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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kalleya, Calista Pramono, Andi Angelina, Chelsea Putri Cuarista, Wilbert Alvin Prabowo, Riefky Maulana, Fairuz Iqbal Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Marketplace development is increasing, as is the number of people with active internet access. Unive is a startup that runs an e-commerce platform connecting Student Activity Units with consumers. Focuses on fostering student entrepreneurship through practical business experience. For its development, this startup needs an interior for business escalation. Designers need software integrated with artificial intelligence (AI) in interior design. This study explores the integration of AI-powered rendering tools in the design of e-commerce platforms, focusing on how these tools enhance the user experience and satisfaction. The research employs a mixed-method approach, combining qualitative and quantitative analyses. The author collected data through user surveys, expert interviews, and performance metrics of AI rendering tools. The author based the evaluation criteria on adherence to interior design principles and the quality of rendering results. Findings indicate that selecting furniture and supporting components that adhere to interior design principles results in optimal rendering outcomes. These outcomes are close to user expectations, significantly improving user engagement and satisfaction. Detailed analysis shows the effectiveness of AI rendering in various design scenarios. The discussion highlights the implications of integrating AI rendering tools in e-commerce platforms. It addresses the potential of these tools to revolutionize user interaction and provides insights into future developments and applications in the field. This study demonstrates the value of AI-powered rendering tools in enhancing e-commerce platform design. By adhering to interior design principles, these tools can achieve rendering results that meet user expectations, ultimately leading to improved user experiences. © 2024 The Authors.

关键词： Marketplaces

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Forebrain excitatory neuron-specific loss of Brpf1 attenuates excitatory synaptic transmission and impairs spatial and fear memory

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Neural Regeneration Research 2024年第5期19卷 1133-1141页

作者： Baicheng Zhao Hang Zhang Ying Liu Gaoyu Zu Yuxiao Zhang Jiayi Hu Shuai Liu Linya You Department of Human Anatomy&Histoembryology School of Basic Medical SciencesFudan UniversityShanghaiChina Shanghai Key Laboratory of Brain Functional Genomics(Ministry of Education) Affiliated Mental Health Center(ECNU)School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina Shanghai Changning Mental Health Center ShanghaiChina NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai ShanghaiChina Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai ShanghaiChina

Bromodomain and plant homeodomain(PHD)finger containing protein 1(Brpf1)is an activator and scaffold protein of a multiunit complex that includes other components involving lysine acetyltransferase(KAT)6A/6B/***1,KAT6A,and KAT6B mutations were identified as the causal genes of neurodevelopmental disorders leading to intellectual *** previous work revealed strong and specific expression of Brpf1 in both the postnatal and adult forebrain,especially the hippocampus,which has essential roles in learning and ***,we hypothesized that Brpf1 plays critical roles in the function of forebrain excitatory neurons,and that its deficiency leads to learning and memory *** test this,we knocked out Brpf1 in forebrain excitatory neurons using *** found that Brpf1 deficiency reduced the frequency of miniature excitatory postsynaptic currents and downregulated the expression of genes Pcdhgb1,Slc16a7,Robo3,and Rho,which are related to neural development,synapse function,and memory,thereby damaging spatial and fear memory in *** findings help explain the mechanisms of intellectual impairment in patients with BRPF1 mutation.

关键词： behavioral test Brpf1 CAMKIIa-Cre intellectual disability miniature excitatory postsynaptic current mRNA-Seq

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Selecting between visuomotor lotteries to measure mental effort in risky decisions 44

Selecting between visuomotor lotteries to measure mental eff...

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Mehrotra, Samarth Srivastava, Nisheeth Department of Cognitive Science IIT Kanpur India Department of Computer Sciencee and Engineering IIT Kanpur India

It is intuitive to believe that humans take considerations of mental effort into account when making decisions. However, it has proved difficult to differentiate theories of mental effort in the absence of direct measurements of this psychological construct. Existing measurements of mental effort using response times and revealed preferences have low reliability. In this paper, we present a new experimental task - selecting between visuomotor lotteries using eye-tracking for sampling lotteries - that enables direct measurement of mental effort. Unlike response time-based measures, effort measurements in this task are not confounded by actual effort allocation. Unlike revealed preference-based measures, effort measurements in this task are acquired on a natural scale unitized by automatic visual selection processes. We also report results from a simple experiment conducted using this task, which reproduce existing findings of costly effort-aversion, and also demonstrate adaptive adjustment of mental effort. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Decision making

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Fine-tuning Transformers with Additional Context to Classify Discursive Moves in Mathematics Classrooms 17

Fine-tuning Transformers with Additional Context to Classify...

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17th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2022

作者： Suresh, Abhijit Jacobs, Jennifer Perkoff, Margaret Martin, James H. Sumner, Tamara Department of Computer Science University of Colorado Boulder United States Institute of Cognitive Science University of Colorado Boulder United States

ISBN: (纸本)9781955917834

"Talk moves" are specific discursive strategies used by teachers and students to facilitate conversations in which students share their thinking, and actively consider the ideas of others, and engage in rich discussions. Experts in instructional practices often rely on cues to identify and document these strategies, for example by annotating classroom transcripts. Prior efforts to develop automated systems to classify teacher talk moves using transformers achieved a performance of 76.32% F1. In this paper, we investigate the feasibility of using enriched contextual cues to improve model performance. We applied state-of-the-art deep learning approaches for Natural Language Processing (NLP), including Robustly optimized bidirectional encoder representations from transformers (Roberta) with a special input representation that supports previous and subsequent utterances as context for talk moves classification. We worked with the publically available TalkMoves dataset, which contains utterances sourced from real-world classroom sessions (human- transcribed and annotated). Through a series of experimentations, we found that a combination of previous and subsequent utterances improved the transformers’ ability to differentiate talk moves (by 2.6% F1). These results constitute a new state of the art over previously published results and provide actionable insights to those in the broader NLP community who are working to develop similar transformer-based classification models. © 2022 Association for Computational Linguistics.

关键词： Deep learning

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News Classification Based On News Headline Using SVC Classifier 16

News Classification Based On News Headline Using SVC Classif...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Leonard, Goldius Sisnadi, Fukriandy Wardhana, Nicholas Vigo Aziz Al-Ghofari, Muhammad Abdul Girsang, Abba Suganda School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665455947

News gives new insight and information from all over the world. News has many categories, such as politic, economy, science, and other common news categories. Every news will have their own category based on its content. The classification of news is usually done manually by inputting the category during the news posting. Some of the categories may be inputted incorrectly. The news classifier can be the solution for problem, but the news classifications out there are usually based on the news content. The classifier will receive the word vector inputs that are taken from the news content and try to classify it into one of certain categories. Unfortunately, news contents can be longer and harder to be processed rather than processing the news headline. The news headline is shorter and packs a decent information for the classifier to find out what category it is. Besides the news headline usage, the classifier also needs to be chosen correctly. In this paper, the SVC model will be tested using the news headline data to classify the news and compare with several other models, such as Linear Regression, Multinomial Naive Bayes, Decision Tree, and Random Forest. The common variables to be compared are the accuracy, recall, and precision to evaluate the SVC model. © 2022 IEEE.

关键词： Classification (of information)

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Ensemble-Stacking Machine Learning Model for Multi-Class Skin Diseases Identification 5

Ensemble-Stacking Machine Learning Model for Multi-Class Ski...

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5th International Conference on Biomedical Engineering, IBIOMED 2024

作者： Tandelon, Dennis Christiono, David Lawrence Andryani, Nur Afny Catur Gondokaryono, Srie P. Rahardja, July I. Danarti, Retno School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Department Jakarta Indonesia Indonesian Society of Dermatology & Venereology Padjadjaran University Faculty of Medicine Indonesia Dermatovenereology Department Indonesia Indonesian Society of Dermatology & Venereology Gadjah Mada University Faculty of Medicine Indonesia Dermatovenereology Department Indonesia

ISBN: (纸本)9798350389265

Algorithm development on skin diseases identification with high cardinality classification is still very challenging. In addition, the high similarity appearance among diseases makes the computer vision approach meet high difficulties on providing clear distinct feature representation. In general, some literature shows that the performance of single machine learning or single deep learning techniques can be further improved when merged by ensemble mechanism. This approach has been applied on binary skin's cancer diseases diagnosis prediction and successfully improve the prediction ability. This paper elaborates the potential of ensemble machine learning approaches using stacking techniques on conducting feature extraction and classification model for identifying more general skin diseases based on dermoscopy images and mobile phone images. The proposed model combines three different CNN architectures, ResNet50, Xception, and VGG16 to extract features from the image then stacked as input for the model trained using five different algorithms, which are logistic regression, support vector machines, multi-layer perceptron, k-nearest neighbors, random forests and the combination of these algorithm in stacking classifier as the base estimators and final estimator. The simulation results present the proposed ensemble - stacking model succeeds to improve the identification ability in all utilized datasets. In addition, the proposed ensemble - stacking models show better recall which indicates good sensitivity on handling false negative. It is beneficial for medical application. © 2024 IEEE.

关键词： Federated learning

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Multi-Resolution Location-Based Training for Multi-Channel Continuous Speech Separation 48

Multi-Resolution Location-Based Training for Multi-Channel C...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Taherian, Hassan Wang, DeLiang The Ohio State University Department of Computer Science and Engineering United States The Ohio State University Center for Cognitive and Brain Sciences United States

ISBN: (纸本)9781728163277

The performance of automatic speech recognition (ASR) systems severely degrades when multi-talker speech overlap occurs. In meeting environments, speech separation is typically performed to improve the robustness of ASR systems. Recently, location-based training (LBT) was proposed as a new training criterion for multi-channel talker-independent speaker separation. Assuming fixed array geometry, LBT outperforms widely-used permutation-invariant training in fully overlapped utterances and matched reverberant conditions. This paper extends LBT to conversational multi-channel speaker separation. We introduce multi-resolution LBT to estimate the complex spectrograms from low to high time and frequency resolutions. With multi-resolution LBT, convolutional kernels are assigned consistently based on speaker locations in physical space. Evaluation results show that multi-resolution LBT consistently outperforms other competitive methods on the recorded LibriCSS corpus. © 2023 IEEE.

关键词： Source separation

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Deep neural networks and weighted word embeddings for sentiment analysis of drug product reviews 7

Deep neural networks and weighted word embeddings for sentim...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Suhartono, Derwin Purwandari, Kartika Jeremy, Nicholaus Hendrik Philip, Samuel Arisaputra, Panji Parmonangan, Ivan Halim Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

One of the major tasks of natural language processing is sentiment analysis. The web is a source of unstructured and rich informa-tion with thousands of opinions and reviews. Individuals, businesses, and governments can all benefit from recognizing sentiment. As part of this study, we propose a deep learning-based approach for sentiment analysis on drug product review data obtained from the UCI machine learning repository. As an alternative to deep learning models, this architecture integrates glove word embedding with convolutional neural networks (CNN). Word2vec and GloVe word embedding schemes have been evaluated empirically for their predictive performance in CNN architectures. Based on a comparison of the deep learning architecture with RoBERTa, itcan be seen that BERT architecture outperforms both of them in training and validation. However, CNN models using Glove word embedding provided superior results in testing. © 2022 Elsevier B.V.. All rights reserved.

关键词： Embeddings

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Enhanced Personality Prediction: Fusing Structural and BERT-Based Lexical Features on Augmented Twitter Data 7

Enhanced Personality Prediction: Fusing Structural and BERT-...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Roslynlia Suhartono, Derwin BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Conventionally, assessing people's personality done by psychology experts requires more time and effort. Hence automatic personality prediction is starting to get noticed by researchers. Nowadays, many people engage themselves in social media platforms, one of which is Twitter. This platform stores actions done in this media which reflect one's personality. This research focuses on improving the performance of personality prediction models based on machine learning algorithms, by augmenting the dataset using Easy Data Augmentation to overcome imbalance dataset, thereafter combining n-grams with POS-tag weighted by TF-IDF. Results show that this method outperforms other machine learning approaches and decreases bias, with a maximum of 72.62% of the best average accuracy and 72.72% best average Fl score using Support Vector Machine classifier. The second-best model is obtained by XGBoost and, lastly, Naive Bayes classifier. Overall, this study shows that data augmentation with the addition of POS-tag feature is evident in improving the performance of personality prediction models. However, larger data is needed. © 2022 IEEE.

关键词： Learning algorithms

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SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion 63

SYNLOCO: Synthesizing Central Pattern Generator and Reinforc...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Xinyu Xiao, Zhiyuan Zhang, Qingrui Pan, Wei Shenzhen Campus of Sun Yatsen University School of Aeronautics and Astronautics Shenzhen518107 China The University of Manchester Department of Computer Science United Kingdom Delft University of Technology Department of Cognitive Robotics Netherlands

ISBN: (纸本)9798350316339

The Central Pattern Generator (CPG) is adept at generating rhythmic gait patterns characterized by consistent timing and adequate foot clearance. Yet, its open-loop configuration often fails to adjust the system's control performance in response to environmental variations. On the other hand, Reinforcement Learning (RL), celebrated for its model-free properties, has gained significant traction in robotics due to its inherent adaptability and robustness. However, initiating traditional RL approaches from the ground up presents a risk of converging to suboptimal local minima and slow learning convergence. In this paper, we propose a quadruped locomotion framework-called SYNLOCO-by synthesizing CPG and RL, which can ingeniously integrate the strengths of both methods, enabling the development of a locomotion controller that is both stable and natural with partial state observations (e.g., no velocity measurements). To optimize the learning trajectory of SYNLOCO, a two-phase training strategy is presented. Both ablation analysis and experimental comparison are performed using a real quadruped robot under varied conditions, including distinct velocities, terrains, and payload capacities. The experiments showcase SYNLOCO's efficiency in producing consistent and clear-footed gaits across diverse scenarios, despite no velocity measurements. The developed controller exhibits resilience against substantial parameter variations, underscoring its potential for robust real-world applications. © 2024 IEEE.

关键词： Reinforcement learning

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