OpenRaiser/Intern-Atlas huggingface dataset:
An Analytics Framework for Heuristic Inference Attacks against Industrial Control Systems
Venue: arXiv cs.CR 2021
Year: 2021

Industrial control systems (ICS) of critical infrastructure are increasingly connected to the Internet for remote site management at scale. However, cyber attacks against ICS - especially at the communication channels between humanmachine interface (HMIs) and programmable logic controllers (PLCs) - are increasing at a rate which outstrips the rate of mitigation. In this paper, we introduce a vendor-agnostic analytics framework which allows security researchers to analyse attacks against ICS systems, even if the researchers have zero control automation domain knowledge or are faced with a myriad of heterogenous ICS systems. Unlike existing works that require expertise in domain knowledge and specialised tool usage, our analytics framework does not require prior knowledge about ICS communication protocols, PLCs, and expertise of any network penetration testing tool. Using `digital twin' scenarios comprising industry-representative HMIs, PLCs and firewalls in our test lab, our framework's steps were demonstrated to successfully implement a stealthy deception attack based on false data injection attacks (FDIA). Furthermore, our framework also demonstrated the relative ease of attack dataset collection, and the ability to leverage well-known penetration testing tools. We also introduce the concept of `heuristic inference attacks', a new family of attack types on ICS which is agnostic to PLC and HMI brands/models commonly deployed in ICS. Our experiments were also validated on a separate ICS dataset collected from a cyber-physical scenario of water utilities. Finally, we utilized time complexity theory to estimate the difficulty for the attacker to conduct the proposed packet analyses, and recommended countermeasures based on our findings.
Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee
Venue: arXiv cs.AI 2021
Year: 2021

Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants showing that our automated persuasion system based on this technology is superior to a baseline system that does not take the beliefs and concerns into account in its strategy.
Copula-based conformal prediction for Multi-Target Regression
Venue: arXiv cs.LG 2021
Year: 2021

There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression. This paper focuses on the problem of providing valid (i.e., frequency calibrated) multi-variate predictions. To do so, we propose to use copula functions applied to deep neural networks for inductive conformal prediction. We show that the proposed method ensures efficiency and validity for multi-target regression problems on various data sets.
Contrastive analysis for scatterplot-based representations of dimensionality reduction
Venue: arXiv cs.HC 2021
Year: 2021

Cluster interpretation after dimensionality reduction (DR) is a ubiquitous part of exploring multidimensional datasets. DR results are frequently represented by scatterplots, where spatial proximity encodes similarity among data samples. In the literature, techniques support the understanding of scatterplots' organization by visualizing the importance of the features for cluster definition with layout enrichment strategies. However, current approaches usually focus on global information, hampering the analysis whenever the focus is to understand the differences among clusters. Thus, this paper introduces a methodology to visually explore DR results and interpret clusters' formation based on contrastive analysis. We also introduce a bipartite graph to visually interpret and explore the relationship between the statistical variables employed to understand how the data features influence cluster formation. Our approach is demonstrated through case studies, in which we explore two document collections related to news articles and tweets about COVID-19 symptoms. Finally, we evaluate our approach through quantitative results to demonstrate its robustness to support multidimensional analysis.
A Neural Few-Shot Text Classification Reality Check
Venue: arXiv cs.CL 2021
Year: 2021

Modern classification models tend to struggle when the amount of annotated data is scarce. To overcome this issue, several neural few-shot classification models have emerged, yielding significant progress over time, both in Computer Vision and Natural Language Processing. In the latter, such models used to rely on fixed word embeddings before the advent of transformers. Additionally, some models used in Computer Vision are yet to be tested in NLP applications. In this paper, we compare all these models, first adapting those made in the field of image processing to NLP, and second providing them access to transformers. We then test these models equipped with the same transformer-based encoder on the intent detection task, known for having a large number of classes. Our results reveal that while methods perform almost equally on the ARSC dataset, this is not the case for the Intent Detection task, where the most recent and supposedly best competitors perform worse than older and simpler ones (while all are given access to transformers). We also show that a simple baseline is surprisingly strong. All the new developed models, as well as the evaluation framework, are made publicly available.
S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training
Venue: arXiv cs.CR 2021
Year: 2021

We introduce S++, a simple, robust, and deployable framework for training a neural network (NN) using private data from multiple sources, using secret-shared secure function evaluation. In short, consider a virtual third party to whom every data-holder sends their inputs, and which computes the neural network: in our case, this virtual third party is actually a set of servers which individually learn nothing, even with a malicious (but non-colluding) adversary. Previous work in this area has been limited to just one specific activation function: ReLU, rendering the approach impractical for many use-cases. For the first time, we provide fast and verifiable protocols for all common activation functions and optimize them for running in a secret-shared manner. The ability to quickly, verifiably, and robustly compute exponentiation, softmax, sigmoid, etc., allows us to use previously written NNs without modification, vastly reducing developer effort and complexity of code. In recent times, ReLU has been found to converge much faster and be more computationally efficient as compared to non-linear functions like sigmoid or tanh. However, we argue that it would be remiss not to extend the mechanism to non-linear functions such as the logistic sigmoid, tanh, and softmax that are fundamental due to their ability to express outputs as probabilities and their universal approximation property. Their contribution in RNNs and a few recent advancements also makes them more relevant.
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
Venue: arXiv cs.NE 2021
Year: 2021

Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology. The challenge lies in that visual encoding in brain is highly complex and not fully revealed. Inspired by the theory that visual features are hierarchically represented in cortex, we propose to break the complex visual signals into multi-level components and decode each component separately. Specifically, we decode shape and semantic representations from the lower and higher visual cortex respectively, and merge the shape and semantic information to images by a generative adversarial network (Shape-Semantic GAN). This 'divide and conquer' strategy captures visual information more accurately. Experiments demonstrate that Shape-Semantic GAN improves the reconstruction similarity and image quality, and achieves the state-of-the-art image reconstruction performance.
Eye: Program Visualizer for CS2
Venue: arXiv cs.CY 2021
Year: 2021

In recent years, programming has witnessed a shift towards using standard libraries as a black box. However, there has not been a synchronous development of tools that can help demonstrate the working of such libraries in general programs, which poses an impediment to improved learning outcomes and makes debugging exasperating. We introduce Eye, an interactive pedagogical tool that visualizes a program's execution as it runs. It demonstrates properties and usage of data structures in a general environment, thereby helping in learning, logical debugging, and code comprehension. Eye provides a comprehensive overview at each stage during run time including the execution stack and the state of data structures. The modular implementation allows for extension to other languages and modification of the graphics as desired. Eye opens up a gateway for CS2 students to more easily understand myriads of programs that are available on online programming websites, lowering the barrier towards self-learning of coding. It expands the scope of visualizing data structures from standard algorithms to general cases, benefiting both teachers as well as programmers who face issues in debugging. Line by line interpreting allows Eye to describe the execution and not only the current state. We also conduct experiments to evaluate the efficacy of Eye for debugging and comprehending a new piece of code. Our findings show that it becomes faster and less frustrating to debug certain problems using this tool, and also makes understanding new code a much more pleasant experience.
Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network
Venue: arXiv cs.IT 2021
Year: 2021

Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available.
An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms
Venue: arXiv cs.LG 2021
Year: 2021

The increasing complexity of algorithms for analyzing medical data, including de-identification tasks, raises the possibility that complex algorithms are learning not just the general representation of the problem, but specifics of given individuals within the data. Modern legal frameworks specifically prohibit the intentional or accidental distribution of patient data, but have not addressed this potential avenue for leakage of such protected health information. Modern deep learning algorithms have the highest potential of such leakage due to complexity of the models. Recent research in the field has highlighted such issues in non-medical data, but all analysis is likely to be data and algorithm specific. We, therefore, chose to analyze a state-of-the-art free-text de-identification algorithm based on LSTM (Long Short-Term Memory) and its potential in encoding any individual in the training set. Using the i2b2 Challenge Data, we trained, then analyzed the model to assess whether the output of the LSTM, before the compression layer of the classifier, could be used to estimate the membership of the training data. Furthermore, we used different attacks including membership inference attack method to attack the model. Results indicate that the attacks could not identify whether members of the training data were distinguishable from non-members based on the model output. This indicates that the model does not provide any strong evidence into the identification of the individuals in the training data set and there is not yet empirical evidence it is unsafe to distribute the model for general use.
A Survey on Personality-Aware Recommendation Systems
Venue: arXiv cs.IR 2021
Year: 2021

With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personality-aware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.
Beyond Capacity: The Joint Time-Rate Region
Venue: arXiv cs.IT 2021
Year: 2021

The traditional notion of capacity studied in the context of memoryless network communication builds on the concept of block-codes and requires that, for sufficiently large blocklength n, all receiver nodes simultaneously decode their required information after n channel uses. In this work, we generalize the traditional capacity region by exploring communication rates achievable when some receivers are required to decode their information before others, at different predetermined times; referred here as the "time-rate" region. Through a reduction to the standard notion of capacity, we present an inner-bound on the time-rate region. The time-rate region has been previously studied and characterized for the memoryless broadcast channel (with a sole common message) under the name "static broadcasting".
Putting gradual types to work
Venue: arXiv cs.PL 2021
Year: 2021

In this paper, we describe our experience incorporating gradual types in a statically typed functional language with Hindley-Milner style type inference. Where most gradually typed systems aim to improve static checking in a dynamically typed language, we approach it from the opposite perspective and promote dynamic checking in a statically typed language. Our approach provides a glimpse into how languages like SML and OCaml might handle gradual typing. We discuss our implementation and challenges faced -- specifically how gradual typing rules apply to our representation of composite and recursive types. We review the various implementations that add dynamic typing to a statically typed language in order to highlight the different ways of mixing static and dynamic typing and examine possible inspirations while maintaining the gradual nature of our type system. This paper also discusses our motivation for adding gradual types to our language, and the practical benefits of doing so in our industrial setting.
A Pub-Sub Architecture to Promote Blockchain Interoperability
Venue: arXiv cs.DC 2021
Year: 2021

The maturing of blockchain technology leads to heterogeneity, where multiple solutions specialize in a particular use case. While the development of different blockchain networks shows great potential for blockchains, the isolated networks have led to data and asset silos, limiting the applications of this technology. Blockchain interoperability solutions are essential to enable distributed ledgers to reach their full potential. Such solutions allow blockchains to support asset and data transfer, resulting in the development of innovative applications. This paper proposes a novel blockchain interoperability solution for permissioned blockchains based on the publish/subscribe architecture. We implemented a prototype of this platform to show the feasibility of our design. We evaluate our solution by implementing examples of the different publisher and subscriber networks, such as Hyperledger Besu, which is an Ethereum client, and two different versions of Hyperledger Fabric. We present a performance analysis of the whole network that indicates its limits and bottlenecks. Finally, we discuss the extensibility and scalability of the platform in different scenarios. Our evaluation shows that our system can handle a throughput in the order of the hundreds of transactions per second.
sGrapp: Butterfly Approximation in Streaming Graphs
Venue: arXiv cs.DB 2021
Year: 2021

We study the fundamental problem of butterfly (i.e. (2,2)-bicliques) counting in bipartite streaming graphs. Similar to triangles in unipartite graphs, enumerating butterflies is crucial in understanding the structure of bipartite graphs. This benefits many applications where studying the cohesion in a graph shaped data is of particular interest. Examples include investigating the structure of computational graphs or input graphs to the algorithms, as well as dynamic phenomena and analytic tasks over complex real graphs. Butterfly counting is computationally expensive, and known techniques do not scale to large graphs; the problem is even harder in streaming graphs. In this paper, following a data-driven methodology, we first conduct an empirical analysis to uncover temporal organizing principles of butterflies in real streaming graphs and then we introduce an approximate adaptive window-based algorithm, sGrapp, for counting butterflies as well as its optimized version sGrapp-x. sGrapp is designed to operate efficiently and effectively over any graph stream with any temporal behavior. Experimental studies of sGrapp and sGrapp-x show superior performance in terms of both accuracy and efficiency.
Enabling Robots to Draw and Tell: Towards Visually Grounded Multimodal Description Generation
Venue: arXiv cs.RO 2021
Year: 2021

Socially competent robots should be equipped with the ability to perceive the world that surrounds them and communicate about it in a human-like manner. Representative skills that exhibit such ability include generating image descriptions and visually grounded referring expressions. In the NLG community, these generation tasks are largely investigated in non-interactive and language-only settings. However, in face-to-face interaction, humans often deploy multiple modalities to communicate, forming seamless integration of natural language, hand gestures and other modalities like sketches. To enable robots to describe what they perceive with speech and sketches/gestures, we propose to model the task of generating natural language together with free-hand sketches/hand gestures to describe visual scenes and real life objects, namely, visually-grounded multimodal description generation. In this paper, we discuss the challenges and evaluation metrics of the task, and how the task can benefit from progress recently made in the natural language processing and computer vision realms, where related topics such as visually grounded NLG, distributional semantics, and photo-based sketch generation have been extensively studied.
$r$-indexing Wheeler graphs
Venue: arXiv cs.DS 2021
Year: 2021

Let $G$ be a Wheeler graph and $r$ be the number of runs in a Burrows-Wheeler Transform of $G$, and suppose $G$ can be decomposed into $\upsilon$ edge-disjoint directed paths whose internal vertices each have in- and out-degree exactly 1. We show how to store $G$ in $O (r + \upsilon)$ space such that later, given a pattern $P$, in $O (|P| \log \log |G|)$ time we can count the vertices of $G$ reachable by directed paths labelled $P$, and then report those vertices in $O (\log \log |G|)$ time per vertex.
Deep Triplet Hashing Network for Case-based Medical Image Retrieval
Venue: arXiv cs.CV 2021
Year: 2021

Deep hashing methods have been shown to be the most efficient approximate nearest neighbor search techniques for large-scale image retrieval. However, existing deep hashing methods have a poor small-sample ranking performance for case-based medical image retrieval. The top-ranked images in the returned query results may be as a different class than the query image. This ranking problem is caused by classification, regions of interest (ROI), and small-sample information loss in the hashing space. To address the ranking problem, we propose an end-to-end framework, called Attention-based Triplet Hashing (ATH) network, to learn low-dimensional hash codes that preserve the classification, ROI, and small-sample information. We embed a spatial-attention module into the network structure of our ATH to focus on ROI information. The spatial-attention module aggregates the spatial information of feature maps by utilizing max-pooling, element-wise maximum, and element-wise mean operations jointly along the channel axis. The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes. Extensive experiments on two case-based medical datasets demonstrate that our proposed ATH can further improve the retrieval performance compared to the state-of-the-art deep hashing methods and boost the ranking performance for small samples. Compared to the other loss methods, the triplet cross-entropy loss can enhance the classification performance and hash code-discriminability
Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping
Venue: arXiv cs.RO 2021
Year: 2021

This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness.