pwc-archive/papers-with-abstracts huggingface dataset:
A Sociotechnical View of Algorithmic Fairness
Authors: Mateusz Dolata, Stefan Feuerriegel, Gerhard Schwabe
Date: 2021-09-27 00:00:00
Tasks: Articles, Decision Making, Fairness

Algorithmic fairness has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept and that technologies for algorithmic fairness should therefore be approached through a sociotechnical lens. We advance the discourse on algorithmic fairness as a sociotechnical phenomenon. Our research objective is to embed AF in the sociotechnical view of IS. Specifically, we elaborate on why outcomes of a system that uses algorithmic means to assure fairness depends on mutual influences between technical and social structures. This perspective can generate new insights that integrate knowledge from both technical fields and social studies. Further, it spurs new directions for IS debates. We contribute as follows: First, we problematize fundamental assumptions in the current discourse on algorithmic fairness based on a systematic analysis of 310 articles. Second, we respond to these assumptions by theorizing algorithmic fairness as a sociotechnical construct. Third, we propose directions for IS researchers to enhance their impacts by pursuing a unique understanding of sociotechnical algorithmic fairness. We call for and undertake a holistic approach to AF. A sociotechnical perspective on algorithmic fairness can yield holistic solutions to systemic biases and discrimination.
Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management
Authors: Ashwin, William Agnew, Umut Pajaro, Hetvi Jethwani, Arjun Subramonian
Date: 2021-09-21 00:00:00
Tasks: Diversity, Fairness, Management

Trustworthy artificial intelligence (AI) has become an important topic because trust in AI systems and their creators has been lost. Researchers, corporations, and governments have long and painful histories of excluding marginalized groups from technology development, deployment, and oversight. As a result, these technologies are less useful and even harmful to minoritized groups. We argue that any AI development, deployment, and monitoring framework that aspires to trust must incorporate both feminist, non-exploitative participatory design principles and strong, outside, and continual monitoring and testing. We additionally explain the importance of considering aspects of trustworthiness beyond just transparency, fairness, and accountability, specifically, to consider justice and shifting power to the disempowered as core values to any trustworthy AI system. Creating trustworthy AI starts by funding, supporting, and empowering grassroots organizations like Queer in AI so the field of AI has the diversity and inclusion to credibly and effectively develop trustworthy AI. We leverage the expert knowledge Queer in AI has developed through its years of work and advocacy to discuss if and how gender, sexuality, and other aspects of queer identity should be used in datasets and AI systems and how harms along these lines should be mitigated. Based on this, we share a gendered approach to AI and further propose a queer epistemology and analyze the benefits it can bring to AI. We additionally discuss how to regulate AI with this queer epistemology in vision, proposing frameworks for making policies related to AI & gender diversity and privacy & queer data protection.
SafeAccess+: An Intelligent System to make Smart Home Safer and Americans with Disability Act Compliant
Authors: Shahinur Alam
Date: 2021-09-14 00:00:00
Tasks: Image Description

Smart homes are becoming ubiquitous, but they are not Americans with Disability Act (ADA) compliant. Smart homes equipped with ADA compliant appliances and services are critical for people with disabilities (i.e., visual impairments and limited mobility) to improve independence, safety, and quality of life. Despite all advancements in smart home technologies, some fundamental design and implementation issues remain. For example, people with disabilities often feel insecure to respond when someone knocks on the door or rings the doorbell. In this paper, we present an intelligent system called "SafeAccess+" to build safer and ADA compliant premises (e.g. smart homes, offices). The key functionalities of the SafeAccess+ are: 1) Monitoring the inside/outside of premises and identifying incoming people; 2) Providing users relevant information to assess incoming threats (e.g., burglary, robbery) and ongoing crimes 3) Allowing users to grant safe access to homes for friends/family members. We have addressed several technical and research challenges: - developing models to detect and recognize person/activity, generating image descriptions, designing ADA compliant end-end system. In addition, we have designed a prototype smart door showcasing the proof-of-concept. The premises are expected to be equipped with cameras placed in strategic locations that facilitate monitoring the premise 24/7 to identify incoming persons and to generate image descriptions. The system generates a pre-structured message from the image description to assess incoming threats and immediately notify the users. The completeness and generalization of models have been ensured through a rigorous quantitative evaluation. The users' satisfaction and reliability of the system has been measured using PYTHEIA scale and was rated excellent (Internal Consistency-Cronbach's alpha is 0.784, Test-retest reliability is 0.939 )
Distributed Optimization using Heterogeneous Compute Systems
Authors: Vineeth S
Date: 2021-10-03 00:00:00
Tasks: Distributed Optimization

Hardware compute power has been growing at an unprecedented rate in recent years. The utilization of such advancements plays a key role in producing better results in less time -- both in academia and industry. However, merging the existing hardware with the latest hardware within the same ecosystem poses a challenging task. One of the key challenges, in this case, is varying compute power. In this paper, we consider the training of deep neural networks on a distributed system of workers with varying compute power. A naive implementation of synchronous distributed training will result in the faster workers waiting for the slowest worker to complete processing. To mitigate this issue, we propose to dynamically adjust the data assigned for each worker during the training. We assign each worker a partition of total data proportional to its computing power. Our experiments show that dynamically adjusting the data partition helps to improve the utilization of the system and significantly reduces the time taken for training. Code is available at the repository: \url{https://github.com/vineeths96/Heterogeneous-Systems}.
Dynamic Tolling for Inducing Socially Optimal Traffic Loads
Authors: Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu, Shankar Sastry
Date: 2021-10-17 00:00:00

How to design tolls that induce socially optimal traffic loads with dynamically arriving travelers who make selfish routing decisions? We propose a two-timescale discrete-time stochastic dynamics that adaptively adjusts the toll prices on a parallel link network while accounting for the updates of traffic loads induced by the incoming and outgoing travelers and their route choices. The updates of loads and tolls in our dynamics have three key features: (i) The total demand of incoming and outgoing travelers is stochastically realized; (ii) Travelers are myopic and selfish in that they choose routes according to a perturbed best response given the current latency and tolls on parallel links; (iii) The update of tolls is at a slower timescale as compared to the the update of loads. We show that the loads and the tolls eventually concentrate in a neighborhood of the fixed point, which corresponds to the socially optimal load and toll price. Moreover, the fixed point load is also a stochastic user equilibrium with respect to the toll price. Our results are useful for traffic authorities to efficiently manage traffic loads in response to the arrival and departure of travelers.
Graph Wedgelets: Adaptive Data Compression on Graphs based on Binary Wedge Partitioning Trees and Geometric Wavelets
Authors: Wolfgang Erb
Date: 2021-10-17 00:00:00
Tasks: Data Compression

We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the partitionings, a key ingredient to obtain sparse representations of a graph signal, is realized in terms of recursive wedge splits adapted to the signal. For this, we transfer adaptive partitioning and compression techniques known for 2D images to general graph structures and develop discrete variants of continuous wedgelets and binary space partitionings. We prove that continuous results on best m-term approximation with geometric wavelets can be transferred to the discrete graph setting and show that our wedgelet representation of graph signals can be encoded and implemented in a simple way. Finally, we illustrate that this graph-based method can be applied for the compression of images as well.
Measuring Total Transverse Reference-free Displacements of Railroad Bridges using 2 Degrees of Freedom (2DOF): Experimental Validation
Authors: Lingkun Chen, Can Zhu, Zeyu Wu, Xinxing Yuan, Fernando Moreu
Date: 2021-10-17 00:00:00
Tasks: Decision Making

Railroad bridge engineers are interested in the displacement of railroad bridges when the train is crossing the bridge for engineering decision making of their assets. Measuring displacements under train crossing events is difficult. If simplified reference-free methods would be accurate and validated, owners would conduct objective performance assessment of their bridge inventories under trains. Researchers have developed new sensing technologies (reference-free) to overcome the limitations of reference point-based displacement sensors. Reference-free methods use accelerometers to estimate displacements, by decomposing the total displacement in two parts: a high-frequency dynamic displacement component, and a low-frequency pseudo-static displacement component. In the past, researchers have used the Euler-Bernoulli beam theory formula to estimate the pseudo-static displacement assuming railroad bridge piles and columns can be simplified as cantilever beams. However, according to railroad bridge managers, railroad bridges have a different degree of fixity for each pile of each bent. Displacements can be estimated assuming a similar degree of fixity for deep foundations, but inherent errors will affect the accuracy of displacement estimation. This paper solves this problem expanding the 1 Degree of Freedom (1DOF) solution to a new 2 Degrees of Freedom (2DOF), to collect displacements under trains and enable cost-effective condition-based information related to bridge safety. Researchers developed a simplified beam to demonstrate the total displacement estimation using 2DOF and further conducted experimental results in the laboratory. The estimated displacement of the 2DOF model is more accurate than that of the 1DOF model for ten train crossing events. With only one sensor added to the ground of the pile, this method provides owners with approximately 40% more accurate displacements.
Tackling the Score Shift in Cross-Lingual Speaker Verification by Exploiting Language Information
Authors: Jenthe Thienpondt, Brecht Desplanques, Kris Demuynck
Date: 2021-10-18 00:00:00
Tasks: Language Identification, Speaker Recognition, Speaker Verification

This paper contains a post-challenge performance analysis on cross-lingual speaker verification of the IDLab submission to the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). We show that current speaker embedding extractors consistently underestimate speaker similarity in within-speaker cross-lingual trials. Consequently, the typical training and scoring protocols do not put enough emphasis on the compensation of intra-speaker language variability. We propose two techniques to increase cross-lingual speaker verification robustness. First, we enhance our previously proposed Large-Margin Fine-Tuning (LM-FT) training stage with a mini-batch sampling strategy which increases the amount of intra-speaker cross-lingual samples within the mini-batch. Second, we incorporate language information in the logistic regression calibration stage. We integrate quality metrics based on soft and hard decisions of a VoxLingua107 language identification model. The proposed techniques result in a 11.7% relative improvement over the baseline model on the VoxSRC-21 test set and contributed to our third place finish in the corresponding challenge.
Similarity-and-Independence-Aware Beamformer with Iterative Casting and Boost Start for Target Source Extraction Using Reference
Authors: Atsuo Hiroe
Date: 2021-10-18 00:00:00
Tasks: Speech Enhancement, speech-recognition, Speech Recognition

Target source extraction is significant for improving human speech intelligibility and the speech recognition performance of computers. This study describes a method for target source extraction, called the similarity-and-independence-aware beamformer (SIBF). The SIBF extracts the target source using a rough magnitude spectrogram as the reference signal. The advantage of the SIBF is that it can obtain a more accurate signal than the spectrogram generated by target-enhancing methods such as speech enhancement based on deep neural networks. For the extraction, we extend the framework of deflationary independent component analysis (ICA) by considering the similarities between the reference and extracted target sources, in addition to the mutual independence of all the potential sources. To solve the extraction problem by maximum-likelihood estimation, we introduce three source models that can reflect the similarities. The major contributions of this study are as follows. First, the extraction performance is improved using two methods, namely boost start for faster convergence and iterative casting for generating a more accurate reference. The effectiveness of these methods is verified through experiments using the CHiME3 dataset. Second, a concept of a fixed point pertaining to accuracy is developed. This concept facilitates understanding the relationship between the reference and SIBF output in terms of accuracy. Third, a unified formulation of the SIBF and mask-based beamformer is realized to apply the expertise of conventional BFs to the SIBF. The findings of this study can also improve the performance of the SIBF and promote research on ICA and conventional beamformers. Index Terms: beamformer, independent component analysis, source separation, speech enhancement, target source extraction
Supervised Metric Learning for Music Structure Features
Authors: Ju-Chiang Wang, Jordan B. L. Smith, Wei-Tsung Lu, Xuchen Song
Date: 2021-10-18 00:00:00
Tasks: Metric Learning

Music structure analysis (MSA) methods traditionally search for musically meaningful patterns in audio: homogeneity, repetition, novelty, and segment-length regularity. Hand-crafted audio features such as MFCCs or chromagrams are often used to elicit these patterns. However, with more annotations of section labels (e.g., verse, chorus, and bridge) becoming available, one can use supervised feature learning to make these patterns even clearer and improve MSA performance. To this end, we take a supervised metric learning approach: we train a deep neural network to output embeddings that are near each other for two spectrogram inputs if both have the same section type (according to an annotation), and otherwise far apart. We propose a batch sampling scheme to ensure the labels in a training pair are interpreted meaningfully. The trained model extracts features that can be used in existing MSA algorithms. In evaluations with three datasets (HarmonixSet, SALAMI, and RWC), we demonstrate that using the proposed features can improve a traditional MSA algorithm significantly in both intra- and cross-dataset scenarios.
VISinger: Variational Inference with Adversarial Learning for End-to-End Singing Voice Synthesis
Authors: Yongmao Zhang, Jian Cong, Heyang Xue, Lei Xie, Pengcheng Zhu, Mengxiao Bi
Date: 2021-10-17 00:00:00
Tasks: Decoder, Rhythm, Singing Voice Synthesis, Variational Inference

In this paper, we propose VISinger, a complete end-to-end high-quality singing voice synthesis (SVS) system that directly generates audio waveform from lyrics and musical score. Our approach is inspired by VITS, which adopts VAE-based posterior encoder augmented with normalizing flow-based prior encoder and adversarial decoder to realize complete end-to-end speech generation. VISinger follows the main architecture of VITS, but makes substantial improvements to the prior encoder based on the characteristics of singing. First, instead of using phoneme-level mean and variance of acoustic features, we introduce a length regulator and a frame prior network to get the frame-level mean and variance on acoustic features, modeling the rich acoustic variation in singing. Second, we further introduce an F0 predictor to guide the frame prior network, leading to stabler singing performance. Finally, to improve the singing rhythm, we modify the duration predictor to specifically predict the phoneme to note duration ratio, helped with singing note normalization. Experiments on a professional Mandarin singing corpus show that VISinger significantly outperforms FastSpeech+Neural-Vocoder two-stage approach and the oracle VITS; ablation study demonstrates the effectiveness of different contributions.
Planning of EM Skins for Improved Quality-of-Service in Urban Areas
Authors: Arianna Benoni, Marco Salucci, Giacomo Oliveri, Paolo Rocca, Baozhu Li, Andrea Massa
Date: 2021-10-18 00:00:00

The optimal planning of electromagnetic skins (EMSs) installed on the building facades to enhance the received signal strength, thus the wireless coverage and/or the quality-of-service (QoS) in large-scale urban areas, is addressed. More specifically, a novel instance of the System-by-Design (SbD) paradigm is proposed towards the implementation of a smart electromagnetic environment (SEME) where low-cost passive static reflective skins are deployed to enhance the level of the power received within selected regions-of-interest (RoIs). Thanks to the ad-hoc customization of the SbD functional blocks, which includes the exploitation of a digital twin (DT) for the accurate yet fast assessment of the wireless coverage condition, effective solutions are yielded. Numerical results, dealing with real-world test-beds, are shown to assess the capabilities, the potentialities, and the current limitations of the proposed EMSs planning strategy.
On the Design of Modular Reflecting EM Skins for Enhanced Urban Wireless Coverage
Authors: Paolo Rocca, Pietro Da Rù, Nicola Anselmi, Marco Salucci, Giacomo Oliveri, Danilo Erricolo, Andrea Massa
Date: 2021-10-18 00:00:00

The design of modular, passive, and static artificial metasurfaces to be used as electromagnetic skins (EMSs) of buildings for improving the coverage in urban millimeter-wave communication scenarios is addressed. Towards this end, an ad-hoc design strategy is presented to determine optimal trade-off implementative solutions that assure a suitable coverage of the areas of interest, where the signal from the base station is too weak, with the minimum complexity. More specifically, the admissible surface in the building facade is first partitioned into tiles, which are the minimum-size elements of the artificial coating (i.e., the building block of an EMS). Then, the search for the optimal EMS layout (i.e., the minimum number and the positions of the tiles to be installed) is carried out with a binary multi-objective optimization method. Representative numerical results are reported and discussed to point out the features and the potentialities of the EMS solution in the smart electromagnetic environment (SEME) as well as the effectiveness of the proposed design method.
Building a Smart EM Environment -- AI-Enhanced Aperiodic Micro-Scale Design of Passive EM Skins
Authors: Giacomo Oliveri, Francesco Zardi, Paolo Rocca, Marco Salucci, Andrea Massa
Date: 2021-10-18 00:00:00

An innovative process for the design of static passive smart skins (SPSSs) is proposed to take into account, within the synthesis, the electromagnetic (EM) interactions due to their finite (macro-level) size and aperiodic (micro-scale) layouts. Such an approach leverages on the combination of an inverse source (IS) formulation, to define the SPSS surface currents, and of an instance of the System-by-Design paradigm, to synthesize the unit cell (UC) descriptors suitable for supporting these currents. As for this latter step, an enhanced Artificial Intelligence (IA)-based digital twin (DT) is built to efficiently and reliably predict the relationships among the UCs and the non-uniform coupling effects arising when the UCs are irregularly assembled to build the corresponding SPSS. Towards this end and unlike state-of-the-art approaches, an aperiodic finite small-scale model of the SPSS is derived to generate the training database for the DT implementation. A set of representative numerical experiments, dealing with different radiation objectives and smart skin apertures, is reported to assess the reliability of the conceived design process and to illustrate the radiation features of the resulting layouts, validated with accurate full-wave simulations, as well.
An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System
Authors: Yijun Xu, Jaber Valinejad, Mert Korkali, Lamine Mili, Yajun Wang, Xiao Chen, Zongsheng Zheng
Date: 2021-10-18 00:00:00
Tasks: Bayesian Inference

The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.