Reviewerly/exHarmony huggingface dataset:
Cryptocurrency Trading Agent Using Deep Reinforcement Learning

Cryptocurrencies are peer-to-peer digital assets monitored and organised by a blockchain network. Price prediction has been a significant focus point with various machine learning algorithms, especially concerning cryptocurrency. This work addresses the challenge faced by traders of short-term profit maximisation. The study presents a deep reinforcement learning algorithm to trade in cryptocurrency markets, Duelling DQN. The environment has been designed to simulate actual trading behaviour, observing historical price movements and taking action on real-time prices. The proposed algorithm was tested with Bitcoin, Ethereum, and Litecoin. The respective portfolio returns are used as a metric to measure the algorithm's performance against the buy-and-hold benchmark, with the buy-and-hold outperforming the results produced by the Duelling DQN agent.
Runtime Recovery for Integer Overflows

Despite decades of effort in research and engineering, integer overflows remain a severe threat to software security. Many tools are developed to detect integer overflows at runtime. However, the vast majority of them terminates program execution when an integer overflow is detected. This essentially causes denial-of-service, which is undesirable in many scenarios in practice. We propose a recovery mechanism designed for safe recovery from integer overflows. The recovery mechanism detects integer overflows and rectifies the values involved in arithmetic operations causing integer overflows so that it prevents the occurrence of the integer overflows and enables the program to continue execute safely. We have designed and developed a tool called RIO that can automatically synthesize and instrument our recovery mechanism into target programs. Our evaluation shows that RIO can successfully synthesize and instrument the recovery mechanism into programs containing real world vulnerabilities and the instrumented recovery mechanism allows the programs to recover safely in the face of exploits intending to trigger the vulnerabilities.
On Perceptual Uncertainty in Autonomous Driving under Consideration of Contextual Awareness

Despite recent advances in automotive sensor technology and artificial intelligence that lead to breakthroughs in sensing capabilities, environment perception in the field of autonomous driving (AD) is still too unreliable for safe operation. Evaluating and managing uncertainty will aid autonomous vehicles (AV) in recognizing perceptual limitations in order to adequately react in critical situations. In this work, we propose an uncertainty evaluation framework in AD based on Dempster-Shafer (DS) theory, that takes context awareness into consideration, a factor that has been so far under-investigated. We formulate uncertainty as a function of context awareness, and examine the effect of redundancy on uncertainty. We also present a modular simulation tool that enables assessing perception architectures in realistic traffic use cases. Our findings show that considering context awareness decreases uncertainty by at least one order of magnitude. We also show that uncertainty behaves exponentially as a function of sensor redundancy.
Challenges with Providing Reliability Assurance for Self-Adaptive Cyber-Physical Systems

Self-adaptive systems are evolving systems that can adjust their behaviour to accommodate dynamic requirements or to better serve the goal. These systems can vary in their architecture, operation, or adaptive strategies based on the application. Moreover, the evaluation can happen in different ways depending on system architecture and its requirements. Self-adaptive systems can be prone to situations like adaptation faults, inconsistencies in context or low performance on tasks due to their dynamism and complexity. That is why it is important to have reliability assurance of the system to monitor such situations which can compromise the system functionality. In this paper, we provide a brief background on different types of self-adaptive systems and various ways a system can evolve. We discuss the different mechanisms that have been applied in the last two decades for reliability evaluation of such systems and identify challenges and limitations as research opportunities related to the self-adaptive system’s reliability evaluation.
Outlier Detection using Ensemble Learning

The growth of internet in recent years have led to emergence of several types of network attacks and outlier. Hence development of techniques to deal with network attacks and outlier has become an important area of research. Several methods for detecting outliers have been found in the current literature. In this paper, we propose a method for outlier detection which is based on ensemble learning and other well-known methods like LOF, KNN, HBOS, iForest, COPOD and PCA as the component of our ensemble model for base estimator. The proposed method deals with highdimensional data by projecting the original data (JL projection) onto a sub space with smaller number of samples and for each data point their anomaly score will be calculated. There after the scores are combined using some common combination methods i.e., score aggregation, maximization and majority voting. The base estimator of our ensemble model as well as other existing models are trained and evaluated using seven benchmark datasets. The proposed model delivered higher detection performance in terms of accuracy and ROC. The use of ensemble learning approach is found to be helpful in dealing with high dimensional data and unsupervised methods with respect to outlier detection.
Sequential Frequency Estimation Using Auxiliary Particle Filter

Frequency estimation is an important and challenging task in signal processing and related applications. However, the measurement signals are contaminated with undesired noise sources in practice, and the true frequencies, hence, cannot be extracted directly. Particle filtering (PF) is a sequential Monte Carlo (SMC) method that is proved efficient and effective in estimating the frequencies from the timefrequency representations (TFRs). There are many techniques proposed to enhance estimation performance of PF. This work conducts a study on the performance in estimating frequencies from the spectrogram of the signal (computed using shorttime Fourier transform (STFT) using sequential importance resampling (SIR) PF and auxiliary PF (APF). APF is proved to providing superior performance in frequency estimation at low signal to noise ratios (SNRs).
Evaluation of Masked Face Recognition of FaceNet Implemented with Machine Learning Algorithms

In image processing, Convolutional Neural Network (CNN) is an important tool for isolating image attributes for using with applications such as facial recognition. According to an outbreak of COVID-19, wearing masks has made face recognition less effective since face details are covered. FaceNet platform is a face feature extraction that is commonly applied to classification applications. Those applications embed FaceNet platform with supervised learning machine learning types to classify the considered objected on the detected image. Recently, Reinforcement Learning (RL) has been used in many applications on both prediction and classification tasks. However, the learning efficiency of RL has not been implemented and evaluated on masked face recognition yet. Therefore, the efficiency of the supervised learning techniques, ANN, KNN and SVM, are also implemented with the FaceNet platform for masked face recognition and they are compared with FaceNet platform implemented with the RL. The simulation results showed that ANN is the most efficient technique and followed by RL, KNN and SVM. The difference in efficiency (F1-scroce) between RL and the neural network was only 2%, but RL took four times more training time.
Semi-automatic short answers clustering and grading with K-Means and Keyword Matching algorithms

Appropriate and timely feedback enhances student learning. Such activities are challenging for professors to do in large classes. This paper provides two semi-automated methods for analyzing short responses: keyword matching and unsupervised document grouping. Three types of questions are tested. For fixed answers, keyword matching is most effective. Document clustering, on the other hand, is effective with a query that is more open-ended. This helps teachers to more efficiently grade students. They might be used in MOOCs to provide students with immediate feedback.
The SWC-based Security Analysis Tool for Smart Contract Vulnerability Detection

Smart contracts publicly deployed on blockchain have been shown to contain several vulnerabilities that users can maliciously exploit. To detect smart contract vulnerabilities as categorized in the Smart Contract Weakness Classification (SWC) standard, we propose the SWC-based Analysis Tool (SWAT). It operates by matching patterns in Solidity codes, making it easy to use and allowing any Solidity developers to conveniently check their smart contracts before deploying. We conducted experiments to compare the proposed solution against state-of-the-art tools in identifying 46 SWC registries. SWAT outperformed the existing tools in terms of the number of SWC vulnerabilities supported. Moreover, our solution is memory-efficient as it uses 44.58% less memory than other tools.
Web based keyword extraction on crowdfunding projects with novel features

Most of the existing research on contradiction focuses on detecting social media-based fake news or fake charity crowdfunding projects, but research on fake or infeasible crowdfunding projects is very limited. Therefore, we focus on how to gain an understanding of fake crowdfunding projects based on keyword extraction and selection. We used 12 features to extract keywords from web based information about crowdfunding projects. We evaluate this method with crowdfunding projects. Experiments showed that BERT with DT provide results of 69.33% in F-measure, which is better than other algorithms.
Classification of News Articles using Gradient Boosting based Deep Learning Model

The Newspaper articles offer us insights into several news in today’s world. With the popularization of the internet, people are directly influenced by the content they surf on the internet in their daily life and online news is not among an exception. It necessitates the need to classify such news article for its readily availability. In this paper we propose a novel method named Long short-term memory-Gradient boosting (LSTM-GB) deep learning model for classification of news articles and compare the result with existing traditional classifiers. Furthermore, the proposed model attains 99.8% accuracy which outplays other classifiers.
Shape Recognition Using Unconstrained Pill Images Based on Deep Convolution Network

The correctness of pill identification and inspection based on visual appearance is a very important issue. The shape of the pill is the most popular visual feature. To locate an individual pill and extract the pill shape from the image, we adopt two object detection models, namely you only look once v3 (YOLOv3) and Mask Regional Convolutional Neuron Network (Mask R-CNN). In this research, we trained each algorithm on our pill image dataset. We analyzed the efficiency of these two models to determine the best pill shape recognition model that can be employed in real world usage conditions. Our results showed that Mask R-CNN successfully identifies shapes with a F1-Score up to 99.5S%, and YOLOv3 yields a F1-Score up to 97.50%. Both models correctly located an individual pill in an image with 98% accuracy.
Guideline for Data Anonymization for Data Privacy in Thailand

PDPA is the first Data protection law in Thailand. The laws have been effectively enforced since June 2022. A data breach is a serious problem for all firms that hold personal data. The other bigger problem is data privacy. All government agencies have mandates to collect a lot of personal data but have to care about how to publish that information to their users. National statistics agencies are responsible for collecting data from surveys. Hospitals or the healthcare industries need to release microdata of medical records for research and other public benefit purposes. Those organizations are forced by laws to protect data privacy. Data anonymization is a promising technique for data privacy but there is no absolute guideline for a real implementation. A K-anonymity algorithm is implemented for demonstration. There is a trade-off between anonymity and utility. There is no standard or guideline for data anonymization in Thailand; therefore, this research paper is to construct a guideline for data anonymization in Thailand.
Three-dimensional Image and Terrain Matching Based on Interferometric Synthetic Aperture Radar Technology

In recent years, due to underground resource extraction and underground space construction, the internal structure of the ground strata has been seriously damaged, leading to frequent disasters such as road collapse, bridge collapse and landslide, which pose serious threats to people's lives and safety. In order to improve the spatial resolution and deformation measurement accuracy, this paper also adopts the interferometric synthetic aperture radar (InSAR) technology to match the image with the topographic data in three dimensions by geometric mapping, and the organic combination of the two technologies with complementary advantages greatly improves the accuracy and reliability of surface deformation data in the monitoring area. Finally, the measured results are analyzed by comparing whether the difference of coordinate changes at the same point in a short period of time exceeds the threshold value for disaster pre-disaster judgment, and disaster prediction is made by wireless network or Beidou short message function. Several sets of experimental results show that the measurement accuracy of deformation in the horizontal direction of the ground surface is better than 3 mm and the measurement accuracy of elevation settlement deformation is better than 5 mm by using the method of this paper, which greatly improves the accuracy and reliability of deformation monitoring.
Analyse of Influence of Adversarial Samples on Neural Network Attacks with Different Complexities

In latest years, neural networks have produced impressive text mining and computer vision achievements. The neural networks are becoming increasingly sophisticated and can perform many recognition tasks requiring accuracy. For example, VGG-16 has sixteen convolutional layers and fully connected layers, and the depth of the network can be increased by multiple non-linear layers to assure learning more intricate patterns. However, in real application scenarios, neural networks reveal problems such as poor resistance to interference. Researchers found that after designing the adversarial objects as normal objects with slight perturbation, the neural network models misclassify the images even though they have almost no effect on the human eye. A set of these adversarial objects produced by a particular attack and defense approach is referred to as an adversarial sample. The emergence of adversarial samples has seriously affected the development of artificial intelligence, and different neural networks have their own advantages and disadvantages in defending against adversarial samples, and no defense method has been found to completely defend against adversarial sample attacks. In this article, we look into the security issue of adversarial samples in the context of deep learning for image classification and test the resistance of several neural networks to the adversarial sample attack. The Fast Gradient Sign Attack technique (FGSM) is used in this study to produce adversarial samples for neural network assaults of varying complexity.
Design of Archives Management Information System Based on Blockchain Technology

At present, the archives management of enterprises is facing great work pressure, mainly focusing on data sharing and information operation. This paper mainly studies the application of blockchain technology, and explores the feasibility of blockchain technology applied to archives management information system through system design and simulation system implementation. This paper studies the traditional file management system, analyzes the application of the traditional enterprise file management system, and uses the blockchain technology in the system. Based on the content research and technical innovation of blockchain technology application, the existing traditional enterprise archives management system in China is investigated. On this basis, the system design based on Bubi blockchain is proposed, and the four functional modules of the simulation system are designed and implemented. Finally, the archive information management system based on blockchain Technology is tested, and the implementation success rate can reach 100%, and the CPU occupancy rate does not exceed 70%, The average response time is less than 1s.
A Fair and Reliable Data Trading Scheme based on Blockchain

To solve the single point of failure and opaque transaction problems in the current centralized data trading model, we propose a blockchain-based data trading scheme. Firstly, we construct a blockchain-based data transaction model to provide users with peer-to-peer trusted transaction services. Then, we design a two-stage one-to-many data pricing method based on smart contracts and the Rubinstein model, which can guarantee the reasonableness of data transaction pricing and achieve a fair price between a single data demander and multiple data owners. Next, we suggest a forensic dispute arbitration method based on on-chain and off-chain data collaboration and a data integrity verification method, which aims to ensure the reliability of de-centralized data transactions and prevent malicious acts such as data owners providing false data, data requesters refusing to pay, and data stored in the cloud being tampered with. Finally, we perform experiments based on consortium blockchain. Experimental results show that the scheme of this paper has better functionality and performance than other schemes of data transactions.
A Data Provenance Scheme Based on Blockchain for Internet of Things

Data provenance technology has played a very important role in the era of big data. However, the existing provenance technologies using blockchain and smart contract still need to be improved in data privacy protection. In this paper, we propose a data provenance scheme based on blockchain for Internet of Things (IoT). Specifically, we develop a smart contract with reasonable access control policy on the blockchain, so that the rights of data owners and users are limited to ensure data security. We also select several important smart contracts to test the time cost of transactions. The experimental results show that the proposed scheme can meet the functional requirements of data provenance of the general IoT.
Cryptocurrency Adoption in the Era of Industry 5.0

Viewed as entrepreneurship in financial solutions and based on blockchain technology, cryptocurrency is the most recent development in digital currency and is gaining substantial interest in recent years. The intersection of Industry 5.0 and cryptocurrency adoption could lead to new business models and revenue streams, with cryptocurrency potentially being used as a secure and efficient means of payment in Industry 5.0. This study aims to explore cryptocurrency adoption literature using a thorough literature review and bibliometric analysis. The study primarily used the Scopus database for the extraction of relevant literature. The literature review and bibliometric analysis have been done both quantitatively and qualitatively based on various techniques such as network analysis (keyword Co-occurrence analysis) and science mapping techniques. i.e., word cloud and word tree map with the help of tools such as VOSviewer, and R Studio Biblioshiny. The study reveals that the use of cryptocurrency in Industry 5.0 has the potential to improve efficiency, security, and transparency.
Smart corruption: Satirical strategies for gaming accountability

Although new forms of data can be used to hold power to account, they also grant the powerful new resources to game accountability. We dub the latter behavior “smart corruption.” The concept highlights the possibility of appropriating algorithms, infrastructures, and data publics to accumulate benefits and obscure responsibility while leaning into the positive associations of transparency. Unlike conventional forms of corruption, smart corruption is disguised as progressive, and is thus difficult to spot or analyze through existing legal or ethical frameworks. To illustrate, we outline a satirical strategy for gaming accountability. Identifying the particular mechanisms and outcomes of transgressive activities carried out under the veneer of data-driven transparency, as well as the key actors and organizations most active in gaming accountability, is an important research and political project.
Augmented Reality in Engineering Education – A Comparison of Students’ and Teachers’ Perceptions

Augmented Reality (AR) can be applied in more and more fields. One of these areas is Industry 4.0, which is highly relevant for education in Higher Technical Vocational Colleges. A positive perception of AR education might help to motivate students to deep-dive into Industry 4.0. This is important since there is a considerable demand for a highly-skilled workforce from many companies from all industries. Teachers play a crucial role in motivating students to feel addicted to these topics. The central aim of this study is to get insights into the perception of teachers about AR education in Higher Technical Vocational Colleges. Their perceptions are then compared with the perception of students. Based on hypotheses, two questionnaires were developed to collect empirical data to be analyzed with statistical methods. Overall, 172 students and 45 teachers took part in this survey. The results demonstrate that students and teachers alike positively perceive AR education. Correlation analysis of selected items from the questionnaires reveals that specific items could play a central role in a generally positive perception. Further research is needed to explore and identify a cause-effect relationship between items.
Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations

Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.