OpenRaiser/Intern-Atlas huggingface dataset:
Categorizing Service Worker Attacks and Mitigations
Venue: arXiv cs.CR 2021
Year: 2021

Service Workers (SWs) are a powerful feature at the core of Progressive Web Apps, namely web applications that can continue to function when the user's device is offline and that have access to device sensors and capabilities previously accessible only by native applications. During the past few years, researchers have found a number of ways in which SWs may be abused to achieve different malicious purposes. For instance, SWs may be abused to build a web-based botnet, launch DDoS attacks, or perform cryptomining; they may be hijacked to create persistent cross-site scripting (XSS) attacks; they may be leveraged in the context of side-channel attacks to compromise users' privacy; or they may be abused for phishing or social engineering attacks using web push notifications-based malvertising. In this paper, we reproduce and analyze known attack vectors related to SWs and explore new abuse paths that have not previously been considered. We systematize the attacks into different categories, and then analyze whether, how, and estimate when these attacks have been published and mitigated by different browser vendors. Then, we discuss a number of open SW security problems that are currently unmitigated, and propose SW behavior monitoring approaches and new browser policies that we believe should be implemented by browsers to further improve SW security. Furthermore, we implement a proof-of-concept version of several policies in the Chromium code base, and also measure the behavior of SWs used by highly popular web applications with respect to these new policies. Our measurements show that it should be feasible to implement and enforce stricter SW security policies without a significant impact on most legitimate production SWs.
Robust and Accurate Object Detection via Self-Knowledge Distillation
Venue: arXiv cs.CV 2021
Year: 2021

Object detection has achieved promising performance on clean datasets, but how to achieve better tradeoff between the adversarial robustness and clean precision is still under-explored. Adversarial training is the mainstream method to improve robustness, but most of the works will sacrifice clean precision to gain robustness than standard training. In this paper, we propose Unified Decoupled Feature Alignment (UDFA), a novel fine-tuning paradigm which achieves better performance than existing methods, by fully exploring the combination between self-knowledge distillation and adversarial training for object detection. We first use decoupled fore/back-ground features to construct self-knowledge distillation branch between clean feature representation from pretrained detector (served as teacher) and adversarial feature representation from student detector. Then we explore the self-knowledge distillation from a new angle by decoupling original branch into a self-supervised learning branch and a new self-knowledge distillation branch. With extensive experiments on the PASCAL-VOC and MS-COCO benchmarks, the evaluation results show that UDFA can surpass the standard training and state-of-the-art adversarial training methods for object detection. For example, compared with teacher detector, our approach on GFLV2 with ResNet-50 improves clean precision by 2.2 AP on PASCAL-VOC; compared with SOTA adversarial training methods, our approach improves clean precision by 1.6 AP, while improving adversarial robustness by 0.5 AP. Our code will be available at https://github.com/grispeut/udfa.
Towards annotation of text worlds in a literary work
Venue: arXiv cs.CL 2021
Year: 2021

Literary texts are usually rich in meanings and their interpretation complicates corpus studies and automatic processing. There have been several attempts to create collections of literary texts with annotation of literary elements like the author's speech, characters, events, scenes etc. However, they resulted in small collections and standalone rules for annotation. The present article describes an experiment on lexical annotation of text worlds in a literary work and quantitative methods of their comparison. The experiment shows that for a well-agreed tag assignment annotation rules should be set much more strictly. However, if borders between text worlds and other elements are the result of a subjective interpretation, they should be modeled as fuzzy entities.
Geofreebie: A Location-Based Freecycling App to Support Forced Migrant Resettlement
Venue: arXiv cs.HC 2021
Year: 2021

Germany has witnessed an influx of forced migrants in recent years. Promoting social interaction with the local community is key to supporting the resettlement of these newcomers. Location-based freecycling services present important benefits due to freecycling's potential to bolster social engagement and location-based services' ability to adapt to the user's context. Yet, their potential to support forced migrants' resettlement is yet to be examined. We conducted needs assessment interviews with 11 participants in Muenster, Germany. We analyzed the interview results to develop user requirements for location-based freecycling services. We then implemented a subset of the user requirements as a prototype mobile app called Geofreebie. The evaluation of the app with 22 participants showed that Geofreebie offered two key advantages for forced migrants' resettlement: it increased the size of their social network, and created a sense of community on their side. These findings can benefit researchers and developers of location-based services to support forced migrant resettlement.
What Should We Optimize in Participatory Budgeting? An Experimental Study
Venue: arXiv cs.MA 2021
Year: 2021

Participatory Budgeting (PB) is a process in which voters decide how to allocate a common budget; most commonly it is done by ordinary people -- in particular, residents of some municipality -- to decide on a fraction of the municipal budget. From a social choice perspective, existing research on PB focuses almost exclusively on designing computationally-efficient aggregation methods that satisfy certain axiomatic properties deemed "desirable" by the research community. Our work complements this line of research through a user study (N = 215) involving several experiments aimed at identifying what potential voters (i.e., non-experts) deem fair or desirable in simple PB settings. Our results show that some modern PB aggregation techniques greatly differ from users' expectations, while other, more standard approaches, provide more aligned results. We also identify a few possible discrepancies between what non-experts consider \say{desirable} and how they perceive the notion of "fairness" in the PB context. Taken jointly, our results can be used to help the research community identify appropriate PB aggregation methods to use in practice.
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines
Venue: arXiv cs.LG 2021
Year: 2021

Understanding the results of deep neural networks is an essential step towards wider acceptance of deep learning algorithms. Many approaches address the issue of interpreting artificial neural networks, but often provide divergent explanations. Moreover, different hyperparameters of an explanatory method can lead to conflicting interpretations. In this paper, we propose a technique for aggregating the feature attributions of different explanatory algorithms using Restricted Boltzmann Machines (RBMs) to achieve a more reliable and robust interpretation of deep neural networks. Several challenging experiments on real-world datasets show that the proposed RBM method outperforms popular feature attribution methods and basic ensemble techniques.
A Machine Learning Approach for Recruitment Prediction in Clinical Trial Design
Venue: arXiv cs.LG 2021
Year: 2021

Significant advancements have been made in recent years to optimize patient recruitment for clinical trials, however, improved methods for patient recruitment prediction are needed to support trial site selection and to estimate appropriate enrollment timelines in the trial design stage. In this paper, using data from thousands of historical clinical trials, we explore machine learning methods to predict the number of patients enrolled per month at a clinical trial site over the course of a trial's enrollment duration. We show that these methods can reduce the error that is observed with current industry standards and propose opportunities for further improvement.
HAD-Net: Hybrid Attention-based Diffusion Network for Glucose Level Forecast
Venue: arXiv cs.LG 2021
Year: 2021

Data-driven models for glucose level forecast often do not provide meaningful insights despite accurate predictions. Yet, context understanding in medicine is crucial, in particular for diabetes management. In this paper, we introduce HAD-Net: a hybrid model that distills knowledge into a deep neural network from physiological models. It models glucose, insulin and carbohydrates diffusion through a biologically inspired deep learning architecture tailored with a recurrent attention network constrained by ODE expert models. We apply HAD-Net for glucose level forecast of patients with type-2 diabetes. It achieves competitive performances while providing plausible measurements of insulin and carbohydrates diffusion over time.
Attentive Federated Learning for Concept Drift in Distributed 5G Edge Networks
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning (ML) is expected to play a major role in 5G edge computing. Various studies have demonstrated that ML is highly suitable for optimizing edge computing systems as rapid mobility and application-induced changes occur at the edge. For ML to provide the best solutions, it is important to continually train the ML models to include the changing scenarios. The sudden changes in data distributions caused by changing scenarios (e.g., 5G base station failures) is referred to as concept drift and is a major challenge to continual learning. The ML models can present high error rates while the drifts take place and the errors decrease only after the model learns the distributions. This problem is more pronounced in a distributed setting where multiple ML models are being used for different heterogeneous datasets and the final model needs to capture all concept drifts. In this paper, we show that using Attention in Federated Learning (FL) is an efficient way of handling concept drifts. We use a 5G network traffic dataset to simulate concept drift and test various scenarios. The results indicate that Attention can significantly improve the concept drift handling capability of FL.
Deep Learning based Urban Vehicle Trajectory Analytics
Venue: arXiv cs.LG 2021
Year: 2021

A `trajectory' refers to a trace generated by a moving object in geographical spaces, usually represented by of a series of chronologically ordered points, where each point consists of a geo-spatial coordinate set and a timestamp. Rapid advancements in location sensing and wireless communication technology enabled us to collect and store a massive amount of trajectory data. As a result, many researchers use trajectory data to analyze mobility of various moving objects. In this dissertation, we focus on the `urban vehicle trajectory,' which refers to trajectories of vehicles in urban traffic networks, and we focus on `urban vehicle trajectory analytics.' The urban vehicle trajectory analytics offers unprecedented opportunities to understand vehicle movement patterns in urban traffic networks including both user-centric travel experiences and system-wide spatiotemporal patterns. The spatiotemporal features of urban vehicle trajectory data are structurally correlated with each other, and consequently, many previous researchers used various methods to understand this structure. Especially, deep-learning models are getting attentions of many researchers due to its powerful function approximation and feature representation abilities. As a result, the objective of this dissertation is to develop deep-learning based models for urban vehicle trajectory analytics to better understand the mobility patterns of urban traffic networks. Particularly, this dissertation focuses on two research topics, which has high necessity, importance and applicability: Next Location Prediction, and Synthetic Trajectory Generation. In this study, we propose various novel models for urban vehicle trajectory analytics using deep learning.
Measuring Outcomes in Healthcare Economics using Artificial Intelligence: with Application to Resource Management
Venue: arXiv cs.AI 2021
Year: 2021

The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e. Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial), lead to shifts in planning and budgeting, but most importantly, reduces confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This manuscript presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.
A Survey on AI Assurance
Venue: arXiv cs.AI 2021
Year: 2021

Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide library of algorithms for different problems. One important notion for the adoption of AI algorithms into operational decision process is the concept of assurance. The literature on assurance, unfortunately, conceals its outcomes within a tangled landscape of conflicting approaches, driven by contradicting motivations, assumptions, and intuitions. Accordingly, albeit a rising and novel area, this manuscript provides a systematic review of research works that are relevant to AI assurance, between years 1985 - 2021, and aims to provide a structured alternative to the landscape. A new AI assurance definition is adopted and presented and assurance methods are contrasted and tabulated. Additionally, a ten-metric scoring system is developed and introduced to evaluate and compare existing methods. Lastly, in this manuscript, we provide foundational insights, discussions, future directions, a roadmap, and applicable recommendations for the development and deployment of AI assurance.
Randomized Classifiers vs Human Decision-Makers: Trustworthy AI May Have to Act Randomly and Society Seems to Accept This
Venue: arXiv cs.CY 2021
Year: 2021

As \emph{artificial intelligence} (AI) systems are increasingly involved in decisions affecting our lives, ensuring that automated decision-making is fair and ethical has become a top priority. Intuitively, we feel that akin to human decisions, judgments of artificial agents should necessarily be grounded in some moral principles. Yet a decision-maker (whether human or artificial) can only make truly ethical (based on any ethical theory) and fair (according to any notion of fairness) decisions if full information on all the relevant factors on which the decision is based are available at the time of decision-making. This raises two problems: (1) In settings, where we rely on AI systems that are using classifiers obtained with supervised learning, some induction/generalization is present and some relevant attributes may not be present even during learning. (2) Modeling such decisions as games reveals that any -- however ethical -- pure strategy is inevitably susceptible to exploitation. Moreover, in many games, a Nash Equilibrium can only be obtained by using mixed strategies, i.e., to achieve mathematically optimal outcomes, decisions must be randomized. In this paper, we argue that in supervised learning settings, there exist random classifiers that perform at least as well as deterministic classifiers, and may hence be the optimal choice in many circumstances. We support our theoretical results with an empirical study indicating a positive societal attitude towards randomized artificial decision-makers, and discuss some policy and implementation issues related to the use of random classifiers that relate to and are relevant for current AI policy and standardization initiatives.
Searching for TrioNet: Combining Convolution with Local and Global Self-Attention
Venue: arXiv cs.CV 2021
Year: 2021

Recently, self-attention operators have shown superior performance as a stand-alone building block for vision models. However, existing self-attention models are often hand-designed, modified from CNNs, and obtained by stacking one operator only. A wider range of architecture space which combines different self-attention operators and convolution is rarely explored. In this paper, we explore this novel architecture space with weight-sharing Neural Architecture Search (NAS) algorithms. The result architecture is named TrioNet for combining convolution, local self-attention, and global (axial) self-attention operators. In order to effectively search in this huge architecture space, we propose Hierarchical Sampling for better training of the supernet. In addition, we propose a novel weight-sharing strategy, Multi-head Sharing, specifically for multi-head self-attention operators. Our searched TrioNet that combines self-attention and convolution outperforms all stand-alone models with fewer FLOPs on ImageNet classification where self-attention performs better than convolution. Furthermore, on various small datasets, we observe inferior performance for self-attention models, but our TrioNet is still able to match the best operator, convolution in this case. Our code is available at https://github.com/phj128/TrioNet.
Design and Evaluation Frameworks for Advanced RISC-based Ternary Processor
Venue: arXiv cs.AR 2021
Year: 2021

In this paper, we introduce the design and verification frameworks for developing a fully-functional emerging ternary processor. Based on the existing compiling environments for binary processors, for the given ternary instructions, the software-level framework provides an efficient way to convert the given programs to the ternary assembly codes. We also present a hardware-level framework to rapidly evaluate the performance of a ternary processor implemented in arbitrary design technology. As a case study, the fully-functional 9-trit advanced RISC-based ternary (ART-9) core is newly developed by using the proposed frameworks. Utilizing 24 custom ternary instructions, the 5-stage ART-9 prototype architecture is successfully verified by a number of test programs including dhrystone benchmark in a ternary domain, achieving the processing efficiency of 57.8 DMIPS/W and 3.06 x 10^6 DMIPS/W in the FPGA-level ternary-logic emulations and the emerging CNTFET ternary gates, respectively.
DNN gradient lossless compression: Can GenNorm be the answer?
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, the problem of optimal gradient lossless compression in Deep Neural Network (DNN) training is considered. Gradient compression is relevant in many distributed DNN training scenarios, including the recently popular federated learning (FL) scenario in which each remote users are connected to the parameter server (PS) through a noiseless but rate limited channel. In distributed DNN training, if the underlying gradient distribution is available, classical lossless compression approaches can be used to reduce the number of bits required for communicating the gradient entries. Mean field analysis has suggested that gradient updates can be considered as independent random variables, while Laplace approximation can be used to argue that gradient has a distribution approximating the normal (Norm) distribution in some regimes. In this paper we argue that, for some networks of practical interest, the gradient entries can be well modelled as having a generalized normal (GenNorm) distribution. We provide numerical evaluations to validate that the hypothesis GenNorm modelling provides a more accurate prediction of the DNN gradient tail distribution. Additionally, this modeling choice provides concrete improvement in terms of lossless compression of the gradients when applying classical fix-to-variable lossless coding algorithms, such as Huffman coding, to the quantized gradient updates. This latter results indeed provides an effective compression strategy with low memory and computational complexity that has great practical relevance in distributed DNN training scenarios.
On the validation of pansharpening methods
Venue: arXiv cs.CV 2021
Year: 2021

Validation of the quality of pansharpening methods is a difficult task because the reference is not directly available. In the meantime, two main approaches have been established: validation in reduced resolution and original resolution. In the former approach it is still not clear how the data are to be processed to a lower resolution. Other open issues are related to the question which resolution and measures should be used. In the latter approach the main problem is how the appropriate measure should be selected. In the most comparison studies the results of both approaches do not correspond, that means in each case other methods are selected as the best ones. Thus, the developers of the new pansharpening methods still stand in the front of dilemma: how to perform a correct or appropriate comparison/evaluation/validation. It should be noted, that the third approach is possible, that is to perform the comparison of methods in a particular application with the usage of their ground truth. But this is not always possible, because usually developers are not working with applications. Moreover, it can be an additional computational load for a researcher in a particular application. In this paper some of the questions/problems raised above are approached/discussed. The following component substitution (CS) and high pass filtering (HPF) pansharpening methods with additive and multiplicative models and their enhancements such as haze correction, histogram matching, usage of spectral response functions (SRF), modulation transfer function (MTF) based lowpass filtering are investigated on remote sensing data of WorldView-2 and WorldView-4 sensors.
Low-Subpacketization Multi-Antenna Coded Caching for Dynamic Networks
Venue: arXiv cs.IT 2021
Year: 2021

Multi-antenna coded caching combines a global caching gain, proportional to the total cache size in the network, with an additional spatial multiplexing gain that stems from multiple transmitting antennas. However, classic centralized coded caching schemes are not suitable for dynamic networks as they require prior knowledge of the number of users to indicate what data should be cached at each user during the placement phase. On the other hand, fully decentralized schemes provide comparable gains to their centralized counterparts only when the number of users is very large. In this paper, we propose a novel multi-antenna coded caching scheme for dynamic networks, where instead of defining individual cache contents, we associate users with a limited set of predefined caching profiles. Then, during the delivery phase, we aim at achieving a combined caching and spatial multiplexing gain, comparable to a large extent with the ideal case of fully centralized schemes. The resulting scheme imposes small subpacketization and beamforming overheads, is robust under dynamic network conditions, and incurs small finite-SNR performance loss compared with centralized schemes.