Flow Graph Research Articles

A collaborative method for code clone detection using a deep learning model

Code cloning (CC) is the process of copying and reconfiguring a code fragment and using it in another part of a software project. This clones increases the running overhead of the software. As a result, Code Clone Detection (CCD) has become an active research area in software development research. The detection of Large-Variance Code Clones (LV-CCs) is very difficult when the lines of codes (LOCs) in the source code are very large. The distance metrics have been used in LV-CC detection by calculating the distance between training feature sets of source codes and testing feature sets of source codes. However, threshold selection for detecting clones is a challenging issue in distance-based LV-CC detection. To solve this, a Collaborative CCD using Deep Learning (CCCD-DL) is developed in this paper by utilising lexical, syntactic, semantic and structural features for identifying all types of clones together. A lexical feature is extracted from Clone Pairs (CPs) identified by LV-Mapper. Syntactic and semantic features are identified by the Abstract Syntax Tree (AST) and Control Flow Graph (CFG). The structural features are extracted by code size metrics (CZMs) and object-oriented metrics (OOMs). All features are coordinated and fed into the input layer of DNN. The hidden layer then transforms the inputs into the neural vertices in the multi-classification stage using linear transformation preceded by suppressing non-linearity. This process can generate a complicated and non-hypothetical prototype with a weight matrix for fitting the training sequence. Thus, the feed-forward step has been successfully completed. This model then uses back-propagation in the following element to modify the weight matrix based on the training set. Finally, a softmax layer converts the clone detection task into a classification process. The results of the experiments show that the proposed method solves distance-based problems more quickly and effectively than the traditional methods for the CCD.

Pluto: Exposing Vulnerabilities in Inter-Contract Scenarios

Attacks on smart contracts have caused considerable losses to digital assets. Many techniques based on symbolic execution, fuzzing, and static analysis are used to detect contract vulnerabilities. Most of the current analyzers only consider vulnerability detection intra-contract scenarios. However, Ethereum contracts usually interact with others by calling their functions. A bug hidden in a path that depends on information from external contract calls is defined as an inter-contract vulnerability. Failure to deal with this kind of bug can result in potential false negatives and false positives. In this work, we propose Pluto, which supports vulnerability detection in inter-contract scenarios. It first builds an Inter-contract Control Flow Graph (ICFG) to extract semantic information among contract calls. Afterward, it symbolically explores the ICFG and deduces Inter-Contract Path Constraints (ICPC) to check the reachability of execution paths more accurately. Finally, Pluto detects whether there is a vulnerability based on some predefined rules. For evaluation, we compare Pluto with five state-of-the-art tools, including Oyente, Mythril, Securify, ILF, and Clairvoyance on a labeled benchmark and 39,443 real-world Ethereum smart contracts. The result shows that other tools can only detect 10% of the inter-contract vulnerabilities, while Pluto can detect 80% of them on the labeled dataset. Beyond that, Pluto has detected 451 confirmed vulnerabilities on real-world contracts, including 36 vulnerabilities in inter-contract scenarios. Two bugs have been assigned with unique CVE identifiers by the US National Vulnerability Database (NVD). On average, Pluto costs 16.9 seconds to analyze a contract, which is as fast as the state-of-the-art tools.

A Pipelined Algorithm and Area-Efficient Architecture for Serial Real-Valued FFT

In this brief, we present a novel pipelined architecture for real-valued fast Fourier transform (RFFT), which is dedicated to processing serial input data. An optimized algorithm is proposed for stage division in RFFT to achieve an area-efficient RFFT computing structure with full hardware utilization. A single path butterfly and a real rotator are merged into one processing element (PE) in each stage, except the last stage, to reduce hardware resource utilization. In addition, a novel shift-adder is designed for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> -point RFFT with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> -bit signals, and a new data management method based on the PEs is proposed, which saves resources with a more regular flow graph.

HCIP: Hybrid Short Long History Table-based Cache Instruction Prefetcher

In modern applications, instruction cache misses have become a performance constraint, and numerous prefetchers have been developed to conceal memory latency. With today's client and server workloads, large instruction working sets require more. These working sets are typically large enough to fit in the Last Level Cache (LLC). However, the Level 1 Instruction (L1-I) cache has a high miss rate, which typically prevents the processor front-end from receiving instructions. Instruction prefetching is a latency hiding method that allows the LLC to send instructions to the L1-I cache. In order to design a high-performance cache architecture, prefetching instructions in the L1-I cache is a fundamental approach. When developing an efficient and effective prefetcher, accuracy and coverage are the most important parameters to be considered. This paper proposed a novel Hybrid Short Long History Table-based Cache Instruction Prefetcher (HCIP) for the L1-I cache. The HCIP makes use of a hybrid configuration of the two history-based prefetchers tables that are Long History Table (LST) and Short History Table (SHT). The transitive closure of the control flow graph is the PRE+PC table used in HCIP. In contrast to PIPS and NOPREF, HCIP indicates maximum coverage of 67% for the majority of the benchmarks given.

Combine sliced joint graph with graph neural networks for smart contract vulnerability detection

Smart contract security has drawn extensive attention in recent years because of the enormous economic losses caused by vulnerabilities. Even worse, fixing bugs in a deployed smart contract is difficult, so developers must detect security vulnerabilities in a smart contract before deployment. Existing smart contract vulnerability detection efforts heavily rely on fixed rules defined by experts, which are inefficient and inflexible.To overcome the limitations of existing vulnerability detection approaches, we propose a GNN based approach for smart contract vulnerability detection. First, we construct a graph representation for a smart contract function with syntactic and semantic features by combining abstract syntax tree (AST), control flow graph (CFG), and program dependency graph (PDG). To further strengthen the presentation ability of our approach, we perform program slicing to normalize the graph and eliminate the redundant information unrelated to vulnerabilities. Then, we use a Bidirectional Gated Graph Neural-Network model with hybrid attention pooling to identify potential vulnerabilities in smart contract functions. Empirical results show that our approach can achieve 89.2% precision and 92.9% recall in smart contract vulnerability detection on our dataset and reveal the effectiveness and efficiency of our approach.

Data pre-processing with high-level-synthesis and dataflow programming using HLS C++ dataflow template library

The readout of detectors with FPGAs is a common practice. Traditionally, this is done using a hardware description language such as VHDL, Verilog, or System Verilog. Data is preprocessed, filtered, and passed to high-performance computing clusters for the next processing steps. Many experiments require a continuous, trigger-less data stream, which significantly increases the algorithmic requirements. In this work, we show how to implement the increasingly complex algorithms using methods of dataflow programming and methods of Modern HLS C++ Template Programming. The focus is also on resource consumption, which must remain below an acceptable limit compared to a pure VHDL implementation. Additional design goals are fast and shorter development time, easy maintenance, the ability to adapt algorithms to changing needs, and high throughput to handle continuous data streams. We present the concept of a dataflow template library that can be used to realize the above design goals. The template library allows an algorithm to be represented as a data flow graph. It is designed in such a way that the maximum possible data throughput can be implemented with an initiation interval of 1. The locality of the algorithm is instantiated by local streaming buffers. It is shown how these buffers are implemented in such a way that a nearly balanced graph is obtained.

Basic block coverage for search-based unit testing and crash reproduction

Search-based techniques have been widely used for white-box test generation. Many of these approaches rely on the approach level and branch distance heuristics to guide the search process and generate test cases with high line and branch coverage. Despite the positive results achieved by these two heuristics, they only use the information related to the coverage of explicit branches (e.g., indicated by conditional and loop statements), but ignore potential implicit branchings within basic blocks of code. If such implicit branching happens at runtime (e.g., if an exception is thrown in a branchless-method), the existing fitness functions cannot guide the search process. To address this issue, we introduce a new secondary objective, called Basic Block Coverage (BBC), which takes into account the coverage level of relevant basic blocks in the control flow graph. We evaluated the impact of BBC on search-based unit test generation (using the DynaMOSA algorithm) and search-based crash reproduction (using the STDistance and WeightedSum fitness functions). Our results show that for unit test generation, BBC improves the branch coverage of the generated tests. Although small (sim 1.5%), this improvement in the branch coverage is systematic and leads to an increase of the output domain coverage and implicit runtime exception coverage, and of the diversity of runtime states. In terms of crash reproduction, in the combination of STDistance and WeightedSum, BBC helps in reproducing 3 new crashes for each fitness function. BBC significantly decreases the time required to reproduce 43.5% and 45.1% of the crashes using STDistance and WeightedSum, respectively. For these crashes, BBC reduces the consumed time by 71.7% (for STDistance) and 68.7% (for WeightedSum) on average.

Flows, growth rates, and the veering polynomial

AbstractFor a pseudo-Anosov flow $\varphi $ without perfect fits on a closed $3$ -manifold, Agol–Guéritaud produce a veering triangulation $\tau $ on the manifold M obtained by deleting the singular orbits of $\varphi $ . We show that $\tau $ can be realized in M so that its 2-skeleton is positively transverse to $\varphi $ , and that the combinatorially defined flow graph $\Phi $ embedded in M uniformly codes the orbits of $\varphi $ in a precise sense. Together with these facts, we use a modified version of the veering polynomial, previously introduced by the authors, to compute the growth rates of the closed orbits of $\varphi $ after cutting M along certain transverse surfaces, thereby generalizing the work of McMullen in the fibered setting. These results are new even in the case where the transverse surface represents a class in the boundary of a fibered cone of M. Our work can be used to study the flow $\varphi $ on the original closed manifold. Applications include counting growth rates of closed orbits after cutting along closed transverse surfaces, defining a continuous, convex entropy function on the ‘positive’ cone in $H^1$ of the cut-open manifold, and answering a question of Leininger about the closure of the set of all stretch factors arising as monodromies within a single fibered cone of a $3$ -manifold. This last application connects to the study of endperiodic automorphisms of infinite-type surfaces and the growth rates of their periodic points.

Parasitic Oscillation Analysis of Trench IGBT During Short-Circuit Type II Using TCAD-Based Signal Flow Graph Model

The oscillation phenomenon of trench-type insulated gate bipolar transistors during short-circuit (SC) type II was investigated experimentally and theoretically. The gate resistance required to suppress oscillations decreased with an increasing collector voltage. The oscillation conditions were calculated from the signal flow graph model using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> -parameter based on a technology computer-aided design simulation. The calculation results reproduced the locus of the collector voltage dependence of the experimentally measured gate resistance. The oscillation mechanism was investigated using the device simulation. The response of carrier density modulation at the base-drift layer boundary was found to transmit to the collector side through the electron–hole plasma region during the oscillation, indicating that the transfer characteristics of the carrier density modulation in the drift region at the specific collector voltage influence the collector voltage dependence of the SC oscillation. The influence of circuit parameters on the oscillation was also investigated. An increase in the emitter inductance suppressed the oscillations, whereas an increase in gate inductance increases oscillations.

Tracking the flow of excreta across the sanitation service chain in Nkubu town

Inadequate access to improved sanitation and water remains a huge factor contributing to increased mortality and morbidity rates, especially among children. Despite the growth in economy and recognition of sanitation as a basic right, investment in sanitation infrastructure has not yet been achieved, particularly in low- and middle-income area. As such, nearly 827 000 people die yearly due to inadequate sanitation, water and hygiene. To track the flow of excreta in Urban areas, the existing sanitation systems were assessed, the proportion of community with access to safely managed feacal sludge was examined, the challenges facing service delivery was assessed and finally, a Shit Flow Diagram for Nkubu town was developed. A descriptive cross-sectional survey was employed. The research instruments involved were use of key informants’ interviews, observation for primary data. The secondary data was gotten from existing data. The Susana platform and the shit flow diagram tools were used for data analysis and generating SFD for Nkubu town. The data collected has been presented using a shit flow diagram, tables, graphs and charts. The results indicate that only 28% of the excreta is safely managed. The pit latrines were the most used containment method with 45% and 3% of the population practicing open defecation. Only 40% of the feacal sludge taken to the treatment was properly treated and disposed. The challenges experienced in the management of fecal sludge were high water table in the area and high license and service fee charged. The findings imply that the unsafely excreta management practices in Nkubu town pose a risk to the health of residents in and around the town and the quality of water sources. This study points to the possible areas of interventions such as proper planning of the town and also creating an enabling environment for feacal sludge management.

Metamorphic relation automation: Rationale, challenges, and solution directions

AbstractMetamorphic testing addresses the issue of the oracle problem by comparing results transformation from multiple test executions. The relationship that governs the output transformation is called metamorphic relation. Metamorphic relations require expert knowledge and the generation of them is considered a time‐consuming task. Researchers have proposed various techniques to automate metamorphic testing, generation, and selection. Although there are several research articles on this issue, there is a lack of overview of the state‐of‐the‐art of metamorphic relation automation. As such, we performed a systematic literature review study to collect, extract, and synthesize the required data. Based on our research questions, the literature was categorized and summarized into different categories. We found that the automation of metamorphic relation is most effective in mathematical and scientific applications. We concluded that some approaches involve analysis of different forms of software‐related information such as control flow graph and program dependence graph as well as an initial set of metamorphic relations. On the other hand, other methods involve analysis of executions of the software functions with random and specific inputs. The results show that this field is still in its infancy with opportunities for novel work, especially in methods utilizing machine learning.

Unified Meaning Representation Format (UMRF) - A Task Description and Execution Formalism for HRI

To facilitate continuous development of novel HRI systems, it is beneficial to have tools that enable quick adjustments, flexibility, or re-invention of the human interfaces when system requirements change due to updates in the state-of-the-art, application domain, etc. Thus, modularity is a key design principle which promotes software reuse and scalability, and reduces development time and cost. Hence, a robot’s autonomous capabilities should not depend on the command interface and should be decoupled via a common format that possesses the descriptive capabilities for outlining tasks and has a sensible syntax for HRI. In this paper, we propose the Unified Meaning Representation Format (UMRF) , which provides the syntax and semantics for passing both simple and complex commands modelled as control flow graphs. UMRF is a standalone meaning representation container that supports embedding other meaning representation formalisms, such as predicate-argument semantics and graphical meaning representation formats, making it adoptable as a standard task description format for semi-autonomous systems in HRI domains. In this article, we define the UMRF syntax and semantics, summarize its unique aspects relative to related task description formats, and demonstrate its descriptiveness by navigating a robot via concurrent (e.g., gestures and speech) and interchangeable input systems (e.g., Google Assistant, Amazon Alexa).

Dynamics of a Stratified Population of Optimum Seeking Agents on a Network—Part II: Steady-State Analysis

In this second part of our work, we study the steady state of the population and the social utility for a general class of dynamics that converge to the set of Nash equilibria and follow a certain positive correlation property. This class of dynamics includes the three dynamics introduced in the first part. We provide sufficient conditions on the network based on a maximum payoff density parameter of each node under which there exists a unique Nash equilibrium. We then utilize the positive correlation properties of the dynamics to reduce the flow graph in order to provide an upper bound on the steady-state social utility. Finally, we extend the idea behind the sufficient condition for the existence of a unique Nash equilibrium to partition the graph appropriately in order to provide a lower bound on the steady-state social utility. We also illustrate interesting cases as well as our results using simulations.

Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

Traffic speed forecasting plays an important role in intelligent traffic monitoring systems. Existing methods mostly predefine a fixed adjacency matrix to capture the spatial correlation between sensors in a traffic network. However, there are multiple hidden spatial correlations between sensors. A single fixed adjacency matrix cannot adaptively capture multiple spatial correlations. To overcome this limitation, we proposed a novel multiadaptive spatiotemporal flow graph neural network (MAF-GNN) for traffic speed forecasting. Specifically, MAF-GNN mainly consists of a multiadaptive adjacency matrix mechanism and a spatiotemporal flow mechanism. The multiadaptive adjacency matrix mechanism was proposed to adaptively capture multiple hidden spatial correlations between sensors. The spatiotemporal flow mechanism was proposed to further enhance the capture of temporal and spatial correlations. The experimental results on two real-world traffic datasets, METR-LA and PeMS-Bay, demonstrated the superiority of MAF-GNN. MAF-GNN outperformed baseline models in 1-h ahead forecasting.

Sharded Blockchain for Collaborative Computing in the Internet of Things: Combined of Dynamic Clustering and Deep Reinforcement Learning Approach

Immutability, decentralization, and linear promoted scalability make the sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportion of cross-shard transactions (CSTs). On the other hand, the assemblage characteristic of the collaborative computing in IoT has not been received attention. Therefore, in this article, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-means</i> -clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.

DL-FHMC: Deep Learning-Based Fine-Grained Hierarchical Learning Approach for Robust Malware Classification

The acceptance of the Internet of Things (IoT) for both household and industrial applications is accompanied by the rapid growth of IoT malware. With the increase of their attack surface, analyzing, understanding, and detecting IoT malicious behavior are crucial. Traditionally, machine and deep learning-based approaches are used for malware detection and behavioral understanding. However, recent research has shown the susceptibility of those approaches to adversarial attacks by introducing noise to the feature space. In this work, we introduce DL-FHMC, a fine-grained hierarchical learning approach for robust IoT malware detection. DL-FHMC utilizes Control Flow Graph (CFG)-based behavioral patterns for adversarial IoT malicious software detection. In particular, we extract a comprehensive list of behavioral patterns from a large dataset of malicious IoT binaries, represented by the shared execution flows, and use them as a modality for malicious behavior detection. Leveraging machine learning and subgraph isomorphism matching algorithms, DL-FHMC provides state-of-the-art performance in detecting malware samples and adversarial examples (AEs). We first highlight the caveats of CFG-based IoT malware detection systems, showing the adversarial capabilities in generating practical functionality-preserving AEs with reduced overhead using Graph Embedding and Augmentation (GEA) techniques. We then introduce Suspicious Behavior Detector, a component that extracts comprehensive behavioral patterns from three popular IoT malicious families, Gafgyt, Mirai, and Tsunami, for AEs detection with high accuracy. The proposed detector operates as a model-independent standalone module, with no prior assumptions of the adversarial attacks nor their configurations.

Software Defined Radio-Based Wireless Sensing System.

In this paper, we investigate the application of using software-defined radio (SDR) and surface acoustic wave (SAW) device for wireless measurement of the response of in situ sensors. SDR uses software to realize different communication functions. After collecting the magnitude and phase of the response at discrete frequencies, we apply inverse Fourier transform to analyze the time domain responses which, in turn, allows for monitoring the changes of the response of the in situ sensor. We employ microwave signal flow graph concepts to improve the quality of the received signals. Comparing the normalized results obtained by SDR with those obtained from a commercial vector network analyzer (VNA), we demonstrate that the results are sufficiently close, and the SDR-based experiments can provide satisfactory measurement of the in-situ sensors. The objective is to eventually employ this wireless measurement system for soil nutrient sensing.

Efficient Binary Static Code Data Flow Analysis Using Unsupervised Learning

The ever-increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/ assembly language format in support of manual static code analysis. Because of the highly manual and resource-intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis.

SmartProvenance: User‐friendly provenance system for internet of things applications based on event flow graphs

SmartProvenance: User‐friendly provenance system for internet of things applications based on event flow graphs

System Dynamics Models of Online Lending Platform Based on Vensim Simulation Technology and Analysis of Interest Rate Evolution Trend.

This paper mainly focuses on the construction of a system dynamics model of an online lending platform and the application of Vensim simulation technology in the analysis of the evolution trend of the investment interest rate. The system dynamics flow graph models of the investment subsystem, loan subsystem, and interest rate subsystem of the online lending platform were innovatively constructed; the level variable, flow rate variable, auxiliary variable, and mathematical relations between these variables as they relate to the platform were innovatively studied. Based on the key variables of the online lending platform, a system dynamics model of the online lending platform was innovatively constructed. The parameters were assigned using data from China's online lending industry, and the logical consistency, sensitivity, and validity of the simulation data of the system dynamics model were tested. Finally, we used Vensim simulation technology to simulate and analyze the impact of emergency scenarios on the interest rate evolution trend. Overall, this paper provides a scientific simulation technology and data analysis method for examining online lending platforms.