OpenRaiser/Intern-Atlas huggingface dataset:
Design and Implementation of English To Yorùbá Verb Phrase Machine Translation System
Venue: arXiv cs.CL 2023
Year: 2023

We aim to develop an English-to-Yoruba machine translation system which can translate English verb phrase text to its Yoruba equivalent.Words from both languages Source Language and Target Language were collected for the verb phrase group in the home domain. The lexical translation is done by assigning values of the matching word in the dictionary. The syntax of the two languages was realized using Context-Free Grammar, we validated the rewrite rules with finite state automata. The human evaluation method was used and expert fluency was scored. The evaluation shows the system performed better than that of sampled Google translation with over 70 percent of the response matching that of the system's output.
On migration to Perpetual Enterprise System
Venue: arXiv cs.SE 2023
Year: 2023

This document describes a pragmatic approach on how to migrate an organisation computer system towards a new system that could evolve forever, addresses the whole organisation and it is integrated. Governance aspects are as important, if not more, than purely technical IT aspects: human resources, call for tenders, and similar. Migration implies that one is not starting from a green field.
Recurrent Equilibrium Networks: Flexible Dynamic Models with Guaranteed Stability and Robustness
Venue: arXiv cs.LG 2023
Year: 2023

This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear dynamical models} for applications in machine learning, system identification and control. The new model class admits ``built in'' behavioural guarantees of stability and robustness. All models in the proposed class are contracting -- a strong form of nonlinear stability -- and models can satisfy prescribed incremental integral quadratic constraints (IQC), including Lipschitz bounds and incremental passivity. RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks and echo state networks, all deep feedforward neural networks, and all stable Wiener/Hammerstein models, and can approximate all fading-memory and contracting nonlinear systems. RENs are parameterized directly by a vector in R^N, i.e. stability and robustness are ensured without parameter constraints, which simplifies learning since \HL{generic methods for unconstrained optimization such as stochastic gradient descent and its variants can be used}. The performance and robustness of the new model set is evaluated on benchmark nonlinear system identification problems, and the paper also presents applications in data-driven nonlinear observer design and control with stability guarantees.
A New Pathway to Approximate Energy Expenditure and Recovery of an Athlete
Venue: arXiv cs.NE 2023
Year: 2023

This work proposes to use evolutionary computation as a pathway to allow a new perspective on the modeling of energy expenditure and recovery of an individual athlete during exercise. We revisit a theoretical concept called the "three component hydraulic model" which is designed to simulate metabolic systems during exercise and which is able to address recently highlighted shortcomings of currently applied performance models. This hydraulic model has not been entirely validated on individual athletes because it depends on physiological measures that cannot be acquired in the required precision or quantity. This paper introduces a generalized interpretation and formalization of the three component hydraulic model that removes its ties to concrete metabolic measures and allows to use evolutionary computation to fit its parameters to an athlete.
Vec2GC -- A Graph Based Clustering Method for Text Representations
Venue: arXiv cs.IR 2023
Year: 2023

NLP pipelines with limited or no labeled data, rely on unsupervised methods for document processing. Unsupervised approaches typically depend on clustering of terms or documents. In this paper, we introduce a novel clustering algorithm, Vec2GC (Vector to Graph Communities), an end-to-end pipeline to cluster terms or documents for any given text corpus. Our method uses community detection on a weighted graph of the terms or documents, created using text representation learning. Vec2GC clustering algorithm is a density based approach, that supports hierarchical clustering as well.
Heads in the Clouds: Measuring the Implications of Universities Migrating to Public Clouds
Venue: arXiv cs.CY 2023
Year: 2023

With the emergence of remote education and work in universities due to COVID-19, the `zoomification' of higher education, i.e., the migration of universities to the clouds, reached the public discourse. Ongoing discussions reason about how this shift will take control over students' data away from universities, and may ultimately harm the privacy of researchers and students alike. However, there has been no comprehensive measurement of universities' use of public clouds and reliance on Software-as-a-Service offerings to assess how far this migration has already progressed. We perform a longitudinal study of the migration to public clouds among universities in the U.S. and Europe, as well as institutions listed in the Times Higher Education (THE) Top100 between January 2015 and October. We find that cloud adoption differs between countries, with one cluster (Germany, France, Austria, Switzerland) showing a limited move to clouds, while the other (U.S., U.K, the Netherlands, THE Top100) frequently outsources universities' core functions and services -- starting long before the COVID-19 pandemic. We attribute this clustering to several socio-economic factors in the respective countries, including the general culture of higher education and the administrative paradigm taken towards running universities. We then analyze and interpret our results, finding that the implications reach beyond individuals' privacy towards questions of academic independence and integrity.
A Learning-Based Approach for Estimating Inertial Properties of Unknown Objects from Encoder Discrepancies
Venue: arXiv cs.RO 2023
Year: 2023

Many robots utilize commercial force/torque sensors to identify inertial properties of unknown objects. However, such sensors can be difficult to apply to small-sized robots due to their weight, size, and cost. In this paper, we propose a learning-based approach for estimating the mass and center of mass (COM) of unknown objects without using force/torque sensors at the end-effector or on the joints. In our method, a robot arm carries an unknown object as it moves through multiple discrete configurations. Measurements are collected when the robot reaches each discrete configuration and stops. A neural network is designed to estimate joint torques from encoder discrepancies. Given multiple samples, we derive the closed-form relation between joint torques and the object's inertial properties. Based on the derivation, the mass and COM of object are identified by weighted least squares. In order to improve the accuracy of inferred inertial properties, an attention model is designed to generate weights of joints, which indicate the relative importance for each joint. Our framework requires only encoder measurements without using any force/torque sensors, but still maintains accurate estimation capability. The proposed approach has been demonstrated on a 4 degree of freedom (DOF) robot arm.
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
Venue: arXiv cs.LG 2023
Year: 2023

Graph neural networks (GNNs) have been demonstrated as a powerful tool for analyzing non-Euclidean graph data. However, the lack of efficient distributed graph learning systems severely hinders applications of GNNs, especially when graphs are big and GNNs are relatively deep. Herein, we present GraphTheta, the first distributed and scalable graph learning system built upon vertex-centric distributed graph processing with neural network operators implemented as user-defined functions. This system supports multiple training strategies and enables efficient and scalable big-graph learning on distributed (virtual) machines with low memory. To facilitate graph convolutions, GraphTheta puts forward a new graph learning abstraction named NN-TGAR to bridge the gap between graph processing and graph deep learning. A distributed graph engine is proposed to conduct the stochastic gradient descent optimization with a hybrid-parallel execution, and a new cluster-batched training strategy is supported. We evaluate GraphTheta using several datasets with network sizes ranging from small-, modest- to large-scale. Experimental results show that GraphTheta can scale well to 1,024 workers for training an in-house developed GNN on an industry-scale Alipay dataset of 1.4 billion nodes and 4.1 billion attributed edges, with a cluster of CPU virtual machines (dockers) of small memory each (5$\sim$12GB). Moreover, GraphTheta can outperform DistDGL by up to $2.02\times$, with better scalability, and GraphLearn by up to $30.56\times$. As for model accuracy, GraphTheta is capable of learning as good GNNs as existing frameworks. To the best of our knowledge, this work presents the largest edge-attributed GNN learning task in the literature.
Bijective proofs for Eulerian numbers of types B and D
Venue: arXiv cs.LO 2023
Year: 2023

Let $\Bigl\langle\matrix{n\cr k}\Bigr\rangle$, $\Bigl\langle\matrix{B_n\cr k}\Bigr\rangle$, and $\Bigl\langle\matrix{D_n\cr k}\Bigr\rangle$ be the Eulerian numbers in the types A, B, and D, respectively -- that is, the number of permutations of n elements with $k$ descents, the number of signed permutations (of $n$ elements) with $k$ type B descents, the number of even signed permutations (of $n$ elements) with $k$ type D descents. Let $S_n(t) = \sum_{k = 0}^{n-1} \Bigl\langle\matrix{n\cr k}\Bigr\rangle t^k$, $B_n(t) = \sum_{k = 0}^n \Bigl\langle\matrix{B_n\cr k}\Bigr\rangle t^k$, and $D_n(t) = \sum_{k = 0}^n \Bigl\langle\matrix{D_n\cr k}\Bigr\rangle t^k$. We give bijective proofs of the identity $$B_n(t^2) = (1 + t)^{n+1}S_n(t) - 2^n tS_n(t^2)$$ and of Stembridge's identity $$D_n(t) = B_n(t) - n2^{n-1}tS_{n-1}(t).$$ These bijective proofs rely on a representation of signed permutations as paths. Using this representation we also establish a bijective correspondence between even signed permutations and pairs $(w, E)$ with $([n], E)$ a threshold graph and $w$ a degree ordering of $([n], E)$, which we use to obtain bijective proofs of enumerative results for threshold graphs.
Stronger Bounds for Weak Epsilon-Nets in Higher Dimensions
Venue: arXiv cs.CG 2023
Year: 2023

Given a finite point set $P$ in ${\mathbb R}^d$, and $ε>0$ we say that $N\subseteq{ \mathbb R}^d$ is a weak $ε$-net if it pierces every convex set $K$ with $|K\cap P|\geq ε|P|$. We show that for any finite point set in dimension $d\geq 3$, and any $ε>0$, one can construct a weak $ε$-net whose cardinality is $\displaystyle O^*\left(\frac{1}{ε^{2.558}}\right)$ in dimension $d=3$, and $\displaystyle o\left(\frac{1}{ε^{d-1/2}}\right)$ in all dimensions $d\geq 4$. To be precise, our weak $ε$-net has cardinality $\displaystyle O\left(\frac{1}{ε^{α_d+γ}}\right)$ for any $γ>0$, with $$ α_d= \left\{ \begin{array}{l} 2.558 & \text{if} \ d=3 \\3.48 & \text{if} \ d=4 \\\left(d+\sqrt{d^2-2d}\right)/2 & \text{if} \ d\geq 5. \end{array}\right\} $$ This is the first significant improvement of the bound of $\displaystyle \tilde{O}\left(\frac{1}{ε^d}\right)$ that was obtained in 1993 by Chazelle, Edelsbrunner, Grigni, Guibas, Sharir, and Welzl for general point sets in dimension $d\geq 3$.
Impact of Spatial Frequency Based Constraints on Adversarial Robustness
Venue: arXiv cs.LG 2023
Year: 2023

Adversarial examples mainly exploit changes to input pixels to which humans are not sensitive to, and arise from the fact that models make decisions based on uninterpretable features. Interestingly, cognitive science reports that the process of interpretability for human classification decision relies predominantly on low spatial frequency components. In this paper, we investigate the robustness to adversarial perturbations of models enforced during training to leverage information corresponding to different spatial frequency ranges. We show that it is tightly linked to the spatial frequency characteristics of the data at stake. Indeed, depending on the data set, the same constraint may results in very different level of robustness (up to 0.41 adversarial accuracy difference). To explain this phenomenon, we conduct several experiments to enlighten influential factors such as the level of sensitivity to high frequencies, and the transferability of adversarial perturbations between original and low-pass filtered inputs.
Ten-tier and multi-scale supplychain network analysis of medical equipment: Random failure and intelligent attack analysis
Venue: arXiv cs.SI 2023
Year: 2023

Motivated by the COVID-19 pandemic, this paper explores the supply chain viability of medical equipment, an industry whose supply chain was put under a crucial test during the pandemic. This paper includes an empirical network-level analysis of supplier reachability under Random Failure Experiment (RFE) and Intelligent Attack Experiment (IAE). Specifically, this study investigates the effect of RFA and IAE across multiple tiers and scales. The global supply chain data was mined and analyzed from about 45,000 firms with about 115,000 intertwined relationships spanning across 10 tiers of the backward supply chain of medical equipment. This complex supply chain network was analyzed at four scales, namely: firm, country-industry, industry, and country. A notable contribution of this study is the application of a supply chain tier optimization tool to identify the lowest tier of the supply chain that can provide adequate resolution for the study of the supply chain pattern. We also developed data-driven-tools to identify the thresholds for breakdown and fragmentation of the medical equipment supply chain when faced with random failures or different intelligent attack scenarios. The novel network analysis tools utilized in the study can be applied to the study of supply chain reachability and viability in other industries.
Non-Deterministic Functions as Non-Deterministic Processes (Extended Version)
Venue: arXiv cs.LO 2023
Year: 2023

We study encodings of the lambda-calculus into the pi-calculus in the unexplored case of calculi with non-determinism and failures. On the sequential side, we consider lambdafail, a new non-deterministic calculus in which intersection types control resources (terms); on the concurrent side, we consider spi, a pi-calculus in which non-determinism and failure rest upon a Curry-Howard correspondence between linear logic and session types. We present a typed encoding of lambdafail into spi and establish its correctness. Our encoding precisely explains the interplay of non-deterministic and fail-prone evaluation in lambdafail via typed processes in spi. In particular, it shows how failures in sequential evaluation (absence/excess of resources) can be neatly codified as interaction protocols.
Using Small MUSes to Explain How to Solve Pen and Paper Puzzles
Venue: arXiv cs.AI 2023
Year: 2023

In this paper, we present Demystify, a general tool for creating human-interpretable step-by-step explanations of how to solve a wide range of pen and paper puzzles from a high-level logical description. Demystify is based on Minimal Unsatisfiable Subsets (MUSes), which allow Demystify to solve puzzles as a series of logical deductions by identifying which parts of the puzzle are required to progress. This paper makes three contributions over previous work. First, we provide a generic input language, based on the Essence constraint language, which allows us to easily use MUSes to solve a much wider range of pen and paper puzzles. Second, we demonstrate that the explanations that Demystify produces match those provided by humans by comparing our results with those provided independently by puzzle experts on a range of puzzles. We compare Demystify to published guides for solving a range of different pen and paper puzzles and show that by using MUSes, Demystify produces solving strategies which closely match human-produced guides to solving those same puzzles (on average 89% of the time). Finally, we introduce a new randomised algorithm to find MUSes for more difficult puzzles. This algorithm is focused on optimised search for individual small MUSes.
Technical Reports Compilation: Detecting the Fire Drill Anti-pattern Using Source Code and Issue-Tracking Data
Venue: arXiv cs.SE 2023
Year: 2023

Detecting the presence of project management anti-patterns (AP) currently requires experts on the matter and is an expensive endeavor. Worse, experts may introduce their individual subjectivity or bias. Using the Fire Drill AP, we first introduce a novel way to translate descriptions into detectable AP that are comprised of arbitrary metrics and events such as logged time or maintenance activities, which are mined from the underlying source code or issue-tracking data, thus making the description objective as it becomes data-based. Secondly, we demonstrate a novel method to quantify and score the deviations of real-world projects to data-based AP descriptions. Using fifteen real-world projects that exhibit a Fire Drill to some degree, we show how to further enhance the translated AP. The ground truth in these projects was extracted from two individual experts and consensus was found between them. We introduce a novel method called automatic calibration, that optimizes a pattern such that only necessary and important scores remain that suffice to confidently detect the degree to which the AP is present. Without automatic calibration, the proposed patterns show only weak potential for detecting the presence. Enriching the AP with data from real-world projects significantly improves the potential. We also introduce a no-pattern approach that exploits the ground truth for establishing a new, quantitative understanding of the phenomenon, as well as for finding gray-/black-box predictive models. We conclude that the presence detection and severity assessment of the Fire Drill anti-pattern, as well as some of its related and similar patterns, is certainly possible using some of the presented approaches.
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability
Venue: arXiv cs.LG 2023
Year: 2023

Adversarial defenses protect machine learning models from adversarial attacks, but are often tailored to one type of model or attack. The lack of information on unknown potential attacks makes detecting adversarial examples challenging. Additionally, attackers do not need to follow the rules made by the defender. To address this problem, we take inspiration from the concept of Applicability Domain in cheminformatics. Cheminformatics models struggle to make accurate predictions because only a limited number of compounds are known and available for training. Applicability Domain defines a domain based on the known compounds and rejects any unknown compound that falls outside the domain. Similarly, adversarial examples start as harmless inputs, but can be manipulated to evade reliable classification by moving outside the domain of the classifier. We are the first to identify the similarity between Applicability Domain and adversarial detection. Instead of focusing on unknown attacks, we focus on what is known, the training data. We propose a simple yet robust triple-stage data-driven framework that checks the input globally and locally, and confirms that they are coherent with the model's output. This framework can be applied to any classification model and is not limited to specific attacks. We demonstrate these three stages work as one unit, effectively detecting various attacks, even for a white-box scenario.
The Modern Mathematics of Deep Learning
Venue: arXiv cs.LG 2023
Year: 2023

We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects of an architecture affect the behavior of a learning task in which way. We present an overview of modern approaches that yield partial answers to these questions. For selected approaches, we describe the main ideas in more detail.
Online Algorithms and Policies Using Adaptive and Machine Learning Approaches
Venue: arXiv cs.LG 2023
Year: 2023

This paper considers the problem of real-time control and learning in dynamic systems subjected to parametric uncertainties. We propose a combination of a Reinforcement Learning (RL) based policy in the outer loop suitably chosen to ensure stability and optimality for the nominal dynamics, together with Adaptive Control (AC) in the inner loop so that in real-time AC contracts the closed-loop dynamics towards a stable trajectory traced out by RL. Two classes of nonlinear dynamic systems are considered, both of which are control-affine. The first class of dynamic systems utilizes equilibrium points %with expansion forms around these points and a Lyapunov approach while second class of nonlinear systems uses contraction theory. AC-RL controllers are proposed for both classes of systems and shown to lead to online policies that guarantee stability using a high-order tuner and accommodate parametric uncertainties and magnitude limits on the input. In addition to establishing a stability guarantee with real-time control, the AC-RL controller is also shown to lead to parameter learning with persistent excitation for the first class of systems. Numerical validations of all algorithms are carried out using a quadrotor landing task on a moving platform.
Rescue Network: Using UAVs (drones) in Earthquake Crisis Management
Venue: arXiv cs.NI 2023
Year: 2023

Earthquake is one of the natural disasters which cannot be either controlled or predicted absolutely. Since preventing earthquake is impossible, preventing its damages is also difficult. Unfortunately, after each earthquake and its financial and life losses, the initial panic of the people results in the second wave of accidents and damages. Inrush of confused people to escape the cities, streets and houses is a great problem. Apart from training in seismic areas which is very important, considering security arrangements and observing security principles in construction, instructing the people is also important. Other than searching for and rescuing the people who are trapped under detrimental or are in danger, those who thieve the damaged area is another important issue after each earthquake. Thus, a solution is proposed to use modern technology to reduce threats of natural disasters including earthquake. Today, UAVs are being used in natural disasters and accidents. To this end and considering the ever-increasing developments of network technologies and communication including IoT and cloud, an efficient design is presented which increases rescue factor of live creatures in natural disasters that can be used to rescue human lives and prevent subsequent outcomes after a few seconds. In this study, focus is on time of occurrence of earthquake and after earthquake