 and 2.15 under strip-tillage practices. Thus, our study suggests that CA could be an appropriate practice for sustaining soil fertility and crop yield under R-M systems in light-textured soils or other similar soils in Banglades

Static Safety for an Actor Dedicated Process Calculus by Abstract Interpretation

The actor model eases the definition of concurrent programs with non uniform behaviors. Static analysis of such a model was previously done in a data-flow oriented way, with type systems. This approach was based on constraint set resolution and was not able to deal with precise properties for communications of behaviors. We present here a new approach, control-flow oriented, based on the abstract interpretation framework, able to deal with communication of behaviors. Within our new analyses, we are able to verify most of the previous properties we observed as well as new ones, principally based on occurrence counting

Adam Deep Learning with SOM for Human Sentiment Classification

Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing

SMT-based Model Checking for Recursive Programs

We present an SMT-based symbolic model checking algorithm for safety verification of recursive programs. The algorithm is modular and analyzes procedures individually. Unlike other SMT-based approaches, it maintains both "over-" and "under-approximations" of procedure summaries. Under-approximations are used to analyze procedure calls without inlining. Over-approximations are used to block infeasible counterexamples and detect convergence to a proof. We show that for programs and properties over a decidable theory, the algorithm is guaranteed to find a counterexample, if one exists. However, efficiency depends on an oracle for quantifier elimination (QE). For Boolean Programs, the algorithm is a polynomial decision procedure, matching the worst-case bounds of the best BDD-based algorithms. For Linear Arithmetic (integers and rationals), we give an efficient instantiation of the algorithm by applying QE "lazily". We use existing interpolation techniques to over-approximate QE and introduce "Model Based Projection" to under-approximate QE. Empirical evaluation on SV-COMP benchmarks shows that our algorithm improves significantly on the state-of-the-art.Comment: originally published as part of the proceedings of CAV 2014; fixed typos, better wording at some place

A Logical Verification Methodology for Service-Oriented Computing

We introduce a logical verification methodology for checking behavioural properties of service-oriented computing systems. Service properties are described by means of SocL, a branching-time temporal logic that we have specifically designed to express in an effective way distinctive aspects of services, such as, e.g., acceptance of a request, provision of a response, and correlation among service requests and responses. Our approach allows service properties to be expressed in such a way that they can be independent of service domains and specifications. We show an instantiation of our general methodology that uses the formal language COWS to conveniently specify services and the expressly developed software tool CMC to assist the user in the task of verifying SocL formulae over service specifications. We demonstrate feasibility and effectiveness of our methodology by means of the specification and the analysis of a case study in the automotive domain