pwc-archive/papers-with-abstracts huggingface dataset:
Community detection and Social Network analysis based on the Italian wars of the 15th century
Authors: J. Fumanal-Idocin, A. Alonso-Betanzos, O. Cordón, H. Bustince, M. Minárová
Date: 2020-07-06 00:00:00
Tasks: Clustering, Community Detection

In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions among each pair of actors in a network. By using these functions, we develop a new community detection algorithm, the Borgia Clustering, where communities naturally arise from the multi-agent interaction in the network. We also discuss the effects of size and scale for communities regarding this case, as well as how we cope with the additional complexity present when big communities arise. Finally, we compare our community detection solution with other representative algorithms, finding favourable results.
Reducing Misinformation in Query Autocompletions
Authors: Djoerd Hiemstra
Date: 2020-07-06 00:00:00
Tasks: Misinformation

Query autocompletions help users of search engines to speed up their searches by recommending completions of partially typed queries in a drop down box. These recommended query autocompletions are usually based on large logs of queries that were previously entered by the search engine's users. Therefore, misinformation entered -- either accidentally or purposely to manipulate the search engine -- might end up in the search engine's recommendations, potentially harming organizations, individuals, and groups of people. This paper proposes an alternative approach for generating query autocompletions by extracting anchor texts from a large web crawl, without the need to use query logs. Our evaluation shows that even though query log autocompletions perform better for shorter queries, anchor text autocompletions outperform query log autocompletions for queries of 2 words or more.
Reflection-based Word Attribute Transfer
Authors: Yoichi Ishibashi, Katsuhito Sudoh, Koichiro Yoshino, Satoshi Nakamura
Date: 2020-07-06 00:00:00
Proceeding: ACL 2020 6
Tasks: Attribute, Word Attribute Transfer, Word Embeddings

Word embeddings, which often represent such analogic relations as king - man + woman = queen, can be used to change a word's attribute, including its gender. For transferring king into queen in this analogy-based manner, we subtract a difference vector man - woman based on the knowledge that king is male. However, developing such knowledge is very costly for words and attributes. In this work, we propose a novel method for word attribute transfer based on reflection mappings without such an analogy operation. Experimental results show that our proposed method can transfer the word attributes of the given words without changing the words that do not have the target attributes.
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation using Pretraining Language Model
Authors: Shilei Liu, Yu Guo, Bochao Li, Feiliang Ren
Date: 2020-07-06 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Common Sense Reasoning, Language Modeling, Language Modelling, Multiple-choice, Sentence, Transfer Learning

This paper describes our submission to subtask a and b of SemEval-2020 Task 4. For subtask a, we use a ALBERT based model with improved input form to pick out the common sense statement from two statement candidates. For subtask b, we use a multiple choice model enhanced by hint sentence mechanism to select the reason from given options about why a statement is against common sense. Besides, we propose a novel transfer learning strategy between subtasks which help improve the performance. The accuracy scores of our system are 95.6 / 94.9 on official test set and rank 7$^{th}$ / 2$^{nd}$ on Post-Evaluation leaderboard.
ModeNet: Mode Selection Network For Learned Video Coding
Authors: Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges
Date: 2020-07-06 00:00:00
Tasks: Image Compression, Video Compression

In this paper, a mode selection network (ModeNet) is proposed to enhance deep learning-based video compression. Inspired by traditional video coding, ModeNet purpose is to enable competition among several coding modes. The proposed ModeNet learns and conveys a pixel-wise partitioning of the frame, used to assign each pixel to the most suited coding mode. ModeNet is trained alongside the different coding modes to minimize a rate-distortion cost. It is a flexible component which can be generalized to other systems to allow competition between different coding tools. Mod-eNet interest is studied on a P-frame coding task, where it is used to design a method for coding a frame given its prediction. ModeNet-based systems achieve compelling performance when evaluated under the Challenge on Learned Image Compression 2020 (CLIC20) P-frame coding track conditions.
Jump Operator Planning: Goal-Conditioned Policy Ensembles and Zero-Shot Transfer
Authors: Thomas J. Ringstrom, Mohammadhosein Hasanbeig, Alessandro Abate
Date: 2020-07-06 00:00:00

In Hierarchical Control, compositionality, abstraction, and task-transfer are crucial for designing versatile algorithms which can solve a variety of problems with maximal representational reuse. We propose a novel hierarchical and compositional framework called Jump-Operator Dynamic Programming for quickly computing solutions within a super-exponential space of sequential sub-goal tasks with ordering constraints, while also providing a fast linearly-solvable algorithm as an implementation. This approach involves controlling over an ensemble of reusable goal-conditioned polices functioning as temporally extended actions, and utilizes transition operators called feasibility functions, which are used to summarize initial-to-final state dynamics of the polices. Consequently, the added complexity of grounding a high-level task space onto a larger ambient state-space can be mitigated by optimizing in a lower-dimensional subspace defined by the grounding, substantially improving the scalability of the algorithm while effecting transferable solutions. We then identify classes of objective functions on this subspace whose solutions are invariant to the grounding, resulting in optimal zero-shot transfer.
Space of Reasons and Mathematical Model
Authors: Florian Richter
Date: 2020-07-06 00:00:00
Tasks: model

Inferential relations govern our concept use. In order to understand a concept it has to be located in a space of implications. There are different kinds of conditions for statements, i.e. that the conditions represent different kinds of explanations, e.g. causal or conceptual explanations. The crucial questions is: How can the conditionality of language use be represented. The conceptual background of representation in models is discussed and in the end I propose how implications of propositional logic and conceptual determinations can be represented in a model of a neural network.
Inferences and Modal Vocabulary
Authors: Florian Richter
Date: 2020-07-06 00:00:00
Tasks: Formal Logic

Deduction is the one of the major forms of inferences and commonly used in formal logic. This kind of inference has the feature of monotonicity, which can be problematic. There are different types of inferences that are not monotonic, e.g. abductive inferences. The debate between advocates and critics of abduction as a useful instrument can be reconstructed along the issue, how an abductive inference warrants to pick out one hypothesis as the best one. But how can the goodness of an inference be assessed? Material inferences express good inferences based on the principle of material incompatibility. Material inferences are based on modal vocabulary, which enriches the logical expressivity of the inferential relations. This leads also to certain limits in the application of labeling in machine learning. I propose a modal interpretation of implications to express conceptual relations.
Logic, Language, and Calculus
Authors: Florian Richter
Date: 2020-07-06 00:00:00
Tasks: Natural Language Inference, Natural Language Understanding

The difference between object-language and metalanguage is crucial for logical analysis, but has yet not been examined for the field of computer science. In this paper the difference is examined with regard to inferential relations. It is argued that inferential relations in a metalanguage (like a calculus for propositional logic) cannot represent conceptual relations of natural language. Inferential relations govern our concept use and understanding. Several approaches in the field of Natural Language Understanding (NLU) and Natural Language Inference (NLI) take this insight in account, but do not consider, how an inference can be assessed as a good inference. I present a logical analysis that can assesss the normative dimension of inferences, which is a crucial part of logical understanding and goes beyond formal understanding of metalanguages.
Learning Personalized Risk Preferences for Recommendation
Authors: Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun, Yongfeng Zhang
Date: 2020-07-06 00:00:00
Tasks: Recommendation Systems

The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions. With this information, they can infer the quality of products to reduce the risk of purchase. Specifically, items with high rating scores and good reviews tend to be less risky, while items with low rating scores and bad reviews might be risky to purchase. On the other hand, the purchase behaviors will also be influenced by consumers' tolerance of risks, known as the risk attitudes. Economists have studied risk attitudes for decades. These studies reveal that people are not always rational enough when making decisions, and their risk attitudes may vary in different circumstances. Most existing works over recommendation systems do not consider users' risk attitudes in modeling, which may lead to inappropriate recommendations to users. For example, suggesting a risky item to a risk-averse person or a conservative item to a risk-seeking person may result in the reduction of user experience. In this paper, we propose a novel risk-aware recommendation framework that integrates machine learning and behavioral economics to uncover the risk mechanism behind users' purchasing behaviors. Concretely, we first develop statistical methods to estimate the risk distribution of each item and then draw the Nobel-award winning Prospect Theory into our model to learn how users choose from probabilistic alternatives that involve risks, where the probabilities of the outcomes are uncertain. Experiments on several e-commerce datasets demonstrate that our approach can achieve better performance than many classical recommendation approaches, and further analyses also verify the advantages of risk-aware recommendation beyond accuracy.
Understanding Echo Chambers in E-commerce Recommender Systems
Authors: Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, Yongfeng Zhang
Date: 2020-07-06 00:00:00
Tasks: Recommendation Systems

Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper items based on user interests. However, significant efforts are missing to understand how the recommendations influence user preferences and behaviors, e.g., if and how recommendations result in \textit{echo chambers}. Extensive efforts have been made in examining the phenomenon in online media and social network systems. Meanwhile, there are growing concerns that recommender systems might lead to the self-reinforcing of user's interests due to narrowed exposure of items, which may be the potential cause of echo chamber. In this paper, we aim to analyze the echo chamber phenomenon in Alibaba Taobao -- one of the largest e-commerce platforms in the world. Echo chamber means the effect of user interests being reinforced through repeated exposure to similar contents. Based on the definition, we examine the presence of echo chamber in two steps. First, we explore whether user interests have been reinforced. Second, we check whether the reinforcement results from the exposure of similar contents. Our evaluations are enhanced with robust metrics, including cluster validity and statistical significance. Experiments are performed on extensive collections of real-world data consisting of user clicks, purchases, and browse logs from Alibaba Taobao. Evidence suggests the tendency of echo chamber in user click behaviors, while it is relatively mitigated in user purchase behaviors. Insights from the results guide the refinement of recommendation algorithms in real-world e-commerce systems.
A modified axiomatic foundation of the analytic hierarchy process
Authors: Fang Liu, Wei-Guo Zhang
Date: 2020-07-06 00:00:00

This paper reports a modified axiomatic foundation of the analytic hierarchy process (AHP), where the reciprocal property of paired comparisons is broken. The novel concept of reciprocal symmetry breaking is proposed to characterize the considered situation without reciprocal property. It is found that the uncertainty experienced by the decision maker can be naturally incorporated into the modified axioms. Some results are derived from the new axioms involving the new concept of approximate consistency and the method of eliciting priorities. The phenomenon of ranking reversal is revisited from a theoretical viewpoint under the modified axiomatic foundation. The situations without ranking reversal are addressed and called ranking equilibrium. The likelihood of ranking reversal is captured by introducing a possibility degree index based on the Kendall's coefficient of concordance. The modified axioms and the derived facts form a novel operational basis of the AHP choice model under some uncertainty. The observations reveal that a more flexible expression of decision information could be accepted as compared to the judgments with reciprocal property.
CORD19STS: COVID-19 Semantic Textual Similarity Dataset
Authors: Xiao Guo, Hengameh Mirzaalian, Ekraam Sabir, Ayush Jaiswal, Wael Abd-Almageed
Date: 2020-07-05 00:00:00
Tasks: Information Retrieval, Language Modelling, Medical Diagnosis, Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence, STS

In order to combat the COVID-19 pandemic, society can benefit from various natural language processing applications, such as dialog medical diagnosis systems and information retrieval engines calibrated specifically for COVID-19. These applications rely on the ability to measure semantic textual similarity (STS), making STS a fundamental task that can benefit several downstream applications. However, existing STS datasets and models fail to translate their performance to a domain-specific environment such as COVID-19. To overcome this gap, we introduce CORD19STS dataset which includes 13,710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge. To be specific, we generated one million sentence pairs using different sampling strategies. We then used a finetuned BERT-like language model, which we call Sen-SCI-CORD19-BERT, to calculate the similarity scores between sentence pairs to provide a balanced dataset with respect to the different semantic similarity levels, which gives us a total of 32K sentence pairs. Each sentence pair was annotated by five Amazon Mechanical Turk (AMT) crowd workers, where the labels represent different semantic similarity levels between the sentence pairs (i.e. related, somewhat-related, and not-related). After employing a rigorous qualification tasks to verify collected annotations, our final CORD19STS dataset includes 13,710 sentence pairs.
Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes
Authors: Hyeju Jang, Emily Rempel, Giuseppe Carenini, Naveed Janjua
Date: 2020-07-05 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has significantly impacted on people's lives, it is essential to capture how people react to public health interventions and understand their concerns. In this paper, we aim to investigate people's reactions and concerns about COVID-19 in North America, especially focusing on Canada. We analyze COVID-19 related tweets using topic modeling and aspect-based sentiment analysis, and interpret the results with public health experts. We compare timeline of topics discussed with timing of implementation of public health interventions for COVID-19. We also examine people's sentiment about COVID-19 related issues. We discuss how the results can be helpful for public health agencies when designing a policy for new interventions. Our work shows how Natural Language Processing (NLP) techniques could be applied to public health questions with domain expert involvement.
Partial Order Resolution of Event Logs for Process Conformance Checking
Authors: Han van der Aa, Henrik Leopold, Matthias Weidlich
Date: 2020-07-05 00:00:00

While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.
Starfish: A Prototype for Universal Preprocessing and Text-Embedded Programming
Authors: Vlado Keselj
Date: 2020-07-05 00:00:00

We present a novel concept of universal text preprocessing and text-embedded programming (PTEP). Preprocessing and text-embedded programming has been widely used in programming languages and frameworks in a fragmented and mutually isolated way. The PTEP ideas can be found in the implementation of the \TeX\ typesetting system; they are prominent in PHP and similar web languages, and finally they are used in the Jupyter data science framework. This paper presents this area of research and related work in a more unified framework, and we describe the implemented system Starfish that satisfies the following novel principles of PTEP: universality, update and replace modes, flexiblity, configurability, and transparency. We describe the operating model and design of Starfish, which is an open-source system implementing universal preprocessing and text-embedded programming in Perl. The system is transparent and its design allows direct implementation in other programming languages as well.
Mission schedule of agile satellites based on Proximal Policy Optimization Algorithm
Authors: Xinrui Liu
Date: 2020-07-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Mission schedule of satellites is an important part of space operation nowadays, since the number and types of satellites in orbit are increasing tremendously and their corresponding tasks are also becoming more and more complicated. In this paper, a mission schedule model combined with Proximal Policy Optimization Algorithm(PPO) is proposed. Different from the traditional heuristic planning method, this paper incorporate reinforcement learning algorithms into it and find a new way to describe the problem. Several constraints including data download are considered in this paper.
Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media
Authors: Hui Yin, Shuiqiao Yang, Jian-Xin Li
Date: 2020-07-05 00:00:00

The outbreak of the novel Coronavirus Disease (COVID-19) has greatly influenced people's daily lives across the globe. Emergent measures and policies (e.g., lockdown, social distancing) have been taken by governments to combat this highly infectious disease. However, people's mental health is also at risk due to the long-time strict social isolation rules. Hence, monitoring people's mental health across various events and topics will be extremely necessary for policy makers to make the appropriate decisions. On the other hand, social media have been widely used as an outlet for people to publish and share their personal opinions and feelings. The large scale social media posts (e.g., tweets) provide an ideal data source to infer the mental health for people during this pandemic period. In this work, we propose a novel framework to analyze the topic and sentiment dynamics due to COVID-19 from the massive social media posts. Based on a collection of 13 million tweets related to COVID-19 over two weeks, we found that the positive sentiment shows higher ratio than the negative sentiment during the study period. When zooming into the topic-level analysis, we find that different aspects of COVID-19 have been constantly discussed and show comparable sentiment polarities. Some topics like ``stay safe home" are dominated with positive sentiment. The others such as ``people death" are consistently showing negative sentiment. Overall, the proposed framework shows insightful findings based on the analysis of the topic-level sentiment dynamics.
Proving Non-Inclusion of Büchi Automata based on Monte Carlo Sampling
Authors: Yong Li, Andrea Turrini, Xuechao Sun, Lijun Zhang
Date: 2020-07-05 00:00:00

The search for a proof of correctness and the search for counterexamples (bugs) are complementary aspects of verification. In order to maximize the practical use of verification tools it is better to pursue them at the same time. While this is well-understood in the termination analysis of programs, this is not the case for the language inclusion analysis of B\"uchi automata, where research mainly focused on improving algorithms for proving language inclusion, with the search for counterexamples left to the expensive complementation operation. In this paper, we present $\mathsf{IMC}^2$, a specific algorithm for proving B\"uchi automata non-inclusion $\mathcal{L}(\mathcal{A}) \not\subseteq \mathcal{L}(\mathcal{B})$, based on Grosu and Smolka's algorithm $\mathsf{MC}^2$ developed for Monte Carlo model checking against LTL formulas. The algorithm we propose takes $M = \lceil \ln \delta / \ln (1-\epsilon) \rceil$ random lasso-shaped samples from $\mathcal{A}$ to decide whether to reject the hypothesis $\mathcal{L}(\mathcal{A}) \not\subseteq \mathcal{L}(\mathcal{B})$, for given error probability $\epsilon$ and confidence level $1 - \delta$. With such a number of samples, $\mathsf{IMC}^2$ ensures that the probability of witnessing $\mathcal{L}(\mathcal{A}) \not\subseteq \mathcal{L}(\mathcal{B})$ via further sampling is less than $\delta$, under the assumption that the probability of finding a lasso counterexample is larger than $\epsilon$. Extensive experimental evaluation shows that $\mathsf{IMC}^2$ is a fast and reliable way to find counterexamples to B\"uchi automata inclusion.