OpenRaiser/Intern-Atlas huggingface dataset:
Modelling and Verification of Reconfigurable Multi-Agent Systems
Venue: arXiv cs.LO 2021
Year: 2021

We propose a formalism to model and reason about reconfigurable multi-agent systems. In our formalism, agents interact and communicate in different modes so that they can pursue joint tasks; agents may dynamically synchronize, exchange data, adapt their behaviour, and reconfigure their communication interfaces. Inspired by existing multi-robot systems, we represent a system as a set of agents (each with local state), executing independently and only influence each other by means of message exchange. Agents are able to sense their local states and partially their surroundings. We extend LTL to be able to reason explicitly about the intentions of agents in the interaction and their communication protocols. We also study the complexity of satisfiability and model-checking of this extension.
Effectively Sampling Higher Order Mutants Using Causal Effect
Venue: arXiv cs.SE 2021
Year: 2021

Higher Order Mutation (HOM) has been proposed to avoid equivalent mutants and improve the scalability of mutation testing, but generating useful HOMs remain an expensive search problem on its own. We propose a new approach to generate Strongly Subsuming Higher Order Mutants (SSHOM) using a recently introduced Causal Program Dependence Analysis (CPDA). CPDA itself is based on program mutation, and provides quantitative estimation of how often a change of the value of a program element will cause a value change of another program element. Our SSHOM generation approach chooses pairs of program elements using heuristics based on CPDA analysis, performs First Order Mutation to the chosen pairs, and generates an HOM by combining two FOMs.
10 Years of the PCG workshop: Past and Future Trends
Venue: arXiv cs.AI 2021
Year: 2021

As of 2020, the international workshop on Procedural Content Generation enters its second decade. The annual workshop, hosted by the international conference on the Foundations of Digital Games, has collected a corpus of 95 papers published in its first 10 years. This paper provides an overview of the workshop's activities and surveys the prevalent research topics emerging over the years.
Methodology for Detecting Cyber Intrusions in e-Learning Systems during COVID-19 Pandemic
Venue: arXiv cs.CR 2021
Year: 2021

In the scenarios of specific conditions and crises such as the coronavirus pandemic, the availability of e-learning ecosystem elements is further highlighted. The growing importance for securing such an ecosystem can be seen from DDoS (Distributed Denial of Service) attacks on e-learning components of the Croatian e-learning system. The negative impact of the conducted attack is visible in numerous users who were prevented from participating in and implementing the planned teaching process. Network anomalies such as conducted DDoS attacks were identified as one of the crucial threats to the e-learning systems. In this paper, an overview of the network anomaly phenomenon was given and botnets' role in generating DDoS attacks, especially IoT device impact. The paper analyzes the impact of the COVID-19 pandemic on the e-learning systems in Croatia. Based on the conclusions, a research methodology has been proposed to develop a cyber-threat detection model that considers the specifics of the application of e-learning systems in crisis, distinguishing flash crowd events from anomalies in the communication network. The proposed methodology includes establishing a theoretical basis on DDoS and flash crowd event traffic, defining a laboratory testbed setup for data acquisition, development of DDoS detection model, and testing the applicability of the developed model on the case study. The implementation of the proposed methodology can improve the quality of the teaching process through timely DDoS detection and it gives other socio-economic contributions such as developing a specific research domain, publicly available dataset of network traffic, and raising the cyber-security of the e-learning systems.
Splitting authentication codes with perfect secrecy: new results, constructions and connections with algebraic manipulation detection codes
Venue: arXiv cs.CR 2021
Year: 2021

A splitting BIBD is a type of combinatorial design that can be used to construct splitting authentication codes with good properties. In this paper we show that a design-theoretic approach is useful in the analysis of more general splitting authentication codes. Motivated by the study of algebraic manipulation detection (AMD) codes, we define the concept of a group generated splitting authentication code. We show that all group-generated authentication codes have perfect secrecy, which allows us to demonstrate that algebraic manipulation detection codes can be considered to be a special case of an authentication code with perfect secrecy. We also investigate splitting BIBDs that can be "equitably ordered". These splitting BIBDs yield authentication codes with splitting that also have perfect secrecy. We show that, while group generated BIBDs are inherently equitably ordered, the concept is applicable to more general splitting BIBDs. For various pairs $(k,c)$, we determine necessary and sufficient (or almost sufficient) conditions for the existence of $(v, k \times c,1)$-splitting BIBDs that can be equitably ordered. The pairs for which we can solve this problem are $(k,c) = (3,2), (4,2), (3,3)$ and $(3,4)$, as well as all cases with $k = 2$.
Digestive System Dynamics in Molecular Communication Perspectives
Venue: arXiv cs.CE 2021
Year: 2021

Consumption of food in excess of the required optimal nutritional requirements has already resulted in a global crisis and this is from the perspective of human health, such as obesity, as well as food waste and sustainability. In order to minimize the impact of these issues, there is a need to develop novel innovative and effective solutions that can optimally match the food consumption to the demand. This requires accurate understanding of the food digestion dynamics and its impact on each individual's physiological characteristics. This study proposes a model to characterize digestive system dynamics by using concepts from the field of Molecular Communications (MC), and this includes integrating advection-diffusion and reaction mechanisms and its role in characterizing the digestion process as a communication system. The model is then used to explore starch digestion dynamics by using communication system metrics such as delay and path loss. Our simulations found that the long gastric emptying time increases the delay in starch digestion and in turn the glucose production and absorption into the blood stream. At the same time, the enzyme activity on the hydrolyzed starch directly impacts the path loss, as higher reaction rates and lower half saturation concentration of starch results in lower path loss. Our work can lead to provide insights formulated for each individuals by creating a digital twin digestion model
Survey on Modeling Intensity Function of Hawkes Process Using Neural Models
Venue: arXiv cs.LG 2021
Year: 2021

The event sequence of many diverse systems is represented as a sequence of discrete events in a continuous space. Examples of such an event sequence are earthquake aftershock events, financial transactions, e-commerce transactions, social network activity of a user, and the user's web search pattern. Finding such an intricate pattern helps discover which event will occur in the future and when it will occur. A Hawkes process is a mathematical tool used for modeling such time series discrete events. Traditionally, the Hawkes process uses a critical component for modeling data as an intensity function with a parameterized kernel function. The Hawkes process's intensity function involves two components: the background intensity and the effect of events' history. However, such parameterized assumption can not capture future event characteristics using past events data precisely due to bias in modeling kernel function. This paper explores the recent advancement using novel deep learning-based methods to model kernel function to remove such parametrized kernel function. In the end, we will give potential future research directions to improve modeling using the Hawkes process.
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions
Venue: arXiv cs.LG 2021
Year: 2021

Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models. However, mainstream evaluation criteria experience limitations, even yielding discrepancies among results under different settings. By examining various attack algorithms, including gradient-based and query-based attacks, we notice the lack of a consensus on a uniform standard for unbiased performance evaluation. Accordingly, we propose a Piece-wise Sampling Curving (PSC) toolkit to effectively address the aforementioned discrepancy, by generating a comprehensive comparison among adversaries in a given range. In addition, the PSC toolkit offers options for balancing the computational cost and evaluation effectiveness. Experimental results demonstrate our PSC toolkit presents comprehensive comparisons of attack algorithms, significantly reducing discrepancies in practice.
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Venue: arXiv cs.LG 2021
Year: 2021

Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. To overcome this limitation, numerous gradient compression techniques have been proposed and have demonstrated high compression ratios. However, most existing methods do not scale well to large scale distributed systems (due to gradient build-up) and/or fail to evaluate model fidelity (test accuracy) on large datasets. To mitigate these issues, we propose a new compression technique, Scalable Sparsified Gradient Compression (ScaleCom), that leverages similarity in the gradient distribution amongst learners to provide significantly improved scalability. Using theoretical analysis, we show that ScaleCom provides favorable convergence guarantees and is compatible with gradient all-reduce techniques. Furthermore, we experimentally demonstrate that ScaleCom has small overheads, directly reduces gradient traffic and provides high compression rates (65-400X) and excellent scalability (up to 64 learners and 8-12X larger batch sizes over standard training) across a wide range of applications (image, language, and speech) without significant accuracy loss.
Topological Simplifications of Hypergraphs
Venue: arXiv cs.HC 2021
Year: 2021

We study hypergraph visualization via its topological simplification. We explore both vertex simplification and hyperedge simplification of hypergraphs using tools from topological data analysis. In particular, we transform a hypergraph to its graph representations known as the line graph and clique expansion. A topological simplification of such a graph representation induces a simplification of the hypergraph. In simplifying a hypergraph, we allow vertices to be combined if they belong to almost the same set of hyperedges, and hyperedges to be merged if they share almost the same set of vertices. Our proposed approaches are general, mathematically justifiable, and they put vertex simplification and hyperedge simplification in a unifying framework.
Multiscale Vision Transformers
Venue: arXiv cs.CV 2021
Year: 2021

We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features. We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10x more costly in computation and parameters. We further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers. Code is available at: https://github.com/facebookresearch/SlowFast
Methodology proposal for proactive detection of network anomalies in e-learning system during the COVID-19 scenario
Venue: arXiv cs.CR 2021
Year: 2021

In specific conditions and crisis situations such as the pandemic of coronavirus (SARS-CoV-2), or the COVID-19 disease, e-learning systems be-came crucial for the smooth performing of teaching and other educational pro-cesses. In such scenarios, the availability of e-learning ecosystem elements is further highlighted. An indicator of the importance for securing the availability of such an ecosystem is evident from the DDoS (Distributed Denial of Service) attack on AAI@EduHr as a key authentication service for number of e-learning users in Republic of Croatia. In doing so, numerous users (teach-ers/students/administrators) were prevented from implementing and participat-ing in the planned teaching process. Given that DDoS as an anomaly of network traffic has been identified as one of the key threats to the e-learning ecosystem in crisis scenarios, this research will focus on overview of methodology for de-veloping a model for proactive detection of DDoS traffic. The challenge in de-tection is to effectively differentiate the increased traffic intensity and service requests caused by legitimate user activity (flash crowd) from the illegitimate traffic caused by a DDoS attack. The DDoS traffic detection model developed by following analyzed methodology would serve as a basis for providing further guidelines and recommendations in the form of response to events that may negatively affect the availability of e-learning ecosystem elements such as DDoS attack.
Constructing a personalized learning path using genetic algorithms approach
Venue: arXiv cs.AI 2021
Year: 2021

A substantial disadvantage of traditional learning is that all students follow the same learning sequence, but not all of them have the same background of knowledge, the same preferences, the same learning goals, and the same needs. Traditional teaching resources, such as textbooks, in most cases pursue students to follow fixed sequences during the learning process, thus impairing their performance. Learning sequencing is an important research issue as part of the learning process because no fixed learning paths will be appropriate for all learners. For this reason, many research papers are focused on the development of mechanisms to offer personalization on learning paths, considering the learner needs, interests, behaviors, and abilities. In most cases, these researchers are totally focused on the student's preferences, ignoring the level of difficulty and the relation degree that exists between various concepts in a course. This research paper presents the possibility of constructing personalized learning paths using genetic algorithm-based model, encountering the level of difficulty and relation degree of the constituent concepts of a course. The experimental results shows that the genetic algorithm is suitable to generate optimal learning paths based on learning object difficulty level, duration, rating, and relation degree between each learning object as elementary parts of the sequence of the learning path. From these results compared to the quality of the traditional learning path, we observed that even the quality of the weakest learning path generated by our GA approach is in a favor compared to quality of the traditional learning path, with a difference of 3.59\%, while the highest solution generated in the end resulted 8.34\% in favor of our proposal compared to the traditional learning paths.
A Machine Learning Framework for Real-time Inverse Modeling and Multi-objective Process Optimization of Composites for Active Manufacturing Control
Venue: arXiv cs.LG 2021
Year: 2021

For manufacturing of aerospace composites, several parts may be processed simultaneously using convective heating in an autoclave. Due to uncertainties including tool placement, convective Boundary Conditions (BCs) vary in each run. As a result, temperature histories in some of the parts may not conform to process specifications due to under-curing or over-heating. Thermochemical analysis using Finite Element (FE) simulations are typically conducted prior to fabrication based on assumed range of BCs. This, however, introduces unnecessary constraints on the design. To monitor the process, thermocouples (TCs) are placed under tools near critical locations. The TC data may be used to back-calculate BCs using trial-and-error FE analysis. However, since the inverse heat transfer problem is ill-posed, many solutions are obtained for given TC data. In this study, a novel machine learning (ML) framework is presented capable of optimizing air temperature cycle in real-time based on TC data from multiple parts, for active control of manufacturing. The framework consists of two recurrent Neural Networks (NN) for inverse modeling of the ill-posed curing problem at the speed of 300 simulations/second, and a classification NN for multi-objective optimization of the air temperature at the speed of 35,000 simulations/second. A virtual demonstration of the framework for process optimization of three composite parts with data from three TCs is presented.
Normalized multivariate time series causality analysis and causal graph reconstruction
Venue: arXiv cs.AI 2021
Year: 2021

Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as real physical notion so as to formulate it from first principles, however, seems to go unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized, and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a huge challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.
Analysis and Modeling of Driver Behavior with Integrated Feedback of Visual and Haptic Information Under Shared Control
Venue: arXiv cs.RO 2021
Year: 2021

The thesis presents contributions made to the evaluation and design of a haptic guidance system on improving driving performance in cases of normal and degraded visual information, which are based on behavior experiments, modeling and numerical simulations. The effect of shared control on driver behavior in cases of normal and degraded visual information has been successfully evaluated experimentally and numerically. The evaluation results indicate that the proposed haptic guidance system is capable of providing reliable haptic information, and is effective on improving lane following performance in the conditions of visual occlusion from road ahead and declined visual attention under fatigue driving. Moreover, the appropriate degree of haptic guidance is highly related to the reliability of visual information perceived by the driver, which suggests that designing the haptic guidance system based on the reliability of visual information would allow for greater driver acceptance. Furthermore, the parameterized driver model, which considers the integrated feedback of visual and haptic information, is capable of predicting driver behavior under shared control, and has the potential of being used for designing and evaluating the haptic guidance system.
Recording Reusable and Guided Analytics From Interaction Histories
Venue: arXiv cs.HC 2021
Year: 2021

The use of visual analytics tools has gained popularity in various domains, helping users discover meaningful information from complex and large data sets. Users often face difficulty in disseminating the knowledge discovered without clear recall of their exploration paths and analysis processes. We introduce a visual analysis tool that allows analysts to record reusable and guided analytics from their interaction logs. To capture the analysis process, we use a decision tree whose node embeds visualizations and guide to define a visual analysis task. The tool enables analysts to formalize analysis strategies, build best practices, and guide novices through systematic workflows.
Age of information distribution under dynamic service preemption
Venue: arXiv cs.PF 2021
Year: 2021

Age of Information (AoI) has emerged as an important quality-of-service measure for applications that prioritize delivery of the freshest information, e.g., virtual or augmented reality over mobile devices and wireless sensor networks used in the control of cyber-physical systems. We derive the Laplace transform of the stationary AoI for the M/GI/1/2 system with a "dynamic" service preemption and pushout policy depending on the existing service time of the in-service message. Thus, our system generalizes both the static M/GI/1/2 queue-pushout system without service preemption and the M/GI/1/1 bufferless system with service preemption - two systems considered to provide very good AoI performance. Based on our analysis, for a service-time distribution that is a mixture of deterministic and exponential, we numerically show that the dynamic policy has lower mean AoI than that of these two static policies and also that of the well studied M/GI/1/1 blocking system.