OpenRaiser/Intern-Atlas huggingface dataset:
PCaaD: Towards Automated Determination and Exploitation of Industrial Processes
Venue: arXiv cs.CR 2021
Year: 2021

Over the last decade, Programmable Logic Controllers (PLCs) have been increasingly targeted by attackers to obtain control over industrial processes that support critical services. Such targeted attacks typically require detailed knowledge of system-specific attributes, including hardware configurations, adopted protocols, and PLC control-logic, i.e. process comprehension. The consensus from both academics and practitioners suggests stealthy process comprehension obtained from a PLC alone, to conduct targeted attacks, is impractical. In contrast, we assert that current PLC programming practices open the door to a new vulnerability class based on control-logic constructs. To support this, we propose the concept of Process Comprehension at a Distance (PCaaD), as a novel methodological and automatable approach for system-agnostic exploitation of PLC library functions, leading to the targeted exfiltration of operational data, manipulation of control-logic behavior, and establishment of covert command and control channels through unused memory. We validate PCaaD on widely used PLCs, by identification of practical attacks.
Rapid Multi-Physics Simulation for Electro-Thermal Origami Systems
Venue: arXiv cs.RO 2021
Year: 2021

Electro-thermally actuated origami provides a novel method for creating 3-D systems with advanced morphing and functional capabilities. However, it is currently difficult to simulate the multi-physical behavior of such systems because the electro-thermal actuation and large folding deformations are highly interdependent. In this work, we introduce a rapid multi-physics simulation framework for electro-thermally actuated origami systems that can simultaneously capture: thermo-mechancially coupled actuation, inter panel contact, heat transfer, large deformation folding, and other complex loading applied onto the origami. Comparisons with finite element models validate the proposed framework for simulating origami heat transfer with different system geometries, materials, and surrounding environments. Verification of the simulated folding behaviors against physical electro-thermal micro-origami further demonstrates the validity of the proposed model. Simulations of more complex origami patterns and a case study for origami optimization are provided as application examples to show the capability and efficiency of the model. The framework provides a novel simulation tool for analysis, design, control, and optimization of active origami systems, pushing the boundary for feasible shape morphing and functional capability.
Performance Prediction Recipes for Optical Links
Venue: arXiv cs.IT 2021
Year: 2021

Although forward error-correction (FEC) coding is an essential part of modern fiber-optic communication systems, it is impractical to implement and evaluate FEC in transmission experiments and simulations. Therefore, it is desirable to accurately predict the end-to-end link performance including FEC from transmission data recorded without FEC. In this tutorial, we provide ready-to-implement "recipes" for such prediction techniques, which apply to arbitrary channels and require no knowledge of information or coding theory. The appropriate choice of recipe depends on properties of the FEC encoder and decoder. The covered metrics include bit error rate, symbol error rate, achievable information rate, and asymptotic information, in all cases computed using a mismatched receiver. Supplementary software implementations are available.
Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging
Venue: arXiv cs.CL 2021
Year: 2021

While NMT has achieved remarkable results in the last 5 years, production systems come with strict quality requirements in arbitrarily niche domains that are not always adequately covered by readily available parallel corpora. This is typically addressed by training domain specific models, using fine-tuning methods and some variation of back-translation on top of in-domain monolingual corpora. However, industrial practitioners can rarely afford to focus on a single domain. A far more typical scenario includes a set of closely related, yet succinctly different sub-domains. At Booking.com, we need to translate property descriptions, user reviews, as well as messages, (for example those sent between a customer and an agent or property manager). An editor might need to translate articles across a set of different topics. An e-commerce platform would typically need to translate both the description of each item and the user generated content related to them. To this end, we propose MDT: a novel method to simultaneously fine-tune on several sub-domains by passing multidimensional sentence-level information to the model during training and inference. We show that MDT achieves results competitive to N specialist models each fine-tuned on a single constituent domain, while effectively serving all N sub-domains, therefore cutting development and maintenance costs by the same factor. Besides BLEU (industry standard automatic evaluation metric known to only weakly correlate with human judgement) we also report rigorous human evaluation results for all models and sub-domains as well as specific examples that better contextualise the performance of each model in terms of adequacy and fluency. To facilitate further research, we plan to make the code available upon acceptance.
Keyboards as a new model of computation
Venue: arXiv cs.FL 2021
Year: 2021

We introduce a new formalisation of languages, called keyboards. We consider a set of elementary operations (writing/erasing a letter, going to the right or to the left,...) and we define a keyboard as a set of finite sequences of such operations, called keys. The corresponding language is the set of words obtained by applying some sequence of those keys. Unlike classical models of computation, every key can be applied anytime. We define various classes of languages based on different sets of elementary operations, and compare their expressive powers. We also compare them to well-known classes of languages (Chomsky hierarchy). We obtain a strict hierarchy of languages, whose expressivity is orthogonal to the one of the aforementionned classical models. -- Nous introduisons une nouvelle représentation de langages, les claviers. On se munit d'un ensemble d'opérations élémentaires (ajout, effacement d'une lettre, déplacement à droite, à gauche, ...), et on définit un clavier comme un ensemble de suites finies d'opérations élémentaires, appelées touches. Son langage sera l'ensemble des mots obtenus en appliquant une suite quelconque de touches. Contrairement à des modèles de calcul classiques, toutes les touches peuvent être appliquées à tout moment. En premier lieu nous définissons différentes classes de claviers en faisant varier l'ensemble des opérations élémentaires autorisées, et nous comparons l'expressivité des classes de langages obtenues. Nous comparons également ces classes à la hiérarchie de Chomsky. Nous obtenons que toutes les classes étudiées sont différentes, et nous caractérisons les classes inclues dans les rationnels et les algébriques. L'expressivité des claviers semble orthogonale à celle des modèles évoqués précédemment.
GMA: A Pareto Optimal Distributed Resource-Allocation Algorithm
Venue: arXiv cs.NI 2021
Year: 2021

To address the rising demand for strong packet delivery guarantees in networking, we study a novel way to perform graph resource allocation. We first introduce allocation graphs, in which nodes can independently set local resource limits based on physical constraints or policy decisions. In this scenario we formalize the distributed path-allocation (PAdist) problem, which consists in allocating resources to paths considering only local on-path information -- importantly, not knowing which other paths could have an allocation -- while at the same time achieving the global property of never exceeding available resources. Our core contribution, the global myopic allocation (GMA) algorithm, is a solution to this problem. We prove that GMA can compute unconditional allocations for all paths on a graph, while never over-allocating resources. Further, we prove that GMA is Pareto optimal with respect to the allocation size, and it has linear complexity in the input size. Finally, we show with simulations that this theoretical result could be indeed applied to practical scenarios, as the resulting path allocations are large enough to fit the requirements of practically relevant applications.
Logics of Dependence and Independence: The Local Variants
Venue: arXiv cs.LO 2021
Year: 2021

Modern logics of dependence and independence are based on team semantics, which means that formulae are evaluated not on a single assignment of values to variables, but on a set of such assignments, called a team. This leads to high expressive power, on the level of existential second-order logic. As an alternative, Baltag and van Benthem have proposed a local variant of dependence logic, called logic of functional dependence (LFD). While its semantics is also based on a team, the formulae are evaluated locally on just one of its assignments, and the team just serves as the supply of the possible assignments that are taken into account in the evaluation process. This logic thus relies on the modal perspective of generalized assignments semantics, and can be seen as a fragment of first-order logic. For the variant of LFD without equality, the satisfiability problem is decidable. We extend the idea of localising logics of dependence and independence in a systematic way, taking into account local variants of standard atomic dependency properties: besides dependence and independence, also inclusion, exclusion, and anonymity. We study model-theoretic and algorithmic questions of the localised logics, and also resolve some of the questions that had been left open by Baltag and van Benthem. In particular, we study decidability issues of the local logics, and prove that satisfiability of LFD with equality is undecidable. Further, we establish characterisation theorems via appropriate notions of bisimulation and study the complexity of model checking problems for these logics.
Towards Teachable Conversational Agents
Venue: arXiv cs.HC 2021
Year: 2021

The traditional process of building interactive machine learning systems can be viewed as a teacher-learner interaction scenario where the machine-learners are trained by one or more human-teachers. In this work, we explore the idea of using a conversational interface to investigate the interaction between human-teachers and interactive machine-learners. Specifically, we examine whether teachable AI agents can reliably learn from human-teachers through conversational interactions, and how this learning compare with traditional supervised learning algorithms. Results validate the concept of teachable conversational agents and highlight the factors relevant for the development of machine learning systems that intend to learn from conversational interactions.
Cybersecurity Awareness Platform with Virtual Coach and Automated Challenge Assessment
Venue: arXiv cs.SE 2021
Year: 2021

Over the last years, the number of cyber-attacks on industrial control systems has been steadily increasing. Among several factors, proper software development plays a vital role in keeping these systems secure. To achieve secure software, developers need to be aware of secure coding guidelines and secure coding best practices. This work presents a platform geared towards software developers in the industry that aims to increase awareness of secure software development. The authors also introduce an interactive game component, a virtual coach, which implements a simple artificial intelligence engine based on the laddering technique for interviews. Through a survey, a preliminary evaluation of the implemented artifact with real-world players (from academia and industry) shows a positive acceptance of the developed platform. Furthermore, the players agree that the platform is adequate for training their secure coding skills. The impact of our work is to introduce a new automatic challenge evaluation method together with a virtual coach to improve existing cybersecurity awareness training programs. These training workshops can be easily held remotely or off-line.
Raising Secure Coding Awareness for Software Developers in the Industry
Venue: arXiv cs.SE 2021
Year: 2021

Many industrial IT security standards and policies mandate the usage of a secure coding methodology in the software development process. This implies two different aspects: first, secure coding must be based on a set of secure coding guidelines, and second software developers must be aware of these secure coding practices. On the one side, secure coding guidelines seems a bit like a black-art: while there exist abstract guidelines that are widely accepted, low-level secure coding guidelines for different programming languages are scarce. On the other side, once a set of secure coding guidelines is chosen, a good methodology is needed to make them known by the people which should be using them, i.e. software developers. Motivated both by the secure coding requirements from industry standards and also by the mandate to train staff on IT security by the global industry initiative "Charter of Trust", this paper presents an overview of important research questions on how to choose secure coding guidelines and on how to raise software developer awareness for secure coding using serious games.
CyberSecurity Challenges: Serious Games for Awareness Training in Industrial Environments
Venue: arXiv cs.SE 2021
Year: 2021

Awareness of cybersecurity topics, e.g., related to secure coding guidelines, enables software developers to write secure code. This awareness is vital in industrial environments for the products and services in critical infrastructures. In this work, we introduce and discuss a new serious game designed for software developers in the industry. This game addresses software developers' needs and is shown to be well suited for raising secure coding awareness of software developers in the industry. Our work results from the experience of the authors gained in conducting more than ten CyberSecurity Challenges in the industry. The presented game design, which is shown to be well accepted by software developers, is a novel alternative to traditional classroom training. We hope to make a positive impact in the industry by improving the cybersecurity of products at their early production stages.
A Novel Key Generation Scheme Using Quaternary PUF Responses and Wiretap Polar Coding
Venue: arXiv cs.CR 2021
Year: 2021

Physical unclonable functions (PUFs) are widely considered in secret key generation for resource constrained devices. However, PUFs require additional hardware overhead. In this paper, we focus on developing a PUF-efficient, robust, and secure key generation scheme. First, a novel method for extracting quaternary PUF responses is proposed to increase the entropy of a PUF response, in which a 2-bit response is extracted from evaluating a single PUF cell multiple times. The probability masses of the responses can be adjusted by setting parameters appropriately. Then, a chosen secret model based fuzzy extractor (FE) is designed to extract secret keys from the quaternary PUF responses. To improve the security of this FE, it is modeled as a wiretap channel system, and wiretap polar coding is adopted to reduce secrecy leakage. An upper bound of secrecy leakage is also given in this paper, and it suggests that an arbitrarily small (even zero) leakage can be achieved by properly choosing parameters of the quaternary PUF responses generation. Comparison results show that the required number of PUF cells to achieve the same level of secrecy in our scheme is as low as half that of the state-of-the-art schemes.
An ecologically valid examination of event-based and time-based prospective memory using immersive virtual reality: the effects of delay and task type on everyday prospective memory
Venue: arXiv cs.CY 2021
Year: 2021

Recent research has focused on assessing either event- or time-based prospective memory (PM) using laboratory tasks. Yet, the findings pertaining to PM performance on laboratory tasks are often inconsistent with the findings on corresponding naturalistic experiments. Ecologically valid neuropsychological tasks resemble the complexity and cognitive demands of everyday tasks, offer an adequate level of experimental control, and allow a generalisation of the findings to everyday performance. The Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive virtual reality neuropsychological battery with enhanced ecological validity, was implemented to comprehensively assess everyday PM (i.e., focal and non-focal event-based, and time-based). The effects of the length of delay between encoding and initiating the PM intention and the type of PM task on everyday PM performance were examined. The results revealed that everyday PM performance was affected by the length of delay rather than the type of PM task. The effect of the length of delay differentially affected performance on the focal, non-focal, and time-based tasks and was proportional to the PM cue focality (i.e., semantic relationship with the intended action). This study also highlighted methodological considerations such as the differentiation between functioning and ability, distinction of cue attributes, and the necessity of ecological validity.
Learning Deep Features for Shape Correspondence with Domain Invariance
Venue: arXiv cs.CV 2021
Year: 2021

Correspondence-based shape models are key to various medical imaging applications that rely on a statistical analysis of anatomies. Such shape models are expected to represent consistent anatomical features across the population for population-specific shape statistics. Early approaches for correspondence placement rely on nearest neighbor search for simpler anatomies. Coordinate transformations for shape correspondence hold promise to address the increasing anatomical complexities. Nonetheless, due to the inherent shape-level geometric complexity and population-level shape variation, the coordinate-wise correspondence often does not translate to the anatomical correspondence. An alternative, group-wise approach for correspondence placement explicitly models the trade-off between geometric description and the population's statistical compactness. However, these models achieve limited success in resolving nonlinear shape correspondence. Recent works have addressed this limitation by adopting an application-specific notion of correspondence through lifting positional data to a higher dimensional feature space. However, they heavily rely on manual expertise to create domain-specific features and consistent landmarks. This paper proposes an automated feature learning approach, using deep convolutional neural networks to extract correspondence-friendly features from shape ensembles. Further, an unsupervised domain adaptation scheme is introduced to augment the pretrained geometric features with new anatomies. Results on anatomical datasets of human scapula, femur, and pelvis bones demonstrate that features learned in supervised fashion show improved performance for correspondence estimation compared to the manual features. Further, unsupervised learning is demonstrated to learn complex anatomy features using the supervised domain adaptation from features learned on simpler anatomy.
User interface for in-vehicle systems with on-wheel finger spreading gestures and head-up displays
Venue: arXiv cs.HC 2021
Year: 2021

Interacting with an in-vehicle system through a central console is known to induce visual and biomechanical distractions, thereby delaying the danger recognition and response times of the driver and significantly increasing the risk of an accident. To address this problem, various hand gestures have been developed. Although such gestures can reduce visual demand, they are limited in number, lack passive feedback, and can be vague and imprecise, difficult to understand and remember, and culture-bound. To overcome these limitations, we developed a novel on-wheel finger spreading gestural interface combined with a head-up display (HUD) allowing the user to choose a menu displayed in the HUD with a gesture. This interface displays audio and air conditioning functions on the central console of a HUD and enables their control using a specific number of fingers while keeping both hands on the steering wheel. We compared the effectiveness of the newly proposed hybrid interface against a traditional tactile interface for a central console using objective measurements and subjective evaluations regarding both the vehicle and driver behaviour. A total of 32 subjects were recruited to conduct experiments on a driving simulator equipped with the proposed interface under various scenarios. The results showed that the proposed interface was approximately 20% faster in emergency response than the traditional interface, whereas its performance in maintaining vehicle speed and lane was not significantly different from that of the traditional one.
CheckSoft : A Scalable Event-Driven Software Architecture for Keeping Track of People and Things in People-Centric Spaces
Venue: arXiv cs.SE 2021
Year: 2021

We present CheckSoft, a scalable event-driven software architecture for keeping track of people-object interactions in people-centric applications such as airport checkpoint security areas, automated retail stores, smart libraries, and so on. The architecture works off the video data generated in real time by a network of surveillance cameras. Although there are many different aspects to automating these applications, the most difficult part of the overall problem is keeping track of the interactions between the people and the objects. CheckSoft uses finite-state-machine (FSM) based logic for keeping track of such interactions which allows the system to quickly reject any false detections of the interactions by the video cameras. CheckSoft is easily scalable since the architecture is based on multi-processing in which a separate process is assigned to each human and to each "storage container" for the objects. A storage container may be a shelf on which the objects are displayed or a bin in which the objects are stored, depending on the specific application in which CheckSoft is deployed.
Delayed Rewards Calibration via Reward Empirical Sufficiency
Venue: arXiv cs.LG 2021
Year: 2021

Appropriate credit assignment for delay rewards is a fundamental challenge for reinforcement learning. To tackle this problem, we introduce a delay reward calibration paradigm inspired from a classification perspective. We hypothesize that well-represented state vectors share similarities with each other since they contain the same or equivalent essential information. To this end, we define an empirical sufficient distribution, where the state vectors within the distribution will lead agents to environmental reward signals in the consequent steps. Therefore, a purify-trained classifier is designed to obtain the distribution and generate the calibrated rewards. We examine the correctness of sufficient state extraction by tracking the real-time extraction and building different reward functions in environments. The results demonstrate that the classifier could generate timely and accurate calibrated rewards. Moreover, the rewards are able to make the model training process more efficient. Finally, we identify and discuss that the sufficient states extracted by our model resonate with the observations of humans.