pwc-archive/papers-with-abstracts huggingface dataset:
Study of software developers' experience using the Github Copilot Tool in the software development process
Authors: Mateusz Jaworski, Dariusz Piotrkowski
Date: 2023-01-12 00:00:00
Tasks: Survey

In software development there is a constant pressure to produce code faster and faster without compromising on quality. New tools supporting developers are created in response to this demand. Currently a new generation of such solutions is about to be launched - Artificial Intelligence driven tools. On 29 June 2021 Github Copilot was announced. It uses trained model to generate code based on human understandable language. The focus of this research was to investigate software developers' approach to this tool. For this purpose a survey containing 18 questions was prepared and shared with programmers. A total of 42 answers were gathered. The results of the research indicate that developers' opinions are divided. Most of them met Github Copilot before attending the survey. The attitude to the tool was mostly positive but not many participants were willing to use it. Concerns are caused by security issues associated with using of Github Copilot.
Taking Search to Task
Authors: Chirag Shah, Ryen W. White, Paul Thomas, Bhaskar Mitra, Shawon Sarkar, Nicholas Belkin
Date: 2023-01-12 00:00:00
Tasks: Information Retrieval, Retrieval

The importance of tasks in information retrieval (IR) has been long argued for, addressed in different ways, often ignored, and frequently revisited. For decades, scholars made a case for the role that a user's task plays in how and why that user engages in search and what a search system should do to assist. But for the most part, the IR community has been too focused on query processing and assuming a search task to be a collection of user queries, often ignoring if or how such an assumption addresses the users accomplishing their tasks. With emerging areas of conversational agents and proactive IR, understanding and addressing users' tasks has become more important than ever before. In this paper, we provide various perspectives on where the state-of-the-art is with regard to tasks in IR, what are some of the bottlenecks in deriving and using task information, and how do we go forward from here. In addition to covering relevant literature, the paper provides a synthesis of historical and current perspectives on understanding, extracting, and addressing task-focused search. To ground ongoing and future research in this area, we present a new framing device for tasks using a tree-like structure and various moves on that structure that allow different interpretations and applications. Presented as a combination of synthesis of ideas and past works, proposals for future research, and our perspectives on technical, social, and ethical considerations, this paper is meant to help revitalize the interest and future work in task-based IR.
Pylon: Semantic Table Union Search in Data Lakes
Authors: Tianji Cong, Fatemeh Nargesian, H. V. Jagadish
Date: 2023-01-12 00:00:00
Tasks: Contrastive Learning, Representation Learning, Retrieval

The large size and fast growth of data repositories, such as data lakes, has spurred the need for data discovery to help analysts find related data. The problem has become challenging as (i) a user typically does not know what datasets exist in an enormous data repository; and (ii) there is usually a lack of a unified data model to capture the interrelationships between heterogeneous datasets from disparate sources. In this work, we address one important class of discovery needs: finding union-able tables. The task is to find tables in a data lake that can be unioned with a given query table. The challenge is to recognize union-able columns even if they are represented differently. In this paper, we propose a data-driven learning approach: specifically, an unsupervised representation learning and embedding retrieval task. Our key idea is to exploit self-supervised contrastive learning to learn an embedding model that takes into account the indexing/search data structure and produces embeddings close by for columns with semantically similar values while pushing apart columns with semantically dissimilar values. We then find union-able tables based on similarities between their constituent columns in embedding space. On a real-world data lake, we demonstrate that our best-performing model achieves significant improvements in precision ($16\% \uparrow$), recall ($17\% \uparrow $), and query response time (7x faster) compared to the state-of-the-art.
Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access
Authors: Christine Pinney, Amifa Raj, Alex Hanna, Michael D. Ekstrand
Date: 2023-01-12 00:00:00
Tasks: Fairness, Information Retrieval, Recommendation Systems, Retrieval

Information access research (and development) sometimes makes use of gender, whether to report on the demographics of participants in a user study, as inputs to personalized results or recommendations, or to make systems gender-fair, amongst other purposes. This work makes a variety of assumptions about gender, however, that are not necessarily aligned with current understandings of what gender is, how it should be encoded, and how a gender variable should be ethically used. In this work, we present a systematic review of papers on information retrieval and recommender systems that mention gender in order to document how gender is currently being used in this field. We find that most papers mentioning gender do not use an explicit gender variable, but most of those that do either focus on contextualizing results of model performance, personalizing a system based on assumptions of user gender, or auditing a model's behavior for fairness or other privacy-related issues. Moreover, most of the papers we review rely on a binary notion of gender, even if they acknowledge that gender cannot be split into two categories. We connect these findings with scholarship on gender theory and recent work on gender in human-computer interaction and natural language processing. We conclude by making recommendations for ethical and well-grounded use of gender in building and researching information access systems.
Blockchain For Mobile Health Applications: Acceleration With GPU Computing
Authors: Georgios Drakopoulos, Michail Marountas, Xenophon Liapakis, Giannis Tzimas, Phivos Mylonas, Spyros Sioutas
Date: 2023-01-11 00:00:00
Tasks: GPU

Blockchain is a linearly linked, distributed, and very robust data structure. Originally proposed as part of the Bitcoin distributed stack, it found a number of applications in a number of fields, most notably in smart contracts, social media, secure IoT, and cryptocurrency mining. It ensures data integrity by distributing strongly encrypted data in widely redundant segments. Each new insertion requires verification and approval by the majority of the users of the blockchain. Both encryption and verification are computationally intensive tasks which cannot be solved with ordinary off-the-shelf CPUs. This has resulted in a renewed scientific interest in secure distributed communication and coordination protocols. Mobile health applications are growing progressively popular and have the enormous advantage of timely diagnosis of certain conditions. However, privacy concerns have been raised as mobile health application by default have access to highly sensitive personal data. This chapter presents concisely how blockchain can be applied to mobile health applications in order to enhance privacy.
Analysis of Thai Capital Market Linkages: Part III. Vine Copula and Monte Carlo Value-at-Risk/Expected Shortfall
Authors: Poomjai Nacaskul, Pitikorn Khlaisamniang, Puvarith Veerabulyarith
Date: 2023-01-13 00:00:00
Proceeding: www.researchgate.net 2023 1

Analytically thorough understanding of financial-market dynamics is fundamental to the promotion of capital-market innovation, efficiency, and resilience; innovative, efficient, and resilient capital market, in turn, is fundamental to the sustainable economic development of the nation and the robust financial stability of its economy. Based on our earlier papers, which saw (Bivariate) Copula (semi-parametric statistics) techniques used to analyse pairwise probabilistic linkages amongst 14 variables representing domestic (Thai) and international (US/Emerging Market/Asia) foreign-exchange, fixed-income, and equity market movements, as well as foreign portfolio-investment flows into Thai equity shares and bonds [23][24], as well as comparison of Copula-based vs. Granger-Causality-based assessment of probabilistic linkages and their implication on resulting Katz/Betweenness Centrality Analysis [25][26], here we set out to build a multivariate distribution “from the ground up” using each capital market variable’s own marginal distributions and pairwise bivariate copulas under so-called Vine Copula construction. With individual marginal distributions linked up with said vine copula, we can extract practical insights from the resultant multivariate distribution, notably via the application of Monte Carlo simulation method. To demonstrate this application usage, we choose a few of our 14 capital market variables to define a demonstrative investment universe, from which we can then compute Monte Carlo Value-at-Risk (VaR) and Expected Shortfall (ES) for any hypothetical portfolio allocation.
InstantDL-An easy-to-use deep learning pipeline for image segmentation and classification
Authors: Dominik J. E. Waibel, Sayedali Shetab Boushehri, Carsten Marr
Date: 2021-03-02 00:00:00
Proceeding: BMC Bioinformatics 2021 3
Tasks: Deep Learning, Image Segmentation, Instance Segmentation, Semantic Segmentation

Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application.
We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented.
With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.
Testing for Coefficient Randomness in Local-to-Unity Autoregressions
Authors: Mikihito Nishi
Date: 2023-01-12 00:00:00
Tasks: Unity

In this study, we propose a test for the coefficient randomness in autoregressive models where the autoregressive coefficient is local to unity, which is empirically relevant given the results of earlier studies. Under this specification, we theoretically analyze the effect of the correlation between the random coefficient and disturbance on tests' properties, which remains largely unexplored in the literature. Our analysis reveals that the correlation crucially affects the power of tests for coefficient randomness and that tests proposed by earlier studies can perform poorly when the degree of the correlation is moderate to large. The test we propose in this paper is designed to have a power function robust to the correlation. Because the asymptotic null distribution of our test statistic depends on the correlation $\psi$ between the disturbance and its square as earlier tests do, we also propose a modified version of the test statistic such that its asymptotic null distribution is free from the nuisance parameter $\psi$. The modified test is shown to have better power properties than existing ones in large and finite samples.
WIRE: Wavelet Implicit Neural Representations
Authors: Vishwanath Saragadam, Daniel LeJeune, Jasper Tan, Guha Balakrishnan, Ashok Veeraraghavan, Richard G. Baraniuk
Date: 2023-01-05 00:00:00
Proceeding: CVPR 2023 1
Tasks: Denoising, Image Denoising, Image Inpainting, Novel View Synthesis, Super-Resolution

Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of the nonlinear activation function employed in its multilayer perceptron (MLP) network. A wide range of nonlinearities have been explored, but, unfortunately, current INRs designed to have high accuracy also suffer from poor robustness (to signal noise, parameter variation, etc.). Inspired by harmonic analysis, we develop a new, highly accurate and robust INR that does not exhibit this tradeoff. Wavelet Implicit neural REpresentation (WIRE) uses a continuous complex Gabor wavelet activation function that is well-known to be optimally concentrated in space-frequency and to have excellent biases for representing images. A wide range of experiments (image denoising, image inpainting, super-resolution, computed tomography reconstruction, image overfitting, and novel view synthesis with neural radiance fields) demonstrate that WIRE defines the new state of the art in INR accuracy, training time, and robustness.
Define, Evaluate, and Improve Task-Oriented Cognitive Capabilities for Instruction Generation Models
Authors: Lingjun Zhao, Khanh Nguyen, Hal Daumé III
Date: 2022-12-21 00:00:00
Tasks: Language Modelling

Recent work studies the cognitive capabilities of language models through psychological tests designed for humans. While these studies are helpful for understanding the general capabilities of these models, there is no guarantee that a model possessing sufficient capabilities to pass those tests would actually use those capabilities in performing real-life tasks. In this work, we formulate task-oriented cognitive capabilities, which are human-like cognitive capabilities that language models leverage to perform tasks. These capabilities are (i) the ability to quickly generate good candidate utterances (the search capability) (ii) the ability to predict how a listener interprets those utterances and choose the most appropriate one (the pragmatic capability). We design an evaluation scheme for comparing these capabilities of a language model with those of a human. Applying this scheme to examine various models in a navigation instruction generation problem, we find that their pragmatic capability is severely lacking. This insight leads us to augment them with better models of the listener and obtain a significant boost of 11% in success rate in guiding real humans. Our work advocates for having a principled procedure for aligning language models with humans that involves (i) formulating task-oriented capabilities, (ii) devising a method to quantify their deficiency, and (iii) iteratively improving them.
UAVs Formation Flight under Multiple Obstacle and Communication Constraints
Authors: Hu Jin
Date: 2023-01-12 00:00:00

In order to improve the communication of the UAV network when the UAV formation in a complex military mission environment. This paper proposed formation reconfiguration strategy under complexities mission environment. We take the problem of multi-UAV formation reconfiguration transform into nonlinear inequality constraints optimization by mathematic approach. (i)design multi-objective multi-constraints optimization function for different task of UAVs and complexities mission environment, and get an optimal model.(ii) Using Pareto optimal theory transform the multi-objective optimization problem into single optimization problem.(iii)finally, we apply the primal-dual Newton interior point algorithm to process the convex optimal problem. In order to verify the high efficiency of this method, a series of simulation experiments are designed. The experiment shows that the method in this paper has the lowest energy consumption while maintaining the connectivity of the UAV formation network.
Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning
Authors: Wenting Zhao, Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas, Mossad Helali, Essam Mansour
Date: 2023-01-05 00:00:00
Tasks: Code Completion

Dynamically typed languages such as Python have become very popular. Among other strengths, Python's dynamic nature and its straightforward linking to native code have made it the de-facto language for many research areas such as Artificial Intelligence. This flexibility, however, makes static analysis very hard. While creating a sound, or a soundy, analysis for Python remains an open problem, we present in this work Serenity, a framework for static analysis of Python that turns out to be sufficient for some tasks. The Serenity framework exploits two basic mechanisms: (a) reliance on dynamic dispatch at the core of language translation, and (b) extreme abstraction of libraries, to generate an abstraction of the code. We demonstrate the efficiency and usefulness of Serenity's analysis in two applications: code completion and automated machine learning. In these two applications, we demonstrate that such analysis has a strong signal, and can be leveraged to establish state-of-the-art performance, comparable to neural models and dynamic analysis respectively.
High-Accuracy Approximation of Evolutionary Pairwise Games on Complex Networks
Authors: Hongyu Wang, Aming Li, Long Wang
Date: 2023-01-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Previous studies have shown that the topological properties of a complex network, such as heterogeneity and average degree, affect the evolutionary game dynamics on it. However, traditional numerical simulations are usually time-consuming and demand a lot of computational resources. In this paper, we propose the method of dynamical approximate master equations (DAMEs) to accurately approximate the evolutionary outcomes on complex networks. We demonstrate that the accuracy of DAMEs supersedes previous standard pairwise approximation methods, and DAMEs require far fewer computational resources than traditional numerical simulations. We use prisoner's dilemma and snowdrift game on regular and scale-free networks to demonstrate the applicability of DAMEs. Overall, our method facilitates the investigation of evolutionary dynamics on a broad range of complex networks, and provides new insights into the puzzle of cooperation.
A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation
Authors: Amir Farakhor, Di wu, Yebin Wang, Huazhen Fang
Date: 2023-01-12 00:00:00
Tasks: Management

This paper presents a novel modular, reconfigurable battery energy storage system. The proposed design is characterized by a tight integration of reconfigurable power switches and DC/DC converters. This characteristic enables isolation of faulty cells from the system and allows fine power control for individual cells toward optimal system-level performance. An optimal power management approach is developed to extensively exploit the merits of the proposed design. Based on receding-horizon convex optimization, this approach aims to minimize the total power losses in charging/discharging while allocating the power in line with each cell's condition to achieve state-of-charge (SoC) and temperature balancing. By appropriate design, the approach manages to regulate the power of a cell across its full SoC range and guarantees the feasibility of the optimization problem. We perform extensive simulations and further develop a lab-scale prototype to validate the proposed system design and power management approach.
Impacts of Distribution Network Reconfiguration on Aggregated DER Flexibility
Authors: Andrey Churkin, Miguel Sanchez-Lopez, Mohammad Iman Alizadeh, Florin Capitanescu, Eduardo A. Martínez Ceseña, Pierluigi Mancarella
Date: 2023-01-12 00:00:00

The ongoing integration of controllable distributed energy resources (DER) makes distribution networks capable of aggregating flexible power and providing flexibility services at both transmission and distribution levels. The aggregated flexibility of an active distribution network (ADN) can be represented as its feasible operating area in the P-Q space. The limits of this area are pivotal for arranging flexibility markets and coordinating transmission and distribution system operators (TSOs and DSOs). However, motivated by the current technical limitations of distribution networks (e.g., protection schemes), existing literature on ADN flexibility and TSO-DSO coordination mostly focuses on radial networks, overlooking the potential benefits of network reconfiguration. This paper, using a realistic meshed distribution system from the UK and the exact ACOPF model for flexibility estimation, demonstrates that network reconfiguration can increase the limits of ADN aggregated flexibility and improve the economic efficiency of flexibility markets.
Novel Stability Conditions for Nonlinear Monotone Systems and Consensus in Multi-Agent Networks
Authors: Diego Deplano, Mauro Franceschelli, Alessandro Giua
Date: 2023-01-12 00:00:00

In this work, we characterize a class of nonlinear monotone dynamical systems that have a certain translation invariance property which goes by the name of plus-homogeneity; usually called "topical" systems. Such systems need not be asymptotically stable, since they are merely nonexpansive but not contractive. Thus, we introduce a stricter version of monotonicity, termed "type-K" in honor of Kamke, and we prove the asymptotic stability of the equilibrium points, as well as the convergence of all trajectories to such equilibria for type-K monotone and plus-homogeneous systems: we call them "K-topical". Since topical maps are the natural nonlinear counterpart of linear maps defined by row-stochastic matrices, which are a cornerstone in the convergence analysis of linear multi-agent systems (MASs), we exploit our results for solving the consensus problem over nonlinear K-topical MASs. We first provide necessary and sufficient conditions on the local interaction rules of the agents ensuring the K-topicality of a MAS. Then, we prove that the agents achieve consensus asymptotically if the graph describing their interactions contains a globally reachable node. Finally, several examples for continuous-time and discrete-time systems are discussed to corroborate the enforceability of our results in different applications.
Observability and Identifiability Analyses of Process Models for Agricultural Anaerobic Digestion Plants
Authors: Simon Hellmann, Arne-Jens Hempel, Stefan Streif, Sören Weinrich
Date: 2023-01-12 00:00:00
Tasks: parameter estimation, State Estimation

Dynamic operation of anaerobic digestion plants requires advanced process monitoring and control. Different simplifications of the Anaerobic Digestion Model No. 1 (ADM1) have been proposed recently, which appear promising for model-based process automation and state estimation. As a fundamental requirement, observability and identifiability of these models are analyzed in this work, which was pursued through algebraic and geometric analysis. Manual algebraic assessment was successfull for small models such as the ADM1-R4 and simplified versions of the ADM1-R3, which were derived in this context. However, for larger model classes the algebraic approach showed to be insufficient. By contrast, the geometric approach, implemented in the STRIKE_GOLDD toolbox, allowed to show observability for more complex models (including ADM1-R4 and ADM1-R3), employing two independent algorithms. The present study lays the groundwork for state observer design, parameter estimation and advanced control resting upon ADM1-based models.