OpenRaiser/Intern-Atlas huggingface dataset:
Voter Verification of BMD Ballots Is a Two-Part Question: Can They? Mostly, They Can. Do They? Mostly, They Don't
Venue: arXiv cs.HC 2020
Year: 2020

The question of whether or not voters actually verify ballots produced by ballot marking devices (BMDs) is presently the subject of some controversy. Recent studies (e.g., Bernhard, et al. 2020) suggest the verification rate is low. What is not clear from previous research is whether this is more a result of voters being unable to do so accurately or whether this is because voters simply choose not to attempt verification in the first place. In order to understand this problem, we conducted an experiment in which 108 participants participated in a mock election where the BMD displayed the voters' true choices, but then changed a subset of those choices on the printed ballot. The design of the printed ballot, the length of the ballot, the number of changes that were made to the ballot, the location of those changes, and the instructions provided to the voters were manipulated as part of the experiment. Results indicated that of those voters who chose to examine the printed ballot, 76% detected anomalies, indicating that voters can reliably detect errors on their ballot if they will simply review it. This suggests that administrative remedies, rather than attempts to alter fundamental human perceptual capabilities, could be employed to encourage voters to check their ballots, which could prove as an effective countermeasure.
Fissile Locks
Venue: arXiv cs.OS 2020
Year: 2020

Classic test-and-test (TS) mutual exclusion locks are simple, and enjoy high performance and low latency of ownership transfer under light or no contention. However, they do not scale gracefully under high contention and do not provide any admission order guarantees. Such concerns led to the development of scalable queue-based locks, such as a recent Compact NUMA-aware (CNA) lock, a variant of another popular queue-based MCS lock. CNA scales well under load and provides certain admission guarantees, but has more complicated lock handover operations than TS and incurs higher latencies at low contention. We propose Fissile locks, which capture the most desirable properties of both TS and CNA. A Fissile lock consists of two underlying locks: a TS lock, which serves as a fast path, and a CNA lock, which serves as a slow path. The key feature of Fissile locks is the ability of threads on the fast path to bypass threads enqueued on the slow path, and acquire the lock with less overhead than CNA. Bypass is bounded (by a tunable parameter) to avoid starvation and ensure long-term fairness. The result is a highly scalable NUMA-aware lock with progress guarantees that performs like TS at low contention and like CNA at high contention.
Crop Knowledge Discovery Based on Agricultural Big Data Integration
Venue: arXiv cs.DB 2020
Year: 2020

Nowadays, the agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. The analysis of this big data enables farmers, companies and agronomists to extract high business and scientific knowledge, improving their operational processes and product quality. However, before analysing this data, different data sources need to be normalised, homogenised and integrated into a unified data representation. In this paper, we propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models. We also apply some methods to extract knowledge with the view to improve crop yield; these include finding suitable quantities of soil properties, herbicides and insecticides for both increasing crop yield and protecting the environment.
An Ontology-based Context Model in Intelligent Environments
Venue: arXiv cs.DC 2020
Year: 2020

Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services.
Learning-Based Human Segmentation and Velocity Estimation Using Automatic Labeled LiDAR Sequence for Training
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose an automatic labeled sequential data generation pipeline for human segmentation and velocity estimation with point clouds. Considering the impact of deep neural networks, state-of-the-art network architectures have been proposed for human recognition using point clouds captured by Light Detection and Ranging (LiDAR). However, one disadvantage is that legacy datasets may only cover the image domain without providing important label information and this limitation has disturbed the progress of research to date. Therefore, we develop an automatic labeled sequential data generation pipeline, in which we can control any parameter or data generation environment with pixel-wise and per-frame ground truth segmentation and pixel-wise velocity information for human recognition. Our approach uses a precise human model and reproduces a precise motion to generate realistic artificial data. We present more than 7K video sequences which consist of 32 frames generated by the proposed pipeline. With the proposed sequence generator, we confirm that human segmentation performance is improved when using the video domain compared to when using the image domain. We also evaluate our data by comparing with data generated under different conditions. In addition, we estimate pedestrian velocity with LiDAR by only utilizing data generated by the proposed pipeline.
Constellation: A High Performance Geo-Distributed Middlebox Framework
Venue: arXiv cs.NI 2020
Year: 2020

Middleboxes are increasingly deployed across geographically distributed data centers. In these scenarios, the WAN latency between different sites can significantly impact the performance of stateful middleboxes. The deployment of middleboxes across such infrastructures can even become impractical due to the high cost of remote state accesses. We introduce Constellation, a framework for the geo distributed deployment of middleboxes. Constellation uses asynchronous replication of specialized state objects to achieve high performance and scalability. The evaluation of our implementation shows that, compared with the state-of-the-art [80], Constellation improves the throughput by a factor of 96 in wide area networks.
Magic: the Gathering is as Hard as Arithmetic
Venue: arXiv cs.AI 2020
Year: 2020

Magic: the Gathering is a popular and famously complicated card game about magical combat. Recently, several authors including Chatterjee and Ibsen-Jensen (2016) and Churchill, Biderman, and Herrick (2019) have investigated the computational complexity of playing Magic optimally. In this paper we show that the ``mate-in-$n$'' problem for Magic is $Δ^0_n$-hard and that optimal play in two-player Magic is non-arithmetic in general. These results apply to how real Magic is played, can be achieved using standard-size tournament legal decks, and do not rely on stochasticity or hidden information. Our paper builds upon the construction that Churchill, Biderman, and Herrick (2019) used to show that this problem was at least as hard as the halting problem.
Predicting the Amount of GDPR Fines
Venue: arXiv cs.CY 2020
Year: 2020

The General Data Protection Regulation (GDPR) was enforced in 2018. After this enforcement, many fines have already been imposed by national data protection authorities in the European Union (EU). This paper examines the individual GDPR articles referenced in the enforcement decisions, as well as predicts the amount of enforcement fines with available meta-data and text mining features extracted from the enforcement decision documents. According to the results, articles related to the general principles, lawfulness, and information security have been the most frequently referenced ones. Although the amount of fines imposed vary across the articles referenced, these three particular articles do not stand out. Furthermore, good predictions are attainable even with simple machine learning techniques for regression analysis. Basic meta-data (such as the articles referenced and the country of origin) yields slightly better performance compared to the text mining features.
A generalized Sylvester-Gallai type theorem for quadratic polynomials
Venue: arXiv cs.CC 2020
Year: 2020

In this work we prove a version of the Sylvester-Gallai theorem for quadratic polynomials that takes us one step closer to obtaining a deterministic polynomial time algorithm for testing zeroness of $Σ^{[3]}ΠΣΠ^{[2]}$ circuits. Specifically, we prove that if a finite set of irreducible quadratic polynomials $\mathcal{Q}$ satisfy that for every two polynomials $Q_1,Q_2\in \mathcal{Q}$ there is a subset $\mathcal{K}\subset \mathcal{Q}$, such that $Q_1,Q_2 \notin \mathcal{K}$ and whenever $Q_1$ and $Q_2$ vanish then also $\prod_{i\in \mathcal{K}} Q_i$ vanishes, then the linear span of the polynomials in $\mathcal{Q}$ has dimension $O(1)$. This extends the earlier result [Shpilka19] that showed a similar conclusion when $|\mathcal{K}| = 1$. An important technical step in our proof is a theorem classifying all the possible cases in which a product of quadratic polynomials can vanish when two other quadratic polynomials vanish. I.e., when the product is in the radical of the ideal generates by the two quadratics. This step extends a result from [Shpilka19]that studied the case when one quadratic polynomial is in the radical of two other quadratics.
Combined Centrality Measures for an Improved Characterization of Influence Spread in Social Networks
Venue: arXiv cs.SI 2020
Year: 2020

Influence Maximization (IM) aims at finding the most influential users in a social network, i. e., users who maximize the spread of an opinion within a certain propagation model. Previous work investigated the correlation between influence spread and nodal centrality measures to bypass more expensive IM simulations. The results were promising but incomplete, since these studies investigated the performance (i. e., the ability to identify influential users) of centrality measures only in restricted settings, e. g., in undirected/unweighted networks and/or within a propagation model less common for IM. In this paper, we first show that good results within the Susceptible- Infected-Removed (SIR) propagation model for unweighted and undirected networks do not necessarily transfer to directed or weighted networks under the popular Independent Cascade (IC) propagation model. Then, we identify a set of centrality measures with good performance for weighted and directed networks within the IC model. Our main contribution is a new way to combine the centrality measures in a closed formula to yield even better results. Additionally, we also extend gravitational centrality (GC) with the proposed combined centrality measures. Our experiments on 50 real-world data sets show that our proposed centrality measures outperform well-known centrality measures and the state-of-the art GC measure significantly. social networks, influence maximization, centrality measures, IC propagation model, influential spreaders
Cycle-Accurate Evaluation of Software-Hardware Co-Design of Decimal Computation in RISC-V Ecosystem
Venue: arXiv cs.AR 2020
Year: 2020

Software-hardware co-design solutions for decimal computation can provide several Pareto points to development of embedded systems in terms of hardware cost and performance. This paper demonstrates how to accurately evaluate such co-design solutions using RISC-V ecosystem. In a software-hardware co-design solution, a part of solution requires dedicated hardware. In our evaluation framework, we develop new decimal oriented instructions supported by an accelerator. The framework can realize cycle-accurate analysis for performance as well as hardware overhead for co-design solutions for decimal computation. The obtained performance result is compared with an estimation with dummy functions.
How the Brain might use Division
Venue: arXiv cs.AI 2020
Year: 2020

One of the most fundamental questions in Biology or Artificial Intelligence is how the human brain performs mathematical functions. How does a neural architecture that may organise itself mostly through statistics, know what to do? One possibility is to extract the problem to something more abstract. This becomes clear when thinking about how the brain handles large numbers, for example to the power of something, when simply summing to an answer is not feasible. In this paper, the author suggests that the maths question can be answered more easily if the problem is changed into one of symbol manipulation and not just number counting. If symbols can be compared and manipulated, maybe without understanding completely what they are, then the mathematical operations become relative and some of them might even be rote learned. The proposed system may also be suggested as an alternative to the traditional computer binary system. Any of the actual maths still breaks down into binary operations, while a more symbolic level above that can manipulate the numbers and reduce the problem size, thus making the binary operations simpler. An interesting result of looking at this is the possibility of a new fractal equation resulting from division, that can be used as a measure of good fit and would help the brain decide how to solve something through self-replacement and a comparison with this good fit.
Fairness by Explicability and Adversarial SHAP Learning
Venue: arXiv cs.LG 2020
Year: 2020

The ability to understand and trust the fairness of model predictions, particularly when considering the outcomes of unprivileged groups, is critical to the deployment and adoption of machine learning systems. SHAP values provide a unified framework for interpreting model predictions and feature attribution but do not address the problem of fairness directly. In this work, we propose a new definition of fairness that emphasises the role of an external auditor and model explicability. To satisfy this definition, we develop a framework for mitigating model bias using regularizations constructed from the SHAP values of an adversarial surrogate model. We focus on the binary classification task with a single unprivileged group and link our fairness explicability constraints to classical statistical fairness metrics. We demonstrate our approaches using gradient and adaptive boosting on: a synthetic dataset, the UCI Adult (Census) dataset and a real-world credit scoring dataset. The models produced were fairer and performant.
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Venue: arXiv cs.AI 2020
Year: 2020

Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies.
On the Effect of Correlation on the Capacity of Backscatter Communication Systems
Venue: arXiv cs.IT 2020
Year: 2020

We analyse the effect of correlation between the forward and backward links on the capacity of backscatter communication systems. To that aim, we obtain an analytical expression for the average capacity under a correlated Rayleigh product fading channel, as well as closed-form asymptotic expressions for the high and low signal-to-noise ratio (SNR) regimes. Our results show that correlation is indeed detrimental for a fixed target SNR; contrarily to the common belief, we also see that correlation can be actually beneficial in some instances when a fixed power budget is considered.
Semantic Holism and Word Representations in Artificial Neural Networks
Venue: arXiv cs.CL 2020
Year: 2020

Artificial neural networks are a state-of-the-art solution for many problems in natural language processing. What can we learn about language and meaning from the way artificial neural networks represent it? Word representations obtained from the Skip-gram variant of the word2vec model exhibit interesting semantic properties. This is usually explained by referring to the general distributional hypothesis, which states that the meaning of the word is given by the contexts where it occurs. We propose a more specific approach based on Frege's holistic and functional approach to meaning. Taking Tugendhat's formal reinterpretation of Frege's work as a starting point, we demonstrate that it is analogical to the process of training the Skip-gram model and offers a possible explanation of its semantic properties.
Code Clone Matching: A Practical and Effective Approach to Find Code Snippets
Venue: arXiv cs.SE 2020
Year: 2020

Finding the same or similar code snippets in source code is one of fundamental activities in software maintenance. Text-based pattern matching tools such as grep is frequently used for such purpose, but making proper queries for the expected result is not easy. Code clone detectors could be used but their features and result are generally excessive. In this paper, we propose Code Clone matching (CC matching for short) that employs a combination of token-based clone detection and meta-patterns enhanced with meta-tokens. The user simply gives a query code snippet possibly with a few meta-tokens and then gets the resulting snippets, forming type 1, 2, or 3 code clone pairs between the query and result. By using a code snippet with meta-tokens as the query, the resulting matches are well controlled by the users. CC matching has been implemented as a practical and efficient tool named ccgrep, with grep-like user interface. The evaluation shows that ccgrep~ is a very effective to find various kinds of code snippets.
Machine Learning on Volatile Instances
Venue: arXiv cs.LG 2020
Year: 2020

Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes. However, running distributed SGD can be prohibitively expensive because it may require specialized computing resources such as GPUs for extended periods of time. We propose cost-effective strategies to exploit volatile cloud instances that are cheaper than standard instances, but may be interrupted by higher priority workloads. To the best of our knowledge, this work is the first to quantify how variations in the number of active worker nodes (as a result of preemption) affects SGD convergence and the time to train the model. By understanding these trade-offs between preemption probability of the instances, accuracy, and training time, we are able to derive practical strategies for configuring distributed SGD jobs on volatile instances such as Amazon EC2 spot instances and other preemptible cloud instances. Experimental results show that our strategies achieve good training performance at substantially lower cost.
Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation
Venue: arXiv cs.NI 2020
Year: 2020

Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G) and beyond wireless communication systems owing to higher spectrum efficiency, spatial gains, and energy efficiency. Reaping the benefits of MIMO transmission can be fully harnessed if the channel state information (CSI) is available at the transmitter side. However, the acquisition of transmitter side CSI entails many challenges. In this paper, we propose a deep learning assisted CSI estimation technique in highly mobile vehicular networks, based on the fact that the propagation environment (scatterers, reflectors) is almost identical thereby allowing a data driven deep neural network (DNN) to learn the non-linear CSI relations with negligible overhead. Moreover, we formulate and solve a dynamic network slicing based resource allocation problem for vehicular user equipments (VUEs) requesting enhanced mobile broadband (eMBB) and ultra-reliable low latency (URLLC) traffic slices. The formulation considers a threshold rate violation probability minimization for the eMBB slice while satisfying a probabilistic threshold rate criterion for the URLLC slice. Simulation result shows that an overhead reduction of 50% can be achieved with 12% increase in threshold violations compared to an ideal case with perfect CSI knowledge.
Cyber Security Incident Handling, Warning and Response System for the European Critical Information Infrastructures (CyberSANE)
Venue: arXiv cs.CY 2020
Year: 2020

This paper aims to enhance the security and resilience of Critical Information Infrastructures (CIIs) by providing a dynamic collaborative, warning and response system (CyberSANE system) supporting and guiding security officers and operators (e.g. Incident Response professionals) to recognize, identify, dynamically analyse, forecast, treat and respond to their threats and risks and handle their daily cyber incidents. The proposed solution provides a first of a kind approach for handling cyber security incidents in the digital environments with highly interconnected, complex and diverse nature.
SARDO: An Automated Search-and-Rescue Drone-based Solution for Victims Localization
Venue: arXiv cs.NI 2020
Year: 2020

Natural disasters affect millions of people every year. Finding missing persons in the shortest possible time is of crucial importance to reduce the death toll. This task is especially challenging when victims are sparsely distributed in large and/or difficult-to-reach areas and cellular networks are down. In this paper we present SARDO, a drone-based search and rescue solution that exploits the high penetration rate of mobile phones in the society to localize missing people. SARDO is an autonomous, all-in-one drone-based mobile network solution that does not require infrastructure support or mobile phones modifications. It builds on novel concepts such as pseudo-trilateration combined with machine-learning techniques to efficiently locate mobile phones in a given area. Our results, with a prototype implementation in a field-trial, show that SARDO rapidly determines the location of mobile phones (~3 min/UE) in a given area with an accuracy of few tens of meters and at a low battery consumption cost (~5%). State-of-the-art localization solutions for disaster scenarios rely either on mobile infrastructure support or exploit onboard cameras for human/computer vision, IR, thermal-based localization. To the best of our knowledge, SARDO is the first drone-based cellular search-and-rescue solution able to accurately localize missing victims through mobile phones.