Abstract

We leverage pre-trained language models to solve the task of complex NER for two lowresource languages: Chinese and Spanish. We use the technique of Whole Word Masking (WWM) to boost the performance of masked language modeling objective on large and unsupervised corpora. We experiment with multiple neural network architectures, incorporating CRF, BiLSTMs, and Linear Classifiers on top of a fine-tuned BERT layer. All our models outperform the baseline by a significant margin and our best performing model obtains a competitive position on the evaluation leaderboard for the blind test set

Abstract

The paper aims to identify cited text spans in the reference paper related to the given citance in the citing paper. We refer to it as cited text span retrieval (CTSR). Most current methods attempt this task by relying on pre-trained offthe-shelf deep learning models like SciBERT. Though these models are pre-trained on large datasets, they underperform in out-of-domain settings. We introduce CitRet, a novel hybrid model for CTSR that leverages unique semantic and syntactic structural characteristics of scientific documents. This enables us to use significantly less data for finetuning. We use only 1040 documents for finetuning. Our model augments mildly-trained SBERT-based contextual embeddings with pre-trained non-contextual Word2Vec embeddings to calculate semantic textual similarity. We demonstrate the performance of our model on the CLSciSumm shared tasks. It improves the state-of-the-art results by over 15% on the F1 score evaluation

Abstract

Biomedical Named Entity Recognition (BMNER) is one of the most important tasks in the field of biomedical text mining. Most work so far on this task has not focused on identification of discontinuous and overlapping entities, even though they are present in significant fractions in real-life biomedical datasets. In this paper, we introduce a novel annotation schema to capture complex entities, and explore the effects of distant supervision on our deep-learning sequence labelling model. For BMNER task, our annotation schema outperforms other BIO-based annotation schemes on the same model. We also achieve higher F1- scores than state-of-the-art models on multiple corpora without fine-tuning embeddings, highlighting the efficacy of neural feature extraction using our model.

Abstract

It is widely accepted that data preparation is one of the most timeconsuming steps of the machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of data directly influences the quality of a model. In this tutorial, we will discuss the importance and the role of exploratory data analysis (EDA) and data visualisation techniques to find data quality issues and for data preparation, relevant to building ML pipelines. We will also discuss the latest advances in these fields and bring out areas that need innovation. To make the tutorial actionable for practitioners, we will also discuss the most popular open-source packages that one can get started with along with their strengths and weaknesses. Finally, we will discuss on the challenges posed by industry workloads and the gaps to be addressed to make data-centric AI real in industry settings

Abstract

We formalize a framework for coordinating the funding of projects and sharing the costs among agents with quasi-linear utility functions and individual budgets. Our model contains the classical discrete participatory budgeting model as a special case, while capturing other well-motivated problems. We propose several important axioms and objectives and study how well they can be simultaneously satisfied. One of our main results is that whereas welfare maximization admits an FPTAS, welfare maximization subject to a well-motivated and very weak participation requirement leads to a strong inapproximability result. We show that this result is bypassed if we consider some natural restricted valuations or when we take an average-case heuristic approach.

Abstract

We study fairness in the context of feature-based price discrimination in monopoly markets. We propose a new notion of individual fairness, namely, α-fairness, which guarantees that individuals with similar features face similar prices. First, we study discrete valuation space and give an analytical solution for optimal fair feature-based pricing. We show that the cost of fair pricing is defined as the ratio of expected revenue in optimal featurebased pricing to the expected revenue in an optimal fair feature-based pricing (COF) can be arbitrarily large in general. When the revenue function is continuous and concave with respect to the prices, we show that one can achieve COF strictly less than 2, irrespective of the model parameters. Finally, we provide an algorithm to compute fair feature-based pricing strategy that achieves this COF

Abstract

2022 IEEE International Conference on Blockchain (Blockchain) BLOC KVAC: A Un iversally Acceptable and Ideal Vaccination System on Blockchain Manik a Sharma Center for Securit y, Theory, and Algorithmic Research Intern ationa l Institute of Inform ation Technology, Hyderabad Gach ibowli, Hyderabad, India 500 032 manik a.sharm a @research.iiit.ac.in Kishore Kothap alli Cent er for Securit y, Theo ry, and Algorithmic Research Interna tional Institute of Information Technology, Hyderabad Gachibowli, Hyderabad , Indi a 500 032 kisho re @iiit.ac .in Sujit Gujar Machin e Learning Lab International Institute of Information Technology, Hyderabad Gachib owli, Hyderabad , India 500 032 suj it.gujar @iiit.ac.in Abstract-Vaccines are essential in pro tecting indivi dua ls and commun ities from the risk of ha rmful an d fatal diseases. The wor ld is migra ting towards the digitization of vacci nation docu- mentation. Still, the current digita l vacci nation is a centra lized mode l, lacking a nnive rsal acceptance. A Universally Acceptable an d Id eal Vacci nat ion System (UAIVS ) sho nld possess (a) a sta ndard vaccination cert ificate ver ificat ion algorithm, (h) p r i- vacy to per sonal in form a tion, (c) sca lab ilit y, (d) s npport lor all vacci nat ions, (e) acco u ntab ility for vaccination information access, and (f) a sco pe for scientific researc h a nd d evelopm en t. Blockc ha in tec hno logy int ro dnces d ecentr alizat ion, immn tability, acco unta bility, per sistence, and liveliness. T hese pro per ties ma ke block cha in an ideal can di da te to solve the existing pro blems in digital vacci nat ion. In this pa per, we prop ose a novel blockchain- base d syste m, n am ely BLO CKVAC, to implem ent a UA IVS . F urt her more , o ur wor k provi des a way to track an individ nal's an d fa mily's vacc ination histo ry, which is esse ntial for sc ient ific research a nd developm ent . BLOCKVAC is highly modular, sca l- a ble, an d cost -efficien t, pr oviding eno ugh roo m for fnt n re feature addi tio ns. The c urre nt works lack cost and sca lab ility ana lysis. WI' show thc scalability of o ur system t hro ugh a th oron gh a nalys is. O ur impl em entation shows t hat add ing one vacc inati on re cor d costs 7 . l(j ~2 ~ x 10- 6 ETH ( ~ U.U26US D) for reg istratio n, 2.5089 x 10- 5 ETH ( ~ 0.07 8USD) for vaccina tion, a nd th e syste m ca n easi ly han dl e np to 20K vaccinations per hon r. Ind ex Terms- B1ockc ha in, Eth ere um, Sm art Contrac ts, Public Acco unta bility, Sca lab ilit y I. I N TR O D UC TI O N The vaccination process, also known as imm unization , was introduced in the late 18th century by the British physician Edward Jenn er against the deadly smallpox virus [ I]. Since then. it has become an inevitab le medical proc edur e, protecting individuals and communities from harmful and fatal diseases. The World Hea lth Organi zation (WHO) estimates that vac- cination saves around 2.5 millio n child lives every year [2]. A record of vacci nation details massively aid s in providing qualit y health care to individuals [3]. Whi le secure storage and verificat ion of vaccination details are challenges even within a country, it is tediou s to merge them from across all the countries [4 ]. Moreover. a definitive process is the need of the hour to verify the required vaccinati on certification for travelers when they enter a country. We wou ld like to than k MeitY and Ripp le for fundin g this research . O ver view of existing vac cina tion mod els: Traditional paper- based vaccination records [5] are prone to challenges such as the possibil ity of losing, damaging, or forging the records without any accountability. Digital solutions replace the need for paper, provide an immutable card, and reduc e manual labor [6]. However, the current vacci nation systems follow a centralized model, where the users need to trust the cent ral autho rity. Further. these sys tems are prone to single-point failures and jeopardize data security P 1. Moreover, current vaccination portal s mainly deal with just slot bookings and issuin g non -verifiab le vaccination certificates. Mo tiva tion: Th e Covid-19 pandemic has caused a digital revolution in vaccination data managem ent. Countri es are shifting towards a digital solution to secure vaccination data ami issue certificates. However, such a system should be universally acceptabl e. We identi fy the properties requ ired for such a sys tem and call it a Universally Acceptable and Ideal Vaccination System (UA IVS). We furt her categorize the proper- ties of UAIVS into two subcategories: Universally Acceptable Vaccination System and Ideal Va ccination System. A Universally Acce ptable Vaccination System co

Abstract

We propose a new approach to improving student outcomes - the provision of the dream option that allows a student with a job offer to participate in the placement process again. Comparing three different student preference structures, we show that the dream option improves stability; however, it does not always improve student happiness and rank efficiency. Nevertheless, imposing a particular structure on student preferences, namely the sequential preference condition, improves both student happiness and rank efficiency. We recommend when to use the dream option and when not to use it. © 2022 Published by Elsevier Ltd on behalf of Indian Institute of Management Bangalore. This is an open access article under the CC BY-NC-ND license

Abstract

Blockchains lie at the heart of Bitcoin and other cryptocurrencies that have shown great promise to revolutionize finance and commerce. Although they are gaining increasing popularity, they face technical challenges when it comes to scaling to support greater demand while maintaining their desirable security properties. In an exciting line of recent work, many researchers have proposed various scalable blockchain protocols that demonstrate the potential to solve these challenges. However, many of these protocols come with the assumptions of honest majority and symmetric network access which may not accurately reflect the real world where the participants may be self-interested or rational. Secondly, these works show that their protocol works in an ideal environment where each party has equal access to the network whereas different parties have varying latencies and network speeds. These assumptions may render the protocols susceptible to security threats in the real world, as highlighted by the literature focused on exploring gametheoretic attacks on these protocols. We propose a scalable blockchain protocol, Interlude, which comes with the typical security guarantees while focusing on game-theoretic soundness and network fairness. The novelty of Interlude is that it has a relatively simple design consisting of a sequence of parallel blocks containing disjoint transaction sets that can be mined quickly followed by a series block that is slow to mine and gives the honest parties in the network time to synchronize. Thus, between the chaos of parallel blocks, our blockchain protocol masquerades an interlude moment of harmony in series blocks that synchronize the network.

Abstract

Neural networks have shown state-of-the-art performance in designing auctions, where the network learns the optimal allocations and payment rule to ensure desirable properties. Motivated by the same, we focus on learning fair division of resources, with no payments involved. Our goal is to allocate the items, goods and/or chores efficiently among the fair allocations. By fair, we mean an allocation that is Envy-free (EF). However, such an allocation may not always exist for indivisible resources. Therefore, we consider the relaxed notion, Envy-freeness up to one item (EF1) that is guaranteed to exist. However, it is not enough to guarantee EF1 since the allocation of empty bundles is also EF1. Hence, we add the further constraint of efficiency, maximum utilitarian social welfare (USW). In general finding, USW allocations among EF1 is an NP-Hard problem even when valuations are additive. In this work, we design a network for this task which we refer to as EEF1-NN. We propose an UNet inspired architecture, Lagrangian loss function, and training procedure to obtain desired results. We show that EEF1-NN finds allocation close to optimal USW allocation and ensures EF1 with a high probability for different distributions over input valuations. Compared to existing approaches EEF1-NN empirically guarantees higher USW. Moreover, EEF1-NN is scalable and determines the allocations much faster than solving it as a constrained optimization problem.