pwc-archive/papers-with-abstracts huggingface dataset:
Developing a Ranking Problem Library (RPLIB) from a data-oriented perspective
Authors: Paul E. Anderson, Brandon Tat, Charlie Ward, Amy N. Langville, Kathryn E. Pedings-Behling
Date: 2022-06-21 00:00:00

We present an improved library for the ranking problem called RPLIB. RPLIB includes the following data and features. (1) Real and artificial datasets of both pairwise data (i.e., information about the ranking of pairs of items) and feature data (i.e., a vector of features about each item to be ranked). These datasets range in size (e.g., from small $n=10$ item datasets to large datasets with hundred of items), application (e.g., from sports to economic data), and source (e.g. real versus artificially generated to have particular structures). (2) RPLIB contains code for the most common ranking algorithms such as the linear ordering optimization method and the Massey method. (3) RPLIB also has the ability for users to contribute their own data, code, and algorithms. Each RPLIB dataset has an associated .JSON model card of additional information such as the number and set of optimal rankings, the optimal objective value, and corresponding figures.
Case Study: The Impact of Location on Bias in Search Results
Authors: Gizem Gezici
Date: 2022-06-21 00:00:00
Tasks: Retrieval

In this work, we aim to investigate the impact of location (different countries) on bias in search results. For this, we use the search results of Google and Bing in the UK and US locations. The query set is composed of controversial queries obtained from ProCon.org that have specific ideological leanings as conservative or liberal. In a previous work, researchers analyse search results in terms of stance and ideological bias with rank and relevance based measures. Yet, in the scope of this work, by using the query subset of controversial queries we examine the effect of location on the existence of bias as well as the magnitude of bias difference between Bing and Google. Note that this study follows a similar evaluation procedure. Our preliminary results show that location might affect the retrieval performance of search engines as well as the bias in the search results returned by Bing and Google towards the controversial queries.
Urdu News Article Recommendation Model using Natural Language Processing Techniques
Authors: Syed Zain Abbas, Dr. Arif ur Rahman, Abdul Basit Mughal, Syed Mujtaba Haider
Date: 2022-05-29 00:00:00
Tasks: Articles, Language Modeling, Language Modelling

There are several online newspapers in urdu but for the users it is difficult to find the content they are looking for because these most of them contain irrelevant data and most users did not get what they want to retrieve. Our proposed framework will help to predict Urdu news in the interests of users and reduce the users searching time for news. For this purpose, NLP techniques are used for pre-processing, and then TF-IDF with cosine similarity is used for gaining the highest similarity and recommended news on user preferences. Moreover, the BERT language model is also used for similarity, and by using the BERT model similarity increases as compared to TF-IDF so the approach works better with the BERT language model and recommends news to the user on their interest. The news is recommended when the similarity of the articles is above 60 percent.
Towards End-to-End Private Automatic Speaker Recognition
Authors: Francisco Teixeira, Alberto Abad, Bhiksha Raj, Isabel Trancoso
Date: 2022-06-23 00:00:00
Tasks: Privacy Preserving, Speaker Recognition, Speaker Verification

The development of privacy-preserving automatic speaker verification systems has been the focus of a number of studies with the intent of allowing users to authenticate themselves without risking the privacy of their voice. However, current privacy-preserving methods assume that the template voice representations (or speaker embeddings) used for authentication are extracted locally by the user. This poses two important issues: first, knowledge of the speaker embedding extraction model may create security and robustness liabilities for the authentication system, as this knowledge might help attackers in crafting adversarial examples able to mislead the system; second, from the point of view of a service provider the speaker embedding extraction model is arguably one of the most valuable components in the system and, as such, disclosing it would be highly undesirable. In this work, we show how speaker embeddings can be extracted while keeping both the speaker's voice and the service provider's model private, using Secure Multiparty Computation. Further, we show that it is possible to obtain reasonable trade-offs between security and computational cost. This work is complementary to those showing how authentication may be performed privately, and thus can be considered as another step towards fully private automatic speaker recognition.
On Fuzzy Cardinal Semantic Transformations
Authors: Alexander Chunikhin, Vadym Zhytniuk
Date: 2022-05-27 00:00:00

The concept of fuzzy cardinal semantic transformation as a basis for creating fuzzy semantic numeration systems is introduced in this work. Both fuzziness of the initial data - cardinals of abstract entities - and fuzziness of the parameters of the cardinal semantic operators are considered. We also expressed cardinal semantic transformations for discrete fuzzy numbers and for continuous triangular fuzzy numbers. The principle of formation of the fuzzy common carry in the cardinal semantic operators with multiple inputs is formed.
Dynamic consensus with prescribed convergence time for multi-leader formation tracking
Authors: Rodrigo Aldana-López, David Gómez-Gutiérrez, Rosario Aragüés, Carlos Sagüés
Date: 2022-06-23 00:00:00

This work addresses the problem of distributed formation tracking for a group of follower holonomic mobile robots around a reference signal. The reference signal is comprised of the geometric center of the positions of multiple leaders. This work's main contribution is a novel Modulated Distributed Virtual Observer (MDVO) for the reference signal. Moreover, the proposed MDVO is based on an exact dynamic consensus algorithm with a prescribed convergence time. In addition, we provide simulation examples showcasing two different application scenarios for the proposal.
Modeling the System-Level Reliability towards a Convergence of Communication, Computing and Control
Authors: Bin Han, Hans D. Schotten
Date: 2022-06-23 00:00:00

Enabled and driven by modern advances in wireless telecommunication and artificial intelligence, the convergence of communication, computing, and control is becoming inevitable in future industrial applications. Analytical and optimizing frameworks, however, are not yet readily developed for this new technical trend. In this work we discuss the necessity and typical scenarios of this convergence, and propose a new approach to model the system-level reliability across all involved domainss
Speaker-Independent Microphone Identification in Noisy Conditions
Authors: Antonio Giganti, Luca Cuccovillo, Paolo Bestagini, Patrick Aichroth, Stefano Tubaro
Date: 2022-06-23 00:00:00
Tasks: Denoising

This work proposes a method for source device identification from speech recordings that applies neural-network-based denoising, to mitigate the impact of counter-forensics attacks using noise injection. The method is evaluated by comparing the impact of denoising on three state-of-the-art features for microphone classification, determining their discriminating power with and without denoising being applied. The proposed framework achieves a significant performance increase for noisy material, and more generally, validates the usefulness of applying denoising prior to device identification for noisy recordings.
Adversarial Multi-Task Learning for Disentangling Timbre and Pitch in Singing Voice Synthesis
Authors: Tae-Woo Kim, Min-Su Kang, Gyeong-Hoon Lee
Date: 2022-06-23 00:00:00
Tasks: Generative Adversarial Network, Multi-Task Learning, Singing Voice Synthesis

Recently, deep learning-based generative models have been introduced to generate singing voices. One approach is to predict the parametric vocoder features consisting of explicit speech parameters. This approach has the advantage that the meaning of each feature is explicitly distinguished. Another approach is to predict mel-spectrograms for a neural vocoder. However, parametric vocoders have limitations of voice quality and the mel-spectrogram features are difficult to model because the timbre and pitch information are entangled. In this study, we propose a singing voice synthesis model with multi-task learning to use both approaches -- acoustic features for a parametric vocoder and mel-spectrograms for a neural vocoder. By using the parametric vocoder features as auxiliary features, the proposed model can efficiently disentangle and control the timbre and pitch components of the mel-spectrogram. Moreover, a generative adversarial network framework is applied to improve the quality of singing voices in a multi-singer model. Experimental results demonstrate that our proposed model can generate more natural singing voices than the single-task models, while performing better than the conventional parametric vocoder-based model.
Automatic Generation of Programming Exercises and Code Explanations using Large Language Models
Authors: Sami Sarsa, Paul Denny, Arto Hellas, Juho Leinonen
Date: 2022-06-03 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Text Generation

This article explores the natural language generation capabilities of large language models with application to the production of two types of learning resources common in programming courses. Using OpenAI Codex as the large language model, we create programming exercises (including sample solutions and test cases) and code explanations, assessing these qualitatively and quantitatively. Our results suggest that the majority of the automatically generated content is both novel and sensible, and in some cases ready to use as is. When creating exercises we find that it is remarkably easy to influence both the programming concepts and the contextual themes they contain, simply by supplying keywords as input to the model. Our analysis suggests that there is significant value in massive generative machine learning models as a tool for instructors, although there remains a need for some oversight to ensure the quality of the generated content before it is delivered to students. We further discuss the implications of OpenAI Codex and similar tools for introductory programming education and highlight future research streams that have the potential to improve the quality of the educational experience for both teachers and students alike.
SVSHI: Secure and Verified Smart Home Infrastructure
Authors: Andrea Veneziano, Samuel Chassot
Date: 2022-06-23 00:00:00
Tasks: Code Generation

Smart infrastructures uses are growing and with them the need for dependability and correctness. To provide better correctness guarantees and bring formal verification into the equation, we present SVSHI, a platform for developing, verifying, and running Python applications in KNX installations, one of the most used smart buildings standards. SVSHI leverages abstract syntax tree (AST) manipulation, code generation, symbolic execution, and static configuration verification to make writing advanced apps easy, quick, and safe. With SVSHI, the reliability and compatibility of the applications are guaranteed without foregoing users' productivity.
Flexible and disposable paper- and plastic-based gel micropads for nematode handling, imaging, and chemical testing
Authors: Zach Njus, Taejoon Kong, Upender Kalwa, Christopher Legner, Matthew Weinstein, Shawn Flanigan, Jenifer Saldanha, Santosh Pandey
Date: 2022-06-23 00:00:00

Today, the area of point-of-care diagnostics is synonymous with paper microfluidics where cheap, disposable, and on-the-spot detection toolkits are being developed for a variety of chemical tests. In this work, we present a novel application of microfluidic paper-based analytical devices (microPADs) to study the behavior of a small model nematode, Caenorhabditis elegans. We describe schemes of microPAD fabrication on paper and plastic substrates where membranes are created in agarose and Pluronic gel. Methods are demonstrated for loading, visualizing, and transferring single and multiple nematodes. Using an anthelmintic drug, levamisole, we show that chemical testing on C. elegans is easily performed because of the open device structure. A custom program is written to automatically recognize individual worms on the microPADs and extract locomotion parameters in real-time. The combination of microPADs and the nematode tracking program provides a relatively low-cost, simple-to-fabricate imaging and screening assay (compared to standard agarose plates or polymeric microfluidic devices) for non-microfluidic, nematode laboratories.
Fundamental limitations on the control of lossless systems
Authors: Johan Lindberg, Richard Pates
Date: 2022-06-23 00:00:00

In this paper we derive fundamental limitations on the levels of $H_2$ and $H_\infty{}$ performance that can be achieved when controlling lossless systems. The results are applied to the swing equation power system model, where it is shown that the fundamental limit on the $H_2$ norm scales with the inverse of the harmonic mean of the inertias in the system. This indicates that power systems may see a degradation in performance as more renewables are integrated, further motivating the need for new control solutions to aid the energy transition.
Decentralized and Coordinated Vf Control for Islanded Microgrids Considering DER Inadequacy and Demand Control
Authors: Buxin She, Fangxing Li, Hantao Cui, Jinning Wang, Liang Min, Oroghene Oboreh Snapps, Rui Bo
Date: 2022-06-22 00:00:00

This paper proposes a decentralized and coordinated voltage and frequency (Vf) control framework for islanded microgrids, with full consideration of the limited capacity of distributed energy resources (DERs) and Vf dependent load. First, the concept of DER inadequacy is illustrated with the challenges it poses. Then, a decentralized and coordinated control framework is proposed to regulate the output of inverter based generations and reallocate limited DER capacity for Vf control. The control framework is composed of a power regulator and a Vf regulator, which generates the supplementary signals for the primary controller. The power regulator regulates the output of grid forming inverters according to the real time capacity constraints of DERs, while the Vf regulator improves the Vf deviation by leveraging the load sensitivity to Vf. Next, the static feasibility and small signal stability of the proposed method are rigorously proven through mathematical formulation and eigenvalue analysis. Finally, a MATLAB Simulink simulation demonstrates the functionalities of the control framework. A few goals are fulfilled within the decentralized and coordinated framework, such as making the best use of limited DERs capacity, enhancing the DC side stability of inverter based generations, and reducing involuntary load shedding.
Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision
Authors: Buxin She, Fangxing Li, Hantao Cui, Jingqiu Zhang, Rui Bo
Date: 2022-06-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A high-level research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.
Multi-Access Point Coordination for Next-Gen Wi-Fi Networks Aided by Deep Reinforcement Learning
Authors: Lyutianyang Zhang, Hao Yin, Sumit Roy, Liu Cao
Date: 2022-06-22 00:00:00
Tasks: Deep Reinforcement Learning, Fairness, Management, Meta-Learning

Wi-Fi in the enterprise - characterized by overlapping Wi-Fi cells - constitutes the design challenge for next-generation networks. Standardization for recently started IEEE 802.11be (Wi-Fi 7) Working Groups has focused on significant medium access control layer changes that emphasize the role of the access point (AP) in radio resource management (RRM) for coordinating channel access due to the high collision probability with the distributed coordination function (DCF), especially in dense overlapping Wi-Fi networks. This paper proposes a novel multi-AP coordination system architecture aided by a centralized AP controller (APC). Meanwhile, a deep reinforcement learning channel access (DLCA) protocol is developed to replace the binary exponential backoff mechanism in DCF to enhance the network throughput by enabling the coordination of APs. First-Order Model-Agnostic Meta-Learning further enhances the network throughput. Subsequently, we also put forward a new greedy algorithm to maintain proportional fairness (PF) among multiple APs. Via the simulation, the performance of DLCA protocol in dense overlapping Wi-Fi networks is verified to have strong stability and outperform baselines such as Shared Transmission Opportunity (SH-TXOP) and Request-to-Send/Clear-to-Send (RTS/CTS) in terms of the network throughput by 10% and 3% as well as the network utility considering proportional fairness by 28.3% and 13.8%, respectively.
From Small-Gain Theory to Compositional Construction of Barrier Certificates for Large-Scale Stochastic Systems
Authors: Mahathi Anand, Abolfazl Lavaei, Majid Zamani
Date: 2021-01-18 00:00:00

This paper is concerned with a compositional approach for the construction of control barrier certificates for large-scale interconnected stochastic systems while synthesizing hybrid controllers against high-level logic properties. Our proposed methodology involves decomposition of interconnected systems into smaller subsystems and leverages the notion of control sub-barrier certificates of subsystems, enabling one to construct control barrier certificates of interconnected systems by employing some max-type small-gain conditions. The main goal is to synthesize hybrid controllers enforcing complex logic properties including the ones represented by the accepting language of deterministic finite automata, while providing probabilistic guarantees on the satisfaction of given specifications in bounded-time horizons. To do so, we propose a systematic approach to first decompose high-level specifications into simple reachability tasks by utilizing automata corresponding to the complement of specifications. We then construct control sub-barrier certificates and synthesize local controllers for those simpler tasks and combine them to obtain a hybrid controller that ensures satisfaction of the complex specification with some lower bound on the probability of satisfaction. To compute control sub-barrier certificates and corresponding local controllers, we provide two systematic approaches based on sum-of-squares (SOS) optimization program and counter-example guided inductive synthesis (CEGIS) framework. We finally apply our proposed techniques to two physical case studies.
Proposing Dynamic Model of Functional Interactions of IoT Technological Innovation System by Using System Dynamics and Fuzzy DEMATEL
Authors: Mohammad Mousakhani, Fatemeh Saghafi, Mohammad Hasanzadeh, Mohammad Ebrahim Sadeghi
Date: 2022-06-17 00:00:00
Tasks: Decision Making

One of the emerging technologies, which is expected to have tremendous effects on community development, is the Internet of Things technology. Given the prospects for this technology and the country's efforts to its development, policymaking for this technology is very crucial. The technological innovation system is one of the most important dynamic approaches in the field of modern technology policy. In this approach, by analyzing various functions which are influencing the development of a technology, the proper path to technological advancement is explained. For this reason, 10 major factors influencing the development of emerging technologies have been identified based on previous studies in this area and the effect of ten factors on each other was identified by using the system dynamics and the fuzzy DEMATEL method and the interactions between these functions were modeled. Market formation, resource mobilization, exploitation of the regime and policymaking and coordination functions have the most direct effect on the other functions. Also, policymaking and coordination, market formation, entrepreneurial activities, creating structure and resource mobilization have the most total impact on the other functions. Regard to resource constraint in the system, the policy makers should focus on those factors which have the most direct and total impact on the others that in this research are market formation, entrepreneurial activities, resource mobilization and policymaking and coordination. Given the dynamic nature of technology development, this model can help policymakers in the decision making process for the development of the Internet of Things.
Providing a model for the issue of multi-period ambulance location
Authors: Hamed Kazemipoor, Mohammad Ebrahim Sadeghi, Agnieszka Szmelter-Jarosz, Mohadese Aghabozorgi
Date: 2022-06-17 00:00:00

In this study, two mathematical models have been developed for assigning emergency vehicles, namely ambulances, to geographical areas. The first model, which is based on the assignment problem, the ambulance transfer (moving ambulances) between locations has not been considered. As ambulance transfer can improve system efficiency by decreasing the response time as well as operational cost, we consider this in the second model, which is based on the transportation problem. Both models assume that the demand of all geographical locations must be met. The major contributions of this study are: ambulance transfer between locations, day split into several time slots, and demand distribution of the geographical zone. To the best of our knowledge the first two have not been studied before. These extensions allow us to have a more realistic model of the real-world operation. Although, in previous studies, maximizing coverage has been the main objective of the goal, here, minimizing operating costs is a function of the main objective, because we have assumed that the demand of all geographical areas must be met.