Papers by Mario G . C . A . Cimino
The increasing volume of urban human mobility data arises unprecedented opportunities to monitor ... more The increasing volume of urban human mobility data arises unprecedented opportunities to monitor and understand city dynamics. Identifying events which do not conform to the expected patterns can enhance the awareness of decision makers for a variety of purposes, such as the management of social events or extreme weather situations [1]. For this purpose GPS-equipped vehicles provide huge amount of reliable data about urban dynamics, exhibiting correlation with human activities, events and city structure [2]. For example, in [3] the impact of a social event is evaluated by analyzing taxi traces data. Here, the authors model typical passenger flow in an area, in order to compute the probability that an event happens. Then, the event impact is measured by analyzing abnormal traffic flows in the area via Discrete Fourier Transform. In [4] GPS trajectories are mapped through an Interactive Voting-based Map Matching Algorithm. This mapping is used for off-line characterization of normal d...
Business Processes (BPs) are the key instrument to understand how companies operate at an organiz... more Business Processes (BPs) are the key instrument to understand how companies operate at an organizational level, taking an as-is view of the workflow, and how to address their issues by identifying a to-be model. In last year's, the BP Model and Notation (BPMN) has become a de-facto standard for modeling processes. However, this standard does not incorporate explicitly the Problem-Solving (PS) knowledge in the Process Modeling (PM) results. Thus, such knowledge cannot be shared or reused. To narrow this gap is today a challenging research area. In this paper we present a framework able to capture the PS knowledge and to improve a workflow. This framework extends the BPMN specification by incorporating new general-purpose elements. A pilot scenario is also presented and discussed.
Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019
This paper focuses on the problem of coordinating multiple UAVs for distributed targets detection... more This paper focuses on the problem of coordinating multiple UAVs for distributed targets detection and tracking, in different technological and environmental settings. The proposed approach is founded on the concept of swarm behavior in multi-agent systems, i.e., a self-formed and self-coordinated team of UAVs which adapts itself to mission-specific environmental layouts. The swarm formation and coordination are inspired by biological mechanisms of flocking and stigmergy, respectively. These mechanisms, suitably combined, make it possible to strike the right balance between global search (exploration) and local search (exploitation) in the environment. The swarm adaptation is based on an evolutionary algorithm with the objective of maximizing the number of tracked targets during a mission or minimizing the time for target discovery. A simulation testbed has been developed and publicly released, on the basis of commercially available UAVs technology and real-world scenarios. Experimental results show that the proposed approach extends and sensibly outperforms a similar approach in the literature.
Journal of Computational Science
A swarm of autonomous drones with self-coordination and environment adaptation can offer a robust... more A swarm of autonomous drones with self-coordination and environment adaptation can offer a robust, scalable and flexible manner to localize objects in an unexplored, dangerous or unstructured environment. We design a novel coordination algorithm combining three biologically-inspired processes: stigmergy, flocking and evolution. Stigmergy, a form of coordination exhibited by social insects, is exploited to attract drones in areas with potential targets. Flocking enables efficient cooperation between flock mates upon target detection, while keeping an effective scan. The two mechanisms can interoperate if their structural parameters are correctly tuned for a given scenario. Differential evolution adapts the swarm coordination according to environmental conditions. The performance of the proposed algorithm is examined with synthetic and real-world scenarios.
Using an autoencoder in the design of an anomaly detector for smart manufacturing
Pattern Recognition Letters
Regional innovation is more and more considered an important enabler of welfare. It is no coincid... more Regional innovation is more and more considered an important enabler of welfare. It is no coincidence that the European Commission has started looking at regional peculiarities and dynamics, in order to focus Research and Innovation Strategies for Smart Specialization towards effective investment policies. In this context, this work aims to support policy makers in the analysis of innovation-relevant trends. We exploit a European database of the regional patent application to determine the dynamics of a set of technological innovation indicators. For this purpose, we design and develop a software system for assessing unfolding trends in such indicators. In contrast with conventional knowledge-based design, our approach is biologically-inspired and based on self-organization of information. This means that a functional structure, called track, appears and stays spontaneous at runtime when local dynamism in data occurs. A further prototyping of tracks allows a better distinction of th...
Pattern recognition in financial time series is not a trivial task, due to level of noise, volati... more Pattern recognition in financial time series is not a trivial task, due to level of noise, volatile context, lack of formal definitions and high number of pattern variants. A current research trend involves machine learning techniques and online computing. However, medium-term trading is still based on human-centric heuristics, and the integration with machine learning support remains relatively unexplored. The purpose of this study is to investigate potential and perspectives of a novel architectural topology providing modularity, scalability and personalization capabilities. The proposed architecture is based on the concept of Receptive Fields (RF), i.e., sub-modules focusing on specific patterns, that can be connected to further levels of processing to analyze the price dynamics on different granularities and different abstraction levels. Both Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) have been experimented as a RF. Early experiments have been carried out ove...
Given the great opportunities provided by Open Collaborative Networks (OCNs), their success depen... more Given the great opportunities provided by Open Collaborative Networks (OCNs), their success depends on the effective integration of composite business logic at all stages. However, a dilemma between cooperation and competition is often found in environments where the access to business knowledge can provide absolute advantages over the competition. Indeed, although it is apparent that business logic should be automated for an effective integration, chain participants at all segments are often highly protective of their own knowledge. In this paper, we propose a solution to this problem by outlining a novel approach with a supporting architectural view. In our approach, business rules are modeled via semantic web and their execution is coordinated by a workflow model. Each company’s rule can be kept as private, and the business rules can be combined together to achieve goals with defined interdependencies and responsibilities in the workflow. The use of a workflow model allows assemb...
A current research trend in neurocomputing involves the design of novel artificial neural network... more A current research trend in neurocomputing involves the design of novel artificial neural networks incorporating the concept of time into their operating model. In this paper, a novel architecture that employs stigmergy is proposed. Computational stigmergy is used to dynamically increase (or decrease) the strength of a connection, or the activation level, of an artificial neuron when stimulated (or released). This study lays down a basic framework for the derivation of a stigmergic NN with a related training algorithm. To show its potential, some pilot experiments have been reported. The XOR problem is solved by using only one single stigmergic neuron with one input and one output. A static NN, a stigmergic NN, a recurrent NN and a long short-term memory NN have been trained to solve the MNIST digits recognition benchmark.
Using Call Data and Stigmergic Similarity to Assess the Integration of Syrian Refugees in Turkey
By absorbing more than 3.4 million Syrians, Turkey has shown remarkable resilience. But the host ... more By absorbing more than 3.4 million Syrians, Turkey has shown remarkable resilience. But the host community tensions toward these newcomers is rising. Thus, the formulation of effective integration policies is needed. However, assessing the effectiveness of such policies demands tools able to measure the integration of refugees despite the complexity of such phenomena. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing the CDR dataset of the challenge. Specifically, we aim at assessing the integration of refugees, by exploiting the similarity between refugees and locals in terms of calling behavior and mobility, considering different spatial and temporal features. Together with the already known methods for data analysis, in this work we use a novel computational approach to analyze users’ mobility: computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational stigme...
According to the smart manufacturing paradigm, the analysis of assets’ time series with a machine... more According to the smart manufacturing paradigm, the analysis of assets’ time series with a machine learning approach can effectively prevent unplanned production downtimes by detecting assets’ anomalous operational conditions. To support smart manufacturing operators with no data science background, we propose an anomaly detection approach based on deep learning and aimed at providing a manageable machine learning pipeline and easy to interpret outcome. To do so we combine (i) an autoencoder, a deep neural network able to produce an anomaly score for each provided time series, and (ii) a discriminator based on a general heuristics, to automatically discern anomalies from regular instances. We prove the convenience of the proposed approach by comparing its performances against isolation forest with different case studies addressing industrial laundry assets’ power consumption and bearing vibrations.
Technological troubleshooting based on sentence embedding with deep transformers
In nowadays manufacturing, each technical assistance operation is digitally tracked. This results... more In nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine ...
Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017
A significant phenomenon in microblogging is that certain occurrences of terms self-produce incre... more A significant phenomenon in microblogging is that certain occurrences of terms self-produce increasing mentions in the unfolding event. In contrast, other terms manifest a spike for each moment of interest, resulting in a wake-up-and-sleep dynamic. Since spike morphology and background vary widely between events, to detect spikes in microblogs is a challenge. Another way is to detect the spikiness feature rather than spikes. We present an approach which detects and aggregates spikiness contributions by combination of spike patterns, called archetypes. The soft similarity between each archetype and the time series of term occurrences is based on computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Archetypes are arranged into an architectural module called Stigmergic Receptive Field (SRF). The final spikiness indicator is computed through linear combination of SRFs, whose weights are determined with the Least Square Error minimization on a spikiness training set. The structural parameters of the SRFs are instead determined with the Differential Evolution algorithm, minimizing the error on a training set of archetypal series. Experimental studies have generated a spikiness indicator in a real-world scenario. The indicator has enhanced a cloud representation of social discussion topics, where the more spiky cloud terms are more blurred.
Business Processes (BPs) are the key instrument to understand how companies operate at an organiz... more Business Processes (BPs) are the key instrument to understand how companies operate at an organizational level, taking an as-is view of the workflow, and how to address their issues by identifying a to-be model. In last year’s, the BP Model and Notation (BPMN) has become a de-facto standard for modeling processes. However, this standard does not incorporate explicitly the ProblemSolving (PS) knowledge in the Process Modeling (PM) results. Thus, such knowledge cannot be shared or reused. To narrow this gap is today a challenging research area. In this paper we present a framework able to capture the PS knowledge and to improve a workflow. This framework extends the BPMN specification by incorporating new general-purpose elements. A pilot scenario is also presented and discussed. Keywords—Business Process Management, BPMN, Problem Solving, Process mapping.
Stock Price Forecasting Over Adaptive Timescale Using Supervised Learning and Receptive Fields
Mining Intelligence and Knowledge Exploration
Recent staff cuts for the Italian university system reduced the teaching manpower, making face-to... more Recent staff cuts for the Italian university system reduced the teaching manpower, making face-to-face student/teacher interaction all but impossible in large classes (e.g., 100-150 students). On the other hand, new generations of students call for evolved self-learning instruments, and teaching practices need to meet this demand by adopting systems that allow for short-loop feedback and scalable class management. This paper discusses the adoption of Web-CAT, an opensource computer-assisted teaching software, within the BSc in Computer Engineering at the university of Pisa. We present in detail the motivations for adopting it, the customization effort and the expected benefits.
A Real-Time Deep Learning Approach for Real-World Video Anomaly Detection
The 16th International Conference on Availability, Reliability and Security
Detection and Mapping of a Toxic Cloud Using UAVs and Emergent Techniques
Mining Intelligence and Knowledge Exploration
2019 International Conference on Robotics and Automation (ICRA), May 1, 2019
Modern cities are growing ecosystems that face new challenges due to the increasing population de... more Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems. CONFIDENTIAL. Limited circulation. For review only.
Sensors
In settings wherein discussion topics are not statically assigned, such as in microblogs, a need ... more In settings wherein discussion topics are not statically assigned, such as in microblogs, a need exists for identifying and separating topics of a given event. We approach the problem by using a novel type of similarity, calculated between the major terms used in posts. The occurrences of such terms are periodically sampled from the posts stream. The generated temporal series are processed by using marker-based stigmergy, i.e., a biologically-inspired mechanism performing scalar and temporal information aggregation. More precisely, each sample of the series generates a functional structure, called mark, associated with some concentration. The concentrations disperse in a scalar space and evaporate over time. Multiple deposits, when samples are close in terms of instants of time and values, aggregate in a trail and then persist longer than an isolated mark. To measure similarity between time series, the Jaccard's similarity coefficient between trails is calculated. Discussion topics are generated by such similarity measure in a clustering process using Self-Organizing Maps, and are represented via a colored term cloud. Structural parameters are correctly tuned via an adaptation mechanism based on Differential Evolution. Experiments are completed for a real-world scenario, and the resulting similarity is compared with Dynamic Time Warping (DTW) similarity.
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Papers by Mario G . C . A . Cimino