OpenRaiser/Intern-Atlas huggingface dataset:
Data Collection and Utilization Framework for Edge AI Applications
Venue: arXiv cs.LG 2021
Year: 2021

As data being produced by IoT applications continues to explode, there is a growing need to bring computing power closer to the source of the data to meet the response time, power dissipation and cost goals of performance-critical applications in various domains like the Industrial Internet of Things (IIoT), Automated Driving, Medical Imaging or Surveillance among others. This paper proposes a data collection and utilization framework that allows runtime platform and application data to be sent to an edge and cloud system via data collection agents running close to the platform. Agents are connected to a cloud system able to train AI models to improve overall energy efficiency of an AI application executed on an edge platform. In the implementation part, we show the benefits of FPGA-based platform for the task of object detection. Furthermore, we show that it is feasible to collect relevant data from an FPGA platform, transmit the data to a cloud system for processing and receiving feedback actions to execute an edge AI application energy efficiently. As future work, we foresee the possibility to train, deploy and continuously improve a base model able to efficiently adapt the execution of edge applications.
Implicit energy regularization of neural ordinary-differential-equation control
Venue: arXiv cs.LG 2021
Year: 2021

Although optimal control problems of dynamical systems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and computationally intractable. In this Letter we present a versatile neural ordinary-differential-equation control (NODEC) framework with implicit energy regularization and use it to obtain neural-network-generated control signals that can steer dynamical systems towards a desired target state within a predefined amount of time. We demonstrate the ability of NODEC to learn control signals that closely resemble those found by corresponding optimal control frameworks in terms of control energy and deviation from the desired target state. Our results suggest that NODEC is capable to solve a wide range of control and optimization problems, including those that are analytically intractable.
SOFOS: Demonstrating the Challenges of Materialized View Selection on Knowledge Graphs
Venue: arXiv cs.DB 2021
Year: 2021

Analytical queries over RDF data are becoming prominent as a result of the proliferation of knowledge graphs. Yet, RDF databases are not optimized to perform such queries efficiently, leading to long processing times. A well known technique to improve the performance of analytical queries is to exploit materialized views. Although popular in relational databases, view materialization for RDF and SPARQL has not yet transitioned into practice, due to the non-trivial application to the RDF graph model. Motivated by a lack of understanding of the impact of view materialization alternatives for RDF data, we demonstrate SOFOS, a system that implements and compares several cost models for view materialization. SOFOS is, to the best of our knowledge, the first attempt to adapt cost models, initially studied in relational data, to the generic RDF setting, and to propose new ones, analyzing their pitfalls and merits. SOFOS takes an RDF dataset and an analytical query for some facet in the data, and compares and evaluates alternative cost models, displaying statistics and insights about time, memory consumption, and query characteristics.
PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data
Venue: arXiv cs.CV 2021
Year: 2021

Computational Fluid Dynamics (CFD) simulations are a very important tool for many industrial applications, such as aerodynamic optimization of engineering designs like cars shapes, airplanes parts etc. The output of such simulations, in particular the calculated flow fields, are usually very complex and hard to interpret for realistic three-dimensional real-world applications, especially if time-dependent simulations are investigated. Automated data analysis methods are warranted but a non-trivial obstacle is given by the very large dimensionality of the data. A flow field typically consists of six measurement values for each point of the computational grid in 3D space and time (velocity vector values, turbulent kinetic energy, pressure and viscosity). In this paper we address the task of extracting meaningful results in an automated manner from such high dimensional data sets. We propose deep learning methods which are capable of processing such data and which can be trained to solve relevant tasks on simulation data, i.e. predicting drag and lift forces applied on an airfoil. We also propose an adaptation of the classical hand crafted features known from computer vision to address the same problem and compare a large variety of descriptors and detectors. Finally, we compile a large dataset of 2D simulations of the flow field around airfoils which contains 16000 flow fields with which we tested and compared approaches. Our results show that the deep learning-based methods, as well as hand crafted feature based approaches, are well-capable to accurately describe the content of the CFD simulation output on the proposed dataset.
ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity
Venue: arXiv cs.CE 2021
Year: 2021

Mechanical modelling of poroelastic media under finite strain is usually carried out via phenomenological models neglecting complex micro-macro scales interdependency. One reason is that the mathematical two-scale analysis is only straightforward assuming infinitesimal strain theory. Exploiting the potential of ANNs for fast and reliable upscaling and localisation procedures, we propose an incremental numerical approach that considers rearrangement of the cell properties based on its current deformation, which leads to the remodelling of the macroscopic model after each time increment. This computational framework is valid for finite strain and large deformation problems while it ensures infinitesimal strain increments within time steps. The full effects of the interdependency between the properties and response of macro and micro scales are considered for the first time providing more accurate predictive analysis of fluid-saturated porous media which is studied via a numerical consolidation example. Furthermore, the (nonlinear) deviation from Darcy's law is captured in fluid filtration numerical analyses. Finally, the brain tissue mechanical response under uniaxial cyclic test is simulated and studied.
Interface to Query and Visualise Definitions from a Knowledge Base
Venue: arXiv cs.HC 2021
Year: 2021

The semantic linked data model is at the core of the Web due to its ability to model real world entities, connect them via relationships and provide context, which could help to transform data into information and information into knowledge. Linked Data, in the form of ontologies and knowledge graphs could be stored locally or could be made available to everyone online. For example, the DBpedia knowledge base, which provides global and unified access to knowledge graphs is open access. However, both access and usage of Linked Data require individuals to have expert knowledge in the field of the Semantic Web. Many of the existing solutions that are powered by Linked Data are developed for specific use cases such as building and exploring ontologies visually and are aimed at researchers with knowledge of semantic technology. The solutions that are aimed at non-experts are generic and, in most cases, information visualisation is not available. Instead, information is presented in textual format, which does not ease cognitive processes such as comprehension and could lead to problems such as information overload. In this paper, we present a web application with a user interface (UI), which combines features from applications for both experts and non-experts. The UI allows individuals with no previous knowledge of the Semantic Web to query the DBpedia knowledge base for definitions of a specific word and to view a graphical visualisation of the query results (the search keyword itself and concepts related to it).
Towards Determining the Effect of Age and Educational Level on Cyber-Hygiene
Venue: arXiv cs.CY 2021
Year: 2021

As internet related challenges increase such as cyber-attacks, the need for safe practises among users to maintain computer system's health and online security have become imperative, and this is known as cyber-hygiene. Poor cyber-hygiene among internet users is a very critical issue undermining the general acceptance and adoption of internet technology. It has become a global issue and concern in this digital era when virtually all business transactions, learning, communication and many other activities are performed online. Virus attack, poor authentication technique, improper file backups and the use of different social engineering approaches by cyber-attackers to deceive internet users into divulging their confidential information with the intention to attack them have serious negative implications on the industries and organisations, including educational institutions. Moreover, risks associated with these ugly phenomena are likely to be more in developing countries such as Nigeria. Thus, authors of this paper undertook an online pilot study among students and employees of University of Nigeria, Nsukka and a total of 145 responses were received and used for the study. The survey seeks to find out the effect of age and level of education on the cyber hygiene knowledge and behaviour of the respondents, and in addition, the type of devices used and activities they engage in while on the internet. Our findings show wide adoption of internet in institution of higher learning, whereas, significant number of the internet users do not have good cyber hygiene knowledge and behaviour. Hence, our findings can instigate an organised training for students and employees of higher institutions in Nigeria.
Evaluation of Morphological Embeddings for the Russian Language
Venue: arXiv cs.CL 2021
Year: 2021

A number of morphology-based word embedding models were introduced in recent years. However, their evaluation was mostly limited to English, which is known to be a morphologically simple language. In this paper, we explore whether and to what extent incorporating morphology into word embeddings improves performance on downstream NLP tasks, in the case of morphologically rich Russian language. NLP tasks of our choice are POS tagging, Chunking, and NER -- for Russian language, all can be mostly solved using only morphology without understanding the semantics of words. Our experiments show that morphology-based embeddings trained with Skipgram objective do not outperform existing embedding model -- FastText. Moreover, a more complex, but morphology unaware model, BERT, allows to achieve significantly greater performance on the tasks that presumably require understanding of a word's morphology.
Hiding solutions in model RB: Forced instances are almost as hard as unforced ones
Venue: arXiv cs.CC 2021
Year: 2021

In this paper we study the forced instance spaces of model RB, where one or two arbitrary satisfying assignments have been imposed. We prove rigorously that the expected number of solutions of forced RB instances is asymptotically the same with those of unforced ones. Moreover, the distribution of forced RB instances in the corresponding forced instance space is asymptotically the same with that of unforced RB instances in the unforced instance space. These results imply that the hidden assignments will not lead to easily solvable formulas, and the hardness of solving forced RB instances will be the same with unforced RB instances.
Firms' Challenges and Social Responsibilities during Covid-19: a Twitter Analysis
Venue: arXiv cs.SI 2021
Year: 2021

The Covid-19 pandemic caused disruptive effects for individuals, firms, and societies. In this paper, we offer insights on the major issues and challenges firms are facing in the Covid-19 pandemic, as well as their concerns for Corporate Social Responsibility (CSR) themes. To do so, we investigate large Italian firms' discussion on Twitter in the first nine months of the pandemic. We downloaded all Twitter posts from 1st of March, 2020, to 17th of November, 2020 by the accounts of the largest Italian firms, i.e. those with 250 or more employees. We then built the bipartite network of accounts and hashtags and, using an entropy-based null model as a benchmark, we projected the information contained in the network into the accounts layers, identifying a network of accounts in which a link indicates a non trivial similarity in terms of their usage of hashtags. We find that the conversation is focused around 13 communities, 10 of which include Covid-19 themes. The core of the network is formed of 5 communities, which deal with environmental sustainability, digital innovation and safety. Firms' ownership type does not seem to influence the conversation. 10 communities out of 13 mention hashtags related to CSR, with the environmental and social dimensions as the prevalent ones. Interestingly enough, the social dimension seems more relevant in the communities dealing with digital innovation and safety. However, the relevance of CSR hashtags is very small at the single message level, but with some peculiarities arising in specific communities. Overall, our paper highlights the role of network methods on Twitter data as a tool which can support managers and policy makers to design their strategies and decision making, capturing firms' emerging issues and relevant themes.
Ordered Semiautomatic Rings with Applications to Geometry
Venue: arXiv cs.FL 2021
Year: 2021

The present work looks at semiautomatic rings with automatic addition and comparisons which are dense subrings of the real numbers and asks how these can be used to represent geometric objects such that certain operations and transformations are automatic. The underlying ring has always to be a countable dense subring of the real numbers and additions and comparisons and multiplications with constants need to be automatic. It is shown that the ring can be selected such that equilateral triangles can be represented and rotations by 30 degrees are possible, while the standard representation of the b-adic rationals does not allow this.
Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm
Venue: arXiv cs.CV 2021
Year: 2021

The COVID-19 virus has caused a global pandemic since March 2020. The World Health Organization (WHO) has provided guidelines on how to reduce the spread of the virus and one of the most important measures is social distancing. Maintaining a minimum of one meter distance from other people is strongly suggested to reduce the risk of infection. This has created a strong interest in monitoring the social distances either as a safety measure or to study how the measures have affected human behavior and country-wise differences in this. The need for automatic social distance estimation algorithms is evident, but there is no suitable test benchmark for such algorithms. Collecting images with measured ground-truth pair-wise distances between all the people using different camera settings is cumbersome. Furthermore, performance evaluation for social distance estimation algorithms is not straightforward and there is no widely accepted evaluation protocol. In this paper, we provide a dataset of varying images with measured pair-wise social distances under different camera positionings and focal length values. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate social distance estimation algorithms. We also propose a method for automatic social distance estimation. Our method takes advantage of object detection and human pose estimation. It can be applied on any single image as long as focal length and sensor size information are known. The results on our benchmark are encouraging with 92% human detection rate and only 28.9% average error in distance estimation among the detected people.
ESPBench: The Enterprise Stream Processing Benchmark
Venue: arXiv cs.PF 2021
Year: 2021

Growing data volumes and velocities in fields such as Industry 4.0 or the Internet of Things have led to the increased popularity of data stream processing systems. Enterprises can leverage these developments by enriching their core business data and analyses with up-to-date streaming data. Comparing streaming architectures for these complex use cases is challenging, as existing benchmarks do not cover them. ESPBench is a new enterprise stream processing benchmark that fills this gap. We present its architecture, the benchmarking process, and the query workload. We employ ESPBench on three state-of-the-art stream processing systems, Apache Spark, Apache Flink, and Hazelcast Jet, using provided query implementations developed with Apache Beam. Our results highlight the need for the provided ESPBench toolkit that supports benchmark execution, as it enables query result validation and objective latency measures.
Stochastic Package Queries in Probabilistic Databases
Venue: arXiv cs.DB 2021
Year: 2021

We provide methods for in-database support of decision making under uncertainty. Many important decision problems correspond to selecting a package (bag of tuples in a relational database) that jointly satisfy a set of constraints while minimizing some overall cost function; in most real-world problems, the data is uncertain. We provide methods for specifying -- via a SQL extension -- and processing stochastic package queries (SPQs), in order to solve optimization problems over uncertain data, right where the data resides. Prior work in stochastic programming uses Monte Carlo methods where the original stochastic optimization problem is approximated by a large deterministic optimization problem that incorporates many scenarios, i.e., sample realizations of the uncertain data values. For large database tables, however, a huge number of scenarios is required, leading to poor performance and, often, failure of the solver software. We therefore provide a novel SummarySearch algorithm that, instead of trying to solve a large deterministic problem, seamlessly approximates it via a sequence of smaller problems defined over carefully crafted summaries of the scenarios that accelerate convergence to a feasible and near-optimal solution. Experimental results on our prototype system show that SummarySearch can be orders of magnitude faster than prior methods at finding feasible and high-quality packages.
BODAME: Bilevel Optimization for Defense Against Model Extraction
Venue: arXiv cs.LG 2021
Year: 2021

Model extraction attacks have become serious issues for service providers using machine learning. We consider an adversarial setting to prevent model extraction under the assumption that attackers will make their best guess on the service provider's model using query accesses, and propose to build a surrogate model that significantly keeps away the predictions of the attacker's model from those of the true model. We formulate the problem as a non-convex constrained bilevel optimization problem and show that for kernel models, it can be transformed into a non-convex 1-quadratically constrained quadratic program with a polynomial-time algorithm to find the global optimum. Moreover, we give a tractable transformation and an algorithm for more complicated models that are learned by using stochastic gradient descent-based algorithms. Numerical experiments show that the surrogate model performs well compared with existing defense models when the difference between the attacker's and service provider's distributions is large. We also empirically confirm the generalization ability of the surrogate model.
Adapting User Interfaces with Model-based Reinforcement Learning
Venue: arXiv cs.HC 2021
Year: 2021

Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user -- for example, due to surprise or relearning effort -- or "trap" the process to a suboptimal design immaturely. However, effects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It finds beneficial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning method plans sequences of adaptations and consults predictive HCI models to estimate their effects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy.
On Medical Device Cybersecurity Compliance in EU
Venue: arXiv cs.CR 2021
Year: 2021

The medical device products at the European Union market must be safe and effective. To ensure this, medical device manufacturers must comply to the new regulatory requirements brought by the Medical Device Regulation (MDR) and the In Vitro Diagnostic Medical Device Regulation (IVDR). In general, the new regulations increase regulatory requirements and oversight, especially for medical software, and this is also true for requirements related to cybersecurity, which are now explicitly addressed in the legislation. The significant legislation changes currently underway, combined with increased cybersecurity requirements, create unique challenges for manufacturers to comply with the regulatory framework. In this paper, we review the new cybersecurity requirements in the light of currently available guidance documents, and pinpoint four core concepts around which cybersecurity compliance can be built. We argue that these core concepts form a foundations for cybersecurity compliance in the European Union regulatory framework.
On Medical Device Software CE Compliance and Conformity Assessment
Venue: arXiv cs.CY 2021
Year: 2021

Manufacturing of medical devices is strictly controlled by authorities, and manufacturers must conform to the regulatory requirements of the region in which a medical device is being marketed for use. In general, these requirements make no difference between the physical device, embedded software running inside a physical device, or software that constitutes the device in itself. As a result, standalone software with intended medical use is considered to be a medical device. Consequently, its development must meet the same requirements as the physical medical device manufacturing. This practice creates a unique challenge for organizations developing medical software. In this paper, we pinpoint a number of regulatory requirement mismatches between physical medical devices and standalone medical device software. The view is based on experiences from industry, from the development of all-software medical devices as well as from defining the manufacturing process so that it meets the regulatory requirements.