OpenRaiser/Intern-Atlas huggingface dataset:
Specification-Driven Predictive Business Process Monitoring
Venue: arXiv cs.AI 2019
Year: 2019

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.
Learning the Right Expansion-ordering Heuristics for Satisfiability Testing in OWL Reasoners
Venue: arXiv cs.AI 2019
Year: 2019

Web Ontology Language (OWL) reasoners are used to infer new logical relations from ontologies. While inferring new facts, these reasoners can be further optimized, e.g., by properly ordering disjuncts in disjunction expressions of ontologies for satisfiability testing of concepts. Different expansion-ordering heuristics have been developed for this purpose. The built-in heuristics in these reasoners determine the order for branches in search trees while each heuristic choice causes different effects for various ontologies depending on the ontologies' syntactic structure and probably other features as well. A learning-based approach that takes into account the features aims to select an appropriate expansion-ordering heuristic for each ontology. The proper choice is expected to accelerate the reasoning process for the reasoners. In this paper, the effect of our methodology is investigated on a well-known reasoner that is JFact. Our experiments show the average speedup by a factor of one to two orders of magnitude for satisfiability testing after applying learning methodology for selecting the right expansion-ordering heuristics.
Model-free Deep Reinforcement Learning for Urban Autonomous Driving
Venue: arXiv cs.LG 2019
Year: 2019

Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in sub-optimal solutions and is expensive to develop, generalize and maintain at scale. On the other hand, with reinforcement learning (RL), a policy can be learned and improved automatically without any manual designs. However, current RL methods generally do not work well on complex urban scenarios. In this paper, we propose a framework to enable model-free deep reinforcement learning in challenging urban autonomous driving scenarios. We design a specific input representation and use visual encoding to capture the low-dimensional latent states. Several state-of-the-art model-free deep RL algorithms are implemented into our framework, with several tricks to improve their performance. We evaluate our method in a challenging roundabout task with dense surrounding vehicles in a high-definition driving simulator. The result shows that our method can solve the task well and is significantly better than the baseline.
EOP: An Encryption-Obfuscation Solution for Protecting PCBs Against Tampering and Reverse Engineering
Venue: arXiv cs.CR 2019
Year: 2019

PCBs are the core components for the devices ranging from the consumer electronics to military applications. Due to the accessibility of the PCBs, they are vulnerable to the attacks such as probing, eavesdropping, and reverse engineering. In this paper, a solution named EOP is proposed to migrate these threats. EOP encrypts the inter-chip communications with the stream cipher. The encryption and decryption are driven by the dedicated clock modules. These modules guarantee the stream cipher is correctly synchronized and free from tampering. Additionally, EOP also incorporates the PCB-level obfuscation for protection against reverse engineering. EOP is designated to be accomplished by utilizing the COTS components. For the validation, EOP is implemented in a Zynq SoC based system. Both the normal operation and tampering detection performance are verified. The results show that EOP can deliver the data from one chip to another without any errors. It is proved to be sensitive to any active tampering attacks.
Snaxels on a Plane
Venue: arXiv cs.GR 2019
Year: 2019

While many algorithms exist for tracing various contours for illustrating a meshed object, few algorithms organize these contours into region-bounding closed loops. Tracing closed-loop boundaries on a mesh can be problematic due to switchbacks caused by subtle surface variation, and the organization of these regions into a planar map can lead to many small region components due to imprecision and noise. This paper adapts "snaxels," an energy minimizing active contour method designed for robust mesh processing, and repurposes it to generate visual, shadow and shading contours, and a simplified visual-surface planar map, useful for stylized vector art illustration of the mesh. The snaxel active contours can also track contours as the mesh animates, and frame-to-frame correspondences between snaxels lead to a new method to convert the moving contours on a 3-D animated mesh into 2-D SVG curve animations for efficient embedding in Flash, PowerPoint and other dynamic vector art platforms.
A Parallel Bitstream Generator for Stochastic Computing
Venue: arXiv cs.ET 2019
Year: 2019

Stochastic computing (SC) presents high error tolerance and low hardware cost, and has great potential in applications such as neural networks and image processing. However, the bitstream generator, which converts a binary number to bitstreams, occupies a large area and energy consumption, thus weakening the superiority of SC. In this paper, we propose a novel technique for generating bitstreams in parallel, which needs only one clock for conversion and significantly reduces the hardware cost. Synthesis results demonstrate that the proposed parallel bitstream generator improves 2.5x area and 712x energy consumption.
Genuine Personal Identifiers and Mutual Sureties for Sybil-Resilient Community Formation
Venue: arXiv cs.MA 2019
Year: 2019

While most of humanity is suddenly on the net, the value of this singularity is hampered by the lack of credible digital identities: Social networking, person-to-person transactions, democratic conduct, cooperation and philanthropy are all hampered by the profound presence of fake identities, as illustrated by Facebook's removal of 5.4Bn fake accounts since the beginning of 2019. Here, we introduce the fundamental notion of a \emph{genuine personal identifier}---a globally unique and singular identifier of a person---and present a foundation for a decentralized, grassroots, bottom-up process in which every human being may create, own, and protect the privacy of a genuine personal identifier. The solution employs mutual sureties among owners of personal identifiers, resulting in a mutual-surety graph reminiscent of a web-of-trust. Importantly, this approach is designed for a distributed realization, possibly using distributed ledger technology, and does not depend on the use or storage of biometric properties. For the solution to be complete, additional components are needed, notably a mechanism that encourages honest behavior and a sybil-resilient governance system.
Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System
Venue: arXiv cs.CL 2019
Year: 2019

Deep pre-training and fine-tuning models (like BERT, OpenAI GPT) have demonstrated excellent results in question answering areas. However, due to the sheer amount of model parameters, the inference speed of these models is very slow. How to apply these complex models to real business scenarios becomes a challenging but practical problem. Previous works often leverage model compression approaches to resolve this problem. However, these methods usually induce information loss during the model compression procedure, leading to incomparable results between compressed model and the original model. To tackle this challenge, we propose a Multi-task Knowledge Distillation Model (MKDM for short) for web-scale Question Answering system, by distilling knowledge from multiple teacher models to a light-weight student model. In this way, more generalized knowledge can be transferred. The experiment results show that our method can significantly outperform the baseline methods and even achieve comparable results with the original teacher models, along with significant speedup of model inference.
Fine-Grained Argument Unit Recognition and Classification
Venue: arXiv cs.CL 2019
Year: 2019

Prior work has commonly defined argument retrieval from heterogeneous document collections as a sentence-level classification task. Consequently, argument retrieval suffers both from low recall and from sentence segmentation errors making it difficult for humans and machines to consume the arguments. In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling. For this, we define the task as Argument Unit Recognition and Classification (AURC). We present a dataset of arguments from heterogeneous sources annotated as spans of tokens within a sentence, as well as with a corresponding stance. We show that and how such difficult argument annotations can be effectively collected through crowdsourcing with high interannotator agreement. The new benchmark, AURC-8, contains up to 15% more arguments per topic as compared to annotations on the sentence level. We identify a number of methods targeted at AURC sequence labeling, achieving close to human performance on known domains. Further analysis also reveals that, contrary to previous approaches, our methods are more robust against sentence segmentation errors. We publicly release our code and the AURC-8 dataset.
Energy Consumption of Group Search on a Line
Venue: arXiv cs.DM 2019
Year: 2019

Consider two robots that start at the origin of the infinite line in search of an exit at an unknown location on the line. The robots can only communicate if they arrive at the same location at exactly the same time, i.e. they use the so-called face-to-face communication model. The group search time is defined as the worst-case time as a function of $d$, the distance of the exit from the origin, when both robots can reach the exit. It has long been known that for a single robot traveling at unit speed, the search time is at least $9d-o(d)$. It was shown recently that $k\geq2$ robots traveling at unit speed also require at least $9d$ group search time. We investigate energy-time trade-offs in group search by two robots, where the energy loss experienced by a robot traveling a distance $x$ at constant speed $s$ is given by $s^2 x$. Specifically, we consider the problem of minimizing the total energy used by the robots, under the constraints that the search time is at most a multiple $c$ of the distance $d$ and the speed of the robots is bounded by $b$. Motivation for this study is that for the case when robots must complete the search in $9d$ time with maximum speed one, a single robot requires at least $9d$ energy, while for two robots, all previously proposed algorithms consume at least $28d/3$ energy. When the robots have bounded memory, we generalize existing algorithms to obtain a family of optimal (and in some cases nearly optimal) algorithms parametrized by pairs of $b,c$ values that can solve the problem for the entire spectrum of these pairs for which the problem is solvable. We also propose a novel search algorithm, with unbounded memory, that simultaneously achieves search time $9d$ and consumes energy $8.42588d$. Our result shows that two robots can search on the line in optimal time $9d$ while consuming less total energy than a single robot within the same search time.
Maximum Lifetime Analytics in IoT Networks
Venue: arXiv cs.NI 2019
Year: 2019

This paper studies the problem of allocating bandwidth and computation resources to data analytics tasks in Internet of Things (IoT) networks. IoT nodes are powered by batteries, can process (some of) the data locally, and the quality grade or performance of how data analytics tasks are carried out depends on where these are executed. The goal is to design a resource allocation algorithm that jointly maximizes the network lifetime and the performance of the data analytics tasks subject to energy constraints. This joint maximization problem is challenging with coupled resource constraints that induce non-convexity. We first show that the problem can be mapped to an equivalent convex problem, and then propose an online algorithm that provably solves the problem and does not require any a priori knowledge of the time-varying wireless link capacities and data analytics arrival process statistics. The algorithm's optimality properties are derived using an analysis which, to the best of our knowledge, proves for the first time the convergence of the dual subgradient method with time-varying sets. Our simulations seeded by real IoT device energy measurements, show that the network connectivity plays a crucial role in network lifetime maximization, that the algorithm can obtain both maximum network lifetime and maximum data analytics performance in addition to maximizing the joint objective, and that the algorithm increases the network lifetime by approximately 50% compared to an algorithm that minimizes the total energy consumption.
Calculation of distributed system imbalance in condition of multifractal load
Venue: arXiv cs.NI 2019
Year: 2019

The method of calculating a distributed system imbalance based on the calculation of node system load was proposed in the work. Calculation of node system load is carried out by calculating the average coefficient of utilization of CPU, memory, bandwidth of each system node, taking into account multifractal properties of input data flows. The simulation of the proposed method for different multifractal parameters was conducted. The simulation showed that the characteristics of multifractal traffic have a considerable effect on the system imbalance. Using the proposed method allows to distribute requests across the servers so that the deviation of the load servers from the average value was minimal, which allows to get higher system performance metrics and faster processing flows.
GPS-Based Vehicle Tracking System-on-Chip
Venue: arXiv cs.DC 2019
Year: 2019

Modern powerful reconfigurable systems are suited in the implementation of various data-stream, data-parallel, and other applications. An application that needs real-time, fast, and reliable data processing is the global positioning system (GPS)-based vehicle tracking system (VTS). In this paper, we build on a recently produced VTS (The Aram Locator) offering a system-on-chip (SOC) replacement of the current microcontroller-based implementation. The proposed SOC is built on a field programmable gate array (FPGA) promising a cheaper design, a more cohesive architecture, a faster processing time and an enhanced system interaction. Different designs, and their hardware implementations, are proposed with different levels of integration. Performance analysis and evaluation of the investigated designs are included.
Polymorphic Higher-order Termination
Venue: arXiv cs.LO 2019
Year: 2019

We generalise the termination method of higher-order polynomial interpretations to a setting with impredicative polymorphism. Instead of using weakly monotonic functionals, we interpret terms in a suitable extension of System F-omega. This enables a direct interpretation of rewrite rules which make essential use of impredicative polymorphism. In addition, our generalisation eases the applicability of the method in the non-polymorphic setting by allowing for the encoding of inductive data types. As an illustration of the potential of our method, we prove termination of a substantial fragment of full intuitionistic second-order propositional logic with permutative conversions.
Translating between models of concurrency
Venue: arXiv cs.FL 2019
Year: 2019

Hoare's Communicating Sequential Processes (CSP) admits a rich universe of semantic models closely related to the van Glabbeek spectrum. In this paper we study finite observational models, of which at least six have been identified for CSP, namely traces, stable failures, revivals, acceptances, refusal testing and finite linear observations. We show how to use the recently-introduced \emph{priority} operator to transform refinement questions in these models into trace refinement (language inclusion) tests. Furthermore, we are able to generalise this to any (rational) finite observational model. As well as being of theoretical interest, this is of practical significance since the state-of-the-art refinement checking tool FDR4 currently only supports two such models. In particular we study how it is possible to check refinement in a discrete version of the Timed Failures model that supports Timed CSP.
Health Behaviour Change Techniques in Diabetes Management Applications: A Systematic Review
Venue: arXiv cs.CY 2019
Year: 2019

The rapid growth in mobile healthcare technology could significantly help control chronic diseases, such as diabetes. This paper presents a systematic review to characterise type 1 & type 2 diabetes management applications available in Apple's iTunes store. We investigated "Health & Fitness" and "Medical" apps following a two-step filtering process (Selection and Analysis phases). We firstly investigated the apps compliance to the persuasive system design (PSD) model. We then characterised the behaviour change techniques (BCTs) of top-ranked apps for diabetes management. Finally, we checked the apps regarding the stages of disease continuum. The findings revealed apps incorporation some PSD principles based on their configuration and behaviour change techniques. Most apps miss the element of BCT and focus on measuring exercise and caloric intake. Few apps consider managing specific diabetes type, which raises doubts about the effectiveness of those apps in providing sustainable diabetes management. Moreover, people may need multiple apps to initiate and maintain a healthy behaviour.
A programmable actuator for combined motion and connection and its application to modular robot
Venue: arXiv cs.RO 2019
Year: 2019

This paper proposes a new type of actuator at millimeter scale, which is based on Simplified Electro-Permanent (SEP) magnets. The new actuator can achieve connection and smooth motion by controlling the polarity of SEP magnets. Analyses based on numerical simulation are used to design a prototype. A dead-time controllable H-bridge and its multiplex design are proposed for controlling the new actuator and simplifying the electronic circuit. Finally, the new actuator is implemented in the DILI modular reconfigurable robot system. The experimental results show that with this new actuator, the DILI module can move smoothly and connect to other modules without power supply during connection. The maximum speed of DILI module is 20mm/s.
Detecting ADS-B Spoofing Attacks using Deep Neural Networks
Venue: arXiv cs.CR 2019
Year: 2019

The Automatic Dependent Surveillance-Broadcast (ADS-B) system is a key component of the Next Generation Air Transportation System (NextGen) that manages the increasingly congested airspace. It provides accurate aircraft localization and efficient air traffic management and also improves the safety of billions of current and future passengers. While the benefits of ADS-B are well known, the lack of basic security measures like encryption and authentication introduces various exploitable security vulnerabilities. One practical threat is the ADS-B spoofing attack that targets the ADS-B ground station, in which the ground-based or aircraft-based attacker manipulates the International Civil Aviation Organization (ICAO) address (a unique identifier for each aircraft) in the ADS-B messages to fake the appearance of non-existent aircraft or masquerade as a trusted aircraft. As a result, this attack can confuse the pilots or the air traffic control personnel and cause dangerous maneuvers. In this paper, we introduce SODA - a two-stage Deep Neural Network (DNN)-based spoofing detector for ADS-B that consists of a message classifier and an aircraft classifier. It allows a ground station to examine each incoming message based on the PHY-layer features (e.g., IQ samples and phases) and flag suspicious messages. Our experimental results show that SODA detects ground-based spoofing attacks with a probability of 99.34%, while having a very small false alarm rate (i.e., 0.43%). It outperforms other machine learning techniques such as XGBoost, Logistic Regression, and Support Vector Machine. It further identifies individual aircraft with an average F-score of 96.68% and an accuracy of 96.66%, with a significant improvement over the state-of-the-art detector.
The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
Venue: arXiv cs.CY 2019
Year: 2019

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it.
A Survey on Practical Applications of Multi-Armed and Contextual Bandits
Venue: arXiv cs.LG 2019
Year: 2019

In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize state-of-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.