Reviewerly/exHarmony huggingface dataset:
Bayesian Methods and Monte Carlo Simulations

Bayesian methods provide the means for studying probabilistic models of linear as well as non-linear stochastic systems. They allow tracking changes in probability distributions by applying Bayes’s theorem and the chain rule for factoring the probabilities. However, an excessive complexity of resulting distributions often dictates the use of numerical methods when performing statistical and causal inferences over probabilistic models. In this chapter, the Bayesian methods for intractable distributions are first introduced as sampling, filtering, approximation, and likelihood-free methods. Their fundamental principles are explained, and the key challenges are identified. The concise survey of Bayesian methods is followed by outlining their applications. In particular, Bayesian experiment design aims at maximizing information gain or utility, and it is often combined with an optimum model selection. Bayesian hypothesis testing introduces optimality in the data-driven decision making. Bayesian machine learning assumes data labels to be random variables. Bayesian optimization is a powerful strategy for configuring and optimizing large-scale complex systems, for which conventional optimization techniques are usually ineffective. The chapter is concluded by examining Bayesian Monte Carlo simulations. It is proposed that augmented Monte Carlo simulations can achieve explainability and also provide much better information efficiency.
Perspective Chapter: Text Watermark Analysis – Concept, Technique, and Applications

Watermarking is a modern technology in which identifying information is embedded in a data carrier. It is not easy to notice without affecting data usage. A text watermark is an approach to inserting a watermark into text documents. This is an extremely complex undertaking, especially given the scarcity of research in this area. This process has proven to be very complex, especially since there has only been a limited amount of research done in this field. Conducting an in-depth analysis, analysis, and implementation of the evaluation, is essential for its success. The overall aim of this chapter is to develop an understanding of the theory, methods, and applications of text watermarking, with a focus on procedures for defining, embedding, and extracting watermarks, as well as requirements, approaches, and linguistic implications. Detailed examination of the new classification of text watermarks is provided in this chapter as are the integration process and related issues of attacks and language applicability. Research challenges in open and forward-looking research are also explored, with emphasis on information integrity, information accessibility, originality preservation, information security, and sensitive data protection. The topics include sensing, document conversion, cryptographic applications, and language flexibility.
Analysis of Contextual Factors Influence on Auditors Informal Learning

In the next decade, more than 1 billion people will need reskilling as professional roles evolve. Even more importantly, the COVID-19 pandemic is forcing organizations to rethink the entire role of organizational learning in both the short- and long-term. As a consequence of this change, professionals need to be motivated to continually further their own skill sets in addition to organizational needs. The purpose of this study was to investigate the hours auditors engage in learning activities integrated into daily work tasks and factors that support engagement in learning. Further, the study provided insight into auditors’ informal learning preferences at different levels of work experience and auditors’ tendency to participate given the level of the perceived organizational learning culture.
Image Compression with Channel-wise Decoder

In this paper, we propose an image compression scheme which uses a channel-wise decoder in the decoding network. The proposed method uses a wavelet based auto-encoder with multiple decoders where each decoder is responsible for each channel in the color image, i.e., the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R,\ G$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> channels. The separated channel-wise decoders result in a better reconstructed color image than a single decoder and therefore also in a better compression rate.
A Lightweight Traffic Police Action Recognition Deep Learning Network for Edge Device

The traffic police gesture recognition is necessary for the perception task of autonomous vehicles. Most of the previous SOTA algorithms are considering accuracy instead of network capability to run edge devices of autonomous vehicles. Since the hardware resources of autonomous vehicles are limited, we propose a lightweight model that can recognize the action of traffic police. We compared our proposed network with SOTA methods and experimented with an open dataset. The results show that the proposed method archives an accuracy rate of 100% same as the SOTA method. And inference speed is faster than the SOTA method and higher than real-time requirements at RTX3090(150 fps) and 33.3 fps at the NVIDIA Jetson Xavior NX board.
Automatic Method of Camera Calibration for The Traffic Surveillance

This work solves the camera calibration problem in the surveillance scenario. We recommend a method to calibrate the camera automatically. We are easily implemented in any surveillance camera system. Our approach consists of three steps. In the first step, we extract the edges of the objects in the image frames. Step two applies the Hough transform algorithm to interpolate the line. Step three take two vanishing points and calibrate the camera. Our proposal is a new method to obtain the internal and external parameters of the camera, which can automatically calibrate the camera with high reliability.
Displacement Detection of Wooden Cultural Properties Using Unsupervised Learning

Since supervised learning requires expert labeling work that requires a lot of time and money to acquire data, an alternative is required in the field of cultural property management. In addition, the existing method based on contact type sensors to detect displacement occurring in wooden cultural heritage has a risk of damage to cultural heritage. This paper proposes to apply an artificial intelligence model using f-AnoGAN, which shows good performance in detecting abnormalies, to overcome the difficulty of obtaining abnormal data in wooden cultural assets with an unsupervised learning approach and to explore alternatives to the conventional contact type sensor-based method. The applied anomaly detection model, f-AnoGAN, is characterized by the fact that learning is performed to simultaneously minimize the error in the image space and the error in the feature space when a new image is mapped to the latent space and then returned to the image space. Our experimental results show that the f-AnoGAN model successfully performs judgment on whether or not displacement has occurred in wooden cultural properties using actual CCTV color images, and further presents the location of displacement when displacement occurs.
Efficient Training of EfficientNetV2-S Using AdaBelief Optimizer

The EfficientNetV2 architectures have classification accuracies exceeding 80% on the Imagenet-1K/21K datasets. Here, the networks used to be trained using stochastic gradient descent with momentum ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SGD+M$</tex> ) as the optimizer. Such a method is known to be effective but takes too much time, making it impractical to train the networks from scratch especially when computational power is limited. To address this, we provide a guide on how to use the AdaBelief Optimizer for training EfficientNetV2-S. Results show that, even without complex training configurations, using AdaBelief can lead the EfficientNetV2-S network to achieve a top-1 accuracy as high as 80% on the Imagenet-1K validation set within 80 epochs of training.
Applying Bilateral Guided Multi-Viewed Fusion on Asymmetrical 3D Convolution Networks for 3D LiDAR semantic segmentation

We present a novel approach for 3D semantic segmentation using LiDAR sensor. In this work, we focus on improving the accuracy of the cylindrical partition and asymmetrical 3D convolution networks with a modification on their dimension-decomposition based context modeling module and asymmetrical residual block. The initial version simply performs multiplication and addition operators to combine two feature branches. In our modification, we apply the bilateral guided multi-viewed fusion module to provide better features for the classification stages. We trained and tested our model on SemanticKITTI dataset, our implementation improves better accuracy than the default asymmetrical 3D convolution networks, in which uses cylindrical voxel for the point cloud representation.
Element-wise Partial Product Quantization for Efficient Deep Learning Accelerators

For efficient DNN edge computing, the model inference must be performed quickly while reducing memory requirement and computation. To this end, deep learning quantization algorithms and dedicated accelerators are actively studied. However, in previous studies, algorithms and accelerators are not co-optimized, requiring complex high-precision operations when processing them in accelerators, or additional resources to store intermediate computation results. To address these problems, we propose a novel quantization algorithm named element-wise quantization and a co-optimized accelerator design. We apply quantization directly to the element-wise partial product results of DNN parameters. Through this process, the size of the intermediate values generated in the inference process is reduced, allowing the accelerator to process it using fewer resources. We fix all the floating-point parameters of the quantization process in the form of power of 2 so that the accelerator can efficiently process them. Through SW-HW co-optimization, we can efficiently process model inference using only integer operation while saving 13.5%, 7.5%, and 15% on Flip flops, LUTs, and I/Os respectively, with only 3∼4% accuracy loss.
Quantized YOLOv5x6 for Traffic Object Detection

There are many powerful object detectors proposed up to this day. Those models are mostly targeted to general purpose object detection. However, this is unnecessary in the realm of autonomous driving. In fact, the complexity of the models seriously requires huge computational power from workstation-scale GPUs, rendering the models inapplicable in embedded environments. We attempt to address this issue by performing quantization using TensorRT. Choosing the YOLOv5x6 as the object detection model, the model is trained and tested on the KITTI 2D Object Dataset. The model is then quantized to half-precision floating point. Results show that the model can achieve faster speed when the trained model parameters are quantized, while having the detection mAP similar to the non-quantized version.
Investigation of Solar Energy Potential and PV-Outputs in Rural and Desert Areas: Case Study Egypt

This study aims to identify the best strategic sites in Egypt available to build the PV systems needed to support required electricity to the country and the desert areas, in addition to deduce the total monthly, annual electricity budget and their generation costs. Egypt has a high solar energy potential which is crucial for sustainable development, as it receives 3450 hours of solar radiation (SR) throughout the year with a high intensity ranged from 8.5KWh/m 2 /d in southwest part to 2.1 KWh/m 2 /d at the north coast. The observed SR at four stations (Asyut, Aswan, Kharga, Cairo) were used to assess the accuracy of the ERA5 reanalysis dataset. Moreover, seven strategic sites were chosen across the country including the Western Desert (El-Golf, Habatta, and Sewa), the Eastern Desert (Saffaga, Shiab-Elbanat), and the Sinai Peninsula (El-Agma and El-Arish) to identify the most suitable stations for the construction of PV systems. Three different types of PV cells with different efficiency (η1=19.1%, η2=22.6% and η3=22.4 %) were utilized to compute the energy that could be generated at the lowest cost at the seven selected sites across Egypt. The results showed that the monthly total cost from η1 was 0.011 $/W during June and exceeded 0.015 $/W in December, while the annual total costs are ranged from 0.0119 $/W in El-Golf and 0.01494 $/W in El-Arish. Also, the result for η2 was ranged from 0.01$/W to 0.026 over El-Golf and Shiab-Elbanat, while the last cell η3 was recorded high cost over all sites but gives more energy. Finally, it is concluded that El-Golf, Shiab-Elbanat, and Saffaga are the best three sites with the lowest coast for building a PV system followed by Habatta and El-Agma, while the energy generation costs at both El-Arish and Sewa were the highest.
A Comparative Study on Graph Construction Methods for Survival Prediction using Histopathology Images

Survival analysis from whole slide histopathology images (WSIs) is challenging due to the high dimensionality of WSIs. Graph-based approaches thus have been extensively studied to analyze WSIs. Recent success of graph neural networks further promotes graph-based approaches for survival analysis. However, the performance of graph-based methods is heavily dependent on graph construction methods. In this paper, we compare and analyze different graph construction methods for survival prediction from WSIs on the TCGA bladder and brain cancer pathology image datasets
Generating Pixel Art Character Sprites using GANs

Iterating on creating pixel art character sprite sheets is essential to the game development process. However, it can take a lot of effort until the final versions containing different poses and animation clips are achieved. This paper reports an investigation on the use of conditional generative adversarial networks to aid the designers in creating such sprite sheets. We propose an architecture based on Pix2Pix to generate images of characters facing a target side (e.g., right) given sprites of them in a source pose (e.g., front). Experiments with small pixel art datasets yielded promising results, producing models with varying degrees of generalization, sometimes capable of generating images very close to the ground truth. We analyzed the results through visual inspection and quantitatively with FID.
Multi Head Network for Effective Multiple Image Processing in the Medical Field

Recently, with the development of convolutional neural networks, medical imaging studies are attracting attention. However, existing neural networks do not carefully consider that medical professionals perform their diagnosis by considering various anatomical structures. Therefore, we proposed a multi-head convolutional neural network to process multiple two-dimensional images effectively. Specifically, the model presented in this study firmly learns the unique characteristics of all input feature images effeiciently by using multi-head design and recent neural network techniques. As a result, our model achieves higher predictive performance with a smaller parameter size than the existing architectures for thyroid-associated ophthalmopathy patients' images acquired in an anonymous hospital.
Comparative study in anomaly diagnosis technique for time series data

In this paper, by comparing the values of the anomaly index in various anomaly diagnosis techniques on the same dataset, it is intended to help in selecting an anomaly diagnosis technique suitable for the desired task. We compare the models from the commonly used CNN or LSTM-based autoencoder anomaly diagnosis technique to its variant model DAGMM (Deep Autoencoding Gaussian Mixture model), deep learning-based SVDD (Support Vector Data Description), and recently USAD (Unsupervised Anomaly Detection) with each other. Since the anomaly index value at the time of occurrence of anomalies varies depending on the model, it is possible to consider the model selection according to the data distribution through these index values.
An Efficient Method to Improve the Performance of Small-Scale Convolutional Neural Networks by Hybrid Selective Initial Parameter

Transfer learning (TL) is a method used in various fields of convolutional neural networks (CNN) based applications. TL provide the opportunity for CNN, able to learn with limited datasets. A generally used TL involves pre-trained model from large-scale datasets. Pre-trained model help prevent the restriction of performance from learn with limited datasets. There are many experiments on limited datasets, but few that assume small CNN models together. Limited-resources, which involves small-scale CNN and limited datasets, can have different learning tendency compare to the large-scale CNN. This paper proposes an efficient method that could achieve better performance using hybrid selection which based on the channel importance calculated from experimental results of limited resources. With a 38% reduction training time, proposed model achieve an accuracy of 12.33% higher than randomly-initialized model. And achieve a 10% reduction training time with 1.2% accuracy increment compare to the TL model.
Standardization Trends in CNN-Based In-Loop Filtering

Inspired by the recent success of convolutional neural networks (CNNs) in image restoration and artifact removal, CNN-based in-loop filters are actively developed in video compression standardization. This paper presents standardization trends of video coding, especially focusing on the techniques presented in the latest JVET-AA standard meeting.
Generating Rooms using Generative Grammars and Genetic Algorithms

Bi-dimensional game environments are usually not designed as a single piece, but built on top of smaller pieces called tiles which, are typically grouped into tilesets. These work as a palette for creating backgrounds, so the resulting map also demands careful positioning of game objects. However, many modern games demand a high volume of scenarios composed of a myriad of tilesets, i.e., creating this kind of game content may be difficult and challenging for humans, so Procedural Content Generation (PCG) techniques can be used to address this problem. In this paper, we propose a novel PCG technique to speed up this process. We model generative grammars whose association rules yield strings that represent the objects arranged in the scene. We show that it is possible to define simple generation directives leaving it to a genetic algorithm process to control the best distributions of weights on the rules. We evaluate our technique under the scenario of generating game rooms for the popular JRPG (Japanese Role-Playing Game), resulting in varied and good-looking rooms.
A Model for Dynamic Game Content Using Semantic Web and Prolog

A common approach for developing video game content is to define it statically. The content is normally created during the game concept layout in order to enable fine control over decisions and story direction, without much space for emergent narrative. For educational games, the narrative can become outdated due to new scientific developments, and may have a low replay value after its concluded. In this paper, we propose a framework capable of collecting data using the semantic web in order to be used as a basis for creating relationships. To do so, we employ Prolog and define facts and rules based on data collected from the semantic web. The proposed approach follows the server-client architecture, allowing multiple clients to work on different data domains and relationships. To evaluate our approach, we developed Dyna Quiz, a trivia-like game that uses dynamically generated content from the semantic web through custom facts and rules considering four domain topics (country, movie, planet, and sport). In addition, we evaluate its latency over network and showed that it takes less than 8 milliseconds to perform 100 inferences at once using a local network, which is sufficient to provide an uninterrupted experience to the final user.
SAR Ship Detection with Deep Land Detection Networks and Land Masking

We propose a new Synthetic Aperture Radar (SAR) ship detection framework using deep learning that makes SAR land mask to improve SAR ship detection performance. To overcome the disadvantage of a small number of SAR dataset, the land detection networks take a large amount of small image patches as input and effectively learn land feature for detecting land mask. We then eliminate land using the detected land mask and apply artificial noise to preserve image distribution. With these schemes, SAR ship detector can concentrate on ship feature rather than land feature. Experimental results demonstrated that the proposed method outperforms baseline.
A Spatial Lexical Analyzer and 3D Grammars that Recognize Voxel Based Structures Using Linear Positional Grammars in Minecraft

We introduce the use of 3D grammars for the identification of objects formed by voxels, i.e., discrete element cells that compose a 3D grid. We also show how to apply these grammars in a bot that can recognize structures in Minecraft. Our main contribution is a new spatial lexical analyzer that can decide the direction it must follow to obtain the next input by looking at the future positions and the expected next token in a Linear Positional Grammar. Moreover, we define different positional relations and specific grammars for various Minecraft structures.
Comparing Regression Models Predicting the Price of Used Cars in Big Data

We built and compared regression models to find the optimal solution using Spark Big Data engine, which predicts the price of used cars. We have the Cargurus dataset to perform predictive analysis, which is consumer data set as of 9.29 GB. The traditional systems have difficulty to handle the large scale data greater than hundreds of Mega-Bytes. Our Spark cluster can resolve this issue by storing and training the models with the large-scale data as distributed parallel computing systems. We adopt multiple regression algorithms to train models using Spark library in the big data environment. Then, we compare the models by measuring the computing time and accuracy. We observed that the GBT model shows the best accuracy in RMSE and R2 but relatively long computation time. The Random Forest model has a similar RMSE and R2 as GBT but with less computing time.
Comparison of Controllable Image Generation Methods for Face Synthesis

Generative Adversarial Networks (GAN) is widely used in the field of image generation because they can synthesize images reflecting various properties such as color, edges, drawing style, or background. In particular, GAN excels at realistically synthesizing faces, and they have had great success in manually controlling face attributes. However, when features extracted from face images are entangled, failure cases still occur during image generation. In this paper, we select two representative methods that can successfully solve these problems. We then analyze their strengths and weaknesses by direct performance comparison on CelebA. In these experiments, we identified which parts of the model are key to controlling face attributes when generating images.