Reviewerly/exHarmony huggingface dataset:
AutoML in heavily constrained applications

Abstract Optimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hyperparameters for the given training dataset. Yet, depending on the AutoML system’s own second-order meta-configuration, the performance of the AutoML process can vary significantly. Current AutoML systems cannot automatically adapt their own configuration to a specific use case. Further, they cannot compile user-defined application constraints on the effectiveness and efficiency of the pipeline and its generation. In this paper, we propose Caml , which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand. The dynamic AutoML strategy of Caml takes user-defined constraints into account and obtains constraint-satisfying pipelines with high predictive performance.
A Combined Priori and Purity Gaussian OverSampling Algorithm for Imbalanced Data Classification

The imbalanced data classification presents pervasive challenges in real-world data mining scenarios. To tackle these challenges, sampling techniques have emerged as effective approaches. However, the prevailing technique, SMOTE(Synthetic Minority Over-sampling Technique), and its derivatives make the assumption that each minority class observation carries an equal amount of information, neglecting the distribution of minority class observations and their relationship with neighboring majority class observations. Consequently, the synthetic samples generated by these methods deviate from the original data distribution, resulting in an increased overlap with the majority samples. To address this limitation, we introduce a novel sampling technique called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Combined Priori and Purity Gaussian OverSampling</i> (PPGO) in this paper. The proposed method incorporates prior probabilities and sample purity to calculate the weight assigned to each minority class sample. This weight is used to determine the quantity of synthetic samples to be generated for each minority class, as well as the level of dispersion during the Gaussian sampling process. This approach aims to restore the original distribution of the observations and minimize the overlap with the majority class regions. The experimental results demonstrate a significant improvement in the G-mean and AUC measures when using the proposed method compared to conventional approaches. These results were obtained through experiments conducted on 32 datasets obtained from the KEEL repository.
Research Trends, Detection Methods, Practices, and Challenges in Code Smell: SLR

Context: A code smell indicates a flaw in the design, implementation, or maintenance process that could degrade the software’s quality and potentially cause future disruptions. Since being introduced by Beck and Fowler, the term code smell has attracted several studies from researchers and practitioners. However, over time, studies are needed to discuss whether this issue is still interesting and relevant. Objective: Conduct a thorough systematic literature review to learn the most recent state of the art for studying code smells, including detection methods, practices, and challenges. Also, an overview of trends and future relevance of the topic of code smell, whether it is still developing, or if there has been a shift in the discussion. Method: The search methodology was employed to identify pertinent scholarly articles from reputable databases such as ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, ProQuest, and CiteSeerX. The application of inclusion and exclusion criteria serves to filter the search results. In addition, forward and backward snowballing techniques are employed to enhance the comprehensiveness of the results. Results: The inquiry yielded 354 scholarly articles published over the timeframe spanning from January 2013 to July 2022. After inclusion, exclusion, and snowballing techniques were applied, 69 main studies regarding code smells were identified. Many researchers focus on detecting code smells, primarily via machine learning techniques and, to a lesser extent, deep learning methods. Additional subjects encompass the ramifications of code smells; code smells within specific contexts, the correlation between code smells and software metrics, and facets about security, refactoring, and development habits. Contexts and types of code smells vary in the focus of the study. Some tools used are Jspirit, aDoctor, CAME, and SonarQube. The study also explores the concept of design smells and anti-pattern detection. While a singular dominating technique to code smell detection has yet to be thoroughly investigated, other aspects of code smell detection remain that still need to be examined. Conclusion: The findings underscore scholarly attention’s evolution towards code smells over the years. This study identified significant journals and conferences and influential researchers in this field. The detection methods used include empirical, machine learning, and deep learning. However, challenges include subjective interpretation and limited contextual applicability.
A Systematic Literature Review on Social Media Slang Analytics in Contemporary Discourse

Social media slang, encompassing informal language, words, phrases, and acronyms on digital platforms, reflects the dynamic nature of online communication. Analyzing social media slang offers valuable insights for organizations and researchers, enabling a deeper understanding of communication trends, sentiment analysis, and user behavior in the digital sphere. It plays a pivotal role in shaping effective marketing strategies and enhancing communication, ultimately facilitating informed decision-making in the digital age. In our study, we conducted a systematic review of research articles from the Web of Science and Scopus databases, spanning the years 2016 to 2023. Our rigorous selection process, based on quality assessments as per PRISMA guidelines, revealed several following key findings. Social media slang exhibits a remarkable adaptability to different platforms, mirroring the communication styles and user cultures found on each. Notably, it influences user behavior, impacting interactions, content engagement, and decision-making, particularly in marketing and communication strategies. Furthermore, our research highlights the value of social media slang in sentiment analysis, providing insights into public sentiment and supporting well-informed decision-making. Our study underscores the versatile applications of slang analytics across various industries and research domains, emphasizing its pivotal role in providing specialized insights and enhancing communication strategies. In conclusion, our research offers a comprehensive understanding of the dynamic landscape of informal language in the context of contemporary digital communication, furnishing valuable insights that inform decision-making, refine marketing strategies, and enhance communication.
Organized Sound, Sounds Heard, and Silence

In this paper I argue that composer John Cage’s so-called ‘silent piece’, 4’33”, is music. I first defend it against the charge that it does not involve the organization of sound, which has been taken to be a necessary feature of music. I then argue that 4’33” satisfies the only other condition that must be met for it to be music: it bears the right socio-historical connections to its predecessors within its tradition (Western art music). I argue further that one cannot understand the organized sound condition and the socio-historical condition separately and that understanding their interaction has theoretical benefits—not least of which is providing a groundwork for a more culturally inclusive philosophy of music. Finally, I consider a number of outstanding questions concerning the content of the organized sound condition for Western art music in the wake of 4’33”.
An Interpretable Preference Learning Model Admitting Dynamic and Context-Dependent Preferences

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RTSNet: Learning to Smooth in Partially Known State-Space Models

The smoothing task is core to many signal-processing applications. A widely popular smoother is the Rauch-Tung-Striebel (RTS) algorithm, which achieves minimal mean-squared error recovery with low complexity for linear Gaussian state-space (SS) models, yet is limited in systems that are only partially known, as well as nonlinear and non-Gaussian. In this work, we propose RTSNet, a highly efficient model-based and data-driven smoothing algorithm suitable for partially known SS models. RTSNet integrates dedicated trainable models into the flow of the classical RTS smoother, while iteratively refining its sequence estimate via deep unfolding methodology. As a result, RTSNet learns from data to reliably smooth when operating under model mismatch and nonlinearities while retaining the efficiency and interpretability of the traditional RTS smoothing algorithm. Our empirical study demonstrates that RTSNet overcomes nonlinearities and model mismatch, outperforming classic smoothers operating with both mismatched and accurate domain knowledge. Moreover, while RTSNet is based on compact neural networks, which leads to faster training and inference times, it demonstrates improved performance over previously proposed deep smoothers in nonlinear settings.
Addressing the Faults Landscape in the Internet of Things: Toward Datacentric and System Resilience

In the Internet of Things (IoT) context, the landscape of weaknesses in the IoT spectrum sheds light on addressing faults by researchers due to the number of IoT components that unveil immense vulnerabilities to failures. Hence, there is a need to comprehend the faults dynamics to facilitate identifying potential hazards in a developer’s design, deliver methodologies to mitigate the risks, and ensure the data quality and resiliency of the IoT’s deployment. This article comprehensively aims to analyze faults occurrences in the IoT, their impacts on functionality, and their repercussions on data. It highlights the intricate patterns of data faults by addressing various aspects, such as duration, cause, pitfalls, component, type, and source.
Vignetting Correction Using an Optical Model and Constant Chromaticity Prior

Vignetting correction is a key pre-processing module for most imaging systems related to computer vision applications. Vignetting estimation is difficult for the current imaging systems due to the complex optical components and challenging vignetting feature extraction. In this work, we propose an algorithm for single-image vignetting correction. We present an optical aperture limit model that uses an occlusion parameter to estimate vignetting effect caused by light occlusion. Moreover, we derive a novel prior that the pixel chromaticity is not affected by vignetting. Our algorithm efficiently selects regions for feature extraction according to the chromatic and spatial analysis of the image, and then uses the intensity ratios to predict the vignetting model parameters and optical center guided by the constant chromaticity prior. We evaluate our algorithm on both synthetic and real-world images. Experimental results indicate that, compared with the state-of-the-art methods, our algorithm achieves better correction under different types of vignetting. Source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://ivlab.org/publications.html</uri> .
Practice and Exploration of Smart Homework to Promote Large-Scale Adaptive Teaching

To implement the “double-reduction” initiative of the Ministry of Education and meet the individualized growth of students in a large-scale teaching environment, most teachers, students, and parents of Zhanqian Road School in Nanchang City have great confusion about their homework, which is mainly manifested in the following aspects.
Light Practical Smart Experiment Classroom and Smart Education Application Solutions in Popular Science Corner

Jingle Magic is based on a new generation of emerging key technologies such as AI, AR, XR and 5G. With the joint operator and The Qingdao West Coast New Area, we have carried out the “5G+ Smart Education Application-Experimental Classroom and Popular Science Corner Project”, which is mainly applied to the experimental teaching of physics and chemistry students in middle schools.
Making tradable white certificates trustworthy, anonymous, and efficient using blockchains

Fossil fuel pollution has contributed to dramatic changes in the Earth's climate, and this trend will continue as fossil fuels are burned at an ever-increasing rate. Many countries around the world are currently making efforts to reduce greenhouse gas emissions, and one of the methods is the Tradable White Certificate (TWC) mechanism. The mechanism allows organizations to reduce their energy consumption to generate energy savings certificates, and those that achieve greater energy savings can sell their certificates to those that fall short. However, there are some challenges to implementing this mechanism, such as the centralized and costly verification and control of energy savings. Moreover, the verification process is not transparent, which could lead to fraud or manipulation of the system. Therefore, in this paper, we propose a blockchain-based TWC mechanism to automatically create, verify, and audit the TWC certificates. In addition, we propose a smart-contract-based TWC trading mechanism that enables traders to trade their TWCs without exposing their private information in an untrusted environment. Evaluations show that the proposed TWC framework is scalable for 1000 TWC traders simultaneously, and optimization problem can be solved in less than 120ms. Moreover, it has been shown that Polygon Matic incurs least gas cost compared to other blockchain-based solutions.
Making Sense of Touch and Sensors with p5.js

Moving from Processing to p5.js enables not only a wider reach by being able to put your sketches on the web and make them accessible to a potentially huge audience. It also means that your sketches can run on mobile devices such as tablets and phones. This is more than being able to carry your creative work around; tablets and phones have built-in sensors and interaction capabilities that you would not easily find on a desktop computer. In this chapter, we will focus on multi-touch input and accessing sensors that provide us with data about acceleration and rotation. And, of course, we will use this in a creative way.
How to Detect AI-Generated Texts?

Recent advances in large language models (LLMs) have significantly improved the quality of synthetic text data. LLMs imitate human writing patterns to produce highly natural text, raising serious ethical, moral, legal, social, and economic concerns in various industries. To address these issues, we present two methods to distinguish Synthetically Generated Text (SGT) from Human-Written Text (HWT): machine learning and text similarity. Our methods include procedures for dataset creation, feature engineering, model training, and result analysis with feature importance analysis and explanation models. As part of our research, we created two datasets - a Wikipedia-based and a US Election 2024 news article-based dataset using ChatGPT, from which we obtained promising results of up to 99.xxx% accuracy. These datasets can be used as open-source datasets in future studies. We also identified a set of handcrafted features that can serve as the baseline feature set for future research.
Investigating Drone Data Recovery Beyond the Obvious Using Digital Forensics

The rapid advancement of drone technology has opened up new opportunities across a wide range of sectors, including agriculture, surveillance, and delivery services. Specifically, the accessibility and affordability of drones suggest a surge in their prevalence in the coming years. Nonetheless, this growth opened new doors for bad actors to use drones for nefarious activities; thereby, raising safety and security concerns attributed to the unmanned aerial vehicles (UAVs). In this paper, we focus on systematically investigating the data recovery mechanisms for confiscated drones. First, we discuss the potential security threats associated with the drones and different detection methods to spot drones. Then, we propose a structured data recovery framework to effectively retrieve crucial data from a malicious drone. In particular, we analyze the popular DJI Mini 2 SE drone and show how our proposed framework can facilitate forensic investigators to uncover crucial data beyond the drone’s metadata. Our experiments show that investigators can effectively recover sensitive historical data that was even deleted from the drone’s external secure digital (SD) card.
S.H.E.R.P.A (Super Helpful Engine Recognizing People’s Audio)

Hands-free technology could be an integral tool for those who are differently abled, the elderly or just someone who wishes to perform another task while accessing technology. Speech to text to command programs are comprised of an ASR (automatic speech recognition) module and a command implementation module. Their purpose is to provide a means for a user to navigate their technology using only spoken words. We propose the implementation of OpenAI’s Whisper model in conjunction with a Graphical User Interface (GUI) and a command library. Utilizing the pretrained model we were able to create a program that allows for hands-free user interaction and command with certain browser, email, and desktop applications.
Semantic-Aware Blockchain Architecture Design for Edge-enabled Metaverse

The evolution of the Metaverse places higher demands on task transmission and computation to provide users with enhanced quality services. In this paper, we are first to address the timely task allocation and processing for heterogeneous services described using semantics in the Metaverse. We consider a mobile edge computing network composed of Unmanned Aerial Vehicles (UAVs) and adopt blockchain technology to support secure task allocation. The task allocation problem is formulated using semantic matching and penalty mechanisms and solved by Particle Swarm optimization (PSO) and Learning-based algorithms. We have analyzed the impact of the penalty mechanism on system rewards, task server matching rates, and the success rate of task service. Our results show significant improvements of our architecture and algorithms compared to existing approaches.
Do API Migrations revert commits - A Preliminary Investigation

Reverted change is a popular and crude approach to recovering from problematic commits in software development. These changes hinder the development progress and complicate the maintenance of the software systems, especially in the case of changes related to API migration. This study examined the reverted commits related to API migration of six open-source systems to investigate (i) the ratio of undoing the problematic changes (i.e., reverted commits) and (ii) the effectiveness of API migration commits in reverting problematic changes caused by other evolution tasks. The outcomes of the study demonstrate that (i) API-migration activities aren’t solely responsible for the reverted commits, and (ii) the number of problematic changes caused by the other evolutionary tasks and reverted by API migration tasks is statistically significant.
Deep Fake and Digital Forensics

Deepfakes have posed a significant challenge to digital forensics, and there is an increasing need for high accuracy deepfake (DF) detection models in real-world scenarios. This research examines and fine-tunes the MesoNet model to improve its performance on a large dataset of 140K authentic and manipulated images. The original MesoNet model achieved an accuracy of 87.1%. However, after fine-tuning and optimizing the model’s weights, the accuracy improved to 96.20%. This was accompanied by a sensitivity of 97.48% and a specificity of 94.75%, indicating that the model is highly effective at detecting genuine images and accurately identifying forged ones. This research contributes to the advancement of DF detection mechanisms in real-world scenarios.
Development of Trustable Deep Learning Model in Remote Sensing through Explainable-AI Method Selection

Remote sensing-based sensors are considered a pivotal medium to monitor remote objects by security-critical government organizations, e.g., DOD, DHS, and EPA. Historically, human operators used manual mapping in monitoring or detecting an object using remote sensing sensors, e.g., multi-spectral images. Deep learning algorithms can automatically identify and learn multi-spectral, multi-temporal, and multi-modality features of remote sensing datasets while detecting objects. They are also known as black box models because they lack clarity in decision-making. In this paper, we compared two explainable AI models, Grad-CAM and Score-CAM, to interpret the decisions in two multispectral datasets: UC-Merced and EuroSat. These datasets contain classes of land use, such as airplanes and buildings, and objects related to land cover, like forests and rivers. In the empirical analysis, we identified that ScoreCAM performed better than GradCAM based on three evaluation metrics: ROAD, SIC, and infidelity in UC-Merced and EuroSat datasets.
Defining Metrics for Comparing Threat Intelligence Solutions Through the Lens of the Analyst

Threat intelligence can be a valuable tool for any cybersecurity team. However, paid solutions are often costly. Furthermore, it is difficult to determine what solution is suitable for the organization, especially when the perspective of the analyst implementing the solution is often not consulted. This paper derives metrics from comparing three threat intelligence solutions with free options through the analyst’s lens to rank them. The metrics enumerate the challenges an analyst may face when adopting the solutions. Three main criteria are identified when evaluating a solution: the quality of the user interface, the quality of the programmatic interface, and the quality of the data for threat intelligence operations. Drawing from existing research on these topics, further criteria and questions are developed for each metric to “score” the solution. The scores are combined to rank the solutions. These scores and the criteria that inform them lead to a baseline that an employer or vendor can utilize to evaluate the successful implementation of a threat intelligence solution.
Building a Robot to Identify Electric Vehicles from Infrared Images

Identification of electric vehicles (EV) is important from the perspective of monitoring the environmental, social, and governance (ESG) goals around sustainable transport choices. We present steps for developing a system capable of improving image quality to help the automated process of counting EV and ICE vehicles. We have built a pan-tilt camera motion system to keep the target vehicle centered in the camera and then, collected imagery by developing a data collection robot based on the Nvidia Jetson nano. The pan-tilt system was developed in two phases. In the first place, we implemented a proportional algorithm and then went on a proportional-integral, which increase the result for placing the target in the region of interest. We will be presenting the steps made to improve the collecting system.