Automatic risk estimation is paramount to prioritizing public contracts auditing efforts, and Machine Learning Risk Prediction (MLRP) models are a promising solution to the classification task of identifying high-risk contracts. Current approaches are focused on the federal level of government, at the same time, face limiting challenges such as the absence of exhaustive ground truth, the difficulty of gaining access to critical databases to build model features, as well as the absence of public literature on the relevance of proposed features. In this work, we attempt to propose MLRP models at the municipal level and overcome those issues by exploring the space of challenges and opportunities in applying MLRP to a setting of Brazilian public contracts. With grounds on the prosecutors' practical experience, we combine three data sources to produce a novel dataset that is more detailed and precise than those used in previous works, first to establish a baseline measuring the gains of applying MLRP to Brazilian Public contracts at the municipal level, second to compare the performance of MLRP and a sample of ad-hoc state-of-the-practice data. Next, we leverage semantic features from contract descriptions in order to evaluate the impact of the contract area on the model's prediction. Also, we experiment using urban and economic characteristics to improve model performance. We measure the impact of access to each datasource on model performance, quantifying the importance of non-open data for this task. Our results suggest that the ad-hoc approach at the firm level has little practical efficacy when evaluated through a more granular/actionable perspective. Contract-level MLRP may be a promising approach, especially when using economic indicators to characterize municipalities, such as GDP per capita. Also, we found no difference between the impact of each feature set on the models' predictions.

Abstract This work explores how the design and situatedness of data representations affect people's compassion with a case study concerning harassment episodes in a public place. Results contribute to advancing the understanding of how visualizations can evoke emotions and their impact on prosocial behaviors, such as helping people in need. Recent literature examined the effect of different on‐screen data representations on emotion or prosociality, but little has been done concerning visualizations shown in a public place — especially a space contextually relevant to the data — or presented through unconventional media formats such as physical marks. We conducted two in‐the‐wild studies to investigate how different factors affect people's self‐reported compassion and intention to donate. We compared three ways of presenting data about the harassment cases: (1) communicating data only verbally; (2) using a printed poster with aggregated information; and (3) using a physicalization with detailed information about each story. We found that the physicalization influenced people to donate more than only hearing about the data, but it is unclear if the same applied to the poster visualization. Also, passers‐by reported a likely small increase in compassion when they saw the physicalization instead of the poster. We also examined the role of situatedness by showing the physicalization in a site that is not contextually relevant to the data. Our results suggest that people had a similar intention to donate and levels of compassion in both places. Those findings may indicate that using specific visualization designs to support campaigns about sensitive causes (e.g., sexual harassment) can increase the emotional response of passers‐by and may motivate them to help, independently of where the data representation is shown. Finally, this work also informs on the strengths and weaknesses of using research in the wild to evaluate data visualizations in public spaces.

When showing data about people, visualization designers and data journalists often use design strategies that presumably help the audience relate to those people. The term anthropographics has been recently coined to refer to this practice and the resulting visualizations. Anthropographics is a rich and growing area, but the work so far has remained scattered. Despite preliminary empirical work and a few web essays written by practitioners, there is a lack of clear language for thinking about and communicating about anthropographics. We address this gap by introducing a conceptual framework and a design space for anthropographics. Our design space consists of seven elementary design dimensions that can be reasonably hypothesized to have some effect on prosocial feelings or behavior. It extends a previous design space and is informed by an analysis of 105 visualizations collected from newspapers, websites, and research articles. We use our conceptual framework and design space to discuss trade-offs, common design strategies, as well as future opportunities for design and research in the area of anthropographics.

Visualizations designed to make readers compassionate with the persons whose data is represented have been called anthropographics and are commonly employed by practitioners. Empirical studies have recently examined whether anthropographics indeed promote empathy, compassion, or the likelihood of prosocial behavior, but findings have been inconclusive so far. This work contributes a detailed overview of past experiments, and two new experiments that use large samples and a combination of design strategies to maximize the possibility of finding an effect. We tested an information-rich anthropographic against a simple bar chart, asking participants to allocate hypothetical money in a crowdsourcing study. We found that the anthropographic had, at best, a small effect on money allocation. Such a small effect may be relevant for large-scale donation campaigns, but the large sample sizes required to observe an effect and the noise involved in measuring it make it very difficult to study in more depth. Data and code are available at https://osf.io/xqae2/.

The workshop features presentations of accepted contributions to the RecSys Challenge 2020, organized by Politecnico di Bari, Free University of Bozen-Bolzano, TU Wien, University of Colorado, Boulder, and Universidade Federal de Campina Grande, and sponsored by Twitter. The challenge focuses on a real-world task of Tweet engagement prediction in a dynamic environment. The goal is to predict the probability for different types of engagement (Like, Reply, Retweet, and Retweet with comment) of a target user for a set of Tweets, based on heterogeneous input data. To this end, Twitter has released a large public dataset of ~160M public Tweets, obtained by subsampling within ~2 weeks, that contains engagement features, user features, and Tweet features. A peculiarity of this challenge is related to the recent regulations on data protection and privacy. The challenge data set was compliant: if a user deleted a Tweet, or their data from Twitter, the dataset was promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics.

This work explores the user experience with two situated visualizations that lie on different points of design space. The first visualization - the Activity Clock - displays the aggregate presence of laboratory members into a wall clock. The second - Personal Activities - represents the same persons individually, in a conventional poster media. We interviewed 17 participants and leverage a theoretical lens of Continuous Engagement and Sense-Making to study how design decisions impact the user experience with respect to (1) which design factors attract users, (2) how design features affect users' understanding of the visualization, and (3) what kind of reflections are evoked by design. We discuss how the defamiliarizing effect of the Activity Clock plays a dual role in attracting users while also hindering their understanding of the data. We also consider the evidence that fine representation granularity in the Personal Activities evokes deeper reflections.

StackExchange is a network of Question & Answer (Q&A) sites that support collaborative knowledge exchange on a variety of topics. Prior research found a significant imbalance between those who contribute content to Q&A sites (predominantly people from Western countries) and those who passively use the site (the so-called "lurkers"). One possible explanation for such participation differences between countries could be a mismatch between culturally related preferences of some users and the values ingrained in the design of the site. To examine this hypothesis, we conducted a value-sensitive analysis of the design of the StackExchange site Stack Overflow and contrasted our findings with those of participants from societies with varying cultural backgrounds using a series of focus groups and interviews. Our results reveal tensions between collectivist values, such as the openness for social interactions, and the performance-oriented, individualist values embedded in Stack Overflow's design and community guidelines. This finding confirms that socio-technical sites like Stack Overflow reflect the inherent values of their designers, knowledge that can be leveraged to foster participation equity.

The availability of historical data on the global positioning systems' trajectories of vehicles and passenger boarding information for public bus fleets of large municipalities has given researchers and practitioners the opportunity to explore new challenges regarding the analysis of public transportation systems. This paper performs one such analysis as a case study examining the margin of improvement that passengers of a 1.8M people Brazilian city have when choosing their daily bus trips. In doing so, we document a number of not readily apparent challenges that must be overcome to leverage public transportation big data to policymakers, transportation systems operators, and citizens. Solutions are devised to each of these challenges and demonstrated on the analysis of the aforementioned 1.8M people city.

This paper examines the impact of heavy churn on the robustness of decentralized social network-based Sybil defense (SNSD) schemes. Our analysis reveals that (i) heavy churn disintegrates the social overlay network that is fundamental to these schemes into multiple disconnected components, resulting in poor network connectivity, and (ii) a naive solution that adds links from each node to all its 2-hop neighbors improves network connectivity but comes at a significant cost of poor attack resilience of these schemes.

Q&A sites currently enable large numbers of contributors to collectively build valuable knowledge bases. Naturally, these sites are the product of contributors acting in different ways - creating questions, answers or comments and voting in these - contributing in diverse amounts, and creating content of varying quality. This paper advances present knowledge about Q&A sites using a multifaceted view of contributors that accounts for diversity of behavior, motivation and expertise to characterize their profiles in five sites. This characterization resulted in the definition of ten behavioral profiles that group users according to the quality and quantity of their contributions. Using these profiles, we find that the five sites have remarkably similar distributions of contributor profiles. We also conduct a longitudinal study of contributor profiles in one of the sites, identifying common profile transitions, and finding that although users change profiles with some frequency, the site composition is mostly stable over time.

P2P communities that use credits to incentivize their members to contribute have emerged over the last few years. In particular, private BitTorrent communities keep track of the total upload and download of each member and impose a minimum threshold for their upload/download ratio, which is known as their sharing ratio. It has been shown that these private communities have significantly better download performance than public communities. However, this performance is based on oversupply, and it has also been shown that it is hard for users to maintain a good sharing ratio to avoid being expelled from the community. In this paper, we address this problem by introducing a speculative download mechanism to automatically manage user contribution in BitTorrent private communities. This mechanism, when integrated in a BitTorrent client, identifies the swarms that have the biggest upload potential, and automatically downloads and seeds them. In other words, it tries to invests the bandwidth of the user in a profitable way. In order to accurately asses the upload potential of swarms we analyze a private BitTorrent community and derive through multiple regression a predictor for the upload potential based on simple parameters accessible to each peer. The speculative download mechanism uses the predictor to build a cache of profitable swarms to which the peer can contribute. Our results show that 75 % of investment decisions result in an increase in upload bandwidth utilization, with a median 207 % return on investment.

EigenTrust (ET) is a renowned algorithm for reputation management in adversarial P2P systems. It incorporates the opinions of all peers in the network to compute a global trust score for each peer based on its past behavior, and relies on a set of pre-trusted nodes to guarantee that malicious nodes cannot subvert the system. In this paper, we show that ET is vulnerable to community structure and a novel targeted attack based on eigenvector centrality, since ET ranks nodes close to the pre-trusted ones higher than those further away. To address these shortcomings, we propose Personalized EigenTrust (PET) which (i) enables each user to choose her trusted peers from the social network of peers, thereby eliminating the need of pre-trusted nodes and making the system autonomous, (ii) is effective in networks operating under various transaction models based on distributions such as random, community-like and power-law, and (iii) is robust to many types of attacks including the targeted one based on eigenvector centrality. Our simulation results reveal that PET outperforms ET under diverse transaction models and attack strategies.

Due to the possibility of cheap identity creation, decentralized online reputation mechanisms are susceptible to sybil attacks. Barter Cast is a reputation mechanism used in the Internet-deployed Tribler file-sharing client. In this paper we study the opportunities for sybil attacks in Barter Cast and we devise a method for making Barter Cast sybil resilient, which is incorporated in a protocol called Sybil Res. Like in Barter Cast, in Sybil Res each peer maintains a local subjective weighted directed graph reflecting data transfer actions in Tribler, from which it computes the reputations of other peers using a flow based algorithm taking the edge weights as flows. In Sybil Res, after an upload action, the uploading peer discounts the weights of the edges on the paths from the down loader to itself. As a consequence, due to the way reputations are computed, the reputation of a peer performing a sybil attack decreases fast. To mitigate the negative impact of edge weight discounting on the reputations of honest peers, after a download action, the downloading peer increases the weights of the edges on the paths from the up loader to itself. We demonstrate that Sybil Res is effective in practice by means of trace-driven simulations using data collected from the Tribler network. The results show that Sybil Res effectively marginalizes attackers while having a minimal effect on the reputations of honest peers.

A considerable body of research shows that Bit-Torrent provides very efficient resource allocation inside single swarms. Many BitTorrent clients also allow users to participate in multiple swarms simultaneously, and implement inter-swarm resource-allocation mechanisms that are used by millions of people. However, resource allocation across multiple swarms in BitTorrent has received much less attention. In this paper, we investigate whether currently prevalent inter-swarm resource allocation mechanisms perform acceptably or call for improvements. We use data from two BitTorrent communities and present results from trace-based simulations. Two use-cases for allocation mechanisms drive our evaluation: (1) file-sharing communities, whose objective is maximizing throughput, and (2) video-streaming communities, whose objective is maximizing the number of users receiving sufficient resources for uninterrupted streaming. To put the results from the analyzed mechanisms into perspective, we devise theoretical efficiency bounds for inter-swarm resource allocation, for which we map the resource allocation problem to a graph-theoretical flow network problem. In this formalism, the goal of the file-sharing use-case, throughput maximization, is equivalent to maximizing the flow in the network. The goal of the video-streaming use-case translates into finding a max-min fair allocation for BitTorrent downloading sessions, a problem for which we devise a new algorithm.

The efficiency of Bit Torrent in disseminating content has inspired a number of P2P protocols for on-demand video streaming (VoD). Prior work on adapting Bit Torrent to VoD mainly focused on the piece selection policy, since streaming requires a somewhat "in order" download progress. Conversely, not much effort has been spent into adapting Bit Torrent's peer selection policy, where nodes mainly serve those that have recently uploaded to them at the highest rates. This mechanism incentivizes cooperation among peers but, in a heterogeneous system (i.e. where peers have different bandwidth capacities), it causes faster peers to receive higher download speeds than slower peers. This might hurt the system's ability of providing as many nodes as possible with the minimum download speed necessary to sustain the video playback rate. Furthermore, peers gain little utility in downloading at rates much higher than the video playback rate. Inspired by these observations, in this work, we extend the peer selection mechanism of an existing Bit Torrent-like VoD protocol, give-to-get (G2G), with techniques that allow peers to relax their reciprocity-based peer selection and choose more random nodes when their current QoS is high. In this way, more peers can be granted a good QoS and free-riding is tolerated only when bandwidth resources are abundant. To demonstrate the benefits of our approach, we present extensive simulations of the introduced techniques.

P2P systems can benefit from reputation mechanisms to promote cooperation and help peers to identify good service providers. However, in spite of a large number of proposed reputation mechanisms, few have been investigated in real situations. BarterCast is a distributed reputation mechanism used by our Internet-deployed Bittorent-based file-sharing client Tribler. In BarterCast, each peer uses messages received from other peers to build a weighted, directed subjective graph that represents the upload and download activity in the system. A peer calculates the reputations of other peers by applying the maxflow algorithm to its subjective graph. For efficiency reasons, only paths of at most two hops are considered in this calculation. In this paper, we identify and assess three potential modifications to BarterCast for improving its accuracy and coverage (fraction of peers for which a reputation value can be computed). First, a peer executes maxflow from the perspective of the node with the highest betweenness centrality in its subjective graph instead of itself. Second, we assume a gossiping protocol that gives each peer complete information about upload and download activities in the system, and third, we lift the path length restriction in the maxflow algorithm. To assess these modifications, we crawl the Tribler network and collect the upload and download actions of the peers for three months. We apply BarterCast with and without the modifications on the collected data and measure accuracy and coverage.

Today, several data-storage systems allow applications to create and manage custom metadata to improve data search and navigability in large scale storage systems.

Peer-to-peer grids are large-scale, dynamic environments where autonomous sites share computing resources. Producing and maintaining relevant and up-to-date resource information in such environments is a challenging problem, due to the grid scale, the resource heterogeneity, and the variety of user demand. This work proposes a peer-to-peer annotation approach where users can freely annotate available resources as a solution to this problem. We advocate that the proposed approach (i) is scalable, as the job of updating the resource information is divided among users; (ii) will improve resources' utilization, by reducing the amount of resources which are allocated to users without matching their applications constraints; and (iii) will allow resource allocators to increase users' utility, leveraging access to more detailed preference descriptions. The paper also discusses the challenges in implementing and deploying such approach and present solutions to tackle these challenges.

BitTorrent is currently a de facto standard for scalable content-distribution. However, its peer-to-peer model for resource allocation does not provide high availability and its performance depends on best-effort contributions given by peers. This has motivated several content-providers to use a hybrid model in which they operate a superpeer in order to attain a higher quality of service. In this paper, we use BitTorrent traces and analytical modelling to investigate the cost incurred by such an entity in relation to the benefits it can provide to the system.

Abstract Here we present and evaluate relative accounting, an autonomous accounting scheme that provides accurate results even when the parties (consumer and provider) do not trust each other. Relative accounting relies on the observed relative performance amongst the parties. As such, the basic requirement to use it is that resource consumers must also be resource providers. Relative accounting is totally autonomous in the sense that it uses only local information, i.e. there is no exchange of information between the parties. This allows for the deployment of the autonomous accounting without requiring any sort of identification infrastructure, such as certificate authorities. Not requiring trust or sophisticated infrastructure makes relative accounting a perfect fit for peer‐to‐peer Grids, which aim to scale much further than traditional Grids by allowing free unidentified entry into the Grid. Our results show that relative accounting performs very close to a perfect accounting, whose implementation is infeasible in most systems, including those we target. Relative accounting was developed to work with OurGrid, a peer‐to‐peer Grid in production since December 2004, but it can also be used in other peer‐to‐peer Grids. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper we study reciprocation-based mechanisms to encourage donation in peer-to-peer grids in which multiple services, such as processing power and data transfers, are shared explicitly. We have modeled such a system and established how peers should assess whether it is profitable to exchange services with another peer, an issue that is not present in the single service case. Unfortunately, this assessment relies on information provided by untrustworthy peers. As an alternative, we have extended, to the case of multiple services, a reciprocation-based mechanism which uses only reliable information gathered locally. We have assessed this mechanism by simulating scenarios in which services are exchanged that are combinations of two different basic services. In the explored scenarios the mechanism performs very well, and can marginalize free riders even when the cost to peers of donating a service is nearly as large as the utility gained by receiving it

We investigate the circumstances under which it is possible to discourage free-riding in a peer-to-peer system for resource-sharing by prioritizing resource allocation to peers with higher reputation. We use a model to predict conditions necessary for any reputation scheme to succeed in discouraging free-riding by this method. We show with simulations that for representative cases, a very simple autonomous reputation scheme works nearly as well at discouraging free-riding as an ideal reputation scheme. Finally, we investigate the expected dynamic behavior of the system.

In this paper we discuss the difficulties involved in the scheduling of applications on computational grids. We highlight two main sources of difficulties: 1) the size of the grid rules out the possibility of using a centralized scheduler; 2) since resources are managed by different parties, the scheduler must consider several different policies. Thus, we argue that scheduling applications on a grid require the orchestration of several schedulers, with possibly conflicting goals. We discuss how we have addressed this issue in the context of PAUA, a grid for Bag-of-Tasks applications (i.e. parallel applications whose tasks are independent) that we are currently deploying throughout Brazil.