pwc-archive/papers-with-abstracts huggingface dataset:
Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels
Authors: Si Chen, Yuqiu Qian, Hui Li, Chen Lin
Date: 2021-02-13 00:00:00
Tasks: Graph Neural Network, Multi-Task Learning, Recommendation Systems, Sequential Recommendation

Online gaming is growing faster than ever before, with increasing challenges of providing better user experience. Recommender systems (RS) for online games face unique challenges since they must fulfill players' distinct desires, at different user levels, based on their action sequences of various action types. Although many sequential RS already exist, they are mainly single-sequence, single-task, and single-user-level. In this paper, we introduce a new sequential recommendation model for multiple sequences, multiple tasks, and multiple user levels (abbreviated as M$^3$Rec) in Tencent Games platform, which can fully utilize complex data in online games. We leverage Graph Neural Network and multi-task learning to design M$^3$Rec in order to model the complex information in the heterogeneous sequential recommendation scenario of Tencent Games. We verify the effectiveness of M$^3$Rec on three online games of Tencent Games platform, in both offline and online evaluations. The results show that M$^3$Rec successfully addresses the challenges of recommendation in online games, and it generates superior recommendations compared with state-of-the-art sequential recommendation approaches.
Impact of h-index on authors ranking: A comparative analysis of Scopus and WoS
Authors: Parul Khurana, Kiran Sharma
Date: 2021-02-13 00:00:00

In academia, the research performance of the faculty members is either evaluated by the number of publications or the number of citations. Most of the time h-index is widely used during the hiring process or the faculty performance evaluation. The calculation of the h-index is shown in various databases; however, there is no recent or systematic evidence about the differences between them. In this study, we compare the difference in the h-index compiled with Scopus and Web of Science (WoS) with the aim of analyzing the ranking of the authors within a university. We analyze the publication records of 350 authors from Monash University (Australia). We also investigate the discipline wise variation in the authors ranking. 31% of the author's profiles show no variation in the two datasets whereas 55% of the author's profiles show a higher count in Scopus and 9% in WoS. The maximum difference in h-index count among Scopus and WoS is 3. On average 12.4% of publications per author are unique in Scopus and 4.1% in WoS. 53.5% of publications are common in both Scopus and WoS. Despite larger unique publications in Scopus, there is no difference shown in the Spearman correlation coefficient between WoS and Scopus citation counts and h-index.
Automatically Matching Bug Reports With Related App Reviews
Authors: Marlo Häring, Christoph Stanik, Walid Maalej
Date: 2021-02-14 00:00:00

App stores allow users to give valuable feedback on apps, and developers to find this feedback and use it for the software evolution. However, finding user feedback that matches existing bug reports in issue trackers is challenging as users and developers often use a different language. In this work, we introduce DeepMatcher, an automatic approach using state-of-the-art deep learning methods to match problem reports in app reviews to bug reports in issue trackers. We evaluated DeepMatcher with four open-source apps quantitatively and qualitatively. On average, DeepMatcher achieved a hit ratio of 0.71 and a Mean Average Precision of 0.55. For 91 problem reports, DeepMatcher did not find any matching bug report. When manually analyzing these 91 problem reports and the issue trackers of the studied apps, we found that in 47 cases, users actually described a problem before developers discovered and documented it in the issue tracker. We discuss our findings and different use cases for DeepMatcher.
Edge Minimizing the Student Conflict Graph
Authors: Joshua S. Friedman
Date: 2021-02-12 00:00:00

In many schools, courses are given in sections. Prior to timetabling students need to be assigned to individual sections. We give a hybrid approximation sectioning algorithm that minimizes the number of edges (potential conflicts) in the student conflict graph (SCG). We start with a greedy algorithm to obtain a starting solution and then continue with a constraint programming based algorithm (CP-SAT) that reduces the number of edges. We apply the sectioning algorithm to a highly constrained timetabling model which we specify.
Planning and Learning Using Adaptive Entropy Tree Search
Authors: Piotr Kozakowski, Mikołaj Pacek, Piotr Miłoś
Date: 2021-02-12 00:00:00

Recent breakthroughs in Artificial Intelligence have shown that the combination of tree-based planning with deep learning can lead to superior performance. We present Adaptive Entropy Tree Search (ANTS) - a novel algorithm combining planning and learning in the maximum entropy paradigm. Through a comprehensive suite of experiments on the Atari benchmark we show that ANTS significantly outperforms PUCT, the planning component of the state-of-the-art AlphaZero system. ANTS builds upon recent work on maximum entropy planning methods - which however, as we show, fail in combination with learning. ANTS resolves this issue to reach state-of-the-art performance. We further find that ANTS exhibits superior robustness to different hyperparameter choices, compared to the previous algorithms. We believe that the high performance and robustness of ANTS can bring tree search planning one step closer to wide practical adoption.
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
Authors: Pashootan Vaezipoor, Andrew Li, Rodrigo Toro Icarte, Sheila Mcilraith
Date: 2021-02-13 00:00:00
Tasks: Deep Reinforcement Learning, Diversity, Reinforcement Learning (RL)

We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments. Instructions are expressed in a well-known formal language -- linear temporal logic (LTL) -- and can specify a diversity of complex, temporally extended behaviours, including conditionals and alternative realizations. Our proposed learning approach exploits the compositional syntax and the semantics of LTL, enabling our RL agent to learn task-conditioned policies that generalize to new instructions, not observed during training. To reduce the overhead of learning LTL semantics, we introduce an environment-agnostic LTL pretraining scheme which improves sample-efficiency in downstream environments. Experiments on discrete and continuous domains target combinatorial task sets of up to $\sim10^{39}$ unique tasks and demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions.
Goods Transportation Problem Solving via Routing Algorithm
Authors: Mikhail Shchukin, Aymen Ben Said, Andre Lobo Teixeira
Date: 2021-02-13 00:00:00

This paper outlines the ideas behind developing a graph-based heuristic-driven routing algorithm designed for a particular instance of a goods transportation problem with a single good type. The proposed algorithm solves the optimization problem of satisfying the demand of goods on a given undirected transportation graph with minimizing the estimated cost for each traversed segment of the delivery path. The operation of the routing algorithm is discussed and overall evaluation of the proposed problem solving technique is given.
Mitigating Negative Side Effects via Environment Shaping
Authors: Sandhya Saisubramanian, Shlomo Zilberstein
Date: 2021-02-13 00:00:00

Agents operating in unstructured environments often produce negative side effects (NSE), which are difficult to identify at design time. While the agent can learn to mitigate the side effects from human feedback, such feedback is often expensive and the rate of learning is sensitive to the agent's state representation. We examine how humans can assist an agent, beyond providing feedback, and exploit their broader scope of knowledge to mitigate the impacts of NSE. We formulate this problem as a human-agent team with decoupled objectives. The agent optimizes its assigned task, during which its actions may produce NSE. The human shapes the environment through minor reconfiguration actions so as to mitigate the impacts of the agent's side effects, without affecting the agent's ability to complete its assigned task. We present an algorithm to solve this problem and analyze its theoretical properties. Through experiments with human subjects, we assess the willingness of users to perform minor environment modifications to mitigate the impacts of NSE. Empirical evaluation of our approach shows that the proposed framework can successfully mitigate NSE, without affecting the agent's ability to complete its assigned task.
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
Authors: Haitian Sun, Pat Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W. Cohen
Date: 2021-02-14 00:00:00
Tasks: Language Modeling, Language Modelling, Open-Domain Question Answering, Question Answering, World Knowledge

We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text. Large Knowledge Bases (KBs) are indispensable for a wide-range of industry applications such as question answering and recommendation. Typically, KBs encode world knowledge in a structured, readily accessible form derived from laborious human annotation efforts. Unfortunately, while they are extremely high precision, KBs are inevitably highly incomplete and automated methods for enriching them are far too inaccurate. Instead, OPQL constructs a VKB by encoding and indexing a set of relation mentions in a way that naturally enables reasoning and can be trained without any structured supervision. We demonstrate that OPQL outperforms prior VKB methods on two different KB reasoning tasks and, additionally, can be used as an external memory integrated into a language model (OPQL-LM) leading to improvements on two open-domain question answering tasks.
Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment
Authors: Ganesh Ghalme, Vineet Nair, Vishakha Patil, Yilun Zhou
Date: 2021-02-14 00:00:00
Tasks: Decision Making, Fairness

Fairness has emerged as an important concern in automated decision-making in recent years, especially when these decisions affect human welfare. In this work, we study fairness in temporally extended decision-making settings, specifically those formulated as Markov Decision Processes (MDPs). Our proposed notion of fairness ensures that each state's long-term visitation frequency is at least a specified fraction. This quota-based notion of fairness is natural in many resource-allocation settings where the dynamics of a single resource being allocated is governed by an MDP and the distribution of the shared resource is captured by its state-visitation frequency. In an average-reward MDP (AMDP) setting, we formulate the problem as a bilinear saddle point program and, for a generative model, solve it using a Stochastic Mirror Descent (SMD) based algorithm. The proposed solution guarantees a simultaneous approximation on the expected average-reward and fairness requirement. We give sample complexity bounds for the proposed algorithm and validate our theoretical results with experiments on simulated data.
Why Talking about ethics is not enough: a proposal for Fintech's AI ethics
Authors: Cristina Godoy Bernardo de Oliveira, Evandro Eduardo Seron Ruiz
Date: 2021-02-14 00:00:00
Tasks: Ethics

As the potential applications of Artificial Intelligence (AI) in the financial sector increases, ethical issues become gradually latent. The distrust of individuals, social groups, and governments about the risks arising from Fintech's activities is growing. Due to this scenario, the preparation of recommendations and Ethics Guidelines is increasing and the risks of being chosen the principles and ethical values most appropriate to companies are high. Thus, this exploratory research aims to analyze the benefits of the application of the stakeholder theory and the idea of Social License to build an environment of trust and for the realization of ethical principles by Fintech. The formation of a Fintech association for the creation of a Social License will allow early-stage Fintech to participate from the beginning of its activities in the elaboration of a dynamic ethical code and with the participation of stakeholders.
Responsibility Management through Responsibility Networks
Authors: Ruijun Chen, Jiong Qiu, Xuejiao Tang
Date: 2021-02-14 00:00:00
Tasks: Management

The safety management is critically important in the workplace. Unfortunately, responsibility issues therein such as inefficient supervision, poor evaluation and inadequate perception have not been properly addressed. To this end, in this paper, we deploy the Internet of Responsibilities (IoR) for responsibility management. Through the building of IoR framework, hierarchical responsibility management, automated responsibility evaluation at all level and efficient responsibility perception are achieved. The practical deployment of IoR system showed its effective responsibility management capability in various workplaces.
Cross-modal Adversarial Reprogramming
Authors: Paarth Neekhara, Shehzeen Hussain, Jinglong Du, Shlomo Dubnov, Farinaz Koushanfar, Julian McAuley
Date: 2021-02-15 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

With the abundance of large-scale deep learning models, it has become possible to repurpose pre-trained networks for new tasks. Recent works on adversarial reprogramming have shown that it is possible to repurpose neural networks for alternate tasks without modifying the network architecture or parameters. However these works only consider original and target tasks within the same data domain. In this work, we broaden the scope of adversarial reprogramming beyond the data modality of the original task. We analyze the feasibility of adversarially repurposing image classification neural networks for Natural Language Processing (NLP) and other sequence classification tasks. We design an efficient adversarial program that maps a sequence of discrete tokens into an image which can be classified to the desired class by an image classification model. We demonstrate that by using highly efficient adversarial programs, we can reprogram image classifiers to achieve competitive performance on a variety of text and sequence classification benchmarks without retraining the network.
AI Ethics Needs Good Data
Authors: Angela Daly, S Kate Devitt, Monique Mann
Date: 2021-02-15 00:00:00
Tasks: Ethics, Fairness

In this chapter we argue that discourses on AI must transcend the language of 'ethics' and engage with power and political economy in order to constitute 'Good Data'. In particular, we must move beyond the depoliticised language of 'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good' given the limitations of ethics as a frame through which AI issues can be viewed. In order to circumvent these limits, we use instead the language and conceptualisation of 'Good Data', as a more expansive term to elucidate the values, rights and interests at stake when it comes to AI's development and deployment, as well as that of other digital technologies. Good Data considerations move beyond recurring themes of data protection/privacy and the FAT (fairness, transparency and accountability) movement to include explicit political economy critiques of power. Instead of yet more ethics principles (that tend to say the same or similar things anyway), we offer four 'pillars' on which Good Data AI can be built: community, rights, usability and politics. Overall we view AI's 'goodness' as an explicly political (economy) question of power and one which is always related to the degree which AI is created and used to increase the wellbeing of society and especially to increase the power of the most marginalized and disenfranchised. We offer recommendations and remedies towards implementing 'better' approaches towards AI. Our strategies enable a different (but complementary) kind of evaluation of AI as part of the broader socio-technical systems in which AI is built and deployed.
OntoZSL: Ontology-enhanced Zero-shot Learning
Authors: Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z. Pan, Zhiquan Ye, Zonggang Yuan, Yantao Jia, Huajun Chen
Date: 2021-02-15 00:00:00
Tasks: image-classification, Image Classification, Knowledge Graph Completion, Word Embeddings, Zero-Shot Learning

Zero-shot Learning (ZSL), which aims to predict for those classes that have never appeared in the training data, has arisen hot research interests. The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e.g., features) from training classes (i.e., seen classes) to unseen classes. However, the priors adopted by the existing methods are relatively limited with incomplete semantics. In this paper, we explore richer and more competitive prior knowledge to model the inter-class relationship for ZSL via ontology-based knowledge representation and semantic embedding. Meanwhile, to address the data imbalance between seen classes and unseen classes, we developed a generative ZSL framework with Generative Adversarial Networks (GANs). Our main findings include: (i) an ontology-enhanced ZSL framework that can be applied to different domains, such as image classification (IMGC) and knowledge graph completion (KGC); (ii) a comprehensive evaluation with multiple zero-shot datasets from different domains, where our method often achieves better performance than the state-of-the-art models. In particular, on four representative ZSL baselines of IMGC, the ontology-based class semantics outperform the previous priors e.g., the word embeddings of classes by an average of 12.4 accuracy points in the standard ZSL across two example datasets (see Figure 4).
Jira: a Kurdish Speech Recognition System Designing and Building Speech Corpus and Pronunciation Lexicon
Authors: Hadi Veisi, Hawre Hosseini, Mohammad Mohammadamini, Wirya Fathy, Aso Mahmudi
Date: 2021-02-15 00:00:00
Tasks: Language Modelling, Sentence, speech-recognition, Speech Recognition

In this paper, we introduce the first large vocabulary speech recognition system (LVSR) for the Central Kurdish language, named Jira. The Kurdish language is an Indo-European language spoken by more than 30 million people in several countries, but due to the lack of speech and text resources, there is no speech recognition system for this language. To fill this gap, we introduce the first speech corpus and pronunciation lexicon for the Kurdish language. Regarding speech corpus, we designed a sentence collection in which the ratio of di-phones in the collection resembles the real data of the Central Kurdish language. The designed sentences are uttered by 576 speakers in a controlled environment with noise-free microphones (called AsoSoft Speech-Office) and in Telegram social network environment using mobile phones (denoted as AsoSoft Speech-Crowdsourcing), resulted in 43.68 hours of speech. Besides, a test set including 11 different document topics is designed and recorded in two corresponding speech conditions (i.e., Office and Crowdsourcing). Furthermore, a 60K pronunciation lexicon is prepared in this research in which we faced several challenges and proposed solutions for them. The Kurdish language has several dialects and sub-dialects that results in many lexical variations. Our methods for script standardization of lexical variations and automatic pronunciation of the lexicon tokens are presented in detail. To setup the recognition engine, we used the Kaldi toolkit. A statistical tri-gram language model that is extracted from the AsoSoft text corpus is used in the system. Several standard recipes including HMM-based models (i.e., mono, tri1, tr2, tri2, tri3), SGMM, and DNN methods are used to generate the acoustic model. These methods are trained with AsoSoft Speech-Office and AsoSoft Speech-Crowdsourcing and a combination of them. The best performance achieved by the SGMM acoustic model which results in 13.9% of the average word error rate (on different document topics) and 4.9% for the general topic.
The corruptive force of AI-generated advice
Authors: Margarita Leib, Nils C. Köbis, Rainer Michael Rilke, Marloes Hagens, Bernd Irlenbusch
Date: 2021-02-15 00:00:00

Artificial Intelligence (AI) is increasingly becoming a trusted advisor in people's lives. A new concern arises if AI persuades people to break ethical rules for profit. Employing a large-scale behavioural experiment (N = 1,572), we test whether AI-generated advice can corrupt people. We further test whether transparency about AI presence, a commonly proposed policy, mitigates potential harm of AI-generated advice. Using the Natural Language Processing algorithm, GPT-2, we generated honesty-promoting and dishonesty-promoting advice. Participants read one type of advice before engaging in a task in which they could lie for profit. Testing human behaviour in interaction with actual AI outputs, we provide first behavioural insights into the role of AI as an advisor. Results reveal that AI-generated advice corrupts people, even when they know the source of the advice. In fact, AI's corrupting force is as strong as humans'.
Crowdsourcing Parallel Corpus for English-Oromo Neural Machine Translation using Community Engagement Platform
Authors: Sisay Chala, Bekele Debisa, Amante Diriba, Silas Getachew, Chala Getu, Solomon Shiferaw
Date: 2021-02-15 00:00:00
Tasks: Diversity, Machine Translation, Sentence, Translation

Even though Afaan Oromo is the most widely spoken language in the Cushitic family by more than fifty million people in the Horn and East Africa, it is surprisingly resource-scarce from a technological point of view. The increasing amount of various useful documents written in English language brings to investigate the machine that can translate those documents and make it easily accessible for local language. The paper deals with implementing a translation of English to Afaan Oromo and vice versa using Neural Machine Translation. But the implementation is not very well explored due to the limited amount and diversity of the corpus. However, using a bilingual corpus of just over 40k sentence pairs we have collected, this study showed a promising result. About a quarter of this corpus is collected via Community Engagement Platform (CEP) that was implemented to enrich the parallel corpus through crowdsourcing translations.