pwc-archive/papers-with-abstracts huggingface dataset:
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Authors: Fuyuan Lyu, Xing Tang, Hong Zhu, Huifeng Guo, Yingxue Zhang, Ruiming Tang, Xue Liu
Date: 2022-08-09 00:00:00
Tasks: Click-Through Rate Prediction, Recommendation Systems

Learning embedding table plays a fundamental role in Click-through rate(CTR) prediction from the view of the model performance and memory usage. The embedding table is a two-dimensional tensor, with its axes indicating the number of feature values and the embedding dimension, respectively. To learn an efficient and effective embedding table, recent works either assign various embedding dimensions for feature fields and reduce the number of embeddings respectively or mask the embedding table parameters. However, all these existing works cannot get an optimal embedding table. On the one hand, various embedding dimensions still require a large amount of memory due to the vast number of features in the dataset. On the other hand, decreasing the number of embeddings usually suffers from performance degradation, which is intolerable in CTR prediction. Finally, pruning embedding parameters will lead to a sparse embedding table, which is hard to be deployed. To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models. Specifically, we propose pruning the redundant embeddings regarding corresponding features' importance by learnable pruning thresholds. Furthermore, we consider assigning various embedding dimensions as one single candidate architecture. To efficiently search the optimal embedding dimensions, we design a uniform embedding dimension sampling scheme to equally train all candidate architectures, meaning architecture-related parameters and learnable thresholds are trained simultaneously in one supernet. We then propose an evolution search method based on the supernet to find the optimal embedding dimensions for each field. Experiments on public datasets show that OptEmbed can learn a compact embedding table which can further improve the model performance.
AI in Telemedicine: An Appraisal on Deep Learning-Based Approaches to Virtual Diagnostic Solutions (VDS)
Authors: Ozioma Collins Oguine, Kanyifeechukwu Jane Oguine
Date: 2022-07-31 00:00:00
Tasks: Deep Learning, Descriptive, Diagnostic

Advancements in Telemedicine as an approach to healthcare delivery have heralded a new dawn in modern Medicine. Its fast-paced development in our contemporary society is credence to the advances in Artificial Intelligence and Information Technology. This paper carries out a descriptive study to broadly explore AI's implementations in healthcare delivery with a more holistic view of the usability of various Telemedical Innovations in enhancing Virtual Diagnostic Solutions (VDS). This research further explores notable developments in Deep Learning model optimizations for Virtual Diagnostic Solutions. A further research review on the prospects of Virtual Diagnostic Solutions (VDS) and foreseeable challenges was also highlighted. Conclusively, this research gives a general overview of Artificial Intelligence in Telemedicine with a central focus on Deep Learning-based approaches to Virtual Diagnostic Solutions.
Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning towards Improved Problem Solving
Authors: Preya Shabrina, Behrooz Mostafavi, Mark Abdelshiheed, Min Chi, Tiffany Barnes
Date: 2022-07-27 00:00:00

Learning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal labeled instructional materials with explanations in traditional problem solving and within tutoring systems to help novices learn to subgoal. However, only a little research is found on problem-solving strategies in relationship with subgoal learning. Also, these strategies are under-explored within computer-based tutors and learning environments. Backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty. In this paper, we explore a training strategy for backward strategy learning within an intelligent logic tutor that teaches logic proof construction. The training session involved backward worked examples (BWE) and problem-solving (BPS) to help students learn backward strategy towards improving their subgoaling and problem-solving skills. To evaluate the training strategy, we analyzed students' 1) experience with and engagement in learning backward strategy, 2) performance, and 3) proof construction approaches in new problems that they solved independently without tutor help after each level of training and in post-test. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training. Additionally, students trained with both BWE and BPS derived subgoals during proof construction with significantly higher efficiency than the other two groups.
Let it RAIN for Social Good
Authors: Mattias Brännström, Andreas Theodorou, Virginia Dignum
Date: 2022-07-26 00:00:00
Tasks: Ethics

Artificial Intelligence (AI) as a highly transformative technology take on a special role as both an enabler and a threat to UN Sustainable Development Goals (SDGs). AI Ethics and emerging high-level policy efforts stand at the pivot point between these outcomes but is barred from effect due the abstraction gap between high-level values and responsible action. In this paper the Responsible Norms (RAIN) framework is presented, bridging this gap thereby enabling effective high-level control of AI impact. With effective and operationalized AI Ethics, AI technologies can be directed towards global sustainable development.
AI Approaches in Processing and Using Data in Personalized Medicine
Authors: Mirjana Ivanovic, Serge Autexier, Miltiadis Kokkonidis
Date: 2022-07-26 00:00:00
Tasks: Explainable artificial intelligence, Federated Learning, Transfer Learning

In modern dynamic constantly developing society, more and more people suffer from chronic and serious diseases and doctors and patients need special and sophisticated medical and health support. Accordingly, prominent health stakeholders have recognized the importance of development of such services to make patients life easier. Such support requires the collection of huge amount of patients complex data like clinical, environmental, nutritional, daily activities, variety of data from smart wearable devices, data from clothing equipped with sensors etc. Holistic patients data must be properly aggregated, processed, analyzed, and presented to the doctors and caregivers to recommend adequate treatment and actions to improve patients health related parameters and general wellbeing. Advanced artificial intelligence techniques offer the opportunity to analyze such big data, consume them, and derive new knowledge to support personalized medical decisions. New approaches like those based on advanced machine learning, federated learning, transfer learning, explainable artificial intelligence open new paths for more quality use of health and medical data in future. In this paper, we will present some crucial aspects and characteristic examples in the area of application of a range of artificial intelligence approaches in personalized medical decisions.
COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics
Authors: Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård
Date: 2022-07-19 00:00:00

Due to the current COVID-19 virus, which has already been declared a pandemic by the World Health Organization (WHO), we are witnessing the greatest pandemic of the decade. Millions of people are being infected, resulting in thousands of deaths every day across the globe. Even it was difficult for the best healthcare-providing countries could not handle the pandemic because of the strain of treating thousands of patients at a time. The count of infections and deaths is increasing at an alarming rate because of the spread of the virus. We believe that innovative technologies could help reduce pandemics to a certain extent until we find a definite solution from the medical field to handle and treat such pandemic situations. Technology innovation has the potential to introduce new technologies that could support people and society during these difficult times. Therefore, this paper proposes the idea of using drones as a companion to tackle current and future pandemics. Our COROID drone is based on the principle of crowdsourcing sensors data of the public's smart devices, which can correlate the reading of the infrared cameras equipped on the COROID drones. To the best of our knowledge, this concept has yet to be investigated either as a concept or as a product. Therefore, we believe that the COROID drone is innovative and has a huge potential to tackle COVID-19 and future pandemics.
Classification of Stress via Ambulatory ECG and GSR Data
Authors: Zachary Dair, Muhammad Muneeb Saad, Urja Pawar, Samantha Dockray, Ruairi O'Reilly
Date: 2022-07-19 00:00:00
Tasks: Classification, Imputation, Specificity

In healthcare, detecting stress and enabling individuals to monitor their mental health and wellbeing is challenging. Advancements in wearable technology now enable continuous physiological data collection. This data can provide insights into mental health and behavioural states through psychophysiological analysis. However, automated analysis is required to provide timely results due to the quantity of data collected. Machine learning has shown efficacy in providing an automated classification of physiological data for health applications in controlled laboratory environments. Ambulatory uncontrolled environments, however, provide additional challenges requiring further modelling to overcome. This work empirically assesses several approaches utilising machine learning classifiers to detect stress using physiological data recorded in an ambulatory setting with self-reported stress annotations. A subset of the training portion SMILE dataset enables the evaluation of approaches before submission. The optimal stress detection approach achieves 90.77% classification accuracy, 91.24 F1-Score, 90.42 Sensitivity and 91.08 Specificity, utilising an ExtraTrees classifier and feature imputation methods. Meanwhile, accuracy on the challenge data is much lower at 59.23% (submission #54 from BEaTS-MTU, username ZacDair). The cause of the performance disparity is explored in this work.
Towards a General Pre-training Framework for Adaptive Learning in MOOCs
Authors: Qingyang Zhong, Jifan Yu, Zheyuan Zhang, Yiming Mao, Yuquan Wang, Yankai Lin, Lei Hou, Juanzi Li, Jie Tang
Date: 2022-07-18 00:00:00
Tasks: Knowledge Tracing

Adaptive learning aims to stimulate and meet the needs of individual learners, which requires sophisticated system-level coordination of diverse tasks, including modeling learning resources, estimating student states, and making personalized recommendations. Existing deep learning methods have achieved great success over statistical models; however, they still lack generalization for diverse tasks and suffer from insufficient capacity since they are composed of highly-coupled task-specific architectures and rely on small-scale, coarse-grained recommendation scenarios. To realize the idea of general adaptive systems proposed in pedagogical theory, with the emerging pre-training techniques in NLP, we try to conduct a practical exploration on applying pre-training to adaptive learning, to propose a unified framework based on data observation and learning style analysis, properly leveraging heterogeneous learning elements. Through a series of downstream tasks of Learning Recommendation, Learning Resource Evaluation, Knowledge Tracing, and Dropout Prediction, we find that course structures, text, and knowledge are helpful for modeling and inherently coherent to student non-sequential learning behaviors and that indirectly relevant information included in the pre-training foundation can be shared across downstream tasks to facilitate effectiveness. We finally build a simplified systematic application of adaptive learning and reflect on the insights brought back to pedagogy. The source code and dataset will be released.
The Transform-o-meter: A method to forecast the transformative impact of innovation
Authors: Hector G. T. Torres
Date: 2022-07-15 00:00:00

With the advent of Transformative Artificial Intelligence, it is now more important than ever to be able to both measure and forecast the transformative impact/potential of innovation. However, current methods fall short when faced with this task. This paper introduces the Transform-o-meter; a methodology that can be used to achieve the aforementioned goal, and be applied to any innovation, both material and immaterial. While this method can effectively be used for the mentioned purpose, it should be taken as a first approach; to be iterated, researched, and expanded further upon.
Areas of Strategic Visibility: Disability Bias in Biometrics
Authors: Jennifer Mankoff, representing the Center for Research, Education on Accessible Technology, Experiences, Devva Kasnitz, Disability Studies, L Jean Camp, Jonathan Lazar, Harry Hochheiser
Date: 2022-07-14 00:00:00

This response to the RFI considers the potential for biometrics to help or harm disabled people2. Biometrics are already integrated into many aspects of daily life, from airport travel to mobile phone use. Yet many of these systems are not accessible to people who experience different kinds of disability exclusion . Different personal characteristics may impact any or all of the physical (DNA, fingerprints, face or retina) and behavioral (gesture, gait, voice) characteristics listed in the RFI as examples of biometric signals.
Computationally Identifying Funneling and Focusing Questions in Classroom Discourse
Authors: Sterling Alic, Dorottya Demszky, Zid Mancenido, Jing Liu, Heather Hill, Dan Jurafsky
Date: 2022-07-08 00:00:00
Proceeding: NAACL (BEA) 2022 7
Tasks: Math

Responsive teaching is a highly effective strategy that promotes student learning. In math classrooms, teachers might "funnel" students towards a normative answer or "focus" students to reflect on their own thinking, deepening their understanding of math concepts. When teachers focus, they treat students' contributions as resources for collective sensemaking, and thereby significantly improve students' achievement and confidence in mathematics. We propose the task of computationally detecting funneling and focusing questions in classroom discourse. We do so by creating and releasing an annotated dataset of 2,348 teacher utterances labeled for funneling and focusing questions, or neither. We introduce supervised and unsupervised approaches to differentiating these questions. Our best model, a supervised RoBERTa model fine-tuned on our dataset, has a strong linear correlation of .76 with human expert labels and with positive educational outcomes, including math instruction quality and student achievement, showing the model's potential for use in automated teacher feedback tools. Our unsupervised measures show significant but weaker correlations with human labels and outcomes, and they highlight interesting linguistic patterns of funneling and focusing questions. The high performance of the supervised measure indicates its promise for supporting teachers in their instruction.
The History of AI Rights Research
Authors: Jamie Harris
Date: 2022-07-06 00:00:00

This report documents the history of research on AI rights and other moral consideration of artificial entities. It highlights key intellectual influences on this literature as well as research and academic discussion addressing the topic more directly. We find that researchers addressing AI rights have often seemed to be unaware of the work of colleagues whose interests overlap with their own. Academic interest in this topic has grown substantially in recent years; this reflects wider trends in academic research, but it seems that certain influential publications, the gradual, accumulating ubiquity of AI and robotic technology, and relevant news events may all have encouraged increased academic interest in this specific topic. We suggest four levers that, if pulled on in the future, might increase interest further: the adoption of publication strategies similar to those of the most successful previous contributors; increased engagement with adjacent academic fields and debates; the creation of specialized journals, conferences, and research institutions; and more exploration of legal rights for artificial entities.
Applying data technologies to combat AMR: current status, challenges, and opportunities on the way forward
Authors: Leonid Chindelevitch, Elita Jauneikaite, Nicole E. Wheeler, Kasim Allel, Bede Yaw Ansiri-Asafoakaa, Wireko A. Awuah, Denis C. Bauer, Stephan Beisken, Kara Fan, Gary Grant, Michael Graz, Yara Khalaf, Veranja Liyanapathirana, Carlos Montefusco-Pereira, Lawrence Mugisha, Atharv Naik, Sylvia Nanono, Anthony Nguyen, Timothy Rawson, Kessendri Reddy, Juliana M. Ruzante, Anneke Schmider, Roman Stocker, Leonhardt Unruh, Daniel Waruingi, Heather Graz, Maarten van Dongen
Date: 2022-07-05 00:00:00
Tasks: Management

Antimicrobial resistance (AMR) is a growing public health threat, estimated to cause over 10 million deaths per year and cost the global economy 100 trillion USD by 2050 under status quo projections. These losses would mainly result from an increase in the morbidity and mortality from treatment failure, AMR infections during medical procedures, and a loss of quality of life attributed to AMR. Numerous interventions have been proposed to control the development of AMR and mitigate the risks posed by its spread. This paper reviews key aspects of bacterial AMR management and control which make essential use of data technologies such as artificial intelligence, machine learning, and mathematical and statistical modelling, fields that have seen rapid developments in this century. Although data technologies have become an integral part of biomedical research, their impact on AMR management has remained modest. We outline the use of data technologies to combat AMR, detailing recent advancements in four complementary categories: surveillance, prevention, diagnosis, and treatment. We provide an overview on current AMR control approaches using data technologies within biomedical research, clinical practice, and in the "One Health" context. We discuss the potential impact and challenges wider implementation of data technologies is facing in high-income as well as in low- and middle-income countries, and recommend concrete actions needed to allow these technologies to be more readily integrated within the healthcare and public health sectors.
A Temperature Independent Readout Circuit for ISFET-Based Sensor Applications
Authors: Elmira Moussavi, Dominik Sisejkovic, Animesh Singh, Daniyar Kizatov, Rainer Leupers, Sven Ingebrandt, Vivek Pachauri, Farhad Merchant
Date: 2022-08-09 00:00:00

The ion-sensitive field-effect transistor (ISFET) is an emerging technology that has received much attention in numerous research areas, including biochemistry, medicine, and security applications. However, compared to other types of sensors, the complexity of ISFETs make it more challenging to achieve a sensitive, fast and repeatable response. Therefore, various readout circuits have been developed to improve the performance of ISFETs, especially to eliminate the temperature effect. This paper presents a new approach for a temperature-independent readout circuit that uses the threshold voltage differences of an ISFET-MOSFET pair. The Linear Technology Simulation Program with Integrated Circuit Emphasis (LTspice) is used to analyze the ISFET performance based on the proposed readout circuit characteristics. A macro-model is used to model ISFET behavior, including the first-level Spice model for the MOSFET part and Verilog-A to model the surface potential, reference electrode, and electrolyte of the ISFET to determine the relationships between variables.In this way, the behavior of the ISFET is monitored by the output voltage of the readout circuit based on a change in the electrolyte's hydrogen potential (pH), determined by the simulation. The proposed readout circuit has a temperature coefficient of 11.9 $ppm/{\deg}C$ for a temperature range of 0-100 ${\deg}C$ and pH between 1 and 13. The proposed ISFET readout circuit outperforms other designs in terms of simplicity and not requiring an additional sensor.
Quantifying the Capacity Gains in Coarsely Quantized SISO Systems with Nonlinear Analog Operators
Authors: Farhad Shirani, Hamidreza Aghasi
Date: 2022-08-08 00:00:00
Tasks: Quantization

The power consumption of high-speed, high-resolution analog to digital converters (ADCs) is a limiting factor in implementing large-bandwidth mm-wave communication systems. A mitigating solution, which has drawn considerable recent interest, is to use a few low-resolution ADCs at the receiver. While reducing the number and resolution of the ADCs decreases power consumption, it also leads to a reduction in channel capacity due to the information loss induced by coarse quantization. This implies a rate-energy tradeoff governed by the number and resolution of ADCs. Recently, it was shown that given a fixed number of low-resolution ADCs, the application of practically implementable nonlinear analog operators, prior to sampling and quantization, may significantly reduce the aforementioned rate-loss. Building upon these observations, this work focuses on single-input single-output (SISO) communication scenarios, and i) characterizes capacity expressions under various assumptions on the set of implementable nonlinear analog functions, ii) provides computational methods to calculate the channel capacity numerically, and iii) quantifies the gains due to the use of nonlinear operators in SISO receiver terminals. Furthermore, circuit-level simulations, using a 65 nm Bulk CMOS technology, are provided to show the implementability of the desired nonlinear operators in the analog domain. The power requirements of the proposed circuits are quantified for various analog operators.
Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters
Authors: Yunseong Cho, Jinseok Choi, Brian L. Evans
Date: 2022-08-08 00:00:00
Tasks: Quantization

A multicell-coordinated beamforming solution for massive multiple-input multiple-output orthogonal frequency-division multiplexing (OFDM) systems is presented when employing low-resolution data converters and per-antenna level constraints. For a more realistic deployment, we aim to find the downlink (DL) beamformer that minimizes the maximum power on transmit antenna array of each basestation under received signal quality constraints while minimizing per-antenna transmit power. We show that strong duality holds between the primal DL formulation and its manageable Lagrangian dual problem which can be interpreted as the virtual uplink (UL) problem with adjustable noise covariance matrices. For a fixed set of noise covariance matrices, we claim that the virtual UL solution is effectively used to compute the DL beamformer and noise covariance matrices can be subsequently updated with an associated subgradient. Our primary contributions are then (1) formulating the quantized DL OFDM antenna power minimax problem and deriving its associated dual problem, (2) showing strong duality and interpreting the dual as a virtual quantized UL OFDM problem, and (3) developing an iterative minimax algorithm based on the dual problem. Simulations validate the proposed algorithm in terms of the maximum antenna transmit power and peak-to-average-power ratio.