HUM-CARD: A human crowded annotated real dataset Di Gennaro, Giovanni; Greco, Claudia; Buonanno, Amedeo ...
Information systems (Oxford),
September 2024, 2024-09-00, Letnik:
124
Journal Article
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The growth of data-driven approaches typical of Machine Learning leads to an ever-increasing need for large quantities of labeled data. Unfortunately, these attributions are often made automatically ...and/or crudely, thus destroying the very concept of “ground truth” they are supposed to represent. To address this problem, we introduce HUM-CARD, a dataset of human trajectories in crowded contexts manually annotated by nine experts in engineering and psychology, totaling approximately 5000 hours. Our multidisciplinary labeling process has enabled the creation of a well-structured ontology, accounting for both individual and contextual factors influencing human movement dynamics in shared environments. Preliminary and descriptive analyzes are presented, highlighting the potential benefits of this dataset and its methodology in various research challenges.
Fast and frugal targeting with incentives Cordasco, Gennaro; Gargano, Luisa; Peters, Joseph G. ...
Theoretical computer science,
04/2020, Letnik:
812
Journal Article
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A widely studied model of influence diffusion in social networks represents the network as a graph G=(V,E), with an integer influence threshold t(v) for each node, and the diffusion process as ...follows: Initially the members of a chosen set S⊆V are influenced and, during each subsequent round, the set of influenced nodes is augmented by including every new node v that has at least t(v) previously influenced neighbours. The general problem is to find a small initial set that influences the whole network. In this paper we extend this model by using incentives to reduce the thresholds of some nodes. The goal is to minimize the total amount of the incentive required to ensure that the information diffusion process terminates within a given number of rounds λ. The problem is hard to approximate in general networks. We present optimal polynomial-time algorithms for paths, cycles, trees, and complete networks for any λ. For the special case λ=1, we present a polynomial-time algorithm with a logarithmic approximation guarantee for any network.
Many modern computing platforms are “task-hungry”: their performance is enhanced by always having as many tasks available for execution as possible.
IC-scheduling
, a master-worker framework for ...executing static computations that have intertask dependencies (modeled as dags), was developed with precisely the goal of rendering a computation-dag’s tasks eligible for execution at the maximum possible rate. The current paper addresses the problem of enhancing IC-scheduling so that it can accommodate the varying computational resources of different workers, by clustering a computation-dag’s tasks, while still producing eligible (now, clustered) tasks at the maximum possible rate. The task-clustering strategies presented exploit the structure of the computation being performed, ranging from a strategy that works for any dag, to ones that build increasingly on the explicit structure of the dagbeing scheduled.
Given a network represented by a graph
G
=
(
V
,
E
)
, we consider a dynamical process of influence diffusion in
G
that evolves as follows: Initially only the nodes of a given
S
⊆
V
are influenced; ...subsequently, at each round, the set of influenced nodes is augmented by all the nodes in the network that have a sufficiently large number of already influenced neighbors. The question is to determine a small subset of nodes
S
(
a target set
) that can influence the whole network. This is a widely studied problem that abstracts many phenomena in the social, economic, biological, and physical sciences. It is known that the above optimization problem is hard to approximate within a factor of
2
log
1
-
ϵ
|
V
|
, for any
ϵ
>
0
. In this paper, we present a fast and surprisingly simple algorithm that exhibits the following features: (1) when applied to trees, cycles, or complete graphs, it always produces an optimal solution (i.e, a minimum size target set); (2) when applied to arbitrary networks, it always produces a solution of cardinality which improves on previously known upper bounds; (3) when applied to real-life networks, it always produces solutions that substantially outperform the ones obtained by previously published algorithms (for which no proof of optimality or performance guarantee is known in any class of graphs).
Given a network represented by a weighted directed graph G, we consider the problem of finding a bounded cost set of nodes S such that the influence spreading from S in G, within a given time bound, ...is as large as possible. The dynamics that governs the spread of influence is the following: initially only elements in S are influenced; subsequently at each round, the set of influenced elements is augmented by all nodes in the network that have a sufficiently large number of already influenced neighbors. We prove that the problem is NP-hard, even in simple networks like complete graphs and trees. We also derive a series of positive results. We present exact pseudo-polynomial time algorithms for general trees, that become polynomial time in case the trees are unweighted. This last result improves on previously published results. We also design polynomial time algorithms for general weighted paths and cycles, and for unweighted complete graphs.
This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms ...for its automatic detection. For this purpose, an original
approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders (Formula: see text), and patients with a clinical diagnosis of depression (Formula: see text). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features' categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.
Agent-based simulations represent an effective scientific tool, with numerous applications from social sciences to biology, which aims to emulate or predict complex phenomena through a set of simple ...rules performed by multiple agents. To simulate a large number of agents with complex models, practitioners have developed high-performance parallel implementations, often specialized for particular scenarios and target hardware. It is, however, difficult to obtain portable simulations, which achieve high performance and at the same time are easy to write and to reproduce on different hardware. This article gives a complete presentation of OpenABL, a domain-specific language and a compiler for agent-based simulations that enable users to achieve high-performance parallel and distributed agent simulations with a simple and portable programming environment. OpenABL is comprised of (1) an easy-to-program language, which relies on domain abstractions and explicitly exposes agent parallelism, synchronization and locality, (2) a source-to-source compiler, and (3) a set of pluggable compiler backends, which generate target code for multi-core CPUs, GPUs, and cloud-based systems. We evaluate OpenABL on simulations from different fields. In particular, our analysis includes predator–prey and keratinocyte, two complex simulations with multiple step functions, heterogeneous agent types, and dynamic creation and removal of agents. The results show that OpenABL-generated codes are portable to different platforms, perform similarly to manual target-specific implementations, and require significantly fewer lines of codes.
•OpenABL is domain-specific language and compiler for simulating Agent-based Model.•A variety of domain semantics enables quick model prototypes with few lines of code.•A source-to-source compiler exposes parallelism into an intermediate representation.•Pluggable backends generate codes for different parallel and distributed platforms.•Experimental validation on seven real-world applications from various fields.
Many modern computing platforms-notably clouds and desktop grids-exhibit dynamic heterogeneity: the availability and computing power of their constituent resources can change unexpectedly and ...dynamically, even in the midst of a computation. We introduce a new quality metric, AREA, for schedules that execute computations having interdependent constituent chores (jobs, tasks, etc.) on such platforms. AREA measures the average number of chores that a schedule renders eligible for execution at each step of a computation. Even though the definition of AREA does not mention any properties of host platforms (such as volatility), intuition suggests that rendering chores eligible at a faster rate will have a benign impact on the performance of volatile platforms. We report on simulation experiments that support this intuition. Earlier work has derived the basic properties of the AREA metric and has shown how to efficiently craft AREA-maximizing (A-M) schedules for several classes of significant computations. Even though A-M schedules always exist for every computation, it is not always known how to derive such schedules efficiently. In response, the current study develops an efficient algorithm that produces AREA-Oriented (A-O) schedules, which aim to efficiently approximate the AREAs of A-M schedules on arbitrary computations. The simulation experiments reported on here suggest that, in common with A-M schedules, A-O schedules complete computations on volatile heterogeneous platforms faster than a variety of heuristics that range from lightweight ones to computationally intensive ones-albeit not to the same degree as A-M schedules do. Our experiments suggest that schedules having larger AREAs have smaller completion times-but no proof of that yet exists.
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