pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Two Types of Global Graph for Sequential Fashion Recommendation
Authors: Yujuan Ding, Yunshan Ma, Wai Keung Wong, Tat-Seng Chua
Date: 2021-05-17 00:00:00
Tasks: Vocal Bursts Valence Prediction

Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce. The key to building an effective sequential fashion recommendation model lies in capturing two types of patterns: the personal fashion preference of users and the transitional relationships between adjacent items. The two types of patterns are usually related to user-item interaction and item-item transition modeling respectively. However, due to the large sets of users and items as well as the sparse historical interactions, it is difficult to train an effective and efficient sequential fashion recommendation model. To tackle these problems, we propose to leverage two types of global graph, i.e., the user-item interaction graph and item-item transition graph, to obtain enhanced user and item representations by incorporating higher-order connections over the graphs. In addition, we adopt the graph kernel of LightGCN for the information propagation in both graphs and propose a new design for item-item transition graph. Extensive experiments on two established sequential fashion recommendation datasets validate the effectiveness and efficiency of our approach.
Chord Recognition- Music and Audio Information Retrieval
Authors: Shah Riya Chiragkumar
Date: 2021-05-14 00:00:00
Tasks: Chord Recognition, Information Retrieval, Music Information Retrieval, Retrieval

Music Information Retrieval (MIR) is a collaborative scientific study that help to build innovative information research themes, novel frameworks, and developing connected delivery mechanisms in addition to making the world's massive collection of music open for everyone. Modern rock music proved to be difficult to estimate tempo and chord recognition did not work. All of the findings indicate that modern rock and metal music can be analysed, despite its complexity, but that further research is needed in this area to make it useful. Using a neural network has been one of the simplest ways of dealing with it. The pitch class profile vector is used in the neural network method. Because the vector only contains 12 elements of semi-tone values, it is enough for chord recognition. Of course, there are other ways of achieving this work, most of them depend on pitch class profiling to transform the chord into a type that can be recognised, but the recognition process is time-consuming centred on extremely complicated and memory-intensive methods.
Hard Choices and Hard Limits for Artificial Intelligence
Authors: Bryce Goodman
Date: 2021-05-04 00:00:00
Tasks: Decision Making, Sentence

Artificial intelligence (AI) is supposed to help us make better choices. Some of these choices are small, like what route to take to work, or what music to listen to. Others are big, like what treatment to administer for a disease or how long to sentence someone for a crime. If AI can assist with these big decisions, we might think it can also help with hard choices, cases where alternatives are neither better, worse nor equal but on a par. The aim of this paper, however, is to show that this view is mistaken: the fact of parity shows that there are hard limits on AI in decision making and choices that AI cannot, and should not, resolve.
The Flipped Classroom model for teaching Conditional Random Fields in an NLP course
Authors: Manex Agirrezabal
Date: 2021-05-04 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

In this article, we show and discuss our experience in applying the flipped classroom method for teaching Conditional Random Fields in a Natural Language Processing course. We present the activities that we developed together with their relationship to a cognitive complexity model (Bloom's taxonomy). After this, we provide our own reflections and expectations of the model itself. Based on the evaluation got from students, it seems that students learn about the topic and also that the method is rewarding for some students. Additionally, we discuss some shortcomings and we propose possible solutions to them. We conclude the paper with some possible future work.
The Online Pivot: Lessons Learned from Teaching a Text and Data Mining Course in Lockdown, Enhancing online Teaching with Pair Programming and Digital Badges
Authors: Beatrice Alex, Clare Llewellyn, Pawel Michal Orzechowski, Maria Boutchkova
Date: 2021-05-03 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

In this paper we provide an account of how we ported a text and data mining course online in summer 2020 as a result of the COVID-19 pandemic and how we improved it in a second pilot run. We describe the course, how we adapted it over the two pilot runs and what teaching techniques we used to improve students' learning and community building online. We also provide information on the relentless feedback collected during the course which helped us to adapt our teaching from one session to the next and one pilot to the next. We discuss the lessons learned and promote the use of innovative teaching techniques applied to the digital such as digital badges and pair programming in break-out rooms for teaching Natural Language Processing courses to beginners and students with different backgrounds.
Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?
Authors: David Leslie, Anjali Mazumder, Aidan Peppin, Maria Wolters, Alexa Hagerty
Date: 2021-04-30 00:00:00

Among the most damaging characteristics of the covid-19 pandemic has been its disproportionate effect on disadvantaged communities. As the outbreak has spread globally, factors such as systemic racism, marginalisation, and structural inequality have created path dependencies that have led to poor health outcomes. These social determinants of infectious disease and vulnerability to disaster have converged to affect already disadvantaged communities with higher levels of economic instability, disease exposure, infection severity, and death. Artificial intelligence (AI) technologies are an important part of the health informatics toolkit used to fight contagious disease. AI is well known, however, to be susceptible to algorithmic biases that can entrench and augment existing inequality. Uncritically deploying AI in the fight against covid-19 thus risks amplifying the pandemic's adverse effects on vulnerable groups, exacerbating health inequity. In this paper, we claim that AI systems can introduce or reflect bias and discrimination in three ways: in patterns of health discrimination that become entrenched in datasets, in data representativeness, and in human choices made during the design, development, and deployment of these systems. We highlight how the use of AI technologies threaten to exacerbate the disparate effect of covid-19 on marginalised, under-represented, and vulnerable groups, particularly black, Asian, and other minoritised ethnic people, older populations, and those of lower socioeconomic status. We conclude that, to mitigate the compounding effects of AI on inequalities associated with covid-19, decision makers, technology developers, and health officials must account for the potential biases and inequities at all stages of the AI process.
Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application
Authors: Abhijit Suresh, Jennifer Jacobs, Vivian Lai, Chenhao Tan, Wayne Ward, James H. Martin, Tamara Sumner
Date: 2021-04-29 00:00:00
Tasks: speech-recognition, Speech Recognition

TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application combines state-of-the-art natural language processing capabilities with automated speech recognition to automatically analyze classroom recordings and provide teachers with personalized feedback on their use of specific types of discourse aimed at broadening and deepening classroom conversations about mathematics. These specific discourse strategies are referred to as "talk moves" within the mathematics education community and prior research has documented the ways in which systematic use of these discourse strategies can positively impact student engagement and learning. In this article, we describe the TalkMoves application's cloud-based infrastructure for managing and processing classroom recordings, and its interface for providing teachers with feedback on their use of talk moves during individual teaching episodes. We present the series of model architectures we developed, and the studies we conducted, to develop our best-performing, transformer-based model (F1 = 79.3%). We also discuss several technical challenges that need to be addressed when working with real-world speech and language data from noisy K-12 classrooms.
An Experimental Analysis of Work-Life Balance Among The Employees using Machine Learning Classifiers
Authors: Karampudi Radha, Mekala Rohith
Date: 2021-04-28 00:00:00
Tasks: BIG-bench Machine Learning

Researchers today have found out the importance of Artificial Intelligence, and Machine Learning in our daily lives, as well as they can be used to improve the quality of our lives as well as the cities and nations alike. An example of this is that it is currently speculated that ML can provide ways to relieve workers as it can predict effective working schedules and patterns which increase the efficiency of the workers. Ultimately this is leading to a Work-Life Balance for the workers. But how is this possible? It is practically possible with the Machine Learning algorithms to predict, calculate the factors affecting the feelings of the worker's work-life balance. In order to actually do this, a sizeable amount of 12,756 people's data has been taken under consideration. Upon analysing the data and calculating under various factors, we have found out the correlation of various factors and WLB(Work-Life Balance in short). There are some factors that have to be taken into serious consideration as they play a major role in WLB. We have trained 80% of our data with Random Forest Classifier, SVM and Naive Bayes algorithms. Upon testing, the algorithms predict the WLB with 71.5% as the best accuracy.
Leveraging Classification Metrics for Quantitative System-Level Analysis with Temporal Logic Specifications
Authors: Apurva Badithela, Tichakorn Wongpiromsarn, Richard M. Murray
Date: 2021-05-16 00:00:00
Tasks: Multi-class Classification

In many autonomy applications, performance of perception algorithms is important for effective planning and control. In this paper, we introduce a framework for computing the probability of satisfaction of formal system specifications given a confusion matrix, a statistical average performance measure for multi-class classification. We define the probability of satisfaction of a linear temporal logic formula given a specific initial state of the agent and true state of the environment. Then, we present an algorithm to construct a Markov chain that represents the system behavior under the composition of the perception and control components such that the probability of the temporal logic formula computed over the Markov chain is consistent with the probability that the temporal logic formula is satisfied by our system. We illustrate this approach on a simple example of a car with pedestrian on the sidewalk environment, and compute the probability of satisfaction of safety requirements for varying parameters of the vehicle. We also illustrate how satisfaction probability changes with varied precision and recall derived from the confusion matrix. Based on our results, we identify several opportunities for future work in developing quantitative system-level analysis that incorporates perception models.
Dual-Stage Low-Complexity Reconfigurable Speech Enhancement
Authors: Jun Yang, Nico Brailovsky
Date: 2021-05-17 00:00:00
Tasks: Speech Enhancement

This paper proposes a dual-stage, low complexity, and reconfigurable technique to enhance the speech contaminated by various types of noise sources. Driven by input data and audio contents, the proposed dual-stage speech enhancement approach performs a coarse and fine processing in the first-stage and second-stage, respectively. In this paper, we demonstrate that the proposed speech enhancement solution significantly enhances the metrics of 3-fold QUality Evaluation of Speech in Telecommunication (3QUEST) consisting of speech mean-opinion-score (SMOS) and noise MOS (NMOS) for near-field and far-field applications. Moreover, the proposed speech enhancement approach greatly improves both the signal-to-noise ratio (SNR) and subjective listening experience. For comparisons, the traditional speech enhancement methods reduce the SMOS although they increase NMOS and SNR. In addition, the proposed speech enhancement scheme can be easily adopted in both capture path and speech render path for speech communication and conferencing systems, and voice-trigger applications.
X-Vectors with Multi-Scale Aggregation for Speaker Diarization
Authors: Myungjong Kim, Vijendra Raj Apsingekar, Divya Neelagiri
Date: 2021-05-16 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Speaker Verification

Speaker diarization is the process of labeling different speakers in a speech signal. Deep speaker embeddings are generally extracted from short speech segments and clustered to determine the segments belong to same speaker identity. The x-vector, which embeds segment-level speaker characteristics by statistically pooling frame-level representations, is one of the most widely used deep speaker embeddings in speaker diarization. Multi-scale aggregation, which employs multi-scale representations from different layers, has recently successfully been used in short duration speaker verification. In this paper, we investigate a multi-scale aggregation approach in an x-vector embedding framework for speaker diarization by exploiting multiple statistics pooling layers from different frame-level layers. Thus, it is expected that x-vectors with multi-scale aggregation have the potential to capture meaningful speaker characteristics from short segments, effectively taking advantage of different information at multiple layers. Experimental evaluation on the CALLHOME dataset showed that our approach provides substantial improvement over the baseline x-vectors.
Distributionally Robust Chance-Constrained Flexibility Planning for Integrated Energy System
Authors: Sen Zhan, Peng Hou, Guangya Yang
Date: 2021-05-17 00:00:00

Inflexible combined heat and power (CHP) plants and uncertain wind power production result in excess power in distribution networks, which leads to inverse power flow challenging grid operations. Power-to-X facilities such as electrolysers and electric boilers can offer extra flexibility to the integrated energy system. In this regard, we aim to jointly determine the optimal Power-to-X facility sizing and integrated energy system operations in this study. To account for wind power uncertainties, a distributionally robust chance-constrained model is developed to characterize wind power uncertainties using ambiguity sets. Linear decision rules are applied to analytically express real-time recourse actions when uncertainties are exposed, which allows the propagation of wind power uncertainties to gas and heat systems. Accordingly, the developed three-stage distributionally robust chance-constrained model is converted into a computationally tractable single-stage mixed-integer conic model. A case study validates the effectiveness of introducing the electrolyser and electric boiler into the integrated energy system, with respect to the decreased system cost, expanded CHP plant flexibility and reduced inverse power flow. The developed distributionally robust optimization model exhibits better effectiveness and robustness compared to a chance-constrained optimization model assuming wind forecast errors follow Gaussian distribution. Detailed profit analysis reveals that although the overall system cost is minimized, the profit is distributed unevenly across various stakeholders in the system.
Complex Frequency
Authors: Federico Milano
Date: 2021-05-17 00:00:00

The paper introduces the concept of complex frequency. The imaginary part of the complex frequency is the variation with respect of a synchronous reference of the local bus frequency as commonly defined in power system studies. The real part is defined based on the variation of the voltage magnitude. The latter term is crucial for the correct interpretation and analysis of the variation of the frequency at each bus of the network. The paper also develops a set of differential equations that describe the link between complex powers and complex frequencies at network buses in transient conditions. No simplifications are assumed except for constant elements of the network admittance matrix. A variety of analytical and numerical examples show the applications and potentials of the proposed concept.
Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems
Authors: Victor G. Lopez, Mohammad Alsalti, Matthias A. Müller
Date: 2021-05-17 00:00:00
Tasks: Q-Learning

This paper introduces and analyzes an improved Q-learning algorithm for discrete-time linear time-invariant systems. The proposed method does not require any knowledge of the system dynamics, and it enjoys significant efficiency advantages over other data-based optimal control methods in the literature. This algorithm can be fully executed off-line, as it does not require to apply the current estimate of the optimal input to the system as in on-policy algorithms. It is shown that a persistently exciting input, defined from an easily tested matrix rank condition, guarantees the convergence of the algorithm. A data-based method is proposed to design the initial stabilizing feedback gain that the algorithm requires. Robustness of the algorithm in the presence of noisy measurements is analyzed. We compare the proposed algorithm in simulation to different direct and indirect data-based control design methods.
Low-Input Accurate Periodic Motion of an Underactuated Mechanism: Mass Distribution and Nonlinear Spring Shaping
Authors: Andrea Tilli, Elena Ruggiano, Alessandro Bosso, Alessandro Samorì
Date: 2021-05-17 00:00:00

This work presents a control-oriented structural design approach for a 2-DOF underactuated mechanical system, with the purpose of generating an optimal oscillatory behavior of the end-effector. To achieve the desired periodic motion, we propose to adjust the dynamic response of the mechanism by selecting its mass distribution and the characteristic of a nonlinear spring. In particular, we introduce a two-step optimization strategy to shape the system's zero dynamics, obtained via input-output linearization. The first part of the procedure aims to minimize the root-mean-square value of the input torque by optimizing the mechanism's mass distribution. In this context, we show that a perfect matching with the desired trajectory can be reached by assuming the ability to design an arbitrary shape of the system's elastic properties. Then, in order to favor a simpler physical implementation of the structure, we dedicate the second optimization step to the piecewise linear approximation of the previously defined stiffness characteristic. The proposed procedure is finally tested in detailed numerical simulations, confirming its effectiveness in generating a complex and efficient periodic motion.
On the Matching Equations of Kinetic Energy Shaping in IDA-PBC
Authors: M. Reza J. Harandi, Hamid D. Taghirad
Date: 2020-11-30 00:00:00

Interconnection and damping assignment passivity-based control scheme has been used to stabilize many physical systems such as underactuated mechanical systems through total energy shaping. In this method, some partial differential equations (PDEs) arisen by kinetic and potential energy shaping, shall be solved analytically. Finding a suitable desired inertia matrix as the solution of nonlinear PDEs related to kinetic energy shaping is a challenging problem. In this paper, a systematic approach to solve this matching equation for systems with one degree of underactuation is proposed. A special structure for desired inertia matrix is proposed to simplify the solution of the corresponding PDE. It is shown that the proposed method is more general than that of some reported methods in the literature. In order to derive a suitable desired inertia matrix, a necessary condition is also derived. The proposed method is applied to three examples, including VTOL aircraft, pendubot and 2D SpiderCrane system.
Dissipation of Oscillation Energy and Distribution of Damping Power in a Multimachine Power System: A Small-signal Analysis
Authors: Kaustav Chatterjee, Nilanjan Ray Chaudhuri
Date: 2021-05-17 00:00:00

This paper revisits the concept of damping torque in a multimachine power system and its relation to the dissipation of oscillation energy in synchronous machine windings. As a multimachine extension of an existing result on a single-machine-infinite-bus (SMIB) system, we show that the total damping power for a mode stemming from the interaction of electromagnetic torques and rotor speeds is equal to the sum of average power dissipations in the generator windings corresponding to the modal oscillation. Further, counter-intuitive to the SMIB result, we demonstrate that, although the equality holds on an aggregate, such is not the case for individual machines in an interconnected system. To that end, distribution factors are derived for expressing the average damping power of each generator as a linear combination of average powers of modal energy dissipation in the windings of all machines in the system. These factors represent the distribution of damping power in a multimachine system. The results are validated on IEEE 4-machine and 16-machine test systems.
Distinguishing Aerial Intruders from Trajectory Data: A Model-Based Hypothesis-Testing Approach
Authors: David Petrizze, Kasra Koorehdavoudi, Mengran Xue, Sandip Roy
Date: 2021-05-16 00:00:00

Motivated by security needs in unmanned aerial system (UAS) operations, an algorithm for identifying airspace intruders (e.g., birds vs. drones) is developed. The algorithm is structured to use sensed intruder velocity data from Internet-of-Things platforms together with limited knowledge of physical models. The identification problem is posed as a statistical hypothesis testing or detection problem, wherein inertial feedback-controlled objects subject to stochastic actuation must be distinguished by speed data. The maximum a posteriori probability detector is obtained, and then is simplified to an explicit computation based on two points in the sample autocorrelation of the data. The simplified form allows computationally-friendly implementation of the algorithm, and simplified learning from archived data. Also, the total probability of error of the detector is computed and characterized. Simulations based on synthesized data are presented to illustrate and supplement the formal analyses.