pwc-archive/papers-with-abstracts huggingface dataset:
A COVID-19 Search Engine (CO-SE) with Transformer-based Architecture
Authors: Shaina Raza
Date: 2022-06-07 00:00:00

Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and researchers require expert-specific methods to stay current on scientific knowledge and research findings. However, there are a lot of research papers being written on the subject, which makes it hard to keep up with the most recent research. This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications. The CO-SE has a retriever component trained on the TF-IDF vectorizer that retrieves the relevant documents from the system. It also consists of a reader component that consists of a Transformer-based model, which is used to read the paragraphs and find the answers related to the query from the retrieved documents. The proposed model has outperformed previous models, obtaining an exact match ratio score of 71.45% and a semantic answer similarity score of 78.55%. It also outperforms other benchmark datasets, demonstrating the generalizability of the proposed approach.
Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
Authors: Ning Wu, Yaobo Liang, Houxing Ren, Linjun Shou, Nan Duan, Ming Gong, Daxin Jiang
Date: 2022-06-07 00:00:00
Tasks: Language Modelling, Passage Retrieval, Retrieval, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Retrieval

Recent research demonstrates the effectiveness of using pretrained language models (PLM) to improve dense retrieval and multilingual dense retrieval. In this work, we present a simple but effective monolingual pretraining task called contrastive context prediction~(CCP) to learn sentence representation by modeling sentence level contextual relation. By pushing the embedding of sentences in a local context closer and pushing random negative samples away, different languages could form isomorphic structure, then sentence pairs in two different languages will be automatically aligned. Our experiments show that model collapse and information leakage are very easy to happen during contrastive training of language model, but language-specific memory bank and asymmetric batch normalization operation play an essential role in preventing collapsing and information leakage, respectively. Besides, a post-processing for sentence embedding is also very effective to achieve better retrieval performance. On the multilingual sentence retrieval task Tatoeba, our model achieves new SOTA results among methods without using bilingual data. Our model also shows larger gain on Tatoeba when transferring between non-English pairs. On two multi-lingual query-passage retrieval tasks, XOR Retrieve and Mr.TYDI, our model even achieves two SOTA results in both zero-shot and supervised setting among all pretraining models using bilingual data.
Theorizing Information Sources for Hope: Belief, Desire, Imagination, and Metacognition
Authors: Tim Gorichanaz
Date: 2022-06-06 00:00:00
Tasks: Ethics, Philosophy

Introduction. Hope is a positive attitude oriented toward a possible (yet uncertain), desired outcome. Though hope is a virtue, hopelessness is widespread and seems related not only to current events but also to information about current events. This paper examines how hope can be sparked through information. Method. This study uses the philosophical methods of conceptual analysis and design to advance a theoretical argument. Analysis. First, a conceptualization of hope is offered, drawing on work primarily in virtue ethics. Then, four types of information sources for hope are theorized, building on and synthesizing work from philosophy and psychology. Results. Four categories of information source conducive to hopefulness are identified: information for forming beliefs about the past or future; information for engaging the moral imagination regarding possibilities for the future; information for sparking desire for particular moral outcomes; and information for metacognition, or about how we become informed with respect to hope. Conclusions. Hope is, in many cases, responsive to information. This suggests a moral opportunity for information professionals and scholars to work toward connecting people with information for hope, particularly in difficult times. Avenues for further research, particularly in information behavior and practices, are suggested.
Enhancing Strong PUF Security with Non-monotonic Response Quantization
Authors: Kleber Stangherlin, Zhuanhao Wu, Hiren Patel, Manoj Sachdev
Date: 2022-06-07 00:00:00
Tasks: Quantization

Strong physical unclonable functions (PUFs) provide a low-cost authentication primitive for resource constrained devices. However, most strong PUF architectures can be modeled through learning algorithms with a limited number of CRPs. In this paper, we introduce the concept of non-monotonic response quantization for strong PUFs. Responses depend not only on which path is faster, but also on the distance between the arriving signals. Our experiments show that the resulting PUF has increased security against learning attacks. To demonstrate, we designed and implemented a non-monotonically quantized ring-oscillator based PUF in 65 nm technology. Measurement results show nearly ideal uniformity and uniqueness, with bit error rate of 13.4% over the temperature range from 0 C to 50 C.
Rites de Passage: Elucidating Displacement to Emplacement of Refugees on Twitter
Authors: Aparup Khatua, Wolfgang Nejdl
Date: 2022-05-30 00:00:00

Social media deliberations allow to explore refugee-related is-sues. AI-based studies have investigated refugee issues mostly around a specific event and considered unimodal approaches. Contrarily, we have employed a multimodal architecture for probing the refugee journeys from their home to host nations. We draw insights from Arnold van Gennep's anthropological work 'Les Rites de Passage', which systematically analyzed an individual's transition from one group or society to another. Based on Gennep's separation-transition-incorporation framework, we have identified four phases of refugee journeys: Arrival of Refugees, Temporal stay at Asylums, Rehabilitation, and Integration of Refugees into the host nation. We collected 0.23 million multimodal tweets from April 2020 to March 2021 for testing this proposed frame-work. We find that a combination of transformer-based language models and state-of-the-art image recognition models, such as fusion of BERT+LSTM and InceptionV4, can out-perform unimodal models. Subsequently, to test the practical implication of our proposed model in real-time, we have considered 0.01 million multimodal tweets related to the 2022 Ukrainian refugee crisis. An F1-score of 71.88 % for this 2022 crisis confirms the generalizability of our proposed framework.
Flexible Group Fairness Metrics for Survival Analysis
Authors: Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee, Sebastian Vollmer
Date: 2022-05-26 00:00:00
Tasks: BIG-bench Machine Learning, Fairness, Prognosis, Survival Analysis

Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorithmic fairness in regression and classification however there has been little exploration of the field for survival analysis. Survival analysis is the prediction task in which one attempts to predict the probability of an event occurring over time. Survival predictions are particularly important in sensitive settings such as when utilising machine learning for diagnosis and prognosis of patients. In this paper we explore how to utilise existing survival metrics to measure bias with group fairness metrics. We explore this in an empirical experiment with 29 survival datasets and 8 measures. We find that measures of discrimination are able to capture bias well whereas there is less clarity with measures of calibration and scoring rules. We suggest further areas for research including prediction-based fairness metrics for distribution predictions.
The risk ethics of autonomous vehicles: a continuous trolley problem in regular road traffic
Authors: Sebastian Krügel, Matthias Uhl
Date: 2022-05-24 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance, Ethics

Is the ethics of autonomous vehicles (AVs) restricted to weighing lives in unavoidable accidents? We argue that AVs distribute risks between road users in regular traffic situations, either explicitly or implicitly. This distribution of risks raises ethically relevant questions that cannot be evaded by simple heuristics such as "hitting the brakes." Using an interactive, graphical representation of different traffic situations, we measured participants' preferences on driving maneuvers of AVs in a representative survey in Germany. Our participants' preferences deviated significantly from mere collision avoidance. Interestingly, our participants were willing to take risks themselves for the benefit of other road users suggesting that the social dilemma of AVs may lessen in a context of risk.
Optimists at Heart: Why Do We Research Game AI? (Extended Version)
Authors: Michael Cook
Date: 2022-05-17 00:00:00
Tasks: Survey

In this paper we survey the motivations behind contemporary game AI research by analysing individual publications, the researchers themselves, and the institutions that influence them. In doing so, we identify some negative effects on our field, caused both by external forces outside of our control as well as institutionalised behaviours that are easily overlooked. We suggest how we might begin to address some of these issues as a community, and reassert ourselves as the primary driving force behind the field.
Does Crypto Kill? Relationship between Electricity Consumption Carbon Footprints and Bitcoin Transactions
Authors: Altanai Bisht, Arielle Wilson, Zachary Jeffreys, Shadrokh Samavi
Date: 2022-05-16 00:00:00

Cryptocurrencies are gaining more popularity due to their security, making counterfeits impossible. However, these digital currencies have been criticized for creating a large carbon footprint due to their algorithmic complexity and decentralized system design for proof of work and mining. We hypothesize that the carbon footprint of cryptocurrency transactions has a higher dependency on carbon-rich fuel sources than green or renewable fuel sources. We provide a machine learning framework to model such transactions and correlate them with the electricity generation patterns to estimate and analyze their carbon cost.
Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and law
Authors: Thierry Kirst, Olivia Tambou, Virginie Do, Alexis Tsoukiàs
Date: 2022-05-14 00:00:00
Tasks: Decision Making, Fairness

The paper offers a contribution to the interdisciplinary constructs of analyzing fairness issues in automatic algorithmic decisions. Section 1 shows that technical choices in supervised learning have social implications that need to be considered. Section 2 proposes a contextual approach to the issue of unintended group discrimination, i.e. decision rules that are facially neutral but generate disproportionate impacts across social groups (e.g., gender, race or ethnicity). The contextualization will focus on the legal systems of the United States on the one hand and Europe on the other. In particular, legislation and case law tend to promote different standards of fairness on both sides of the Atlantic. Section 3 is devoted to the explainability of algorithmic decisions; it will confront and attempt to cross-reference legal concepts (in European and French law) with technical concepts and will highlight the plurality, even polysemy, of European and French legal texts relating to the explicability of algorithmic decisions. The conclusion proposes directions for further research.
The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis
Authors: Lionel Nganyewou Tidjon, Foutse khomh
Date: 2022-05-12 00:00:00
Tasks: Ethics

Artificial Intelligence (AI) is transforming our daily life with several applications in healthcare, space exploration, banking and finance. These rapid progresses in AI have brought increasing attention to the potential impacts of AI technologies on society, with ethically questionable consequences. In recent years, several ethical principles have been released by governments, national and international organisations. These principles outline high-level precepts to guide the ethical development, deployment, and governance of AI. However, the abstract nature, diversity, and context-dependency of these principles make them difficult to implement and operationalize, resulting in gaps between principles and their execution. Most recent work analysed and summarized existing AI principles and guidelines but they did not provide findings on principle-implementation gaps and how to mitigate them. These findings are particularly important to ensure that AI implementations are aligned with ethical principles and values. In this paper, we provide a contextual and global evaluation of current ethical AI principles for all continents, with the aim to identify potential principle characteristics tailored to specific countries or applicable across countries. Next, we analyze the current level of AI readiness and current implementations of ethical AI principles in different countries, to identify gaps in the implementation of AI principles and their causes. Finally, we propose recommendations to mitigate the principle-implementation gaps.
The Beyond the Fence Musical and Computer Says Show Documentary
Authors: Simon Colton, Maria Teresa Llano, Rose Hepworth, John Charnley, Catherine V. Gale, Archie Baron, Francois Pachet, Pierre Roy, Pablo Gervas, Nick Collins, Bob Sturm, Tillman Weyde, Daniel Wolff, James Robert Lloyd
Date: 2022-05-11 00:00:00

During 2015 and early 2016, the cultural application of Computational Creativity research and practice took a big leap forward, with a project where multiple computational systems were used to provide advice and material for a new musical theatre production. Billed as the world's first 'computer musical... conceived by computer and substantially crafted by computer', Beyond The Fence was staged in the Arts Theatre in London's West End during February and March of 2016. Various computational approaches to analytical and generative sub-projects were used to bring about the musical, and these efforts were recorded in two 1-hour documentary films made by Wingspan Productions, which were aired on SkyArts under the title Computer Says Show. We provide details here of the project conception and execution, including details of the systems which took on some of the creative responsibility in writing the musical, and the contributions they made. We also provide details of the impact of the project, including a perspective from the two (human) writers with overall control of the creative aspects the musical.
A Transparency Index Framework for AI in Education
Authors: Muhammad Ali Chaudhry, Mutlu Cukurova, Rose Luckin
Date: 2022-05-09 00:00:00
Tasks: Ethics, Fairness

Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with different stakeholders of AI in education, including educators, ed-tech experts, and AI practitioners. We map the requirements of transparency for different categories of stakeholders of AI in education and demonstrate that transparency considerations are embedded in the entire AI development process from the data collection stage until the AI system is deployed in the real world and iteratively improved. We also demonstrate how transparency enables the implementation of other ethical AI dimensions in Education like interpretability, accountability, and safety. In conclusion, we discuss the directions for future research in this newly emerging field. The main contribution of this study is that it highlights the importance of transparency in developing AI-powered educational technologies and proposes an index framework for its conceptualization for AI in education.
A Perspective on K-12 AI Education
Authors: Nathan Wang, Paul Tonko, Nikil Ragav, Michael Chungyoun, Jonathan Plucker
Date: 2022-05-04 00:00:00

Artificial intelligence (AI), which enables machines to learn to perform a task by training on diverse datasets, is one of the most revolutionary developments in scientific history. Although AI and especially deep learning is relatively new, it has already had transformative impact on medicine, biology, transportation, entertainment, and beyond. As AI changes our daily lives at an increasingly fast pace, we are challenged with preparing our society for an AI-driven future. To this end, a critical step is to ensure an AI-ready workforce through education. Advocates of beginning instruction of AI basics at the K-12 level typically note benefits to the workforce, economy, and national security. In this complementary perspective, we discuss why learning AI is beneficial for motivating students and promoting creative thinking, and how to develop a module-based approach that optimizes learning outcomes. We hope to excite and engage more members of the education community to join the effort to advance K-12 AI education in the USA and worldwide.
Data Governance in the Age of Large-Scale Data-Driven Language Technology
Authors: Yacine Jernite, Huu Nguyen, Stella Biderman, Anna Rogers, Maraim Masoud, Valentin Danchev, Samson Tan, Alexandra Sasha Luccioni, Nishant Subramani, Gérard Dupont, Jesse Dodge, Kyle Lo, Zeerak Talat, Isaac Johnson, Dragomir Radev, Somaieh Nikpoor, Jörg Frohberg, Aaron Gokaslan, Peter Henderson, Rishi Bommasani, Margaret Mitchell
Date: 2022-05-04 00:00:00
Tasks: Management

The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data. This work proposes an approach to global language data governance that attempts to organize data management amongst stakeholders, values, and rights. Our proposal is informed by prior work on distributed governance that accounts for human values and grounded by an international research collaboration that brings together researchers and practitioners from 60 countries. The framework we present is a multi-party international governance structure focused on language data, and incorporating technical and organizational tools needed to support its work.
Negative Imaginary State Feedback Equivalence for a Class of Nonlinear Systems
Authors: Kanghong Shi, Ian R. Petersen, Igor G. Vladimirov
Date: 2022-06-07 00:00:00

In this paper, we investigate the necessary and sufficient conditions under which a class of nonlinear systems are state feedback equivalent to nonlinear negative imaginary (NI) systems with positive definite storage functions. The nonlinear systems of interest have a normal form of relative degree less than or equal to two. The nonlinearity of the system is restricted with respect to a subset of the state variables, which are the state variables that have external dynamics. Under mild assumptions, such systems are state feedback equivalent to nonlinear NI systems and nonlinear output strictly negative imaginary (OSNI) systems if and only if they are weakly minimum phase. Such a state feedback control approach can also asymptotically stabilize the systems in question against nonlinear OSNI system uncertainties. A numerical example is provided to show the process of the state feedback equivalence control and stabilization of uncertain systems.
Intelligent Sliding Mode Control of an Overhead Container Crane
Authors: Wallace Moreira Bessa, Svenja Otto, Edwin Kreuzer, Robert Seifried
Date: 2022-06-07 00:00:00

In this contribution, an intelligent controller is proposed for an underactuated overhead container crane subject to both parameter uncertainties and unmodeled dynamics. The adopted approach is based on the sliding mode method to confer robustness against modeling inaccuracies and external disturbances. Additionally, an adaptive fuzzy inference system is embedded within the control law to improve set-point regulation and trajectory tracking. In order to evaluate the performance of the proposed intelligent scheme, the control law was implemented and tested in a 1:6 scale experimental container crane, available at the Institute of Mechanics and Ocean Engineering at Hamburg University of Technology. The obtained experimental results demonstrate not only the feasibility of the proposed scheme, but also its improved efficacy for both stabilization and trajectory tracking problems.
Total controllability analysis discovers explainable drugs for Covid-19 treatment
Authors: Xinru Wei, Chunyu Pan, Xizhe Zhang, Weixiong Zhang
Date: 2022-06-07 00:00:00

Network medicine has been pursued for Covid-19 drug repurposing. One such approach adopts structural controllability, a theory for controlling a network (the cell). Motivated to protect the cell from viral infections, we extended this theory to total controllability and introduced a new concept of control hubs. Perturbation to any control hub renders the cell uncontrollable by exogenous stimuli, e.g., viral infections, so control hubs are ideal drug targets. We developed an efficient algorithm for finding all control hubs and applied it to the largest homogenous human protein-protein interaction network. Our new method outperforms several popular gene-selection methods, including that based on structural controllability. The final 65 druggable control hubs are enriched with functions of cell proliferation, regulation of apoptosis, and responses to cellular stress and nutrient levels, revealing critical pathways induced by SARS-CoV-2. These druggable control hubs led to drugs in 4 major categories: antiviral and anti-inflammatory agents, drugs on central nerve systems, and dietary supplements and hormones that boost immunity. Their functions also provided deep insights into the therapeutic mechanisms of the drugs for Covid-19 therapy, making the new approach an explainable drug repurposing method. A remarkable example is Fostamatinib that has been shown to lower mortality, shorten the length of ICU stay, and reduce disease severity of hospitalized Covid-19 patients. The drug targets 10 control hubs, 9 of which are kinases that play key roles in cell differentiation and programmed death. One such kinase is RIPK1 that directly interacts with viral protein nsp12, the RdRp of the virus. The study produced many control hubs that were not targets of existing drugs but were enriched with proteins on membranes and the NF-$\kappa$B pathway, so are excellent candidate targets for new drugs.