Reviewerly/exHarmony huggingface dataset:
DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning

As machine learning (ML) has been widely developed in real-world applications, the privacy of ML models draws an increasing concern. In this paper, we study how to forget specific data records from ML models to preserve the privacy of these data. Although some studies propose efficient unlearning algorithms on random forests and extremely randomized trees, Gradient Boosting Decision Trees (GBDT), which are widely used in practice, have not been explored. The efficient unlearning of GBDT faces two major challenges: 1) the training of each tree is deterministic and non-robust; 2) the training of a tree depends on all the previous trees. To solve the first challenge, we propose a robust GBDT-like ML model DeltaBoost that enables efficient and accurate deletion according to our theoretical analysis. For the second challenge, we design a training algorithm for DeltaBoost that minimizes the dependency among trees. Our experiments on five datasets demonstrate that DeltaBoost can remove data records from the trained model efficiently and effectively. Our unlearning approach achieves up to two orders of magnitude speedup compared to retraining GBDT. Besides, DeltaBoost produces competitive performance to existing decision-tree-based ML models.
Application of Computer VR Technology in Digital Media System Design

The integration of virtual reality (VR) technology in the design of digital media systems offers a promising approach to address the limitations of traditional digital media systems. With the ability to utilize diversified forms of digital media expressions, VR technology can enhance the user experience by creating more immersive and interactive environments. In this paper, we propose a digital media technology architecture based on VR technology, and we design a digital media function module using an interaction algorithm to promote efficient interaction. The proposed system was evaluated through experimental studies, which demonstrated that it has a short interaction time and good performance, meeting the interaction requirements of digital media. The developed technology architecture can be applied to various digital media systems to improve user satisfaction and experience. By embracing the potential of VR technology, digital media designers can create more dynamic and interactive experiences that can captivate users and enhance their overall experience. This study highlights the potential benefits of incorporating VR technology in the design of digital media systems, and underscores the importance of creating immersive and engaging digital media experiences.
Steered Training Data Generation for Learned Semantic Type Detection

In this paper, we introduce STEER to adapt learned semantic type extraction approaches to a new, unseen data lake. STEER provides a data programming framework for semantic labeling which is used to generate new labeled training data with minimal overhead. At its core, STEER comes with a novel training data generation procedure called Steered-Labeling that can generate high quality training data not only for non-numeric but also for numerical columns. With this generated training data STEER is able to fine-tune existing learned semantic type extraction models. We evaluate our approach on four different data lakes and show that we can significantly improve the performance of two different types of learned models across all data lakes.
Global and Local Differentially Private Release of Count-Weighted Graphs

Many complex natural and technological systems are commonly modeled as count-weighted graphs, where nodes represent entities, edges model relationships between them, and edge weights define some counting statistics associated with each relationship. As graph data usually contain sensitive information about entities, preserving privacy when releasing this type of data becomes an important issue. In this context, differential privacy (DP) has become the de facto standard for data release under strong privacy guarantees. When dealing with DP for weighted graphs, most state-of-the-art works assume that the graph topology is known. However, in several real-world applications, the privacy of the graph topology also needs to be ensured. In this paper, we aim to bridge the gap between DP and count-weighted graph data release, considering both graph structure and edge weights as private information. We first adapt the weighted graph DP definition to take into account the privacy of the graph structure. We then develop two novel approaches to privately releasing count-weighted graphs under the notions of global and local DP. We also leverage the post-processing property of DP to improve the accuracy of the proposed techniques considering graph domain constraints. Experiments using real-world graph data demonstrate the superiority of our approaches in terms of utility over existing techniques, enabling subsequent computation of a variety of statistics on the released graph with high utility, in some cases comparable to the non-private results.
Efficient Deep Attentive Pixels Network in Face Super-Resolution at Scale Factor of 16

Nowadays, Face Super-Resolution (FSR) models utilize the fusion approach, which combines the attention technique with the super-resolution network. The fusion approach has been proposed and solves the problem of FSR. Facial attributes have been effectively used to guide low-level information of the face to perform robust face image restoration. Iterative techniques appraised the value of facial landmarks to boost the reconstruction capability of the super-resolution network. Nevertheless, the network parameters in FSR are high, while the learning rate is still low. This paper proposes an attention mechanism combined with the Face Alignment Network (FAN). The proposed attention mechanism consists of channel attention and a non-local module. Our proposed model outperforms at the scale of $\times 16$ compared to the other state-of-the-art models.
Log4shell Investigate Based On Generic Computer Forensic Investigation Model

Log 4Shel1 (CVE-2021-44228) Vulnerability was disclosed on November 24, 2021. It is loophole from JAVA application which is a ubiquitous library of Log4j for logging framework. The hacker invokes JNDI lookup component for call Lightweight Directory Access Protocol (LDAP) which attacked by several ways such as Remote Code Execute (RCE) which is a critical effect on the vulnerable server. Therefore, this research paper proposes a study to investigate Log 4Shell under the control of the Generic Computer Forensic Investigation Model (GCFIM). The purpose of this investigation is to determine the management of the analysis process for accessing guideline information, gathering data, and analyzing digital evidence related to the Log4Shell attack. Furthermore, the paper provides a report documenting the source of the attacker that attacked the servers, which can be used as evidence in legal documents in court.
Chiang Rai Phra That Nine Choms Attraction Promoting Mobile Application for Cultural Tourism

This study aimed to create a mobile application to promote and guide the cultural tourism attraction Phra That Nine Choms in Chiang Rai Province, Thailand. The critical information about Phra That Nine Choms is gathered and analyzed to identify the problem and user requirements. The operation of the mobile application is designed using a use case diagram, class diagram, and sequence diagram. The Java programming language creates the mobile application through the Eclipse program, Android SDK, and JAVA JDK development tools. 95 Thai and foreign tourists tested the resulting application. Their satisfaction was evaluated through a questionnaire and analyzed. The interpreting results showed that the mobile application is suitable for providing travel recommendations and can serve as a guide for future development to support tourism activities, particularly for cultural and local attractions.
Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work.
Explainability and causality for robust, fair, and trustworthy artificial reasoning

Artificial intelligence (AI) and machine learning (ML) systems are required to be fair and trustworthy. They must be capable of bias detection and mitigation to achieve robustness. To this end, a plethora of research fields have seen growth in research related to making AI/ML systems more trustworthy. Causal learning and Explainable AI (XAI) are two such fields that have been used extensively in the past few years to achieve explainability and fairness. However, they have been used as separate methodologies, not together. This paper provides a new perspective in using causal learning and XAI together to create a more robust and trustworthy system. Having causality and explainability together in the same model presents an extra layer of robustness, that is not achieved by using either of them individually. We present a use case for combining causality via causal discovery, and explainability via feature relevance. Using causal discovery, the generated causal graphs are compared to the feature relevance plots from the ML model. Directed causal graphs can display the features that are causally relevant for the predictions, and these causally relevant features can be directly compared to the features listed from correlation-based explanations from XAI.
A Novel Encryption Approach with Fractional Discrete Cosine Transform and Cascading Discrete Orthonormal Stockwell Transform

This paper proposes a new cryptographic algorithm based on fractional discrete cosine transform (FrDCT) and discrete orthonormal Stockwell (DOST) transform to secure multimedia data. Fractionality makes the cryptosystem more reliable and ensures the security of the data transmitted in the unsecured channel. The proposed method describes the advantage of using FrDCT instead of fractional Fourier transform (FrFT). At the same time, the energy compaction property of DOST guarantees lesser memory and storage requirements. The proposed method has been compared with state-of-the-methods to ensure practicality in different applications. The standard quantitative parameters such as information entropy (≈ 8), NPCR (≈ 99%), UACI (≈ 33%) etc., show its immunity against unauthenticated user(s).
Image Watermarking Scheme using a Fractional-Order Discrete-Time Chaotic System

This article proposes a new image watermarking scheme using a fractional-order discrete-time chaotic system. A modified-Hénon system of fractional-order is considered as a transmitter to this scheme, whereas a fractional-order step-by-step observer is considered as the receiver. This transmission scheme ensures perfect synchronization and, consequently, perfect retrieval of system states. The watermark insertion technique inside our fractional-order chaotic system’s dynamics is done by inclusion; the new encrypted image serves as watermark to a selected image. The watermarking of the cipher is realized through the application of a watermarking algorithm in DWT domain, then extracted and decrypted at the receiver level. Some results of simulation are given to prove that our approach is applicable and robust.
Feature reduction for hand gesture classification: Sparse coding approach

Hand grasping patterns are highly complex and necessitate sophisticated hand kinematic models. To effectively investigate and study hand gestures in realistic and daily-life scenarios, it is crucial to reduce the dimensionality of hand kinematics. Many studies have proposed low-dimensional kinematic models using dimension reduction techniques, revealing that only a few dimensions of the kinematic model are significant for accurately recognizing hand gestures. In this paper, we propose a novel feature selection technique based on sparse coding to classify hand gestures, with a specific focus on grasping objects. Our technique outperforms Principal Component Analysis (PCA), which is a commonly used dimension reduction technique. By utilizing sparse coding, we are able to extract the most informative features from the kinematic data, resulting in a more precise and efficient classification of hand gestures. Our approach has significant potential for real-world applications in areas such as human-robot interaction, prosthetics, and virtual reality.
Effect of Principal Component Analysis on Genetic Algorithm Feature Selection

A genetic algorithm feature selection module is used to find the optimal number of features that yield the best quality classifiers. Two groups of data are used and compared. The first group uses original data features in creating population. In the second group, the principal component analysis technique is used on data sets from the first group to obtain principal components. These principal components are used to create population instead of original features. The results indicated that genetic algorithm works well with both sets of data but did not show any improvement in terms of execution time or accuracy score. However, the interpretation of the results from the genetic algorithm feature selection module in the second case is easier to understand. Since principal components are sorted in descending order, it can be clearly seen that the components with higher significance tend to always be chosen by the genetic algorithm.
Improving a text classifier using text augmentation: road traffic content from Twitter

The purpose of this study is to develop a more effective method for categorizing Thai-language tweets related to traffic. The categorization consists of five categories. Previous studies have utilized CNN and BERT for classification, but have faced the challenge of needing balanced data for improved performance. To address this, we propose the use of BPEmb to augmentation the data and calculate cosine similarity. The subsequent step will be to create a balanced dataset to train a combination of CNN and bi-LSTM models for tweet classification. Our experiment demonstrates a significant improvement in tweet classification with a 14.3% increase in F1-score compared to the baseline method.
Programmer Cognition Failures as the Root Cause of Software Vulnerabilities: A Preliminary Review

The causal analysis of software vulnerabilities can be an effective way for building and evolving a dependable and reliable software system. Vulnerable source code can be leveraged by the attackers to break the system. Assisting the programmers so that they can avoid writing vulnerable code can cut down the effort and cost of protecting the software from security incidents. Security vulnerabilities can be prevented by identifying those programmer behavior related root causes that are the recurring reasons for the security bugs. Such repeated erroneous behavioral patterns have been coined as human cognition failures or human errors. In the case of software development, these erroneous behavioral patterns can lead the programmers to write vulnerable code. The goal of this research is to explore the available literature to identify frequently occurring programmer human errors in software implementation so as to provide programmers a handy list of cognitive issues that can be avoided by just being aware of them. Our literature review identified eight papers from where we extracted 20 human errors by programmers that have the potential to lead to writing vulnerable code.
Deep Learning Based Real Time Face Recognition For University Attendance System

The current face recognition systems struggle with pose, lighting, and expression variations in real-world scenarios. To overcome these challenges, face recognition systems have to apply advanced algorithms and machine learning techniques to analyze and compare facial features. Several studies have proposed face recognition attendance systems based on real-time video processing using deep convolutional neural networks. These systems aim to address problems related to the accuracy rate, stability, and interface settings of the attendance system. In this study, we propose a deep learning approach for face recognition in real-time video data. We constructed a new dataset of real-time videos by automatically and incrementally detecting, labeling, tracing, and purifying faces. We then fine-tune a convolutional neural network with the labeled dataset. Our experimental results on a testing dataset from an attendance management system show that the fine-tuned network achieves an accuracy of 95.3%, outperforming the non-fine-tuned network, which has an accuracy of 81.4%.
Membership and Participation in Object Oriented and Procedural Paradigms

Analyzing technical debt (TD) periodically with software tools is an important activity to help mitigate maintenance issues and sustain high levels of quality in software. Software written in Object Oriented languages (OO) have more dedicated TD analysis tools than software written in Procedural languages (PL) since many of the analysis techniques describe metrics that implicitly use a method's membership to a class in their calculations. The research described in this paper provides the conceptual foundation for using a membership matrix (used in current TD analysis techniques) and a participation matrix in TD analysis. Our early-stage research work proposes the participation matrix as a superset of the membership matrix and defines the mathematical requirements that must be met by a participation matrix constructed by any procedure. The participation matrix is defined as a fuzzy estimate of memberships and can be used to leverage TD analysis designed for OO to analyze PL programs. The operational implications of this work can allow practitioners to significantly benefit from improved TD tools that are also available in PL software.
Colorectal Tissue Image Classification Across Datasets

This work studies the performance of the pretrained models to classify colorectal tissues and the effectiveness of tissue image classification models when it is applied across different datasets that may exhibit different color characteristics. In particular, the study is conducted on three pre-trained CNN models, ResNet50, VGG19, and EfficientNetB3, where they are used as to extract a feature vector of the image patch. Then the multi-layer perceptron-based neural network is used to perform the task of multiclass classification. The result shows that the best accuracy to classify the colorectal histopathological images was achieved by the ResNet model with 93.87% test accuracy on the Kather-texture2016 image dataset. And for the accuracy of the classification model when it is trained in the public dataset and applied to the local datasets, the normalized color image test dataset showed the best accuracy rate at 80.69% when compared to raw images and grayscale images among the local datasets. This result suggests that the pre-trained classification models could be useful for tissue image classification tasks across various laboratory sources and image color adjustment could help enhance the recognition performance even further of the image classification model. Therefore, consideration for image color correction would be needed to preserve the recognition performance of the model if the pre-trained tissue image classification model is to be used off-the-shelf across laboratories.
From Characters to Chaos: On the Feasibility of Attacking Thai OCR with Adversarial Examples

Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary can influence the DNN model’s prediction by carefully manipulating input to the model. Prior work has demonstrated the security impacts of adversarial attacks on various OCR languages. However, to date, no studies have been conducted and evaluated on an OCR system tailored to the Thai language. To bridge this gap, this work presents a feasibility study of performing adversarial attacks on a specific Thai OCR application – Thai License Plate Recognition (LPR). Moreover, we propose a new type of adversarial attacks based on the semi-targeted scenario and show that this scenario is highly realistic in LPR applications. Our experimental results show the feasibility of our attacks as they can be performed on a commodity computer desktop with over 90% attack success rate.
Fighting Deepfakes by Detecting DCT Frequency Anomalies

The recent widespread use of deep learning has led media synthesis and manipulation to reach previously unheard-of degrees of realism. Deepfake has been a standard media manipulation tool adopted in synthesis of fake media, including videos, images, and audio recordings. Deepfake technology today has reached a potential to tremendously impact the trustworthiness of multimedia data considerably. This has significant ramifications for people, organizations, and society at large. Current literature presents numerous cases of fake news to sway public opinion as well as personal defamation cases. In this paper, we investigate the frequency domain spectrum of deepfake videos and propose the exploitation of high-frequency DCT coefficients gathered from videos to serve as a distinctive fingerprint. We construct a resilient model capable of distinguishing between pristine videos and its deepfake derivative, based on variance of differences between consecutive video frames' DCT coefficients.
Development of a Class Materials Search System using LINE Chatbot

Self-learning enables students to learn at their own pace. The process itself has changed significantly from reading textbooks to viewing ebooks on electronic devices, asking questions on online discussion boards or watching video clips from other parts of the world. The change has come with a massive increase in the amount of information available. It is time-consuming for a student to obtain the exact item of class material wanted. We developed a search system that can store relevant class materials in its own database and give suggestions when a student needs them. The system is built as a chatbot in the LINE application, using the NLP algorithm, to interact between users and the system via Artificial Intelligence technology. The system was developed for two subjects in Electrical Engineering: Signals and Systems and Control Systems. It was able to correctly interpret the users’ requests with up to 80% accuracy. It can give suggestions of class materials based on rankings given by users’ scores. This search system makes self-learning less time consuming and may provide more encouragement to students.