Reviewerly/exHarmony huggingface dataset:
Computational Modeling in Various Cognitive Fields

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An Overview of Computational Cognitive Sciences

This chapter provides an introduction and an overview of computational cognitive sciences. Computational cognitive sciences explore the essence of cognition and various cognitive functionalities through developing mechanistic, process-based understanding by specifying corresponding computational models. These models impute computational processes onto cognitive functions and thereby produce runnable programs. Detailed simulations and other operations can then be conducted. Understanding the human mind strictly from observations of, and experiments with, human behavior is ultimately untenable. Computational modeling is therefore both useful and necessary. Computational cognitive models are theoretically important because they represent detailed cognitive theories in a unique, indispensable way. Computational cognitive modeling has thus far deepened the understanding of the processes and the mechanisms of the mind in a variety of ways.
Obstacle avoidance based on double closed loop control of autonomous underwater vehicle for submarine cable laying

When the autonomous underwater vehicle (AUV) lays the submarine cable in complex environment, it should balance the laying quality and its own safety. In this paper it designs an engineering modeling method under the condition of irregular obstacles and an obstacle avoidance method based on double closed loop control for AUV to lay the submarine cable. The model of irregular obstacle of bulge terrain and depression terrain are established respectively. Then the outer-loop controller based on model predictive control is designed to find the optimal path of AUV in order to avoid the unexpected obstacle. The segmented progressive optimization technique is used to keep the suspension height with the seabed. It could keep the minimal change of suspension height and obtain the shortest path. The best cabling path is designed by detecting the contour feature point and distinguishing the different work situations. The inner-loop controller based on Gaussian process is designed to reduce the influence of current interference. It compensates the control input with the mean value of Gaussian process by the outer-loop controller. Also this controller could reduce the outer-loop following error efficiently. In the simulation it compares double closed loop control with sphere model predictive control and double model predictive control respectively with the different obstacles. The result shows that double closed loop control improves the quality of the submarine cable laying and AUV's safety in the submarine complex environment effectively.
Symbolic and Hybrid Models of Cognition

For decades, symbolic models of cognition were the dominant computational approaches of cognition. Today they coexist with subsymbolic, statistical, and hybrid models, but they are still the de facto standard for modeling human reasoning processes. This chapter summarizes important aspects of symbolic and hybrid models of cognition, approaching the topic from different perspectives. After some discussion on historical aspects and the theoretical basis of symbolic models of cognition, cognitive architectures as models for intelligent agents are examined. Subsequently, the role of symbolic computational approaches towards processing natural language, representation of human knowledge, and commonsense reasoning are considered. Then the focus is put on the crucial question of learning new representations and theories, before finally looking at hybrid and neural-symbolic systems combining reasoning and learning and bridging between symbolic and subsymbolic elements.
Logic-Based Modeling of Cognition

After a brief orientation to logic-based (computational) cognitive modeling, the necessary preliminaries are discussed in this chapter (e.g., what a logic is, and what it is for one to “capture" human cognition are explained). Three “microworlds" or domains that all readers should be comfortably familiar with (natural numbers and arithmetic; everyday vehicles; and residential schools, e.g., colleges and universities) are introduced, in order to facilitate exposition in the chapter. Then the ever-expanding universe U of formal logics, with an emphasis on three categories therein, is given: deductive logics having no provision for directly modeling cognitive states; nondeductive logics suitable for modeling rational belief through time without machinery to directly model cognitive states such as believes and knows; and finally, nondeductive logics that enable the kind of direct modeling of cognitive states absent from the first two types of logic. Then, there follows a focus spcifically on two important aspects of human-level cognition to be modeled in logic-based fashion: the processing of quantification, and defeasible (or nonmonotonic) reasoning. Finally, a brief evaluation of logic-based cognitive modeling is provided, together with some comparison to other approaches to cognitive modeling, and the future of the discipline is considered. The chapter presupposes nothing more than high-school mathematics of the standard sort on the part of the reader.
Natural Language Understanding and Generation

In the framework of computational cognitive modeling, natural language understanding and generation must be integrated with other cognitive capabilities, such as reasoning and learning. The language understanding component of an intelligent agent extracts and formally represents the meaning of texts and dialog turns. The output of language understanding must reflect the speaker’s intended meaning and be sufficiently detailed to serve as input to reasoning and action in artificial intelligent agents. One kind of agent action is verbal, so agents must include a language generation capability. This chapter describes a particular language understanding system that meets the requirements for the above language capabilities and also puts forward methodological arguments about the interplay between theories, models, and computational systems.
Cognitive Modeling in Social Simulation

This chapter discusses cognitive social simulation, which lies at the intersection of cognitive modeling and social simulation – two forms of computational modeling. Cognitive modeling focuses on producing precise computational models of individual mental processes, while social simulation centers on models of social processes (such as interaction of individuals or collective decision making). By combining cognitive and social models, cognitive social simulation is poised to address issues concerning both individuals and society. Detailed simulation enables precise analysis of possible scenarios and outcomes (social or individual). A number of examples of cognitive social simulation are sketched in this chapter. Issues involved are discussed. Some promising directions are outlined.
A Study on Defense Strategy According to Japan’s Perception of Security Crisis in Northeast Asia

본 연구는 2022년 12월 16일에 일본 각의에서 결정한 국가안전보장전략을 고찰하기 위해 근대 이후 일본의 동북아 안보위기 인식에 따른 안전보장 전략을 검토했다. 일본은 러시아의 우크라이나 침략, 중국의 힘에 의한 국익 추구, 북한의 위협 고조 등으로 인해 현재 ‘전후 가장 어렵고 복잡한 안전보장 환경’에 직면해 있다고 인식하면서 방위력의 기본 방향을 전면 수정했다. 일본이 침공을 받았을 때 적의 미사일 기지 등을 공격할 수 있는 ‘반격능력’을 보유하고, 5년 뒤에는 방위비를 국내총생산(GDP)의 2% 이상으로 늘린다는 내용의 국가안전보장전략이다. 이러한 적극적인 안보정책은 메이지 시기에도 존재했다. 일본 본토를 의미하는 ‘주권선’과 그 안전을 확보하기 위한 ‘이익선’론을 적용하여, ‘이익선’을 조선에서 만주와 동아시아로 확대하여 아시아･태평양전쟁 기간 동안에 영토확장을 위한 논리로 활용했다. 이처럼 대립이 격화되어있는 가운데 자국의 안전을 위한 위협적인 전략 수정은 새로운 대립 격화를 초래할 우려가 있다. 포스트 냉전 시대의 종결을 초래한 러시아의 우크라이나 침공은 또 다른 안보의 가치를 요구하고 있다. 이러한 시점에서 일본의 공세적인 안보전략은 동북아의 안보에 또 다른 위협으로 인식될 수 있기 때문에 보다 신중한 태도로 임해야 할 것이다. This study reviewed Japan’s security strategy through recognizing the security crisis in Northeast Asia after modern times in order to consider the national security strategy decided by Japan on December 16, 2022. Japan recognizes that it is currently facing ‘the most difficult and complex security environment after the war’ due to Russia’s invasion of Ukraine, China’s pursuit of national interests, and the escalating threat from North Korea. And, in the National Security Strategy, Japan decided to have a ‘counterattack capability’ that can attack enemy missile bases when Japan is invaded, and to increase defense spending to more than 2% of GDP five years later. This active security policy existed even during the Meiji period. By applying the ‘sovereign line’ meaning the Japanese mainland and the ‘profit line’ to ensure its safety, the ‘profit line’ was expanded from Joseon to Manchuria and East Asia, and was used as a logic for territorial expansion during the Asia-Pacific War. However, in the midst of intensifying confrontation, a threatening strategy change for the sake of national security could lead to a new escalation of confrontation. Russia’s invasion of Ukraine, which brought about the end of the post-Cold War era, is demanding another security value. At this point, Japan’s aggressive security strategy can be perceived as another threat to the security of Northeast Asia, so we need to be more cautious. 本研究は2022年12月16日に日本が決定した国家安全保障戦略を考察するために、近代以後の日本の北東アジア安保危機認識を通じた安全保障戦略を検討した。明治期の利益線と敗戦後の冷戦による再軍備論議、2022年の国家安全保障戦略の決定過程を比較することで、日本が安保危機に対処する方式を分析し、これが北東アジアの平和に及ぼす影響を考察した。日本はロシアのウクライナ侵略、中国の力による国益追求、北朝鮮の脅威の高まりなどにより、現在‘戦後最も困難で複雑な安全保障環境’に直面していると認識し、防衛力の基本方向を全面修正した。その内容は、日本が侵攻を受けた時、敵のミサイル基地などを攻撃できる‘反撃能力’を保有し、5年後には防衛費を国内総生産(GDP)の2%以上に増やすということである。このような積極的な安保政策は明治期にも存在した。日本本土を意味する‘主権線’とその安全を確保するための‘利益線’を適用し、‘利益線’を朝鮮から満州と東アジアに拡大し、アジア･太平洋戦争期の領土拡張のための論理として活用した。しかし、対立が激化している中、自国の安全のための脅威的な戦略修正は新たな対立激化を招く恐れがある。ポスト冷戦時代の終結を招いたロシアのウクライナ侵攻は、さらなる安保の価値を求めている。このような時点で、日本の攻勢的な安保戦略は北東アジアの安保にさらなる脅威と認識されかねないため、より慎重な態度で臨まなければならないだろう。
An Antiforgetting Model for Higher Vocational English Learning Using BP Neural Network

Currently, high-quality vocational education is an important foundation for the aspirations of China to create massive professionals and talented employees. Because of these, the advancement of modern vocational education is becoming increasingly important in the current era. Aside from these benefits, the use of artificial intelligence expertise in higher vocational English learning can effectively solve the unscientific problem of learning plan formulation in higher vocational English learning, increase the level of scientific learning, and strengthen students’ autonomous learning ability. As a result, this research provides a BP neural network technique based on the Ebbinghaus forgetting curve that can effectively complete sample incremental training. It achieves performance comparable to the complete sample batch training approach while using many fewer samples to train. From experimental work, it is clear that the suggested work can effectively improve the training efficiency of the BP neural network to provide learners with a more suitable learning plan.
Visual Analytics for Innovation and R&amp;D Intelligence

Overview: Innovation is critical to the growth and survival of companies across industries and geographies. Over time, however, sources of innovation have become much more diverse, and a systematic understanding of the complex and dynamic innovation landscape is challenging. This paper describes the value and impact of using a data-driven, visual analytic approach for innovation and R&D intelligence. I outline a methodology with which to approach visual analytic innovation and R&D intelligence, present illustrative mini-case studies, and frame the findings into key considerations and elements of the intelligence decision space. The paper contributes to practical and theoretical understanding of how emerging computational and analytical approaches can support innovation and R&D activities. It concludes with practical implications and future research directions.
Disease estimation using robust AI methods

Human blood scrutinization is an indispensable step to analyze a particular health condition, comprise of a complete blood cell (CBC) count. CBC accentuates the counting of White blood cells (WBCs), red blood cells (RBCs), and Platelets which are implicitly significant for the analysis of severe maladies such as leukemia, thrombocytopenia, and anemia. Traditional approaches like manual counting and automated analyzer were extensively used, which is monotonous, time intensive, and entail a lot of medical experts. To get rid of aforesaid leisure techniques, here by using a machine learning-based object detection and classification algorithm you only look once (YOLO) to count the blood cells. YOLO with modified configuration has been trained on the customized dataset to detect the WBCs, RBCs, and platelets.
Visualizing Research on Explainable Artificial Intelligence for Medical and Healthcare

Understanding complex machine learning and artificial intelligence models have always been challenging because these models are black-box, and often we don't know what information models rely upon to infer. Explainable Artificial Intelligence (XAI) has emerged as a new exciting field to explain and understand these machine learning models as humans can understand and improve them. In the past few years, there have been numerous research articles on explainable artificial intelligence for medical and healthcare. 1687 documents are being studied and analysed using bibliometric methods in this work. There are certain systematic reviews on the same topic, but this study is the first of its kind to use a quantitative method to analyze a large number of publications. The results of this study show that the research in this field took pace in 2011, and there have been quite many publications in the following years. We have also identified top-cited journals and articles. Through thematic analysis, we have found some important thematic areas of research in the field of XAI for medical and healthcare. The findings showed that the USA is the global leader in XAI research, followed by China and Canada at second and third place, respectively.
Skip Connections' Importance in Biomedical Image Segmentation

This examination explores the impacts of both extensive and brief pass associations on Fully Biomedical Fully Convolutional Networks (FCN) Image division. In ordinary, just drawn-out pass associations are employed to pass highlights from the contracting way to the growing way to get better spatial insights lost during down testing. We increment FCNs by utilizing fast detour associations which can be similar to the ones introduced in leftover organizations to expand extremely profound FCNs. The presence of each lengthy and brief skip association is appropriate for incredibly profound FCN, concerning an evaluation of the slope stream. At long last, we show that in the EM dataset, an exceptionally profound FCN may likewise yield outcomes that may be almost the most recent with practically no extra submit handling.
Car Types and Semantics Classification Using Weka

This paper presents a method of using Weka, a machine learning tool, to identify the difference of types and product semantics between fuel vehicles and electric vehicles. Pictures of 58 fuel vehicles and 42 pictures of electric vehicles during time period from 2021 to 2023 are selectively collected from Consumer Reports website to build the dataset. The fuel vehicle brands include Audi, BMW, Cadillac, and Lexus with 3 types, namely, SUV, Sedan and Luxury. In addition to the above-mentioned brands, Tesla is the 5th brand of electric vehicles. The perception of each picture is labelled by questionnaires of Automotive Model Semantics. Results reveal that even the smaller dataset can be trained to have highly accuracy models for classifying fuel vehicles and electric vehicles, different types as well as product semantics. The method presented is promising for studying car styling and exploring new applications of image classification to branding and product design.
Crafting tasteful experiences: Designing artificial intelligence and voice user interfaces for home delivery contexts

This research article crafted, evaluated, and revised a theoretically underpinned design concept with the purpose of enhancing customers’ dine-in experiences. The design concept was motivated by the considerable interest in artificial intelligence (AI), voice user interfaces (VUI) within Human-Computer Interaction (HCI), and the rapid digitalization of online food ordering as a result of the COVID-19 pandemic. The study applied the concept-driven design research approach because it offered to make theoretical contributions while at the same time being design and concept-oriented. The result of this research is a revised design concept that has the potential to digitalize the dine-in restaurant business further and add to the understanding of human experience while interacting with a voice user interface. Finally, the research article manifests as an example of how interaction designers make theoretical contributions through design and how technologies can be combined in new contexts.
Developing a Hybrid Neural Network for Part-Of-Speech Tagging and Named Entity Recognition

Enabling our computers to understand human languages is very important as we move towards the 4th to 5th industrial revolution. A lot of efforts are already made to fast track this development most especially for highly resourced languages such as English, French, German, among others. The most notable open-source NLP tool built is the Natural Language toolkit, a collection of modules and corpora that provides researchers in Natural Language Processing (NLP) with extremely useful tools and resources. In the Philippines, several researchers have contributed to this advancement mostly for the Filipino language. In this paper, we introduce the design architecture of a hybrid neural network model that combines the best component features of the existing architectures for the POS Tagging and NER tasks. We also present initial experiment results.
The drone detection based on improved YOLOv5

The wide application of drones not only brings convenience to production and life, but also poses a threat to public safety. Therefore, the detection of s is crucial. However, tiny drones make it difficult to cope with traditional detection methods such as radar and photoelectricity because of their tiny size. Therefore, this paper proposed a tiny drones detection method based on YOLOv5 framework. By optimizing the size of Anchor box, embedding the Convolutional Block Attention Module (CBAM) and optimized loss function (CIoU), the detection performance of the original algorithm for drones under complex background is improved. The improved YOLOv5 algorithm is trained and tested on the self-built dataset, and its mean Average Precision, Accuracy and Recall reach 96.9%, 97.8% and 95.6% respectively. Finally, the improved YOLOv5 is used for drone detection in complex background environments. Compared with the original algorithm, it can correctly identify drone targets in harsh environments.
Implementation of an Undergraduate Admission Chatbot Using Microsoft Azure's Question Answering and Bot Framework

Artificial intelligence (AI) and chatbots have been gaining popularity in recent years because of their ability to converse naturally and their wide array of applications. A chatbot is a software tool that can be used to simulate human conversation with audio or textual technologies. This study developed a chatbot web application that can help the Pamantasan ng Lungsod ng Maynila to effectively answer applicants' concerns through the web chatbot. The web chatbot application can be used by applicants to ask questions, can learn to address repetitive questions using the history of questions in training, and avoids pile-ups and delays by answering applicants’ queries instantly. The findings of the study revealed promising results with a score of 92.84 out of 100 in terms of overall usability and user experience.
Technology-Enhanced Systemic Quality Assurance with the Aid of Text-Based Emotion Recognition of Facebook Comments for Higher Education Institutions

One of the difficult and recently-emerging problems in the realm of natural language processing is the recognition and analysis of emotions (NLP). A current area of research involves identifying a person's emotional state through textual data in addition to recognizing emotions from face and auditory records. Numerous disciplines, including higher education institutions, can use the study of emotions to their advantage. This is especially true given the widespread usage of social media in today's world, when everything is done online. This information could be very helpful in guiding an organization's decisions. The abundant text found in social media, blogs, and other places can be used to explore different text mining findings, such as emotions. This study tackles on making use of the vast amount of information available online especially in social media platforms through the development of iMosyon, an emotion recognition system to help aid higher institutions in their decision-making process. Experimental results were executed and researchers then decided to use the Support Vector Machine (SVM) model due to the fact that it received the highest accuracy score of 78% among the classifiers. The beta testing which made use of Evaluation of Performance Functionality and Software Product Quality shows an overall rating of 4.5 out of 5.0 that indicates that the respondents accepted its functionality and the feedback was good.
A multimodal sentiment recognition method based on attention mechanism

Effective sentiment analysis on social media data can help to better understand the public's sentiment and opinion tendencies. Combining multimodal content for sentiment classification uses the correlation information of data between modalities, thereby avoiding the situation that a single modality does not fully grasp the overall sentiment. This paper proposes a multimodal sentiment recognition model based on the attention mechanism. Through transfer learning, the latest pre-trained model is used to extract preliminary features of text and image, and the attention mechanism is deployed to achieve further feature extraction of prominent image key regions and text keywords, better mining the internal information of modalities and learning the interaction between modalities. In view of the different contribution of each modal to sentiment classification, a decision-level fusion method is proposed to design fusion rules to integrate the classification results of each modal to obtain the final sentiment recognition result. This model integrates various unimodal features well, and effectively mines the emotional information expressed in Internet social media comments. This method is experimentally tested on the Twitter dataset, and the results show that the classification accuracy of sentiment recognition is significantly improved compared with the single-modal method.