OpenRaiser/Intern-Atlas huggingface dataset:
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
Venue: arXiv cs.CL 2021
Year: 2021

Pre-trained Natural Language Processing (NLP) models can be easily adapted to a variety of downstream language tasks. This significantly accelerates the development of language models. However, NLP models have been shown to be vulnerable to backdoor attacks, where a pre-defined trigger word in the input text causes model misprediction. Previous NLP backdoor attacks mainly focus on some specific tasks. This makes those attacks less general and applicable to other kinds of NLP models and tasks. In this work, we propose \Name, the first task-agnostic backdoor attack against the pre-trained NLP models. The key feature of our attack is that the adversary does not need prior information about the downstream tasks when implanting the backdoor to the pre-trained model. When this malicious model is released, any downstream models transferred from it will also inherit the backdoor, even after the extensive transfer learning process. We further design a simple yet effective strategy to bypass a state-of-the-art defense. Experimental results indicate that our approach can compromise a wide range of downstream NLP tasks in an effective and stealthy way.
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
Venue: arXiv cs.LG 2021
Year: 2021

Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-the-cloud setting to distributed training over edge devices in order to strengthen data privacy. An essential but rarely studied challenge in FL is label deficiency at the edge. This problem is even more pronounced in FL compared to centralized training due to the fact that FL users are often reluctant to label their private data. Furthermore, due to the heterogeneous nature of the data at edge devices, it is crucial to develop personalized models. In this paper we propose self-supervised federated learning (SSFL), a unified self-supervised and personalized federated learning framework, and a series of algorithms under this framework which work towards addressing these challenges. First, under the SSFL framework, we demonstrate that the standard FedAvg algorithm is compatible with recent breakthroughs in centralized self-supervised learning such as SimSiam networks. Moreover, to deal with data heterogeneity at the edge devices in this framework, we have innovated a series of algorithms that broaden existing supervised personalization algorithms into the setting of self-supervised learning. We further propose a novel personalized federated self-supervised learning algorithm, Per-SSFL, which balances personalization and consensus by carefully regulating the distance between the local and global representations of data. To provide a comprehensive comparative analysis of all proposed algorithms, we also develop a distributed training system and related evaluation protocol for SSFL. Our findings show that the gap of evaluation accuracy between supervised learning and unsupervised learning in FL is both small and reasonable. The performance comparison indicates the representation regularization-based personalization method is able to outperform other variants.
The Variability of Model Specification
Venue: arXiv cs.LG 2021
Year: 2021

It's regarded as an axiom that a good model is one that compromises between bias and variance. The bias is measured in training cost, while the variance of a (say, regression) model is measure by the cost associated with a validation set. If reducing bias is the goal, one will strive to fetch as complex a model as necessary, but complexity is invariably coupled with variance: greater complexity implies greater variance. In practice, driving training cost to near zero does not pose a fundamental problem; in fact, a sufficiently complex decision tree is perfectly capable of driving training cost to zero; however, the problem is often with controlling the model's variance. We investigate various regression model frameworks, including generalized linear models, Cox proportional hazard models, ARMA, and illustrate how misspecifying a model affects the variance.
Tight Bounds on the Optimal UL Sum-Rate of MU RIS-aided Wireless Systems
Venue: arXiv cs.IT 2021
Year: 2021

The objective of this paper is to develop simple techniques to bound the optimal uplink sum-rate of multi-user RIS-aided wireless systems. Specifically, we develop a novel technique called \textit{channel separation} which provides a new understanding as to how the RIS phases affect the sum-rate. Leveraging channel separation, we derive upper and lower bounds on the optimal sum-rate. In addition, we propose a low-complexity alternating optimization algorithm to obtain near-optimal sum-rate results. Numerical results demonstrate the tightness of the bounds and show that the alternating optimization approach delivers sum-rate values similar to the results of a full numerical optimization procedure. Furthermore, in practical scenarios where hardware limitations cause the RIS phases to be quantized, our lower bound can still be applied and shows that the sum-rate is robust to quantization, even with low resolution.
Stegomalware: A Systematic Survey of MalwareHiding and Detection in Images, Machine LearningModels and Research Challenges
Venue: arXiv cs.CR 2021
Year: 2021

Malware distribution to the victim network is commonly performed through file attachments in phishing email or from the internet, when the victim interacts with the source of infection. To detect and prevent the malware distribution in the victim machine, the existing end device security applications may leverage techniques such as signature or anomaly-based, machine learning techniques. The well-known file formats Portable Executable (PE) for Windows and Executable and Linkable Format (ELF) for Linux based operating system are used for malware analysis, and the malware detection capabilities of these files has been well advanced for real-time detection. But the malware payload hiding in multimedia using steganography detection has been a challenge for enterprises, as these are rarely seen and usually act as a stager in sophisticated attacks. In this article, to our knowledge, we are the first to try to address the knowledge gap between the current progress in image steganography and steganalysis academic research focusing on data hiding and the review of the stegomalware (malware payload hiding in images) targeting enterprises with cyberattacks current status. We present the stegomalware history, generation tools, file format specification description. Based on our findings, we perform the detail review of the image steganography techniques including the recent Generative Adversarial Networks (GAN) based models and the image steganalysis methods including the Deep Learning(DL) models for hiding data detection. Additionally, the stegomalware detection framework for enterprise is proposed for anomaly based stegomalware detection emphasizing the architecture details for different network environments. Finally, the research opportunities and challenges in stegomalware generation and detection are also presented.
3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation
Venue: arXiv cs.CV 2021
Year: 2021

3D object detection using LiDAR data remains a key task for applications like autonomous driving and robotics. Unlike in the case of 2D images, LiDAR data is almost always collected over a period of time. However, most work in this area has focused on performing detection independent of the temporal domain. In this paper we present 3D-FCT, a Siamese network architecture that utilizes temporal information to simultaneously perform the related tasks of 3D object detection and tracking. The network is trained to predict the movement of an object based on the correlation features of extracted keypoints across time. Calculating correlation across keypoints only allows for real-time object detection. We further extend the multi-task objective to include a tracking regression loss. Finally, we produce high accuracy detections by linking short-term object tracklets into long term tracks based on the predicted tracks. Our proposed method is evaluated on the KITTI tracking dataset where it is shown to provide an improvement of 5.57% mAP over a state-of-the-art approach.
Simulation of Derivatives Post-Trade Services using an Authoritative Data Store and the ISDA Common Domain Model
Venue: arXiv cs.CY 2021
Year: 2021

In this paper, we present a summary of the design and implementation of a simulation of post-trade services for interest rate swaps, from execution to maturity. We use an authoritative data store (ADS) and the International Swaps and Derivatives Association (ISDA) Common Domain Model (CDM) to simulate a potential future architecture. We start by providing a brief overview of the CDM and the lifecycle of an interest rate swap. We then compare our simulated future state architecture with a typical current state architecture. Next, we present the key requirements of the simulated system, several suitable design patterns, and a summary of the implementation. The simulation uses the CDM to address the industry problems of inconsistent processes and inconsistent data, and an authoritative data store to address the industry problem of duplicated data.
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for visiting. However, existing methods could not handle well the pairing and precedence relationships in the pickup and delivery problem (PDP), which is a representative variant of VRP. To address this challenging issue, we leverage a novel neural network integrated with a heterogeneous attention mechanism to empower the policy in deep reinforcement learning to automatically select the nodes. In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the nodes while taking into account the precedence constraint, i.e., the pickup node must precede the pairing delivery node. Further integrated with a masking scheme, the learnt policy is expected to find higher-quality solutions for solving PDP. Extensive experimental results show that our method outperforms the state-of-the-art heuristic and deep learning model, respectively, and generalizes well to different distributions and problem sizes.
Entropy Regularised Deterministic Optimal Control: From Path Integral Solution to Sample-Based Trajectory Optimisation
Venue: arXiv cs.RO 2021
Year: 2021

Sample-based trajectory optimisers are a promising tool for the control of robotics with non-differentiable dynamics and cost functions. Contemporary approaches derive from a restricted subclass of stochastic optimal control where the optimal policy can be expressed in terms of an expectation over stochastic paths. By estimating the expectation with Monte Carlo sampling and reinterpreting the process as exploration noise, a stochastic search algorithm is obtained tailored to (deterministic) trajectory optimisation. For the purpose of future algorithmic development, it is essential to properly understand the underlying theoretical foundations that allow for a principled derivation of such methods. In this paper we make a connection between entropy regularisation in optimisation and deterministic optimal control. We then show that the optimal policy is given by a belief function rather than a deterministic function. The policy belief is governed by a Bayesian-type update where the likelihood can be expressed in terms of a conditional expectation over paths induced by a prior policy. Our theoretical investigation firmly roots sample based trajectory optimisation in the larger family of control as inference. It allows us to justify a number of heuristics that are common in the literature and motivate a number of new improvements that benefit convergence.
One-Dimensional Fragment over Words and Trees
Venue: arXiv cs.LO 2021
Year: 2021

One-dimensional fragment of first-order logic is obtained by restricting quantification to blocks of existential (universal) quantifiers that leave at most one variable free. We investigate this fragment over words and trees, presenting a complete classification of the complexity of its satisfiability problem for various navigational signatures, and comparing its expressive power with other important formalisms. These include the two-variable fragment with counting and the unary negation fragment.
Concrete Categorical Model of a Quantum Circuit Description Language with Measurement
Venue: arXiv cs.LO 2021
Year: 2021

In this paper, we introduce dynamic lifting to a quantum circuit-description language, following the Proto-Quipper language approach. Dynamic lifting allows programs to transfer the result of measuring quantum data -- qubits -- into classical data -- booleans -- . We propose a type system and an operational semantics for the language and we state safety properties. Next, we introduce a concrete categorical semantics for the proposed language, basing our approach on a recent model from Rios\&Selinger for Proto-Quipper-M. Our approach is to construct on top of a concrete category of circuits with measurements a Kleisli category, capturing as a side effect the action of retrieving classical content out of a quantum memory. We then show a soundness result for this semantics.
Generalizing Neural Networks by Reflecting Deviating Data in Production
Venue: arXiv cs.LG 2021
Year: 2021

Trained with a sufficiently large training and testing dataset, Deep Neural Networks (DNNs) are expected to generalize. However, inputs may deviate from the training dataset distribution in real deployments. This is a fundamental issue with using a finite dataset. Even worse, real inputs may change over time from the expected distribution. Taken together, these issues may lead deployed DNNs to mis-predict in production. In this work, we present a runtime approach that mitigates DNN mis-predictions caused by the unexpected runtime inputs to the DNN. In contrast to previous work that considers the structure and parameters of the DNN itself, our approach treats the DNN as a blackbox and focuses on the inputs to the DNN. Our approach has two steps. First, it recognizes and distinguishes "unseen" semantically-preserving inputs. For this we use a distribution analyzer based on the distance metric learned by a Siamese network. Second, our approach transforms those unexpected inputs into inputs from the training set that are identified as having similar semantics. We call this process input reflection and formulate it as a search problem over the embedding space on the training set. This embedding space is learned by a Quadruplet network as an auxiliary model for the subject model to improve the generalization. We implemented a tool called InputReflector based on the above two-step approach and evaluated it with experiments on three DNN models trained on CIFAR-10, MNIST, and FMINST image datasets. The results show that InputReflector can effectively distinguish inputs that retain semantics of the distribution (e.g., blurred, brightened, contrasted, and zoomed images) and out-of-distribution inputs from normal inputs.
Analysis of Trade-offs in RF Photonic Links based on Multi-Bias Tuning of Silicon Photonic Ring-Assisted Mach Zehnder Modulators
Venue: arXiv cs.IT 2021
Year: 2021

Recent progress in silicon-based photonic integrated circuits (PICs) have opened new avenues for analog circuit designers to explore hybrid integration of photonics with CMOS ICs. Traditionally, optoelectronic systems are designed using discrete optics and electronics. Silicon photonic (SiP) platforms provide the opportunity to realize these systems in a compact chip-scale form factor and alleviate long-standing challenges in optoelectronics. In this work, we analyze multi-bias tuning in Ring-Assisted Mach Zehnder Modulator (RAMZM) and resulting trade-offs in analog RF photonic links realized using RAMZMs. Multi-bias tuning in the rings and the Mach-Zehnder arms allow informed trade-offs between link noise figure and linearity. We derive performance metrics including gain, noise figure, and linearity metrics associated with tuning of multiple bias settings in RAMZM based links and present resulting design optimization. Compared to MZM, an improvement of 18 dB/Hz$^{\frac{2}{3}}$ in SFDR is noted when RAMZM is linearized. We also propose a biasing scheme for RAMZM that provides 6x improvement in slope efficiency, or equivalently, 15.56dB in power Gain over MZMs (single drive) while still providing similar SFDR performance ($\sim$ 109 dB/Hz$^{\frac{2}{3}}$) as MZMs. Moreover, a method to improve gain in photodiode saturation limited links is presented and studied.
A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation
Venue: arXiv cs.LG 2021
Year: 2021

There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario. However, characterising such behaviour usually requires large-scale testing of the model that can be extremely computationally expensive for complex real-world tasks. For example, tasks involving compute intensive object detectors as one of their components. In this work, we propose an approach that enables efficient large-scale testing using simplified low-fidelity simulators and without the computational cost of executing expensive deep learning models. Our approach relies on designing an efficient surrogate model corresponding to the compute intensive components of the task under test. We demonstrate the efficacy of our methodology by evaluating the performance of an autonomous driving task in the Carla simulator with reduced computational expense by training efficient surrogate models for PIXOR and CenterPoint LiDAR detectors, whilst demonstrating that the accuracy of the simulation is maintained.
Visibility Reasoning for Concurrent Snapshot Algorithms
Venue: arXiv cs.PL 2021
Year: 2021

Visibility relations have been proposed by Henzinger et al. as an abstraction for proving linearizability of concurrent algorithms that obtains modular and reusable proofs. This is in contrast to the customary approach based on exhibiting the algorithm's linearization points. In this paper we apply visibility relations to develop modular proofs for three elegant concurrent snapshot algorithms of Jayanti. The proofs are divided by signatures into components of increasing level of abstraction; the components at higher abstraction levels are shared, i.e., they apply to all three algorithms simultaneously. Importantly, the interface properties mathematically capture Jayanti's original intuitions that have previously been given only informally.
WHO-Hand Hygiene Gesture Classification System
Venue: arXiv cs.CV 2021
Year: 2021

The recent ongoing coronavirus pandemic highlights the importance of hand hygiene practices in our daily lives, with governments and worldwide health authorities promoting good hand hygiene practices. More than one million cases of hospital-acquired infections occur in Europe annually. Hand hygiene compliance may reduce the risk of transmission by reducing the number of infections as well as healthcare expenditures. In this paper, the World Health Organization, hand hygiene gestures are recorded and analyzed with the construction of an aluminum frame, placed at the laboratory sink. The hand hygiene gestures are recorded for thirty participants after conducting a training session about hand hygiene gestures demonstration. The video recordings are converted into image files and are organized into six different hand hygiene classes. The Resnet50 framework selection for the classification of multiclass hand hygiene stages. The model is trained with the first set of classes; Fingers Interlaced, P2PFingers Interlaced, and Rotational Rub for 25 epochs. An accuracy of 44 percent for the first set of experiments with a loss score greater than 1.5 in the validation set is achieved. The training steps for the second set of classes; Rub hands palm to palm, Fingers Interlocked, Thumb Rub are 50 epochs. An accuracy of 72 percent is achieved for the second set with a loss score of less than 0.8 for the validation set. In this work, a preliminary analysis of a robust hand hygiene dataset with transfer learning takes place. The future aim for deploying a hand hygiene prediction system for healthcare workers in real-time.
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers
Venue: arXiv cs.LG 2021
Year: 2021

Recent work applying deep reinforcement learning (DRL) to solve traveling salesman problems (TSP) has shown that DRL-based solvers can be fast and competitive with TSP heuristics for small instances, but do not generalize well to larger instances. In this work, we propose a novel approach named MAGIC that includes a deep learning architecture and a DRL training method. Our architecture, which integrates a multilayer perceptron, a graph neural network, and an attention model, defines a stochastic policy that sequentially generates a TSP solution. Our training method includes several innovations: (1) we interleave DRL policy gradient updates with local search (using a new local search technique), (2) we use a novel simple baseline, and (3) we apply curriculum learning. Finally, we empirically demonstrate that MAGIC is superior to other DRL-based methods on random TSP instances, both in terms of performance and generalizability. Moreover, our method compares favorably against TSP heuristics and other state-of-the-art approach in terms of performance and computational time.
RevASIDE: Assignment of Suitable Reviewer Sets for Publications from Fixed Candidate Pools
Venue: arXiv cs.DL 2021
Year: 2021

Scientific publishing heavily relies on the assessment of quality of submitted manuscripts by peer reviewers. Assigning a set of matching reviewers to a submission is a highly complex task which can be performed only by domain experts. We introduce RevASIDE, a reviewer recommendation system that assigns suitable sets of complementing reviewers from a predefined candidate pool without requiring manually defined reviewer profiles. Here, suitability includes not only reviewers' expertise, but also their authority in the target domain, their diversity in their areas of expertise and experience, and their interest in the topics of the manuscript. We present three new data sets for the expert search and reviewer set assignment tasks and compare the usefulness of simple text similarity methods to document embeddings for expert search. Furthermore, an quantitative evaluation demonstrates significantly better results in reviewer set assignment compared to baselines. A qualitative evaluation also shows their superior perceived quality.