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&quot;How can Artificial Intelligence help a stateless minority from online abuse? Research efforts in hate speech detection thus far have largely focused on identifying and subsequently filtering out negative content that specifically targets them. In this paper, we highlight a recent work that tackles a different aspect of web-vulnerability of marginalized communities: sparsity of pro-minority voices championing their cause. The highlighted paper advocates that blocking hate alone may not be sufficient in these cases as the internet shapes community perception to a great extent in modern times and supportive comments to a vulnerable community serve
 a different purpose. Using an Active Sampling approach, the paper constructs a nuanced voice-for-the-voiceless classifier that automatically discovers comments supporting an (allegedly) persecuted minority. In the context of the Rohingya refugee crisis, one of the biggest humanitarian crises of modern times, the paper presents promising results that can substantially aid content moderation efforts in finding positive content supporting the Rohingyas.&quot;

ECAI 2020

The Refugee Experience Online: Surfacing Positivity Amidst Hate

rohingya refugee crisis

help speech detection

active sampling

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

"How can Artificial Intelligence help a stateless minority from online abuse? Research efforts in hate speech detection thus far have largely focused on identifying and subsequently filtering out negative content that specifically targets them. In this paper, we highlight a recent work that tackles a different aspect of web-vulnerability of marginalized communities: sparsity of pro-minority voices championing their cause. The highlighted paper advocates that blocking hate alone may not be sufficient in these cases as the internet shapes community perception to a great extent in modern times and supportive comments to a vulnerable community serve
 a different purpose. Using an Active Sampling approach, the paper constructs a nuanced voice-for-the-voiceless classifier that automatically discovers comments supporting an (allegedly) persecuted minority. In the context of the Rohingya refugee crisis, one of the biggest humanitarian crises of modern times, the paper presents promising results that can substantially aid content moderation efforts in finding positive content supporting the Rohingyas."

poster

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Modelling a problem as finding a shortest path in a graph has been successful in many AI problems. However, the classical shortest path framework might not be suitable for domains such as planning a career pathway optimized to the skills needed along the way. To address this gap, we introduce graphs with item sets, where each node has a set of items from a finite, global set. We define a cost function for paths in graphs with item sets and show that it lacks the prefix optimality property. We further define two decision problems inspired from career planning. We show that these are NP-complete, unlike the traditional shortest path problem, which is solved in polynomial time. Our study gives a theoretical understanding that could be used as a basis for designing efficient solving approaches to the problems studied.

The Challenge of Optimal Paths in Graphs with Item Sets

As "Big Data" has become pervasive, an increasing amount of research has connected the dots between human behaviour in the offline and online worlds. Consequently, researchers have exploited these new findings to create models that better predict different aspects of human life and recommend future behaviour. To date, however, we do not yet fully understand the similarities and differences of human behaviour in these virtual and physical worlds. Here, we analyse and discuss the mobility and application usage of 400,000 individuals over eight months. We find an astonishing similarity between people's mobility in the physical space and how they move from app to app in smartphones. Our data shows that individuals use and visit a finite number of apps and places, but they keep exploring over time. In particular, two distinct profiles of individuals emerge: those that keep changing places and services, and those that are stable over time, named as "explorers" and "keepers". We see these findings as crucial to enrich a discussion for the potentials and the challenges of building human-centric AI systems, which might leverage recent results in Computational Social Science.

Understanding individual behaviour: from virtual to physical patterns

The prevailing methodology for integrating decision making and ethics is to modify autonomous systems in an ad hoc way to incorporate moral sensibility. However, these provisional modifications often lead to behavior that jeopardizes the intentions of developers or the values of stakeholders. We propose a novel approach for building moral autonomous systems that optimally completes a task and follows an ethical framework by decoupling ethical compliance from task completion. This paper offers a formal definition of our approach along with its key properties, an example based on prima facie duties, and a demonstration that uses our open source library.

An Integrated Approach to Moral Autonomous Systems

Contracts are the cornerstone of legal agreements in the industry. As essential legal documents, contracts are subject to negotiations, demanding extensive analysis and evaluation efforts. Although the emergence of machine learning has enabled assistance tools for text analysis tasks, the specificity and constraints of each
 business context remain obstacles for the automation of contracts evaluation. In this paper, we propose an AutoML based approach for automated negotiation assistance that uses expert annotated contracts and the business-specific knowledge of acceptance policies. Driven by policies rules, our approach generates a classification process composed of hierarchies of complementary classifiers, each being automatically prepared according, but not limited to, feature extraction, learning model and data granularity. Experiments conducted on real-world service contracts have yielded promising results.

Toward a Generic AutoML-Based Assistant for Contracts Negotiation

Malaria is one of the primary causes of death in Mozambique, responsible for an estimated 15,000 deaths in 2017. Human-mediated parasite movement combined with spatial and seasonal changes in transmission threatens the success of malaria interventions by reintroducing parasites to areas targeted for elimination. In this study we used pseudonymized and aggregated call detail records from a sample of over 8.5 million subscribers of a mobile phone operator in Mozambique to quantify human movements for four months in 2017. We calculated parasite importation and exportation rates to identify potential net sinks and sources of malaria across districts. We used a community structure algorithm to explore the connectedness of provinces in Mozambique, and we calculated parasite mobility between urban and rural areas. This work will help provide evidence to inform malaria elimination strategies regionally.

Malaria parasite mobility in Mozambique estimated using mobile phone records

In AI and ML, several methods rely on stochasticity or randomization: mutation and crossover in evolutionary algorithms; dropout and stochastic gradient descent in deep learning; stochasticity in stochastic local search (SLS); and randomization in systematic search. SLS algorithms, which we study here, are competitive in solving computationally hard problems such as satisfiability (SAT), sparse signal recovery, scheduling, and most probable explanations in Bayesian networks (BNs). Essentially, SLS algorithms are greedy optimizers that also make random moves in order to avoid getting trapped in local but non-global optima. 
 
 Clearly, SLS is a well-established idea in AI. However, SLS is sometimes underutilized and there are certain potential difficulties when applying SLS. This highlight showcases recent developments that address some of these difficulties - related to ML, formalization with Markov chains, and setting hyperparameters - and briefly points to future research directions with potential.

Stochastic Local Search and Machine Learning: From Theory to Applications and Vice Versa

In this paper, we identify a series of problems to track and understand what AI is capable of, surveying some previous initiatives. We present the AIcollaboratory, a data-driven framework to collect and explore data about AI results, progress and ultimately capabilities, being developed in the context of AI Watch, the European Commission (EC) knowledge service to monitor the development, uptake and impact of AI in Europe. We close the paper with some challenges for the community emerging around the AIcollaboratory.

Tracking AI: The Capability is (Not) Near

Ontologies are widely used to formally represent abstract domain knowledge. Logic reasoning ensures the logical consistency of ontologies, and infers knowledge implicitly encoded in ontologies. It has been shown both theoretically and empirically that for large and complex ontologies, reasoning is still time-consuming and resource-intensive. Meta-reasoning exploits machine learning techniques to tackle the important problems of understanding the source of reasoning hardness and to predict reasoning efficiency, with the overall goal of improving reasoning efficiency. In this paper, we highlight recent advances in meta-reasoning for Semantic Web ontologies, briefly present technical innovations and results, and discuss important problems for future research.

Towards Meta-reasoning for Ontologies: A Roadmap

The WikiTalk open-domain dialogue system performs Wikipedia-based talking. This paper highlights the potential for a new Wikipedia-based listening capability. The aim is to enable social robots to follow the changing topics in human-human conversations, opening the possibility for robots to join in human conversations by  making topically relevant contributions at appropriate points.

WikiTalk and WikiListen: Towards listening robots that can join in conversations with topically relevant contributions

Recent advances in language model (LM) pre-training from large- scale corpora have shown to improve various natural language processing tasks. They achieve performances comparable to non-expert humans on the GLUE benchmark for natural language understanding (NLU). While the improvement of the different contextualized representations comes from (i) the usage of more and more data, (ii) changing the types of lexical pre-training tasks or (iii) increasing the model size, NLU is more than memorizing word co-occurrences. But how much world knowledge and common sense can those language model capture? How much can those models infer from just the lexical information? To overcome this problem, some approaches include semantic information in the training process. We highlight existing approaches to combine different types of semantics with language models during the pre-training or fine-tuning phase

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The Challenge of Optimal Paths in Graphs with Item Sets

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