OpenRaiser/Intern-Atlas huggingface dataset:
L2R2: Leveraging Ranking for Abductive Reasoning
Venue: arXiv cs.IR 2021
Year: 2021

The abductive natural language inference task ($α$NLI) is proposed to evaluate the abductive reasoning ability of a learning system. In the $α$NLI task, two observations are given and the most plausible hypothesis is asked to pick out from the candidates. Existing methods simply formulate it as a classification problem, thus a cross-entropy log-loss objective is used during training. However, discriminating true from false does not measure the plausibility of a hypothesis, for all the hypotheses have a chance to happen, only the probabilities are different. To fill this gap, we switch to a ranking perspective that sorts the hypotheses in order of their plausibilities. With this new perspective, a novel $L2R^2$ approach is proposed under the learning-to-rank framework. Firstly, training samples are reorganized into a ranking form, where two observations and their hypotheses are treated as the query and a set of candidate documents respectively. Then, an ESIM model or pre-trained language model, e.g. BERT or RoBERTa, is obtained as the scoring function. Finally, the loss functions for the ranking task can be either pair-wise or list-wise for training. The experimental results on the ART dataset reach the state-of-the-art in the public leaderboard.
Leveraging WiFi Network Logs to Infer Student Collocation and its Relationship with Academic Performance
Venue: arXiv cs.HC 2021
Year: 2021

A comprehensive understanding of collocation can help understand performance outcomes. For university cohorts, this needs data that describes large groups over a long period. Harnessing user devices to infer this, while tempting, is challenged by privacy concerns, power consumption, and maintenance issues. Alternatively, embedding new sensors in the environment is limited by the expense of covering the entire campus. We investigate the feasibility of leveraging WiFi association logs for this purpose. While these provide coarse approximations of location, these are easily obtainable and depict multiple users on campus over a semester. We explore how these coarse collocations are related to individual performance. Specifically, we inspect the association between individual performance and the collocation behaviors of project group members. We study 163 students (in 54 project groups) over 14 weeks. After describing how we determine collocation with the WiFi logs, we present a study to analyze how collocation within groups relates to a student's final score. We find collocation behaviors show a significant correlation (Pearson's r = 0.24) with performance -- better than both peer feedback or individual behaviors like attendance. Finally, we discuss how repurposing WiFi logs can facilitate applications for domains like mental wellbeing and physical health.
Unleashing the power of disruptive and emerging technologies amid COVID-19: A detailed review
Venue: arXiv cs.CY 2021
Year: 2021

The unprecedented outbreak of the novel coronavirus (COVID-19), during early December 2019 in Wuhan, China, has quickly evolved into a global pandemic, became a matter of grave concern, and placed government agencies worldwide in a precarious position. The scarcity of resources and lack of experiences to endure the COVID-19 pandemic, combined with the fear of future consequences has established the need for adoption of emerging and future technologies to address the upcoming challenges. Since the last five months, the amount of pandemic impact has reached its pinnacle that is altering everyone's life; and humans are now bound to adopt safe ways to survive under the risk of being affected. Technological advances are now accelerating faster than ever before to stay ahead of the consequences and acquire new capabilities to build a safer world. Thus, there is a rising need to unfold the power of emerging, future and disruptive technologies to explore all possible ways to fight against COVID-19. In this review article, we attempt to study all emerging, future, and disruptive technologies that can be utilized to mitigate the impact of COVID-19. Building on background insights, detailed technological specific use cases to fight against COVID-19 have been discussed in terms of their strengths, weaknesses, opportunities, and threats (SWOT). As concluding remarks, we highlight prioritized research areas and upcoming opportunities to blur the lines between the physical, digital, and biological domain-specific challenges and also illuminate collaborative research directions for moving towards a post-COVID-19 world.
Frontal Attack: Leaking Control-Flow in SGX via the CPU Frontend
Venue: arXiv cs.CR 2021
Year: 2021

We introduce a new timing side-channel attack on Intel CPU processors. Our Frontal attack exploits timing differences that arise from how the CPU frontend fetches and processes instructions while being interrupted. In particular, we observe that in modern Intel CPUs, some instructions' execution times will depend on which operations precede and succeed them, and on their virtual addresses. Unlike previous attacks that could only profile branches if they contained different code or had known branch targets, the Frontal attack allows the adversary to distinguish between instruction-wise identical branches. As the attack requires OS capabilities to set the interrupts, we use it to exploit SGX enclaves. Our attack further demonstrates that secret-dependent branches should not be used even alongside defenses to current controlled-channel attacks. We show that the adversary can use the Frontal attack to extract a secret from an SGX enclave if that secret was used as a branching condition for two instruction-wise identical branches. We successfully tested the attack on all the available Intel CPUs with SGX (until 10th gen) and used it to leak information from two commonly used cryptographic libraries.
ConFuzzius: A Data Dependency-Aware Hybrid Fuzzer for Smart Contracts
Venue: arXiv cs.CR 2021
Year: 2021

Smart contracts are Turing-complete programs that are executed across a blockchain. Unlike traditional programs, once deployed, they cannot be modified. As smart contracts carry more value, they become more of an exciting target for attackers. Over the last years, they suffered from exploits costing millions of dollars due to simple programming mistakes. As a result, a variety of tools for detecting bugs have been proposed. Most of these tools rely on symbolic execution, which may yield false positives due to over-approximation. Recently, many fuzzers have been proposed to detect bugs in smart contracts. However, these tend to be more effective in finding shallow bugs and less effective in finding bugs that lie deep in the execution, therefore achieving low code coverage and many false negatives. An alternative that has proven to achieve good results in traditional programs is hybrid fuzzing, a combination of symbolic execution and fuzzing. In this work, we study hybrid fuzzing on smart contracts and present ConFuzzius, the first hybrid fuzzer for smart contracts. ConFuzzius uses evolutionary fuzzing to exercise shallow parts of a smart contract and constraint solving to generate inputs that satisfy complex conditions that prevent evolutionary fuzzing from exploring deeper parts. Moreover, ConFuzzius leverages dynamic data dependency analysis to efficiently generate sequences of transactions that are more likely to result in contract states in which bugs may be hidden. We evaluate the effectiveness of ConFuzzius by comparing it with state-of-the-art symbolic execution tools and fuzzers for smart contracts. Our evaluation on a curated dataset of 128 contracts and 21K real-world contracts shows that our hybrid approach detects more bugs (up to 23%) while outperforming state-of-the-art in terms of code coverage (up to 69%), and that data dependency analysis boosts bug detection up to 18%.
Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori Knowledge
Venue: arXiv cs.CR 2021
Year: 2021

Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by trained human attackers and its success critically depended on the available expertise. Automating this practice constitutes a non-trivial problem, as the range of actions that a human expert may attempts against a system and the range of knowledge she relies on to take her decisions are hard to capture. In this paper, we focus our attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and we analyze how model-free reinforcement learning algorithms may help to solve them. In modeling these capture the flag competitions as reinforcement learning problems we highlight that a specific challenge that characterize penetration testing is the problem of discovering the structure of the problem at hand. We then show how this challenge may be eased by relying on different forms of prior knowledge that may be provided to the agent. In this way we demonstrate how the feasibility of tackling penetration testing using reinforcement learning may rest on a careful trade-off between model-free and model-based algorithms. By using techniques to inject a priori knowledge, we show it is possible to better direct the agent and restrict the space of its exploration problem, thus achieving solutions more efficiently.
Refining Implicit Argument Annotation for UCCA
Venue: arXiv cs.CL 2021
Year: 2021

Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.
Coordinated Multicell Beamforming and Power Allocation for Massive MIMO with Low-Resolution ADC/DAC
Venue: arXiv cs.IT 2021
Year: 2021

In this work, we present a solution for coordinated beamforming and power allocation when base stations employ a massive number of antennas equipped with low-resolution analog-to-digital and digital-to-analog converters. We address total power minimization problems of the coarsely quantized uplink (UL) and downlink (DL) communication systems with target signal-to-interference-plus-noise ratio (SINR) constraints. By combining the UL problem with minimum mean square error combiners and deriving the Lagrangian dual of the DL problem, we prove UL-DL duality and show there is no duality gap even with coarse data converters. Inspired by strong duality, we devise an iterative algorithm to determine the optimal UL transmit powers, and then linearly amplify the UL combiners with proper weights to acquire the optimal DL precoder. Simulation results validate strong duality and evaluate the proposed method in terms of total power consumption and achieved SINR.
SeqL: Secure Scan-Locking for IP Protection
Venue: arXiv cs.CR 2021
Year: 2021

Existing logic-locking attacks are known to successfully decrypt functionally correct key of a locked combinational circuit. It is possible to extend these attacks to real-world Silicon-based Intellectual Properties (IPs, which are sequential circuits) through scan-chains by selectively initializing the combinational logic and analyzing the responses. In this paper, we propose SeqL, which achieves functional isolation and locks selective flip-flop functional-input/scan-output pairs, thus rendering the decrypted key functionally incorrect. We conduct a formal study of the scan-locking problem and demonstrate automating our proposed defense on any given IP. We show that SeqL hides functionally correct keys from the attacker, thereby increasing the likelihood of the decrypted key being functionally incorrect. When tested on pipelined combinational benchmarks (ISCAS,MCNC), sequential benchmarks (ITC) and a fully-fledged RISC-V CPU, SeqL gave 100% resilience to a broad range of state-of-the-art attacks including SAT[1], Double-DIP[2], HackTest[3], SMT[4], FALL[5], Shift-and-Leak[6] and Multi-cycle attacks[7].
Efficacy of Satisfiability Based Attacks in the Presence of Circuit Reverse Engineering Errors
Venue: arXiv cs.CR 2021
Year: 2021

Intellectual Property (IP) theft is a serious concern for the integrated circuit (IC) industry. To address this concern, logic locking countermeasure transforms a logic circuit to a different one to obfuscate its inner details. The transformation caused by obfuscation is reversed only upon application of the programmed secret key, thus preserving the circuit's original function. This technique is known to be vulnerable to Satisfiability (SAT)-based attacks. But in order to succeed, SAT-based attacks implicitly assume a perfectly reverse-engineered circuit, which is difficult to achieve in practice due to reverse engineering (RE) errors caused by automated circuit extraction. In this paper, we analyze the effects of random circuit RE-errors on the success of SAT-based attacks. Empirical evaluation on ISCAS, MCNC benchmarks as well as a fully-fledged RISC-V CPU reveals that the attack success degrades exponentially with increase in the number of random RE-errors. Therefore, the adversaries either have to equip RE-tools with near perfection or propose better SAT-based attacks that can work with RE-imperfections.
Scalable Polyhedral Verification of Recurrent Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

We present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and nonlinear recurrent update functions by combining sampling, optimization, and Fermat's theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.
Modular Politics: Toward a Governance Layer for Online Communities
Venue: arXiv cs.CY 2021
Year: 2021

Governance in online communities is an increasingly high-stakes challenge, and yet many basic features of offline governance legacies--juries, political parties, term limits, and formal debates, to name a few--are not in the feature-sets of the software most community platforms use. Drawing on the paradigm of Institutional Analysis and Development, this paper proposes a strategy for addressing this lapse by specifying basic features of a generalizable paradigm for online governance called Modular Politics. Whereas classical governance typologies tend to present a choice among wholesale ideologies, such as democracy or oligarchy, Modular Politics would enable platform operators and their users to build bottom-up governance processes from computational components that are modular and composable, highly versatile in their expressiveness, portable from one context to another, and interoperable across platforms. This kind of approach could implement pre-digital governance systems as well as accelerate innovation in uniquely digital techniques. As diverse communities share and connect their components and data, governance could occur through a ubiquitous network layer. To that end, this paper proposes the development of an open standard for networked governance.
Optimal Anticodes, Diameter Perfect Codes, Chains and Weights
Venue: arXiv cs.IT 2021
Year: 2021

Let $P$ be a partial order on $[n] = \{1,2,\ldots,n\}$, $\mathbb{F}_{q}^n$ be the linear space of $n$-tuples over a finite field $\mathbb{F}_{q}$ and $w$ be a weight on $\mathbb{F}_{q}$. In this paper, we consider metrics on $\mathbb{F}_{q}^n$ induced by chain orders $P$ over $[n]$ and weights $w$ over $\mathbb{F}_q$, and we determine the cardinality of all optimal anticodes and completely classify them. Moreover, we determine all diameter perfect codes for a set of relevant instances on the aforementioned metric spaces.
Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity
Venue: arXiv cs.LG 2021
Year: 2021

We study a model for adversarial classification based on distributionally robust chance constraints. We show that under Wasserstein ambiguity, the model aims to minimize the conditional value-at-risk of the distance to misclassification, and we explore links to adversarial classification models proposed earlier and to maximum-margin classifiers. We also provide a reformulation of the distributionally robust model for linear classification, and show it is equivalent to minimizing a regularized ramp loss objective. Numerical experiments show that, despite the nonconvexity of this formulation, standard descent methods appear to converge to the global minimizer for this problem. Inspired by this observation, we show that, for a certain class of distributions, the only stationary point of the regularized ramp loss minimization problem is the global minimizer.
TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples
Venue: arXiv cs.CV 2021
Year: 2021

The challenges of high intra-class variance yet low inter-class fluctuations in fine-grained visual categorization are more severe with few labeled samples, \textit{i.e.,} Fine-Grained categorization problems under the Few-Shot setting (FGFS). High-order features are usually developed to uncover subtle differences between sub-categories in FGFS, but they are less effective in handling the high intra-class variance. In this paper, we propose a Target-Oriented Alignment Network (TOAN) to investigate the fine-grained relation between the target query image and support classes. The feature of each support image is transformed to match the query ones in the embedding feature space, which reduces the disparity explicitly within each category. Moreover, different from existing FGFS approaches devise the high-order features over the global image with less explicit consideration of discriminative parts, we generate discriminative fine-grained features by integrating compositional concept representations to global second-order pooling. Extensive experiments are conducted on four fine-grained benchmarks to demonstrate the effectiveness of TOAN compared with the state-of-the-art models.
Discovering Domain Orders through Order Dependencies
Venue: arXiv cs.DB 2021
Year: 2021

Much real-world data come with explicitly defined domain orders; e.g., lexicographic order for strings, numeric for integers, and chronological for time. Our goal is to discover implicit domain orders that we do not already know; for instance, that the order of months in the Chinese Lunar calendar is Corner < Apricot < Peach. To do so, we enhance data profiling methods by discovering implicit domain orders in data through order dependencies. We enumerate tractable special cases and proceed towards the most general case, which we prove is NP-complete. We show that the general case nevertheless can be effectively handled by a SAT solver. We also devise an interestingness measure to rank the discovered implicit domain orders, which we validate with a user study. Based on an extensive suite of experiments with real-world data, we establish the efficacy of our algorithms, and the utility of the domain orders discovered by demonstrating significant added value in three applications (data profiling, query optimization, and data mining).
A Survey on Blockchain Interoperability: Past, Present, and Future Trends
Venue: arXiv cs.DC 2021
Year: 2021

Blockchain interoperability is emerging as one of the crucial features of blockchain technology, but the knowledge necessary for achieving it is fragmented. This fact makes it challenging for academics and the industry to seamlessly achieve interoperability among blockchains. Given the novelty and potential of this new domain, we conduct a literature review on blockchain interoperability, by collecting 262 papers, and 70 grey literature documents, constituting a corpus of 332 documents. From those 332 documents, we systematically analyzed and discussed 80 documents, including both peer-reviewed papers and grey literature. Our review classifies studies in three categories: Cryptocurrency-directed interoperability approaches, Blockchain Engines, and Blockchain Connectors. Each category is further divided into sub-categories based on defined criteria. We discuss not only studies within each category and subcategory but also across categories, providing a holistic overview of blockchain interoperability, paving the way for systematic research in this domain. Our findings show that blockchain interoperability has a much broader spectrum than cryptocurrencies. The present survey leverages an interesting approach: we systematically contacted the authors of grey literature papers and industry solutions to obtain an updated view of their work. Finally, this paper discusses supporting technologies, standards, use cases, open challenges, and provides several future research directions.
Fairness-Aware PageRank
Venue: arXiv cs.SI 2021
Year: 2021

Algorithmic fairness has attracted significant attention in the past years. Surprisingly, there is little work on fairness in networks. In this work, we consider fairness for link analysis algorithms and in particular for the celebrated PageRank algorithm. We provide definitions for fairness, and propose two approaches for achieving fairness. The first modifies the jump vector of the Pagerank algorithm to enfonce fairness, and the second imposes a fair behavior per node. We also consider the problem of achieving fairness while minimizing the utility loss with respect to the original algorithm. We present experiments with real and synthetic graphs that examine the fairness of Pagerank and demonstrate qualitatively and quantitatively the properties of our algorithms.