We propose and develop a framework for validating smart contracts derived from e-contracts. The goal is to ensure the generated smart contracts fulfil all the conditions outlined in their corresponding e-contracts. By confirming alignment between the smart contracts and their original agreements, this approach enhances trust and reliability in automated contract execution. The proposed framework will systematically compare and validate the terms and clauses of the e-contracts with the logic of the smart contracts. This validation confirms that the agreement is accurately translated into executable code. Automated verification identifies issues between the e-contracts and their smart contract counterparts. This proposed work will solve the problems of gap between legal language and code execution, this framework ensures seamless integration of smart contracts into the existing legal framework.

Unique-Cause MC/DC (UCM) is the most desired form of MC/DC in many safety-critical applications. For a given predicate, the UCM considers the independent pair of each condition by flipping the corresponding condition and fixing the other conditions. For the given N conditions in a predicate, the existing static symbolic execution tool, CBMC, generates MC/DC (Modified Condition/Decision) goal constraints of size, N + 1 that constitutes its minimal independent pairs. However, we propose a novel UCM Sequence Generator (UCM-Gen) that generates all possible inequality comparisons of the sequences/combinations of the N conditions which helps in computing the independent pairs further. The UCM-Gen outputs UCM Annotated Program which when given to the program verifiers, produces the UCM Score (%). In our work, we have considered CBMC to get the SAT/UNSAT results for each sequence of the UCM Annotated Program. Upon analysing these results, we calculate the total number of independently affected conditions (i.e., I value) for all the predicates in the given program. Furthermore, this work is compared with the CBMC's mode of MC/DC implementation. Interestingly, our proposed approach based UCM score (%) is always greater than the CBMC's MC/DC score (%) and hence claiming that their corresponding test cases contribute in effective bug finding.

Fuzz testing is one of the popular techniques for evaluating software reliability. Its effectiveness largely depends on the quality and diversity of the initial seed inputs. Traditionally, these seeds are generated randomly, which may limit the effectiveness of the fuzzing process. However, generating seeds based on an analysis of the target code can significantly improve the performance of these tools. To address this, we proposed a Large Language Model (LLM)-based seed generation approach for effective fuzzing and named it gptPromptFuzz. In our approach, initially, one meta-prompt is designed in accordance with the objective of diverse seed generation. To further enhance diversity, we construct ten additional prompts that are semantically equivalent to the meta-prompt. Each of these prompts is independently processed by LLM to produce unique seeds. The experimental results demonstrated that the proposed gptPromptFuzz outperformed random AFL in generating seeds effectively, with reduced execution time, in all 45 benchmark C programs. Further, a larger number of paths are obtained in 41 out of 45 programs.

Missing values can greatly affect analyses and decision-making in many fields. In the context of Responsible Artificial Intelligence (AI), ensuring the robustness of machine learning models is essential because Responsible AI emphasizes reliability and interpretability in decision-making processes. However, traditional imputation and ensemble learning methods often fail to preserve critical relationships between independent and dependent variables, introducing bias or noise into the data and undermining the development of robust classification models. To address these challenges, we propose a novel classification approach that aligns with Responsible AI principles. Our Resilient Decision Tree classifier is specifically designed to handle incomplete datasets. We employ subspace classifiers that operate on different non overlapping subsets of features without relying on imputation. By combining these subspace models into a weighted ensemble classifier, we enhance prediction accuracy for test datasets with missing values. The experimental results obtained on real-life and synthetic datasets demonstrate that our methodology produces an effective ensemble classifier.

Advancements in blockchain technologies empower society with trust-based applications. Smart contracts, which are programs designed to facilitate activities on the blockchain, serve as important instruments for executing agreements. Smart contracts are established among involved parties to codify their respective requirements and commitments. In various situations, where a smart contract manages substantial and valuable transactions, the likelihood of encountering issues and asset losses increases significantly. Therefore, it becomes essential to verify and test smart contracts thoroughly. In this paper, we present a new tool to measure condition coverage criterion for smart contracts using Solidity-based model checkers. We demonstrate the process of annotating the original smart contract by the condition coverage properties and employ the model checker to validate the feasibility of the specified properties. Further, we assess the properties instrumented to compute the condition coverage score. We conducted experiments on 70 smart contracts, employing both the Bounded Model Checker (BMC) and Constrained Horn Clauses (CHC). Our findings demonstrate BMC's superior performance compared to CHC. The tool we propose assists smart contract developers in verifying their code through condition coverage analysis. Utilizing both model checkers in tandem contributes to enhancing the quality of smart contracts, as the outcome may vary, and either of the checkers might yield superior condition coverage. Video-cast: https://youtu.beI13kuIjpPGPI?si=YQIWYPJhp7vzORI4

The important contribution that large language models (LLMs) have made to the development of a new software testing era is the main objective of this proposed approach. It emphasizes the role that LLMs play in producing complex and diverse input seeds, which opens the way for efficient bug discovery. In the study we also introduce a systematic approach for combining various input values, employing the principles of Combinatorial testing using the PICT (Pairwise independent Combinatorial testing). By promoting a more varied set of inputs for thorough testing, PICT enhances the seed production process. Then we show how these different seeds may be easily included in the American Fuzzy Lop (AFL) tool, demonstrating how AFL can effectively use them to find and detect software flaws. This integrated technique offers a powerful yet straightforward approach to software Quality.

Fuzzing is a popular and highly effective technique for software testing especially vulnerability detection. Fuzzing includes the random mutation of well-formed program inputs using dynamic program analysis. Though fuzzing is an active area of research, less systematic efforts have been investigated to understand as well as to generate powerful input seeds for a fuzzer. Reactive systems are used in different applications such as web services, decision support systems, and logical controllers. These systems are quite complex and bigger, hence the validation process becomes tedious. In this work, we propose a static seed generator that helps to accelerate the performance of existing fuzzers. In this paper, we validate the reactive systems using our approach by detecting vulnerability. To evaluate the performance of our developed seeder, we experimented with 100 Rigorous Ex-amination of Reactive Systems (RERS) C-programs. Experimental results show that our approach SSG-AFL is superior as compared to the AFL with random seeds. SSG-AFL shows 59.75% winning programs after running all four phases as compared to Random-AFL.

Most of the oil industries have a substantial volume of physical subsurface data generated as part of the exploration study. This data is collected over many decades and exists in various formats such as tapes, cartridges, CDs, DVDs, paper media comprising of maps, technical well reports, and seismic logs. These items are usually stored in large offsite repositories across the globe and is maintained by third-party vendors. Access to this historical data is crucial for oil companies as it helps to find potential prospects for oil extraction which otherwise require an exploratory study by geologists using satellite imagery, surface rocks, terrain, and seismology. Storing large volumes of technical data in offsite repositories also posts many key challenges such as high storage cost, high retrieval time and inaccessibility of information. To address the above challenges, companies are digitizing the physical data and complementing with rich metadata extraction by Optical Character Recognition(OCR). This introduces some more technical challenges while dealing with lower Dots Per Inch (DPI) scans, poor quality scans, and huge file size. Several frameworks are developed which store the data in local repositories but these frameworks have limitations with respect to the number of documents processed, huge file size and storage scalability. To deal with above-mentioned problems, we present a high-performance computing cloud-based framework by storing the digitized data in the cloud, metadata enrichment through OCR along with image enhancement by a series of Image Processing (IP) techniques and provide high data availability to users using cloud-based search. We have tested this framework with big oil and gas company's data on a huge scale and the results are encouraging. Although this paper addresses oil industries domain problem, the proposed framework can be applied to other domains that have huge physical data.

In IoT applications, sensors produce raw data, and smart solutions consume the processed data. IoT driven smart solutions typically use data that is aggregated, filtered and processed to drive the smart solution. Such aggregated data has different semantics in comparison to raw sensor data, and it needs to be customized simultaneously for different application requirements. There is a need for proper data logistics for the movement of data from the sensors producing raw data to smart solutions consuming solution relevant data. In this paper, we present a data logistics system that includes (i) architecture to support the movement of data from sensors to smart applications, and (ii) event-driven solution to trigger workflows of smart solution. Workflows in our solution supports the data fulfillment requirements for the enactment of smart solutions.

Community structures in networks are helpful to understand the network structure and analyze the network properties. Majority of the studies in this area tend to discover communities by analyzing the linkages in the networks, which may result in communities with diversified attributes. In this paper, we model community detection as an optimization problem. This paper proposes a novel community detection approach by leveraging the concepts of particle swarm optimization with dynamic neighborhood topology by analyzing the similarity between the node attributes. The process starts by dividing the particles into several sub swarms, and the particles in each sub swarm iteratively form communities through information exchange and integrating the nodes which satisfy a threshold similarity score. The presented method does not require any prior knowledge about the communities. Experiments are carried out on real-life datasets, and the process is executed in parallel, asynchronous environments. We evaluated our method with the evaluation properties namely Omega index, Silhouette coefficient and Tanimoto coefficient to evaluate the performance and quality of the clusters. We also compared our approach with existing approaches, and the results demonstrate that our method performed better in terms of measured metrics.

Recommendation systems play an important role in suggesting relevant information to users. In this paper, we introduce community-wise social interactions as a new dimension for recommendations and present a social recommendation system using collaborative filtering and community detection approaches. We use (i) community detection algorithm to extract friendship relations among users by analyzing user-user social graph and (ii) user-item based collaborative filtering for rating prediction. We developed our approach using map-reduce framework. Our approach improves scalability, coverage and cold start issue of collaborative filtering based recommendation system. We carried out experiments on MovieLens and Facebook datasets, to predict the rating of the movie and produce top-k recommendations for new (cold start) user. The results are compared with traditional collaborative filtering based recommendation system.

Detecting communities is of great importance in various disciplines such as social media, biology and telephone networks, where systems are often represented as graphs. Community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group. The communities have different properties such as node degree, betweenness, centrality, cluster coefficient and modularity. Discovering communities from social networks of big data scale on a single se quential machine is a tedious task. In this paper, we present a Scalable Community Detection Algorithm which relaxes the performance issues due to many I/Os. We adopt Girvan-Newman's modularity based hierarchical community detection algorithm in bottom u p a pproach an d proposed an approximation algorithm for community detection in a distributed environment. We developed our approach using MapReduce and Giraph computing platforms. Experimental results demonstrate that the proposed approach is more efficient than standard MapReduce approach and easily scaled to graph of any size.

E-contract evolves over a period of time due to changes in e-contract environment. E-contract evolution adversely affects the execution of e-contracts. An e-contract is specified by a model at conceptual level, supported by a database management system (DBMS) at logical level and by both DBMS and workflow management system (WFMS) at implementation level. Any changes in the design as well as runtime environment during e-contract enactment must be reflected at all levels. Conventional modeling approaches simply model the e-contracts as specified workflows and execute them. Since, e-contracts are complex in nature, such models have to undergo large number of transformations during e-contract enactment. Metamodeling approach guides the correctness of transformed models by generating appropriate model instances according to e-contract constraints to support evolution. A metamodel has structural artifacts to capture the relationships among contract elements and model the required specifications and semantics present in an e-contract as a template. In this paper, we develop 1) an active metamodeling approach by a) introducing the taxonomy of evolution operations and b) handling metaevents to facilitate the structural and behavioral conformance during e-contracts evolution, and 2) an $({\rm ER}^{\ast{\rm EC}})$ architecture for enacting evolving e-contracts. Our methodology actively capture behavior features from e-contract executions to drive e-contract evolution.

Energy efficiency is one of the main challenge hat data centers are facing nowadays. A considerable portion of the consumed energy in these environments is wasted because of idling resources. To avoid wastage, offering services with variety of SLAs (with different prices and priorities) is a common practice. The question we investigate in this research is how the energy consumption of a data center that offers various SLAs can be reduced. To answer this question we propose an adaptive energy management policy that employs virtual machine(VM) preemption to adjust the energy consumption based on user performance requirements. We have implementedour proposed energy management policy in Haize a as a real scheduling platform for virtualized data centers. Experimental results reveal 18% energy conservation (up to 4000 kWh in 30 days) comparing with other baseline policies without any major increase in SLA violation.

Due to increased adoption of digital consumer services, Mobile Ad-hoc Networks (MANETs) are gaining importance in real-life business applications. MANETs are wireless networks consisting of a set of mobile nodes and enables multi-hop peer-to-peer routing without any requirement of predefined infrastructure for its deployment. There are several works in literature for improving the Quality of Service (QoS) such as BRAWN, AODV and DSDV. However, these approaches have to be augmented with additional parameters mainly to avoid congestion and propagation delays. In this paper, we present an improved protocol to maintain higher QoS in MANETS by determining multiple efficient paths and ranking them. In addition to Bandwidth and Data rate, we also incorporate additional parameters namely Queuing delay and Distance in order to dynamically compute the paths for a specific communication with minimum delay. We demonstrate the viability of our proposed protocol with an example.

Automation of industrial asset management has gained significant attention due to recent breakthrough in technologies for object identification and tracking. Unavailability of small assets used in industrial assembly floors, owing to post use misplacement, impacts the productivity and results in erroneous information in inventory management systems. In this work we present a solution for identifying small industrial assets along with their storage location using a machine vision based approach. Proposed system will assist the store manager to track and consolidate industrial assets periodically on the assembly floor after production. We have developed a machine vision based application on a mobile handheld device for asset tracking. This application is capable of acquiring asset images and finding their storage location. A mobile application prototype is implemented on an open software platform for evaluation.

Due to increased adoption of digital inclusion in various businesses, location based services are gaining importance to provide value-added services for their customers. In this work, we present a computer vision based system for tracking customer locations by recognizing individual shopping carts inside shopping malls in order to facilitate location based services. We provide an efficient approach for cart recognition that consists of two stages: cart detection and then cart recognition. A binary pattern is placed between two pre-defined color markers and attached to each cart for recognition. The system takes live video feed as input from the cameras mounted on the aisles of the shopping mall and processes frames in real-time. In the cart detection stage, color segmentation, feature extraction and classification are used for detection of binary pattern along with color markers. In recognition stage, segmented binary strip is processed using spatial image processing techniques to decode the cart identification number.

Modeling and deployment of e-contracts is a challenging task because of the involvement of both technological and business aspects. There are several frameworks and systems available in the literature. Some works mainly deal with the automatic handling of paper contracts and others provide monitoring and enactment of contracts. Because contracts evolve, it is useful to have a system that models and enacts the evolution of e-contracts.

Rough set theory operates on an information system that consists of a set of objects. A core concept of rough set theory is that of equivalence between objects called indiscernibility. Indiscernibility reflects a total impossibility of distinguishing between objects, considering the available information. Considering a tolerance or similarity relation instead of an indiscernibility relation is quite relevant due to the existence of quantitative attributes in the information systems. Extending indiscernibility to tolerance relation results in weakening of some of the properties of the binary relation in terms of reflexivity, symmetry and transitivity. In this paper, we present a clustering technique using similarity relation with transitivity property being relaxed. The concept of similarity upper approximation has been used to form the initial family of cluster. A relationship based measure has been used to decide the belongingness of uncertain elements. We present an example to illustrate our proposed methodology. This promises to be a useful and interesting area of extension of the theory of rough sets.

Data classification is a well-organized operation in the field of data mining. This article presents an application of the k-nearest neighbor classification technique for mining a fuzzy database. We consider a data set in which attribute values have certain similarities in nature and analyze the observations for the domain of each attribute, on the basis of fuzzy similarity relations. The proposed technique is general and the presented case study demonstrates the suitability of using this fuzzy approach for mining fuzzy databases, especially when the database contains various levels of abstraction. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1277–1290, 2004.

Clustering of data in a large dimension space is of great interest in many data mining applications. In this paper, we propose a method for clustering of web usage data in a high-dimensional space based on a concept hierarchy model. In this method, the relationship present in the web usage data are mapped into a fuzzy proximity relation of user transactions. We also described an approach to present the preference set of URLs to a new user transaction based on the match score with the clusters. The study demonstrates that our approach is general and effective for mining the web data for web personalization.