Reviewerly/exHarmony huggingface dataset:
SSKDN: a semisupervised knowledge distillation network for single image dehazing

Model-based methods utilize atmospheric scattering model to effectively dehaze images but introduce unwanted artifacts. By contrast, recent model-free methods directly restore dehazed images by an end-to-end network and avoid artificial errors. However, their dehazing ability is limited. To address this problem, we combine the advantages of supervised and unsupervised learning and propose a semisupervised knowledge distillation network for single image dehazing named SSKDN. Specially, we respectively build a supervised learning branch and an unsupervised learning branch by four attention-guided feature extraction blocks. In the supervised learning branch, the network is optimized by synthetic images. In the unsupervised learning branch, we dehaze real-world images by dark channel prior and refine dehazing network (RefineDNet) (another dehazing method) and use these dehazed images as fake ground truths to optimize network using prior information and knowledge distillation. Experimental results on synthetic and real-world images demonstrate that the proposed SSKDN performs better than state-of-the-art methods and owns powerful generalization ability.
Blockchain Technologies in Healthcare System for Real Time Applications Using IoT and Deep Learning Techniques

Data transparency, flexible access, immutability, privacy, audit, traceability, data provenance, trust, and security are fundamental issues for modern healthcare data management systems. As a promising new technology, blockchain has the potential to enhance healthcare data management functions by boosting data efficiency and guaranteeing trust. The present research looked into the benefits of blockchain technology in healthcare and the challenges that have prevented its widespread implementation so far. Healthcare organisations around the world are using a variety of methods to modernise into more effective, coordinated and user-cantered structures. There is an increase in both human effort and security risks when dealing with massive amounts of data, such as reports and images for each individual. Internet of Things (IoT) solutions in healthcare aim to address these problems by enhancing patient care while reducing costs through more effective use of healthcare resources. However, many different types of intrusion can pose serious risks to IoT devices. In some cases, doctors will insist that their patients use only certain labs or pharmacies, regardless of the quality of the services they provide, simply to increase the doctor's bottom line. Because of this, protecting data is essential when discussing the Internet of Things. To solve these problems, Blockchain technology has emerged as the most reliable method for protecting the privacy of control systems in real time. In this paper, we will introduce a CNN-based healthcare data security framework using the blockchain technique by generating the hash of each data point, which will alert all users of the blockchain network to any unauthorised changes to data or breaches in the supply of medicines.
Informed Guided Rapidly-exploring Random Trees-Connect for Path Planning of Walking Robots

In this paper, we deal with the problem of full-body path planning for walking robots. The state of walking robots is defined in multi-dimensional space. Path planning requires defining the path of the feet and the robot's body. Moreover, the planner should check multiple constraints like static stability, self-collisions, collisions with the terrain, and the legs' workspace. As a result, checking the feasibility of the potential path is time-consuming and influences the performance of a planning method. In this paper, we verify the feasibility of sampling-based planners in the path planning task of walking robots. We identify the strengths and weaknesses of the existing planners. Finally, we propose a new planning method that improves the performance of path planning of legged robots.
Analisis Sentimen Opini Pengguna Twitter Terhadap Perusahaan Jasa Ekspedisi Menggunakan Algoritma Naïve Bayes Berbasis PSO

Expeditions are used in the process of delivering goods or selling them remotely. Twitter has become a social media for information sharing and opinions, including those on the expeditionary services both negative and positive. The solution for the problem which occurs is that of sentiment analysis, helpful in grouping the data and predicting the tweet. The aim of the research to predict sentient tweeted data using a file classification method, naive bayes's algorithm calculated the value of the tweets of the Anteraja expedition service that had the results accuracy 87,77%, precision 76,67%, recall 52,27%. JNE with accuracy 81,48%, precision 71,43%, dan recall 62,50%. JNT with accuracy 91,46%, precision 48,15%, recall 86,67%. Shopee Express with accuracy 92,68%, precision 9,09%, recall 16,67% and Sicepat with accuracy 91,50%, presision 100,00% dan recall 38,10%. Particle Swarm Optimization (PSO) serves to increase the value of the results of the nave Bayes classification with the results of Anteraja accuracy 91,70%, precision 82,05%, recall 72,73%. JNE accuracy 93,83%, precision 88,00%, recall 91,67%. JNT accuracy 92,18%, precision 70,97%, recall 81,48%. Shopee Express accuracy 94%, precision 20,00%, recall 33,33% and Sicepat accuracy 95,42%, precision 93,75%, recall 71,43%. From the results of naïve bayes research and Particle Swarm Optimization (PSO) it can be compared that Particle Swarm Optimization (PSO) is proven to be able to increase the value of nave Bayes.
Analisis Debit Banjir Rencana Daerah Tangkapan Air Waduk Tugu Menggunakan HEC-HMS

Over the course construction of Tugu Reservoir, it is proposed to build a spillway gate to increase its performance as a flood control infrastructure. The construction planning of spillway gate requires a calculation of design flood discharge for Tugu Reservoir Catchment Area. The latest calculation was carried out during its construction planning in 2010. Therefore, it is necessary to evaluate and recalculate the design flood discharge using the latest data. This study aims to model design flood discharge of Tugu Reservoir Catchment Area using HEC-HMS software. This software is able to simulate rainfall-runoff modeling in a catchment area. Based on field conditions, Tugu Reservoir catchment has parameters, curve number value of 79, impervious value of 5%, and a lag time of 4.81 minutes. The result of the HEC-HMS modeling shows that the design flood discharge of Tugu Reservoir for Q100 is 369,30 m3/s; Q1000 is 656,70 m3/s; and QPMF is 995.30 m3/s. Based on the test using Creager Graph, the design flood discharge for Tugu Reservoir is still in the reasonable category with C value below 100. The result of the HEC-HMS modeling is not much different from the calculation result of Tugu Reservoir Construction Planning in 2010.
IOT THREATS &amp; IMPLEMENTATION OF AI/ML TO ADDRESS EMERGING CYBER SECURITY ISSUES IN IOT WITH CLOUD COMPUTING

The Internet of Things (IoT) has emerged as one of the most innovative and forward-thinking technologies, attracting interest from both the scientific community and the commercial community due to its financial allure.Artificial intelligence/machine learning (AI/ML) is required to deconstruct the data saved in the cloud framework in order to integrate various devices and associate gadgets with people.These IoT devices communicate with one another and exchange data utilizing the web and cloud-based network architecture by using their individual unique identities and the integrated sensor in each device.We are in a big data era where using AI/ML is essential to the cycle of swiftly and accurately analyzing the obtained cloud-based large data.IoT security issues include substantial challenges and risks, including hacking, data fraud, remote access, and cyberattacks.Despite the fact that AI is increasingly playing a larger role in the development of traditional cyber security, both cloud vulnerability and the networking of IoT devices pose serious risks.IoT devices that are not sufficiently protected run the danger of being used in DDoS (Distributed Denial of Service) attacks.These assaults reveal security flaws and interrupt services, which have a severe impact on customer satisfaction and economic output.Additionally, the great majority of IoT devices that are connected remotely and transported by a public entity are always in cyber danger.Because it is hard for humans to manage the network, AI and ML are advantageous and essential to our foreseeable networks.The above-mentioned issues in IoT security need the use of AI/ML as a security tool.The suggested approach to cloud network infrastructure security problems uses predictive analysis to foresee potential assaults.This research then develops the appropriate AI/ML application using 3GPP, MIMO, and datasets from CIADA and Packet.A more practical Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) are the results of the use of AI components, which have been crucial and utilized.
Room-size illusion and recovery of the true appearance

It is widely known among photographers that photographing a room with a wide-angle lens exaggerates the size of the room; nevertheless, such images are commonly found on hotel-reservation web sites. The present paper points out that the size exaggeration is a kind of optical illusion caused by an inappropriate viewpoint from which the image is seen, and presents a method we developed for removing the illusion and thus recovering the true appearance of the room. This method requires only a single image together with the lens center at which the image is taken. From this information, we can generate images that would be obtained if we stand at the same point as the camera and pan around the original scene, changing the view direction. The validity of the method is shown by examples. Possible applications to size-exaggerated images posted on web sites are also discussed.
Dynamic computer-generated moiré profilometry based on high-density binary coding

A dynamic computer-generated moiré profilometry based on high-density binary coding is proposed. For making full use of the maximum refresh rate and the maximum resolution of the digital light projector (DLP), the binary coded fringe is used to replace the conventional 256-gray-scale sinusoidal fringe, which can increase the refresh rate from the traditional 120 Hz to more than 1 kHz and meet the needs of dynamic measurement from the source. To realize the minimum equivalent wavelength and obtain the purest calculated moiré fringe, a minimum four-pixel period high-density binary fringe that satisfies the sampling theorem is designed for the DLP. The measuring accuracy of computer-generated moiré profilometry is effectively improved due to its minimum equivalent wavelength. The experimental results show the feasibility and practicability of the proposed method. It not only possesses higher measuring accuracy, but also possesses a proper potential application in dynamic three-dimensional measurement.
A Certificateless Provable Data Possession Scheme for Cloud-Based EHRs

Electronic health records (EHRs: digital collections of patient health status and diagnosis) are generally shared, analyzed and stored on cloud servers. One operational challenge is to ensure that EHRs are stored correctly, for example using provable data possession (PDP). Seeking to contribute to the literature, we propose a certificateless PDP scheme for cloud-based EHRs. In our scheme, we distribute multiple copies of EHRs on different cloud servers to allow for corrupted EHRs to be recoverable from other intact copies. The scheme is also designed to resist copy-summation attack which assures that cloud servers are storing EHRs honestly. In our approach, EHRs are stored in ciphertext form so that only authorized users can decrypt and gain access to the information. We also design a new data structure – map version marker table (MVMT) – for block-level dynamic operations and data traceability. Specifically, MVMT allows an authorized doctor to access historical EHRs to inform their diagnosis and decision-making. The security and performance analyses show that our scheme is secure (assuming the intractability of the computational Diffie-Hellman problem) and is practical to support cloud-based EHR applications.
Computer-Aided Diagnosis for the Early Breast Cancer Detection

Computer-aided diagnosis (CAD) system is a prominent tool for the detection of different forms of diseases, especially cancers, based on medical imaging. Digital image processing is a critical in the processing and analysis of medical images for the disease diagnosis and detection. This study introduces a CAD system for detecting breast cancer. Once the breast region is segmented from the mammograms image, certain texture and statistical features are extracted. GLRLM feature extraction technique is implemented to extracted texture features. On the other hand, statistical features such as skewness, mean, entropy, and standard deviation are extracted. Consequently, on the basis of the extracted features, SVM and kNN classifier techniques are utilized to classify the segmented region as normal or abnormal. The performance of the proposed approach has been investigated via extensive experiments conducted on the well-known MIAS dataset of mammography images. The experimental findings show that the suggested approach outperforms other existing approaches, with an accuracy rate of 99.7%.
Tangible data visualization of physical activity for children and adolescents: A qualitative study of temporal transition of experiences

Children and adolescents in the UK are increasingly at risk of significant health problems due to physical inactivity. While activity trackers and fitness applications have focused on addressing this problem in youth, poor wear-time compliance and usability and accessibility issues have been frequently reported in the literature as barriers to engagement. Physicalization of data offers an alternative approach to engage with physical activity (PA). In this paper, we present the results of a seven-week qualitative study with 97 primary and secondary school children (8–14 years old). We took a temporal approach to collect children’s and adolescents’ perspectives in short video interviews as they received 3D-printed models representing their faded-weekly PA levels. Our findings showed that children’s and adolescents’ emotional engagement with the models remained high throughout the study, while their reflection on the models and their knowledge of what constitutes PA and its different types evolved over time. The findings from this temporal study suggest that tangible data visualization of PA evokes experiences such as embodied reflection, active learning, emotional engagement, and temporality of PA experience. Therefore, we argue that the motivational impact of regular tangible visualizations as a form of feedback should be considered alongside wearable trackers in addressing childhood inactivity.
Impact of oil spillage on the levels of heavy metals and petroleum hydrocarbons in millipede and soil from Ikot Ada Udo, Akwa Ibom State, South-South Nigeria

The impact of oil spillage on heavy metals and hydrocarbons levels was studied. Millipede (Sprostreptus assiniensis) samples were hand-picked, while soil samples were collected using hand auger, and then analysed for heavy metals and total petroleum hydrocarbon (TPH) using a spectrophotometer. Levels of heavy metals, TPH in millipede, soil samples were higher in the study site compared to control. Mean metals concentration in soil were 0.748 (Pb), 0.754 (Cd), 1.577 (Ni), 0.274 (Cr), 4.749 (Fe), 0.020 (V), 0.103 (Co), 0.181 (As), 5.544 (Mn) and 5.187 mg/kg (TPH); while that of millipede were 1.290 (Pb), 0.524 (Cd), 0.683 (Ni), 1.278 (Cr), 2.283 (Fe), BDL (V), 0.023 (Co), 0.001 (As), 3.016 (Mn) and 3.011 mg/kg (TPH). Mean Ni, Cr, Mn (in millipede); Cd, Cr, Fe, V, as (in soil) were above WHO limits. Thorough clean-up is recommended for the total restoration of the environment.
Tunable single-photon routing between two single-mode waveguides by a giant <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si42.svg" display="inline" id="d1e1058"><mml:mi>Λ</mml:mi></mml:math>-type three-level atom

We explore the influence of a giant Λ-type three-level atom on the modulation of single photon quantum routing in two channels composed of two linear waveguides. The results show that the single photon scattering behavior is strongly dependent on the phase difference between the two atom-waveguide coupling points, and the coupling strengths between the giant atom and the waveguides. To improve the efficiency of the quantum routing, another configuration with a terminated waveguide is proposed. We find the single-photon routing probability can reach 100% through adjusting the phase shifts induced by the giant atom and the terminated waveguide. The effects of the driving field on the quantum routing are also investigated. Our work may provide a feasible approach to control the single-photon quantum routing using the giant-atom setup.
ANALISA PERFORMA ALGORITMA MACHINE LEARNING DALAM PREDIKSI PENYAKIT LIVER

Currently in the world of medicine, determining liver inflammation is something that is not easy to do. But there are medical records that have kept the patient's symptoms and diagnosis of liver inflammation. The weaknesses of the manual method encourage researchers to develop a method that does not depend 100% on humans. The developed method utilizes a computer as a tool to analyze data. This kind of thing is certainly very useful for health experts. They can use existing medical records as an aid in making decisions about the diagnosis of a patient's disease. In this study, we analyzed the performance of machine learning algorithms by comparing the support vector machine, naïve Bayes and k-nearest neighbor algorithms. This study aims to determine the performance of which algorithm has the highest accuracy in liver disease data. From the research results using splinting data 80:20 it can be concluded that the Naïve Bayes algorithm model has better performance than other algorithm models when using the SMOTE technique with an accuracy value of 65.51%, whereas when not using the SMOTE technique the Support Vector Machine algorithm has the highest performance. better than other algorithm models with an accuracy value on the data not 72.41%.
Improving Domain-Generalized Few-Shot Text Classification with Multi-Level Distributional Signatures

Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and meta-tested on unseen datasets with different domains. However, previous methods mostly construct semantic representations by learning from words directly, which is limited in domain adaptability. In this study, we enhance the domain adaptability of the model by utilizing the distributional signatures of texts that indicate domain-related features in specific domains. We propose a Multi-level Distributional Signatures based model, namely MultiDS. Firstly, inspired by pretrained language models, we compute distributional signatures from an extra large news corpus, and we denote these as domain-agnostic features. Then we calculate the distributional signatures from texts in the same domain and texts from the same class, respectively. These two kinds of information are regarded as domain-specific and class-specific features, respectively. After that, we fuse and translate these three distributional signatures into word-level attention values, which enables the model to capture informative features as domain changes. In addition, we utilize domain-specific distributional signatures for the calibration of feature representations in specific domains. The calibration vectors produced by the domain-specific distributional signatures and word embeddings help the model adapt to various domains. Extensive experiments are performed on four benchmarks. The results demonstrate that our proposed method beats the state-of-the-art method with an average improvement of 1.41% on four datasets. Compared with five competitive baselines, our method achieves the best average performance. The ablation studies prove the effectiveness of each proposed module.
Data Mining Selection of Prospective Government Employees with Employment Agreements using Naive Bayes Classifier

The implementation of health in Indonesia is still marked by problems in managing the workforce, especially among honorary health workers. The State Personnel Agency seeks to improve service quality by selecting good human resources to enhance community services. Several Ministries agreed to change the team member recruitment system from Prospective Civil Servants to Government Employees with Work Agreements. In the field, in the Government Employees with Work Agreements acceptance process, there are still pros and cons, both from the rules and the appointment process. The community hopes that the appointment process can be objective and open so that no group is disadvantaged. To achieve these expectations, researchers used data mining to classify health workers who would become Government Employees with Work Agreements. The data mining process uses probability with the Naive Bayes Classifier algorithm from historical data on Government Employees with Work Agreements receipts for 2021. Data on the history of Government Employees with Work Agreements acceptance of health workers as many as 1078 data have been filtered and cleaned. The results of testing the data are 0.00012 for the worthy assumptions and 0.0032 for the unworthy assumptions. Data processing results will be visually displayed using bubble diagrams and Python programming. The researcher concluded that the process of classifying prospective first-aid team members for medical personnel could be done by data mining using the Naïve Bayes algorithm. The results of this classification can be used as a reference for the following year's Government Employees with Work Agreements revenue classification process.
Classification of Banana Ripeness Based on Color and Texture Characteristics

Banana is one of the most consumed fruits globally and is a rich source of vitamins, minerals and carbohydrates. With the many benefits that bananas have, many farmers cultivate this fruit. The problem that occurs when the harvest is produced on a large scale is the process of selecting bananas that are still unripe or ripe. Usually farmers carry out the selection process manually by visually identifying ripeness based on the color of the fruit skin. However, direct observation has several drawbacks such as subjectivity, takes a long time and is inaccurate. For this reason, we need a system that can help determine the maturity level of bananas automatically through a series of banana image processing processes. One way that can be used to determine the maturity level of bananas is by looking at the color and texture of the bananas. This study aims to classify the maturity level of bananas based on the color and texture characteristics of the banana image using the Gray Level Co-occurrence Matrix and K-Nearest Neighbor methods for the classification process. Based on the results of the research analysis that has been carried out, using the parameter k which has a value of 3 obtains very high accuracy.