Constraint-based causal discovery algorithms aim to extract causal relationships between variables of interest by using conditional independence tests (CITs). However, CITs with large conditioning sets often lead to unreliable results due to their low statistical power, propagating errors throughout the course of causal discovery. As the reliability of CITs is crucial for their practical applicability, recent approaches rely on either tricky heuristics or complicated routines with high computational costs to tackle inconsistent test results. Against this background, we propose a principled, simple, yet effective method, coined \textsc{deduce-dep}, which corrects unreliable conditional independence statements by replacing them with deductively reasoned results from lower-order CITs. An appealing property of \textsc{deduce-dep} is that it can be seamlessly plugged into existing constraint-based methods and serves as a modular subroutine. In particular, we showcase the integration of \textsc{deduce-dep} into representative algorithms such as HITON-PC and PC, illustrating its practicality. Empirical evaluation demonstrates that our method properly corrects unreliable CITs, leading to improved performance in causal structure learning.

Heap randomization, in general, has been a well-trodden area; however, the efficacy of byte-granularity randomization has never been fully explored as misalignment raises various concerns. Modern heap exploits often abuse the determinism in word alignment, and modern CPU architecture better supports unaligned access (since Nehalem). Based on such new developments, we conduct an in-depth analysis of evaluating the efficacy of byte-granularity heap randomization in three folds: (i) security effectiveness, (ii) performance impact, and (iii) compatibility analysis to measure deployment cost. Security discussion is based on 20 CVE case studies. To measure performance details, we conduct cycle-level microbenchmarks and report that the performance cost is highly concentrated to edge cases depending on the L1-cache line. Based on such analysis, we design and implement an allocator suited for byte-granularity heap randomization. On the negative side, our analysis suggests that byte-granularity heap randomization has high deployment cost due to various implementation conflicts. We enumerate the problematic compatibility issues using Coreutils, Nginx, and ChakraCore benchmarks.

Object Layout Randomization (OLR) is a memory randomization approach that makes unpredictable in-object memory layout by shuffling and relocating each member fields of the object. This defense approach has significant security effect for mitigating various types of memory error attacks. However, the current state-of-the-art enforces OLR while compile time. It makes diversified object layout for each binary, but the layout remains equal across the execution. This approach can be effective in case the program binary is hidden from attackers. However, there are several limitations: (i) the security efficacy is built with the premise that the binary is safely undisclosed from adversaries, (ii) the randomized object layout is identical across multiple executions, and (iii) the programmer should manually specify which objects should be affected by OLR. In this paper, we introduce Per-allocation Object Layout Randomization(POLaR): the first dynamic approach of OLR suited for public binaries. The randomization mechanism of POLaR is applied at runtime, and the randomization makes unique object layout even for the same type of instances. As a result, POLaR achieves two previously unmet security primitives. (i) The randomization does not break upon the exposure of the binary. (ii) Repeating the same attack does not result in deterministic behavior. In addition, we also implemented the TaintClass framework based on DFSan project to optimize/automate the target object selection process. To show the efficacy of POLaR, we use several public open-source software and SPEC2006 benchmark suites.

A control method is presented for a five-fingered artificial hand using EMG signals. The artificial hand is driven by pneumatic actuators and has 15 degrees of freedom. It is difficult to discriminate all the finger motions from just the EMG signals. Therefore, we describe typical hand motions using Petri net and control the finger motions based on this model. The proposed method enables the operator to incrementally control the joints of the five fingers based on the discrimination of the discrete hand motion. The operator only needs to specify the kind of discrete hand motion (i.e., spherical grasp, power grip, hook grip, key grip, and precision grip) and does not need to consider how to control each finger. Each state of the Petri net stores the on/off pattern of the 15 solenoid valves that corresponds to the posture of the five-fingered hand. The hand posture is incrementally varied to complete the desired motion, transitioning to the state in the Petri net based on EMG motion discrimination.We conducted experiments using four able-bodied subjects. In the experiment, the above-mentioned five motions were successfully performed using six-channel EMG signals measured from the forearm of the operator.

In this paper, we present an event-driven track management method to detect reliably and track robustly while minimizing missing and false detections. No state-of-the-art vehicle detection method can detect all the vehicles on the road without error. A multi-vehicle tracking method is essential to minimize the number of missing and false detections. In a multi-vehicle tracking method, there are three types of errors: false negative alarms, false positive alarms, and track identity switches. Our track management method can reduce the number of these errors remarkably while processing in real time for online application. Our track management method has four states: IDLE, PRE-TRACK, CUR-TRACK, and POST-TRACK. Most false positive alarms are removed in the PRE-TRACK state due to their sparseness. A track state transition to other states is determined by a track score. The track score is calculated by obstacle detection, vehicle recognition, detection-by-tracking, and data association. The proposed method is tested and verified with image sequences in real road environments. The experimental results demonstrate that the event-driven track management method minimizes the number of the false positive and false negative alarms remarkably compared with previous methods.