Information-enabled technology, such as digital contact tracing (DCT), is designed to assist decision-making. However, during the pandemic, DCT also contributes additional factors to consider for individuals and policymakers. In this article, we conceptualise the interactions between individuals and policymakers and propose a leader-followers mean-field Game model to analyse their decision-making processes by integrating key factors, such as individuals’ health state, self-control effort, privacy, and social activities, under the influence of interventions and subsidies from policymakers. The simulation demonstrates that moderate subsidies are sufficient to induce early control efforts and higher collective efforts even with privacy concerns and the negative influence of engaging activities; Equilibrium and system stability can be reached when healthy or the summation of healthy and mediocre healthy populations dominates; The model highlights the crucial elements for future data analysis and collection, such as DCT is vital to indicate population health states.

Fear induction in the form of stories and visual images pervades the history of human culture. Creating a visceral emotion such as fear remains one of the cornerstones of human creativity. As artificial intelligence makes strides in solving challenging analytical problems like chess and Go, an important question still remains: can machines induce extreme human emotions, such as fear? In this work, we propose a deep-learning based collaborative horror writer that collaboratively writes scary stories with people on Twitter. We deploy our system as a bot on Twitter that regularly generates and posts new stories on Twitter, and invites users to participate. Users who interact with the stories produce multiple storylines originating from the same tweet, thereby creating a tree-based story structure. We further perform a validation study on n = 105 subjects to verify whether the generated stories psychologically move people on psychometrically validated measures of effect and anxiety such as I-PANAS-SF [43] and STAI-SF [26]. Our experiments show that 1) stories generated by our bot as well as the stories generated collaboratively between our bot and Twitter users produced statistically significant increases in negative affect and state anxiety compared to the control condition, and 2) collaborated stories are more successful in terms of increasing negative affect and state anxiety than the machine-generated ones. Furthermore, we make three novel datasets used in our framework publicly available at https://github.com/catlab-team/shelley for encouraging further research on this topic.

Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. In light of recent advances in machine intelligence, a number of scientists, philosophers and technologists have revived the discussion about the potentially catastrophic risks entailed by such an entity. In this article, we trace the origins and development of the neo-fear of superintelligence, and some of the major proposals for its containment. We argue that total containment is, in principle, impossible, due to fundamental limits inherent to computing itself. Assuming that a superintelligence will contain a program that includes all the programs that can be executed by a universal Turing machine on input potentially as complex as the state of the world, strict containment requires simulations of such a program, something theoretically (and practically) impossible. This article is part of the special track on AI and Society.

We introduce TuringBox, a platform to democratize the study of AI. On one side of the platform, AI contributors upload existing and novel algorithms to be studied scientifically by others. On the other side, AI examiners develop and post machine intelligence tasks to evaluate and characterize the outputs of algorithms. We outline the architecture of such a platform, and describe two interactive case studies of algorithmic auditing on the platform.

The analysis of the creation, mutation, and propagation of social media content on the Internet is an essential problem in computational social science, affecting areas ranging from marketing to political mobilization. A first step towards understanding the evolution of images online is the analysis of rapidly modifying and propagating memetic imagery or "memes". However, a pitfall in proceeding with such an investigation is the current incapability to produce a robust semantic space for such imagery, capable of understanding differences in Image Macros. In this study, we provide a first step in the systematic study of image evolution on the Internet, by proposing an algorithm based on sparse representations and deep learning to decouple various types of content in such images and produce a rich semantic embedding. We demonstrate the benefits of our approach on a variety of tasks pertaining to memes and Image Macros, such as image clustering, image retrieval, topic prediction and virality prediction, surpassing the existing methods on each. In addition to its utility on quantitative tasks, our method opens up the possibility of obtaining the first large-scale understanding of the evolution and propagation of memetic imagery.

Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption. Recent work analyzing massive datasets advances our understanding of popularity, but one major gap remains: To precisely quantify the relationship between the popularity of an online item and the external promotions it receives. This work supplies the missing link between exogenous inputs from public social media platforms, such as Twitter, and endogenous responses within the content platform, such as YouTube. We develop a novel mathematical model, the Hawkes intensity process, which can explain the complex popularity history of each video according to its type of content, network of diffusion, and sensitivity to promotion. Our model supplies a prototypical description of videos, called an endo-exo map. This map explains popularity as the result of an extrinsic factor -- the amount of promotions from the outside world that the video receives, acting upon two intrinsic factors -- sensitivity to promotion, and inherent virality. We use this model to forecast future popularity given promotions on a large 5-months feed of the most-tweeted videos, and found it to lower the average error by 28.6% from approaches based on popularity history. Finally, we can identify videos that have a high potential to become viral, as well as those for which promotions will have hardly any effect.

The cumulative effect of collective online participation has an important and adverse impact on individual privacy. As an online system evolves over time, new digital traces of individual behavior may uncover previously hidden statistical links between an individual's past actions and her private traits. To quantify this effect, we analyze the evolution of individual privacy loss by studying the edit history of Wikipedia over 13 years, including more than 117,523 different users performing 188,805,088 edits. We trace each Wikipedia's contributor using apparently harmless features, such as the number of edits performed on predefined broad categories in a given time period (e.g. Mathematics, Culture or Nature). We show that even at this unspecific level of behavior description, it is possible to use off-the-shelf machine learning algorithms to uncover usually undisclosed personal traits, such as gender, religion or education. We provide empirical evidence that the prediction accuracy for almost all private traits consistently improves over time. Surprisingly, the prediction performance for users who stopped editing after a given time still improves. The activities performed by new users seem to have contributed more to this effect than additional activities from existing (but still active) users. Insights from this work should help users, system designers, and policy makers understand and make long-term design choices in online content creation systems.

Human interactions that are sensed ubiquitously by mobile phones can improve a significant number of public health problems, particularly helping to track the spread of disease. In this paper, we evaluate multiple avenues for the integration of high-resolution face to face Bluetooth-sensed interaction networks into standard epidemic models. Our goal is to evaluate the capacity of the different avenues of integration to track the spread of seasonal influenza on a real-world community of 72 individuals over a period of 17 weeks. The dataset considered contains real-time tracking of individual flu symptoms over the whole observation period, providing a concrete individualized source for evaluation. We obtain an error of less than 2 infected people on average for predicting the total number of individuals affected by the flu and precision of approximately 30% when predicting exactly which individual will become infected at a given time. To the best of our knowledge, this is the first study considering mobile phone Bluetooth-sensed interaction data for dynamic infectious disease simulation that is evaluated against real human influenza occurrence. Our remarkable results indicate that high-resolution mobile phone data can increase the predictive power of even the simplest of epidemic models.

The present work deals with the problem of information acquisition in a strategic networked environment. To study this problem, Kleinberg and Raghavan (FOCS 2005) introduced the model of query incentive networks, where the root of a binomial branching process wishes to retrieve an information -- known by each node independently with probability 1/n -- by investing as little as possible. The authors considered fixed-payment contracts in which every node strategically chooses an amount to offer its children paid upon information retrieval to convince them to seek the information in their subtrees. Kleinberg and Raghavan discovered that the investment needed at the root exhibits an unexpected threshold behavior that depends on the branching parameter b. For b>2, the investment is linear in the expected distance to the closest information (logarithmic in n, the rarity of the information), while, for 1<b<2, it becomes exponential in the same distance (i.e., polynomial in n). Arcaute et al. (EC 2007) later observed the same threshold behavior for arbitrary Galton-Watson branching processes.

Protein structure prediction is regarded as a highly challenging problem both for the biology and for the computational communities. In recent years, many approaches have been developed, moving to increasingly complex lattice models and off-lattice models. This paper presents a Large Neighborhood Search (LNS) to find the native state for the Hydrophobic-Polar (HP) model on the Face-Centered Cubic (FCC) lattice or, in other words, a self-avoiding walk on the FCC lattice having a maximum number of H-H contacts. The algorithm starts with a tabu-search algorithm, whose solution is then improved by a combination of constraint programming and LNS. The flexible framework of this hybrid algorithm allows an adaptation to the Miyazawa-Jernigan contact potential, in place of the HP model, thus suggesting its potential for tertiary structure prediction. Benchmarking statistics are given for our method against the hydrophobic core threading program HPstruct, an exact method which can be viewed as complementary to our method.

Compression distances have been widely used in knowledge discovery and data mining. They are parameter-free, widely applicable, and very effective in several domains. However, little has been done to interpret their results or to explain their behavior. In this paper, we take a step toward understanding compression distances by performing an experimental evaluation of the impact of several kinds of information distortion on compression-based text clustering. We show how progressively removing words in such a way that the complexity of a document is slowly reduced helps the compression-based text clustering and improves its accuracy. In fact, we show how the nondistorted text clustering can be improved by means of annealing text distortion. The experimental results shown in this paper are consistent using different data sets, and different compression algorithms belonging to the most important compression families: Lempel-Ziv, Statistical and Block-Sorting.

Diffusion processes taking place in social networks are used to model a number of phenomena, such as the spread of human or computer viruses, and the adoption of products in viral marketing campaigns. It is generally difficult to obtain accurate information about how such spreads actually occur, so a variety of stochastic diffusion models are used to simulate spreading processes in networks instead. We show that a canonical genetic algorithm with a spatially distributed population, when paired with specific forms of Holland's synthetic hyperplane-defined objective functions, can simulate a large and rich class of diffusion models for social networks. These include standard diffusion models, such as the Independent Cascade and Competing Processes models. In addition, our Genetic Algorithm Diffusion Model (GADM) can also model complex phenomena such as information diffusion. We demonstrate an application of the GADM to modeling information flow in a large, dynamic social network derived from e-mail headers.

An important question in behavioral epidemiology and public health is to understand how individual behavior is affected by illness and stress. Although changes in individual behavior are intertwined with contagion, epidemiologists today do not have sensing or modeling tools to quantitatively measure its effects in real-world conditions. In this paper, we propose a novel application of ubiquitous computing. We use mobile phone based co-location and communication sensing to measure characteristic behavior changes in symptomatic individuals, reflected in their total communication, interactions with respect to time of day (e.g., late night, early morning), diversity and entropy of face-to-face interactions and movement. Using these extracted mobile features, it is possible to predict the health status of an individual, without having actual health measurements from the subject. Finally, we estimate the temporal information flux and implied causality between physical symptoms, behavior and mental health.

Student plagiarism is a major problem in universities worldwide. In this paper, we focus on plagiarism in answers to computer programming assignments, where students mix and/or modify one or more original solutions to obtain counterfeits. Although several software tools have been developed to help the tedious and time consuming task of detecting plagiarism, little has been done to assess their quality, because determining the real authorship of the whole submission corpus is practically impossible for markers. In this paper, we present a grammar evolution technique which generates benchmarks for testing plagiarism detection tools. Given a programming language, our technique generates a set of original solutions to an assignment, together with a set of plagiarisms of the former set which mimic the basic plagiarism techniques performed by students. The authorship of the submission corpus is predefined by the user, providing a base for the assessment and further comparison of copy-catching tools. We give empirical evidence of the suitability of our approach by studying the behavior of one advanced plagiarism detection tool (AC) on four benchmarks coded in APL2, generated with our technique.

This work presents an Information Retrieval technique based on algorithmic information theory (using the normalized compression distance), statistical data outlier detection, and a novel database structure. The paper shows how they all can be integrated to retrieve information from generic databases using long text-based queries. Two important problems are addressed. On the one hand, we analyze and tyr to solve the detection of a particular case of false positives: when the distance among two documents is outlyingly low but there is not actual similarity. On the other hand, we propose a way to structure the database such that the similarity distance estimation scales well with the length of the size of the query. All design choices are justified with an experimental evaluation.

One of the characteristics of tag prediction mechanisms is that, typically, all user models are constructed with the same granularity. In this paper we hypothesize and empirically demonstrate that in order to increase tag prediction accuracy, the granularity of each user model has to be adapted to the level of usage of each particular user. We have constructed user models for tag prediction using association rules in Bibsonomy, a popular social bookmark and publication sharing system, at three granularity levels: (1) canonical, (2) stereotypical and (3) individual. Our experiments show that prediction accuracy improves if the level of granularity matches the level of participation of the user in the community (i.e., amount of tagging in Bibsonomy).

Recent large scale experiments have shown that the normalized information distance, an algorithmic information measure, is among the best similarity metrics for melody classification. This paper proposes the use of this distance as a fitness function which may be used by genetic algorithms to automatically generate music in a given pre-defined style. The minimization of this distance of the generated music to a set of musical guides makes it possible to obtain computer-generated music which recalls the style of a certain human author. The recombination operator plays an important role in this problem and thus several variations are tested to fine tune the genetic algorithm for this application. The superiority of the relative pitch envelope over other music parameters, such as the lengths of the notes, brought us to develop a simplified algorithm that nevertheless obtains interesting results.

Student plagiarism is a mayor problem in universities worldwide. In this paper,we focus on plagiarism in answers to computer programming assignments,where student mix and/or modify one or more original solutions to obtain counterfeits. Although several software tools have been implemented to help the tedious and time consuming task of detecting plagiarism, little has been done to assess their quality, because, in fact, determining the original subset of the whole solutionset is practically impossible for graders. In this article we present a Grammatical Evolution technique which generates benchmarks. Given a programming language, our technique generates a set of original solutions to an assignment, together with a set of plagiarisms of the former set which mimic the way in which students act. The phylogeny of the coded solutions is predefined, providing a base for the evaluationof the performance of copy-catching tools. We give empirical evidence of the suitability of our approach by studying the behavior of one state-of-the-art detection tool (AC) on four benchmarks coded in APL2, generated with this technique.

This correspondence studies the influence of noise on the normalized compression distance (NCD), a measure based on the use of compressors to compute the degree of similarity of two files. This influence is approximated by a first order differential equation which gives rise to a complex effect, which explains the fact that the NCD may give values greater than 1, observed by other authors. The model is tested experimentally with good adjustment. Finally, the influence of noise on the clustering of files of different types is explored, finding that the NCD performs well even in the presence of quite high noise levels

There are several evolutionary approaches for solving random binary Constraint Satisfaction Problems (CSPs). In most of these strategies we find a complex use of information regarding the problem at hand. Here we present a hybrid Evolutionary Algorithm that outperforms previous approaches in terms of effectiveness and compares well in terms ofefficiency. Our algorithm is conceptual and simple, featuring a GRASP-like (GRASP stands for Greedy Randomized Adaptive Search Procedure) mechanism for genotype-to-phenotype mapping, and without considering any specific knowledge of the problem. Therefore, we provide a simple algorithm that harnesses generality while boosting performance.