Reviewerly/exHarmony huggingface dataset:
Autonomous Control of a Hybrid Rolling and Flying Caged Drone for Leak Detection in HVAC Ducts

In this article, a hybrid, rolling, and flying multirotor caged drone is developed for inspection and leak detection of heating, ventilation, and air conditioning (HVAC) ducts. The developed passive-cage drone is designed, manufactured, and controlled autonomously using direct model reference adaptive control (DMRAC). The platform is equipped with a 2-D LIDAR sensor that provides a scan of the surroundings in a plane. Possible leaks are detected using a thermal camera. DMRAC is used for higher level roll, pitch, and yaw control, while lower level motor speed control is achieved through Proportional-Integral-Derivative (PID) controllers. This article compares, for the first time, PID-based controllers for roll, pitch, and yaw control, with standard DMRAC and DMRAC with gain modifications, in a cascaded setting on a hybrid passive-caged drone. Several hardware tests are used to highlight the differences in the control performance when PID controllers/DMRAC with certain gain modifications are used to control the developed hybrid passive-caged drone.
Assessing Adversarial Replay and Deep Learning-Driven Attacks on Specific Emitter Identification-based Security Approaches

&lt;p&gt;Specific Emitter Identification (SEI) detects, characterizes, and identifies emitters by exploiting distinct, inherent, and unintentional features present in their transmitted signals. Since its introduction a significant amount of work has been conducted; however, most assume the emitters are passive and that their identifying signal features are immutable and difficult to mimic. Suggesting the emitters are reluctant and incapable of developing and implementing effective SEI countermeasures; however, Deep Learning (DL) has been shown capable of learning emitter-specific features directly from their raw in-phase and quadrature signal samples and Software-Defined Radios (SDRs) are capable of manipulating them. Based on these capabilities, it is fair to question the ease at which an emitter can effectively mimic the SEI features of another or manipulate its own to hinder or defeat SEI. This work considers SEI mimicry using three signal features mimicking countermeasures; “off-the-self” DL; two SDRs of different sizes, weights, power, and cost (SWaP-C); handcrafted and DL-based SEI processes, and a “coffee shop” deployment. Our results show “off-the-shelf” DL algorithms and SDR enables SEI mimicry; however, adversary success is hindered by: (i) the use of decoy emitter signals, (ii) integration of a denoising autoencoder, and (iii) SDR SWaP-C constraints. &lt;/p&gt;
Analysis of the Possible Demand of Stablecoins

Stablecoins, which are anchored by certain assets, have been gaining market attention in recent years as a branch of cryptocurrencies. The dollar domination in currency markets exists in cryptocurrency since some mainstream stablecoins anchor dollar. Looking back to when the Bretton Woods system was established, the dollar was pegged to gold and gradually became the worlds main currency. Dollar-based global stablecoins havent been completely developed, and their use on a global scale carries financial risks. This paper discusses the objectives of stablecoins and the differences between vision and reality based on existing literature and research data. In the process of realizing price stability, stablecoins also generate credit risk, and fall into the logical dilemma that leaving fiat currency and achieving price stability cannot be achieved simultaneously. This paper suggests that the compromise of stablecoin to centralization can solve part of current problems. Nevertheless, decentralization needs to be studied continuously in order to achieve stablecoins objectives.
SENTIMENT ANALYSIS WITH A CASE STUDY OF PRACTICE CARD ON TWITTER SOCIAL MEDIA USING NAIVE BAYES METHOD

-In early March 2022, Indonesia experienced a coronavirus pandemic which caused COVID-19 to enter for the first time. Since then, all sectors have been affected by the COVID-19 pandemic, not only health, the economic sector has also been seriously affected by this pandemic. In overcoming employment problems, the government makes a policy of the Pre-Employment Card program. The Pre-Employment Card Program is one of the government's efforts to expand job opportunities and to increase competitiveness which later became one of the social assistance for the community to overcome the Covid-19 pandemic. The implementation of the Pre-Employment Card program received pros and cons from the community, one of which was on Twitter social media. The results of the sentiment analysis of the pre-employment card program are mostly positive. The test results show that the Naïve Bayes Classifier method is successful in classifying sentiment with the highest accuracy value of 96%, the highest precision value of 98%, and the highest recall value of 96%, and AUC of 96%.
Speech Act Patterns in American and Filipino Online Selling Advertisements

The COVID-19 pandemic has resulted in a huge leap from in-person to online shopping.Currently, more consumers rely on products that can be bought online for convenience, variety, and safety.The vast choices in websites and mobile applications have provided the consumers with lots of features for the products to be sold, bought, and reviewed.Yang et al. ( 2019) emphasized how the internet has been extensively developed and applied, especially in business transactions.Phonthanukitithaworn et al. (2019) explain that digital marketing also helps the sellers to expand their channels, distributions, and customers' feedback.
Interpretation of Aeroradiometric Data of Part of Lower Benue Trough of Nigeria for Mineral Deposits

Aeroradiometric data of part of Lower Benue Trough of Nigeria was interpreted in order to ascertain the mineral potentials of the area. The four half-degree radiometric data sheets used for this study (sheet 288, sheet 289, sheet 302 and sheet 303) were acquired from the Nigeria Geological Survey Agency (NGSA). The radioelement composite map, radioelement equivalent ratios and ternary maps were generated from the radiometric data using Oasis Montaj 8.4 and ArcGIS software. High Potassium concentration (0.0861-0.5175%), equivalent Uranium (4.0077-5.5034 ppm) and equivalent Thorium (16.0104-19.8929 ppm) were observed in the study area, especially around Nkalagu, Igumale and Abakaliki areas. Result of the radiometric data interpretation also indicates the presence of a number of magmatic centres around Ejekwe and Igumale areas, and at the southeastern part of Abakaliki, which suggests that the area is potentially mineralized. Key minerals revealed by the study in these areas include feldspar, granite, dolerite, basalt, plead and zinc. The distribution and target extent of these zones of mineralization fall within a broad NE-SW trending axis.
Floquet time-crystals as sensors of AC fields

We discuss the performance of discrete time crystals (DTCs) as quantum sensors. The long-range spatial and time ordering displayed by DTCs, leads to an exponentially slow heating, turning DTC into advantageous sensors. Specifically, their performance (determined by the quantum Fisher information) to estimate AC fields can overcome the shot-noise limit while allowing for extremely long time sensing protocols. Since the collective response of the many-body interactions stabilizes the DTC dynamics against noise, these sensors become moreover robust to imperfections in the protocol. The performance of such a sensor can also be used in a dual role to probe the presence or absence of a many-body localized phase.
Explainable AI using expressive Boolean formulas

We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions. The Boolean formula defines a rule with tunable complexity (or interpretability), according to which input data are classified. Such a formula can include any operator that can be applied to one or more Boolean variables, thus providing higher expressivity compared to more rigid rule-based and tree-based approaches. The classifier is trained using native local optimization techniques, efficiently searching the space of feasible formulas. Shallow rules can be determined by fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers, potentially powered by special purpose hardware or quantum devices. We combine the expressivity and efficiency of the native local optimizer with the fast operation of these devices by executing non-local moves that optimize over subtrees of the full Boolean formula. We provide extensive numerical benchmarking results featuring several baselines on well-known public datasets. Based on the results, we find that the native local rule classifier is generally competitive with the other classifiers. The addition of non-local moves achieves similar results with fewer iterations, and therefore using specialized or quantum hardware could lead to a speedup by fast proposal of non-local moves.
Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers

ChatGPT is a large language model developed by OpenAI. Despite its impressive performance across various tasks, no prior work has investigated its capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of ChatGPT on various benchmark biomedical tasks, such as relation extraction, document classification, question answering, and summarization. To the best of our knowledge, this is the first work that conducts an extensive evaluation of ChatGPT in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot ChatGPT even outperforms the state-of-the-art fine-tuned generative transformer models, such as BioGPT and BioBART. This suggests that ChatGPT's pre-training on large text corpora makes it quite specialized even in the biomedical domain. Our findings demonstrate that ChatGPT has the potential to be a valuable tool for various tasks in the biomedical domain that lack large annotated data.
Muhadhoroh and English Public Speaking Skills: Benefits, Challenges, and Strategies

In the modern era, Islamic boarding school education has changed forms of adaptation to the times. One of them is the existence of the term Modern Islamic Boarding School where the education system combines national and international curricula and adapts to the needs of the students. Extracurricular activities are one of the activities that exist in modern Islamic boarding schools. This activity is known as Muhadhoroh. This study explores Muhadhoroh and English-speaking skills related to the benefits, challenges, and strategies for these activities. This study uses a descriptive qualitative method. The research data were obtained from interviews, observations, and documentation while the participants in this study were three English teachers who teach at Amanatul Ummah Islamic boarding school both Indonesian and foreign teachers. The research data were analyzed in four stages, namely data collection, data display, data reduction, and conclusion. The results of this study indicate that Muhadhoroh could provide opportunities for students to develop talents in the field of English public speaking skills because students had the opportunity to perform various kinds of English performances in public. Related to challenges, this activity has challenges including preparation, time management, and facilities. Even so, several strategies could be taken to overcome these challenges, including arranging preparation and performance schedules, utilizing free time and shifting school schedules into preparation and performance schedules, as well as adding facilities by renting equipment to other parties during activities. This study is limited in location and the number of participants who are only in one Islamic boarding school and the type of research instrument. It is recommended for further research to be able to conduct similar research by adding variations of participants and Islamic boarding schools as well as adding variations of research instruments
Enhancing Chinese Address Parsing in Low-Resource Scenarios through In-Context Learning

Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and GPT modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, mitigating interference from unnecessary variations. We introduce the REB-KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts, and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model's effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.
Non-Face-to-Face P2P (peer-to-peer) Real-time Token Payment Blockchain System

With the increase of intelligent voice phishing and the increasing reliance on open banking systems, there has been a rise in cases where individuals' personal information has been exposed, resulting in significant financial losses for the victims. Non-face-to-face transactions in the financial sector face challenges such as customer identification, ensuring transaction integrity, and preventing transaction rejection. Blockchain-based distributed ledgers have been proposed as a solution, but their adoption is limited due to the difficulty of managing private keys and the burden of gas fees management. This paper proposes a non-face-to-face P2P real-time token payment system that minimizes the risk of key loss by storing private keys in a keystore file and database through a server-based key management module. The proposed system simplifies token creation and management through a server-based token management module and implements an automatic gas charging function for smooth token transactions. Transaction integrity and non-repudiation are ensured through a transaction confirmation module that uses transaction IDs without exposing personal information. Furthermore, advanced security measures such as blocking foreign IP access and DDoS defense are implemented to securely protect user data. The proposed system aims to provide a convenient, secure, and accessible online payment solution to the public by implementing a self-authentication function using a web application that is not limited to smart phones or application platforms.
Evaluation of Defense Methods Against the One-Pixel Attack on Deep Neural Networks

The one-pixel attack is an image attack method for creating adversarial instances with minimal perturbations, i.e., pixel modification. The attack method makes the adversarial instances difficult to detect as it only manipulates a single pixel in the image. In this paper, we study four different defense approaches against adversarial attacks, and more specifically the one-pixel attack, over three different models. The defense methods used are: data augmentation, spatial smoothing, and Gaussian data augmentation used during both training and testing. The empirical experiments involve the following three models: all convolutional network (CNN), network in network (NiN), and the convolutional neural network VGG16. Experiments were executed and the results show that Gaussian data augmentation performs quite poorly when applied during the prediction phase. When used during the training phase, we see a reduction in the number of instances that could be perturbed by the NiN model. However, the CNN model shows an overall significantly worse performance compared to no defense technique. Spatial smoothing shows an ability to reduce the effectiveness of the one-pixel attack, and it is on average able to defend against half of the adversarial examples. Data augmentation also shows promising results, reducing the number of successfully perturbed images for both the CNN and NiN models. However, data augmentation leads to slightly worse overall model performance for the NiN and VGG16 models. Interestingly, it significantly improves the performance for the CNN model. We conclude that the most suitable defense is dependent on the model used. For the CNN model, our results indicate that a combination of data augmentation and spatial smoothing is a suitable defense setup. For the NiN and VGG16 models, a combination of Gaussian data augmentation together with spatial smoothing is more promising. Finally, the experiments indicate that applying Gaussian noise during the prediction phase is not a workable defense against the one-pixel attack.
Optimized Small Waterbird Detection Method Using Surveillance Videos Based on YOLOv7

Waterbird monitoring is the foundation of conservation and management strategies in almost all types of wetland ecosystems. China's improved wetland protection infrastructure, which includes remote devices for the collection of larger quantities of acoustic and visual data on wildlife species, increased the need for data filtration and analysis techniques. Object detection based on deep learning has emerged as a basic solution for big data analysis that has been tested in several application fields. However, these deep learning techniques have not yet been tested for small waterbird detection from real-time surveillance videos, which can address the challenge of waterbird monitoring in real time. We propose an improved detection method by adding an extra prediction head, SimAM attention module, and sequential frame to YOLOv7, termed as YOLOv7-waterbird, for real-time video surveillance devices to identify attention regions and perform waterbird monitoring tasks. With the Waterbird Dataset, the mean average precision (mAP) value of YOLOv7-waterbird was 67.3%, which was approximately 5% higher than that of the baseline model. Furthermore, the improved method achieved a recall of 87.9% (precision = 85%) and 79.1% for small waterbirds (defined as pixels less than 40 × 40), suggesting a better performance for small object detection than the original method. This algorithm could be used by the administration of protected areas or other groups to monitor waterbirds with higher accuracy using existing surveillance cameras and can aid in wildlife conservation to some extent.
Influence of ISO 9001:2015 in Building Project of Sharjah

The goal of this study is to evaluate how organizational culture, leadership, and other factors affect the extent to which the ISO 9001:2015 quality management system is implemented in the construction industry. The 150 construction employees serve as the study sample's population. SEM (Structural Equation Model), together with LISREL software version 8.70, is used in this study's research. The findings demonstrate that motivation, competence, and commitment have a small influence on the effectiveness of the ISO 9001:2015 quality management system implementation; however, leadership and organizational culture have a significant impact on it.
Towards an Improved Particle Swarm Optimization for Feature Selection: A Survey

Over the years, scientists have used natural discoveries such as evolution to solve real-world problems. Addressing the challenges that arise when dealing with high-dimensional data is one such problem. These challenges include difficulties in analyzing, visualizing, and modelling these high-dimensional data. As a result, the Swarm Intelligence (SI) techniquewas developed, which was inspired by natural swarm foraging behaviors. Particle swarm optimization (PSO) is a well-known SI algorithm for addressing a wide range of optimization problems. As a result, it has been used to solve a variety of optimization problems in fields as diverse as genomic analysis and intrusion detection systems. One of the most successful areas of PSO application is feature selection, which entails using computational techniques to select a reduced subset of features that have a sufficient relationship with their corresponding class labels. This, in turn, addresses the mentioned challenges. Nonetheless, progressive research has revealed several problems with PSO, including problems with diversity, and premature convergence among others. As a result, several improvements and extensions were made to various aspects of the algorithm since its inception to make it efficient. This paper organizes and summarizes current research on improvements to the PSO algorithm for solving the feature selection problem. Consequently, it presents current trends and directions for scholars in the field, as well as open challenges and literature gaps to investigate
A Futuristic View of the Secretarial Profession from an Artificial Intelligence Perspective

The first revolution in the 18th century saw the harnessing of water and steam power, exploitation of electricity took place in the second revolution and the exploitation of computer science technology in the third revolution. The fourth and fascinating revolution has started with the advent of new technologies like Artificial Intelligence (AI), Machine Learning and Robot Process Automation (RPA). The above disruptive technologies significantly disrupt the role of a company secretary. A day is not far when Artificial Intelligence is also sitting in the boardroom along with other human Directors. New technology will increasingly change the way the companies work and also the work as a company secretary and governance professional. Technological innovation leads to new corporate governance challenges in terms of ethics, fairness and transparency. The changes need to be embraced with boards and stakeholders suitably advised.