Reviewerly/exHarmony huggingface dataset:
Graph Fourier Transform Based Image Zero-Watermarking

In this paper, we propose an image zero watermarking method using the graph Fourier transform (GFT). We introduce new elements such as sampling suitable for images, convolution of surrounding pixels, and definition of adjacency matrices based on sampling results. To extract features of a host image, a graph signal is produced with an adjacency matrix that depends on the distance from neighboring pixels. Then, the coefficients after the GFT are encoded into binary information using the K-means method. Finally, XOR operation is performed between the obtained features and watermark to achieve zero watermarking. Simulations show that the proposed method works more effectively than a comparative method in resisting a variety of common attacks.
A High Precision Counting Framework for Cerithidea moerchii towards Low Power Implementation

In this work, an adaptive framework which combines traditional image processing approach and machine learning based object detection approach to achieve low power implementation for the counting of Cerithidea moerchii (C. Moerchii) is proposed. The proposed framework categorizes the observing conditions into four types according to different environments of C. moerchii and adaptively select the counting tools with optimized computation complexity and detection accuracy. This proposal is characterized by the ability to distinguish different environments, switch counting modes, and save computational complexity with acceptable accuracy. The simulation results show that totally over 80% counting accuracy can be achieved by the proposed framework.
CNN and MKDE-Based Classification of Synthetic Speech Attribution

Speech synthesis algorithms have been continuously developed for years. They are easily available to the general public and have excellent performance. Therefore, if voice synthesis algorithms are used maliciously, they can be abused for various crimes such as fake news and fraud, including the impersonation of celebrities. Therefore, many studies have been conducted recently to solve problems caused by speech synthesis algorithms. As a result, various synthetic speech detectors have been developed in many previous studies. This paper proposes a synthetic speech classification model that can classify with which algorithms voice is synthesized in a situation where synthetic speech is affected by noise, reverberation, compression, etc. The proposed model consists of a Convolutional Neural Network (CNN) and a Multivariate Kernel Density Estimation (MKDE). The CNN model used the Mel spectrogram of the audio signal as a feature, and the MKDE model used the logit value of the CNN to estimate the Probability Density Function (PDF) of the training class. The data used for model learning and evaluation were provided in the 2022 IEEE Signal Processing Cup, where the final model performance shows an accuracy of 96.38% for the noiseless case and 95.5% for the noisy one, respectively.
An Efficient Neural Network Design for Image Super-Resolution with Knowledge Distillation

This paper proposes a new neural network design for efficient image super-resolution inference. Employing complex-simple sub-networks, the proposed design samples feature to dynamically choose an execution path, leading to considerable computation reduction. However, uniformly random sampling generally causes a large accuracy drop due to highly different feature maps obtained by the sub-networks. To address the problem, we propose two simple yet effective techniques considering both the training and inference stages. First, Knowledge Distillation is utilized during training to minimize the feature map difference. Second, a gradient image which is obtained with the well-known Sobel filter guides the sampling by assigning points on edge and texture regions to the complex sub-network. The experimental results show that the proposed design reduces 50% of computations when only 20% of feature maps are computed by the complex sub-network. More importantly, the proposed sampling method enhances the restoration accuracy by 0.3 dB on average compared to the uniformly random sampling method.
Optimization of Video Repetitive Action Counting for Efficient Inference on Edge Devices

Repetitive actions are prevalent in both natural and man-made environments, offering valuable insights into the analysis of action units and underlying phenomena. Video repetition counting task aims to predict the count and frequency of the repetitive actions. Deep learning models have been developed for this task, enabling the recognition of repetitive motions without physical contact with the moving object. However, these models often perform unnecessary operations during inference due to inefficient data pre-processing. To address this issue, we propose an optimized data frame pre-processing method that minimizes redundant operations, ensuring fast and accurate inference. Furthermore, in order to enable video repetition counting on edge devices, we employ quantization for model compression, allowing the deployment of lightweight models suitable for various applications.
Classifying Cybercrime and Threat on Thai Online News: A Comparison of Supervised Learning Algorithms

Nowadays, there are many news and articles being offered on the Internet from various sources. This news can generally be classified into different domains, such as politics, entertainment, sports, technology, etc. Over the past decade, news related to cybercrime and threats were often classified into either crime or technology domains. However, contents related to such news should be alerted to all citizens as fast as possible in order to prevent further damage. Therefore, in this research paper, we aim to categorize news related to cybercrimes or threats from a pool of news of different topics. The challenge of this work is to be able to handle new types of threats such as call center scams, ransomware as well as other new threats which exploit the vulnerability of new technology/application. To tackle this problem, we have developed methods and processes to classify news into domains related to cybercrimes and threats using various Machine Learning techniques: Naive Bayes, KNN, SVM, Decision Trees, Random Forest, and Gradient Boosting. Using the dataset of 5,000 news articles, which consists of 3,500 general news and 1,500 cybercrime related news, the best technique that did the best in categorizing cybercrime related news is SVM, with an accuracy of 93.66% and a cross-validation accuracy of 92.34%. The second best technique is Random Forest with an accuracy of 92.46% and a cross-validation accuracy of 92.25%.
Analysis of the Effect of Feature Denoising from the Perspective of Corruption Robustness

Adversarial attack is a method that aims to cause incorrect predictions in a deep learning model by making slight perturbations to the input. As a result of this vulnerability, various studies have been conducted to improve adversarial robustness. However, deep learning models are also vulnerable to distribution mismatch between training data and test data. This mismatch can occur due to natural corruption in test data. Research on corruption robustness has been less explored compared to adversarial robustness. This paper analyzes the effect of feature denoising network, which is for improving adversarial robustness, in terms of corruption robustness. Experimental results show that feature denoising network can also lead to improved robustness against common corruptions.
Improvement of Coding Efficiency Method Based on Super-Resolution by Learing Decoded Images in HEVC

Recently, high-resolution videos such as 4K and 8K have become increasingly popular, leading to a significant increase in video data size. Consequently, techniques for more efficient video compression are needed. One approach to address this issue is to downsample the video image to reduce the amount of information and then perform encoding. Upsampling is performed using super-resolution or other methods to restore the original resolution, which is expected to improve coding efficiency. However, super-resolution may not necessarily produce superior results compared to other. As conventional superresolution processing does not consider degradation caused by video encoding processing in the learning process. Therefore, we propose a method of learning super-resolution that considers the degradation caused by both downsampling and video encoding processing. As a result, we can perform super-resolution processing suitable for video encoding and improve encoding efficiency by up to 7%.
Color Correction Method Using Monocular Depth Estimation Model for Underwater Images

In recent years, with the increase in population, the aquaculture industry has been gaining attention as a means of ensuring sustainable food resources. One important technique for the automation of the aquaculture industry is object detection in underwater images. In this paper, we propose a color correction algorithm for improved object detection accuracy underwater. The color correction algorithm depends on the original image and its depth map. In conventional methods, the depth map is generated by Monodepth2. Our proposed method improves the accuracy of the depth map by using a new training dataset. Furthermore, the proposed color correction dataset is confirmed to increase object detection accuracy.
An Optimized Multi-Object Tracking with TensorRT

Multi-object tracking is a crucial task in computer vision, requiring real-time performance, accurate identification, and tracking multiple objects in a scene. However, it is a challenging process due to the large number of objects to track, the need to maintain their identities over time, and the complexity of deep neural networks. To address these challenges, we present an optimized multi-object tracking pipeline using TensorRT. We leverage ByteTrack, a state-of-the-art multi-object tracking framework, and implement the pipeline in C++ for low latency. We optimized the Yolox neural network used in ByteTrack with TensorRT to produce bounding boxes for each frame. We also focus on the fact that ByteTrack utilizes sparse matrices when applying a Kalman filter to generate tracklets for each object. We evaluate the performance of our approach on the palace.mp4 video dataset, which is used in the ByteTrack demo. Our experimental results show a significant improvement in frame rate, increasing from 10.1fps to 14.3fps, while also reducing memory consumption from 1.4GB to 1.1GB. Overall, our optimized multi-object tracking pipeline demonstrates the effectiveness of combining deep learning with TensorRT and classic computer vision techniques. It provides an efficient and accurate solution for real-world multi-object tracking applications, such as surveillance, robotics, and autonomous vehicles.
Implementation of Modulo Multiplication with CAM-Based Massive-Parallel SIMD Matrix Core

Recently, semiconductor technology has been growing rapidly, and mobile devices are required to have digital convergence. In this paper, a CAM-based massive-parallel SIMD matrix core (CAMX) is proposed as a mobile device accelerator for high-performance, programmability, and versatility. The CAMX can process repeated arithmetic and table-lookup coding operations in parallel. Mobile devices need to keep secure communication on the Internet, and the cipher techniques are wide spread for secure communication like the Advanced Encryption Standard (AES), Rivest-Shamir-Adleman (RSA), and so on. In particular, RSA encryption needs to calculate modulo multiplication. In this paper, the CAMX executes modulo multiplication for secure communication. This processing involves multiplication and division processing. This processing is implemented in the CAMX, and the CAMX can execute modulo multiplication at 1,104 clock cycles in parallel. In addition, the clock cycles of the CAMX and an ARM core are compared. From the result, the CAMX can reduce clock cycles by approximately 86 % for 1,024 parallel processing data relative to the ARM core with NEON.
CAESAR: A CNN Accelerator Exploiting Sparsity and Redundancy Pattern

Convolutional Neural Networks (CNN) have shown outstanding performance in many computer vision applications. However, CNN Inference on mobile and edge devices is challenging due to high computation demands. Recently, many prior studies have tried to address this challenge by reducing the data precision with quantization techniques, leading to abundant redundancy in the CNN models. This paper proposes CAESAR, a CNN accelerator that eliminates redundant computations to reduce the computation demands of CNN inference. By analyzing the computation pattern of the convolution layer, CAESAR predicts the location where the redundant computations occur and removes them in the executions. After that, CAESAR remaps the remaining effectual computations on the processing elements originally mapped to the redundant computations so that all processing elements are fully utilized. Based on our evaluation with a cycle-level microarchitecture simulator, CAESAR achieves an overall speedup of up to 2.13x and saves energy by 78% over the TPU-like baseline accelerator.
Literature review of educational immersive virtual reality solution functionality classification

An increase in the immersive virtual reality (IVR) technology accessibility impacts the number of developed solutions that are becoming widely used in education. The developed solutions greatly vary based on the IVR devices they use, their functionalities, and expected learning goals or outcomes. Currently there is a lack of clear definition of the difference between these solutions based on their functional possibilities and presented content. A clear classification scheme of the IVR learning solutions based on functionalities could help better define these solutions. This work presents the findings from a literature review of previous works attempting to identify different types of IVR educational solutions based on their functionalities. The identification of object for the development classification scheme is also described.
Identifying patterns of features used by automatic detectors of gaming the system behaviors

In the last decades, some research on online education has focused on better understanding a particular type of behavior gamed by students, known as gaming the system. In this article, we follow this track and present the first results of a new research approach to analyze and compare existing features commonly assigned to students used by automatic detectors for the behavior gaming the system in different situations, including problem-solving, considering various criteria. We have addressed the following main research question: What information or feature patterns about the students have been considered by automatic detector models of gaming the system behaviors? To answer this question, we have followed a knowledge discovery methodology in two studies: literature review and pattern identification. These studies were carried out in five concurrent stages: Selection of papers, Identification of the relevant works, Extraction of variables, Creating the patterns, and Mapping of the works in the standards. Results indicate that the detectors reported in the literature use eight variable patterns.
Brightness and Contrast Adaptive Face Recognition System

Recently, artificial intelligence has made remarkable advances and is being extensively utilized in many fields. However, there are challenges in adapting to real-world conditions in the field of face recognition where even minor errors are not tolerated. To address this, we propose a face recognition system that can remove brightness and contrast from images, which are common real-world conditions, without retraining the model. Our system can adapt to brightness and contrast accordingly using a brightness and contrast removal algorithm. The algorithm generates nearly identical images for comparison by removing brightness and contrast. Experimental results demonstrate high face recognition accuracy compared to existing systems, improving true positive and false negative accuracy by 4.55% and 10.34%, respectively, with a minimal computing overhead of only 1.40%. Overall, the face recognition accuracy improved by 4.60%.
Method to Eliminate Fruitless Cycles for Pollard's Rho Method with Splitting Table

This research focuses on attacks against a type of curve suitable for pairing, called Barreto-Naehring curves. The accelerated attack will evaluate the level of security with respect to the elliptic curve discrete logarithm problem (ECDLP). Then the rho method is known to efficiently solve ECDLPs, the random walk path sometimes induces unsolvable cycles, called fruitless cycles, and the random walk must be restarted from yet another starting point. The authors confirm the effectiveness of a method for eliminating fruitless cycles in a random walk path with parallel Pollard's rho method using skewed Frobenius maps of curves. In this paper, the authors proposed a parallel rho method using skew Frobenius mapping and several tables with novel approach. The results show that the proposed method can effectively eliminate fruitless cycles.
A Safety System for Industrial Fields using YOLO Object Detection with Deep Learning

This paper proposes a safety system that can be used in various industrial field situations. The safety system detects boundaries with a line detection method and identifies people using YOLO (You Only Look Once) from images captured through a camera. And using the depth image, this system determines which individuals are within the danger range among the detected people. Therefore, this paper includes the selection of a specific YOLO model, performance improvement through training YOLO models with deep learning, depth data correction, line detection method, and system optimization in the proposed hardware.
Developing Conceptualizations of Operational Technology and Service Quality for Critical Infrastructures – »Devcon«

Critical infrastructures (CIs) such as energy grids, communication networks or transportation systems are the essential backbone of any societies’ social and economic processes. Providing appropriate techniques to monitor the efficient and safe operation of their underlying Operational Technology (aka. OTM) is naturally of paramount importance. One crucial challenge is, however, that a CI’s OT is continuously evolving on a high pace, leading to massive heterogeneities, interdependencies and interoperability problems, thereby making OTM and integrative service quality hard to achieve. The research goal of our project »DevCon« is therefore to push forward appropriate techniques to facilitate OTM and integrative service quality even in face of the evolving nature inherent in this kind of systems. In particular, to overcome heterogeneities while explicating interdependencies and ensuring interoperability, conceptualizations of OT objects and service quality are put forward, providing the basis for a pivotal knowledge base. For this, an innovative approach is followed by synergistically combining inductive methods in terms of machine learning and deductive methods in terms of semantic technologies.
Performance Evaluation of Collaborative Filtering Recommender Algorithms

Recommender systems are used with frequency so that content/items are offered in a personalized way for each user, and it is important that these algorithms can accurately recommend content/items to these users (good predictive performance), as well as have a satisfactory computational performance - so that it is not necessary to use too many computational resources. Thus, this paper aims to evaluate some recommendation algorithms that use the memory-based Collaborative Filtering (CF) technique and to evaluate the influence of similarity metrics on the performance of these algorithms. Both algorithms and metrics are available in the scikit-surprise library. Two public databases were used: MovieLens 100k and MovieLens 1M. After the experiments, it was observed that the choice of similarity metric might influence the predictive performance and the prediction and training time of the algorithms. The MSD metric was the one that stood out in influencing, in a positive way, these results. It was also noticed that the database could influence both the predictive performance of the algorithms, as well as the RAM consumption.
Investigating Deep Learning Approaches for Hate Speech Detection in Social Media : Portuguese-BR tweets

Social media constitutes a very open social network space, which lacks of barriers to access. It has become a way for some people to vent anger, fear, happiness, hate, hope, love and sadness without feeling self-conscious. Hate speech and offensive language published and diffused via online environments have the potential to cause harm and suffering to individuals and lead to social disorder beyond virtual space. Over the last decade, machine learning and natural language processing approaches have been developed to detect harmful user content on social media. This work aims to analyze and detect hate speech, on Portuguese language, during the2018 Brazilian presidential electioneering period. In order to achieve this goal, we experiment different configurations of Deep Learning networks, hyperparameter and features. As a result, the paper presents best settings of parameters for practical applications of hate speech mining. We also presented a Portuguese hate lexicon and a labeled dataset. All resources are available on our GitHub.
A tree-organized chatbot proposal to provide a single digital channel to access specific chatbots in a real Brazilian digital government environment

The use of chatbots, or conversational assistants, is increasingly present in several areas of activity. In the governmental sphere, the use of these solutions provides the improvement of the provision of public services, as well as the reduction of their costs. However, the diversity of subjects under the responsibility of the same public body presents itself as a challenge to the development of a chatbot. This is because the more subjects to be dealt with, the more likely the chatbot is ambiguous in their responses. To solve this problem, this work proposes the use of text classification techniques, to enable a tree-based architecture of conversational assistants. In this architecture, a general knowledge chatbot can identify the subject of the user’s message and forward it to specific chatbots. The feasibility of the proposal was evaluated from the performance of experiments, which compared three implementation strategies for text classification: multiclass classifier, binaries classifiers and one-class classifier. The results of the experiments showed a better performance, in relation to the Fl-score and accuracy metrics, of the multiclass and binary classifiers. However, it was concluded that the use of one-class classifiers greater benefits to the hierarchical chatbots, because it allows independence between the models created, which results in greater scalability and simplicity of the proposed architecture.