Reviewerly/exHarmony huggingface dataset:
ChatGPT for Robotics: A New Approach to Human-Robot Interaction and Task Planning

ChatGPT, released by OpenAI, has garnered academic interest due to its powerful natural language processing capabilities. It can accurately understand conversations and generate high-quality responses. This paper explores the potential of applying ChatGPT to two significant research topics in robotics: Human-Robot Interaction and Task Planning. Human-Robot Interaction involves studying the interactions between humans and robots. ChatGPT is well-suited for this purpose as it enables robots to communicate with humans. However, ChatGPT has shortcomings such as outdated knowledge and fabricating answers, making it unsuitable for direct use in Q&A. To address these issues, we propose an architecture called FRC that combines FAQ, retrieval module, and ChatGPT. In this architecture, ChatGPT is used for rephrasing questions and reading comprehension. Experiments show that the architecture can combine multiple rounds of dialogue to answer incomplete questions or questions that need coreference resolution. Task planning involves using an internal model to reason about the world and create a plan of actions to achieve a specific goal. Traditional approaches to robotic task planning rely on search while using ChatGPT for task planning is a novel approach based on generation. Existing methods using ChatGPT do not consider the state of the robot. We propose a method that enables ChatGPT to perform task planning based on the state of the robot and have verified its feasibility through experiments. We also discuss the limitations of ChatGPT in multi-party talk and motion control.
Experience Adapter: Adapting Pre-trained Language Models for Continual Task Planning

In this paper, we investigate the challenge of Pre-trained Language Models (PLMs) for continual task planning. PLM-based planner is difficult to incorporate incremental experience without risking catastrophic forgetting or overwhelming the model parameters. Inspired by human cognition, we propose the Experience Adapter, a novel method that avoids the need for model re-training or fine-tuning. The adapter continually collects experiences externally, including observation memory and human feedback, represented in memory graph and rules. Using these, the adapter directs task planning and corrects behavior not aligning with human expectations. Our method, not relying on the planner’s inherent structure, pairs easily with various foundational planning methods. In experiments on everyday tasks within the VirtualHome environment, we show that our approach significantly improves task success rate from 47% to 64%. This non-invasive method fits seamlessly within existing model-serving pipelines without altering the model training.
Accuracy Evaluation of Marginalized Unscented Kalman Filter

Compared with a conventional unscented Kalman filter (UKF), the recently proposed marginalized unscented Kalman filter (MUKF) uses a partially sampling strategy to achieve similar filter accuracy with fewer sigma points, demonstrating its powerful ability to deal with state estimation with mixed linearity and nonlinearity. However, the hypothesis that the accuracy of MUKF is equivalent to that of UKF is not true for all systems. In this paper, it is proved that when the state equation is expressed as a differential equation, the accuracy of MUKF is equivalent to that of UKF only when the propagations of the states to be sampled and the states not to be sampled in MUKF are independent of each other. The above condition corresponds to a common problem in engineering, which is a system with measurement biases. Through theoretical proof and numerical simulation, it is verified in this paper that for the systems with measurement biases, the differences of the computed means and covariances in filtering recursions between MUKF and UKF are restricted to the same order infinitesimal as the square of the scaling parameter. To sum up, this paper evaluates the accuracy of MUKF, providing references for engineers to choose MUKF or UKF in different systems.
CyberSecureHub: Integrating Cyber Security Tools

In an age of increasing digitalization and interconnectedness, "CyberSecureHub" emerges as a groundbreaking project, integrating a multitude of cybersecurity tools to empower businesses and organizations in safeguarding their digital assets. This innovative platform not only enhances tool accessibility but fosters collective strength against modern cyber threats. Leveraging technologies like React and Node.js the project is meticulously executed through well-defined phases, emphasizing testing and quality control. It delivers not only operational tools but also comprehensive documentation, user guidance, and risk assessment. "CyberSecureHub" is poised to adapt and protect, offering organizations a versatile and secure solution in an evolving digital landscape, making a significant impact in the cybersecurity industry
An AI-Based Framework for Translating American Sign Language to English and Vice Versa

In this paper, we propose a framework to convert American Sign Language (ASL) to English and English to ASL. Within this framework, we use a deep learning model along with the rolling average prediction that captures image frames from videos and classifies the signs from the image frames. The classified frames are then used to construct ASL words and sentences to support people with hearing impairments. We also use the same deep learning model to capture signs from the people with deaf symptoms and convert them into ASL words and English sentences. Based on this framework, we developed a web-based tool to use in real-life application and we also present the tool as a proof of concept. With the evaluation, we found that the deep learning model converts the image signs into ASL words and sentences with high accuracy. The tool was also found to be very useful for people with hearing impairment and deaf symptoms. The main contribution of this work is the design of a system to convert ASL to English and vice versa.
Chinese Multicategory Sentiment of E-Commerce Analysis Based on Deep Learning

With the continuous rise of information technology and social networks, and the explosive growth of network text information, text sentiment analysis technology now plays a vital role in public opinion monitoring and product development analysis on networks. Text data are high-dimensional and complex, and traditional binary classification can only classify sentiment from positive or negative aspects. This does not fully cover the various emotions of users, and, therefore, natural language semantic sentiment analysis has limitations. To solve this deficiency, we propose a new model for analyzing text sentiment that combines deep learning and the bidirectional encoder representation from transformers (BERT) model. We first use an advanced BERT language model to convert the input text into dynamic word vectors; then, we adopt a convolutional neural network (CNN) to obtain the relatively significant partial emotional characteristics of the text. After extraction, we use the bidirectional recurrent neural network (BiGRU) to bidirectionally capture the contextual feature message of the text. Finally, with the MultiHeadAttention mechanism we obtain correlations among the data in different information spaces from different subspaces so that the key information related to emotion in the text can be selectively extracted. The final emotional feature representation obtained is classified using Softmax. Compared with other similar existing methods, our model in this research paper showed a good effect in comparative experiments on an e-commerce text dataset, and the accuracy and F1-score of the classification were significantly improved.
Generating Fingerings for Piano Music with Model-Based Reinforcement Learning

The piano fingering annotation task refers to assigning finger labels to notes in piano sheet music. Good fingering helps improve the smoothness and musicality of piano performance. In this paper, we propose a method for automatically generating piano fingering using a model-based reinforcement learning algorithm. We treat fingering annotation as a partial constraint combinatorial optimization problem and establish an environment model for the piano performance process based on prior knowledge. We design a reward function based on the principle of minimal motion and use reinforcement learning algorithms to decide the optimal fingering combinations. Our innovation lies in establishing a more realistic environment model and adopting a model-based reinforcement learning approach, compared to model-free methods, to enhance the utilization of samples. We also propose a music score segmentation method to parallelize the fingering annotation task. The experimental section shows that our method achieves good results in eliminating physically impossible fingerings and reducing the amount of finger motion required in piano performance.
Improving Adversarial Robustness via Distillation-Based Purification

Despite the impressive performance of deep neural networks on many different vision tasks, they have been known to be vulnerable to intentionally added noise to input images. To combat these adversarial examples (AEs), improving the adversarial robustness of models has emerged as an important research topic, and research has been conducted in various directions including adversarial training, image denoising, and adversarial purification. Among them, this paper focuses on adversarial purification, which is a kind of pre-processing that removes noise before AEs enter a classification model. The advantage of adversarial purification is that it can improve robustness without affecting the model’s nature, while another defense techniques like adversarial training suffer from a decrease in model accuracy. Our proposed purification framework utilizes a Convolutional Autoencoder as a base model to capture the features of images and their spatial structure.We further aim to improve the adversarial robustness of our purification model by distilling the knowledge from teacher models. To this end, we train two Convolutional Autoencoders (teachers), one with adversarial training and the other with normal training. Then, through ensemble knowledge distillation, we transfer the ability of denoising and restoring of original images to the student model (purification model). Our extensive experiments confirm that our student model achieves high purification performance(i.e., how accurately a pre-trained classification model classifies purified images). The ablation study confirms the positive effect of our idea of ensemble knowledge distillation from two teachers on performance.
Multi-Meta Information Embedding Enhanced BERT for Chinese Mechanics Entity Recognition

The automatic extraction of key entities in mechanics problems is an important means to automatically solve mechanics problems. Nevertheless, for standard Chinese, compared with the open domain, mechanics problems have a large number of specialized terms and composite entities, which leads to a low recognition capability. Although recent research demonstrates that external information and pre-trained language models can improve the performance of Chinese Named Entity Recognition (CNER), few efforts have been made to combine the two to explore high-performance algorithms for extracting mechanics entities. Therefore, this article proposes a Multi-Meta Information Embedding Enhanced Bidirectional Encoder Representation from Transformers (MMIEE-BERT) for recognizing entities in mechanics problems. The proposed method integrates lexical information and radical information into BERT layers directly by employing an information adapter layer (IAL). Firstly, according to the characteristics of Chinese, a Multi-Meta Information Embedding (MMIE) including character embedding, lexical embedding, and radical embedding is proposed to enhance Chinese sentence representation. Secondly, an information adapter layer (IAL) is proposed to fuse the above three embeddings into the lower layers of the BERT. Thirdly, a Bidirectional Long Short-Term Memory (BiLSTM) network and a Conditional Random Field (CRF) model are applied to semantically encode the output of MMIEE-BERT and obtain each character’s label. Finally, extensive experiments were carried out on the dataset built by our team and widely used datasets. The results demonstrate that the proposed method has more advantages than the existing models in the entity recognition of mechanics problems, and the precision, recall, and F1 score were improved. The proposed method is expected to provide an automatic means for extracting key information from mechanics problems.
Self-Supervised Steering and Path Labeling for Autonomous Driving

Autonomous driving is a complex task that requires high-level hierarchical reasoning. Various solutions based on hand-crafted rules, multi-modal systems, or end-to-end learning have been proposed over time but are not quite ready to deliver the accuracy and safety necessary for real-world urban autonomous driving. Those methods require expensive hardware for data collection or environmental perception and are sensitive to distribution shifts, making large-scale adoption impractical. We present an approach that solely uses monocular camera inputs to generate valuable data without any supervision. Our main contributions involve a mechanism that can provide steering data annotations starting from unlabeled data alongside a different pipeline that generates path labels in a completely self-supervised manner. Thus, our method represents a natural step towards leveraging the large amounts of available online data ensuring the complexity and the diversity required to learn a robust autonomous driving policy.
ASSESSMENT OF THE HIGHER EDUCATION SYSTEM AVAILABILITY IN THE RUSSIAN FEDERATION FOR THE TRANSITION TO EDUCATION 2.0

The purpose of this work is to highlight and detail the issue of the availability of the higher education system in the Russian Federation for the transition to the new concept of Education 2.0. To achieve this goal, the following methods were used: descriptive methods, statistical methods, economic and mathematical methods, as well as methods of group expert assessment. As a result of the work done, the average level of availability of the higher education system in the Russian Federation for the transition to the new concept of Education 2.0 for a certain date was determined with high accuracy, and its constituent elements were also detailed within the middle levels for key projections: information, technological, legal, social, and economic. As a result, it was concluded that the entire system of higher education in the Russian Federation is not sufficiently prepared in the near term for the analyzed transition. As a recommendation in the area under consideration, it was suggested that it is advisable to implement the Education 2.0 concept locally in higher educational institutions in the Russian Federation with a high relative level of project funding (for example, in extended education programs, in retraining senior management personnel, as well as in the framework of specialized MBA and DBA programs). The novelty of the work done by the authors is determined by the projection structuring of the analyzed problem within five interconnected fields, as well as the obtaining of quantitative results with a high level of objectivity in the presentation of quantitative and qualitative results achieved including through the use of a complex of logically and analytically processed opinions of qualified experts.
Authenticity, and Approval Framework for Bus Transportation Based on Blockchain 2.0 Technology

The intelligent transport system (ITS) has transformed urban transportation, enhancing daily commutes with services like congestion management, vehicle crash prevention, traffic control, roadside safety, breakdown assistance, ticket booking, vehicle registration, and insurance. However, in urban bus transportation, the ITS faces security threats, such as data forgery and manipulation. To counter these challenges, a blockchain-based framework for bus transportation approval is proposed, ensuring data integrity and security. The framework’s performance is evaluated based on processing time, central processing unit (CPU), graphical processing unit (GPU), cloud usage, and memory consumption, and compared to Ethereum and Aurora testnet, in terms of gas cost, security, and performance. Stochastic algorithms, including the genetic algorithm and Tabu search, are used for time complexity analysis, to obtain an optimized solution. The decision-making trial and evaluation laboratory (DEMATEL) analysis is also performed to assess factors like transaction costs, execution time, memory consumption, and security. The results show that execution time, memory consumption, and processing time are crucial, while transaction cost, reliability, and transparency positively impact the system’s effectiveness. By reducing the risk of false data presentation and ensuring accurate records, the proposed framework contributes to a more efficient and reliable transportation system.
Classification For Predicting Heart Disease Using The K Nearest Neighbor Method Sylvani General Hospital Binjai City

The heart is a hollow organ and has four chambers or chambers located between the two lungs in the middle of the thoracic cavity. The heart has an important function in the human body, namely as a pump that presses blood so that it can flow to all parts of the body through arteries or veins. Disease caused by plaque buildup in the coronary arteries which supply oxygen to the heart muscle, resulting in severe damage to the heart is called coronary heart disease. Many factors can increase the risk of heart disease. These risk factors consist of risk factors that cannot be modified such as family history, age and gender and risk factors that can be modified such as hypertension, smoking habits, diabetes, dyslipidemia, obesity, lack of physical activity, diet and stress. K-Nearest Neighbor is a method for classifying new objects based on their (K) closest neighbors. K-NN includes a Supervised Learning algorithm where the results of querying new instances are classified based on the majority of categories in KNN. The class that appears the most will be the classification result class. This algorithm only stores feature vectors and classifies the learning data. In the classification phase, the same features are calculated for the test data (whose classification is unknown). The distance of this new vector to all data vectors is calculated, and the k closest ones are taken. The newly classified point is predicted to be among the most classified of these points. From the data with the majority categories there are Positive and Negative categories. From the majority number (Positive &gt; Negative) it can be concluded that new data (data No. 20) (K1=1, K2=0.5, K3=0, K4=1, K5=0, K6=1, K7=0, 5, K8=1) is included in the Positive category.
Narrative strategy formation in the bilinguals’ stories in Russian: corpus study

The lexical parameters of oral stories of two generations of Russian-German bilinguals and the function of lexical means in the narrative strategy implementation are describes. Through addressing the oral narrative this study is included in the range of modern works considering the features of text generation by bilinguals. The relevance of the study lies both in the material and in the chosen methods of analysis. The aim of the study is to identify the degree of the narrative strategy formation at the lexical level among representatives of two generations of Russian-German bilinguals in comparison with monolingual Russian native speakers. The research material is a corpus of experimentally collected oral stories (47 texts) of German-Russian bilinguals. M. Mayer’s book “Frog, where are you?” was used as a stimulus for subjects. The test subjects were representatives of 22 families: children - 26 people aged 8 to 19 years, parents - 21 people aged 30 to 55 years. To compare the characteristics of the corpora, the similar corpus of oral stories of monolinguals (42 stories) was involved. Quantitative and statistical analysis, corpus analysis, thematic and semantic analysis of vocabulary, elements of structural analysis of narrative were used. The novelty of the study is the corpus analysis identification of lexical parameters of narrative strategies used by two generations of bilinguals. The analysis showed that the degree of lexical skills development in the implementation of narrative strategies in bilinguals is lower than in monolinguals in both age groups, despite the statistical similarity of the texts of both corpora. This concerns, first of all, the completeness and uniformity of the lexical expression of the mandatory components of the narrative stimulated by the material, and the relations between them in the text. At the same time, the general trend of narrative creation was in line with expectations. The identified parameters can be further methodically interpreted in order to prepare educational materials for bilinguals.
A Study on Grammar Categorization and Grammar Terms in TOEIC Textbooks

This paper examines the grammar classification of the TOEIC textbooks, which classify grammar based on five types of sentence patterns and eight word classes according to traditional grammar, and proposes to analyze sentences by introducing 'complement' for essential elements and 'adjunct' for optional elements in the grammatical function category, and to classify determinative as an independent word class. This has the advantage of improving learners' understanding of sentences by making it easier to analyze sentences for obligatory and optional elements, and reducing the confusion of describing grammatical function categories with terms from word classes such as adverbs (phrases) for optional elements. In addition, by categorizing determinative as an independent word class, it is possible to explain the definite and quantitative features of determinative and indicate the finiteness and indefiniteness of noun phrases. Through the study of determinative, learners can understand the finiteness of nouns and avoid grammatical errors. Also, the grammatical explanations in the TOEIC textbooks have explanatory validity through their congruence with modern descriptive and theoretical grammars.
A Study on Conversational Context Sensitivity Development Education

This study focused on the context as the cause of communicators’ misunderstandings or insufficient grasps of events in conversations. We suggested that learners should actively and elaborately utilize the conversation context through classroom experiences dealing with conversational context. Contextualizing activity was highlighted as a constructive expressive and understanding act of learners, context sensitivity was presented as an object of the activity, and oral communication context for education was explored and displayed as a model. Continually, through conversation analysis in drama, we described characters who are and are not contextually sensitive: objective information about the interlocutor, information based on the relationship, background knowledge related to the conversation, and clues of conversation situation. Learners' doing contextualizing and cultivating context sensitivity with texts including authentic context, can help deepen their understanding of you and me, beyond understanding language and its mechanism.