Study on Optimization of Flood Discharge Types in MHSJ Stilling Basin

At the beginning of the operation of a Hydropower Station adopted a new type of stilling basin with multi-horizontal submerged jets (MHSJ), it was found there was a phenomenon of roller shutter door and window vibration in some local area of the downstream region during the flood discharging process. The prototype observation indicated that the flow induced vibration is greatly influenced by flood discharging types with different open combination of the sluice gates. Flow fluctuating pressure is a main load that frequently causes damages to flood discharge structures, which is a crucial incentive that caused flow induced vibration of the downstream region of the hydropower station. In this paper, from the perspective of hydraulics, the flood discharging types with different open combination of the sluice gates under same flood discharge were simulated through a series of hydraulic model experiments. Judged by the values of fluctuating pressure on the bottom plate of stilling basin, it was found the joint discharging type of surface outlets and middle outlets is better than surface outlets discharging type or middle outlets discharging type. The response law between discharge allocation proportion of each outlet and fluctuating pressure characteristics in the still basin was preliminary revealed. The optimal flood discharge types were obtained. The research results can provide technical support for the operation and management of the Hydropower Station. The reduction of vibration intensity from the source is expected

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary. So far, the adversarial examples can only deceive Android malware detectors that rely on syntactic features, and the perturbations can only be implemented by simply modifying Android manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new highly-effective attack that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK using a substitute model. Based on the transferability concept, the perturbations that successfully deceive the substitute model are likely to deceive the original models as well. We develop an automated tool to generate the adversarial examples without human intervention to apply the attacks. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We evaluated the proposed manipulation methods for adversarial examples by using the same datasets that Drebin and MaMadroid (5879 malware samples) used. Our results show that, the malware detection rates decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure

Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing

Knowledge tracing (KT) aims to monitor students' evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students' responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT.Comment: 13 page