Miracle-dz/UniFAR-FTUs huggingface dataset:
Summary of the Third International Workshop on Bots in Software Engineering (BotSE 2021)

Bots automate tasks in software engineering projects and interact with software developers. Bots have been proposed, for example, for testing, maintenance, and automating bug fixes. The research community has been discussing these bots in the International Workshop on Bots in Software Engineering (BotSE), collocated with ICSE (the International Conference on Software Engineering). The workshop participants share experiences and challenges, discuss new usages of bots, and map out future directions. In this paper, we present a summary of the 3rd edition of the workshop, which comprised nine papers, one journal-first presentation, and two keynotes, followed by extensive discussion. More details can be found at http://botse.org/
QFinder: A Framework for Quantity-centric Ranking

Quantities shape our understanding of measures and values, and they are an important means to communicate the properties of objects. Often, search queries contain numbers as retrieval units, e.g., "iPhone that costs less than 800 Euros''. Yet, modern search engines lack a proper understanding of numbers and units. In queries and documents, search engines handle them as normal keywords and therefore are ignorant of relative conditions between numbers, such as greater than or less than, or, more generally, the numerical proximity of quantities. In this work, we demonstrate QFinder, our quantity-centric framework for ranking search results for queries with quantity constraints. We also open-source our new ranking method as an Elasticsearch plug-in for future use. Our demo is available at: https://qfinder.ifi. uni-heidelberg.de/
PTAU: Prompt Tuning for Attributing Unanswerable Questions

Current question answering systems are insufficient when confronting real-life scenarios, as they can hardly be aware of whether a question is answerable given its context. Hence, there is a recent pursuit of unanswerability of a question and its attribution. Attribution of unanswerability requires the system to choose an appropriate cause for an unanswerable question. As the task is sophisticated for even human beings, it is expensive to acquire labeled data, which makes it a low-data regime problem. Moreover, the causes themselves are semantically abstract and complex, and the process of attribution is heavily question- and context-dependent. Thus, a capable model has to carefully appreciate the causes, and then, judiciously contrast the question with its context, in order to cast it into the right cause. In response to the challenges, we present PTAU, which refers to and implements a high-level human reading strategy such that one reads with anticipation. In specific, PTAU leverages the recent prompt-tuning paradigm, and is further enhanced with two innovatively conceived modules: 1) a cause-oriented template module that constructs continuous templates towards certain attributing class in high dimensional vector space; and 2) a semantics-aware label module that exploits label semantics through contrastive learning to render the classes distinguishable. Extensive experiments demonstrate that the proposed design better enlightens not only the attribution model, but also current question answering models, leading to superior performance.
RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation

Sequential recommendation methods are very important in modern recommender systems because they can well capture users' dynamic interests from their interaction history, and make accurate recommendations for users, thereby helping enterprises succeed in business. However, despite the great success of existing sequential recommendation-based methods, they focus too much on item-level modeling of users' click history and lack information about the user's entire click history (such as click order, click time, etc.). To tackle this problem, inspired by recent advances in pre-training techniques in the field of natural language processing, we build a new pre-training task based on the original BERT pre-training framework and incorporate temporal information. Specifically, we propose a new model called the RE arrange S equence prE -training and T ime embedding model via BERT for sequential R ecommendation (RESETBERT4Rec ) \footnoteThis work was completed during JD internship. , it further captures the information of the user's whole click history by adding a rearrange sequence prediction task to the original BERT pre-training framework, while it integrates different views of time information. Comprehensive experiments on two public datasets as well as one e-commerce dataset demonstrate that RESETBERT4Rec achieves state-of-the-art performance over existing baselines.
Identifying Argumentative Questions in Web Search Logs

We present an approach to identify argumentative questions among web search queries. Argumentative questions ask for reasons to support a certain stance on a controversial topic, such as ''Should marijuana be legalized? '' Controversial topics entail opposing stances, and hence can be supported or opposed by various arguments. Argumentative questions pose a challenge for search engines since they should be answered with both pro and con arguments in order to not bias a user toward a certain stance. To further analyze the problem, we sampled questions about 19 controversial topics from a large Yandex search log and let human annotators label them as one of factual, method, or argumentative. The result is a collection of 39,340 labeled questions, 28% of which are argumentative, demonstrating the need to develop dedicated systems for this type of questions. A comparative analysis of the three question types shows that asking for reasons and predictions are among the most important features of argumentative questions. To demonstrate the feasibility of the classification task, we developed a BERT-based classifier to map questions to the question types, reaching a promising macro-averaged F>sub>1-score of 0.78.
A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature Review

Automotive user interfaces constantly change due to increasing automation, novel features, additional applications, and user demands. While in-vehicle interaction can utilize numerous promising modalities, no existing overview includes an extensive set of human sensors and actuators and interaction locations throughout the vehicle interior. We conducted a systematic literature review of 327 publications leading to a design space for in-vehicle interaction that outlines existing and lack of work regarding input and output modalities, locations, and multimodal interaction. To investigate user acceptance of possible modalities and locations inferred from existing work and gaps unveiled in our design space, we conducted an online study (N=48). The study revealed users' general acceptance of novel modalities (e.g., brain or thermal activity) and interaction with locations other than the front (e.g., seat or table). Our work helps practitioners evaluate key design decisions, exploit trends, and explore new areas in the domain of in-vehicle interaction.
Towards Results-level Proportionality for Multi-objective Recommender Systems

The main focus of our work is the problem of multiple objectives optimization (MOO) while providing a final list of recommendations to the user. Currently, system designers can tune MOO by setting importance of individual objectives, usually in some kind of weighted average setting. However, this does not have to translate into the presence of such objectives in the final results. In contrast, in our work we would like to allow system designers or end-users to directly quantify the required relative ratios of individual objectives in the resulting recommendations, e.g., the final results should have 60% relevance, 30% diversity and 10% novelty. If individual objectives are transformed to represent quality on the same scale, these result conditioning expressions may greatly contribute towards recommendations tuneability and explainability as well as user's control over recommendations. To achieve this task, we propose an iterative algorithm inspired by the mandates allocation problem in public elections. The algorithm is applicable as long as per-item marginal gains of individual objectives can be calculated. Effectiveness of the algorithm is evaluated on several settings of relevance-novelty-diversity optimization problem. Furthermore, we also outline several options to scale individual objectives to represent similar value for the user.
V2P: Vision-to-Prompt based Multi-Modal Product Summary Generation

Multi-modal Product Summary Generation is a new yet challenging task, which aims to generate a concise and readable summary for a product given its multi-modal content, e.g., its long text description and image. Although existing methods have achieved great success, they still suffer from three key limitations: 1) overlook the benefit of pre-training, 2) lack the representation-level supervision, and 3) ignore the diversity of the seller-generated data. To address these limitations, in this work, we propose a Vision-to-Prompt based multi-modal product summary generation framework, dubbed as V2P, where a Generative Pre-trained Language Model (GPLM) is adopted as the backbone. In particular, to maintain the original text capability of the GPLM and fully utilize the high-level concepts contained in the product image, we design V2P with two key components: vision-based prominent attribute prediction, and attribute prompt-guided summary generation. The first component works on obtaining the vital semantic attributes of the product from its image by the Swin Transformer, while the second component aims to generate the summary based on the product's long text description and the attribute prompts yielded by the first component with a GPLM. Towards comprehensive supervision over the second component, apart from the conventional output-level supervision, we introduce the representation-level regularization. Meanwhile, we design the data augmentation-based robustness regularization to handle the diverse inputs and improve the robustness of the second component. Extensive experiments on a large-scale Chinese dataset verify the superiority of our model over cutting-edge methods.
Analyzing the Support Level for Tips Extracted from Product Reviews

Useful tips extracted from product reviews assist customers to take a more informed purchase decision, as well as making a better, easier, and safer usage of the product. In this work we argue that extracted tips should be examined based on the amount of support and opposition they receive from all product reviews. A classifier, developed for this purpose, determines the degree to which a tip is supported or contradicted by a single review sentence. These support-levels are then aggregated over all review sentences, providing a global support score, and a global contradiction score, reflecting the support-level of all reviews to the given tip, thus improving the customer confidence in the tip validity. By analyzing a large set of tips extracted from product reviews, we propose a novel taxonomy for categorizing tips as highly-supported, highly-contradicted, controversial (supported and contradicted), and anecdotal (neither supported nor contradicted).
Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison

Information access systems, such as search and recommender systems, often use ranked lists to present results believed to be relevant to the user's information need. Evaluating these lists for their fairness along with other traditional metrics provides a more complete understanding of an information access system's behavior beyond accuracy or utility constructs. To measure the (un)fairness of rankings, particularly with respect to the protected group(s) of producers or providers, several metrics have been proposed in the last several years. However, an empirical and comparative analyses of these metrics showing the applicability to specific scenario or real data, conceptual similarities, and differences is still lacking. We aim to bridge the gap between theoretical and practical ap-plication of these metrics. In this paper we describe several fair ranking metrics from the existing literature in a common notation, enabling direct comparison of their approaches and assumptions, and empirically compare them on the same experimental setup and data sets in the context of three information access tasks. We also provide a sensitivity analysis to assess the impact of the design choices and parameter settings that go in to these metrics and point to additional work needed to improve fairness measurement.
SoChainDB: A Database for Storing and Retrieving Blockchain-Powered Social Network Data

Social networks have become an inseparable part of human activities. Most existing social networks follow a centralized system model, which despite storing valuable information of users, arise many critical concerns such as content ownership and over-commercialization. Recently, decentralized social networks, built primarily on blockchain technology, have been proposed as a substitution to eliminate these concerns. Since decentralized architectures are mature enough to be on par with the centralized ones, decentralized social networks are becoming more and more popular. Decentralized social networks can offer both common options like writing posts and comments and more advanced options such as reward systems and voting mechanisms. They provide rich eco-systems for the influencers to interact with their followers and other users via staking systems based on cryptocurrency tokens. The vast and valuable data of the decentralized social networks open several new directions for the research community to extend human behavior knowledge. However, accessing and collecting data from these social networks is not easy because it requires strong blockchain knowledge, which is not the main focus of computer science and social science researchers. Hence, our work proposes the SoChainDB framework that facilitates obtaining data from these new social networks. To show the capacity and strength of SoChainDB, we crawl and publish Hive data - one of the largest blockchain-based social networks. We conduct extensive analyses to understand the insight of Hive data and discuss some interesting applications, e.g., game, non-fungible tokens market built upon Hive. It is worth mentioning that our framework is well-adaptable to other blockchain social networks with minimal modification. SoChainDB is publicly accessible at http://sochaindb.com and the dataset is available under the CC BY-SA 4.0 license.
Career Records for All Modern Position Players Eligible for the Major League Baseball Hall of Fame

Abstract The dataset "Career Records For All Modern Position Players Eligible For The Major League Baseball Hall of Fame" contains information for the 1340 major league baseball players who had retired prior to the 1993 season and who were eligible for the Major League Baseball Hall of Fame (had played in at least ten seasons). Traditional performance measures included are number of seasons played, games played, official at-bats (AB), runs scored, hits (H), doubles (2B), triples (3B), home runs (HR), runs batted in (RBI), walks (BB), strikeouts (SO), batting average (BA), on base percentage (OBP), slugging percentage (SLG), stolen bases (SB), times caught stealing (CS), fielding average (FA), and primary position played (POS). In addition, the following composite measures are included: adjusted production (AP), batting runs (BR), adjusted batting runs (ABR), runs created (RC), stolen base runs (SBR), fielding runs (FR), and total player rating (TPR). Finally, the dataset includes an indication of whether or not each player has been admitted into the Major League Baseball Hall of Fame and, if so, under what set of rules he was admitted.
Critical review on life cycle inventories and environmental assessments of LED-lamps

ABSTRACT Resource scarcity, hazardous waste, and climate change are the driving forces for developing energy efficient and low-toxic lighting sources. Currently, solid state lighting based on light-emitting diodes is expected to become the most dominant lighting technology of the future, mostly driven by its advantage with respect to energy efficiency. Parallel to the ongoing development of light-emitting diode-based lighting sources, a number of single case and comparative life cycle assessment studies of LED-lamps and components in varying study settings were carried out. However, these studies mostly rely to specific lamp designs, which limits general conclusions. This work includes a summary of the global market of lighting and LED-lamp technologies, followed by a comprehensive review and comparison of published life cycle assessment studies. In addition, we take also some aspects, which are relevant for the well-being of an end-user and which are usually not treated in life cycle assessment studies, like glare and health issues, into consideration. A critical interpretation of the assumptions and outcomes of these studies is given and questions that remain unsettled and therefore should be addressed in future studies are discussed.
Resurgent cities and the socioeconomic divide: the young, educated and affluent city of Copenhagen, Denmark

ABSTRACT The recovery of cities has led to increased wealth while simultaneously amplifying the socioeconomic spatial divide and polarization within the resurgent city. Recently, renewed interest in the socioeconomic consequences of the city’s growth has focused on residential differences in terms of e.g. income and education. In this paper, we examine how the recovery of cities has influenced the internal divide in the socioeconomic structures of demography, education, and employment. Utilizing Danish register data for all residents in Copenhagen during the period from 1992 to 2017, we describe the development of the resurgent city in relation to two sets of processes, academification (formal academic education) and workification (employment rate), with a focus on young adults, and compare with the suburban municipalities bordering Copenhagen. We show that Copenhagen, like other major European and American resurgent cities, has seen a decrease in the mean age of its residents and an increase in young adults who have completed an academic education and found employment in the city. Moreover, we find that the relationship between residence and workplace location has become more divided according to educational background, further emphasizing the socioeconomic inequalities within the city.
Modern tools for holistic assessment of ambiguity and risk: a case study of three Latin American Mega Transport Projects

Abstract The objective of this article has been to use Fuzzy Set Theory (FST) to assess the construction risks of three mega transport projects (MTPs) in Latin America and illustrate the relative importance of ambiguous risk factors in this region. The selected multiple-case study comprised the following three cases: Ferroanel from Sao Paulo in Brazil; Central Railway of Uruguay; and Line 1 of the Bogota Metro in Colombia. By applying FST to this set of MTPs, which includes projects developed in different institutional contexts, this paper shows that ill-defined construction risks are especially important in megaprojects in Latin America. Consequently, it also reveals that risk assessment should not be circumscribed to the sole use of numeric, probability-based analyses, which are incapable of considering ambiguous risks. On the contrary, this paper demonstrates the potential of using other methodologies, such as FST, which also incorporate perceptions of the institutional contexts in order to improve risk management in megaprojects of the region.
The Algebraic Path Problem for Graph Metrics

Finding paths with optimal properties is a foundational problem in computer science. The notions of shortest paths (minimal sum of edge costs), minimax paths (minimal maximum edge weight), reliability of a path and many others all arise as special cases of the “algebraic path problem” ( APP ). Indeed, the APP formalizes the relation between different semirings such as min-plus, min-max and the distances they induce. We here clarify, for the ﬁrst time, the relation between the potential distance and the log-semiring. We also deﬁne a new unifying family of algebraic structures that include all above-mentioned path problems as well as the commute cost and others as special or limiting cases. The family comprises not only semirings but also strong bimonoids (that is, semirings without distributivity). We call this new and very general distance the “log-norm distance”. Finally, we derive some sufﬁcient conditions which ensure that the APP associated with a semiring deﬁnes a metric over an arbitrary graph.
Adaptive Gaussian Process Change Point Detection

Detecting change points in time series, i.e., points in time at which some observed process suddenly changes, is a fundamental task that arises in many real-world applications, with consequences for safety and reliability. In this work, we propose ADAGA , a novel Gaussian process-based solu-tion to this problem, that leverages a powerful heuristics we developed based on statistical hypothesis testing. In contrast to prior approaches, ADAGA adapts to changes both in mean and co-variance structure of the temporal process. In extensive experiments, we show its versatility and applicability to different classes of change points, demonstrating that it is significantly more accurate than current state-of-the-art alternatives.
Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning

We propose a novel formulation for the Inverse Reinforcement Learning (IRL) problem, which jointly accounts for the compatibility with the expert behavior of the identiﬁed reward and its effectiveness for the subsequent forward learning phase. Albeit quite natural, especially when the ﬁnal goal is apprenticeship learning (learning policies from an expert), this aspect has been completely overlooked by IRL approaches so far. We propose a new model-free IRL method that is remarkably able to autonomously ﬁnd a trade-off between the error induced on the learned policy when potentially choosing a sub-optimal reward, and the estimation error caused by using ﬁnite samples in the forward learning phase, which can be controlled by explicitly optimizing also the discount factor of the related learning problem. The approach is based on a min-max formulation for the robust selection of the reward parameters and the discount factor so that the distance be-tween the expert’s policy and the learned policy is minimized in the successive forward learning task when a ﬁnite and possibly small number of samples is available. Differently from the majority of other IRL techniques, our approach does not involve any planning or forward Reinforcement Learning problems to be solved. After presenting the formulation, we provide a numerical scheme for the optimization, and we show its effectiveness on illustrative numerical cases.
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning

Large-scale machine learning systems often involve data distributed across a collection of users. Federated learning algorithms leverage this structure by communicating model updates to a central server, rather than entire datasets. In this paper, we study stochastic optimization algorithms for a personalized federated learning setting involving local and global models subject to user-level (joint) differential privacy. While learning a private global model induces a cost of privacy, local learning is perfectly private. We provide generalization guarantees showing that coordinating local learning with private centralized learning yields a generically useful and improved tradeoff between accuracy and privacy. We illustrate our theoretical results with experiments on synthetic and real-world datasets.