Reviewerly/exHarmony huggingface dataset:
Plannie: A Benchmark Framework for Autonomous Robots Path Planning Algorithms Integrated to Simulated and Real Environments

Over the years, research has enabled robots to become more autonomous, determining their trajectories. Thus, path planning is an important area in autonomous robots. With the growing number of path planning algorithms, it is essential to have a framework capable of analyzing several algorithms in the same scenario to assess their capabilities. This paper presents Plannie, a framework to develop, simulate, benchmark, and test path planning algorithms in 2D and 3D environments in the real world. It supports many path planning algorithms, such as classic, metaheuristic, and machine learning. Furthermore, this framework offers several maps from an external database. However, it is also possible the build new maps in addition to having control, mapping, and localization techniques available for testing in a real environment. Plannie also offers planning modules that involve dynamic obstacle avoidance, coverage, traveling salesman problems, and multi-robot algorithms. Finally, we demonstrate the capability of the Plannie benchmarking several path planning algorithms in different maps. Plannie is open-source <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Controlling a Swarm of Unmanned Aerial Vehicles Using Full-Body k-Nearest Neighbor Based Action Classifier

The intuitive control of robot swarms becomes crucial when humans are working in close proximity with the swarm in unknown environments. In such operations, it is necessary to maintain the autonomy of the swarm while giving the human operator enough means to influence the decision-making process of the robots. This paper presents a human-swarm interaction approach using full-body action recognition to control an autonomous flock of unmanned aerial vehicles. We estimate the full-body pose of the human operator and use a k-nearest neighbor algorithm to classify the action made by the humans. Finally, the swarm uses the identified action to decide its goal direction. We demonstrate the practicality of our approach with a multi-stage experimental setup to evaluate the prediction accuracy and robustness of the system.
Repairing Security Vulnerabilities Using Pre-trained Programming Language Models

Repairing software bugs with automated solutions is a long-standing goal of researchers. Some of the latest automated program repair (APR) tools leverage natural language processing (NLP) techniques to repair software bugs. But natural languages (NL) and programming languages (PL) have significant differences, which leads to the fact that they may not be able to handle PL tasks well. Moreover, due to the difference between the vulnerability repair task and bug repair task, the performance of these tools on vulnerability repair is not yet known. To address these issues, we attempt to use large-scale pre-trained PL models (CodeBERT and GraphCodeBERT) for the vulnerability repair task based on the characteristics of PL and explore the real-world performance of the state-of-the-art data-driven approaches for vulnerability repair. The results show that using pre-trained PL models can better capture and process PL features and accomplish multi-line vulnerability repair. Specifically, our solution achieves advanced results (single-line repair accuracy 95.47%, multi-line repair accuracy 90.06%). These results outperform the state-of-the-art data-driven approaches and demonstrate that adding rich data-dependent features can help solve more complex code repair problems. Besides, we also discuss the previous work and our approach, pointing out some shortcomings and solutions we can work on in the future.
Publicly Accountable Robust Multi-Party Computation

In recent years, lattice-based secure multi-party computation (MPC) has seen a rise in popularity and is used more and more in large scale applications like privacy-preserving cloud computing, electronic voting, or auctions. Many of these applications come with the following high security requirements: a computation result should be publicly verifiable, with everyone being able to identify a malicious party and hold it accountable, and a malicious party should not be able to corrupt the computation, force a protocol restart, or block honest parties or an honest third-party (client) that provided private inputs from receiving a correct result. The protocol should guarantee verifiability and accountability even if all protocol parties are malicious. While some protocols address one or two of these often essential security features, we present the first publicly verifiable and accountable, and (up to a threshold) robust SPDZ-like MPC protocol without restart. We propose protocols for accountable and robust online, offline, and setup computations. We adapt and partly extend the lattice-based commitment scheme by Baum et al. (SCN 2018) as well as other primitives like ZKPs. For the underlying commitment scheme and the underlying BGV encryption scheme we determine ideal parameters. We give a performance evaluation of our protocols and compare them to state-of-the-art protocols both with and without our target security features: public accountability, public verifiability and robustness.
HARDLOG: Practical Tamper-Proof System Auditing Using a Novel Audit Device

Audit systems maintain detailed logs of security-related events on enterprise machines to forensically analyze potential incidents. In principle, these logs should be safely stored in a secure location (e.g., network storage) as soon as they are produced, but this incurs prohibitive slowdown to a monitored machine. Hence, existing audit systems protect batched logs asynchronously (e.g., after tens of seconds), but this allows attackers to tamper with unprotected logs.This paper presents HARDLOG, a practical and effective system that employs a novel audit device to provide fine-grained log protection with minimal performance slowdown. HARDLOG implements criticality-aware log protection: it ensures that logs are synchronously protected in the audit device before an infrequent security-critical event is allowed to execute, but logs are asynchronously protected on frequent non-critical events to minimize performance overhead. Importantly, even on non-critical events, HARDLOG ensures bounded-asynchronous protection: it sends log entries to the audit device within a tiny, bounded delay from their creation using well-known real-time techniques. To demonstrate HARDLOG’S effectiveness, we prototyped an audit device using commodity components and implemented a reference audit system for Linux. Our prototype achieves a bounded protection delay of 15 milliseconds at non-critical events alongside undelayed protection at critical events. We also show that, for diverse real-world programs, HARDLOG incurs a geometric mean performance slowdown of only 6.3%, hence it is suitable for many real-world deployment scenarios.
Sabre: Sender-Anonymous Messaging with Fast Audits

We present Sabre, a family of sender-anonymous messaging protocols with instances supporting both Twitter-like anonymous bulletin boards and Secure Drop-like mailboxes. Both variants provide strong anonymity while potentially scaling to anonymity sets in the tens of millions and beyond. Sabre protocols inherit their basic structure from Riposte and Express while replacing those schemes’ linear-cost audit protocols with new logarithmic-cost ones. Sabre also incorporates a host of innovations that improve concrete performance by an order of magnitude or more under “ideal” circumstances, while providing asymptotic speedups in the face of resource exhaustion-style DoS attacks.
IRQDebloat: Reducing Driver Attack Surface in Embedded Devices

Embedded and IoT devices often come with a wide range of hardware functionality, but any particular end user may only use some small subset of these features. However, even unused hardware features are accompanied by potentially buggy driver code, which increases the attack surface of the device. In this paper, we introduce IRQDebloat, a system for disabling unwanted hardware features through automated firmware rewriting. Building on the insight that external inputs to the system are typically delivered through interrupt requests (IRQs), IRQDebloat systematically explores the interrupt handling code in the target firmware, identifies the handler function for each peripheral, and finally rewrites target firmware to disable the handlers that correspond to undesired hardware features. In our experiments we demonstrate IRQDebloat’s effectiveness and generality by identifying IRQ handlers across four different operating systems (Linux, FreeBSD, VxWorks, and RiscOS) and seven different embedded platforms, and disabling selected peripherals on real-world hardware (a Raspberry Pi and a Valve Steam Link). On the Steam Link, we survey the attack surface and find that disabling selected peripherals could block up to 44 CVEs found in the Linux kernel over the past five years.
Goshawk: Hunting Memory Corruptions via Structure-Aware and Object-Centric Memory Operation Synopsis

Existing tools for the automated detection of memory corruption bugs are not very effective in practice. They typically recognize only standard memory management (MM) APIs (e.g., malloc and free) and assume a naive paired-use model—an allocator is followed by a specific deallocator. However, we observe that programmers very often design their own MM functions and that these functions often manifest two major characteristics: (1) Custom allocator functions perform multi-object or nested allocation which then requires structure-aware deallocation functions. (2) Custom allocators and deallocators follow an unpaired-use model. A more effective detection thus needs to adapt those characteristics and capture memory bugs related to non-standard MM behaviors. In this paper, we present a MM function aware memory bug detection technique by introducing the concept of structure-aware and object-centric Memory Operation Synopsis (MOS). A MOS abstractly describes the memory objects of a given MM function, how they are managed by the function, and their structural relations. By utilizing MOS, a bug detection could explore much less code but is still capable of handling multi-object or nested allocations and does not rely on the paired-use model. In addition, to extensively find MM functions and automatically generate MOS for them, we propose a new identification approach that combines natural language processing (NLP) and data flow analysis, which enables the efficient and comprehensive identification of MM functions, even in very large code bases. We implement a MOS-enhanced memory bug detection system, Goshawk, to discover memory bugs caused by complex and custom MM behaviors. We applied Goshawk to well-tested and widely-used open source projects including OS kernels, server applications, and IoT SDKs. Goshawk outperforms the state-of-the-art data flow analysis driven bug detection tools by an order of magnitude in analysis speed and the number of accurately identified MM functions, reports the discovered bugs with a developer-friendly, MOS based description, and successfully detects 92 new double-free and use-after-free bugs.
Differential Privacy and Swapping: Examining De-Identification’s Impact on Minority Representation and Privacy Preservation in the U.S. Census

There has been considerable controversy regarding the accuracy and privacy of de-identification mechanisms used in the U.S. Decennial Census. We theoretically and experimentally analyze two such classes of mechanisms, swapping and differential privacy, especially examining their effects on ethnoracial minority groups.We first prove that the expected error of queries made on swapped demographic datasets is greater in sub-populations whose racial distributions differ more from the racial distribution of the global population. We also prove that the probability that m unique entries exist in a sub-population shrinks exponentially as the sub-population size grows. These properties suggest that swapping, which prioritizes unique entries, will produce poor accuracy for minority groups.We then empirically analyze the impact of swapping and differential privacy on the accuracy and privacy of a demographic dataset. We evaluate accuracy in several ways, including methods that stress the effect on minority groups. We evaluate privacy by counting the number of re-identified entries in a simulated linkage attack. Finally, we explore the disproportionate presence of minority groups in identified entries.Our empirical lindings corroborate our theoretical results: for minority representation, the utility of differential privacy is comparable to the utility of swapping, while providing a stronger privacy guarantee. Swapping places a disproportionate privacy burden on minority groups, whereas an ε-differentially private mechanism is ε-differentially private for all subgroups.
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems

Differential privacy is a de facto privacy framework that has seen adoption in practice via a number of mature software platforms. Implementation of differentially private (DP) mechanisms has to be done carefully to ensure end-to-end security guarantees. In this paper we study two implementation flaws in the noise generation commonly used in DP systems. First we examine the Gaussian mechanism’s susceptibility to a floating-point representation attack. The premise of this first vulnerability is similar to the one carried out by Mironov in 2011 against the Laplace mechanism. Our experiments show the attack’s success against DP algorithms, including deep learning models trained using differentially-private stochastic gradient descent. In the second part of the paper we study discrete counterparts of the Laplace and Gaussian mechanisms that were previously proposed to alleviate the shortcomings of floating-point representation of real numbers. We show that such implementations unfortunately suffer from another side channel: a novel timing attack. An observer that can measure the time to draw (discrete) Laplace or Gaussian noise can predict the noise magnitude, which can then be used to recover sensitive attributes. This attack invalidates differential privacy guarantees of systems implementing such mechanisms. We demonstrate that several commonly used, state-of-the-art implementations of differential privacy are susceptible to these attacks. We report success rates up to 92.56% for floating point attacks on DP-SGD, and up to 99.65% for end-to-end timing attacks on private sum protected with discrete Laplace. Finally, we evaluate and suggest partial mitigations.
SNARKBlock: Federated Anonymous Blocklisting from Hidden Common Input Aggregate Proofs

Zero-knowledge blocklists allow cross-platform blocking of users but, counter-intuitively, do not link users identities inter- or intra-platform, or to the fact they were blocked. Unfortunately, existing approaches (Tsang et al. ’10) require that servers do work linear in the size of the blocklist for each verification of a non-membership proof.We design and implement SNARKBLOCK, a new protocol for zero-knowledge blocklisting with server-side verification that is logarithmic in the size of the blocklist. SNARKBLOCK is also the first approach to support ad-hoc, federated blocklisting: websites can mix and match their own blocklists from other blocklists and dynamically choose which identity providers they trust.Our core technical advance, of separate interest, is the HICIAP zero-knowledge proof system, which addresses a common problem in privacy-preserving protocols: using zero-knowledge proofs for repeated but unlinakble interactions. Rerandomzing a Groth16 proof achieves unlinkability without the need to recompute the proof for every interaction. But this technique does not apply to applications where each interaction includes multiple Groth16 proofs over a common hidden input (e.g., the user’s identity). Here, the best known approach is to commit to the hidden input and feed it to each proof, but this creates a persistent identifier, forcing recomputation. HICIAP resolves this problem by aggregating n Groth16 proofs into one $O(\log n) -$sized, $O(\log n) -$verification time proof which also shows that the input proofs share a hidden input. Because HICIAP is zero-knowledge, repeated shows of the same aggregate or an updated aggregate are unlinkable even though the underlying Groth16 proofs are never recomputed.
Peekaboo: A Hub-Based Approach to Enable Transparency in Data Processing within Smart Homes

We present Peekaboo, a new privacy-sensitive architecture for smart homes that leverages an in-home hub to pre-process and minimize outgoing data in a structured and enforceable manner before sending it to external cloud servers. Peekaboo's key innovations are (1) abstracting common data preprocessing functionality into a small and fixed set of chainable operators, and (2) requiring that developers explicitly declare desired data collection behaviors (e.g., data granularity, destinations, conditions) in an application manifest, which also specifies how the operators are chained together. Given a manifest, Peekaboo assembles and executes a pre-processing pipeline using operators pre-loaded on the hub. In doing so, developers can collect smart home data on a need-to-know basis; third-party auditors can verify data collection behaviors; and the hub itself can offer a number of centralized privacy features to users across apps and devices, without additional effort from app developers. We present the design and implementation of Peekaboo, along with an evaluation of its coverage of smart home scenarios, system performance, data minimization, and example built-in privacy features.
Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation

Fairness has become an important agenda in computer vision and artificial intelligence. Recent studies have shown that many computer vision models and datasets exhibit demographic biases and proposed mitigation strategies. These works attempt to address accuracy disparity, spurious correlations, or unbalanced representations in datasets in tasks such as face recognition, verification and expression and attribute classification. These tasks, however, all require face detection as the first preprocessing step, and surprisingly, there has been little effort in identifying or mitigating biases in face detection. Biased face detectors themselves pose a threat against fair and ethical AI systems, and their biases may be further passed on to subsequent downstream tasks such as face recognition in a computer vision pipeline. This paper therefore investigates the problem of biases in face detection, focusing on accuracy disparity of detectors between demographic groups including gender, age group, and skin tone. We collect perceived demographic attributes on a popular face detection benchmark dataset, WIDER FACE, report skewed demographic distributions, and compare detection performance between groups. In order to mitigate the biases, we apply three mitigation methods that have been introduced in the recent literature and also propose two novel methods. Experimental results show that these methods are effective in reducing demographic biases. We also discuss how the effectiveness varies by demographic attributes, detection easiness, and multiple detectors, which will shed light on this new topic of addressing face detection bias.
Multitask Summary Scoring with Longformers

Automated scoring of student language is a complex task that requires systems to emulate complex and multi-faceted human evaluation criteria. Summary scoring brings an additional layer of complexity to automated scoring because it involves two texts of differing lengths that must be compared. In this study, we present our approach to automate summary scoring by evaluating a corpus of approximately 5,000 summaries based on 103 source texts, each summary being scored on a 4-point Likert scale for seven different evaluation criteria. We train and evaluate a series of Machine Learning models that use a combination of independent textual complexity indices from the ReaderBench framework and Deep Learning models based on the Transformer architecture in a multitask setup to predict concurrently all criteria. Our models achieve significantly lower errors than previous work using a similar dataset, with MAE ranging from 0.10–0.16 and corresponding R2 values of up to 0.64. Our findings indicate that Longformer-based [1] models are adequate for contextualizing longer text sequences and effectively scoring summaries according to a variety of human-defined evaluation criteria using a single Neural Network.
Eye to Eye: Gaze Patterns Predict Remote Collaborative Problem Solving Behaviors in Triads

We investigated the feasibility of using eye gaze to model collaborative problem solving (CPS) behaviors in 96 triads (N = 288) who used videoconferencing to remotely collaborate on an educational physics game. Trained human raters coded spoken utterances based on a theoretically grounded framework consisting of three CPS facets: constructing shared knowledge, negotiation/coordination, and maintaining team function. We then trained random forest classifiers to identify each of the CPS facets using eye gaze features pertaining to each individual (e.g., number of fixations) and/or shared across individuals (e.g., eye gaze distance between collaborators) in conjunction with information about the unfolding task context. We found that the individual gaze features outperformed the shared features, and together yielded between 6% to 8% improvements in classification accuracy above task-context baseline models, using a cross-validation scheme that generalized across teams. We discuss how our findings support CPS theories and the development of real-time intervention systems that provide actionable feedback to improve collaboration.
Automatic Question Generation for Scaffolding Self-explanations for Code Comprehension

This work presents two systems, Machine Noun Question Generation (QG) and Machine Verb QG, developed to generate short questions and gap-fill questions, which Intelligent Tutoring Systems then use to guide students' self-explanations during code comprehension. We evaluate our system by comparing the quality of questions generated by the system against human expert-generated questions. Our result shows that these systems performed similarly to humans in most criteria. Among the machines, we find that Machine Noun QG performed better.