OpenRaiser/Intern-Atlas huggingface dataset:
Should citations be field-normalized in evaluative bibliometrics? An empirical analysis based on propensity score matching
Venue: arXiv cs.DL 2020
Year: 2020

Field-normalization of citations is bibliometric standard. Despite the observed differences in citation counts between fields, the question remains how strong fields influence citation rates beyond the effect of attributes or factors possibly influencing citations (FICs). We considered several FICs such as number of pages and number of co-authors in this study. We wondered whether there is a separate field-effect besides other effects (e.g., from numbers of pages and co-authors). To find an answer on the question in this study, we applied inverse-probability of treatment weighting (IPW). Using Web of Science data (a sample of 308,231 articles), we investigated whether mean differences among subject categories in citation rates still remain, even if the subject categories are made comparable in the field-related attributes (e.g., comparable of co-authors, comparable number of pages) by IPW. In a diagnostic step of our statistical analyses, we considered propensity scores as covariates in regression analyses to examine whether the differences between the fields in FICs vanish. The results revealed that the differences did not completely vanish but were strongly reduced. We received similar results when we calculated mean value differences of the fields after IPW representing the causal or unconfounded field effects on citations. However, field differences in citation rates remain. The results point out that field-normalization seems to be a prerequisite for citation analysis and cannot be replaced by the consideration of any set of FICs in citation analyses.
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes
Venue: arXiv cs.LG 2020
Year: 2020

In recent years, the interest in leveraging quantum effects for enhancing machine learning tasks has significantly increased. Many algorithms speeding up supervised and unsupervised learning were established. The first framework in which ways to exploit quantum resources specifically for the broader context of reinforcement learning were found is projective simulation. Projective simulation presents an agent-based reinforcement learning approach designed in a manner which may support quantum walk-based speed-ups. Although classical variants of projective simulation have been benchmarked against common reinforcement learning algorithms, very few formal theoretical analyses have been provided for its performance in standard learning scenarios. In this paper, we provide a detailed formal discussion of the properties of this model. Specifically, we prove that one version of the projective simulation model, understood as a reinforcement learning approach, converges to optimal behavior in a large class of Markov decision processes. This proof shows that a physically-inspired approach to reinforcement learning can guarantee to converge.
Minus HELLO: HELLO Devoid Protocols for Energy Preservation in Mobile Ad Hoc Networks
Venue: arXiv cs.NI 2020
Year: 2020

In mobile ad-hoc networks, nodes have to transmit HELLO or Route Maintenance messages at regular intervals, and all nodes residing within its radio range, reply with an acknowledgment message informing their node identifier, current location, and radio-range. Regular transmitting these messages consume a significant amount of battery power in nodes, especially when the set of down-link neighbors does not change over time and the radio-range of the sender node is large. The present article focuses on this aspect and tries to eliminate the number of HELLO messages in existing state-of-art protocols. Also, it shortens radio-ranges of nodes whenever possible. Simulation results show that the average lifetime of nodes greatly increases in proposed Minus HELLO devoid routing protocols along with a great increase in network throughput. Also, the required number of route re-discovery reduces.
Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS
Venue: arXiv cs.LG 2020
Year: 2020

This paper presents a preliminary study comparing different observation and action space representations for Deep Reinforcement Learning (DRL) in the context of Real-time Strategy (RTS) games. Specifically, we compare two representations: (1) a global representation where the observation represents the whole game state, and the RL agent needs to choose which unit to issue actions to, and which actions to execute; and (2) a local representation where the observation is represented from the point of view of an individual unit, and the RL agent picks actions for each unit independently. We evaluate these representations in $μ$RTS showing that the local representation seems to outperform the global representation when training agents with the task of harvesting resources.
Silhouette: Efficient Protected Shadow Stacks for Embedded Systems
Venue: arXiv cs.CR 2020
Year: 2020

Microcontroller-based embedded systems are increasingly used for applications that can have serious and immediate consequences if compromised---including automobile control systems, smart locks, drones, and implantable medical devices. Due to resource and execution-time constraints, C is the primary language used for programming these devices. Unfortunately, C is neither type-safe nor memory-safe, and control-flow hijacking remains a prevalent threat. This paper presents Silhouette: a compiler-based defense that efficiently guarantees the integrity of return addresses, significantly reducing the attack surface for control-flow hijacking. Silhouette combines an incorruptible shadow stack for return addresses with checks on forward control flow and memory protection to ensure that all functions return to the correct dynamic caller. To protect its shadow stack, Silhouette uses store hardening, an efficient intra-address space isolation technique targeting various ARM architectures that leverages special store instructions found on ARM processors. We implemented Silhouette for the ARMv7-M architecture, but our techniques are applicable to other common embedded ARM architectures. Our evaluation shows that Silhouette incurs a geometric mean of 1.3% and 3.4% performance overhead on two benchmark suites. Furthermore, we prototyped Silhouette-Invert, an alternative implementation of Silhouette, which incurs just 0.3% and 1.9% performance overhead, at the cost of a minor hardware change.
Algorithmic Copywriting: Automated Generation of Health-Related Advertisements to Improve their Performance
Venue: arXiv cs.IR 2020
Year: 2020

Search advertising, a popular method for online marketing, has been employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and experimentation, which may not be available to health authorities wishing to elicit such changes, especially when dealing with public health crises such as epidemic outbreaks. Here we develop a framework, comprised of two neural networks models, that automatically generate ads. First, it employs a generator model, which create ads from web pages. It then employs a translation model, which transcribes ads to improve performance. We trained the networks using 114K health-related ads shown on Microsoft Advertising. We measure ads performance using the click-through rates (CTR). Our experiments show that the generated advertisements received approximately the same CTR as human-authored ads. The marginal contribution of the generator model was, on average, 28\% lower than that of human-authored ads, while the translator model received, on average, 32\% more clicks than human-authored ads. Our analysis shows that the translator model produces ads reflecting higher values of psychological attributes associated with a user action, including higher valance and arousal, and more calls-to-actions. In contrast, levels of these attributes in ads produced by the generator model are similar to those of human-authored ads. Our results demonstrate the ability to automatically generate useful advertisements for the health domain. We believe that our work offers health authorities an improved ability to nudge people towards healthier behaviors while saving the time and cost needed to build effective advertising campaigns.
Conflict and Cooperation: AI Research and Development in terms of the Economy of Conventions
Venue: arXiv cs.CY 2020
Year: 2020

Artificial Intelligence (AI) and its relation with societies is increasingly becoming an interesting object of study from the perspective of sociology and other disciplines. Theories such as the Economy of Conventions (EC) are usually applied in the context of interpersonal relations but there is still a clear lack of studies around how this and other theories can shed light on interactions between human an autonomous systems. This work is focused into studying a preliminary step that is a key enabler for the subsequent interaction between machines and humans: how the processes of researching, designing and developing AI related systems reflect different moral registers, represented by conventions within the EC. Having a better understanding of those conventions guiding the advances in AI is considered as the first and required advance to understand the conventions afterwards reflected by those autonomous systems in the interactions with societies. For this purpose, we develop an iterative tool based on active learning to label a data set from the field of AI and Machine Learning (ML) research and present preliminary results of a supervised classifier trained on these conventions. To further demonstrate the feasibility of the approach, the results are contrasted with a classifier trained on software conventions.
Extending Deep Knowledge Tracing: Inferring Interpretable Knowledge and Predicting Post-System Performance
Venue: arXiv cs.CY 2020
Year: 2020

Recent student knowledge modeling algorithms such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Networks (DKVMN) have been shown to produce accurate predictions of problem correctness within the same learning system. However, these algorithms do not attempt to directly infer student knowledge. In this paper we present an extension to these algorithms to also infer knowledge. We apply this extension to DKT and DKVMN, resulting in knowledge estimates that correlate better with a posttest than knowledge estimates from Bayesian Knowledge Tracing (BKT), an algorithm designed to infer knowledge, and another classic algorithm, Performance Factors Analysis (PFA). We also apply our extension to correctness predictions from BKT and PFA, finding that knowledge estimates produced with it correlate better with the posttest than BKT and PFA's standard knowledge estimates. These findings are significant since the primary aim of education is to prepare students for later experiences outside of the immediate learning activity.
Growing axons: greedy learning of neural networks with application to function approximation
Venue: arXiv cs.LG 2020
Year: 2020

We propose a new method for learning deep neural network models that is based on a greedy learning approach: we add one basis function at a time, and a new basis function is generated as a non-linear activation function applied to a linear combination of the previous basis functions. Such a method (growing deep neural network by one neuron at a time) allows us to compute much more accurate approximants for several model problems in function approximation.
Distinguishing numbers and distinguishing indices of oriented graphs
Venue: arXiv cs.DM 2020
Year: 2020

A distinguishing r-vertex-labelling (resp. r-edge-labelling) of an undirected graph G is a mapping $λ$ from the set of vertices (resp. the set of edges) of G to the set of labels {1,. .. , r} such that no non-trivial automorphism of G preserves all the vertex (resp. edge) labels. The distinguishing number D(G) and the distinguishing index D (G) of G are then the smallest r for which G admits a distinguishing r-vertex-labelling or r-edge-labelling, respectively. The distinguishing chromatic number D $χ$ (G) and the distinguishing chromatic index D $χ$ (G) are defined similarly, with the additional requirement that the corresponding labelling must be a proper colouring. These notions readily extend to oriented graphs, by considering arcs instead of edges. In this paper, we study the four corresponding parameters for oriented graphs whose underlying graph is a path, a cycle, a complete graph or a bipartite complete graph. In each case, we determine their minimum and maximum value, taken over all possible orientations of the corresponding underlying graph, except for the minimum values for unbalanced complete bipartite graphs K m,n with m = 2, 3 or 4 and n > 3, 6 or 13, respectively, or m $\ge$ 5 and n > 2 m -- m 2 , for which we only provide upper bounds.
Empirical Analysis of Session-Based Recommendation Algorithms
Venue: arXiv cs.IR 2020
Year: 2020

Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with the user in an ongoing session and do not require the existence of long-term preference profiles. Most recently, a number of deep learning based ("neural") approaches to session-based recommendations were proposed. However, previous research indicates that today's complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy. With this work, our goal is to shed light on the state-of-the-art in the area of session-based recommendation and on the progress that is made with neural approaches. For this purpose, we compare twelve algorithmic approaches, among them six recent neural methods, under identical conditions on various datasets. We find that the progress in terms of prediction accuracy that is achieved with neural methods is still limited. In most cases, our experiments show that simple heuristic methods based on nearest-neighbors schemes are preferable over conceptually and computationally more complex methods. Observations from a user study furthermore indicate that recommendations based on heuristic methods were also well accepted by the study participants. To support future progress and reproducibility in this area, we publicly share the session-rec evaluation framework that was used in our research.
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Venue: arXiv cs.LG 2020
Year: 2020

Active subspace is a model reduction method widely used in the uncertainty quantification community. In this paper, we propose analyzing the internal structure and vulnerability and deep neural networks using active subspace. Firstly, we employ the active subspace to measure the number of "active neurons" at each intermediate layer and reduce the number of neurons from several thousands to several dozens. This motivates us to change the network structure and to develop a new and more compact network, referred to as {ASNet}, that has significantly fewer model parameters. Secondly, we propose analyzing the vulnerability of a neural network using active subspace and finding an additive universal adversarial attack vector that can misclassify a dataset with a high probability. Our experiments on CIFAR-10 show that ASNet can achieve 23.98$\times$ parameter and 7.30$\times$ flops reduction. The universal active subspace attack vector can achieve around 20% higher attack ratio compared with the existing approach in all of our numerical experiments. The PyTorch codes for this paper are available online.
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Convolutional Neural Networks (CNNs) have become indispensable for solving machine learning tasks in speech recognition, computer vision, and other areas that involve high-dimensional data. A CNN filters the input feature using a network containing spatial convolution operators with compactly supported stencils. In practice, the input data and the hidden features consist of a large number of channels, which in most CNNs are fully coupled by the convolution operators. This coupling leads to immense computational cost in the training and prediction phase. In this paper, we introduce LeanConvNets that are derived by sparsifying fully-coupled operators in existing CNNs. Our goal is to improve the efficiency of CNNs by reducing the number of weights, floating point operations and latency times, with minimal loss of accuracy. Our lean convolution operators involve tuning parameters that controls the trade-off between the network's accuracy and computational costs. These convolutions can be used in a wide range of existing networks, and we exemplify their use in residual networks (ResNets). Using a range of benchmark problems from image classification and semantic segmentation, we demonstrate that the resulting LeanConvNet's accuracy is close to state-of-the-art networks while being computationally less expensive. In our tests, the lean versions of ResNet in most cases outperform comparable reduced architectures such as MobileNets and ShuffleNets.
The deal.II finite element library: design, features, and insights
Venue: arXiv cs.MS 2020
Year: 2020

deal.II is a state-of-the-art finite element library focused on generality, dimension-independent programming, parallelism, and extensibility. Herein, we outline its primary design considerations and its sophisticated features such as distributed meshes, $hp$-adaptivity, support for complex geometries, and matrix-free algorithms. But deal.II is more than just a software library: It is also a diverse and worldwide community of developers and users, as well as an educational platform. We therefore also discuss some of the technical and social challenges and lessons learned in running a large community software project over the course of two decades.
Semantic Object Accuracy for Generative Text-to-Image Synthesis
Venue: arXiv cs.CV 2020
Year: 2020

Generative adversarial networks conditioned on textual image descriptions are capable of generating realistic-looking images. However, current methods still struggle to generate images based on complex image captions from a heterogeneous domain. Furthermore, quantitatively evaluating these text-to-image models is challenging, as most evaluation metrics only judge image quality but not the conformity between the image and its caption. To address these challenges we introduce a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption. The SOA uses a pre-trained object detector to evaluate if a generated image contains objects that are mentioned in the image caption, e.g. whether an image generated from "a car driving down the street" contains a car. We perform a user study comparing several text-to-image models and show that our SOA metric ranks the models the same way as humans, whereas other metrics such as the Inception Score do not. Our evaluation also shows that models which explicitly model objects outperform models which only model global image characteristics.
Equipartitions with Wedges and Cones
Venue: arXiv cs.CG 2020
Year: 2020

A famous result about mass partitions is the so called \emph{Ham-Sandwich theorem}. It states that any $d$ mass distributions in $\mathbb{R}^d$ can be simultaneously bisected by a single hyperplane. In this work, we study two related questions. The first one is how many masses we can simultaneously partition with a $k$-fan, that is, $k$ half-hyperplanes in $\mathbb{R}^d$, emanating from a common $(d-2)$-dimensional apex. This question was extensively studied in the plane, but in higher dimensions the only known results are for the case where $k$ is an odd prime. We extend these results to a larger family of values of $k$. We further present a new result for $k=2$, which generalizes to cones. The second question considers bisections with double wedges or, equivalently, Ham-Sandwich cuts after projective transformations. Here we prove that given $d$ families of $d+1$ point sets each, there is always a projective transformation such that after the transformation, each family has a Ham-Sandwich cut. We further prove a result on partitions with parallel hyperplanes after a projective transformation.
Reproducing Scientific Experiment with Cloud DevOps
Venue: arXiv cs.DC 2020
Year: 2020

The reproducibility of scientific experiment is vital for the advancement of disciplines based on previous work. To achieve this goal, many researchers focus on complex methodology and self-invented tools which have difficulty in practical usage. In this article, we introduce the Cloud DevOps infrastructure from software engineering community and shows how it can be used effectively for heterogeneous agents to reproduce experiments for computer science related disciplines. DevOps can be enabled using freely available cloud computing machines for medium-sized experiment and self-hosted computing engines for large-scale computing, thus powering researchers to share their experiment result with others in a more reliable way.
Bounding Regression Errors in Data-driven Power Grid Steady-state Models
Venue: arXiv cs.LG 2020
Year: 2020

Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios, and proposes an evaluation implementation based on Rademacher complexity theory. We answer key questions for data-driven models: how much training data is required to guarantee a certain error bound, and how partial physical knowledge can be utilized to reduce the required amount of data. Our results are crucial for the evaluation and application of data-driven models in power grid analysis. We demonstrate the proposed method by finding generalization error bounds for two applications, i.e. branch flow linearization and external network equivalent under different degrees of physical knowledge. Results identify how the bounds decrease with additional power grid physical knowledge or more training data.