OpenRaiser/Intern-Atlas huggingface dataset:
A Comprehensive Survey on Aspect Based Sentiment Analysis
Venue: arXiv cs.CL 2020
Year: 2020

Aspect Based Sentiment Analysis (ABSA) is the sub-field of Natural Language Processing that deals with essentially splitting our data into aspects ad finally extracting the sentiment information. ABSA is known to provide more information about the context than general sentiment analysis. In this study, our aim is to explore the various methodologies practiced while performing ABSA, and providing a comparative study. This survey paper discusses various solutions in-depth and gives a comparison between them. And is conveniently divided into sections to get a holistic view on the process.
SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices
Venue: arXiv cs.LG 2020
Year: 2020

A fundamental challenge for running machine learning algorithms on battery-powered devices is the time and energy limitations, as these devices have constraints on resources. There are resource-efficient classifier algorithms that can run on these devices, but their accuracy is often sacrificed for resource efficiency. Here, we propose an ultra-low power classifier, SEFR, with linear time complexity, both in the training and the testing phases. SEFR is comparable to state-of-the-art classifiers in terms of classification accuracy, but it is 63 times faster and 70 times more energy efficient than the average of state-of-the-art and baseline classifiers on binary class datasets. The energy and memory consumption of SEFR is very insignificant, and it can even perform both train and test phases on microcontrollers. To our knowledge, this is the first multipurpose classification algorithm specifically designed to perform both training and testing on ultra-low power devices.
What we talk about when we talk about data physicality
Venue: arXiv cs.HC 2020
Year: 2020

Data physicalizations "map data to physical form," yet many canonical examples are not based on data sets. To address this contradiction, I argue that the practice of physicalization forces us to rethink traditional notions of data. This paper proposes a conceptual framework to examine how physicalizations relate to data. This paper develops a two-dimensional conceptual space for comparing different perspectives on data used in physicalization, drawing from design theory and critical data studies literature. One axis distinguishes between epistemological and ontological perspectives, focusing on the relationship between data and the mind. The second axis distinguishes how data relate to the world, differentiating between representational and relational perspectives. To clarify the aesthetic and conceptual implications of these different perspectives, the paper discusses examples of data physicalization for each quadrant of the continuous space. It further uses the framework to examine the explicit and implicit assumptions about data in physicalization literature. As a theoretical paper, it encourages practitioners to think about how data relate to the manifestations and the phenomena they try to capture. It invites exploration of the relationship between data and the world as a generative source of creative tension.
The Dual Information Bottleneck
Venue: arXiv cs.IT 2020
Year: 2020

The Information Bottleneck (IB) framework is a general characterization of optimal representations obtained using a principled approach for balancing accuracy and complexity. Here we present a new framework, the Dual Information Bottleneck (dualIB), which resolves some of the known drawbacks of the IB. We provide a theoretical analysis of the dualIB framework; (i) solving for the structure of its solutions (ii) unraveling its superiority in optimizing the mean prediction error exponent and (iii) demonstrating its ability to preserve exponential forms of the original distribution. To approach large scale problems, we present a novel variational formulation of the dualIB for Deep Neural Networks. In experiments on several data-sets, we compare it to a variational form of the IB. This exposes superior Information Plane properties of the dualIB and its potential in improvement of the error.
RBF Solver for Quaternions Interpolation
Venue: arXiv cs.GR 2020
Year: 2020

In this paper we adapt the RBF Solver to work with quaternions by taking advantage of their Lie Algebra and exponential map. This will allow to work with quaternions as if they were normal vectors in R^3 and blend them in a very efficient way.
Distributed-Ledger-based Authentication with Decentralized Identifiers and Verifiable Credentials
Venue: arXiv cs.DC 2020
Year: 2020

Authentication with username and password is becoming an inconvenient process for the user. End users typically have little control over their personal privacy, and data breaches effecting millions of users have already happened several times. We have implemented a proof of concept decentralized OpenID Connect Provider by marrying it with Self-Sovereign Identity, which gives users the freedom to choose from a very large pool of identity providers instead of just a select few corporations, thus enabling the democratization of the highly centralized digital identity landscape. Furthermore, we propose a verifiable credential powered decentralized Public Key Infrastructure using distributed ledger technologies, which creates a straightforward and verifiable way for retrieving digital certificates.
Outlier Detection Using a Novel method: Quantum Clustering
Venue: arXiv cs.LG 2020
Year: 2020

We propose a new assumption in outlier detection: Normal data instances are commonly located in the area that there is hardly any fluctuation on data density, while outliers are often appeared in the area that there is violent fluctuation on data density. And based on this hypothesis, we apply a novel density-based approach to unsupervised outlier detection. This approach, called Quantum Clustering (QC), deals with unlabeled data processing and constructs a potential function to find the centroids of clusters and the outliers. The experiments show that the potential function could clearly find the hidden outliers in data points effectively. Besides, by using QC, we could find more subtle outliers by adjusting the parameter $σ$. Moreover, our approach is also evaluated on two datasets (Air Quality Detection and Darwin Correspondence Project) from two different research areas, and the results show the wide applicability of our method.
Developing Excel Thought Leadership
Venue: arXiv cs.CY 2020
Year: 2020

Over a period of five years, the Institute of Chartered Accountants in England and Wales (ICAEW) has developed a suite of three 'thought leadership' papers surrounding good practice in spreadsheet use and spreadsheet work environments. We will review the history of these three papers, the key lessons which each has to teach, and discuss how the process of making them has helped ICAEW to develop its position in the field.
How are your robot friends doing? A design exploration of graphical techniques supporting awareness of robot team members in teleoperation
Venue: arXiv cs.HC 2020
Year: 2020

While teleoperated robots continue to proliferate in domains including search and rescue, field exploration, or the military, human error remains a primary cause for accidents or mistakes. One challenge is that teleoperating a remote robot is cognitively taxing as the operator needs to understand the robot's state and monitor all its sensor data. In a multi-robot team, an operator needs to additionally monitor other robots' progress, states, notifications, errors, and so on to maintain team cohesion. One strategy for supporting the operator to comprehend this information is to improve teleoperation interface designs to carefully present data. We present a set of prototypes that simplify complex team robot states and actions, with an aim to help the operator to understand information from the robots easily and quickly. We conduct a series of pilot studies to explore a range of design parameters used in our prototypes (text, icon, facial expression, use of color, animation, and number of team robots), and develop a set of guidelines for graphically representing team robot states in the remote team teleoperation.
Procrustean Orthogonal Sparse Hashing
Venue: arXiv cs.LG 2020
Year: 2020

Hashing is one of the most popular methods for similarity search because of its speed and efficiency. Dense binary hashing is prevalent in the literature. Recently, insect olfaction was shown to be structurally and functionally analogous to sparse hashing [6]. Here, we prove that this biological mechanism is the solution to a well-posed optimization problem. Furthermore, we show that orthogonality increases the accuracy of sparse hashing. Next, we present a novel method, Procrustean Orthogonal Sparse Hashing (POSH), that unifies these findings, learning an orthogonal transform from training data compatible with the sparse hashing mechanism. We provide theoretical evidence of the shortcomings of Optimal Sparse Lifting (OSL) [22] and BioHash [30], two related olfaction-inspired methods, and propose two new methods, Binary OSL and SphericalHash, to address these deficiencies. We compare POSH, Binary OSL, and SphericalHash to several state-of-the-art hashing methods and provide empirical results for the superiority of the proposed methods across a wide range of standard benchmarks and parameter settings.
Novel Perception Algorithmic Framework For Object Identification and Tracking In Autonomous Navigation
Venue: arXiv cs.CV 2020
Year: 2020

This paper introduces a novel perception framework that has the ability to identify and track objects in autonomous vehicle's field of view. The proposed algorithms don't require any training for achieving this goal. The framework makes use of ego-vehicle's pose estimation and a KD-Tree-based segmentation algorithm to generate object clusters. In turn, using a VFH technique, the geometry of each identified object cluster is translated into a multi-modal PDF and a motion model is initiated with every new object cluster for the purpose of robust spatio-temporal tracking. The methodology further uses statistical properties of high-dimensional probability density functions and Bayesian motion model estimates to identify and track objects from frame to frame. The effectiveness of the methodology is tested on a KITTI dataset. The results show that the median tracking accuracy is around 91% with an end-to-end computational time of 153 milliseconds
A New Integer Programming Formulation of the Graphical Traveling Salesman Problem
Venue: arXiv cs.DM 2020
Year: 2020

In the Traveling Salesman Problem (TSP), a salesman wants to visit a set of cities and return home. There is a cost $c_{ij}$ of traveling from city $i$ to city $j$, which is the same in either direction for the Symmetric TSP. The objective is to visit each city exactly once, minimizing total travel costs. In the Graphical TSP, a city may be visited more than once, which may be necessary on a sparse graph. We present a new integer programming formulation for the Graphical TSP requiring only two classes of constraints that are either polynomial in number or polynomially separable, while addressing an open question proposed by Denis Naddef.
Balancing a CartPole System with Reinforcement Learning -- A Tutorial
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we provide the details of implementing various reinforcement learning (RL) algorithms for controlling a Cart-Pole system. In particular, we describe various RL concepts such as Q-learning, Deep Q Networks (DQN), Double DQN, Dueling networks, (prioritized) experience replay and show their effect on the learning performance. In the process, the readers will be introduced to OpenAI/Gym and Keras utilities used for implementing the above concepts. It is observed that DQN with PER provides best performance among all other architectures being able to solve the problem within 150 episodes.
AI Research Considerations for Human Existential Safety (ARCHES)
Venue: arXiv cs.CY 2020
Year: 2020

Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity's long-term prospects for survival as a species. In negative terms, we ask what existential risks humanity might face from AI development in the next century, and by what principles contemporary technical research might be directed to address those risks. A key property of hypothetical AI technologies is introduced, called \emph{prepotence}, which is useful for delineating a variety of potential existential risks from artificial intelligence, even as AI paradigms might shift. A set of \auxref{dirtot} contemporary research \directions are then examined for their potential benefit to existential safety. Each research direction is explained with a scenario-driven motivation, and examples of existing work from which to build. The research directions present their own risks and benefits to society that could occur at various scales of impact, and in particular are not guaranteed to benefit existential safety if major developments in them are deployed without adequate forethought and oversight. As such, each direction is accompanied by a consideration of potentially negative side effects.
A refined dynamic finite-strain shell theory for incompressible hyperelastic materials: equations and two-dimensional shell virtual work principle
Venue: arXiv cs.CE 2020
Year: 2020

Based on previous work for the static problem, in this paper we first derive one form of dynamic finite-strain shell equations for incompressible hyperelastic materials that involve three shell constitutive relations. In order to single out the bending effect as well as to reduce the number of shell constitutive relations, a further refinement is performed, which leads to a refined dynamic finite-strain shell theory with only two shell constitutive relations (deducible from the given three-dimensional (3D) strain energy function) and some new insights are also deduced. By using the weak formulation of the shell equations and the variation of the 3D Lagrange functional, boundary conditions and the two-dimensional (2D) shell virtual work principle are derived. As a benchmark problem, we consider the extension and inflation of an arterial segment. The good agreement between the asymptotic solution based on the shell equations and that from the 3D exact one gives verification of the former. The refined shell theory is also applied to study the plane-strain vibrations of a pressurized artery, and the effects of the axial pre-stretch, pressure and fibre angle on the vibration frequencies are investigated in detail.
Network visualizations with Pyvis and VisJS
Venue: arXiv cs.SI 2020
Year: 2020

Pyvis is a Python module that enables visualizing and interactively manipulating network graphs in the Jupyter notebook, or as a standalone web application. Pyvis is built on top of the powerful and mature VisJS JavaScript library, which allows for fast and responsive interactions while also abstracting away the low-level JavaScript and HTML. This means that elements of the rendered graph visualization, such as node/edge attributes can be specified within Python and shipped to the JavaScript layer for VisJS to render. This declarative approach makes it easy to quickly explore graph visualizations and investigate data relationships. In addition, Pyvis is highly customizable so that colors, sizes, and hover tooltips can be assigned to the rendered graph. The network graph layout is controlled by a front-end physics engine that is configurable from a Python interface, allowing for the detailed placement of the graph elements. In this paper, we outline use cases for Pyvis with specific examples to highlight key features for any analysis workflow. A brief overview of Pyvis' implementation describes how the Python front-end binding uses simple Pyvis calls.
Thinking Taxonomically about Fake Accounts: Classification, False Dichotomies, and the Need for Nuance
Venue: arXiv cs.CY 2020
Year: 2020

It is often said that war creates a fog in which it becomes difficult to discern friend from foe on the battlefield. In the ongoing war on fake accounts, conscious development of taxonomies of the phenomenon has yet to occur, resulting in much confusion on the digital battlefield about what exactly a fake account is. This paper intends to address this problem, not by proposing a taxonomy of fake accounts, but by proposing a systematic way to think taxonomically about the phenomenon. Specifically, we examine fake accounts through both a combined philosophical and computer science-based perspective. Through these lenses, we deconstruct narrow binary thinking about fake accounts, both in the form of general false dichotomies and specifically in relation to the Facebook's conceptual framework "Coordinated Inauthentic Behavior" (CIB). We then address the false dichotomies by constructing a more complex way of thinking taxonomically about fake accounts.
Summarising Big Data: Common GitHub Dataset for Software Engineering Challenges
Venue: arXiv cs.SE 2020
Year: 2020

In open-source software development environments; textual, numerical and relationship-based data generated are of interest to researchers. Various data sets are available for this data, which is frequently used in areas such as software engineering and natural language processing. However, since these data sets contain all the data in the environment, the problem arises in the terabytes of data processing. For this reason, almost all of the studies using GitHub data use filtered data according to certain criteria. In this context, using a different data set in each study makes a comparison of the accuracy of the studies quite difficult. In order to solve this problem, a common dataset was created and shared with the researchers, which would allow us to work on many software engineering problems.
Architectural Blueprints: The 4+1 View Model of Software Architecture
Venue: arXiv cs.SE 2020
Year: 2020

This article presents a model for describing the architecture of software-intensive systems, based on the use of multiple, concurrent views. This use of multiple views allows to address separately the concerns of the various stakeholders of the architecture: end-user, developers, systems engineers, project managers, etc., and to handle separately the functional and non functional requirements. Each of the five views is described, together with a notation to capture it. The views are designed using an architecture-centered, scenario-driven, iterative development process.
The Structure of U.S. College Networks on Facebook
Venue: arXiv cs.SI 2020
Year: 2020

Anecdotally, social connections made in university have life-long impact. Yet knowledge of social networks formed in college remains episodic, due in large part to the difficulty and expense involved in collecting a suitable dataset for comprehensive analysis. To advance and systematize insight into college social networks, we describe a dataset of the largest online social network platform used by college students in the United States. We combine de-identified and aggregated Facebook data with College Scorecard data, campus-level information provided by U.S. Department of Education, to produce a dataset covering the 2008-2015 entry year cohorts for 1,159 U.S. colleges and universities, spanning 7.6 million students. To perform the difficult task of comparing these networks of different sizes we develop a new methodology. We compute features over sampled ego-graphs, train binary classifiers for every pair of graphs, and operationalize distance between graphs as predictive accuracy. Social networks of different year cohorts at the same school are structurally more similar to one another than to cohorts at other schools. Networks from similar schools have similar structures, with the public/private and graduation rate dimensions being the most distinguishable. We also relate school types to specific outcomes. For example, students at private schools have larger networks that are more clustered and with higher homophily by year. Our findings may help illuminate the role that colleges play in shaping social networks which partly persist throughout people's lives.
Universal Vector Neural Machine Translation With Effective Attention
Venue: arXiv cs.CL 2020
Year: 2020

Neural Machine Translation (NMT) leverages one or more trained neural networks for the translation of phrases. Sutskever introduced a sequence to sequence based encoder-decoder model which became the standard for NMT based systems. Attention mechanisms were later introduced to address the issues with the translation of long sentences and improving overall accuracy. In this paper, we propose a singular model for Neural Machine Translation based on encoder-decoder models. Most translation models are trained as one model for one translation. We introduce a neutral/universal model representation that can be used to predict more than one language depending on the source and a provided target. Secondly, we introduce an attention model by adding an overall learning vector to the multiplicative model. With these two changes, by using the novel universal model the number of models needed for multiple language translation applications are reduced.
Can Synthetic Data Improve Object Detection Results for Remote Sensing Images?
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually. In this letter, we propose the use of realistic synthetic data with a wide distribution to improve the performance of remote sensing image aircraft detection. Specifically, to increase the variability of synthetic data, we randomly set the parameters during rendering, such as the size of the instance and the class of background images. In order to make the synthetic images more realistic, we then refine the synthetic images at the pixel level using CycleGAN with real unlabeled images. We also fine-tune the model with a small amount of real data, to obtain a higher accuracy. Experiments on NWPU VHR-10, UCAS-AOD and DIOR datasets demonstrate that the proposed method can be applied for augmenting insufficient real data.
Reproducible and Portable Workflows for Scientific Computing and HPC in the Cloud
Venue: arXiv cs.DC 2020
Year: 2020

The increasing availability of cloud computing services for science has changed the way scientific code can be developed, deployed, and run. Many modern scientific workflows are capable of running on cloud computing resources. Consequently, there is an increasing interest in the scientific computing community in methods, tools, and implementations that enable moving an application to the cloud and simplifying the process, and decreasing the time to meaningful scientific results. In this paper, we have applied the concepts of containerization for portability and multi-cloud automated deployment with industry-standard tools to three scientific workflows. We show how our implementations provide reduced complexity to portability of both the applications themselves, and their deployment across private and public clouds. Each application has been packaged in a Docker container with its dependencies and necessary environment setup for production runs. Terraform and Ansible have been used to automate the provisioning of compute resources and the deployment of each scientific application in a Multi-VM cluster. Each application has been deployed on the AWS and Aristotle Cloud Federation platforms. Variation in data management constraints, Multi-VM MPI communication, and embarrassingly parallel instance deployments were all explored and reported on. We thus present a sample of scientific workflows that can be simplified using the tools and our proposed implementation to deploy and run in a variety of cloud environments.