On Time-sensitive Control Dependencies

We present efficient algorithms for time-sensitive control dependencies (CDs). If statement y is time-sensitively control dependent on statement x , then x decides not only whether y is executed but also how many timesteps after x . If y is not standard control dependent on x , but time-sensitively control dependent, then y will always be executed after x , but the execution time between x and y varies. This allows us to discover, e.g., timing leaks in security-critical software. We systematically develop properties and algorithms for time-sensitive CDs, as well as for nontermination-sensitive CDs. These work not only for standard control flow graphs (CFGs) but also for CFGs lacking a unique exit node (e.g., reactive systems). We show that Cytron’s efficient algorithm for dominance frontiers [ 10 ] can be generalized to allow efficient computation not just of classical CDs but also of time-sensitive and nontermination-sensitive CDs. We then use time-sensitive CDs and time-sensitive slicing to discover cache timing leaks in an AES implementation. Performance measurements demonstrate scalability of the approach.

Quantifying the Tangible Value of a Smart Field Asset Implementation Leveraging on Digital Technology and Automation, Taking into Account the Intangible Value Element

The value calculation for a new digital and innovative technology is often requested by the executive management to justify the cost required for the implementation and maintenance of the technology. The value is normally segregated into a tangible and intangible value that correspond to a quantitative and qualitative description of those value elements. As part of the Digital Oilfield (DOF) assessment, the solution value has been defined using two approaches. Firstly, qualitative value is described using a "FEATURE_BENEFIT_VALUE" model. The qualitative value elements have been grouped to align with the company strategic pillars to achieve its vision. Secondly, the quantitative value has been estimated using an NPV model. It estimates the value of the complete digital solution (combined investment for all domains) being proposed for the Asset. The model estimates the net present value (NPV) of the expected Asset investment in digital enablement and digital capability as defined in the assessment report. Net cash flow graphs are also calculated. The approach used is to calculate an NPV for a GO-NOGO decision. Therefore, a conservative estimate of NPV is made with the mind-set that if even being conservative, the NPV clears the company's hurdle rate for such projects, then the decision to invest is undertaken. Sensitivity analysis has been performed using conservative estimates of production gain enabled by digital and conservative oil prices. The paper will detail out the approach for the value quantification of a DOF solution that will also correspond to the industry guidance. Example on how the value is calculated will also be outlined.

Comparative Analysis and Enhancement of CFG-based Hardware-Assisted CFI Schemes

Subverting the flow of instructions (e.g., by use of code-reuse attacks) still poses a serious threat to the security of today’s systems. Various control flow integrity (CFI) schemes have been proposed as a powerful technique to detect and mitigate such attacks. In recent years, many hardware-assisted implementations of CFI enforcement based on control flow graphs (CFGs) have been presented by academia. Such approaches check whether control flow transfers follow the intended CFG by limiting the valid target addresses. However, these papers all target different platforms and were evaluated with different sets of benchmark applications, which makes quantitative comparisons hardly possible. For this paper, we have implemented multiple promising CFG-based CFI schemes on a common platform comprising a RISC-V within FPGA. By porting almost 40 benchmark applications to this system we can present a meaningful comparison of the various techniques in terms of run-time performance, hardware utilization, and binary size. In addition, we present an enhanced CFI approach that is inspired by what we consider the best concepts and ideas of previously proposed mechanisms. We have made this approach more practical and feature-complete by tackling some problems largely ignored previously. We show with this fine-grained scheme that CFI can be achieved with even less overheads than previously demonstrated.

Extracting and analyzing inorganic material synthesis procedures in the literature

Analyzing material synthesis procedures in the literature is required to collect structural information of material names and synthesis procedures for designing materials computationally. Since synthesis procedures are mostly written in natural language in paper or technical documents, they need to be extracted and structured into a format that can be handled by a computer through information extraction. Moreover, to represent a synthesis procedure, it is necessary to express information such as conditions and the order of operations in the procedure, but existing databases that compile structural information of material names and synthesis procedures of materials do not provide such information about procedures. It is, therefore, necessary to create a framework that extracts and organizes the information of synthesis procedures in text so that the information is enough for material development such as the order of operations and the links among materials, operations, and conditions. In this study, we construct a pipeline system that extracts synthesis procedures from a text in the form of a flow graph. The extraction system consists of preprocessing, deep learning-based entity extraction, rule-based relation extraction, and selection for paragraph-containing procedures. We applied the system to a large body of literature and extracted flow graphs (procedures) that include about 4 million entities and 3 million relations. We took several statistics on the extracted graphs and performed several analyses on the extracted graphs. We experimentally confirmed that some extracted operations were specific to the target material and the frequently extracted sub-graphs include reasonable operations.

STOCHASTIC MODELING OF THE TRAINING DEVELOPMENT PROCESS AT THE ENTERPRISE

The article proposes a stochastic model of the training development process based on W-functions and the theory of closed flow graphs, which allows to take into account the probabilistic nature of the process, the possibility of various problems and risks, and to assess the probability of obtaining the desired result. The use of such modeling can increase the effectiveness of decision-making during the planning and implementation of training programs for employees.

An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts

We propose Dr.Aid, a logic-based AI framework for automated compliance checking of data governance rules over data-flow graphs. The rules are modelled using a formal language based on situation calculus and are suitable for decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid models data rules and flow rules and checks compliance by reasoning about the propagation, combination, modification and application of data rules over the data flow graphs. Our approach is driven and evaluated by real-world datasets using provenance graphs from data-intensive research.

Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing

Control Flow Graphs (CFGs) provide fundamental data for many program analyses, such as malware analysis, vulnerability detection, code similarity analysis, etc. Existing techniques for constructing control flow graphs include static, dynamic, and hybrid analysis, which each having their own advantages and disadvantages. However, due to the difficulty of resolving indirect jump relations, the existing techniques are limited in completeness. In this paper, we propose a practical technique that applies static analysis and dynamic analysis to construct more complete control flow graphs. The main innovation of our approach is to adopt directed gray-box fuzzing (DGF) instead of coverage-based gray-box fuzzing (CGF) used in the existing approach to generate test cases that can exercise indirect jumps. We first employ a static analysis to construct the static CFGs without indirect jump relations. Then, we utilize directed gray-box fuzzing to generate test cases and resolve indirect jump relations by monitoring the execution traces of these test cases. Finally, we combine the static CFGs with indirect jump relations to construct more complete CFGs. In addition, we also propose an iterative feedback mechanism to further improve the completeness of CFGs. We have implemented our technique in a prototype and evaluated it through comparing with the existing approaches on eight benchmarks. The results show that our prototype can resolve more indirect jump relations and construct more complete CFGs than existing approaches.