Reviewerly/exHarmony huggingface dataset:
A Systematic Review of Neural Network Autoregressive Model with Exogenous Input for Solar Radiation Prediction Modelling Development

Neural Network is one of the Machine Learning methods that has been applied in various Artificial Intelligence system development including solar radiation prediction modelling. Since there are multiple approaches had been developed using the Neural Network method, the study has been focusing on the development of a Multi-layer Neural Network model that can handle non-linearities and highly dynamic data. The integration of the Multi-layer Neural Network and the Non-linear Autoregressive Model with Exogenous Input (NARX) developed a compromising non-linear Neural Network model which can be applied in the modelling of solar radiation. This paper develops a systematic review of the Neural Network Autoregressive Model with Exogenous Input (NNARX) for solar radiation prediction modelling starts from the architecture and the comparative selection for the Training Function. The model is developed and analysed using MATLAB R2019a software. Results showed that the Levenberg-Marquardt Training Function performed better with the R2 value of 0.94 for training and 0.91 for testing, making it the most suitable for the NNARX in the development of solar radiation prediction modelling.
Improved Neural Network LADRC for Sensorless Control of PMSM

Active disturbance rejection control (ADRC) has been widely used in permanent magnet synchronous motor control because of its superior interference feedforward compensation performance since it was proposed. However, due to the inaccuracy of the motor parameter estimation and its change with the working state, when the motor is running at low speed, the fixed parameter ADRC strikes a balance between dynamic performance and noise filtering, so it will produce excessive speed fluctuations. To this end, the neural network is introduced into the linear active disturbance rejection control (LADRC), and four states are selected as the inputs of the neural network, and two output values are assigned to the LADRC as system parameters. The use of the dynamic parameters can improve the robustness of the control system, reduce steady-state speed fluctuations, and reduce reliability problems in complex scenarios. Then the effectiveness of the proposed control method is verified by simulation. Finally, the proposed control method is applied to the sensorless control of the high frequency square wave injection mode of permanent magnet synchronous motor, which reduces the speed fluctuation at low speed.
A Keyword Extraction Method for Traditional Chinese Medicine Constitution Identification Based on Batch Data Hybrid Sampling

Keyword extraction for traditional Chinese medicine (TCM) constitution identification refers to using keyword extraction methods to extract critical symptoms from task corpora to achieve the TCM constitution identification task. In this paper, we constructed a dataset for keyword extraction in TCM constitution identification and implemented keyword extraction using sequence labeling methods. However, we discovered an issue of uneven category distribution during the keyword extraction process, which affected the model's performance. To address this problem, we propose a model training method based on batch data hybrid sampling algorithm. This method divides the training dataset into several batches. It strategically balances the ratio of common and rare category data within each batch, achieving an even distribution of data categories within each training batch and reducing the impact of insufficient learning on rare categories by the model. Experimental results demonstrate that our proposed method improves the performance of keyword extraction for TCM constitution identification, with an average F1 score improvement of 4.29% for rare category keyword extraction.
Improved Intelligent Ledger Construction for Realistic IoT Blockchain Networks

Scalability is essential for next-generation blockchain technology to integrate with large mobile networks like Internet of Things (IoT). The IOTA distributed ledger protocol has combined transaction generation and verification to address this, but at the expense of increased reliance on connectivity to resolve conflicts with a novel ledger data structure. Intelligent Ledger Construction (ILC), was proposed as an auditable lightweight reinforcement-learning scheme to address this constraint with proposal of local conflict resolution with machine-learning classification. This effort presents an improved reliability reward model to enhance training for ILC and further reduce adversarial gaming and resource usage. Testing this revision in an idealistic setting showed vast improvement in transaction throughput efficiency and delay while also reducing unapproved transactions by 60% in a realistic IoT-like network. Future work will improve auditability of the underlying intelligence for stronger security.
Mandarin Chinese translation of the Artificial-Social-Agent questionnaire instrument for evaluating human-agent interaction

The Artificial-Social-Agent (ASA) questionnaire is an instrument for evaluating human-ASA interaction. It consists of 19 constructs and related dimensions measured by either 24 questionnaire items (short version) or 90 questionnaire items (long version). The questionnaire was built and validated by a research community effort to make evaluation results more comparable between agents and findings more generalizable. The current questionnaire is in English, which limits its use to only a population with an adequate command of the English language. Translating the questionnaire into more languages allows for the inclusion of other populations and the possibility of comparing them. Therefore, this paper presents a Mandarin Chinese translation of the questionnaire. After three construction cycles that included forward and backward translation, we gave both the final version of the translated and original English questionnaire to 242 bilingual crowd-workers to evaluate 14 ASAs. Results show on average a good level of correlation on the construct/dimension level (ICC M = 0.79, SD = 0.09, range [0.61, 0.95]) and on the item level (ICC M = 0.62, SD = 0.14, range [0.19, 0.92]) between the two languages for the long version, and for the short version (ICC M = 0.66, SD = 0.12, range [0.41, 0.92]). The analysis also established correction values for converting questionnaire item scores between Chinese and English questionnaires. Moreover, we also found systematic differences in English questionnaire scores between the bilingual sample and a previously collected mixed-international English-speaking sample. We hope this and the Chinese questionnaire translation will motivate researchers to study human-ASA interaction among a Chinese literate population and to study cultural similarities and differences in this area.
Delve deep into End-To-End Automatic Speech Recognition Models

Automatic Speech Recognition (ASR) has experienced significant advancements in recent years, with end-to-end approaches emerging as a promising paradigm shift. Unlike traditional ASR systems that rely on a pipeline of separate components, end-to-end models aim to directly transcribe speech inputs into text using deep learning architectures. In this paper, we conduct a comprehensive study on end-to-end ASR models. We review various architectures employed in end-to-end ASR, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models. We explore different training methodologies, loss functions, and optimization algorithms used in end-to-end ASR. Additionally, we discuss large-scale datasets commonly used for training and evaluate the performance of end-to-end models using established evaluation metrics such as word error rate (WER). Furthermore, we analyze the strengths and weaknesses of end-to-end ASR models, highlight their applications in real-world scenarios, and discuss open challenges and future directions. By providing this comprehensive study, we aim to facilitate a deeper understanding of end-to-end ASR models and their potential for driving advancements in speech recognition technology.
Systematic Literature Review of Data Distribution in Preprocessing Stage with Focus on Outliers

Data Preprocessing refers to the steps and techniques applied to raw data before it is ready to be analyzed or modeled as a substantive part of the data flow and aims to transform, clean and organize data in a revised way for the quality, relevance and efficiency of subsequent data analysis tasks. Handling outliers in the N2O Emissions Dataset, Fertilizer Prediction and Crop Yield Prediction Dataset is an important step in the data analysis process. The approach taken will depend on the specific context and purpose of the analysis, and it is important to carefully consider the impact of outliers on the results. Using the methods discussed researchers and analysts can effectively identify and treat outliers in the N2O Emissions Dataset, Fertilizer Prediction, Crop Yield Prediction Dataset, and produce more accurate and reliable results. Implemented a systematic literature that involved searching for articles published from 2015 to 2023 for review. The quality of the existing studies used the assessment criteria of 50 relevant studies identified as having been conducted following systematic literature guidelines.
Linear Time Non-Local Cost Aggregation on Complementary Spatial Tree Structures

Studies on many dense correspondence tasks in the field of computer vision attempt to find spatially smooth results. A typical way to solve these problems is by smoothing the matching costs using edge-preserving filters. However, local filters generate locally optimal results, in that they only take the costs over a small support window into account, and non-local filters based on a minimum spanning tree (MST) tend to overuse the piece-wise constant assumption. In this paper, we propose a linear time non-local cost aggregation method based on two complementary spatial tree structures. The geodesic distances in both the spatial and intensity spaces along the tree structures are used to evaluate the similarity of pixels, and the final aggregated cost is the sum of the outputs from these two trees. The filtering output of a pixel on each tree can be obtained by recursively aggregating the costs along eight sub-trees with linear time complexity. The only difference between the filtering procedures on these two spatial tree structures is the order of the filtering. Experimental results in optical flow estimation and stereo matching on the Middlebury and KITTI datasets demonstrate the effectiveness and efficiency of our method. It turns out that our method outperforms typical non-local filters based on the MST in cost aggregation. Moreover, a comparison of handcrafted features and deep features learned by convolutional neural networks (CNNs) in calculating the matching cost is also provided. The code will be available soon.
Bat-like Sonar Target Distance Estimation Based on Fractional Fourier Transform

To address the distance estimation problem in bat-like target localization, the paper investigates and adopts the fractional-order Fourier transform method to estimate the target object distance based on the linear frequency modulation characteristics of the transmitting wave. Firstly, a data acquisition platform is built based on the long-eared bat ear model and a mobile robot to collect the reflected acoustic waves of the target in multiple spatial orientations, then the transmitting wave of each acoustic signal and the audio signal containing the echo are intercepted as the input, and finally the fractional-order Fourier transform method is used to achieve the target distance estimation. The experimental results show that the error of the test set is less than 30cm, The RMSE is 17.22cm, and the distance estimation accuracy is related to the distance of the target and the size of the pitch angle, the former influence is more significant.
Lower bound on operation time of composite quantum gates robust against pulse length error

Abstract Precise control of quantum systems is a cornerstone for realizing high-quality quantum technology such as quantum computing and quantum communication. The performance of control of systems often deteriorates due to systematic errors. In one-qubit control, the pulse length error (PLE) is a typical systematic error, which is often caused by deviation of the strength of the control field. A composite quantum gate (CQG) is a method for suppressing effects of such systematic errors at the cost of a long operation time. A longer operation time implies stronger decoherence, and thus a shorter CQG is preferable from the viewpoint of noise immunity. However, it has not been clear how short CQG can be implemented. This problem can be regarded as an optimization problem under constraints: optimizing the operation time while requiring the error robustness. In this paper, we find a lower bound on operation time of all CQGs with first-order robustness against the PLE, in which effects of the error are eliminated up to its first order. The derivation of this bound is based on a geometric property of robustness against the PLE. This can be used for search after high-performance CQGs.
Gesture Recognition and Hand Tracking for Anti-Counterfeit Palmvein Recognition

At present, COVID-19 is posing a serious threat to global human health. The features of hand veins in infrared environments have many advantages, including non-contact acquisition, security, privacy, etc., which can remarkably reduce the risks of COVID-19. Therefore, this paper builds an interactive system, which can recognize hand gestures and track hands for palmvein recognition in infrared environments. The gesture contours are extracted and input into an improved convolutional neural network for gesture recognition. The hand is tracked based on key point detection. Because the hand gesture commands are randomly generated and the hand vein features are extracted from the infrared environment, the anti-counterfeiting performance is obviously improved. In addition, hand tracking is conducted after gesture recognition, which prevents the escape of the hand from the camera view range, so it ensures that the hand used for palmvein recognition is identical to the hand used during gesture recognition. The experimental results show that the proposed gesture recognition method performs satisfactorily on our dataset, and the hand tracking method has good robustness.
Perspective Games

We introduce and study perspective games , which model multi-agent systems in which agents can view only the parts of the system that they own. As in standard multi-player turn-based games, the vertices of the game graph are partitioned among the players. Starting from an initial vertex, the players jointly generate a computation, with each player deciding the successor vertex whenever the generated computation reaches a vertex she owns. A perspective strategy for a player depends only on the history of visits in her vertices. Thus, unlike observation-based models of partial visibility, where uncertainty is longitudinal – players partially observe all vertices in the history, uncertainty in the perspective model is transverse – players fully observe part of the vertices in the history. We consider deterministic and probabilistic perspective games, with structural (e.g., Büchi or parity) and behavioral (e.g., LTL formulas) winning conditions. For these settings, we study the theoretical properties of the game as well as the decidability and complexity of the problem of deciding whether a player has a winning perspective strategy, in terms of both the game graph and the objectives. We compare perspective strategies with memoryless ones, and study an extension of the temporal logic ATL ⋆ with path quantifiers that capture perspective and memoryless strategies.
Blockchain combined with Federated Learning technology research

Federated learning has garnered significant attention and been widely explored in various domains as a means to train models while preserving data privacy through local data sharing. However, several challenges persist within federated learning, including privacy information leakage, the reliability of uploading model parameters to servers, and communication costs. In recent years, researchers and practitioners have made substantial progress in addressing these challenges by integrating blockchain technology with federated learning. This fusion has resulted in enhanced data privacy protection, incentivized data sharing, and improved verification accountability. To provide a comprehensive understanding of the latest research findings and development trends in blockchain-based federated learning, this review paper aims to present a detailed analysis. Initially, the paper examines the inherent issues of federated learning and identifies potential security and privacy attacks that blockchain can mitigate. Furthermore, the distinct characteristics of blockchain-based federated learning are elucidated. Subsequently, an in-depth exploration of state-of-the-art approaches in blockchain-based federated learning is conducted, focusing on security and privacy mechanisms, recording and rewarding systems, as well as verification and accountability protocols. Additionally, the paper delves into pertinent topics concerning the integration of blockchain and federated learning, addressing associated challenges and considerations.
METHODOLOGICAL BASIS OF PROFESSIONAL PREPARATION OF TEACHING STAFF OF HIGHER EDUCATION INSTITUTIONS

The work examines the essence of the concept of "professional readiness", its role for the successful professional activity of future teaching staff, substantiated meaningful components of professional readiness, defined individual-personal qualities and
Fighting Against Hate Speech: A Case for Harnessing Interactive Digital Counter-Narratives

This paper delves into the pressing issue of hate speech (HS) by examining the potential of Interactive Digital Narratives (IDN) to develop Interactive Digital Counter-Narratives (IDCN), highlighting the limited research in this area. Drawing inspiration from two cases studies and other examples from the literature, the article explores the unique ways digital media have been used to address HS through counter-narratives. By incorporating principles of decolonial thinking and Critical Race Theory, IDCNs can leverage emerging formats like video games and VR/AR/XR to counter hate speech effectively. Pointing out the inadequacy of hate-speech laws in combating covert hate speech, the paper argues that interactive counter-narratives offer a powerful means to challenge this complex societal phenomenon. According to several studies on HS, counter-narratives may preserve freedom of speech, debunk stereotypes, encourage mutual understanding, and facilitate dialogue to de-escalate conversations.
Knowledge-aware progressive clustering for social image

Abstract Social image data refer to the annotated image with tags in social media, in which the tags are always labeled by users. Integrating the visual and textual information of social image can obtain accurate and comprehensive feature and improve clustering performance. However, the heterogeneous gap between tags and images makes it difficult to reasonably organize the social images. In addition, the tags are often sparse and incomplete due to personal preference and cognition differences of users. To solve these problems, we propose a novel knowledge-aware progressive clustering (KAPC) method, which employs human knowledge to guide the cross-modal clustering of social images. Firstly, we design a dual-similarity semantic expansion strategy to complement the sparse tags with human knowledge, which constructs a more complete semantic similarity matrix for tags through knowledge graphs. Secondly, we define an objective function based on information theory to bridge the heterogeneous gap, which align inter-modal cluster distribution to explore the correlation between visual and textual information. Finally, a progressive iteration method is designed to make the two modalities guide each other and obtain better performance of social image clustering. Extensive experiments on four social image datasets verify the effectiveness of the proposed KAPC method.
Outdated or Not? A Case Study of How 3D Desktop VR Is Accepted Today

Virtual reality (VR) is believed to be a beneficial medium for teaching and learning. While many researchers are pursuing more advanced VR technologies, devices, or systems to provide users or learners with more immersive and interactive VR learning experiences, many people have not even experienced some basic VR systems. In this paper, we investigated a straightforward question, “How do learners perceive their learning experience in a legacy VR system?” using OpenSimulator to host a VR online orientation. The results indicate that it was many participants’ first-time experiencing VR. Even if the VR system we used might seem outdated using today’s criteria, most enjoyed it very much and felt immersed in the VR world. The results implied that, though advanced immersive VR technology provides a better experience, a legacy VR system such as OpenSimulator may still be helpful in particular learning activities for specific learning populations.
School Adaptation Virtual Reality Simulation for Preschoolers: Implementation and Formative Evaluation

Preschoolers’ entrance to elementary school marks a major change in their lives, which may cause difficulties in adjusting to school, such as anxiety and refusal to attend. Accordingly, there is a need for school adaptation content for preschoolers before entering school. We developed a VR game that provides a classroom experience simulation with four different tasks for preschoolers before entering school. We conducted a formative evaluation on the game with 16 participants (5 males, 11 females, mean age: 22.25) of different ages to measure usability, presence and cybersickness. The results indicated that the game has adequate usability (SUS score: 80.88/90; PSSUQ mean: 3.44), with some issues related to quality of instructions, clarity of the user interface, and technical issues. Moreover, most of the participants were able to sense presence (involvement mean: 5.87; adaptation/immersion mean: 6.16), but 43.7% of them reported symptoms of cybersickness. The results of the study play a pioneering role in future work on development of immersive school adaption interventions. Therefore, the study can be of interest to researchers, developers and teachers who are interested in the area.
Analyzing Audience Comments: Improving Interactive Narrative with ChatGPT

This paper presents a novel method that utilizes ChatGPT for the categorization of audience comments in game live streams, treating it as a zero-shot task. Audience participation games have gained significant popularity in the realm of game live streaming, playing a vital role in game promotion and audience engagement. Streamers employ various techniques such as storytelling and interactive narrative to cultivate a larger fan base and enhance the value of their streams. Simultaneously, the audience generates diverse comments that directly impact the streamer’s interactive narrative and storytelling. However, the traditional methods for comment analysis in game live streams are lacking in terms of speed and cost-effectiveness. Therefore, our aim is to investigate whether ChatGPT can fulfill these requirements. Through experimental evaluation, our results indicate a majority choice of 54.34% and a human choice of 82.61%, showcasing that ChatGPT, when employed with suitable prompts, can address the aforementioned need.