OpenRaiser/Intern-Atlas huggingface dataset:
DOVE: A Data-Oblivious Virtual Environment
Venue: arXiv cs.CR 2021
Year: 2021

Users can improve the security of remote communications by using Trusted Execution Environments (TEEs) to protect against direct introspection and tampering of sensitive data. This can even be done with applications coded in high-level languages with complex programming stacks such as R, Python, and Ruby. However, this creates a trade-off between programming convenience versus the risk of attacks using microarchitectural side channels. In this paper, we argue that it is possible to address this problem for important applications by instrumenting a complex programming environment (like R) to produce a Data-Oblivious Transcript (DOT) that is explicitly designed to support computation that excludes side channels. Such a transcript is then evaluated on a Trusted Execution Environment (TEE) containing the sensitive data using a small trusted computing base called the Data-Oblivious Virtual Environment (DOVE). To motivate the problem, we demonstrate a number of subtle side-channel vulnerabilities in the R language. We then provide an illustrative design and implementation of DOVE for R, creating the first side-channel resistant R programming stack. We demonstrate that the two-phase architecture provided by DOT generation and DOVE evaluation can provide practical support for complex programming languages with usable performance and high security assurances against side channels.
Hybrid In-memory Computing Architecture for the Training of Deep Neural Networks
Venue: arXiv cs.AR 2021
Year: 2021

The cost involved in training deep neural networks (DNNs) on von-Neumann architectures has motivated the development of novel solutions for efficient DNN training accelerators. We propose a hybrid in-memory computing (HIC) architecture for the training of DNNs on hardware accelerators that results in memory-efficient inference and outperforms baseline software accuracy in benchmark tasks. We introduce a weight representation technique that exploits both binary and multi-level phase-change memory (PCM) devices, and this leads to a memory-efficient inference accelerator. Unlike previous in-memory computing-based implementations, we use a low precision weight update accumulator that results in more memory savings. We trained the ResNet-32 network to classify CIFAR-10 images using HIC. For a comparable model size, HIC-based training outperforms baseline network, trained in floating-point 32-bit (FP32) precision, by leveraging appropriate network width multiplier. Furthermore, we observe that HIC-based training results in about 50% less inference model size to achieve baseline comparable accuracy. We also show that the temporal drift in PCM devices has a negligible effect on post-training inference accuracy for extended periods (year). Finally, our simulations indicate HIC-based training naturally ensures that the number of write-erase cycles seen by the devices is a small fraction of the endurance limit of PCM, demonstrating the feasibility of this architecture for achieving hardware platforms that can learn in the field.
Biomedical Question Answering: A Survey of Approaches and Challenges
Venue: arXiv cs.CL 2021
Year: 2021

Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access and understand complex biomedical knowledge. There have been tremendous developments of BQA in the past two decades, which we classify into 5 distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base and question entailment approaches. In this survey, we introduce available datasets and representative methods of each BQA approach in detail. Despite the developments, BQA systems are still immature and rarely used in real-life settings. We identify and characterize several key challenges in BQA that might lead to this issue, and discuss some potential future directions to explore.
Bayesian Inference with Certifiable Adversarial Robustness
Venue: arXiv cs.LG 2021
Year: 2021

We consider adversarial training of deep neural networks through the lens of Bayesian learning, and present a principled framework for adversarial training of Bayesian Neural Networks (BNNs) with certifiable guarantees. We rely on techniques from constraint relaxation of non-convex optimisation problems and modify the standard cross-entropy error model to enforce posterior robustness to worst-case perturbations in $ε$-balls around input points. We illustrate how the resulting framework can be combined with methods commonly employed for approximate inference of BNNs. In an empirical investigation, we demonstrate that the presented approach enables training of certifiably robust models on MNIST, FashionMNIST and CIFAR-10 and can also be beneficial for uncertainty calibration. Our method is the first to directly train certifiable BNNs, thus facilitating their deployment in safety-critical applications.
Controlled Experimentation in Continuous Experimentation: Knowledge and Challenges
Venue: arXiv cs.SE 2021
Year: 2021

Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. Method: We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. Results: The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. Conclusions: The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve.
On FGLM Algorithms with Tate Algebras
Venue: arXiv cs.SC 2021
Year: 2021

Tate introduced in [Ta71] the notion of Tate algebras to serve, in the context of analytic geometry over the-adics, as a counterpart of polynomial algebras in classical algebraic geometry. In [CVV19, CVV20] the formalism of Gr{ö}bner bases over Tate algebras has been introduced and advanced signature-based algorithms have been proposed. In the present article, we extend the FGLM algorithm of [FGLM93] to Tate algebras. Beyond allowing for fast change of ordering, this strategy has two other important benefits. First, it provides an efficient algorithm for changing the radii of convergence which, in particular, makes effective the bridge between the polynomial setting and the Tate setting and may help in speeding up the computation of Gr{ö}bner basis over Tate algebras. Second, it gives the foundations for designing a fast algorithm for interreduction, which could serve as basic primitive in our previous algorithms and accelerate them significantly.
On PyTorch Implementation of Density Estimators for von Mises-Fisher and Its Mixture
Venue: arXiv cs.LG 2021
Year: 2021

The von Mises-Fisher (vMF) is a well-known density model for directional random variables. The recent surge of the deep embedding methodologies for high-dimensional structured data such as images or texts, aimed at extracting salient directional information, can make the vMF model even more popular. In this article, we will review the vMF model and its mixture, provide detailed recipes of how to train the models, focusing on the maximum likelihood estimators, in Python/PyTorch. In particular, implementation of vMF typically suffers from the notorious numerical issue of the Bessel function evaluation in the density normalizer, especially when the dimensionality is high, and we address the issue using the MPMath library that supports arbitrary precision. For the mixture learning, we provide both minibatch-based large-scale SGD learning, as well as the EM algorithm which is a full batch estimator. For each estimator/methodology, we test our implementation on some synthetic data, while we also demonstrate the use case in a more realistic scenario of image clustering. Our code is publicly available in https://github.com/minyoungkim21/vmf-lib.
GuiltyWalker: Distance to illicit nodes in the Bitcoin network
Venue: arXiv cs.LG 2021
Year: 2021

Money laundering is a global phenomenon with wide-reaching social and economic consequences. Cryptocurrencies are particularly susceptible due to the lack of control by authorities and their anonymity. Thus, it is important to develop new techniques to detect and prevent illicit cryptocurrency transactions. In our work, we propose new features based on the structure of the graph and past labels to boost the performance of machine learning methods to detect money laundering. Our method, GuiltyWalker, performs random walks on the bitcoin transaction graph and computes features based on the distance to illicit transactions. We combine these new features with features proposed by Weber et al. and observe an improvement of about 5pp regarding illicit classification. Namely, we observe that our proposed features are particularly helpful during a black market shutdown, where the algorithm by Weber et al. was low performing.
Integrating Cardinality Constraints into Constraint Logic Programming with Sets
Venue: arXiv cs.LO 2021
Year: 2021

Formal reasoning about finite sets and cardinality is an important tool for many applications, including software verification, where very often one needs to reason about the size of a given data structure and not only about what its elements are. The Constraint Logic Programming tool {log} provides a decision procedure for deciding the satisfiability of formulas involving very general forms of finite sets, without cardinality. In this paper we adapt and integrate a decision procedure for a theory of finite sets with cardinality into {log}. The proposed solver is proved to be a decision procedure for its formulas. Besides, the new CLP instance is implemented as part of the {log} tool. In turn, the implementation uses Howe and King's Prolog SAT solver and Prolog's CLP(Q) library, as an integer linear programming solver. The empirical evaluation of this implementation based on +250 real verification conditions shows that it can be useful in practice.
Node-Level Membership Inference Attacks Against Graph Neural Networks
Venue: arXiv cs.CR 2021
Year: 2021

Many real-world data comes in the form of graphs, such as social networks and protein structure. To fully utilize the information contained in graph data, a new family of machine learning (ML) models, namely graph neural networks (GNNs), has been introduced. Previous studies have shown that machine learning models are vulnerable to privacy attacks. However, most of the current efforts concentrate on ML models trained on data from the Euclidean space, like images and texts. On the other hand, privacy risks stemming from GNNs remain largely unstudied. In this paper, we fill the gap by performing the first comprehensive analysis of node-level membership inference attacks against GNNs. We systematically define the threat models and propose three node-level membership inference attacks based on an adversary's background knowledge. Our evaluation on three GNN structures and four benchmark datasets shows that GNNs are vulnerable to node-level membership inference even when the adversary has minimal background knowledge. Besides, we show that graph density and feature similarity have a major impact on the attack's success. We further investigate two defense mechanisms and the empirical results indicate that these defenses can reduce the attack performance but with moderate utility loss.
Dompteur: Taming Audio Adversarial Examples
Venue: arXiv cs.CR 2021
Year: 2021

Adversarial examples seem to be inevitable. These specifically crafted inputs allow attackers to arbitrarily manipulate machine learning systems. Even worse, they often seem harmless to human observers. In our digital society, this poses a significant threat. For example, Automatic Speech Recognition (ASR) systems, which serve as hands-free interfaces to many kinds of systems, can be attacked with inputs incomprehensible for human listeners. The research community has unsuccessfully tried several approaches to tackle this problem. In this paper we propose a different perspective: We accept the presence of adversarial examples against ASR systems, but we require them to be perceivable by human listeners. By applying the principles of psychoacoustics, we can remove semantically irrelevant information from the ASR input and train a model that resembles human perception more closely. We implement our idea in a tool named DOMPTEUR and demonstrate that our augmented system, in contrast to an unmodified baseline, successfully focuses on perceptible ranges of the input signal. This change forces adversarial examples into the audible range, while using minimal computational overhead and preserving benign performance. To evaluate our approach, we construct an adaptive attacker that actively tries to avoid our augmentations and demonstrate that adversarial examples from this attacker remain clearly perceivable. Finally, we substantiate our claims by performing a hearing test with crowd-sourced human listeners.
Two Novel Performance Improvements for Evolving CNN Topologies
Venue: arXiv cs.CV 2021
Year: 2021

Convolutional Neural Networks (CNNs) are the state-of-the-art algorithms for the processing of images. However the configuration and training of these networks is a complex task requiring deep domain knowledge, experience and much trial and error. Using genetic algorithms, competitive CNN topologies for image recognition can be produced for any specific purpose, however in previous work this has come at high computational cost. In this work two novel approaches are presented to the utilisation of these algorithms, effective in reducing complexity and training time by nearly 20%. This is accomplished via regularisation directly on training time, and the use of partial training to enable early ranking of individual architectures. Both approaches are validated on the benchmark CIFAR10 data set, and maintain accuracy.
The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions
Venue: arXiv cs.HC 2021
Year: 2021

The explanation dimension of Artificial Intelligence (AI) based system has been a hot topic for the past years. Different communities have raised concerns about the increasing presence of AI in people's everyday tasks and how it can affect people's lives. There is a lot of research addressing the interpretability and transparency concepts of explainable AI (XAI), which are usually related to algorithms and Machine Learning (ML) models. But in decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system. Decision-makers usually need to justify their decision to others in different domains. If that decision is somehow based on or influenced by an AI-system outcome, the explanation about how the AI reached that result is key to building trust between AI and humans in decision-making scenarios. In this position paper, we discuss the role of XAI in decision-making scenarios, our vision of Decision-Making with AI-system in the loop, and explore one case from the literature about how XAI can impact people justifying their decisions, considering the importance of building the human-AI relationship for those scenarios.
Energy-Aware Adaptive Offloading of Soft Real-Time Jobs in Mobile Edge Clouds
Venue: arXiv cs.DC 2021
Year: 2021

We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfillment of job deadlines.
Hyperbolic Generative Adversarial Network
Venue: arXiv cs.LG 2021
Year: 2021

Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data. We propose that it is possible to take advantage of the hierarchical characteristic present in the images by using hyperbolic neural networks in a GAN architecture. In this study, different configurations using fully connected hyperbolic layers in the GAN, CGAN, and WGAN are tested, in what we call the HGAN, HCGAN, and HWGAN, respectively. The results are measured using the Inception Score (IS) and the Fréchet Inception Distance (FID) on the MNIST dataset. Depending on the configuration and space curvature, better results are achieved for each proposed hyperbolic versions than their euclidean counterpart.
Classification of Long Noncoding RNA Elements Using Deep Convolutional Neural Networks and Siamese Networks
Venue: arXiv cs.CV 2021
Year: 2021

In the last decade, the discovery of noncoding RNA(ncRNA) has exploded. Classifying these ncRNA is critical todetermining their function. This thesis proposes a new methodemploying deep convolutional neural networks (CNNs) to classifyncRNA sequences. To this end, this paper first proposes anefficient approach to convert the RNA sequences into imagescharacterizing their base-pairing probability. As a result, clas-sifying RNA sequences is converted to an image classificationproblem that can be efficiently solved by available CNN-basedclassification models. This research also considers the foldingpotential of the ncRNAs in addition to their primary sequence.Based on the proposed approach, a benchmark image classifi-cation dataset is generated from the RFAM database of ncRNAsequences. In addition, three classical CNN models and threeSiamese network models have been implemented and comparedto demonstrate the superior performance and efficiency of theproposed approach. Extensive experimental results show thegreat potential of using deep learning approaches for RNAclassification.
ParmoSense: A Scenario-based Participatory Mobile Urban Sensing Platform with User Motivation Engine
Venue: arXiv cs.SI 2021
Year: 2021

Rapid proliferation of mobile devices with various sensors have enabled Participatory Mobile Sensing (PMS). Several PMS platforms provide multiple functions for various sensing purposes, but they are suffering from the open issues: limited use of their functions for a specific scenario/case and requiring technical knowledge for organizers. In this paper, we propose a novel PMS platform named ParmoSense for easily and flexibly collecting urban environmental information. To reduce the burden on both organizers and participants, in ParmoSense, we employ two novel features: modularization of functions and scenario-based PMS system description. For modularization, we provide the essential PMS functions as modules which can be easily chosen and combined for sensing in different scenarios. The scenario-based description feature allows organizers to easily and quickly set up a new participatory sensing instance and participants to easily install the corresponding scenario and participate in the sensing. Moreover, ParmoSense provides GUI tools as well for creating and distributing PMS system easily, editing and visualizing collected data quickly. It also provides multiple functions for encouraging participants' motivation for sustainable operation of the system. Through performance comparison with existing PMS platforms, we confirmed ParmoSense shows the best cost-performance in the perspective of the workload for preparing PMS system and varieties of functions. In addition, to evaluate the availability and usability of ParmoSense, we conducted 19 case studies, which have different locations, scales, and purposes, over 4 years with cooperation from ordinary citizens. Through the case studies and the questionnaire survey for participants and organizers, we confirmed that ParmoSense can be easily operated and participated by ordinary citizens including non-technical persons.
Training Vision Transformers for Image Retrieval
Venue: arXiv cs.CV 2021
Year: 2021

Transformers have shown outstanding results for natural language understanding and, more recently, for image classification. We here extend this work and propose a transformer-based approach for image retrieval: we adopt vision transformers for generating image descriptors and train the resulting model with a metric learning objective, which combines a contrastive loss with a differential entropy regularizer. Our results show consistent and significant improvements of transformers over convolution-based approaches. In particular, our method outperforms the state of the art on several public benchmarks for category-level retrieval, namely Stanford Online Product, In-Shop and CUB-200. Furthermore, our experiments on ROxford and RParis also show that, in comparable settings, transformers are competitive for particular object retrieval, especially in the regime of short vector representations and low-resolution images.