OpenRaiser/Intern-Atlas huggingface dataset:
Analyzing the Rework Time and Severity of Code Debt: A Case Study Using Technical Debt Catalogs
Venue: arXiv cs.SE 2020
Year: 2020

This paper presents a case study analyzing Hibernate ecosystem software projects to investigate and demonstrate Code Debt behavior in relation to severity and rework time. The case study carried out revealed that the Code Debt with severity related to impact on software maintenance is the most representative and has the largest rework times to be paid in the Hibernate ecosystem. Besides, it was found that the distributions of rework times of Code Debt for all severities undergo variations in the initial versions of the development.
On the Value of Target Data in Transfer Learning
Venue: arXiv cs.LG 2020
Year: 2020

We aim to understand the value of additional labeled or unlabeled target data in transfer learning, for any given amount of source data; this is motivated by practical questions around minimizing sampling costs, whereby, target data is usually harder or costlier to acquire than source data, but can yield better accuracy. To this aim, we establish the first minimax-rates in terms of both source and target sample sizes, and show that performance limits are captured by new notions of discrepancy between source and target, which we refer to as transfer exponents.
Computing the racing line using Bayesian optimization
Venue: arXiv cs.RO 2020
Year: 2020

A good racing strategy and in particular the racing line is decisive to winning races in Formula 1, MotoGP, and other forms of motor racing. The racing line defines the path followed around a track as well as the optimal speed profile along the path. The objective is to minimize lap time by driving the vehicle at the limits of friction and handling capability. The solution naturally depends upon the geometry of the track and vehicle dynamics. We introduce a novel method to compute the racing line using Bayesian optimization. Our approach is fully data-driven and computationally more efficient compared to other methods based on dynamic programming and random search. The approach is specifically relevant in autonomous racing where teams can quickly compute the racing line for a new track and then exploit this information in the design of a motion planner and a controller to optimize real-time performance.
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Venue: arXiv cs.LG 2020
Year: 2020

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical ML and scalable general-purpose GPU computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.
FEAST Eigenvalue Solver v4.0 User Guide
Venue: arXiv cs.MS 2020
Year: 2020

The FEAST library package represents an unified framework for solving various family of eigenvalue problems and achieving accuracy, robustness, high-performance and scalability on parallel architectures. Its originality lies with a new transformative numerical approach to the traditional eigenvalue algorithm design - the FEAST algorithm. The algorithm gathers key elements from complex analysis, numerical linear algebra and approximation theory, to construct an optimal subspace iteration technique using approximate spectral projectors. FEAST can be used for solving both standard and generalized forms of the Hermitian or non-Hermitian problems (linear or non-linear), and it belongs to the family of contour integration eigensolvers. FEAST's main computational task consists of a numerical quadrature computation that involves solving independent linear systems along a complex contour, each with multiple right hand sides. In v4.0, FEAST has been reimplemented using an inverse residual iteration algorithm which enables the linear systems to be solved with very low accuracy (in single precision) with no impact on the FEAST double precision convergence rate. As a result, v4.0 is on average 3-4 times faster than v2.1 and v3.0 using new default optimization parameters (v2.1 has been featured as Intel-MKL's principal HPC eigensolver since 2013). v4.0 also implements new important features such as IFEAST (using Inexact Iterative solver), Non-linear polynomial FEAST, and PFEAST with its 3-MPI levels of parallelism. FEAST is both a comprehensive library package, and an easy to use software. It includes flexible reverse communication interfaces and ready to use driver interfaces for dense, banded and sparse systems.
Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations
Venue: arXiv cs.DC 2020
Year: 2020

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidation-based energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided.
Unveiling the research landscape of Sustainable Development Goals and their inclusion in Higher Education Institutions and Research Centers: major trends in 2000-2017
Venue: arXiv cs.DL 2020
Year: 2020

Sustainable Development Goals are the blueprint to achieve a better and more sustainable future for society. Its legacy is linked with the Millennium Development Goals, set up in 2000. A bibliometric analysis was conducted to 1) measure "core" research output from 2000-2017, with the aim to map the global research of sustainability goals, 2) describe thematic specialization based on keywords co-occurrence analysis and strongest citation burst, 3) present a methodology to classify scientific output (based on an ad-hoc glossary) and assess SDGs interconnections. Sustainability goals publications (core+expand based on direct citations) were identified in-house CWTS Web of Science by using search terms in titles, abstracts, and keywords. 25,299 bibliographic records were analyzed, from which 21,653 (85.59%) are from HEIs and research centres (RC). The purpose of this paper is to analyze the role of these organizations in sustainability research. The findings reveal the increasing participation of these organizations in this research (660 institutions in 2000-2005 to 1744 institutions involved in 2012-2017). In terms of specialization, some institutions present a higher production and specialization on the topic (e.g., London School of Hygiene & Tropical Medicine and World Health Organization); however, others present less production but higher specialization (e.g., Stockholm Environment Institute). Regarding the topics, health (especially in developing countries), women and socio-economic aspects are the most prominent ones. Moreover, it is observed the interlinked nature of SDGs between some SDGs in research output (e.g., SDG11 and SDG3). This study provides important orientation for HEIs and RCs in terms of Research, Development and Innovation (R&D+i) to respond to major societal challenges and could be useful for the policymakers in order to promote the research agenda on this topic.
CROFT: A scalable three-dimensional parallel Fast Fourier Transform (FFT) implementation for High Performance Clusters
Venue: arXiv cs.DC 2020
Year: 2020

The FFT of three-dimensional (3D) input data is an important computational kernel of numerical simulations and is widely used in High Performance Computing (HPC) codes running on a large number of processors. Performance of many scientific applications such as Molecular Dynamic simulations depends on the underlying 3D parallel FFT library being used. In this paper, we present C-DACs three-dimensional Fast Fourier Transform (CROFT) library which implements three-dimensional parallel FFT using pencil decomposition. To exploit the hyperthreading capabilities of processor cores without affecting performance, CROFT is designed to use multithreading along with MPI. CROFT implementation has an innovative feature of overlapping compute and memory-I/O with MPI communication using multithreading. As opposed to other 3D FFT implementations, CROFT uses only two threads where one thread is dedicated for communication so that it can be effectively overlapped with computations. Thus, depending on the number of processes used, CROFT achieves performance improvement of about 51% to 42% as compared to FFTW3 library.
Building Reliable Cloud Services Using P# (Experience Report)
Venue: arXiv cs.PL 2020
Year: 2020

Cloud services must typically be distributed across a large number of machines in order to make use of multiple compute and storage resources. This opens the programmer to several sources of complexity such as concurrency, order of message delivery, lossy network, timeouts and failures, all of which impose a high cognitive burden. This paper presents evidence that technology inspired by formal-methods, delivered as part of a programming framework, can help address these challenges. In particular, we describe the experience of several engineering teams in Microsoft Azure that used the open-source P# programming framework to build multiple reliable cloud services. P# imposes a principled design pattern that allows writing formal specifications alongside production code that can be systematically tested, without deviating from routine engineering practices. Engineering teams that have been using P# have reported dramatically increased productivity (in time taken to push new features to production) as well as services that have been running live for months without any issues in features developed and tested with P#.
Direct Domain Decomposition Method (D3M) for Finite Element Electromagnetic Computations
Venue: arXiv cs.CE 2020
Year: 2020

An exact arithmetic, memory efficient direct solution method for finite element method (FEM) computations is outlined. Unlike conventional black-box or low-rank direct solvers that are opaque to the underlying physical problem, the proposed method leverages physical insights at every stage of the development through a new symmetric domain decomposition method (DDM) with one set of Lagrange multipliers. Comparisons with state-of-the-art exact direct solvers on electrically large problems suggest up to 10 times less memory and better run-time complexity while maintaining the same accuracy.
HushRelay: A Privacy-Preserving, Efficient, and Scalable Routing Algorithm for Off-Chain Payments
Venue: arXiv cs.CR 2020
Year: 2020

Payment channel networks (PCN) are used in cryptocurrencies to enhance the performance and scalability of off-chain transactions. Except for opening and closing of a payment channel, no other transaction requests accepted by a PCN are recorded in the Blockchain. Only the parties which have opened the channel will know the exact amount of fund left at a given instant. In real scenarios, there might not exist a single path which can enable transfer of high value payments. For such cases, splitting up the transaction value across multiple paths is a better approach. While there exists several approaches which route transactions via several paths, such techniques are quite inefficient, as the decision on the number of splits must be taken at the initial phase of the routing algorithm (e.g., SpeedyMurmur [42]). Algorithms which do not consider the residual capacity of each channel in the network are susceptible to failure. Other approaches leak sensitive information, and are quite computationally expensive [28]. To the best of our knowledge, our proposed scheme HushRelay is an efficient privacy preserving routing algorithm, taking into account the funds left in each channel, while splitting the transaction value across several paths. Comparing the performance of our algorithm with existing routing schemes on real instances (e.g., Ripple Network), we observed that HushRelay attains a success ratio of 1, with an execution time of 2.4 sec. However, SpeedyMurmur [42] attains a success ratio of 0.98 and takes 4.74 sec when the number of landmarks is 6. On testing our proposed routing algorithm on the Lightning Network, a success ratio of 0.99 is observed, having an execution time of 0.15 sec, which is 12 times smaller than the time taken by SpeedyMurmur.
NP Reasoning in the Monotone $μ$-Calculus
Venue: arXiv cs.LO 2020
Year: 2020

Satisfiability checking for monotone modal logic is known to be (only) NP-complete. We show that this remains true when the logic is extended with aconjunctive and alternation-free fixpoint operators as well as the universal modality; the resulting logic -- the aconjunctive alternation-free monotone $μ$-calculus with the universal modality -- contains both concurrent propositional dynamic logic (CPDL) and the alternation-free fragment of game logic as fragments. We obtain our result from a characterization of satisfiability by means of Büchi games with polynomially many Eloise nodes.
On the Effective Capacity of an Underwater Acoustic Channel under Impersonation Attack
Venue: arXiv cs.CR 2020
Year: 2020

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GD method.
Parameterized Complexity of Two-Interval Pattern Problem
Venue: arXiv cs.CG 2020
Year: 2020

A \emph{2-interval} is the union of two disjoint intervals on the real line. Two 2-intervals $D_1$ and $D_2$ are \emph{disjoint} if their intersection is empty (i.e., no interval of $D_1$ intersects any interval of $D_2$). There can be three different relations between two disjoint 2-intervals; namely, preceding ($<$), nested ($\sqsubset$) and crossing ($\between$). Two 2-intervals $D_1$ and $D_2$ are called \emph{$R$-comparable} for some $R\in\{<,\sqsubset,\between\}$, if either $D_1RD_2$ or $D_2RD_1$. A set $\mathcal{D}$ of disjoint 2-intervals is $\mathcal{R}$-comparable, for some $\mathcal{R}\subseteq\{<,\sqsubset,\between\}$ and $\mathcal{R}\neq\emptyset$, if every pair of 2-intervals in $\mathcal{R}$ are $R$-comparable for some $R\in\mathcal{R}$. Given a set of 2-intervals and some $\mathcal{R}\subseteq\{<,\sqsubset,\between\}$, the objective of the \emph{2-interval pattern problem} is to find a largest subset of 2-intervals that is $\mathcal{R}$-comparable. The 2-interval pattern problem is known to be $W[1]$-hard when $|\mathcal{R}|=3$ and $NP$-hard when $|\mathcal{R}|=2$ (except for $\mathcal{R}=\{<,\sqsubset\}$, which is solvable in quadratic time). In this paper, we fully settle the parameterized complexity of the problem by showing it to be $W[1]$-hard for both $\mathcal{R}=\{\sqsubset,\between\}$ and $\mathcal{R}=\{<,\between\}$ (when parameterized by the size of an optimal solution); this answers an open question posed by Vialette [Encyclopedia of Algorithms, 2008].
Analysis of Dutch Master Paintings with Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Trained on the works of an artist under study and visually comparable works of other artists, convolutional neural networks can identify forgeries and provide attributions. They can also assign classification probabilities within a painting, revealing mixed authorship and identifying regions painted by different hands.
A Scalable Photonic Computer Solving the Subset Sum Problem
Venue: arXiv cs.ET 2020
Year: 2020

The subset sum problem is a typical NP-complete problem that is hard to solve efficiently in time due to the intrinsic superpolynomial-scaling property. Increasing the problem size results in a vast amount of time consuming in conventionally available computers. Photons possess the unique features of extremely high propagation speed, weak interaction with environment and low detectable energy level, therefore can be a promising candidate to meet the challenge by constructing an a photonic computer computer. However, most of optical computing schemes, like Fourier transformation, require very high operation precision and are hard to scale up. Here, we present a chip built-in photonic computer to efficiently solve the subset sum problem. We successfully map the problem into a waveguide network in three dimensions by using femtosecond laser direct writing technique. We show that the photons are able to sufficiently dissipate into the networks and search all the possible paths for solutions in parallel. In the case of successive primes the proposed approach exhibits a dominant superiority in time consumption even compared with supercomputers. Our results confirm the ability of light to realize a complicated computational function that is intractable with conventional computers, and suggest the subset sum problem as a good benchmarking platform for the race between photonic and conventional computers on the way towards "photonic supremacy".
Public Bayesian Persuasion: Being Almost Optimal and Almost Persuasive
Venue: arXiv cs.GT 2020
Year: 2020

Persuasion studies how an informed principal may influence the behavior of agents by the strategic provision of payoff-relevant information. We focus on the fundamental multi-receiver model by Arieli and Babichenko (2019), in which there are no inter-agent externalities. Unlike prior works on this problem, we study the public persuasion problem in the general setting with: (i) arbitrary state spaces; (ii) arbitrary action spaces; (iii) arbitrary sender's utility functions. We fully characterize the computational complexity of computing a bi-criteria approximation of an optimal public signaling scheme. In particular, we show, in a voting setting of independent interest, that solving this problem requires at least a quasi-polynomial number of steps even in settings with a binary action space, assuming the Exponential Time Hypothesis. In doing so, we prove that a relaxed version of the Maximum Feasible Subsystem of Linear Inequalities problem requires at least quasi-polynomial time to be solved. Finally, we close the gap by providing a quasi-polynomial time bi-criteria approximation algorithm for arbitrary public persuasion problems that, in specific settings, yields a QPTAS.
A Hierarchy of Limitations in Machine Learning
Venue: arXiv cs.CY 2020
Year: 2020

"All models are wrong, but some are useful", wrote George E. P. Box (1979). Machine learning has focused on the usefulness of probability models for prediction in social systems, but is only now coming to grips with the ways in which these models are wrong---and the consequences of those shortcomings. This paper attempts a comprehensive, structured overview of the specific conceptual, procedural, and statistical limitations of models in machine learning when applied to society. Machine learning modelers themselves can use the described hierarchy to identify possible failure points and think through how to address them, and consumers of machine learning models can know what to question when confronted with the decision about if, where, and how to apply machine learning. The limitations go from commitments inherent in quantification itself, through to showing how unmodeled dependencies can lead to cross-validation being overly optimistic as a way of assessing model performance.