Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Distribution Matters in Greybox Fuzzing

Existing energy distribution strategies of AFL and its variants have two limitations. (1) They focus on increasing coverage but ignore the fact that some code regions are more likely to be vulnerable. (2) They randomly select mutators and deterministically specify the number to mutator, therefore lack insights regarding which granularity of mutators are more helpful at that particular stage. We improve the two limitations of AFL's fuzzing energy distribution in a principled way. We direct the fuzzer to strengthen fuzzing toward regions that have a higher probability to contain vulnerabilities based on static semantic metrics of the target program. Furthermore, granularity-aware scheduling of mutators is proposed, which dynamically assigns ratios to different mutation operators. We implemented these improvements as an extension to AFL. Large-scale experimental evaluations showed the effectiveness of each improvement and performance of integration. The proposed tool has helped us find 12 new bugs and expose three new CVEs.
Kernel-Based Reinforcement Learning in Robust Markov Decision Processes

The robust Markov Decision Process (MDP) framework aims to address the problem of parameter uncertainty due to model mismatch, approximation errors or even adversarial behaviors. It is especially relevant when deploying the learned policies in real-world applications. Scaling up the robust MDP framework to large or continuous state space remains a challenging problem. The use of function approximation in this case is usually inevitable and this can only amplify the problem of model mismatch and parameter uncertainties. It has been previously shown that, in the case of MDPs with state aggregation, the robust policies enjoy a tighter performance bound compared to standard solutions due to its reduced sensitivity to approximation errors. We extend these results to the much larger class of kernelbased approximators and show, both analytically and empirically that the robust policies can significantly outperform the non-robust counterpart.
Active 10: Brisk Walking to Support Regular Physical Activity

We describe a methodology and a technology supporting an intervention carried out by Public Health England (PHE) to encourage physically inactive people (doing less than 30 minutes' physical activity per week) to initiate regular physical activity via 10 minutes of daily brisk walking. The intervention is designed to encourage the inclusion of short bouts of continuous brisk walking in everyday activities such as shopping or commuting. To this extent a behaviour change mobile application, Active 10, was developed and distributed freely for Android and iOS. The app was downloaded over 620,000 times and our server infrastructure has collected nearly a billion data points between March 2017 and January 2019. The paper describes the rationale for Active 10, the application supporting the intervention, the data architecture and the data collection approach. Then we discuss the complexity of developing a health tracking technology with such large number of users, producing a significant volume of data. Finally, we describe a preliminary data analysis, focussing on a cohort of 129,010 users who used the app for over 8 weeks: 73% of these users achieved less than ten minutes of brisk walking per day during the first week; by the end of the 8th week this subset of users showed, on average, a 10-fold increase in brisk walking. The most inactive section of the cohort, the 54% of users who showed virtually no brisk walking activity during week 1, seems to achieve the greatest proportional increase, and by the end of week 8 they appear to meet, on average, 10 minutes of continuous brisk walking per day. The increase is more evident within the 15% of the cohort who kept the app for over six months: on average a 12% increase in average activity was observed in this group with no sign of decline.
Improving the Software Logging Practices in DevOps

DevOps refers to a set of practices dedicated to accelerating modern software engineering process. It breaks the barriers between software development and IT operations and aims to produce and maintain high quality software systems. Software logging is widely used in DevOps. However, there are few guidelines and tool support for composing high quality logging code and current application context of log analysis is very limited with respect to feedback for developers and correlations among other telemetry data. This thesis proposes automated approaches to improving software logging practices in DevOps by leveraging various types of software repositories (e.g., historical, communication, bug, and runtime repositories). We aim to support the software development side by providing guidelines and tools on developing and maintaining high quality logging code. We aim to support the IT operation side by enriching the log analysis context through systematic estimating code coverage via executing logs and in-depth problem diagnosis by correlating logs with other telemetry data (e.g., traces and APM data). Case studies show that our approaches can provide useful software logging suggestions to both developers and operators in open source and commercial systems.
Deep Differential Testing of JVM Implementations

The Java Virtual Machine (JVM) is the cornerstone of the widely-used Java platform. Thus, it is critical to ensure the reliability and robustness of popular JVM implementations. However, little research exists on validating production JVMs. One notable effort is classfuzz, which mutates Java bytecode syntactically to stress-test different JVMs. It is shown that classfuzz mainly produces illegal bytecode files and uncovers defects in JVMs' startup processes. It remains a challenge to effectively test JVMs' bytecode verifiers and execution engines to expose deeper bugs. This paper tackles this challenge by introducing classming, a novel, effective approach to performing deep, differential JVM testing. The key of classming is a technique, live bytecode mutation, to generate, from a seed bytecode file f, likely valid, executable (live) bytecode files: (1) capture the seed f 's live bytecode, the sequence of its executed bytecode instructions; (2) repeatedly manipulate the control- and data-flow in f 's live bytecode to generate semantically different mutants; and (3) selectively accept the generated mutants to steer the mutation process toward live, diverse mutants. The generated mutants are then employed to differentially test JVMs. We have evaluated classming on mainstream JVM implementations, including OpenJDK's HotSpot and IBM's J9, by mutating the DaCapo benchmarks. Our results show that classming is very effective in uncovering deep JVM differences. More than 1,800 of the generated classes exposed JVM differences, and more than 30 triggered JVM crashes. We analyzed and reported the JVM runtime differences and crashes, of which 14 have already been confirmed/fixed, including a highly critical security vulnerability in J9 that allowed untrusted code to disable the security manager and elevate its privileges (CVE-2017-1376).
Adversarial Training for Weakly Supervised Event Detection

Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled data. However, these methods typically rely on sophisticated pre-defined rules as well as existing instances in knowledge bases for automatic annotation and thus suffer from low coverage, topic bias, and data noise. To address these issues, we build a large event-related candidate set with good coverage and then apply an adversarial training mechanism to iteratively identify those informative instances from the candidate set and filter out those noisy ones. The experiments on two real-world datasets show that our candidate selection and adversarial training can cooperate together to obtain more diverse and accurate training data for ED, and significantly outperform the state-of-the-art methods in various weakly supervised scenarios. The datasets and source code can be obtained from https://github.com/thunlp/Adv-ED.
Composing Value Functions in Reinforcement Learning

An important property for lifelong-learning agents is the ability to combine existing skills to solve new unseen tasks. In general, however, it is unclear how to compose existing skills in a principled manner. Under the assumption of deterministic dynamics, we prove that optimal value function composition can be achieved in entropyregularised reinforcement learning (RL), and extend this result to the standard RL setting. Composition is demonstrated in a high-dimensional video game, where an agent with an existing library of skills is immediately able to solve new tasks without the need for further learning.
MAF: Method-Anchored Test Fragmentation for Test Code Plagiarism Detection

Software engineering education becomes popular due to the rapid development of the software industry. In order to reduce learning costs and improve learning efficiency, some online practice platforms have emerged. This paper proposes a novel test code plagiarism detection technology, namely MAF, by introducing bidirectional static slicing to anchor methods under test and extract fragments of test codes. Combined with similarity measures, MAF can achieve effective plagiarism detection by avoiding massive unrelated noisy test codes. The experiment is conducted on the dataset of Mooctest, which so far has supported hundreds of test activities around the world in the past 3 years. The experimental results show that MAF can effectively improve the performance (precision, recall and F1-measure) of similarity measures for test code plagiarism detection. We believe that MAF can further expand and promote software testing education, and it can also be extended to use in test recommendation, test reuse and other engineering applications.
Iterative Search for Weakly Supervised Semantic Parsing

Training semantic parsers from question-answer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates between searching for consistent logical forms and maximizing the marginal likelihood of the retrieved ones. This training scheme lets us iteratively train models that provide guidance to subsequent ones to search for logical forms of increasing complexity, thus dealing with the problem of spuriousness. We evaluate these techniques on two hard datasets: WikiTableQuestions (WTQ) and Cornell Natural Language Visual Reasoning (NLVR), and show that our training algorithm outperforms the previous best systems, on WTQ in a comparable setting, and on NLVR with significantly less supervision.
Improving Neural Machine Translation with Neural Syntactic Distance

The explicit use of syntactic information has been proved useful for neural machine translation (NMT). However, previous methods resort to either tree-structured neural networks or long linearized sequences, both of which are inefficient. Neural syntactic distance (NSD) enables us to represent a constituent tree using a sequence whose length is identical to the number of words in the sentence. NSD has been used for constituent parsing, but not in machine translation. We propose five strategies to improve NMT with NSD. Experiments show that it is not trivial to improve NMT with NSD; however, the proposed strategies are shown to improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En–Ch), and +1.0 (Ch–En) BLEU).
A Survey on Situational Awareness of Ransomware Attacks - Detection and Prevention Parameters

In recent years, cybercrime activities have grown significantly, compromising device security and jeopardizing the normal activities of enterprises. The profits obtained through intimidation and the limitations for tracking down the illegal transactions have created a lucrative business based on the hijacking of users’ files. In this context, ransomware takes advantage of cryptography to compromise the user information or deny access to the operating system. Then, the attacker extorts the victim to pay a ransom in order to regain access, recover the data, or keep the information private. Nowadays, the adoption of Situational Awareness (SA) and cognitive approaches can facilitate the rapid identification of ransomware threats. SA allows knowing what is happening in compromised devices and network communications through monitoring, aggregation, correlation, and analysis tasks. The current literature provides some parameters that are monitored and analyzed in order to prevent these kinds of attacks at an early stage. However, there is no complete list of them. To the best of our knowledge, this paper is the first proposal that summarizes the parameters evaluated in this research field and considers the SA concept. Furthermore, there are several articles that tackle ransomware problems. However, there are few surveys that summarize the current situation in the area, not only regarding its evolution but also its issues and future challenges. This survey also provides a classification of ransomware articles based on detection and prevention approaches.
Decoupling Language and Editor - The Impact of the Language Server Protocol on Textual Domain-Specific Languages

: Model-Driven Software Development using Domain-Speciﬁc Languages (DSL) has been widely adopted throughout research and industry. The language workbenches required to efﬁciently build Domain-Speciﬁc Languages and the associated editor support are often deeply integrated into a speciﬁc Integrated Development Environment (IDE). Thereby, the chosen Domain-Speciﬁc Language workbench predicts the IDE required to use the DSL. Yet, this IDE might not be the best choice for further implementing, testing, and debugging the generated code. A case study was conducted to analyze how the Language Server Protocol could be utilized to decouple the DSL implementation from a speciﬁc editor integrated into an IDE. First, the Language Server Protocol capabilities are exempliﬁed by building editor support for an Entity-DSL that is integrated into two different IDEs. Second, a SWOT analysis is carried out to identify strengths and weaknesses as well as opportunities and threats for Domain-Speciﬁc Languages utilizing the Language Server Protocol. The case study’s results indicate that the Language Server Protocol enables efﬁcient multi-editor integration. Further, the results of the SWOT analysis imply potential beneﬁts for cross-functional teams specifying a shared domain model.
LegoNet: Efficient Convolutional Neural Networks with Lego Filters

This paper aims to build efﬁcient convolutional neural networks using a set of Lego ﬁlters. Many successful building blocks, e.g. inception and residual modules, have been designed to refresh state-of-the-art records of CNNs on visual recognition tasks. Beyond these high-level modules, we suggest that an ordinary ﬁlter in the neural network can be upgraded to a sophisticated module as well. Filter modules are established by assem-bling a shared set of Lego ﬁlters that are often of much lower dimensions. Weights in Lego ﬁlters and binary masks to stack Lego ﬁlters for these ﬁlter modules can be simultaneously optimized in an end-to-end manner as usual. Inspired by network engineering, we develop a split-transform-merge strategy for an efﬁcient convolution by exploiting intermediate Lego feature maps. The compression and acceleration achieved by Lego Networks using the proposed Lego ﬁlters have been theoretically discussed. Experimental results on benchmark datasets and deep models demonstrate the advantages of the proposed Lego ﬁlters and their potential real-world applications on mobile devices.
Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds

Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.
On Sparse Linear Regression in the Local Differential Privacy Model

In this paper, we study the sparse linear regression problem under the Local Differential Privacy (LDP) model. We first show that polynomial dependency on the dimensionality <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> of the space is unavoidable for the estimation error in both non-interactive and sequential interactive local models, if the privacy of the whole dataset needs to be preserved. Similar limitations also exist for other types of error measurements and in the relaxed local models. This indicates that differential privacy in high dimensional space is unlikely achievable for the problem. With the understanding of this limitation, we then present two algorithmic results. The first one is a sequential interactive LDP algorithm for the low dimensional sparse case, called Locally Differentially Private Iterative Hard Thresholding (LDP-IHT), which achieves a near optimal upper bound. This algorithm is actually rather general and can be used to solve quite a few other problems, such as (Local) DP-ERM with sparsity constraints and sparse regression with non-linear measurements. The second one is for the restricted (high dimensional) case where only the privacy of the responses (labels) needs to be preserved. For this case, we show that the optimal rate of the error estimation can be made logarithmically dependent on <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> (i.e., <inline-formula> <tex-math notation="LaTeX">$\log p$ </tex-math></inline-formula>) in the local model, where an upper bound is obtained by a label-privacy version of LDP-IHT. Experiments on real world and synthetic datasets confirm our theoretical analysis.
Assuring Cloud QoS through Loop Feedback Controller Assisted Vertical Provisioning

A problem observed in cloud environments is the assurance of Quality of Service (QoS) in the execution of applications. In the context of batch applications, a common goal is ensuring deadline compliance. However, estimating the required amount of resources to assure execution deadline may be difficult and prone to underallocations or superallocations. An alternative is modifying the amount of allocated resources in case the progress is not satisfactory, using horizontal or vertical scaling. Following this strategy, this work proposes a provisioning method based on PID controllers and vertical scaling for batch applications. To evaluate the proposed provisioning method on assuring QoS, we executed two microbenchmarks and one Big Data application on a real cloud infrastructure where the provisioning was controlled by our method. Results show that the provisioning method using the Proportional-Derivative controller is effective on ensuring QoS regarding deadline compliance, allocation smoothness and resource efficiency, though it requires additional adjusts when provisioning resources to applications with non-linear progress.
Trajectory Planning for Automated Merging on Highways

This article introduces a new approach for trajectory planning for merging on highways. The aim of the algorithm, is to find a comfortable driving strategy to merge in a gap on the target lane. Therfore, the proposed algorithm determines a bunch of trajectories to reach surrounding gaps. The trajectory with the lowest costs for each gap is chosen. To obtain the longitudinal component of the trajectory, a five-part section-wise defined polynomial in Frenet space is used to generate comfortable driving behaviour, with as few changes in the acceleration profile as possible. Based on the prediction of surrounding traffic, different variations of deceleration and acceleration are combined. For each longitudinal part, a lateral component to perform a lane change into the target gap is evaluated. The concept allows to evaluate the influence of the longitudinal driving strategy on the dynamics required to change lanes. The algorithm is evaluated in a MATLAB simulation including a runtime estimation.
Transfer of Samples in Policy Search via Multiple Importance Sampling

Algorithm 1 requires a measure of ESS in order to evaluate the quality of a gradient estimate and, consequently, to adapt the batch size. Although several ESS measures for IS have been studied (see, e.g., (Martino et al., 2017)), to the best of our knowledge no measure specifically designed for MIS estimators has been proposed. A recent work by Elvira et al. (2018) has analyzed the classical ESS measure introduced in Section 2 and has empirically demonstrated its effectiveness in MIS. Thus, we have decided to apply it to our context as well. However, since for our application we are satisfied with a lower bound on the ESS, instead of taking the variance of the importance weights under the given proposals, we take it w.r. t. the mixture of these. This is motivated by the following proposition, which follows directly from the fact that the former variance is always smaller than the latter (see (Owen & Zhou, 2000) or Lemma C.1 in Appendix C.5).
Smart Parking Zones using Dual Mode Routed Bluetooth Fogged Meshes

Contemporary parking solutions are often limited by the need for complex sensor infrastructures to perform space occupancy detection, and costly to maintain ingress and egress parking systems. For outdoor lots, network infrastructure and computational requirements often limit the availability of innovative technology. We propose the use of Bluetooth Low Energy (BLE) beacon technology and low power sensor nodes, coupled with sensible placement of computational support and data storage near to the sensor network (a Fog computing paradigm) to provide a seamless parking solution capable of providing parking maintainers with accurate determinations of where vehicles are parked within the lot. Our solution is easy to install, easy to maintain, and does require significant alterations to the existing parking structures.