OpenRaiser/Intern-Atlas huggingface dataset:
Checking marking reachability with the state equation in Petri net subclasses
Venue: arXiv cs.LO 2020
Year: 2020

Although decidable, the marking reachability problem for Petri nets is well-known to be intractable in general, and a non-elementary lower bound has been recently uncovered. In order to alleviate this difficulty, various structural and behavioral restrictions have been considered, allowing to relate reachability to properties that are easier to check. For a given initial marking, the set of potentially reachable markings is described by the state equation solutions and over-approximates the set of reachable markings. In this paper, we delineate several subclasses of weighted Petri nets in which the set of reachable markings equals the set of potentially reachable ones, a property we call the PR-R equality. When fulfilled, this property allows to use linear algebra to answer the reachability questions, avoiding a brute-force analysis of the state space. Notably, we provide conditions under which this equality holds in classes much more expressive than marked graphs, adding places with several ingoing and outgoing transitions, which allows to model real applications with shared buffers. To achieve it, we investigate the relationship between liveness, reversibility, boundedness and potential reachability in Petri nets. We also show that this equality does not hold in classes with close modeling capability when the conditions are relaxed.
Learning With Differential Privacy
Venue: arXiv cs.CR 2020
Year: 2020

The leakage of data might have been an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection system are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility. Differential privacy allows one to access the forest of data by describing their pattern of groups without disclosing any individual trees. The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail. The different aspects of differential privacy, it's application in privacy protection and leakage of information, a comparative discussion, on the current research approaches in this field, its utility in the real world as well as the trade-offs - will be discussed.
An Asymptotically Optimal Algorithm for Online Stacking
Venue: arXiv cs.DS 2020
Year: 2020

Consider a storage area where arriving items are stored temporarily in bounded capacity stacks until their departure. We look into the problem of deciding where to put an arriving item with the objective of minimizing the maximum number of stacks used over time. The decision has to be made as soon as an item arrives, and we assume that we only have information on the departure times for the arriving item and the items currently at the storage area. We are only allowed to put an item on top of another item if the item below departs at a later time. We refer to this problem as online stacking. We assume that the storage time intervals are picked i.i.d. from $[0, 1] \times [0, 1]$ using an unknown distribution with a bounded probability density function. Under this mild condition, we present a simple polynomial time online algorithm and show that the competitive ratio converges to $1$ in probability. The result holds if the stack capacity is $o(\sqrt{n})$, where $n$ is the number of items, including the realistic case where the capacity is a constant. Our experiments show that our results also have practical relevance. To the best of our knowledge, we are the first to present an asymptotically optimal algorithm for online stacking, which is an important problem with many real-world applications within computational logistics.
Automated Sustainability Compliance Checking Using Process Mining and Formal Logic
Venue: arXiv cs.CY 2020
Year: 2020

Business processes need to have certain constraints such that they can lead to sustainable outcomes. These constraints can be manifold and their adherence has to be monitored. In the past compliance checking has been applied in several business domains without considering certain sustainability aspects, such as multi-dimensionality and impact level. With my research I want to contribute to the application of compliance checking techniques for the purpose of sustainability compliance. In order to achieve this, I want to analyse and develop data-driven approaches, which allow to automate the task of compliance checking. The way in which this can be achieved, is be combining methods from process mining with formal languages that can express sustainability rules in a machine-readable manner. The main goal is to develop a compliance engine that can be adapted by ERP systems in order to evaluate sustainability conformance in business processes.
Image Enhancement and Object Recognition for Night Vision Surveillance
Venue: arXiv cs.CV 2020
Year: 2020

Object recognition is a critical part of any surveillance system. It is the matter of utmost concern to identify intruders and foreign objects in the area where surveillance is done. The performance of surveillance system using the traditional camera in daylight is vastly superior as compared to night. The main problem for surveillance during the night is the objects captured by traditional cameras have low contrast against the background because of the absence of ambient light in the visible spectrum. Due to that reason, the image is taken in low light condition using an Infrared Camera and the image is enhanced to obtain an image with higher contrast using different enhancing algorithms based on the spatial domain. The enhanced image is then sent to the classification process. The classification is done by using convolutional neural network followed by a fully connected layer of neurons. The accuracy of classification after implementing different enhancement algorithms is compared in this paper.
Implementation Strategies for Multidimensional Spreadsheets
Venue: arXiv cs.SE 2020
Year: 2020

Seasoned Excel developers were invited to participate in a challenge to implement a spreadsheet with multi-dimensional variables. We analyzed their spreadsheet to see the different implement strategies employed. We identified two strategies: most participants used a projection of three or four-dimensional variables on the two-dimensional plane used by Excel. A few participants used a database approach where the multi-dimensional variables are presented in the form of a dataset table with the appropriate primary key. This approach leads to simpler formulas.
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks
Venue: arXiv cs.NE 2020
Year: 2020

The adaptive learning capabilities seen in biological neural networks are largely a product of the self-modifying behavior emerging from online plastic changes in synaptic connectivity. Current methods in Reinforcement Learning (RL) only adjust to new interactions after reflection over a specified time interval, preventing the emergence of online adaptivity. Recent work addressing this by endowing artificial neural networks with neuromodulated plasticity have been shown to improve performance on simple RL tasks trained using backpropagation, but have yet to scale up to larger problems. Here we study the problem of meta-learning in a challenging quadruped domain, where each leg of the quadruped has a chance of becoming unusable, requiring the agent to adapt by continuing locomotion with the remaining limbs. Results demonstrate that agents evolved using self-modifying plastic networks are more capable of adapting to complex meta-learning learning tasks, even outperforming the same network updated using gradient-based algorithms while taking less time to train.
Improving Dependability of Neuromorphic Computing With Non-Volatile Memory
Venue: arXiv cs.NE 2020
Year: 2020

As process technology continues to scale aggressively, circuit aging in a neuromorphic hardware due to negative bias temperature instability (NBTI) and time-dependent dielectric breakdown (TDDB) is becoming a critical reliability issue and is expected to proliferate when using non-volatile memory (NVM) for synaptic storage. This is because an NVM requires high voltage and current to access its synaptic weight, which further accelerates the circuit aging in a neuromorphic hardware. Current methods for qualifying reliability are overly conservative, since they estimate circuit aging considering worst-case operating conditions and unnecessarily constrain performance. This paper proposes RENEU, a reliability-oriented approach to map machine learning applications to neuromorphic hardware, with the aim of improving system-wide reliability without compromising key performance metrics such as execution time of these applications on the hardware. Fundamental to RENEU is a novel formulation of the aging of CMOS-based circuits in a neuromorphic hardware considering different failure mechanisms. Using this formulation, RENEU develops a system-wide reliability model which can be used inside a design-space exploration framework involving the mapping of neurons and synapses to the hardware. To this end, RENEU uses an instance of Particle Swarm Optimization (PSO) to generate mappings that are Pareto-optimal in terms of performance and reliability. We evaluate RENEU using different machine learning applications on a state-of-the-art neuromorphic hardware with NVM synapses. Our results demonstrate an average 38\% reduction in circuit aging, leading to an average 18% improvement in the lifetime of the hardware compared to current practices. RENEU only introduces a marginal performance overhead of 5% compared to a performance-oriented state-of-the-art.
Rinascimento: using event-value functions for playing Splendor
Venue: arXiv cs.AI 2020
Year: 2020

In the realm of games research, Artificial General Intelligence algorithms often use score as main reward signal for learning or playing actions. However this has shown its severe limitations when the point rewards are very rare or absent until the end of the game. This paper proposes a new approach based on event logging: the game state triggers an event every time one of its features changes. These events are processed by an Event-value Function (EF) that assigns a value to a single action or a sequence. The experiments have shown that such approach can mitigate the problem of scarce point rewards and improve the AI performance. Furthermore this represents a step forward in controlling the strategy adopted by the artificial agent, by describing a much richer and controllable behavioural space through the EF. Tuned EF are able to neatly synthesise the relevance of the events in the game. Agents using an EF show more robust when playing games with several opponents.
Toward Building Safer Smart Homes for the People with Disabilities
Venue: arXiv cs.HC 2020
Year: 2020

Situational awareness is a critical foundation for the protection of human life/properties and is challenging to maintain for people with disabilities (i.e., visual impairments and limited mobility). In this paper, we present a dialog enabled end-to-end assistive solution called "SafeAccess" to build a safer smart home by providing situational awareness. The key functions of SafeAccess are: - 1) monitoring homes and identifying incoming persons; 2) helping users in assessing incoming threats (e.g., burglary, robbery, gun violence); and, 3) allowing users to grant safe access to homes for friends/families. In this work, we focus on building a robust model for detecting and recognizing person, generating image descriptions, and designing a prototype for the smart door. To interact with the system, we implemented a dialog enabled smartphone app, especially for creating a personalized profile from face images or videos of friends/families. A Raspberry pi connected to the home monitoring cameras captures the video frames and performs change detection to identify frames with activities. Then, we detect human presence using Faster r-cnn and extract faces using Multi-task Cascaded Convolutional Networks (MTCNN). Subsequently, we match the detected faces using FaceNet/support vector machine (SVM) classifiers. The system notifies users with an MMS containing the name of incoming persons or as "unknown", scene image, facial description, and contextual information. The users can grant access or call emergency services using the SafeAccess app based on the received notification. Our system identifies persons with an F-score 0.97 and recognizes items to generate image description with an average F-score 0.97.
Product Forms for FCFS Queueing Models with Arbitrary Server-Job Compatibilities: An Overview
Venue: arXiv cs.PF 2020
Year: 2020

In recent years a number of models involving different compatibilities between jobs and servers in queueing systems, or between agents and resources in matching systems, have been studied, and, under Markov assumptions and appropriate stability conditions, the stationary distributions have been shown to have product forms. We survey these results and show how, under an appropriate detailed description of the state, many are corollaries of similar results for the Order Independent Queue. We also discuss how to use the product form results to determine distributions for steady-state response times.
Modeling and Simulation of COVID-19 Pandemic for Cincinnati Tri-State Area
Venue: arXiv cs.SI 2020
Year: 2020

In this paper, we use SIR model to simulate the COVID-19 pandemic for Cincinnati Tri-State Area. We have built a representative population of Cincinnati that includes movements for traveling to stores, schools, workplaces, and traveling to friends houses. Using this model, we simulate the effect of quarantine, return to work, and panic buying. We show that that there will be a second wave of infections when people return to work and significant increase in number of infections when there is panic buying at stores with the announcement of the quarantine measures.
Evaluating the Exploitability of Implicit Interactions in Distributed Systems
Venue: arXiv cs.CR 2020
Year: 2020

Implicit interactions refer to those interactions among the components of a system that may be unintended and/or unforeseen by the system designers. As such, they represent cybersecurity vulnerabilities that can be exploited to mount cyber-attacks causing serious and destabilizing system effects. In this paper, we study implicit interactions in distributed systems specified using the algebraic modeling framework known as Communicating Concurrent Kleene Algebra (C$^2$KA). To identify and defend against a range of possible attack scenarios, we develop a new measure of exploitability for implicit interactions to aid in evaluating the threat posed by the existence of such vulnerabilities in system designs for launching cyber-attacks. The presented approach is based on the modeling and analysis of the influence and response of the system agents and their C$^2$KA specifications. We also demonstrate the applicability of the proposed approach using a prototype tool that supports the automated analysis. The rigorous, practical techniques presented here enable cybersecurity vulnerabilities in the designs of distributed systems to be more easily identified, assessed, and then mitigated, offering significant improvements to overall system resilience, dependability, and security.
Deterministic Gaussian Averaged Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

We present a deterministic method to compute the Gaussian average of neural networks used in regression and classification. Our method is based on an equivalence between training with a particular regularized loss, and the expected values of Gaussian averages. We use this equivalence to certify models which perform well on clean data but are not robust to adversarial perturbations. In terms of certified accuracy and adversarial robustness, our method is comparable to known stochastic methods such as randomized smoothing, but requires only a single model evaluation during inference.
Robust Spammer Detection by Nash Reinforcement Learning
Venue: arXiv cs.SI 2020
Year: 2020

Online reviews provide product evaluations for customers to make decisions. Unfortunately, the evaluations can be manipulated using fake reviews ("spams") by professional spammers, who have learned increasingly insidious and powerful spamming strategies by adapting to the deployed detectors. Spamming strategies are hard to capture, as they can be varying quickly along time, different across spammers and target products, and more critically, remained unknown in most cases. Furthermore, most existing detectors focus on detection accuracy, which is not well-aligned with the goal of maintaining the trustworthiness of product evaluations. To address the challenges, we formulate a minimax game where the spammers and spam detectors compete with each other on their practical goals that are not solely based on detection accuracy. Nash equilibria of the game lead to stable detectors that are agnostic to any mixed detection strategies. However, the game has no closed-form solution and is not differentiable to admit the typical gradient-based algorithms. We turn the game into two dependent Markov Decision Processes (MDPs) to allow efficient stochastic optimization based on multi-armed bandit and policy gradient. We experiment on three large review datasets using various state-of-the-art spamming and detection strategies and show that the optimization algorithm can reliably find an equilibrial detector that can robustly and effectively prevent spammers with any mixed spamming strategies from attaining their practical goal. Our code is available at https://github.com/YingtongDou/Nash-Detect.
Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols
Venue: arXiv cs.CL 2020
Year: 2020

As conversational AI-based dialogue management has increasingly become a trending topic, the need for a standardized and reliable evaluation procedure grows even more pressing. The current state of affairs suggests various evaluation protocols to assess chat-oriented dialogue management systems, rendering it difficult to conduct fair comparative studies across different approaches and gain an insightful understanding of their values. To foster this research, a more robust evaluation protocol must be set in place. This paper presents a comprehensive synthesis of both automated and human evaluation methods on dialogue systems, identifying their shortcomings while accumulating evidence towards the most effective evaluation dimensions. A total of 20 papers from the last two years are surveyed to analyze three types of evaluation protocols: automated, static, and interactive. Finally, the evaluation dimensions used in these papers are compared against our expert evaluation on the system-user dialogue data collected from the Alexa Prize 2020.
Continual Learning for Affective Computing
Venue: arXiv cs.CV 2020
Year: 2020

Real-world application requires affect perception models to be sensitive to individual differences in expression. As each user is different and expresses differently, these models need to personalise towards each individual to adequately capture their expressions and thus, model their affective state. Despite high performance on benchmarks, current approaches fall short in such adaptation. In this work, we propose the use of Continual Learning (CL) for affective computing as a paradigm for developing personalised affect perception.
DNS Tunneling: A Deep Learning based Lexicographical Detection Approach
Venue: arXiv cs.CR 2020
Year: 2020

Domain Name Service is a trusted protocol made for name resolution, but during past years some approaches have been developed to use it for data transfer. DNS Tunneling is a method where data is encoded inside DNS queries, allowing information exchange through the DNS. This characteristic is attractive to hackers who exploit DNS Tunneling method to establish bidirectional communication with machines infected with malware with the objective of exfiltrating data or sending instructions in an obfuscated way. To detect these threats fast and accurately, the present work proposes a detection approach based on a Convolutional Neural Network (CNN) with a minimal architecture complexity. Due to the lack of quality datasets for evaluating DNS Tunneling connections, we also present a detailed construction and description of a novel dataset that contains DNS Tunneling domains generated with five well-known DNS tools. Despite its simple architecture, the resulting CNN model correctly detected more than 92% of total Tunneling domains with a false positive rate close to 0.8%.
Comparing the difficulty of factorization and discrete logarithm: a 240-digit experiment
Venue: arXiv cs.CR 2020
Year: 2020

We report on two new records: the factorization of RSA-240, a 795-bit number, and a discrete logarithm computation over a 795-bit prime field. Previous records were the factorization of RSA-768 in 2009 and a 768-bit discrete logarithm computation in 2016. Our two computations at the 795-bit level were done using the same hardware and software, and show that computing a discrete logarithm is not much harder than a factorization of the same size. Moreover, thanks to algorithmic variants and well-chosen parameters, our computations were significantly less expensive than anticipated based on previous records.The last page of this paper also reports on the factorization of RSA-250.
Privacy-Aware Activity Classification from First Person Office Videos
Venue: arXiv cs.CV 2020
Year: 2020

In the advent of wearable body-cameras, human activity classification from First-Person Videos (FPV) has become a topic of increasing importance for various applications, including in life-logging, law-enforcement, sports, workplace, and healthcare. One of the challenging aspects of FPV is its exposure to potentially sensitive objects within the user's field of view. In this work, we developed a privacy-aware activity classification system focusing on office videos. We utilized a Mask-RCNN with an Inception-ResNet hybrid as a feature extractor for detecting, and then blurring out sensitive objects (e.g., digital screens, human face, paper) from the videos. For activity classification, we incorporate an ensemble of Recurrent Neural Networks (RNNs) with ResNet, ResNext, and DenseNet based feature extractors. The proposed system was trained and evaluated on the FPV office video dataset that includes 18-classes made available through the IEEE Video and Image Processing (VIP) Cup 2019 competition. On the original unprotected FPVs, the proposed activity classifier ensemble reached an accuracy of 85.078% with precision, recall, and F1 scores of 0.88, 0.85 & 0.86, respectively. On privacy protected videos, the performances were slightly degraded, with accuracy, precision, recall, and F1 scores at 73.68%, 0.79, 0.75, and 0.74, respectively. The presented system won the 3rd prize in the IEEE VIP Cup 2019 competition.
Graph Neural Networks for Motion Planning
Venue: arXiv cs.RO 2020
Year: 2020

This paper investigates the feasibility of using Graph Neural Networks (GNNs) for classical motion planning problems. We propose guiding both continuous and discrete planning algorithms using GNNs' ability to robustly encode the topology of the planning space using a property called permutation invariance. We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT). Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.