OpenRaiser/Intern-Atlas huggingface dataset:
A firefly algorithm for power management in wireless sensor networks (WSNs)
Venue: arXiv cs.NI 2021
Year: 2021

In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize the network topology. Therefore, choosing the right route in this type of network due to resource constraints and their operating environment is one of the most important challenges in these networks. Therefore, the main purpose of these networks is to collect appropriate routing information about the environment around the network sensors while observing the energy consumption of the sensors. One of the important approaches to reduce energy consumption in sensor networks is the use of the clustering technique, but in most clustering methods, only the criterion of the amount of energy of the cluster or the distance of members to the cluster has been considered. Therefore, in this paper, a method is presented using the firefly algorithm and using the four criteria of residual energy, noise rate, number of hops, and distance. The proposed method called EM-FIREFLY is introduced which selects the best cluster head with high attractiveness and based on the fitness function and transfers the data packets through these cluster head to the sink. The proposed method is evaluated with NS-2 simulator and compared with the algorithm-PSO and optimal clustering methods. The evaluation results show the efficiency of the EM-FIREFLY method in maximum relative load and network lifetime criteria compared to other methods discussed in this article.
Transfer Learning Approach for Arabic Offensive Language Detection System -- BERT-Based Model
Venue: arXiv cs.CL 2021
Year: 2021

Developing a system to detect online offensive language is very important to the health and the security of online users. Studies have shown that cyberhate, online harassment and other misuses of technology are on the rise, particularly during the global Coronavirus pandemic in 2020. According to the latest report by the Anti-Defamation League (ADL), 35% of online users reported online harassment related to their identity-based characteristics, which is a 3% increase over 2019. Applying advanced techniques from the Natural Language Processing (NLP) field to support the development of an online hate-free community is a critical task for social justice. Transfer learning enhances the performance of the classifier by allowing the transfer of knowledge from one domain or one dataset to others that have not been seen before, thus, supporting the classifier to be more generalizable. In our study, we apply the principles of transfer learning cross multiple Arabic offensive language datasets to compare the effects on system performance. This study aims at investigating the effects of fine-tuning and training Bidirectional Encoder Representations from Transformers (BERT) model on multiple Arabic offensive language datasets individually and testing it using other datasets individually. Our experiment starts with a comparison among multiple BERT models to guide the selection of the main model that is used for our study. The study also investigates the effects of concatenating all datasets to be used for fine-tuning and training BERT model. Our results demonstrate the limited effects of transfer learning on the performance of the classifiers, particularly for highly dialectic comments.
Speeding up Routing Schedules on Aisle-Graphs with Single Access
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we study the Orienteering Aisle-graphs Single-access Problem (OASP), a variant of the orienteering problem for a robot moving in a so-called single-access aisle-graph, i.e., a graph consisting of a set of rows that can be accessed from one side only. Aisle-graphs model, among others, vineyards or warehouses. Each aisle-graph vertex is associated with a reward that a robot obtains when visits the vertex itself. As the robot's energy is limited, only a subset of vertices can be visited with a fully charged battery. The objective is to maximize the total reward collected by the robot with a battery charge. We first propose an optimal algorithm that solves OASP in O(m^2 n^2) time for aisle-graphs with a single access consisting of m rows, each with n vertices. With the goal of designing faster solutions, we propose four greedy sub-optimal algorithms that run in at most O(mn (m+n)) time. For two of them, we guarantee an approximation ratio of 1/2(1-1/e), where e is the base of the natural logarithm, on the total reward by exploiting the well-known submodularity property. Experimentally, we show that these algorithms collect more than 80% of the optimal reward.
Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
Venue: arXiv cs.LG 2021
Year: 2021

Recently introduced EASE algorithm presents a simple and elegant way, how to solve the top-N recommendation task. In this paper, we introduce Neural EASE to further improve the performance of this algorithm by incorporating techniques for training modern neural networks. Also, there is a growing interest in the recsys community to utilize variational autoencoders (VAE) for this task. We introduce deep autoencoder FLVAE benefiting from multiple non-linear layers without an information bottleneck while not overfitting towards the identity. We show how to learn FLVAE in parallel with Neural EASE and achieve the state of the art performance on the MovieLens 20M dataset and competitive results on the Netflix Prize dataset.
Differentiable Implicit Soft-Body Physics
Venue: arXiv cs.LG 2021
Year: 2021

We present a differentiable soft-body physics simulator that can be composed with neural networks as a differentiable layer. In contrast to other differentiable physics approaches that use explicit forward models to define state transitions, we focus on implicit state transitions defined via function minimization. Implicit state transitions appear in implicit numerical integration methods, which offer the benefits of large time steps and excellent numerical stability, but require a special treatment to achieve differentiability due to the absence of an explicit differentiable forward pass. In contrast to other implicit differentiation approaches that require explicit formulas for the force function and the force Jacobian matrix, we present an energy-based approach that allows us to compute these derivatives automatically and in a matrix-free fashion via reverse-mode automatic differentiation. This allows for more flexibility and productivity when defining physical models and is particularly important in the context of neural network training, which often relies on reverse-mode automatic differentiation (backpropagation). We demonstrate the effectiveness of our differentiable simulator in policy optimization for locomotion tasks and show that it achieves better sample efficiency than model-free reinforcement learning.
Common Information Belief based Dynamic Programs for Stochastic Zero-sum Games with Competing Teams
Venue: arXiv cs.MA 2021
Year: 2021

Decentralized team problems where players have asymmetric information about the state of the underlying stochastic system have been actively studied, but \emph{games} between such teams are less understood. We consider a general model of zero-sum stochastic games between two competing teams. This model subsumes many previously considered team and zero-sum game models. For this general model, we provide bounds on the upper (min-max) and lower (max-min) values of the game. Furthermore, if the upper and lower values of the game are identical (i.e., if the game has a \emph{value}), our bounds coincide with the value of the game. Our bounds are obtained using two dynamic programs based on a sufficient statistic known as the common information belief (CIB). We also identify certain information structures in which only the minimizing team controls the evolution of the CIB. In these cases, we show that one of our CIB based dynamic programs can be used to find the min-max strategy (in addition to the min-max value). We propose an approximate dynamic programming approach for computing the values (and the strategy when applicable) and illustrate our results with the help of an example.
Near-field Tracking with Large Antenna Arrays: Fundamental Limits and Practical Algorithms
Venue: arXiv cs.IT 2021
Year: 2021

Applications towards 6G have brought a huge interest towards arrays with a high number of antennas and operating within the millimeter and sub-THz bandwidths for joint communication and localization. With such large arrays, the plane wave approximation is often not accurate because the system may operate in the near-field propagation region (Fresnel region) where the electromagnetic field wavefront is spherical. In this case, the curvature of arrival (CoA) is a measure of the spherical wavefront that can be used to infer the source position using only a single large array. In this paper, we study a near-field tracking problem for inferring the state (i.e., the position and velocity) of a moving source with an ad-hoc observation model that accounts for the phase profile of a large receiving array. For this tracking problem, we derive the posterior Cramér-Rao Lower Bound (P-CRLB) and show the effects when the source moves inside and outside the Fresnel region. We provide insights on how the loss of positioning information outside Fresnel comes from an increase of the ranging error rather than from inaccuracies of angular estimation. Then, we investigate the performance of different Bayesian tracking algorithms in the presence of model mismatches and abrupt trajectory changes. Our results demonstrate the feasibility and high accuracy for most of the tracking approaches without the need of wideband signals and of any synchronization scheme. signals and of any synchronization scheme.
Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches
Venue: arXiv cs.CV 2021
Year: 2021

Automated driving has become a major topic of interest not only in the active research community but also in mainstream media reports. Visual perception of such intelligent vehicles has experienced large progress in the last decade thanks to advances in deep learning techniques but some challenges still remain. One such challenge is the detection of corner cases. They are unexpected and unknown situations that occur while driving. Conventional visual perception methods are often not able to detect them because corner cases have not been witnessed during training. Hence, their detection is highly safety-critical, and detection methods can be applied to vast amounts of collected data to select suitable training data. A reliable detection of corner cases will not only further automate the data selection procedure and increase safety in autonomous driving but can thereby also affect the public acceptance of the new technology in a positive manner. In this work, we continue a previous systematization of corner cases on different levels by an extended set of examples for each level. Moreover, we group detection approaches into different categories and link them with the corner case levels. Hence, we give directions to showcase specific corner cases and basic guidelines on how to technically detect them.
B/Surf: Interactive Bézier Splines on Surfaces
Venue: arXiv cs.GR 2021
Year: 2021

Bézier curves provide the basic building blocks of graphic design in 2D. In this paper, we port Bézier curves to manifolds. We support the interactive drawing and editing of Bézier splines on manifold meshes with millions of triangles, by relying on just repeated manifold averages. We show that direct extensions of the De Casteljau and Bernstein evaluation algorithms to the manifold setting are fragile, and prone to discontinuities when control polygons become large. Conversely, approaches based on subdivision are robust and can be implemented efficiently. We define Bézier curves on manifolds, by extending both the recursive De Casteljau bisection and a new open-uniform Lane-Riesenfeld subdivision scheme, which provide curves with different degrees of smoothness. For both schemes, we present algorithms for curve tracing, point evaluation, and point insertion. We test our algorithms for robustness and performance on all watertight, manifold, models from the Thingi10k repository, without any pre-processing and with random control points. For interactive editing, we port all the basic user interface interactions found in 2D tools directly to the mesh. We also support mapping complex SVG drawings to the mesh and their interactive editing.
Recognizability of languages via deterministic finite automata with values on a monoid: General Myhill-Nerode Theorem
Venue: arXiv cs.FL 2021
Year: 2021

This paper deals with the problem of recognizability of functions l: Sigma* --> M that map words to values in the support set M of a monoid (M,.,1). These functions are called M-languages. M-languages are studied from the aspect of their recognition by deterministic finite automata whose components take values on M (M-DFAs). The characterization of an M-language l is based on providing a right congruence on Sigma* that is defined through l and a factorization on the set of all M-languages, L(Sigma*,M) (in sort L). A factorization on L is a pair of functions (g,f) such that, for each l in L, g(l). f(l)= l, where g(l) in M and f(l) in L. In essence, a factorization is a form of common factor extraction. A general Myhill-Nerode theorem, which is valid for any L(Sigma*, M), is provided. Basically, l is recognized by an M-DFA if and only if there exists a factorization on L, (g,f), such that the right congruence on Sigma* induced by the factorization (g,f) and f(l), has finite index. This paper shows that the existence of M-DFAs guarantees the existence of natural non-trivial factorizations on L without taking account any additional property on the monoid.
Transparent FPGA Acceleration with TensorFlow
Venue: arXiv cs.AR 2021
Year: 2021

Today, artificial neural networks are one of the major innovators pushing the progress of machine learning. This has particularly affected the development of neural network accelerating hardware. However, since most of these architectures require specialized toolchains, there is a certain amount of additional effort for developers each time they want to make use of a new deep learning accelerator. Furthermore the flexibility of the device is bound to the architecture itself, as well as to the functionality of the runtime environment. In this paper we propose a toolflow using TensorFlow as frontend, thus offering developers the opportunity of using a familiar environment. On the backend we use an FPGA, which is addressable via an HSA runtime environment. In this way we are able to hide the complexity of controlling new hardware from the user, while at the same time maintaining a high amount of flexibility. This can be achieved by our HSA toolflow, since the hardware is not statically configured with the structure of the network. Instead, it can be dynamically reconfigured during runtime with the respective kernels executed by the network and simultaneously from other sources e.g. OpenCL/OpenMP.
Reinforcement Learning For Constraint Satisfaction Game Agents (15-Puzzle, Minesweeper, 2048, and Sudoku)
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, reinforcement learning has seen interest because of deep Q-Learning, where the model is a convolutional neural network. Deep Q-Learning has shown promising results in games such as Atari and AlphaGo. Instead of learning the entire Q-table, it learns an estimate of the Q function that determines a state's policy action. We use Q-Learning and deep Q-learning, to learn control policies of four constraint satisfaction games (15-Puzzle, Minesweeper, 2048, and Sudoku). 15-Puzzle is a sliding permutation puzzle and provides a challenge in addressing its large state space. Minesweeper and Sudoku involve partially observable states and guessing. 2048 is also a sliding puzzle but allows for easier state representation (compared to 15-Puzzle) and uses interesting reward shaping to solve the game. These games offer unique insights into the potential and limits of reinforcement learning. The Q agent is trained with no rules of the game, with only the reward corresponding to each state's action. Our unique contribution is in choosing the reward structure, state representation, and formulation of the deep neural network. For low shuffle, 15-Puzzle, achieves a 100% win rate, the medium and high shuffle achieve about 43% and 22% win rates respectively. On a standard 16x16 Minesweeper board, both low and high-density boards achieve close to 45% win rate, whereas medium density boards have a low win rate of 15%. For 2048, the 1024 win rate was achieved with significant ease (100%) with high win rates for 2048, 4096, 8192 and 16384 as 40%, 0.05%, 0.01% and 0.004% , respectively. The easy Sudoku games had a win rate of 7%, while medium and hard games had 2.1% and 1.2% win rates, respectively. This paper explores the environment complexity and behavior of a subset of constraint games using reward structures which can get us closer to understanding how humans learn.
Analysing the Requirements for an Open Research Knowledge Graph: Use Cases, Quality Requirements and Construction Strategies
Venue: arXiv cs.DL 2021
Year: 2021

Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KG) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective and present a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting and reviewing daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications, and outline possible solutions.
The Barrier of meaning in archaeological data science
Venue: arXiv cs.CV 2021
Year: 2021

Archaeologists, like other scientists, are experiencing a data-flood in their discipline, fueled by a surge in computing power and devices that enable the creation, collection, storage and transfer of an increasingly complex (and large) amount of data, such as remotely sensed imagery from a multitude of sources. In this paper, we pose the preliminary question if this increasing availability of information actually needs new computerized techniques, and Artificial Intelligence methods, to make new and deeper understanding into archaeological problems. Simply said, while it is a fact that Deep Learning (DL) has become prevalent as a type of machine learning design inspired by the way humans learn, and utilized to perform automatic actions people might describe as intelligent, we want to anticipate, here, a discussion around the subject whether machines, trained following this procedure, can extrapolate, from archaeological data, concepts and meaning in the same way that humans would do. Even prior to getting to technical results, we will start our reflection with a very basic concept: Is a collection of satellite images with notable archaeological sites informative enough to instruct a DL machine to discover new archaeological sites, as well as other potential locations of interest? Further, what if similar results could be reached with less intelligent machines that learn by having people manually program them with rules? Finally: If with barrier of meaning we refer to the extent to which human-like understanding can be achieved by a machine, where should be posed that barrier in the archaeological data science?
Feature Selection for Multivariate Time Series via Network Pruning
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, there has been an ever increasing amount of multivariate time series (MTS) data in various domains, typically generated by a large family of sensors such as wearable devices. This has led to the development of novel learning methods on MTS data, with deep learning models dominating the most recent advancements. Prior literature has primarily focused on designing new network architectures for modeling temporal dependencies within MTS. However, a less studied challenge is associated with high dimensionality of MTS data. In this paper, we propose a novel neural component, namely Neural Feature Selector (NFS), as an end-2-end solution for feature selection in MTS data. Specifically, NFS is based on decomposed convolution design and includes two modules: firstly each feature stream (a stream corresponds to an univariate series of MTS) within MTS is processed by a temporal CNN independently; then an aggregating CNN combines the processed streams to produce input for other downstream networks. We evaluated the proposed NFS model on four real-world MTS datasets and found that it achieves comparable results with state-of-the-art methods while providing the benefit of feature selection. Our paper also highlights the robustness and effectiveness of feature selection with NFS compared to using recent autoencoder-based methods.
Feature Analyses and Modelling of Lithium-ion Batteries Manufacturing based on Random Forest Classification
Venue: arXiv cs.LG 2021
Year: 2021

Lithium-ion battery manufacturing is a highly complicated process with strongly coupled feature interdependencies, a feasible solution that can analyse feature variables within manufacturing chain and achieve reliable classification is thus urgently needed. This article proposes a random forest (RF)-based classification framework, through using the out of bag (OOB) predictions, Gini changes as well as predictive measure of association (PMOA), for effectively quantifying the importance and correlations of battery manufacturing features and their effects on the classification of electrode properties. Battery manufacturing data containing three intermediate product features from the mixing stage and one product parameter from the coating stage are analysed by the designed RF framework to investigate their effects on both the battery electrode active material mass load and porosity. Illustrative results demonstrate that the proposed RF framework not only achieves the reliable classification of electrode properties but also leads to the effective quantification of both manufacturing feature importance and correlations. This is the first time to design a systematic RF framework for simultaneously quantifying battery production feature importance and correlations by three various quantitative indicators including the unbiased feature importance (FI), gain improvement FI and PMOA, paving a promising solution to reduce model dimension and conduct efficient sensitivity analysis of battery manufacturing.