Reviewerly/exHarmony huggingface dataset:
Research on the strategies of promoting the development of teachers' professional ability through intelligent training

With the CPC Central Committee and The State Council attaching great importance to teaching and research work and the introduction of relevant policy documents on the use of new technologies to innovate teaching and research work methods, the form of teachers' teaching and research has undergone earth-shaking changes. The new generation of intelligent technology promotes the new development of teaching and research, and the teaching and research work in the field of basic education across the country has tried to explore the new form of teachers' teaching and research work in the network environment, and the intelligent research and training platform has emerged. The strategy study case of promoting the development of teachers' professional ability by intelligent training is selected from the third batch of "Intelligent training platform application pilot work school" - Guiyang Qinghua Middle School. The eighth grade Chinese team (11 teachers) of the school took "The skill of killing cattle" as an example to carry out research and training activities, explaining its application and practice process -- using the platform to carry out data tracking of the whole process of preparation, grinding, evaluation, discussion, practice and thinking of lessons, helping teachers to promote the progress of teaching and research activities from "Internet" to "informatization" and then to "intelligence". Provide reference for achieving excellent development of professional competence.
Logistic Regression and Maximum Entropy Model

This chapter first introduces the logistic regressionLogistic regression model, then the maximum entropy modelMaximum Entropy Model, and finally describes the learning algorithms for logistic regressionLogistic regression and maximum entropy models, including the improved iterative scaling algorithm and the Quasi-Newton method.
Clustering

This chapter introduces the basic concepts of the clustering, and two most commonly used clustering algorithms: hierarchical clustering and k-means clustering.
VEHICLE CLASSIFICATION IN URBAN REGIONS OF THE GLOBAL SOUTH FROM AERIAL IMAGERY

Abstract. Land transport is a major contributor to the human-caused climate change; knowing the total number and composition of the vehicle fleet is key for estimating its emissions. Especially for countries of the Global South, emission inventories are associated with high uncertainties because fleet data are often unknown or outdated – classifying vehicles on remote sensing has the potential to change this. We present the XWHEEL dataset based on annotated vehicles in aerial images with six classes depending on the number of wheels, size and motorization. The dataset consists of 73 annotated aerial images of the city of Dar es Salaam (Tanzania) with 15,973 vehicles. To analyze the performance of the dataset, a convolutional neural network, ReDet, and a transformer-based neural network, DINOOBB, are trained with different configurations and validated on the validation and test split, but also on aerial images from other regions. The transformer-based DINO architecture has been adapted to the remote sensing domain and modified to predict Oriented Bounding Boxes. Results show a good performance on the test split from Dar es Salaam, when the two-wheeled classes are merged and the non-motorized three-wheeled vehicles are excluded due to their rare occurrence. The best performing algorithm configurations with four classes were then tested on aerial images of Kathmandu (Nepal) and Kampala (Uganda). The performance drops for cycles and three-wheeled vehicles, as their appearance varies between countries. A main finding is that we can reliably detect the different vehicle classes in Dar es Salaam. When algorithms trained on XWHEEL are generalized to other regions of the Global South, performance decreases for the more difficult classes (bicycles and tricycles). To obtain results that are comparable across the board, we therefore recommend expanding the dataset with additional annotations from other regions of the Global South.
Analysis of Volkswagen’s Diesel Performance Scandal

The research aim of this study is to investigate the reputation risk of the Volkswagen’s diesel performance scandal. Many Volkswagen cars being sold in America were found to have a “defeat device”—or software—in diesel engines that could detect when they were being tested, changing the performance accordingly to improve results. This diesel performance scandal is a deliberate fraud on customers with the purpose of improving sales volumes, which brings reputation risk for Volkswagen. This study uses case study method to make analysis of this event and reputation risk. It is structured as the following: firstly, background and Volkswagen’s fraud scandal event is described. Then, the concept of reputation risk is introduced. Next, Volkswagen’s case is relevant to reputation risk, why the scandal happens, why Volkswagen’s fraud is hidden and solutions that Volkswagen provides for the fraud event are discussed. Finally, several recommendations are provided for Volkswagen’s fraud event.
Improving Signal Signal-to -Noise Ratio (SNR) for Quantum Readout Signals Using Adaptive Hardware Filters on FPGA

This research presents an innovative approach to improving theSignalSignal-to -Noise Ratio (SNR) in superconducting quantumcomputing readout signals, utilizing adaptive filtering on thecontrol board. Our solution FPGA FPGA-based aims to bolsterquantum computations' reliability by providing a more accurateand precise way to obtain our readout traces and achieve ahigher discernability between excitation states in real real-time.
A Question-Answering System for Vietnamese Public Administrative Services

In the realm of legal question-answering (QA) systems, information retrieval (IR) plays a pivotal role. Despite thorough research in numerous languages, the Vietnamese research community has shown limited interest in legal information retrieval, particularly in the context of public administrative services. In this paper, we propose the development of a QA system tailored to the Vietnamese language, specifically focusing on the domain of public administrative services. Our system provides legal-based responses, and it is built upon a combination of retrieval and re-ranking techniques. We employ both lexical-based and semantic-based retrieval models and integrate them to create the final model. Our research shows that the system outperforms existing models in retrieving public administrative information and answering questions related to Vietnamese legal documents.
Integrating Multiple Models For Effective Video Retrieval and Multi-stage Search

Video is one of the most prevalent forms of data due to the widespread availability of recording devices. This makes video retrieval systems essential since they assist in locating a video segment within a dataset that most closely matches a given query. One of the difficulties of video querying is the processing of multimedia data (images, audio, and text). In addition, it is important to integrate temporal information, as inquiries frequently pertain to the depiction of events occurring within a specific time frame. Thus, this study introduces an innovative system capable of not only integrating various types of models but also effectively managing temporal searches through multi-stage processes. The efficacy of the systems was demonstrated at the AI Challenge 2023, which took place in Ho Chi Minh City, where our team got the best accuracy across all other contestants in the qualifying phase and received the top 1 position among the 60 participating teams.
RAC-SAC: An improved Actor-Critic algorithm for Continuous Multi-task manipulation on Robot Arm Control

Controlling a robot arm in a complex environment is a challenging task. In this problem, the robot arm interacts with objects of uncertain shapes. To move an object to a required position where it can be grasped, the robot arm needs the ability to learn autonomously. It must be agile and intelligent enough to identify interaction points with the object, move it, and rotate it in a sensible direction without prior learning in complex environmental conditions.In this paper, we propose Deep Reinforcement Learning approach to tackle this problem, utilizing model-free method called Soft Actor-Critic (SAC) and Realistic Actor Critic (RAC). To enhance accuracy and expedite the learning process, we fine-tune the model by redesigning the reward function to enable the agent to learn more effectively. Additionally, we combine it with methods such as Hindsight Experience Replay (HER), Relay Hindsight Experience Replay (RHER), and Projected Conflicting Gradients (PCGrad) to increase stability in executing sequential actions over extended periods.The experimental results were simulated on a Kuka 7-degree-of-freedom robot arm. The results demonstrate that the proposed method achieves stable and faster convergence with an accuracy of up to 87-97% when pushing objects such as cubes and scissors. Comparative results also indicate that the proposed method is more effective than previous state-of-the-art methods. Additionally, we have built a framework that supports various RL algorithms and can be applied to different objects when addressing the problem.
Learning Algorithm for LesserDNN, a DNN with Quantized Weights

This paper presents LesserDNN, a model that uses a set of floating-point values {-1.0, -0.5, -0.25, -0.125, -0.0625, 0.0625, 0.125, 0.25, 0.5, 1.0} as quantized weights, and a new learning algorithm for the proposed model. In previous studies on deep neural networks (DNNs) with quantized weights, because DNNs employ the gradient descent method as their learning algorithm, quantized weights were applied only during the inference stage. Due to differentiability properties, quantized weights cannot be used when the gradient descent method is applied during training. To address this issue, we devised an algorithm based on simulated annealing. Since simulated annealing has no differentiability requirements, LesserDNN can utilize quantized weights during training. With the use of quantized weights and this simulated annealing-based algorithm, the learning process becomes a combinatorial problem. The proposed algorithm was applied to train networks on MNIST handwriting datasets. The tested models were trained with the simulated annealing-based algorithm and quantized weights, achieving the same level of accuracy as gradient descent-based comparison methods. Thus, LesserDNN has a simple design and small implementation scale because backpropagation is not applied. Moreover, this model achieves a high accuracy.
Strategic Improvements of SqueezeSegV2 for Road-Scene Semantic Segmentation Using 3D LiDAR Point Cloud

Semantic segmentation of LiDAR point clouds for road-scene analysis in autonomous vehicles and driver assistance systems is a challenging task due to the confusion of categories and the sparse distribution of point clouds, thus leading low performance. In this paper, we propose two important improvements to SqueezeSegV2, a deep encoder-decoder neural network, to improve the overall performance of semantic segmentation. The first improvement is the adaptive Fire module, which can be configured to be lightweight or accurate, depending on the service and application requirements. The second one is the steady Fire Deconvolution module, which boosts the accuracy of the segmentation mask reconstruction. Remarkably, both modules are improved by apply manipulating the combination of symmetric and asymmetric grouped convolution with dilation rate to enhance the contextual learning efficiency of the deep model. We evaluate our proposed methods on the Panda dataset and show that they achieve better segmentation accuracy than the original SqueezeSegV2 model by mean accuracy and mean IoU, while also reducing the number of trainable parameters by around .
Next Generation 3D Face Models

course Share on Next Generation 3D Face Models Author: Prashanth Chandran View Profile Authors Info & Claims SA '23: SIGGRAPH Asia 2023 CoursesDecember 2023Article No.: 11Pages 1–144https://doi.org/10.1145/3610538.3614649Published:06 December 2023Publication History 0citation148DownloadsMetricsTotal Citations0Total Downloads148Last 12 Months148Last 6 weeks52 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Machine Learning &amp; Neural Networks

Use and development of computer systems that are able to learn and adapt without following explicit instructions by using algorithms and statistical models to analyze and draw inferences from patterns in data.
FORECASTING SOLAR RADIATION IN BRAZILIAN CITIES USING A UNIFIED MULTILAYER PERCEPTRON MODEL

This comprehensive study investigates the utility of a unified Multilayer Perceptron (MLP) for 1-hour solar radiation forecasting in four significant Brazilian cities: Brasília, Salvador, Manaus, and Porto Alegre. The study's diverse geographical locations ensure a comprehensive evaluation of the MLP model's predictive performance under varying climatic conditions. The unified MLP model exhibited successful performance across all cities, showcasing its adaptability and versatility, with an average MAE of 174.59, Pearson correlation above 0.92, and R² above 0.8. These results offer valuable insights for integrating advanced AI techniques into renewable energy applications, contributing to the sustainable development of solar energy systems.
TextCheater: A Query-Efficient Textual Adversarial Attack in the Hard-Label Setting

Designing a query-efficient attack strategy to generate high-quality adversarial examples under the hard-label black-box setting is a fundamental yet challenging problem, especially in natural language processing (NLP). The process of searching for adversarial examples has many uncertainties (e.g., an unknown impact on the target model's prediction of the added perturbation) when confidence scores cannot be accessed, which must be compensated for with a large number of queries. To address this issue, we propose TextCheater, a decision-based metaheuristic search method that performs a query-efficient textual adversarial attack task by prohibiting invalid searches. The strategies of multiple initialization points and Tabu search are also introduced to keep the search process from falling into a local optimum. We apply our approach to three state-of-the-art language models (i.e., BERT, wordLSTM, and wordCNN) across six benchmark datasets and eight real-world commercial sentiment analysis platforms/models. Furthermore, we evaluate the Robustly optimized BERT pretraining Approach (RoBERTa) and models that enhance their robustness by adversarial training on toxicity detection and text classification tasks. The results demonstrate that our method minimizes the number of queries required for crafting plausible adversarial text while outperforming existing attack methods in the attack success rate, fluency of output sentences, and similarity between the original text and its adversary.
A Comparative Study of Tools for Explicit Content Detection in Images

Cyberworlds offer a vast quantity of knowledge and services on all topics for Internet users. The protection of children is an important issue on Internet and could be solved by detecting automatically explicit content. Another application is to facilitate digital forensic experts when analyzing media such as hard drives to detect child pornography content in criminal affairs. In this work, we focus on images and we study the efficiency of existing methods from the literature mainly based on machine learning and deep learning approaches. We apply a rigorous protocol with significant datasets in order to draw conclusions on the performance we can expect in real conditions. This study shows that this task is not really solved by existing tools. Moreover, the frontier of explicit content is also not always easy to define.
SSE-YOLO : A Lighter and Faster Object Detection Network for Small Targets in Sonar Images

Due to the small size of the targets in sonar images, state-of-the-art target detection algorithms hardly make adaptable improvements to the sonar image. This paper proposes a lightweight target detection network, namely SSE-YOLO, to meet the premise of real-time fast detection of underwater targets under equipment computing power limitations. This improved model designs a lightweight feature extraction module SSE based on YOLOV3 to build a new lightweight backbone network. In order to enhance small target characterization, a new YOLO prediction layer is constructed by fusing more shallow features with deeper features. Moreover, the convolutional attention mechanism module CBAM is used in the neck network to improve the anti-background interference capability by strengthening the detection target features. In particular, a dataset of more than 17,000 sonar images containing three types of targets in a water tank experiment was built. Ultimately, compared with classic lightweight networks, the proposed network has the best performance in the dataset. Results illustrate the effectiveness of the proposed network, achieving a satisfactory balance between performance and consumption, which satisfies the requirements of high accuracy, small model size, and real-time.
Machine Vision Algorithms for a Scaled Autonomous Vehicles

The challenge of achieving vision in autonomous vehicles through traditional processing has been a topic of development for numerous years. To improve accuracy in depth estimation, expensive sensing devices have been introduced. This project aims to perform vision-related tasks, including segmentation and lane identification, using only a conventional monochrome stereoscopic camera and a Raspberry Pi 3b+ computing device. By executing all relevant processing operations directly on the Raspberry Pi, this approach demonstrates that accurate visual perception for autonomous vehicles can potentially be achieved using affordable and accessible computing hardware.
Feature Selection in Machine Learning for Cross-Project Software Defect Number Prediction

Although many studies have investigated the usage of machine learning (ML) algorithms for cross-project software defect prediction, limited works addressed cross-project defect number prediction (CPDNP). This study investigated the robustness of feature selection in five ML algorithms, such as Decision Trees, Random Forests, Gradient Boosted Trees, Generalized Linear Model, and Deep Neural Networks, for CPDNP. The results showed that Gradient Boosted Tree generated models with the lowest errors in most projects. However, models generated by Random Forests and Deep Neural Networks with feature selection were considered the most robust ones. Meanwhile, models generated by Decision Trees were the least robust. The feature selection was sufficient to generate robust ML models for CPDNP.
Template-based design optimization for multiple pairing-friendly curve parameters

Pairing operation is the basis of most protocols of advanced functional cryptography. Major problems of pairing operation are that it has high computation cost and that elliptic curve parameters have not yet been standardized. This paper aims at helping to solve these problems by providing a design automation platform for pairing hardware accelerators and comparing its hardware cost for multiple curves. We proposed an architecture template for pairing processors and demonstrated synthesized results.
MathVR: Teaching Vector Arithmetic Using Virtual Reality

Traditionally, linear algebra has been taught using 2D representations of fundamentally 3D concepts. We present a visualisation of vector arithmetic in virtual reality with full hand presence and lateral motion, demonstrating how this new technology can be used to enhance student engagement and understanding. We detail sufficiently powerful affordances to convey ideas such as compound operations, yet simple enough to be digestible by average undergraduate students in a 1st year university mathematics course. This presentation aims to catalyse discussion and ideas around the use of immersive technologies for education in more abstract disciplines such as mathematics.
Bridging the Gap: Sustainable Collaboration between CG Production and Educational Institutions

One of the major challenges that new employees in Japan's CG entertainment industry faces is the skill gap between the industry and academia. Recognizing the urgent need for an effective approach to bridge this gap, this study with a new initiative with Nihon Kogakuin College, proposes and implements an innovative educational model, drawing analogies from the "Melting Pot" and the "Salad Bowl." The "Salad Bowl" model honors the uniqueness and needs of both participating companies and students, emphasizing the preservation of diversity. Through its implementation, it has been elucidated that this new educational approach brings about an improvement in student skills and high satisfaction levels, paving the way for success in future workplace environments through real-world industry experience. Additionally, it has been effective in resolving recruitment challenges of smaller studios, overcoming faculty shortages, providing a concrete evaluation of soft skills, and creating employment opportunities tailored to each company. These investigative outcomes underscore the significance and effectiveness of establishing a sustainable collaboration between industry and academia, realizing the provision of education programs that cater to the diverse industrial demands of Japan.
Accurate Spectrum Sensing with Improved DeepLabV3+ for 5G-LTE Signals Identification

This paper presents a deep learning approach for fifth-generation (5G) and Long-Term Evolution (LTE) signal discrimination, explicitly focusing on identifying modulated signals in next-generation wireless networks. The mixture of modulated signals, which is essentially difficult to discern in the form of a complex envelope, should be converted into a visually informative spectrogram image by applying the Fast Fourier transform (FFT). To segment spectral regions of 5G new radio (NR) and LTE in a spectrogram, we aptly improve DeepLabV3+, a deep encoder-decoder network for semantic segmentation, by incorporating an adaptive Atrous Spatial Pyramid Pooling (ASPP) block and an attention mechanism to accommodate intrinsic signal characteristics and amplify relevant features, respectively. Besides increasing the learning efficiency in the encoder, the improvement enriches the recovery capability of crucial 5G and LTE details, thus resulting in more accurate signal identification in the spectrogram image. Relying on the simulation results benchmarked on a dataset consisting of spectral images containing both LTE and 5G signals, the new network demonstrated effectiveness when compared to the original version by increasing global accuracy, mean intersection-over-union (IoU), and mean boundary-F1-score (BFScore) up to , and in that order. For medium SNR level, it can achieve global accuracy and mean IoU, while also showing robustness under various practical channel impairments.
An Introduction to Creating Real-Time Interactive Computer Graphic Applications

The emphasis of this course focuses on the capabilities of the WebGL application programming interface (i.e., programming library, often called an API).
Use of Physics-Based AI for Simulations and Modeling in the Era of Digital Twins

What an AI powered future might look like? Rapid evolution of computing capabilities enables transforming process of scientific discovery and inventing new ways to experience and interact with simulation outcomes in real-time, especially in the context of modern Digital Twins.