OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Graph Processing and Preprocessing with Hardware Assisted Propagation Blocking
Venue: arXiv cs.AR 2020
Year: 2020

Extensive prior research has focused on alleviating the characteristic poor cache locality of graph analytics workloads. However, graph pre-processing tasks remain relatively unexplored. In many important scenarios, graph pre-processing tasks can be as expensive as the downstream graph analytics kernel. We observe that Propagation Blocking (PB), a software optimization designed for SpMV kernels, generalizes to many graph analytics kernels as well as common pre-processing tasks. In this work, we identify the lingering inefficiencies of a PB execution on conventional multicores and propose architecture support to eliminate PB's bottlenecks, further improving the performance gains from PB. Our proposed architecture -- COBRA -- optimizes the PB execution of both graph processing and pre-processing alike to provide end-to-end speedups of up to 4.6x (3.5x on average).
Collusion-Resistant Identity-based Proxy Re-Encryption: Lattice-based Constructions in Standard Model
Venue: arXiv cs.CR 2020
Year: 2020

The concept of proxy re-encryption (PRE) dates back to the work of Blaze, Bleumer, and Strauss in 1998. PRE offers delegation of decryption rights, i.e., it securely enables the re-encryption of ciphertexts from one key to another, without relying on trusted parties. PRE allows a semi-trusted third party termed as a ``proxy" to securely divert encrypted files of user A (delegator) to user B (delegatee) without revealing any information about the underlying files to the proxy. To eliminate the necessity of having a costly certificate verification process, Green and Ateniese introduced an identity-based PRE (IB-PRE). The potential applicability of IB-PRE sprung up a long line of intensive research from its first instantiation. Unfortunately, till today, there is no collusion-Resistant unidirectional IB-PRE secure in the standard model, which can withstand quantum attack. In this paper, we present the first concrete constructions of collusion-Resistant unidirectional IB-PRE, for both selective and adaptive identity, which are secure in standard model based on the hardness of learning with error problem.
SRF-GAN: Super-Resolved Feature GAN for Multi-Scale Representation
Venue: arXiv cs.CV 2020
Year: 2020

Recent convolutional object detectors exploit multi-scale feature representations added with top-down pathway in order to detect objects at different scales and learn stronger semantic feature responses. In general, during the top-down feature propagation, the coarser feature maps are upsampled to be combined with the features forwarded from bottom-up pathway, and the combined stronger semantic features are inputs of detector's headers. However, simple interpolation methods (e.g. nearest neighbor and bilinear) are still used for increasing feature resolutions although they cause noisy and blurred features. In this paper, we propose a novel generator for super-resolving features of the convolutional object detectors. To achieve this, we first design super-resolved feature GAN (SRF-GAN) consisting of a detection-based generator and a feature patch discriminator. In addition, we present SRF-GAN losses for generating the high quality of super-resolved features and improving detection accuracy together. Our SRF generator can substitute for the traditional interpolation methods, and easily fine-tuned combined with other conventional detectors. To prove this, we have implemented our SRF-GAN by using the several recent one-stage and two-stage detectors, and improved detection accuracy over those detectors. Code is available at https://github.com/SHLee-cv/SRF-GAN.
Gender Differences in Public Code Contributions: a 50-year Perspective
Venue: arXiv cs.SE 2020
Year: 2020

Gender imbalance in information technology in general, and Free/Open Source Software specifically, is a well-known problem in the field. Still, little is known yet about the large-scale extent and long-term trends that underpin the phenomenon. We contribute to fill this gap by conducting a longitudinal study of the population of contributors to publicly available software source code. We analyze 1.6 billion commits corresponding to the development history of 120 million projects, contributed by 33 million distinct authors over a period of 50 years. We classify author names by gender and study their evolution over time.We show that, while the amount of commits by female authors remains low overall, there is evidence of a stable long-term increase in their proportion over all contributions, providing hope of a more gender-balanced future for collaborative software development.
A Digital Image Processing Approach for Hepatic Diseases Staging based on the Glisson's Capsule
Venue: arXiv cs.CV 2020
Year: 2020

Due to the need for quick and effective treatments for liver diseases, which are among the most common health problems in the world, staging fibrosis through non-invasive and economic methods has become of great importance. Taking inspiration from diagnostic laparoscopy, used in the past for hepatic diseases, in this paper ultrasound images of the liver are studied, focusing on a specific region of the organ where the Glisson's capsule is visible. In ultrasound images, the Glisson's capsule appears in the shape of a line which can be extracted via classical methods in literature. By making use of a combination of standard image processing techniques and Convolutional Neural Network approaches, the scope of this work is to give evidence to the idea that a great informative potential relies on smoothness of the Glisson's capsule surface. To this purpose, several classifiers are taken into consideration, which deal with different type of data, namely ultrasound images, binary images depicting the Glisson's line, and features vector extracted from the original image. This is a preliminary study that has been retrospectively conducted, based on the results of the elastosonography examination.
SoK on Performance Bounds in Anonymous Communication
Venue: arXiv cs.CR 2020
Year: 2020

Communicating anonymously comes at a cost - and large communities have been in a constant tug-of-war between the development of faster protocols, and the improvement of security analyses. Thereby more intricate privacy goals emerged and more detailed bounds on the minimum overhead necessary to achieve them were proven. The entanglement of requirements, scenarios, and protocols complicates analysis, and the published results are hardly comparable, due to deviating, yet specific choices of assumptions and goals (some explicit, most implicit). In this paper, we systematize the field by harmonizing the models, comparing the proven performance bounds, and contextualizing these theoretical results in a broad set of proposed and implemented systems. By identifying inaccuracies, we demonstrate that the attacks, on which the results are based, indeed break much weaker privacy goals than postulated, and tighten the bounds along the way. We further show the equivalence of two seemingly alternative bounds. Finally, we argue how several assumptions and requirements of the papers likely are of limited applicability in reality and suggest relaxations for future work.
MobChain: Three-Way Collusion Resistance in Witness-Oriented Location Proof Systems Using Distributed Consensus
Venue: arXiv cs.CR 2020
Year: 2020

Smart devices have accentuated the importance of geolocation information. Geolocation identification using smart devices has paved the path for incentive-based location-based services (LBS). A location proof is a digital certificate of the geographical location of a user, which can be used to access various LBS. However, a user full control over a device allows the tampering of location proof. Initially, to resist false proofs, two-party trusted centralized location proof systems (LPS) were introduced to aid the users in generating secure location proofs mutually. However, two-party protocols suffered from the collusion attacks by the participants of the protocol. Consequently, many witness-oriented LPS have emerged to mitigate collusion attacks in two-party protocols. However, witness-oriented LPS presented the possibility of three-way collusion attacks (involving the user, location authority, and the witness). The three-way collusion attacks are inevitable in all existing witness-oriented schemes. To mitigate the inability to resist three-way collusion of existing schemes, in this paper, we introduce a decentralized consensus protocol called as MobChain, where the selection of a witness and location authority is achieved through a distributed consensus of nodes in an underlying P2P network of a private blockchain. The persistent provenance data over the blockchain provides strong security guarantees, as a result, the forging and manipulation become impractical. MobChain provides secure location provenance architecture, relying on decentralized decision making for the selection of participants of the protocol to resist three-way collusion problem. Our prototype implementation and comparison with the state-of-the-art solutions show that MobChain is computationally efficient, highly available while improving the security of LPS.
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game
Venue: arXiv cs.LG 2020
Year: 2020

Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. In this work, we introduce a novel formulation for PIC based on a communication game between a speaker and a listener. The speaker attempts to generate natural language captions while the listener encourages the generated captions to contain discriminative information about the input images and personality traits. In this way, we expect that the generated captions can be improved to naturally represent the images and express the traits. In addition, we propose to adapt the language model GPT2 to perform caption generation for PIC. This enables the speaker and listener to benefit from the language encoding capacity of GPT2. Our experiments show that the proposed model achieves the state-of-the-art performance for PIC.
Optimizing voice conversion network with cycle consistency loss of speaker identity
Venue: arXiv cs.SD 2020
Year: 2020

We propose a novel training scheme to optimize voice conversion network with a speaker identity loss function. The training scheme not only minimizes frame-level spectral loss, but also speaker identity loss. We introduce a cycle consistency loss that constrains the converted speech to maintain the same speaker identity as reference speech at utterance level. While the proposed training scheme is applicable to any voice conversion networks, we formulate the study under the average model voice conversion framework in this paper. Experiments conducted on CMU-ARCTIC and CSTR-VCTK corpus confirm that the proposed method outperforms baseline methods in terms of speaker similarity.
Optimal Joint Allocation of Efforts in Inclusive Fitness by Related Individuals
Venue: arXiv cs.GT 2020
Year: 2020

Families are places of affection and cooperation, but also of conflict. In his famous paper Parent-Offspring Conflict, Robert L. Trivers builds upon W. D. Hamilton's concept of inclusive fitness to argue for genetic conflict in parent-offspring relationships, and to derive numerical predictions on the intensity of the conflict. We propose a mathematical model of game theory that depicts how each member of a family allocates her resource budget to maximize her inclusive fitness; this latter is made of the sum of personal fitness plus the sum of relatives fitnesses weighted by Wright's coefficients of relationship. We define an optimal allocation profile as a Nash equilibrium, and we characterize the solutions in function of resource budgets, coefficients of relationship and derivatives of personal fitnesses.
Identification of state functions by physically-guided neural networks with physically-meaningful internal layers
Venue: arXiv cs.LG 2020
Year: 2020

Substitution of well-grounded theoretical models by data-driven predictions is not as simple in engineering and sciences as it is in social and economic fields. Scientific problems suffer most times from paucity of data, while they may involve a large number of variables and parameters that interact in complex and non-stationary ways, obeying certain physical laws. Moreover, a physically-based model is not only useful for making predictions, but to gain knowledge by the interpretation of its structure, parameters, and mathematical properties. The solution to these shortcomings seems to be the seamless blending of the tremendous predictive power of the data-driven approach with the scientific consistency and interpretability of physically-based models. We use here the concept of physically-constrained neural networks (PCNN) to predict the input-output relation in a physical system, while, at the same time fulfilling the physical constraints. With this goal, the internal hidden state variables of the system are associated with a set of internal neuron layers, whose values are constrained by known physical relations, as well as any additional knowledge on the system. Furthermore, when having enough data, it is possible to infer knowledge about the internal structure of the system and, if parameterized, to predict the state parameters for a particular input-output relation. We show that this approach, besides getting physically-based predictions, accelerates the training process, reduces the amount of data required to get similar accuracy, filters partly the intrinsic noise in the experimental data and provides improved extrapolation capacity.
Optimizing Offer Sets in Sub-Linear Time
Venue: arXiv cs.AI 2020
Year: 2020

Personalization and recommendations are now accepted as core competencies in just about every online setting, ranging from media platforms to e-commerce to social networks. While the challenge of estimating user preferences has garnered significant attention, the operational problem of using such preferences to construct personalized offer sets to users is still a challenge, particularly in modern settings where a massive number of items and a millisecond response time requirement mean that even enumerating all of the items is impossible. Faced with such settings, existing techniques are either (a) entirely heuristic with no principled justification, or (b) theoretically sound, but simply too slow to work. Thus motivated, we propose an algorithm for personalized offer set optimization that runs in time sub-linear in the number of items while enjoying a uniform performance guarantee. Our algorithm works for an extremely general class of problems and models of user choice that includes the mixed multinomial logit model as a special case. We achieve a sub-linear runtime by leveraging the dimensionality reduction from learning an accurate latent factor model, along with existing sub-linear time approximate near neighbor algorithms. Our algorithm can be entirely data-driven, relying on samples of the user, where a `sample' refers to the user interaction data typically collected by firms. We evaluate our approach on a massive content discovery dataset from Outbrain that includes millions of advertisements. Results show that our implementation indeed runs fast and with increased performance relative to existing fast heuristics.
Using Explainable Scheduling for the Mars 2020 Rover Mission
Venue: arXiv cs.AI 2020
Year: 2020

Understanding the reasoning behind the behavior of an automated scheduling system is essential to ensure that it will be trusted and consequently used to its full capabilities in critical applications. In cases where a scheduler schedules activities in an invalid location, it is usually easy for the user to infer the missing constraint by inspecting the schedule with the invalid activity to determine the missing constraint. If a scheduler fails to schedule activities because constraints could not be satisfied, determining the cause can be more challenging. In such cases it is important to understand which constraints caused the activities to fail to be scheduled and how to alter constraints to achieve the desired schedule. In this paper, we describe such a scheduling system for NASA's Mars 2020 Perseverance Rover, as well as Crosscheck, an explainable scheduling tool that explains the scheduler behavior. The scheduling system and Crosscheck are the baseline for operational use to schedule activities for the Mars 2020 rover. As we describe, the scheduler generates a schedule given a set of activities and their constraints and Crosscheck: (1) provides a visual representation of the generated schedule; (2) analyzes and explains why activities failed to schedule given the constraints provided; and (3) provides guidance on potential constraint relaxations to enable the activities to schedule in future scheduler runs.
Bootstrap Aggregation for Point-based Generalized Membership Inference Attacks
Venue: arXiv cs.CR 2020
Year: 2020

An efficient scheme is introduced that extends the generalized membership inference attack to every point in a model's training data set. Our approach leverages data partitioning to create variable sized training sets for the reference models. We then train an attack model for every single training example for a reference model configuration based upon output for each individual point. This allows us to quantify the membership inference attack vulnerability of each training data point. Using this approach, we discovered that smaller amounts of reference model training data led to a stronger attack. Furthermore, the reference models do not need to be of the same architecture as the target model, providing additional attack efficiencies. The attack may also be performed by an adversary even when they do not have the complete original data set.
Iterative Semi-parametric Dynamics Model Learning For Autonomous Racing
Venue: arXiv cs.RO 2020
Year: 2020

Accurately modeling robot dynamics is crucial to safe and efficient motion control. In this paper, we develop and apply an iterative learning semi-parametric model, with a neural network, to the task of autonomous racing with a Model Predictive Controller (MPC). We present a novel non-linear semi-parametric dynamics model where we represent the known dynamics with a parametric model, and a neural network captures the unknown dynamics. We show that our model can learn more accurately than a purely parametric model and generalize better than a purely non-parametric model, making it ideal for real-world applications where collecting data from the full state space is not feasible. We present a system where the model is bootstrapped on pre-recorded data and then updated iteratively at run time. Then we apply our iterative learning approach to the simulated problem of autonomous racing and show that it can safely adapt to modified dynamics online and even achieve better performance than models trained on data from manual driving.
Exploring Energy-Accuracy Tradeoffs in AI Hardware
Venue: arXiv cs.LG 2020
Year: 2020

Artificial intelligence (AI) is playing an increasingly significant role in our everyday lives. This trend is expected to continue, especially with recent pushes to move more AI to the edge. However, one of the biggest challenges associated with AI on edge devices (mobile phones, unmanned vehicles, sensors, etc.) is their associated size, weight, and power constraints. In this work, we consider the scenario where an AI system may need to operate at less-than-maximum accuracy in order to meet application-dependent energy requirements. We propose a simple function that divides the cost of using an AI system into the cost of the decision making process and the cost of decision execution. For simple binary decision problems with convolutional neural networks, it is shown that minimizing the cost corresponds to using fewer than the maximum number of resources (e.g. convolutional neural network layers and filters). Finally, it is shown that the cost associated with energy can be significantly reduced by leveraging high-confidence predictions made in lower-level layers of the network.
The COVID19 infodemic. The role and place of academics in science communication
Venue: arXiv cs.CY 2020
Year: 2020

As the COVID19 pandemic has spread across the world, a concurrent pandemic of information has spread with it. Deemed an infodemic by the World Health Organization, and described as an overabundance of information, some accurate, some not, that occurs during an epidemic, this proliferation of data, research and opinions provides both opportunities and challenges for academics. Academics and scientists have a key role to play in the solutions to the infodemic challenge: as educators, influences and communicators, even where their expertise and experience does not align precisely with the SARS-Cov2 virus and its impacts. Successful communication requires a better understanding of how the public seeks, understands and processes scientific information, however, in order to maximise the ways in which experts engage with traditional and social media and to make sure that such engagement does not add to confusion and misinformation alongside efforts to counter or challenge it. This paper will outline the key advantages to be had from greater engagement with COVID19 discussions, the popular channels through which such discussions take place and through which information is disseminated. It also warns against the common pitfalls those who choose to engage might encounter, whilst stressing that the disadvantages of doing so are far outweighed by the advantages such engagement offers.
REALab: An Embedded Perspective on Tampering
Venue: arXiv cs.LG 2020
Year: 2020

This paper describes REALab, a platform for embedded agency research in reinforcement learning (RL). REALab is designed to model the structure of tampering problems that may arise in real-world deployments of RL. Standard Markov Decision Process (MDP) formulations of RL and simulated environments mirroring the MDP structure assume secure access to feedback (e.g., rewards). This may be unrealistic in settings where agents are embedded and can corrupt the processes producing feedback (e.g., human supervisors, or an implemented reward function). We describe an alternative Corrupt Feedback MDP formulation and the REALab environment platform, which both avoid the secure feedback assumption. We hope the design of REALab provides a useful perspective on tampering problems, and that the platform may serve as a unit test for the presence of tampering incentives in RL agent designs.
Learning Canonical Transformations
Venue: arXiv cs.CV 2020
Year: 2020

Humans understand a set of canonical geometric transformations (such as translation and rotation) that support generalization by being untethered to any specific object. We explore inductive biases that help a neural network model learn these transformations in pixel space in a way that can generalize out-of-domain. Specifically, we find that high training set diversity is sufficient for the extrapolation of translation to unseen shapes and scales, and that an iterative training scheme achieves significant extrapolation of rotation in time.