International Joint Conference Research Articles

Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection

The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related potentials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices. However, continuous online error detection faces challenges such as developing efficient and lightweight classification techniques for quick predictions, reducing false alarms from artifacts, and dealing with the non-stationarity of EEG signals. Further research is essential to address the complexities of continuous classification in online sessions. With this study, we demonstrated a comprehensive approach for continuous online EEG-based machine error detection, which emerged as the winner of a competition at the 32nd International Joint Conference on Artificial Intelligence. The competition consisted of two stages: an offline stage for model development using pre-recorded, labeled EEG data, and an online stage 3 months after the offline stage, where these models were tested live on continuously streamed EEG data to detect errors in orthosis movements in real time. Our approach incorporates two temporal-derivative features with an effect size-based feature selection technique for model training, together with a lightweight noise filtering method for online sessions without recalibration of the model. The model trained in the offline stage not only resulted in a high average cross-validation accuracy of 89.9% across all participants, but also demonstrated remarkable performance during the online session 3 months after the initial data collection without further calibration, maintaining a low overall false alarm rate of 1.7% and swift response capabilities. Our research makes two significant contributions to the field. Firstly, it demonstrates the feasibility of integrating two temporal derivative features with an effect size-based feature selection strategy, particularly in online EEG-based BCIs. Secondly, our work introduces an innovative approach designed for continuous online error prediction, which includes a straightforward noise rejection technique to reduce false alarms. This study serves as a feasibility investigation into a methodology for seamless error detection that promises to transform practical applications in the domain of neuroadaptive technology and human-robot interaction.

Selected Papers from the 5th International Joint Conference on Rules and Reasoning (RuleML+RR 2021)

Selected Papers from the 5th International Joint Conference on Rules and Reasoning (RuleML+RR 2021)

Evaluating Datalog Tools for Meta-reasoning over OWL 2 QL

Abstract Metamodeling is a general approach to expressing knowledge about classes and properties in an ontology. It is a desirable modeling feature in multiple applications that simplifies the extension and reuse of ontologies. Nevertheless, allowing metamodeling without restrictions is problematic for several reasons, mainly due to undecidability issues. Practical languages, therefore, forbid classes to occur as instances of other classes or treat such occurrences as semantically different objects. Specifically, meta-querying in SPARQL under the Direct Semantic Entailment Regime uses the latter approach, thereby effectively not supporting meta-queries. However, several extensions enabling different metamodeling features have been proposed over the last decade. This paper deals with the Metamodeling Semantics (MS) over OWL 2 QL and the Metamodeling Semantic Entailment Regime (MSER), as proposed in Lenzerini et al. (2015, Description Logics) and Lenzerini et al. (2020, Information Systems 88, 101294), Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 1–6). A reduction from OWL 2 QL to Datalog for meta-querying was proposed in Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 1–6). In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. These tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set Programming, Hybrid Knowledge Bases). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies with limited resources (time and memory). This paper significantly extends Qureshi and Faber (2021, International Joint Conference on Rules and Reasoning, Springer, 218–233.) by a more detailed experimental analysis and more background.

ReARTSim: an ReRAM ARray Transient Simulator with GPU optimized runtime acceleration

The demand for computation driven by machine learning and deep learning applications has experienced exponential growth over the past five years (Sevilla et al 2022 2022 International Joint Conference on Neural Networks (IJCNN) (IEEE) pp 1-8), leading to a significant surge in computing hardware products. Meanwhile, this rapid increase has exacerbated the memory wall bottleneck within mainstream Von Neumann architectures (Hennessy and Patterson et al 2011 Computer architecture: a quantitative approach (Elsevier)). For instance, NVIDIA graphical processing units (GPUs) have gained nearly a 200x increase in fp32 computing power, transitioning from P100 to H100 in the last five years (NVIDIA Tesla P100 2023 (www.nvidia.com/en-us/data-center/tesla-p100/); NVIDIA H100 Tensor Core GPU 2023 (www.nvidia.com/en-us/data-center/h100/)), accompanied by a mere 8x scaling in memory bandwidth. Addressing the need to mitigate data movement challenges, process-in-memory designs, especially resistive random-access memory (ReRAM)-based solutions, have emerged as compelling candidates (Verma et al 2019 IEEE Solid-State Circuits Mag. 11 43–55; Sze et al 2017 Proc. IEEE 105 2295–329). However, this shift in hardware design poses distinct challenges at the design phase, given the limitations of existing hardware design tools. Popular design tools today can be used to characterize analog behavior via SPICE tools (PrimeSim HSPICE 2023 (www.synopsys.com/implementation-and-signoff/ams-simulation/primesim-hspice.html)), system and logical behavior using Verilog tools (VCS 2023 (www.synopsys.com/verification/simulation/vcs.html)), and mixed signal behavior through toolbox like CPPSIM (Meninger 2023 (www.cppsim.org/Tutorials/wideband_fracn_tutorial.pdf)). Nonetheless, the design of in-memory computing systems, especially those involving non-CMOS devices, presents a unique need for characterizing mixed-signal computing behavior across a large number of cells within a memory bank. This requirement falls beyond the scope of conventional design tools. In this paper, we bridge this gap by introducing the ReARTSim framework—a GPU-accelerated mixed-signal transient simulator for analyzing ReRAM crossbar array. This tool facilitates the characterization of analog circuit and device behavior on a large scale, while also providing enhanced simulation performance for complex algorithm analysis, sign-off, and verification.

Preface

The 5th International Conference on Empathic Architecture (ICEA) is a biannual international conference organized by the Department of Architecture, Faculty of Civil Engineering and Planning, Petra Christian University. The 1st ICEA was initiated on 11-12 September 2014. The 2nd ICEA was held as an International Joint Conference with the 17th SENVAR on 1-2 March 2017. The 3rd ICEA was conducted on 25-27 April 2019, in which all papers presented were published in the IOP Conference Proceeding: Earth and Environmental Science Volume 490. During the pandemic, the 4th ICEA was held online on 26 February 2021.The 5th ICEA was held at Petra Christian University, Surabaya, Indonesia, on 19-20 October 2023, with the theme “Architecture for Health and Wellbeing.” The 5th International Conference on Empathic Architecture (ICEA) aims to gather researchers, scholars, and practitioners to share and exchange their knowledge and breakthroughs in architecture, especially for health and wellbeing. It is important to seek a holistic understanding of the impact of architecture, with its design features, on human health and wellbeing. Thus, an architectural design should be responsive to user needs, inspire, motivate, give meaning, and be spirit caring.The 5th ICEA was honored to present four reputable keynote speakers: (i) A. Ray Pentecost III, DrPH, FAIA, FACHA, LEED AP from UIA-Public Health Group, USA (ii) Dr. Yuan Chao from National University of Singapore, (iii) Ir. Adi Utomo Hatmoko, M. Arch., a professional architect in hospital design from Indonesia, and (iv) Dr. Ruzika Bozovic from the National University of Singapore.The subtopics of the conference include Healing Architecture, Inclusive Architecture Design, Architecture and Perceptual memory, Lighting for well-being, Building Thermal Performance, Indoor Air Quality, Building Envelope for Wellbeing, and Wellness in an Urban Environment. Through these topics, papers from authors have been presented during the conference days as a manifestation of empathy for users and the environment through Architecture and Built Environment. After the inspiring keynote sessions and discussions during the parallel sessions, the papers were curated once more before publication in this IOP Conference Series: Earth and Environmental Science.We would like to appreciate all keynote speakers, scientific committee, moderators, conference coordinators, and participants who have contributed to this conference. Finally, we hope this 5th ICEA proceeding could encourage the development of an architecture that empathizes with the user’s health and wellbeing.The 5th ICEA Editorial Teamhttps://5thicea.petra.ac.id/homeList of Scientific Committee, Organizing Committee are available in the pdf

Preface

On behalf of organizing committee members, it is our great honor to welcome you to the 2022 International Joint Conference on Robotics and Artificial Intelligence (JCRAI 2022).2022 International Joint Conference on Robotics and Artificial Intelligence (JCRAI 2022), organized by the Hong Kong Society of Robotics and Automation (HKSRA) was successfully held virtually on Webex from 14th-17th, October due to the Covid pandemic. The purpose of this gathering is to bring researchers, engineers, scientists, and industry professionals to this open forum where advances in the field of robotics and artificial intelligence can be shared and exchanged.List of Committee is available in this Pdf.

Introduction to the Special Issue on Logic Rules and Reasoning: Selected Papers from the 4th International Joint Conference on Rules and Reasoning (RuleML+RR 2020)

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The first AI4TSP competition: Learning to solve stochastic routing problems

This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCAI-21). The TSP is one of the classical combinatorial optimization problems, with many variants inspired by real-world applications. This first competition asked the participants to develop algorithms to solve an orienteering problem with stochastic weights and time windows (OPSWTW). It focused on two learning approaches: surrogate-based optimization and deep reinforcement learning. In this paper, we describe the problem, the competition setup, and the winning methods, and give an overview of the results. The winning methods described in this work have advanced the state-of-the-art in using AI for stochastic routing problems. Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers. The simulator of the problem has been made open-source and can be used by other researchers as a benchmark for new learning-based methods. The instances and code for the competition are available at https://github.com/paulorocosta/ai-for-tsp-competition.

Special Collection of Extended Selected Papers on “Novel Research Results Presented at the 14th International Joint Conference on Knowledge-based Software Engineering (JCKBSE2022), 22–24 August 2022, Larnaca, Cyprus https://easyconferences.eu/jckbse2022/”

Special Collection of Extended Selected Papers on “Novel Research Results Presented at the 14th International Joint Conference on Knowledge-based Software Engineering (JCKBSE2022), 22–24 August 2022, Larnaca, Cyprus https://easyconferences.eu/jckbse2022/”

Silbido profundo: An open source package for the use of deep learning to detect odontocete whistles.

This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck, Cholewiak, Helble, and Roch [(2020). Proceedings of the International Joint Conference on Neural Networks, July 19-24, Glasgow, Scotland, p. 10] is incorporated into silbido, an established software package for extraction of cetacean tonal calls. The precision and recall of the new system were over 96% and nearly 80%, respectively, when applied to a whistle extraction task on a challenging two-species subset of a conference-benchmark data set. A second data set was examined to assess whether the algorithm generalized to data that were collected across different recording devices and locations. These data included 487 h of weakly labeled, towed array data collected in the Pacific Ocean on two National Oceanographic and Atmospheric Administration (NOAA) cruises. Labels for these data consisted of regions of toothed whale presence for at least 15 species that were based on visual and acoustic observations and not limited to whistles. Although the lack of per whistle-level annotations prevented measurement of precision and recall, there was strong concurrence of automatic detections and the NOAA annotations, suggesting that the algorithm generalizes well to new data.

Special Section Guest Editorial

The International Joint Conference on Biometrics (IJCB) combines two major biometrics conferences: the IEEE Biometrics Theory, Applications, and Systems (BTAS) conference and the International Conference on Biometrics (ICB). The blending of these two conferences happened once every 3 years between 2011 and 2017. Since 2020 – thanks to a special agreement between the IEEE Biometrics Council and the IAPR Technical Committee on Biometrics (TC4) – this joint conference has become an annual event. It represents an exciting event for the worldwide biometrics research community. This Special Issue of the IEEE Transactions on Biometrics, Behavior, and Identity Science collects the extended versions of the best papers presented in the 2021 edition.

Deep Learning-Based Named Entity Recognition System Using Hybrid Embedding

Retrieval of meaningful information out of voluminous data available on the internet is a big challenge these days. Named entity recognition system deals with this challenge efficiently and achieves promising results in accessing information of interest in several NLP applications. The present study proposes a deep learning-based named entity recognition system using hybrid embedding which is the combination of fasttext and bidirectional LSTM based character embedding. These embeddings capture the contextual, syntactic and semantic properties of the text which improves the cognitive power of deep learning methods. We have performed different experiments with important variants of recurrent neural network (RNN) namely long short-term memory network (LSTM) and bidirectional LSTM as well as gated recurrent unit (GRU) and bidirectional GRU on manually annotated Punjabi dataset and annotated Hindi dataset collected from international joint conference on natural language processing (IJCNLP-08) website. We have also explored different word embeddings and character embeddings for named entity recognition task. Out of all the experiments, the bidirectional GRU model using hybrid embedding has outperformed with precision, recall, and f-score values of 84%, 83%, and 83.50% respectively for Punjabi named entity recognition and 75%, 77%, 75.99% respectively for Hindi named entity recognition.

Stepping Into the Next Decade of Ubiquitous and Pervasive Computing: UbiComp and ISWC 2021

The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2021) took place from September 21–26 virtually, around the world; co-located with the International Symposium on Wearable Computers (ISWC). This article discusses some of the interesting research published in the Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies, as well as those accepted at ISWC this year—presented virtually with Live Q&A over Whova, Zoom, and Gather.Town. Furthermore, it covers some of the interesting plenary events, such as the Keynotes by Cynthia Breazeal (MIT Media Labs) and John LePore (Perception), as well as the panel discussion on the 30th anniversary of Mark Weiser’s paper on the Vision of a Computer for the 21st Century. Finally, some of the interesting highlights from the ISWC gadget-show and design-exhibition, alongside multiple virtual workshops as part of the adjunct program have been presented.

The 25th Edition of the International Symposium on Wearable Computers

This year marks the 25th edition of ISWC—International Symposium on Wearable Computers—which is the leading research venue for all the topics related to wearables. The conference was held September 21st—24th, 2021, with workshops dedicated to specialized research topics further extending into the 26th. ISWC was co-hosted with the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing, as has been the case for a few years.

Introduction to the Special Issue on the International Joint Conference on Rules and Reasoning, RuleML+RR 2019

Abstract This special issue of Theory and Practice of Logic Programming consists of extended versions of five selected papers from the 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019). RuleML+RR 2019 was held in conjunction with the 5th Global Conference on Artificial Intelligence, GCAI 2019, as part of the Bolzano Rules and Artificial INtelligence Summit in Bolzano, Italy, from 17 to 19 of September 2019.

Call for nominations

The ACM SIGAI Industry Award for Excellence in Artificial Intelligence (AI) will be given annually to individuals or teams who have transferred original academic research into AI applications in recent years in ways that demonstrate the power of AI techniques via a combination of the following features: originality of the research novelty and technical excellence of the approach; importance of AI techniques to the approach; and actual or predicted societal impact of the application. Awardees receive a plaque accompanied by a prize of $5,000, and will be recognized at the International Joint Conference on Artificial Intelligence through an agreement with the IJCAI Board of Trustees.

Preface

2021 International Joint Conference on Robotics and Artificial Intelligence (JCRAI 2021), which took place in Chengdu, China during Nov. 7-9, 2021. The purpose of this gathering is to bring researchers, engineers, scientists and industry professionals to an open forum where advances in the field of Robotics and Artificial Intelligence can be shared and exchanged. The conference is an ideal platform for keeping up with advances and changes to a consistently evolving field. Leading researchers and industry experts from around the globe will be presenting the latest studies through papers and oral presentations.Against the backdrop of Covid-19 epidemic, the conference cannot be successfully held without the hard work of the conference committee. We would like to express our sincere gratitude to them.The meeting was originally planned to be held in Chengdu, China, however, due to travel restrictions of COVID-19, the conference was held virtually. At the conference, each presenter has 15 minutes to display their achieved research results, and a Q&A session held after the presentation. About 40 presenters attended the conference, and we held an online meeting via Webex.List of Conference committee is available in this pdf.

Proof Complexity of Modal Resolution

We investigate the proof complexity of modal resolution systems developed by Nalon and Dixon (J Algorithms 62(3–4):117–134, 2007) and Nalon et al. (in: Automated reasoning with analytic Tableaux and related methods—24th international conference, (TABLEAUX’15), pp 185–200, 2015), which form the basis of modal theorem proving (Nalon et al., in: Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI’17), pp 4919–4923, 2017). We complement these calculi by a new tighter variant and show that proofs can be efficiently translated between all these variants, meaning that the calculi are equivalent from a proof complexity perspective. We then develop the first lower bound technique for modal resolution using Prover–Delayer games, which can be used to establish “genuine” modal lower bounds for size of dag-like modal resolution proofs. We illustrate the technique by devising a new modal pigeonhole principle, which we demonstrate to require exponential-size proofs in modal resolution. Finally, we compare modal resolution to the modal Frege systems of Hrubeš (Ann Pure Appl Log 157(2–3):194–205, 2009) and obtain a “genuinely” modal separation.

TBIOM Special Issue on “Best Reviewed Papers From IJCB 2020—Editorial”

Recent years have seen considerable advances in biometric recognition techniques leading to a wide-spread deployment of biometric technology across a number of application domains, ranging from security, border control, and criminal investigations to entertainment, social media, autonomous driving and even health services. To highlight some of these advancements and present the latest research achievements related to biometrics, the 4th IEEE/IAPR International Joint Conference on Biometrics (IJCB) was organized in 2020. IJCB combines two major biometrics research conferences, the IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS) conference, and the International Conference on Biometrics (ICB) and is made possible through a special agreement between the IEEE Biometrics Council and the IAPR TC-4. Because IJCB has (so far) only been organized every three years, it represents a particularly prestigious and selective event, where only the highest-quality research is presented.

Artificial neural network for the recognition of human emotions under a backpropagation algorithm

The era of the technological revolution increasingly encourages the development of technologies that facilitate in one way or another people's daily activities, thus generating a great advance in information processing. The purpose of this work is to implement a neural network that allows classifying the emotional states of a person based on the different human gestures. A database is used with information on students from the PUCE-E School of Computer Science and Engineering. Said information are images that express the gestures of the students and with which the comparative analysis with the input data is carried out. The environment in which this work converges proposes that the implementation of this project be carried out under the programming of a multilayer neuralnetwork. Multilayer feeding neural networks possess a number of properties that make them particularly suitable for complex pattern classification problems [8]. Back-Propagation [4], which is a backpropagation algorithm used in the Feedforward neural network, was taken into consideration to solve the classification of emotions.
 Keywords: Image processing, neural networks, gestures, back-propagation, feedforward, classification, emotions.
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