An Assessment of Deep Learning Models and Word Embeddings for Toxicity Detection within Online Textual Comments

Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task

Multimodal human machine interactions in industrial environments

This chapter will present a review of Human Machine Interaction techniques for industrial applications. A set of recent HMI techniques will be provided with emphasis on multimodal interaction with industrial machines and robots. This list will include Natural Language Processing techniques and others that make use of various complementary interfaces: audio, visual, haptic or gestural, to achieve a more natural human-machine interaction. This chapter will also focus on providing examples and use cases in fields related to multimodal interaction in manufacturing, such as augmented reality. Accordingly, the chapter will present the use of Artificial Intelligence and Multimodal Human Machine Interaction in the context of STAR applications

An assessment of deep learning models and word embeddings for toxicity detection within online textual comments

Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task

Semantic Role Labeling for Knowledge Graph Extraction from Text

This paper introduces TakeFive, a new semantic role labeling method that transforms a text into a frame-oriented knowledge graph. It performs dependency parsing, identifies the words that evoke lexical frames, locates the roles and fillers for each frame, runs coercion techniques, and formalizes the results as a knowledge graph. This formal representation complies with the frame semantics used in Framester, a factual-linguistic linked data resource. We tested our method on the WSJ section of the Peen Treebank annotated with VerbNet and PropBank labels and on the Brown corpus. The evaluation has been performed according to the CoNLL Shared Task on Joint Parsing of Syntactic and Semantic Dependencies. The obtained precision, recall, and F1 values indicate that TakeFive is competitive with other existing methods such as SEMAFOR, Pikes, PathLSTM, and FRED. We finally discuss how to combine TakeFive and FRED, obtaining higher values of precision, recall, and F1 measure

NetFPGA Hardware Modules for Input, Output and EWMA Bit-Rate Computation

NetFPGA is a hardware board that it is becoming increasingly popular in various research areas. It is a hardware customizable router and it can be used to study, implement and test new protocols and techniques directly in hardware. It allows researchers to experience a more real experiment environment. In this paper we present a work about the design and development of four new modules built on top of the NetFPGA Reference Router design. In particular, they compute the input and output bit rate run time and provide an estimation of the input bit rate based on an EWMA filter. Moreover we extended the rate limiter module which is embedded within the output queues in order to test our improved Reference Router. Along the paper we explain in detail each module as far as the architecture and the implementation are concerned. Furthermore, we created a testing environment which show the effectiveness and effciency of our module

Performance comparison between the Click Modular Router and the NetFPGA

It is possible to forward minimum-sized packets at rates of hundreds of Mbps using commodity hardware and Linux. We had a preference for the Click Modular Router platform due its flexibility and the fact that it claimed to have equal or higher performance than native forwarding if used with its polling drivers. Moreover, the NetFPGA is an open networking platform accelerator that enables researchers and instructors to build working prototypes of high-speed, hardware-accelerated networking systems. NetFPGA reference designs comprised in the system include an IPv4 router, an Ethernet switch, a four-port NIC, and SCONE (Software Component of NetFPGA). Researchers have used the platform to build advanced network flow processing systems. We have followed the RFC1242 - Benchmarking Terminology for Network Interconnection Devices - and the RFC2544 - Benchmarking Methodology for Network Interconnection Devices - in order to define the specific set of tests to use to describe the performance characteristics of the two routers. We have also shown a test comparison between the NetFPGA and the Click router about a file transfer using the FTP and the HTTP protocol.Overall, the NetFPGA router performance outperforms the Click router performance

Mining Scholarly Data for Fine-Grained Knowledge Graph Construction

Knowledge graphs (KG) are large network of entities and relationships, tipically expressed as RDF triples, relevant to a specific domain or an organization. Scientific Knowledge Graphs (SKGs) focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. The next big challenge in this field regards the generation of SKGs that also contain a explicit representation of the knowledge presented in research publications. In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a KG. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships derived from 12, 007 publications in the field of Semantic Web, and iv) discuss how Deep Learning methods can be applied to overcome some limitations of the current techniques

Diversity of Expertise is Key to Scientific Impact: a Large-Scale Analysis in the Field of Computer Science

Understanding the relationship between the composition of a research team and the potential impact of their research papers is crucial as it can steer the development of new science policies for improving the research enterprise. Numerous studies assess how the characteristics and diversity of research teams can influence their performance across several dimensions: ethnicity, internationality, size, and others. In this paper, we explore the impact of diversity in terms of the authors' expertise. To this purpose, we retrieved 114K papers in the field of Computer Science and analysed how the diversity of research fields within a research team relates to the number of citations their papers received in the upcoming 5 years. The results show that two different metrics we defined, reflecting the diversity of expertise, are significantly associated with the number of citations. This suggests that, at least in Computer Science, diversity of expertise is key to scientific impact.Comment: This paper has been accepted for presentation at STI2023 (https://www.sti2023.org/). It will be presented on September 202