Reviewerly/exHarmony huggingface dataset:
Investigating Security Vulnerability Related to Exposure and TLS Ecosystem in IoT Devices

The Internet of Things (IoT) is popular for its ability to perform various smart and dedicated tasks, but its popularity has also made it a prime target for cyberattacks. Unfortunately, IoT security has been given less priority compared to functionality and performance during the design and implementation stages. Two major reasons behind the weak security of IoT devices are their exposure to potential attacks and the low adoption of secure Secure Sockets Layer(SSL)/Transport Layer Security(TLS) protocols. To address these issues, this paper examines the exposure of different categories of IoT devices and services using tools like Shodan and criminal infrastructure analysis. It also investigates the state of SSL/TLS implementation in IoT infrastructure. The research provides a list of exposed services and ports that can be exploited by attackers, highlighting the significant risks. Additionally, the study reveals that the implementation of SSL/TLS in the IoT ecosystem is concerning, although there has been a slight improvement compared to the previous year.
Enhancing Voting Security and Efficiency

This study explores developing and implementing a novel Electronic Voting Machine (EVM) system integrated with biometric identifiers to enhance voting security and efficiency significantly. Traditionally, voting processes relied on paper ballots, a system fraught with several challenges, including over-voting, the loss or misplacement of ballot papers, environmental harm due to paper consumption, and a lengthy result compilation process. An advanced EVM system is proposed to address these issues, leveraging unique biometric identifiers - facial recognition and fingerprints - for voter authentication and secure vote recording. Our EVM system effectively improves the security against bogus voting and vote repetition, which have been significant concerns in previous voting systems. This robust approach to voter authentication minimizes the likelihood of voting fraud, thus contributing to a more reliable and secure voting process. However, the transition to this advanced EVM system is challenging. The study identifies keyimplications, including the impact on employment due to automation, potential inaccuracies and biases associated with biometric technologies, and vital privacy concerns surrounding using sensitive biometric data. Despite these challenges, the proposed system provides a substantial foundation for future enhancements. Opportunities for further development include the integration of additional biometric identifiers like iris recognition, refining the accuracy of current biometric technologies, and strengthening data privacy measures.
Smooth and Stepwise Self-Distillation for Object Detection

Distilling the structured information captured in feature maps has contributed to improved results for object detection tasks, but requires careful selection of baseline architectures and substantial pre-training. Self-distillation addresses these limitations and has recently achieved state-of-the-art performance for object detection despite making several simplifying architectural assumptions. Building on this work, we propose Smooth and Stepwise Self-Distillation (SSSD) for object detection. Our SSSD architecture forms an implicit teacher from object labels and a feature pyramid network backbone to distill label-annotated feature maps using Jensen-Shannon distance, which is smoother than distillation losses used in prior work. We additionally add a distillation coefficient that is adaptively configured based on the learning rate. We extensively benchmark SSSD against a baseline and two state-of-the-art object detector architectures on the COCO dataset by varying the coefficients and backbone and detector networks. We demonstrate that SSSD achieves higher average precision in most experimental settings, is robust to a wide range of coefficients, and benefits from our stepwise distillation procedure.
Research on recognition method of floating objects on water surface based on Mask R-CNN

In order to detect and deal with floating objects on water surface in time and improve the supervision level of rivers and lakes, we propose a method for recognition of floating objects on water surface, based on Mask R-CNN algorithm. Firstly, we design a set of floating object label classification rules, and establish a real data sample set in the field of rivers and lakes. Then we propose a solution of floating objects recognition, which includes image capture service, AI analysis, and early warning service platform. We compare the floating object recognition method base on the Mask R-CNN model and the SIFT feature and conduct experiments with different feature extraction networks. The results show that the method is significantly better than the traditional SIFT method, the average accuracy is increased by 16.15%, the average recall rate increased by 13.75%, and the ResNet-based method is more capable of identifying irregular floating objects. This method is successfully applied to the river and lake supervision system, and the recognition accuracy of common targets is over 90%.
Generic eigenstate preparation via measurement-based purification

It is not a general opinion that a quantum system could be purified into a target eigenstate via repeated measurements on a coupled qubit rather than direct transitions in the Hamiltonian. Projective measurement on the ancillary qubit gives rise to positive operator-valued measures on the system that can filter out the unwanted states except the target one. In application, we discuss measurement-based entanglement purification by which maximally entangled states (Bell states and Greenberger-Horne-Zeilinger states) can be distilled from maximally mixed states or separable states. We also demonstrate the significant acceleration of a stimulated Raman adiabatic passage assisted by similar measurements. Our scheme allows arbitrary eigenstate preparation and reveals efficiency in multipartite systems for subspace purification. It offers a promising and generic quantum-control framework enriching the functionalities of quantum measurement.
Integration Mixed Reality with Internet of Things by Using Magic Leap

Mixed Reality (MR) is one of the immersive technologies that integrated other technologies. One of the applications of this research is integration with IoT. Usually, controlling an IoT device using an application or web that runs on a smartphone or laptop, is likely replaced with mixed reality that runs on Magic Leap. Mixed reality applications allow users to use mixed reality applications on Magic Leap immersively. In this research, we will develop a mixed reality application integrated with IoT smart devices is created that contains 3D environments and 3D assets and runs on Magic Leap. Users interact using Magic Leap’s Controller and hand tracking. User commands to control smart devices are forwarded to Tuya server through the web service API. Tuya server aims to access controlled Smart devices that are added with a Tuya account registered in Tuya’s cloud service. The results of this study were tested based on data transmission speed, load testing, and user testing. the results of testing the speed of sending commands with the same bandwidth speed get an average response time of 0.9 seconds. From the load delivery test, it was found that the minimum number of users to send simultaneous commands was 5 users with a minimum bandwidth of 8 Mbps. The results of user testing expressed respondents’ satisfaction with the performance of the mixed reality application system.
ANALISA POTENSI ENERGI ANGIN DI PANTAI LHONG KABUPATEN ACEH BESAR MENGGUNAKAN DISTRIBUSI WEIBULL

Aceh Province in the National Energy General Plan is targeted in 2050 for the development of new renewable energy wind power plants to reach 894 MW of power. This wind energy potential still needs to be surveyed or further researched for the suitability of its development location so as to produce the best position as a wind power plant. This study aims to analyze the wind potential on the Lhong coast, Aceh Besar District, Aceh Province using the Weibull distribution and using the wind speed from the ECMWF. The average wind speed distributed by weibull at the location reaches 2 m/s with a frequency of 26% and a speed of 3 m/s with a frequency of 25%. The power generated from the wind potential in the Lhong coastal area of Aceh Besar Regency is 32.411 watt meters with electrical energy produced 168,364 kwh/m/year, which belongs to the category of low-class wind as a wind power plant.Keywords:Power Generation, Renewable Energy, Wind Energy Potential.
An Astute Automaton Model for Objects Extraction Using Outer Totality Cellular Automata (OTCA)

Object formation is imperative to the recent computer vision, pattern recognition, healthcare, and automation applications. The objects are generated from images by defining edges and the segmentation process. This article introduced a novel method, Outer Totality Cellular Automata (OTCA), for defining actual and continuous edges of the image objects. The OTCA analysis nearby 25 neighbourhood pixels of all the pixels and generate a unique and efficient threshold. The proposed method has three primary functions, i.e. vitality, rule mapping, and improved morphological functions. The key objectives are image smoothing, neighbourhood analysis, defining game of life rule, and edges smoothing. Notably, the proposed method aimed to segment different coloured images, i.e., RGB, HSV, and YUV. The proposed method also aimed to produce more truthful results on blurred, reflected, shaded night vision images. The experimental process demonstrates using standard open-source datasets and validated using image quality assessment parameters, i.e., entropy, PSNR, SSIM, and MSE. The results claim 3% - 12% more structural analogous, factual, and accurate than existing classical methods and recent searches.
Research on Image Super-Resolution Using Attention Mechanisms based on Super-Resolution Generative Adversarial Network

With the continuous advancement of technology, Super-Resolution Generative Adversarial Networks (SRGAN) have played a significant role in the field of image super-resolution, significantly enhancing the resolution of images. However, while SRGAN excels in generating details, sometimes the restored details do not always meet people's expectations. To further enhance the quality of images and make image details clearer, this paper introduces improvements to the architecture and loss functions of the SRGAN network. Specifically, this research draws inspiration from the architecture of ESRGAN, removing the original Batch Normalization layers and introducing a newly designed Residual Block. Leveraging insights from attention mechanisms, we incorporate three layers of convolutional operations and introduce attention mechanisms into these new Residual Blocks. Furthermore, to simplify the computational complexity of the model, this paper simplifies the original loss functions, consolidating the previous four losses into two. These enhancements result in a significantly improved model in capturing visual elements, making key objects in the images more prominent compared to SRGAN. Detailed experimental results demonstrate that this model, while maintaining the clarity of details, provides higher visual quality. These achievements provide valuable insights and inspiration for further research and applications in the field of image super-resolution.
AutoConf: Automated Configuration of Unsupervised Learning Systems Using Metamorphic Testing and Bayesian Optimization

Unsupervised learning systems using clustering have gained significant attention for numerous applications due to their unique ability to discover patterns and structures in large unlabeled datasets. However, their effectiveness highly depends on their configuration, which requires domain-specific expertise and often involves numerous manual trials. Specifically, selecting appropriate algorithms and hyperparameters adds to the complexity of the configuration process. In this paper, we propose, apply, and assess an automated approach (AutoConf) for configuring unsupervised learning systems using clustering, leveraging metamorphic testing and Bayesian optimization. Metamorphic testing is utilized to verify the configurations of unsupervised learning systems by applying a series of input transformations. We use Bayesian optimization guided by metamorphic-testing output to automatically identify the optimal configuration. The approach aims to streamline the configuration process and enhance the effectiveness of unsupervised learning systems. It has been evaluated through experiments on six datasets from three domains for anomaly detection. The evaluation results show that our approach can find configurations outperforming the baseline approaches as they achieved a recall of 0.89 and a precision of 0.84 (on average).
Semantic Analysis

This chapter studies semantic analysis, one of the core concepts for learning NLP. There are two basic schemes of semantic analysis: lexical and compositional semantic analysis. After that we will explore word senses and six commonly used lexical semantics followed by word sense disambiguation (WSD) and various WSD schemes. Further, it also studies WordNet and online thesauri for word similarity and various distributed similarity measurement include Point-wise Mutual Information (PMI) and Positive Point-wise Mutual information (PPMI) models with live examples for illustration. Chapters 4 and 5 also serve as the conceptual basis for Workshop#4—Semantic Analysis and Word Vectors using spaCy in Chap. 13 .
Improved <scp>GAN</scp> for image resolution enhancement using <scp>ViT</scp> for breast cancer detection

Abstract Breast cancer has a significant mortality rate and is widespread among women. Detecting it early is crucial for effective treatment. While mammograms provide detailed anatomical visuals, their accuracy is hindered by low sensitivity and high background noise, especially in dense breast tissue. This study introduces a new network architecture integrating generative adversarial networks (GANs) and vision transformers to improve reference‐based super‐resolution. The proposed model combines image generation and classification in a unified framework, eliminating the need for a separate classifier during training. To enhance GAN robustness, a unique two‐channel approach is employed, and transformer and residual learning techniques improve overall efficiency. The study introduces a novel synthetic image‐based model to improve feature detection for breast cancer classification. The proposed model shows the ability to understand information variability across different representations, enhancing accuracy and reducing computational time. Using the INbreast dataset, the results indicate the model's capability to generate high‐quality images with sensitivity and accuracy values of 0.932 and 0.988, respectively.
On the conjecture 10/n=1/x+1/y+1/z

In this paper is solve of the conjecture 10/n=1/x+1/y+1/z.
A Systematic Review of Using Machine Learning and Natural Language Processing in Smart Policing

Smart policing refers to the use of advanced technologies such as artificial intelligence to enhance policing activities in terms of crime prevention or crime reduction. Artificial intelligence tools, including machine learning and natural language processing, have widespread applications across various fields, such as healthcare, business, and law enforcement. By means of these technologies, smart policing enables organizations to efficiently process and analyze large volumes of data. Some examples of smart policing applications are fingerprint detection, DNA matching, CCTV surveillance, and crime prediction. While artificial intelligence offers the potential to reduce human errors and biases, it is still essential to acknowledge that the algorithms reflect the data on which they are trained, which are inherently collected by human inputs. Considering the critical role of the police in ensuring public safety, the adoption of these algorithms demands careful and thoughtful implementation. This paper presents a systematic literature review focused on exploring the machine learning techniques employed by law enforcement agencies. It aims to shed light on the benefits and limitations of utilizing these techniques in smart policing and provide insights into the effectiveness and challenges associated with the integration of machine learning in law enforcement practices.
Data-Driven Estimation of Forward Reachable Sets

The computation of reachable sets is an effective way to characterize and verify the behavior of safety-critical cyber-physical systems. However, many systems of practical interest possess high-dimensional, analytically intractable, and possibly unknown dynamics, which make the computation of safe sets with formal guarantees difficult or impossible. In these cases, we can employ a data-driven approach to reachability analysis instead. The data-driven approach uses a finite ensemble of sample trajectories to compute reachable set estimates which are guaranteed to achieve high accuracy in a probabilistic sense. In addition to being applicable to any system which admits simulation, data-driven reachability is computationally straightforward: computation is typically dominated by trajectory simulations which can be done in parallel. In this chapter, we present two approaches to data-driven forward reachability analysis. The first approach uses the tools of scenario optimization to construct estimates as approximate solutions to chance-constrained optimization problems. The scenario approach can be applied to a range of estimator geometries, of which we examine p-norm balls as a special case. The second approach uses a class of polynomials whose sublevel sets act as non-convex estimates of the reachable set.
Speech Translation with Large Language Models: An Industrial Practice

Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained LLM. By integrating the large language model (LLM) with a speech encoder and employing multi-task instruction tuning, LLM-ST can produce accurate timestamped transcriptions and translations, even from long audio inputs. Furthermore, our findings indicate that the implementation of Chain-of-Thought (CoT) prompting can yield advantages in the context of LLM-ST. Through rigorous experimentation on English and Chinese datasets, we showcase the exceptional performance of LLM-ST, establishing a new benchmark in the field of speech translation. Demo: https://speechtranslation.github.io/llm-st/.
Masked Image Modeling via Dynamic Token Morphing

Masked Image Modeling (MIM) arises as a promising option for Vision Transformers among various self-supervised learning (SSL) methods. The essence of MIM lies in token-wise masked patch predictions, with targets patchified from images; or generated by pre-trained tokenizers or models. We argue targets from the pre-trained models usually exhibit spatial inconsistency, which makes it excessively challenging for the model to follow to learn more discriminative representations. To mitigate the issue, we introduce a novel self-supervision signal based on Dynamic Token Morphing (DTM), which dynamically aggregates contextually related tokens. DTM can be generally applied to various SSL frameworks, yet we propose a simple MIM that employs DTM to effectively improve the performance barely introducing extra training costs. Our experiments on ImageNet-1K and ADE20K evidently demonstrate the superiority of our methods. Furthermore, the comparative evaluation of iNaturalist and Fine-grained Visual Classification datasets further validates the transferability of our method on various downstream tasks. Our code will be released publicly.
Humanoid Soccer Robot Target Detection and Localization

A monocular visual target localization method based on You Only Look Once v8 (YOLOv8) and Perspective-3-Point (P3P) for the RoboCup humanoid soccer robot competition is investigated. In this article, we propose a monocular vision-based object localization method for robot soccer competition using "You Only Look Once" v8 (YOLOv8) and Perspective-3-Point (P3P) algorithms. Firstly, the YOLOv8 algorithm is employed to detect the target object within the input image, providing precise positional information. Subsequently, the P3P algorithm is utilized for accurate localization of the detected targets. Considering the inherent instability in the humanoid robot's motion, the Kalman filtering algorithm is further integrated to enhance the accuracy and stability of both target detection and localization results. The proposed target detection and localization methodology was thoroughly evaluated through extensive experimentation on a custom dataset. The obtained results demonstrate a remarkable mean Average Precision (mAP50) of 0.989 for target detection and 0.916 for mAP50-95. Notably, when the robot's camera was positioned at distances ranging from 100 cm to 300 cm from the target object, the maximum average positioning error was measured at 32.87 cm, accompanied by a maximum average relative error of 10.97%. Conversely, at closer distances (< 100 cm), the maximum average positioning error reduced to 2.95 cm, accompanied by a maximum average relative error of 3.69%. Furthermore, the utilization of Kalman filtering yielded a substantial decrease in the maximum difference in distance calculation results between consecutive frames, reducing it from 79.71 cm to 30.02 cm. The experimental findings substantiate the efficacy and resilience of the proposed approach, thereby validating its suitability for addressing the stringent demands of humanoid soccer robots within the competitive domain of RoboCup.
The Logic of Geology

The Thinker’s Guide to Scientific Thinking focuses on developing the intellectual skills inherent in the well-cultivated practice of every area of scientific research and study. It helps students and practicing scientists come to reason within the logic of science and to see the field as a cohesive whole.
How to Evaluate an Author’s or Experimenter’s Scientific Reasoning

The Thinker’s Guide to Scientific Thinking focuses on developing the intellectual skills inherent in the well-cultivated practice of every area of scientific research and study. It helps students and practicing scientists come to reason within the logic of science and to see the field as a cohesive whole.
Microcontroller-Based Wireless System for Sliding Book Learning Media Tools

This design was performed in the development of the Sliding Book learning media tool by utilizing an Arduino UNO R3-based microcontroller control system. The addition of sensors and controllers in the form of Photodiode sensors and infrared remotes is undertaken with the intention of creating a wireless control system. The data is retrieved in the form of voltage data, current data, and the length of time used in one sliding book viewing. The outcome of this study is a sliding book learning media control design with an Arduino-based wireless control system and the addition of an infrared remote sensor and a photodiode sensor. The test results indicate that the command function of the infrared remote works well to provide wireless control of the motor rotation direction of the sliding book tool on the forward, reverse, and stop displays, and when determining the sliding page position, the photodiode sensor function corresponds to the design position of the page boundary reading. Sliding book control average power consumption was shown to be 1.67 Watts for the forward button for a voltage of 5V, 1.68 Watts for 12V and 1.47 Watts for the reverse button for a voltage of 5V. The power requirement for reverse rotation with a 5V source is 1.47 watts on the reverse button, while a 12V source requires 1.69 watts.