Reviewerly/exHarmony huggingface dataset:
Design and implementation of innovation and entrepreneurship management system based on WEB

During the reform of talent mode in colleges and universities, to improve the level of innovation and entrepreneurship of college students, relevant training plans have begun to be promoted and implemented nationwide. Entrepreneurship for college students from the school to declare innovation project management requirements and the specific circumstances of view as a WEB and Java language as the core, the construction of basic function perfect college students' innovative entrepreneurial training information management platform, not only can satisfy the need of social development, and can also guarantee the higher education sustainable development goals in the innovation game. Therefore, on the basis of understanding the current situation of college students' innovation and entrepreneurship management, according to the research status at home and abroad, this paper discusses how to build an innovation and entrepreneurship management system with WEB as the core. Through the analysis of the system's test operation, it is found that all the functions constructed have passed the test and meet the current requirements of education management operation.
Dimensionality reduction by regularized least squares weighted discriminant projection

In this paper, a new dimensionality reduction method, named regularized least squares weighted discriminant projection (RLSWDP), is developed to process high-dimensional signal which widely exists in real-world applications. In RLSWDP, all the representation coefficients are involved for samples reconstruction to improve reconstruction accuracy. Besides, the representation coefficients of each training sample are further used for weighting the reconstructed between-class scatter and within-class scatter which can preserve structural similarity between the samples resulting in better discrimination in projection space. Thirdly, a scale factor is introduced into the within-class weighting matrix to enhance the within-class compactness. Using the idea of maximum margin criterion, the optimal subspace projection matrix is obtained by eigenvalue decomposition. Experimental results on three face datasets demonstrate that the proposed method has promising performance compared to some popular dimensionality reduction methods.
A Preliminary Study on Taiwanese OCR for Assisting Textual Database Construction from Historical Documents

Currently, there is not enough Taiwanese text available to build a proper language model (LM) to support the construction of emerging Taiwanese automatic speech recognition (ASR) and text-to-speech (TTS) systems. Therefore, this paper reports the first Taiwanese optical character recognition (OCR) [1, 2, 3] system to assist human annotators in converting a vast collection of scanned images of Taiwanese historical documents preserved in the “Memory of the Written Taiwanese” (MoWT) website [4] into a usable textual database for building state-of-the-art Taiwanese ASR and TTS systems in the future. Supplementary information and replication materials are available on GitHub [5].
Emerging properties from Bayesian Non-Parametric for multiple clustering: Application for multi-view image dataset

Artificial Intelligence (AI) in supermarkets is moving fast with the recent advances in deep learning. One important project in the retail sector is the development of AI solutions for smart stores, mainly to improve product recognition. In this paper, we present a new framework to address the multi-view image classification using multiple clustering. The proposed framework combines a pre-trained Vision Transformer with a Bayesian Non-Parametric multiple clustering. In this work, we propose an M CM C- based inference approach to learn the column-partition and the row-partitions. This method infers multiple clustering solutions and allows to find automatically the number of clusters. Our method provides interesting results on a multi-view image dataset and emphasizes, on one hand, the power of pre-trained Vision Transformers combined with the multiple clustering algorithm, on the other hand, the usefulness of the Bayesian Non-Parametric modeling, which automatically performs a model selection.
A Bokeh Image Generation Technique using Machine Learning

In photography, bokeh is the aesthetic effect appearing in out-of-focus areas. Bokeh is often used to emphasize a subject or to express a beautiful background. However, such bokeh effects can be expressed with cameras equipped with large-aperture lenses but are not easy to realize with small-aperture lenses. The main contribution of this paper is to propose a technique to generate a bokeh image from a single overall in-focus image using a machine learning approach. We propose two types of U-Net based network models, mainly composed of CNN and Transformer. In addition to using two different types of networks, a depth map obtained from the input image and a circular bokeh image are also provided to the network as auxiliary inputs in order to add the bokeh effect by lens aperture, which is difficult to reproduce by machine learning and to distinguish foreground and background accurately. The experimental results show that the proposed method produces bokeh images very close to those taken with a real camera. Furthermore, the quantitative evaluation results show that the proposed method is almost equal to or better than the state-of-the-art machine learning approaches.
Towards the Design of Locally Differential Private Hardware System for Edge Computing

A challenging issue for edge computing is how to correct meaningful information from sensor data while keeping the privacy of the data and individuals. One approach is given by the recent work [Choi et al. “Guaranteeing Local Differential Privacy on Ultra-Low-Power Systems” 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA)]. They point out that low resolution and fixed point characteristics of Ultra-Low-Power implementations may break privacy guarantees due to the low quality of noising. For overcoming this weakness, they introduce the techniques of resampling and thresholding. They also implemented in hardware to show the proposed method achieves both low overhead and high utility while keeping local differential privacy, with sensor/IoT benchmarks. Whereas, this research show some flaw in the existing work above and improve the method. We give the case in which infinite privacy loss still occurs because of low resolution even though the width of the output distribution is restricted, which was not investigated yet by the original paper of ISCA2018. Our major contribution is to propose an improvement to avoid privacy loss from low resolution and to enhance privacy protection in this circuit. Furthermore, we report the experimental results with software simulations, which guarantees the utility of our improving method. Finally, we discuss the power and limitation of our improved method with future challenging issues.
Semi-automatic Data Annotation System for Multi-Target Multi-Camera Vehicle Tracking

Multi-target multi-camera tracking (MTMCT) plays an important role in intelligent video analysis, surveillance video retrieval, and other application scenarios. Nowadays, the deep-learning-based MTMCT has been the mainstream and has achieved fascinating improvements regarding tracking accuracy and efficiency. However, according to our investigation, the lacking of datasets focusing on real-world application scenarios limits the further improvements for current learning-based MTMCT models. Specifically, the learning-based MTMCT models training by common datasets usually cannot achieve satisfactory results in real-world application scenarios. Motivated by this, this paper presents a semi-automatic data annotation system to facilitate the real-world MTMCT dataset establishment. The proposed system first employs a deep-learning-based single-camera trajectory generation method to automatically extract trajectories from surveillance videos. Subsequently, the system provides a recommendation list in the following manual cross-camera trajectory matching process. The recommendation list is generated based on side information, including camera location, timestamp relation, and background scene. In the experimental stage, extensive results further demonstrate the efficiency of the proposed system.
Cyber-Crime Detection: Experimental Techniques Comparison Analysis

Cyber-crime is one of the main problems the world face, and machine learning plays a key part in contemporary operating systems for giving better transformation in the security environment and cybercrime detection. While detecting cybercrimes is difficult, it is possible to gain advantages from machine learning to generate models to assist in predicting and detecting cybercrimes. The researchers have proven that the majority of the models can work effectively in identifying cybercrime, they can span from 70% to 90% in accuracy measuring. The objective of this research paper is to conduct experimental techniques comparison analysis for cyber-crime detection by reviewing all possible machine learning algorithms for automatic detection. The key focus of the study is on the use of eight classifiers models which are Logistic Regression (LR), Decision Tree (DT), K-nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and Multiple layer perception (MLP). From the experiment conducted, the high prediction came from MLP which is 96% accuracy of the cyber-crime methods based on existing cyber-crime data.
Augmented Reality Application for Solar System Learning

The purpose of this study is to investigate the applicability of Augmented Reality (AR) technology to solar system learning content. AR is a real-world interactive experience in which real-world items are augmented with computer-generated perceptual information and is used as the main technology for this study. AR in solar system learning provides a way to study natural phenomena of solar system that are impractical to see directly due to their location, duration, and scale in virtual 3D visualization. There are several existing problems of the previous AR solar system learning applications such as users are not allowed to interact with the virtual learning content and the users only can view the 3D animated visualization. Some existing applications were built only for users to learn the fact of the solar system without any quizzes for users to test their understanding. Besides, in the existing systems, only one marker is used for the entire solar system which is less interactive. Thus, in this study, the application was designed and developed to allow users to interact with the virtual 3D content by using touch-based inputs. Different card combinations also allow users to perform vary card interactions to expel different learning content. Also, quizzes are included to boost the users’ understanding. Preliminary testing results based on the identification of applicability of AR technology to solar system learning content showed that the proposed application could be used as an alternative to adequately identify the best method for teaching and learning process on solar system topic.
Aphasia Detection for Cantonese-Speaking and Mandarin-Speaking Patients Using Pre-Trained Language Models

Automatic analysis of aphasic speech based on speech technology has been extensively investigated in recent years, but there has been a few studies on Chinese languages. In this paper, we focus on automatic aphasia detection for Cantonese-and Mandarin-speaking patients using state-of-the-art pre-trained language models that support both traditional and simplified Chinese. Given speech transcriptions of subjects, pre-trained language models are used in two ways: 1) pre-trained language model derived embeddings followed by a classifier; 2) pre-trained language model fine-tuned for aphasia detection task. Both approaches are demonstrated to outperform baseline models using acoustic features and static word embeddings. The best accuracy is obtained with fine-tuned BERT models, achieving 0.98 and 0.94 for Cantonese-speaking and Mandarin-speaking subjects respectively. We also investigate the feasibility of applying the cross-lingual pre-trained language model fine-tuned by aphasia detection task for Cantonese-speaking subjects to Mandarin-speaking subjects with limited data. The promising results will hopefully make it possible to perform detection on those low-resource pathological speech which is difficult to implement a specific detection system.
Improving Rare Words Recognition through Homophone Extension and Unified Writing for Low-resource Cantonese Speech Recognition

Homophone characters are common in tonal syllable-based languages, such as Mandarin and Cantonese. The data-intensive end-to-end Automatic Speech Recognition (ASR) systems are more likely to mis-recognize homophone characters and rare words under low-resource settings. For the problem of low-resource Cantonese speech recognition, this paper presents a novel homophone extension method to integrate human knowledge of the homophone lexicon into the beam search decoding process with language model re-scoring. Besides, we propose an automatic unified writing method to merge the variants of Cantonese characters and standardize speech annotation guidelines, which enables more efficient utilization of labeled utterances by providing more samples for the merged characters. We empirically show that both homophone extension and unified writing improve the recognition performance significantly on both in-domain and out-of-domain test sets, with an absolute Character Error Rate (CER) decrease of around 5% and 18%.
Augmenting Graph Convolution with Distance Preserving Embedding for Improved Learning

Graph convolution incorporates topological information of a graph into learning. Message passing corresponds to traversal of a local neighborhood in classical graph algorithms. We show that incorporating additional global structures, such as shortest paths, through distance preserving embedding can improve performance. Our approach, Gavotte, significantly improves the performance of a range of popular graph neu-ral networks such as GCN, GA T,Graph SAGE, and GCNII for transductive learning. Gavotte also improves the performance of graph neural networks for full-supervised tasks, albeit to a smaller degree. As high-quality embeddings are generated by Gavotte as a by-product, we leverage clustering algorithms on these embed dings to augment the training set and introduce Gavotte+. Our results of Gavotte+ on datasets with very few labels demonstrate the advantage of augmenting graph convolution with distance preserving embedding.
Crowd Counting Method Based on Deep Neural Network

With the progress of society and the rapid development of economy, the pace of urbanization construction is promoted. The number of people flocking to cities increases sharply, and the population density keeps increasing. Therefore, crowd counting has important application value in the field of intelligent security. Aiming at the problems of background interference, serious occlusion between crowds, insufficient information of context feature extraction and huge scale change in Crowd Counting and Crowd density estimation tasks. The CAI-CAN model proposed in this paper is composed of four parts, which are front-end network, CAI Module, CA Module and back-end network. The input image is transmitted to the front-end network for primary feature extraction. CAN module performs multi-scale feature fusion on the image after feature extraction. CA module consists of two parts: coordinate information embedding and coordinate attention generation. Given the input, use two pooling kernels to encode along the horizontal and vertical coordinates. Finally, the processed features are sent to the back-end network of density generation module, and the density map is obtained through convolution. Our method is validated on Shanghaitech dataset, UCF-CC_50 dataset and JHU-Crowd ++ dataset. Experimental results show that compared with CAN and other methods, the proposed method achieves better accuracy and robustness.
Joint Low-rank and Orthogonal Deep Multi-view Subspace Clustering based on Local Fusion

In recent years, a number of multi-view clustering methods have been proposed through a global fusion paradigm. These methods take the entire sample space as the fusion object, where the global complementarity between views is explored and exploited to improve the clustering performance. However, local structures with strong or weak clustering capacity could coexist in each view. The traditional global fusion paradigm ignores the differences in clustering capacity of local structures, which makes it impossible to explore and exploit local complementarity between views. In this paper, a novel deep multi view subspace clustering method based on local fusion is proposed to solve this problem. First, a low rank self-expression layer is inserted into the deep autoencoder to eliminate the influence of noises when obtaining local cluster structure. Then, the fusion object is refined from the entire sample space to the local cluster structure, where a self-weighted strategy is designed to assign contribution weight according to the clustering capacity of the local cluster structure. Meanwhile, we joint orthogonal constraint to enhance the discriminative of local cluster structure that is more suitable for downstream clustering task. Experiments on several real-world datasets show that the proposed method achieves better clustering performance than most traditional multi-view clustering methods based on global fusion.
Feature Selection and Recognition Based on SAR Image

Synthetic Aperture Radar (SAR) image feature selecting can effectively reduce the data dimension of subsequent processing through feature extraction and selection based on a small number of sample images, thereby reduce the complexity of subsequent processing and the workload in the process of feature extraction for a large number of images. In this paper, a method composed of feature difference analysis and Relief algorithm is proposed to select the features. The proposed method is called D-Relief method. Then support vector machine (SVM) is utilized to verify the influence of different feature selection methods on the classification results. The experimental results show that the proposed D-Relief method can further reduce the dimension of feature vector and ensure the performance of classifier.
Interactive Multimedia For Promoting Cultural Heritage Tourism In Penang

Penang, also referred as the "Pearl of the Orient", is one of the most well-known tourist destinations in Malaysia due to its unique cultural, architectural properties and diversity range of heritage arts and crafts. Research revealed that digital technologies in today’s world are almost needed for presenting all types of cultural heritage and boosting the tourism sector especially after the imposition of Covid-19 anti-epidermic measures, resulting in the significant loss of tourists. The main aim of this research project is to promote cultural heritage tourism in Penang with the use of interactive multimedia tools. The methodological approach is formed through the examination and evaluation of a comprehensive reference and an online survey that distributed to the visitors in Penang to discuss the current issues, challenges, and the solutions in promoting the cultural heritage tourism in Penang. This is a concept paper that explores the benefits and methods to develop an interactive website that includes 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> virtual tours, detailed travel guides, animation for introducing the histories of the heritage building and video for promoting the cultural heritage tourism in Penang.
Transparent Latent Space Counterfactual Explanations for Tabular Data

Artificial Intelligence decision-making systems have dramatically increased their predictive performance in recent years, beating humans in many different specific tasks. However, with increased performance has come an increase in the complexity of the black-box models adopted by the AI systems, making them entirely obscure for the decision process adopted. Explainable AI is a field that seeks to make AI decisions more transparent by producing explanations. In this paper, we propose T-LACE, an approach able to retrieve post-hoc counterfactual explanations for a given pre-trained black-box model. T-LACE exploits the similarity and linearity proprieties of a custom-created transparent latent space to build reliable counterfactual explanations. We tested T-LACE on several tabular datasets and provided qualitative evaluations of the generated explanations in terms of similarity, robustness, and diversity. Comparative analysis against various state-of-the-art counterfactual explanation methods shows the higher effectiveness of our approach.
An Evaluation Method of Traffic Radar Tracking Performance in No-truth System

Aiming to solve the problem of tracking performance evaluation of traffic radar, an evaluation method without a truth system is proposed. Firstly, observe the traffic radar data and divide the track into unique ID track and three types of non- ID unique track. Unique ID track refers to the trajectory of a target corresponding to a trajectory. We analyze and propose the corresponding type recognition strategies. Then four performance indexes are established based on different track types to evaluate the tracking performance of traffic radar. Finally, through the verification of the radar data collected on the highway, this method can evaluate the tracking performance of the radar accurately and effectively when real road driving information cannot be obtained.
Design of Radar Display System Based on Human-computer Interaction Interface

Because the man-made natural operation control methods such as voice or gesture are prone to ambiguity or more complicated codec, in the practical exploration, scientific researchers have proposed a human-computer interaction interface, which is mainly combined with the operation method of human-computer cooperation to realize man-machine Interactive. This interface design has a positive effect on the radar display system and helps to present more effective application modules during system operation. This paper mainly analyzes how to use MATLAB software in radar system simulation. Among them, GUI human-computer interaction interface tool realizes simulation analysis of signal processing and power calculation in the overall design of radar. The final experimental results prove that this simulation software interface is convenient and reliable, and it can build a more excellent design platform for the work of the overall radar designer.
An Implementation of a 3D Image Filter for Motion Vector Generation on an FPGA Board

Object detection and tracking technology are essential in many ways with the increasing demand for remote video conferencing and video surveillance. This paper focuses on a method that combines an object detection method based on a CNN (Convolutional Neural Network) with a motion-vector-based object tracking method in specified frames instead of the whole frame stream. Although this method can improve the throughput with a small memory requirement, it requires 3D image filters to reduce the noise in each motion vector. The immense computing power needed for 3D image filters prevents this method from being popularly used. To address this problem, we propose to implement the technique on an FPGA and offload the filters and CNN engine on the FPGA hardwired logic. We implemented slide window-based image filters on a Zynq Ultrascale+ FPGA board and achieved 23.42 times performance improvement for the median filter and 55.46 times for the average filter, respectively. Considering the resource usage, it appeared that we could implement these 3D image filters design with YOLO on the target FPGA.