Dynamic data distribution with control flow analysis

In this paper, we describe an approach to discover the control flow graph of Web services for Web services analysis, verification, and testing. For this purpose, three novel methods are proposed. First, we introduce a domain independent RDF Schemas for concise resource oriented functional specification of Web services operations. Secondly, we describe the use of RDF entailment to accurately derive the control flow from the functional specifications. We developed a transformation from RDF graph to SPARQL query to facilitate the RDF entailment which offers flexibility and extensibility over the direct graph matching approach. The third is a linkage based Web services modeling and analysis framework, within which we apply an improved Google PageRank algorithm to efficiently calculate test coverage potential using the derived control flow. We justify that the proposed linkage based Web services modeling and analysis framework is particularly suitable for testing Web services. A prototype of the proposed methods has been implemented and tested on some standard based Web services. Experimental results show that the control flow analysis is quite efficient and accurate, and the coverage based test results of the proposed approach are very promising.

As the era of new-generation big data applications unfolds, the need for secure and adaptive data stream mining has become increasingly paramount. Evolving databases, characterized by ever-changing data streams and dynamic data distributions, present unique challenges and opportunities. This paper addresses the crucial intersection of security and adaptability in the context of data stream mining for new-generation big data. First, we delve into the evolving landscape of big data, where real-time data streams from diverse sources drive decision-making processes. Ensuring the privacy and security of sensitive information within these data streams is a fundamental concern. We explore cryptographic techniques, anonymization methods, and access control mechanisms that safeguard data while allowing for meaningful analysis. We present novel adaptive algorithms and model update strategies that can continuously learn and adjust to changing data distributions. These approaches enable data stream mining to remain effective and accurate over time. This paper offers insights into the fusion of security and adaptability in data stream mining, providing a foundation for the development of robust and privacy-conscious solutions for the evolving landscape of new-generation big data applications.

Data warehouse store historical records on which analysis queries are executed. Data warehouse are populated from the transaction database through a process called as Extract Transform and Load (ETL). Extract is to identify the transaction database records to be moved to the data warehouse. Transform is to make the records normalized to suit the data warehouse environment. Load is to actually store the records in the data warehouse. Today business wants to perform real time analysis of data. Availability of updated records in the data warehouse is necessary for real time analysis. This could be achieved through the process of near real time (ETL), i.e., to improve existing ETL process. Improvement to existing ETL could be achieved in many ways such as to increase the frequency of loads, to identify relevant changes that are required for analysis and move only those changes. Only through increasing the frequency of load would not be useful in many cases. Hence we should identify the changes and then move if they are useful for analysis making it smart ETL. In this paper we study three such techniques. First one is to create a replica of dimension table in data warehouse and move the changes to the replica this reduces the query response time. Second is to load the data in parallel so that loading time could be reduced. Third is to identify changes and trigger the loading process. We have used the first approach to create replica and loaded the data in parallel and observed that by loading in parallel not only does the loading time reduces but also the query response time. Then in the first approach is the query response time reduces to a certain limit methods suggest to move the data from replica dimensions to the original dimensions. Here we bring in right time trigger to move instead of just the query response time. We found that when we have a combined approach to query response time and right time trigger the number of moves are reduced.

Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution.

In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead to dynamic and non-stationary data distributions in evolving data streams. This phenomenon, known as concept drift, poses a substantial challenge for traditional data-driven digital twin static machine learning (ML) models for anomaly detection and classification. Subsequently, the drift in normal and anomalous data distributions over time causes the model performance to decay, resulting in high false alarm rates and missed anomalies. To address this issue, we present TWIN-ADAPT, a continuous learning model within a digital twin framework designed to dynamically update and optimize its anomaly classification algorithm in response to changing data conditions. This model is evaluated against state-of-the-art concept drift adaptation models and tested under simulated drift scenarios using diverse noise distributions to mimic real-world distribution shift in anomalies. TWIN-ADAPT is applied to three critical CPS datasets of Smart Manufacturing Labs (also known as “Cleanrooms”): Fumehood, Lithography Unit and Vacuum Pump. The evaluation results demonstrate that TWIN-ADAPT’s continual learning model for optimized and adaptive anomaly classification achieves a high accuracy and F1 score of 96.97% and 0.97, respectively, on the Fumehood CPS dataset, showing an average performance improvement of 0.57% over the offline model. For the Lithography and Vacuum Pump datasets, TWIN-ADAPT achieves an average accuracy of 69.26% and 71.92%, respectively, with performance improvements of 75.60% and 10.42% over the offline model. These significant improvements highlight the efficacy of TWIN-ADAPT’s adaptive capabilities. Additionally, TWIN-ADAPT shows a very competitive performance when compared with other benchmark drift adaptation algorithms. This performance demonstrates TWIN-ADAPT’s robustness across different modalities and datasets, confirming its suitability for any IoT-driven CPS framework managing diverse data distributions in real time streams. Its adaptability and effectiveness make it a versatile tool for dynamic industrial settings.

The data distribution problem is very complex, because it involves trade-off decisions between minimizing communication and maximizing parallelism. A common approach towards solving this problem is to break the data mapping into two stages: an alignment stage and a distribution stage. The alignment stage attempts to increase parallelism, while the distribution stage attempts to decrease communication overhead. As opposed to previous approaches, we consider the alignment and distribution problems in a unified framework, and attempt to simultaneously maximize parallelism and minimize communication overhead. The problem becomes harder if dynamic remapping, multi-dimensional distributions, array replications and control flow are taken into account. This paper formulates the full data decomposition problem that addresses all these issues and presents a simple new algorithm to find the optimal solution of the dynamic data distribution problem, given the number of processors and a partitioning of the input program into phases. The algorithm runs efficiently for small search spaces (several hundreds of data distributions).

The sharp increase of smartphone malwares has become one of the most serious security problems. The most significant part of the growth is the variants of existing malwares. A legacy approach for malware, the signature matching, is efficient in temporal dimension, but it is not practical because of its lack of robustness against the variants. A counter approach, the behavior analysis to handle the variant issue, takes too much time and resources. We propose a variant detection mechanism using runtime semantic signature. Our key idea is to reduce the control and data flow analysis overhead by using binary patterns for the control and data flow of critical actions as a signature. The flow information is a significant part of behavior analysis but takes high analysis overhead. In contrast to the previous behavioral signatures, the runtime semantic signature has higher family classification accuracy without the flow analysis overhead, because the binary patterns of flow parts is hardly shared by the out of family members. Using the proposed signature, we detect the new variants of known malwares by static matching efficiently and accurately. We evaluated our mechanism with 1,759 randomly collected real-world Android applications including 79 variants of 4 malware families. As the experimental result, our mechanism showed 99.89% of accuracy on variant detection. We also showed that the mechanism has a linear time complexity as the number of target applications. It is fully practical and advanced performance than the previous works in both of accuracy and efficiency.

A parallel tree code for large N-body simulation: dynamic load balance and data distribution on a CRAY T3D system

Many data-driven software engineering tasks such as discovering programming patterns, mining API specifications, etc., perform source code analysis over control flow graphs (CFGs) at scale. Analyzing millions of CFGs can be expensive and performance of the analysis heavily depends on the underlying CFG traversal strategy. State-of-the-art analysis frameworks use a fixed traversal strategy. We argue that a single traversal strategy does not fit all kinds of analyses and CFGs and propose bespoke control flow analysis (BCFA). Given a control flow analysis (CFA) and a large number of CFGs, BCFA selects the most efficient traversal strategy for each CFG. BCFA extracts a set of properties of the CFA by analyzing the code of the CFA and combines it with properties of the CFG, such as branching factor and cyclicity, for selecting the optimal traversal strategy. We have implemented BCFA in Boa, and evaluated BCFA using a set of representative static analyses that mainly involve traversing CFGs and two large datasets containing 287 thousand and 162 million CFGs. Our results show that BCFA can speedup the large scale analyses by 1%-28%. Further, BCFA has low overheads; less than 0.2%, and low misprediction rate; less than 0.01%.

: This final report describes results of the Phase I SBIR research effort to develop new software testing techniques capable of satisfying the demands of distributed real-time software environments. Traditional software testing techniques are inadequate for distribute systems due to such complicating factors as non-deterministic execution, real-time timing constraints, task interaction event sequencing, race conditions, etc. During the course of the Phase I-effort, OTI investigate a wide variety of candidate technique. For each technique, OTI evaluated the feasibility and defined a research and development approach for the technique and required supporting technologies. Detailed comparison-of the attributes of each technique gives the following prioritization of techniques: Deterministic Execution Testing, Timing Analysis, Control Flow Testing, Dependency Analysis, Network Communication Testing, Data Flow Analysis, Problem Tracking Facility, Mutation Analysis, Static Concurrency Testing. OTI's research approach was based on developing new functionality on top of an existing software testing environment to construct the Environment for Test and Analysis of Distributed Software (ETADS). Distributed software testing, Software testing environment, Control flow analysis, Data flow analysis, Timing analysis, Software instrumentation, Non- determinism, Deterministic replay.

Growing neural gas (GNG) has many applications, including topology preservation, feature extraction, dynamic adaptation, clustering, and dimensionality reduction. These methods have broad applicability in extracting the topological structure of 3D point clouds, enabling unsupervised motion estimation, and depicting objects within a scene. Furthermore, multi-scale batch-learning GNG (MS-BL-GNG) has improved learning convergence. However, it is only implemented on static or stationary datasets, and adapting to dynamic data remains difficult. Similarly, the learning rate cannot be increased if new nodes are added to the existing network after accumulating errors in the sampling data. Next, we propose a new growth approach that, when applied to MS-BL-GNG, significantly increases the learning speed and adaptability of dynamic data distribution input patterns. This method immediately adds data samples as new nodes to existing networks. The probability of adding a new node is determined by the distance between the first, second, and third closest nodes. We applied our method for monitoring a moving object at its pace to demonstrate the usefulness of the proposed model. In addition, optimization methods are used such that processing can be performed in real-time.

Real-world applications have produced massive short text streams. Contrary to the traditional normal texts, they present the characteristics such as short length, only having few labeled data, high-velocity, high-volume and dynamic data distributions, which deteriorate the issues of data sparseness, label missing and concept drift. Obviously, it is a huge challenge for existing short text (stream) classification algorithms due to the poor effectiveness, where they always assume all short texts are completely labeled and little attention is paid on the concept drift issue hidden in short text streams. Therefore, we propose a novel semi-supervised short text steam classification method based on the drift-aware incremental deep learning ensemble model. Specifically, with the sliding window mechanism, we firstly fuse three types of statistical, semantic and structure information to solve the data sparseness issue. Secondly, a semi-supervised incremental deep learning ensemble model based on GCN and the refined LSTM is developed to adapt to the high-volume, high-velocity and label missing short text streams. Thirdly, a label-probability distribution based concept drift detector is introduced to distinguish concept drifts. Finally, as compared with eleven well-known classification methods, extensive experiments demonstrate the effectiveness of the proposed method in the handling of short text streams with limited labeled data.

We present our on-going work to develop techniques for specifying source code signatures of bug patterns. Specifically, we discuss two approaches. The first approach directly analyzes a program in the intermediate representation (IR) of the ROSE compiler infrastructure using ROSE's API. The second analyzes the program using the bddbddb system of Lam, Whaley, et al.. In this approach, we store the IR produced by ROSE as a relational database, express patterns as declarative inference rules on relations in the language Datalog, and bddbddb implements the Datalog programs using binary decision diagram (BDD) techniques. Both approaches readily apply to large-scale applications, since ROSE provides full type analysis, control flow, and other available analysis information. In this paper, we primarily consider bug patterns expressed with respect to the structure of the source code or the control flow, or both. More complex techniques to specify patterns that are functions of data flow properties may be addressed by either of the above approaches, but are not directly treated here. Our Datalog-based work includes explicit support for expressing patterns on the use of the Message Passing Interface (MPI) in parallel distributed memory programs. We show examples of this on-going work as well.

Flow logic offers a compact and versatile notation for expressing the acceptability of solutions to program analysis problems. In contrast to previous logical formulations of program analysis it aims at integrating existing approaches to data flow analysis and control flow analysis. It is able to deal with a broad variety of language paradigms, program properties, kinds of formal semantics, and methods used for computing the best solution. In this paper we illustrate how a compositional flow logic (in “succinct” form) can be systematically transformed into an efficient exhaustive procedure for computing the best solution of a set of constraints generated. This involves transformations to attribute grammars and to specifications of the (“verbose”) form used in control flow analysis.

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