Reviewerly/exHarmony huggingface dataset:
The Effect of Statistical Hypothesis Testing on Machine Learning Model Selection

Statistical tests of hypothesis play a crucial role in evaluating the performance of machine learning (ML) models and selecting the best model among a set of candidates. However, their effectiveness in selecting models over larger periods of time remains unclear. This study aims to investigate the impact of statistical tests on ML model selection in sequential experiments. Specifically, we examine whether selecting models based on statistical tests leads to higher quality models after a significant number of iterations and explore the effect of the number of tests performed and the preferred statistical test for different experimental time horizons. The study on binary classification problems reveals that the use of statistical tests should be approached with caution, particularly in challenging scenarios where generating improved models is difficult. The analysis demonstrates that statistical tests may impede progress and impose overly stringent acceptance criteria for new models, hindering the selection of high-quality models. The findings also indicate that the dominance of versions without statistical tests remained consistent, suggesting the need for further research in this area. Although this study is limited by the number of datasets and the absence of pre-test assumption verification, it emphasizes the importance of understanding the impact of statistical tests on ML model selection.
Consolidating Research Data Management Infrastructure: Towards Sustainable Digital Scholarship

The sustainability of digital research outputs, particularly in the humanities where these frequently comprise archives of digital cultural heritage material, has always offered a challenge to the researchers and institutions who have responsibility for them. The amount of upfront care, effort, and funding that goes into developing a research project during the active (and funded) research phase is rarely replicated within the post-project maintenance and curation of the delivered digital assets or archives. What often defines the sustainability of a research project and its archive is a combination of research method and expected life span for the digital collection. Innovation in research data design is often at the expense of its longevity. But this does not need to be so. The tradeoff between longevity and functionality is a false dichotomy. Yet what is clear is that care and consideration in planning the research data storage or archive for a project can make a big difference. A data management plan that meets grant funder requirements is asked for for many research projects, but it is more than simply a funding document. Good research data management ensures that outputs are available online for years to come and available for future research and innovation. This article offers a practical insight to the methods being employed at the University of Oxford to support Digital Humanities scholars (and beyond) safeguard their digital legacy for future generations.
Clinical Oncology Textual Notes Analysis Using Machine Learning and Deep Learning

Advances in textual classification can foster quality in existing clinical systems. Our research explored experimentally text classification methods applied in non-synthetic oncology clinical notes corpora. The experiments were performed in a dataset with 3,308 medical notes. Experiments evaluated the following machine learning and deep learning classification methods: Multilayer Perceptron Neural network, Logistic Regression, Decision Tree classifier, Random Forest classifier, K-nearest neighbors classifier, and Long-Short Term Memory. An experiment evaluated the influence of the corpora preprocessing step on the results, allowing us to identify that the classifier's mean accuracy was leveraged from 26.1% to 86.7% with the per-clinical-event corpus and 93.9% with the per-patient corpus. The best-performing classifier was the Multilayer Perceptron, which achieved 93.90% accuracy, a Macro F1 score of 93.61%, and a Weighted F1 score of 93.99%.
A Framework for Characterizing What Makes an Instance Hard to Classify

The health domain has been largely benefited by Machine Learning solutions, which can be used for building predictive models to support medical decisions. But, for increasing the reliability of these systems, it is important to understand when the models are prone to failures. In this paper, we investigate what can we learn from the instances of a dataset which are hard to classify by Machine Learning models. Different reasons may explain why one or a set of instances are misclassified, despite the predictive model used. They can be either noisy, anomalous or placed in overlapping regions, to name a few. Our framework works at two levels: the original base dataset and a meta-dataset built to reflect the hardness level of the instances. A two-dimensional hardness embedding is assembled, which can be visually inspected to determine sets of instances to scrutinize better. We show some analysis that can be undertaken in this hardness space that allow to characterize why some of the instances are hard to classify, with case studies on health datasets.
Applying Reinforcement Learning for Multiple Functions in Swarm Intelligence

Swarm intelligence (SI) algorithms have become popular due to their self-learning characteristics and adaptability to external changes. They can find reasonable solutions to complex problems without in-depth knowledge. Much of the success of these algorithms comes from balancing the exploration and exploitation tasks. This work evaluates the application and performance of a reinforcement learning approach applied to a well-known swarm intelligence algorithm, Particle Swarm Optimization (PSO). We use the reinforcement learning agent Proximal Policy Optimization (PPO) to dynamically change the swarm communication topology according to the problem. We analyze the PSO's behavior, influenced by the reinforcement learning agent, through methods such as interaction networks and fitness analysis. We show that the RL approach can transfer the knowledge learned from one function to other functions, and that dynamic changes of topology over time makes PSO much more efficient than setting only one specific topology, even when using a Dynamic topology. Our results then suggest that changing topologies might be more efficient than having a Dynamic topology, and that indeed Local and Global topologies have an important role in the best swarm performance. Our results take a step further on explaining the performance of SI and automatizing their use for non-experts.
Performance Analysis of Generative Adversarial Networks and Diffusion Models for Face Aging

Computational face aging enables predicting a person's future appearance using algorithms, with the goal that the output age is close to the expected age and that the individual's characteristics are maintained. In this work, we evaluate the performance of four generative models on facial aging. Two models are based on generative adversarial networks (GANs), HRFAE, and SAM, and the other two are based on diffusion models, Pix2pix-zero and Instruct-pix2pix. The first two were explicitly trained to generate an aged version of the original person, and the others have a zero-shot generation; in other words, they are generic models that perform different tasks, including facial aging. Since diffusion models have been gaining attention because of their diversity and high-quality image generation, comparing their results against models specifically designed for the task using meaningful metrics is essential. Therefore, we compared these models using the FFHQ Aging database and with the metrics: Mean absolute error (MAE) of the predicted age, Fréchet inception distance (FID), and the cosine similarity of the FaceNet's embeddings.
AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification

Automated Machine Learning (AutoML) has achieved high popularity in recent years. However, most of these studies have investigated alternatives to single-label classification problems, presenting a need for more investigations in the multi-label classification scenario. From the AutoML point of view, the few studies on multi-label classification focus on automatically finding the best models based on mono-objective optimization. These tools train several multi-label classifiers in search of the one with the best performance in a single objective optimization process. In this work, we propose AutoMMLC, a new multi-objective AutoML method for multi-label classification, to find the best models that maximize the f-score measure and minimize the training time. Experiments were carried out with ten multi-label datasets and different versions of the proposed method using two multi-objective optimization algorithms: Multi-objective Random Search and Non-Dominated Sorting Genetic Algorithm II. We evaluated the Pareto front obtained by these methods through the hypervolume metric. The Wilcoxon test demonstrated that AutoMMLC versions had similar results for this metric. Multi-label Classification (MLC) algorithms were obtained from the Pareto frontiers through the Frugality Score and compared with the baseline algorithms. The Friedman test demonstrated that the MLC algorithms from AutoMMLC versions had equal performances to f-score and training time. Furthermore, they had better results than baseline algorithms for f-score and better results than most baseline algorithms for training time.
A Laser Triangulation Displacement Sensor Based on a Cylindrical Annular Reflector

The ellipticity of the spot caused by laser jitter has a significant effect on the measurement accuracy of the traditional laser triangulation method. In order to overcome the influence of laser characteristics on measurement and improve the accuracy of displacement measurement, this paper proposes a measurement model for a laser displacement sensor based on a cylindrical annular reflector. A physical prototype is designed through parameter optimization using NSGA-II, and a two-step detection algorithm is proposed for the imaging ring of the prototype. The algorithm performs contour thinning after rough positioning of the imaging annular ring, and then performs precise detection. Through physical experiments, it is verified that the repeatability error of this method can reach below 0.02%, and the accuracy is significantly improved compared to traditional laser triangulation displacement sensors. Meanwhile, the measurement results of displacement show linearity. The optical path design of this sensor is simpler than the previously proposed rotational symmetrical laser triangulation displacement sensor, which has good application prospects.
Fault-tolerant preparation of quantum polar codes encoding one logical qubit

This paper explores an approach to fault-tolerant quantum computing (FTQC), relying on quantum polar codes. We consider quantum polar codes of Calderbank-Shor-Steane type, encoding one logical qubit, which we refer to as ${\mathcal{Q}}_{1}$ codes. First, we show that a subfamily of ${\mathcal{Q}}_{1}$ codes is equivalent to the well-known family of Shor codes. Moreover, we show that ${\mathcal{Q}}_{1}$ codes significantly outperform Shor codes, of the same length and minimum distance. Second, we consider the fault-tolerant preparation of ${\mathcal{Q}}_{1}$ code states. We give a recursive procedure to prepare a ${\mathcal{Q}}_{1}$ code state, based on two-qubit Pauli measurements only. The procedure is not by itself fault-tolerant; however, the measurement operations therein provide redundant classical bits, which can be advantageously used for error detection. Fault tolerance is then achieved by combining the proposed recursive procedure with an error detection method. Finally, we consider the fault-tolerant error correction of ${\mathcal{Q}}_{1}$ codes. We use Steane error correction, which incorporates the proposed fault-tolerant code state preparation procedure. We provide numerical estimates of the logical error rates for ${\mathcal{Q}}_{1}$ and Shor codes of length 16 and 64 qubits, assuming a circuit-level depolarizing noise model. Remarkably, the ${\mathcal{Q}}_{1}$ code of length 64 qubits achieves a logical error rate very close to ${10}^{\ensuremath{-}6}$ for the physical error rate $p={10}^{\ensuremath{-}3}$, therefore demonstrating the potential of the proposed polar-codes-based approach to FTQC.
Exploring the Landscape of Decentralized Autonomous Organizations: A Comprehensive Review of Blockchain Initiatives

The present study aims to investigate the DAO initiative and scrutinize the diverse methodologies researchers employ for data collection in this area, highlighting any unresolved problems or limitations and suggesting approaches to enhance blockchain technology for future investigations. A remarkable blockchain initiative is the decentralized autonomous organization (DAO), a decentralized blockchain technology system that lets people self-govern through self-executing rules. The methodology is a qualitative analysis that uses contractual and business aspects to create a legally binding smart contract for DAO collaborations; thus, SPESC and Symboleo are smart-contract languages (SCL) that can involve IT and non-IT individuals in contract development. Blockchain technology has created Decentralized Autonomous Organizations (DAOs) that perform autonomously through smart contracts within their ecosystem without the necessity for centralized control or third-party intervention.
Adaptation of stochasticity into activation function of deep learning for stock price forecasting

Stock market is an example of a stochastic environment in the real world. multilayer perceptron (MLP) is often applied to forecast stock price. However, it is widely used to approximate the input-output mapping deterministically. Hence, this study aims to adapt stochasticity into MLP by introducing the Gaussian process into the sigmoid activation function. In addition, the adapted activation function incorporates Roger-Satchell and Yang-Zhang volatity estimators. Besides, the stochastic activation function was considered as a hyperparameter by applying it either only in training time or in both testing and training time. The stochastic multilayer perceptron (S-MLP) is then applied to forecast one day's highest stock price of eight constituents in FTSE Bursa Malaysia KLCI (FBMKLCI). The result shows that the proposed network is inferior in comparison to MLP except for several constituents. In addition, S-MLP with stochastic activation function during both the training and testing time performs better compared to the presence of stochastic activation function in S-MLP during training time only.
Survey on cryptocurrency security attacks and detection mechanisms

Cryptocurrencies have become extremely popular as a form of payment in recent years. They are supported by blockchain, a cutting-edge advanced technology that makes extensive use of cryptographic mechanisms and other sophisticated distributed computing techniques. On these grounds, cryptocurrencies have been a target of several attacks. Cyber-attacks, for example, are exogenous events that can robustly affect cryptocurrencies by influencing their stabilization of price and market valuation. This study describes an overview of cybercriminals’ activities on cryptocurrencies. It provides a detailed discussion on the most popular types of attacks on the cryptocurrency ecosystem. Moreover, it provides possible countermeasures to these attacks. Finally, it produces insights into the most impactful attacks on cryptocurrencies and the best methods that have been proposed for detecting cryptocurrency attacks. The main goal of this survey is to obtain a thorough understanding of cryptocurrency attacks, which have been the subject of major studies concerning financial risks on cryptocurrency. A large number of existing publications have reviewed and assessed various forms of attacks to achieve this goal. However, these works have considerably flawed. To the best of our knowledge, the present survey sheds light on future research directions.
Data-efficient model-based reinforcement learning with trajectory discrimination

Abstract Deep reinforcement learning has always been used to solve high-dimensional complex sequential decision problems. However, one of the biggest challenges for reinforcement learning is sample efficiency, especially for high-dimensional complex problems. Model-based reinforcement learning can solve the problem with a learned world model, but the performance is limited by the imperfect world model, so it usually has worse approximate performance than model-free reinforcement learning. In this paper, we propose a novel model-based reinforcement learning algorithm called World Model with Trajectory Discrimination (WMTD). We learn the representation of temporal dynamics information by adding a trajectory discriminator to the world model, and then compute the weight of state value estimation based on the trajectory discriminator to optimize the policy. Specifically, we augment the trajectories to generate negative samples and train a trajectory discriminator that shares the feature extractor with the world model. Experimental results demonstrate that our method improves the sample efficiency and achieves state-of-the-art performance on DeepMind control tasks.
Framework for Detection of Cyberbullying in Text Data Using Natural Language Processing and Machine Learning

Cyberbullying is a huge problem online that affects young people and adults. It can lead to accidents like suicide and depression. There is a growing need to curate content on social media platforms. In the following study, the authors used data on two different forms of cyberbullying, hate speech tweets on Twitter, and ad hominem-based comments on Wikipedia forums. The authors use machine learning-based natural language processing and textual data to build models based on cyberbullying detection. They study three feature extraction methods and their four classifiers to determine the best method. The model achieves an accuracy of more than 90 degrees on the tweet data and more than 80 degrees on the Wikipedia data.
Attaining CMMI Level 3 With Agile Development in Small-Medium Firms

This research chapter outlines an improvement process for software development and requirements management in small to medium-sized software companies with teams up to 25 members. It integrates elements from the Waterfall Model and Agile to address challenges in developing software for home loan services in Thai commercial banks. The study aims to achieve Capability Level 3 in the CMMI model, analyzing projects in the Thai software industry. Specific practices SP1.1 – SP1.5 and generic goals GP1.1 – GP3.2 of the requirements management process group are considered, referencing IEEE 12207-2008 and IEEE 1074-2006 standards. The outcomes include developed processes, guidelines, and tested tools. SCAMPI assessment by three experts shows improved benefits realization (avg. score: 4.373), work performance (avg. improvement: 34.34%), and process capability (avg. score: 88.22%), affirming Capability Level 3.
MUSIC-COMPUTER TECHNOLOGY SOUND RECORDING ENRICHES STUDENTS' MUSIC KNOWLEDGE AND SKILLS IN AN EXTRACURRICULAR LEARNING ENVIRONMENT

The development of modern society requires the application of new forms of presentation of educational content in order to build personality and form a generation of creative people. It’s necessary that the approach and forms of the structure of the learning process to stimulate students’ development to the maximum extent. Attracting students’ attention and stimulating their activity by presenting the learning content most effectively is a challenge for the teacher. This way, their interest should be increased and they should acquire lasting, long-term knowledge in Art music. This report presents the possibilities of consolidating and enriching students' knowledge and skills in the academic discipline Music outside the traditional music lesson. The research presents the benefit of education through music computer technology Sound Recording in a recording studio for students aged 10-16 as a part of their scheduled observation hours in extracurriculum learning environment. The aim is to increase students' interest in music as an art and to support the gaining of new knowledge. In this way, the students will be provoked to get to know their surroundings in full. At the same time, the research is an example of successful good practice to motivate and support music teachers with new practical ideas.
Ontology-Based Intelligent Interface Personalization for Protection Against Phishing Attacks

Millions of users on the Internet have fallen into phishing website traps. Detection tools are designed to warn users against such attacks, but often fail to achieve this purpose. One crucial reason behind this is that users rarely have a chance to interact and build a relationship with a detection tool that stealthily runs at the backend. A warning message on a rarely seen interface from such a tool hardly inspires users’ trust in its authenticity and accuracy. In this study, we propose an ontology-based intelligent interface personalization (OBIIP) design for the warning interfaces of phishing website detection tools. We first constructed an ontology of warning interface elements (OWIE), which is a comprehensive knowledgebase for warning interface design. We then used OWIE in the design and creation of an OBIIP prototype and assessed it in a laboratory experiment and an online experiment. The results show the significant value of OBIIP in improving users’ performance in terms of self-protection against website phishing attacks and building a stronger relationship with the detection tool in terms of trust in and use of the tool.
Personality for Virtual Assistants: A Self-Presentation Approach

Self-presentation is a sociological line of research relating concrete human behaviors, perceived personality traits, and social interaction goals. According to self-presentation, people engineer their own attributes such as behavior, clothing, and speech to try to affect how they are perceived and to accomplish social goals. Recent studies demonstrate that principles from self-presentation apply not only to how humans interact with other humans but also to how humans interact with machines. Therefore, the rich body of self-presentation research can inform virtual assistant personality and behavior. That is, if a virtual assistant is trying to accomplish x, it can express personality trait y by doing z. In this chapter, I introduce self-presentation and discuss how it provides a data-driven approach to designing and assuring virtual assistant personality.
YouTube discourse of the Oting massacre in Nagaland: investigating affiliations, sentiments and Naga identity negotiation in YouTube comments

Personae identification and understanding the social bonds shared in online interactions are found to be key to combating misinformation and conspiratorial discourse as well as aiding in developing solutions for specific communities. The 2021 Oting massacre in Nagaland that killed 14 civilians sparked public outrage and widespread protests calling for justice and the repeal of the Armed Forces (Special Powers) Act in northeast India. The massacre received a lot of attention, including YouTube videos, and reignited discussion on militarization, Naga identity and a backlash over the botched operation. A close qualitative analysis of 500 randomly extracted comments from 10 selected YouTube videos identified 15 personae with unique linguistic patterns that reveal specific ideational targets and their affiliation strategies in the discourse. A sentiment analysis of 11,294 comments shows a higher negative score linked to condemnation, hate speech and conspiracy than a positive score linked to solidarity and empathy. ‘Nagaism’ as being a Naga is reimagined and reinforced via YouTube, countering misconceptions about Naga identity.