Reviewerly/exHarmony huggingface dataset:
THE USE OF ROUGH CLASSIFICATION AND TWO THRESHOLD TWO DIVISORS FOR DEDUPLICATION

The data deduplication technique efficiently reduces and removes redundant data in big data storage systems. The main issue is that the data deduplication requires expensive computational effort to remove duplicate data due to the vast size of big data. The paper attempts to reduce the time and computation required for data deduplication stages. The chunking and hashing stage often requires a lot of calculations and time. This paper initially proposes an efficient new method to exploit the parallel processing of deduplication systems with the best performance. The proposed system is designed to use multicore computing efficiently. First, The proposed method removes redundant data by making a rough classification for the input into several classes using the histogram similarity and k-mean algorithm. Next, a new method for calculating the divisor list for each class was introduced to improve the chunking method and increase the data deduplication ratio. Finally, the performance of the proposed method was evaluated using three datasets as test examples. The proposed method proves that data deduplication based on classes and a multicore processor is much faster than a single-core processor. Moreover, the experimental results showed that the proposed method significantly improved the performance of Two Threshold Two Divisors (TTTD) and Basic Sliding Window BSW algorithms.
Improved Faster R-CNN Network for Liquid Bag Foreign Body Detection

The production quality of medical fluid bags is closely related to patient health. In this paper, we used medical fluid bags to detect whether they contained foreign bodies. A visual acquisition system for the fluid bag was built. Vignetting correction was performed on the acquired images, and a foreign body recognition detection method based on an improved Faster R-CNN model was proposed. The feature extraction network of Faster R-CNN was discussed and studied regarding the characteristics of small foreign objects in liquid bags, and the ResNet152 network replaced the VGG16 network; furthermore, the feature fusion and attention mechanism were added to the feature extraction, and CIoU replaced the IoU loss function; the anchor box parameters were optimized and improved using the K-means clustering algorithm, and ROI Align replaced the ROI Pooling module. The improved network in this paper was compared with the Faster R-CNN model, which is a modification of feature extraction networks, such as ResNet50, ResNet101, and ResNet152, and the original VGG16 feature extraction network. The results show that the ResNet152 network had the best feature extraction effect among the feature extraction networks, and other optimizations were performed in this paper based on the use of ResNet152. In the precision−recall curve, the network in this paper showed the best effect. The improved algorithm presented in this paper was significantly improved compared with the original algorithm, with a detection accuracy of 97% and an average accuracy improvement of 7.8% in foreign object recognition.
Precisely Synced Clocks Pave the Way for New Science

Researchers working on Hawaiian mountain tops precisely synchronized two clocks with unprecedented efficiency, demonstrating the potential to create networks of such clocks for geodesy and more.
DESIGN AND DEVELOPMENT A SCARA ROBOT FOR CLASSIFY PRODUCTS USING IMAGE PROCESSING

In this paper, a SCARA robot arm is designed and developed using stepper motor to classify objects according to their color. Four stepper motors are mounted at each joint of the robot to provide precise movement. The Arduino Mega board is used to create signal for stepper motor driver. The mechanical model of the robot is designed on the Autodesk Inventor software. All links of robot is fabrication by laser cutting method. The assembly of the parts of the robot and the motor’s mechanical shapes produce the final prototype of the arm. The Arduino has been programmed to provide rotation to each corresponding servo motor to the sliders in the designed mobile application for usage from distance. Product classification algorithm will be developed to detect objects based on color. The robot will pick up the products to different positions according to the detected color.
Certainty Factor and Evidential Reasoning to Handle Uncertainty in Artificial Intelligence

Early knowledge-based program developers, such as MYCIN (which conducted medical diagnostics), used a pragmatic approach to representing ambiguous knowledge. MYCIN, for instance, had mathematics called certainty factors for uncertain reasoning. Each rule was assigned a certainty factor, which is a quantifiable measure of how strongly the antecedent (left side) of the rule supports the conclusion (right side). The experts that produced the rules evaluate the certainty factor for each rule in MYCIN. The certainty factor of a result generated from the application of many rules is calculated as a straightforward arithmetic function of the underlying rules’ certainty factors. MYCIN’s certainty factors enabled efficient inference in the face of uncertainty. The certainty factor method also closely resembles how humans manage numerical measurements of uncertainty. However, on rare occasions, the certainty factor connected to a result generated from numerous rules in MYCIN might deviate dramatically from a domain expert’s first-hand evaluation of the conclusion’s uncertainty. This chapter delivers different concepts related to certainty factors, different case studies, and the advantages and limitations of certainty factors.
Design of polarization, near-infrared, and visible light joint imaging system for advanced imaging

Single sensor imaging is difficult to detect in complex environments. To improve the signal-to-noise ratio of a single image, enhance the robustness of the imaging system, and provide further assistance in complex environmental image identification, we assembled a jointed imaging system consisting of polarization, near-infrared, and visible cameras, accompanied with weighted average image color fusion algorithm. The imaging system was performed in both close-up natural landscape and remote marine target, and the sub-images from each camera and the fused image were analyzed by multiple non-reference quality evaluation indicators. The analysis results show that joint imaging can not only combine information from multiple source images, enhancing image quality, but also resist random noise from each imaging source.
DIGITALISASI SKRIPSI DALAM REPOSITORI PROGRAM STUDI MANAJEMEN SUMBERDAYA PERAIRAN FAKULTAS PERIKANAN DAN ILMU KELAUTAN UNIVERSITAS PATTIMURA

Thesis is one of the references used as a reference to writing a scientific work in an academic environment both by students and lecturers in carrying out research assignments. Existing thesis can be a source of research ideas, information on student research interests and ongoing research development. These benefits become more optimal with the ease of accessing thesis data anytime and anywhere. The aim of this activity is to create a digital-based thesis in a repository at the Study Program in Aquatic Resources Management at Pattimura University. This activity was carried out in October - November 2022. The stages of the activity included coordination, collecting thesis softcopy data, inputting thesis data into Google Drive and the website in the form of a repository, making posters as a medium for outreach to students and lecturers and evaluating the effectiveness of the repository on the website. The results of this activity can be concluded that digitizing thesis into a repository can provide easy access anytime and anywhere for both students and lecturers who need information related to thesis data in the Aquatic Resources Management Study Program.
Improving unified named entity recognition by incorporating mention relevance

Abstract Named entity recognition (NER) is a fundamental task for natural language processing, which aims to detect mentions of real-world entities from text and classifying them into predefined types. Recently, research on overlapped and discontinuous named entity recognition has received increasing attention. However, we note that few studies have considered both overlapped and discontinuous entities. In this paper, we proposed a novel sequence-to-sequence model that is capable of recognizing both overlapped and discontinuous entities based on machine reading comprehension. The model utilizes machine reading comprehension formulation to encode significant inferior information about the entity category. Then input sequence passes through a question-answering model to predict the mention relevance of the given source sentences to the query. Finally, we incorporate the mention relevance into the BART-based generation model. We conducted experiments on three type of NER datasets to show the generality of our model. The experimental results demonstrate that our model beats almost all the current top-performing baselines achieves a vast amount of performance boost over current SOTA models on overlapped and discontinuous NER datasets.
Development of Audit Information System Index KAMI Based on ISO/IEC 27001:2013

Information security is crucial and vital in digital era. Confidentiality and security of assets must be protected to minimize IT risks. An audit is needed to control whether the organization or agency has implemented information system security standards. One of the information system security audits that has an evaluation value of the level of readiness is to use the System Security Index (KAMI) based on ISO/27001 2013. KAMI is designed to assist organizations and institutions to conduct independent assessments and evaluate the level of readiness in implementing information security which includes the criteria for Governance, Risk Management, Framework, Asset Management, Information Security Technology, and Supplements. This study designed a system with an SDLC (System Development Life Cycle) approach consisting of Planning, Analysis, Design, and Implementation. The purpose of the development of the KAMI audit information system is to facilitate auditors and related parties in the audit process carried out using the KAMI Index based on ISO/IEC 27001:2013, so that auditors do not need to use Microsoft Excel again in the audit process. The results of this study are in the form of the KAMI Index information system website based on ISO/IEC 27001:2013. The conclusion from the blackbox testing results above is "Accepted", where the audit information system website based on ISO 27001:13 functions well and serves its users efficiently. There are no missing or incorrect functions, no errors in this test sign, value input test, document input test, and menu test.
Reply to Crockett et al. and Mottelson and Kontogiorgos: Machine learning’s scientific significance and future impact on replicability research

Errors in clinical decision-making are disturbingly common. Here, we show that structured information–sharing networks among clinicians significantly reduce diagnostic errors, and improve treatment recommendations, as compared to groups of ...Errors in clinical decision-making are disturbingly common. Recent studies have found that 10 to 15% of all clinical decisions regarding diagnoses and treatment are inaccurate. Here, we experimentally study the ability of structured information–sharing ...
Modify Multiple Object Detection and Tracking to Improve the Execution Time

Multi-Object Detection and Tracking (MODT) is crucial in various contexts. Despite this, significant advancements in detection and tracking speed were needed to meet the challenge during the implementation phase. Introduce a revised algorithmic framework for (MODT) to speed up processing and make it more stable for use in real-time settings. The object's position and velocity were predicted using a Kalman filter and a background subtraction detection approach. When the detector isn't actively searching for a new object, it can be beneficial to discard the successive two frames and replace them with the Kalman filter's prediction and estimated value for the monitored object to speed up the process. Adding some image filter-like aspect ratio object and motion which help to reduce the effectiveness of the shadow and variations of the lighting conditions in the scene, which improve the proposed algorithm detection and tracking, This is useful for daytime preprocessing in an automated traffic surveillance system and inside pedestrian monitoring, and it can be shown with the help of a video camera. The results of these preliminary tests indicate that the proposed algorithm for this vehicle monitoring system works. It demonstrates that when applied to a single camera, the proposed method can monitor, detect, and track many vehicles or human being simultaneous, with improved execution time by 22% over the standard background subtraction and tolerable complexity.
Light-field AR combiner enabling prescription correction and aesthetic lenses

For Augmented Reality to spread and reach common usage levels, the visual experience needs to be entirely healthy and natural for the user. This is why CREAL has developed a unique AR display that combines light field imagery with ordinary highly transparent ophthalmic lenses, providing a true-to-life depth perception for the human eye and customizable prescription with classical aesthetic look of the lenses. While almost all AR glasses providers are lumbered with trade-offs between focal depth, image resolution and lens aesthetic, CREAL’s newest display finally enables an AR visual experience that “has it all”. This talk with introduce the solution.
Research on Dynamic Rule Mining Method for Incremental Small Sample Experimental Data

In terms of fault diagnosis and prediction, new equipment mainly has the characteristics of small data samples and increasing sample size. In the rulemaking process, there are problems such as incomplete data, insufficient rules support, low accuracy data and information completeness, the current method is difficult to automatically correct forward rules based on continuously accumulated data. There is a lack of basic models and algorithms for rule correction, This method establishes an initial rule library after modeling system faults based on knowledge and data fusion. When a system fault occurs, it will exhibit various abnormal phenomena, and the collection of different types of abnormal phenomena (i.e. fault phenomena) corresponds to typical faults.
Digital Twin Oriented Virtual Viewpoint Rendering Algorithm Based on Layered Image Mapping

Virtual viewpoint synthesis technology can provide a more realistic appearance and perspective for digital twins. The most widely implemented method in virtual viewpoint synthesis technology is Depth Image Based Rendering (DIBR) technology, where depth maps reflect the depth information of objects in the scene. We propose a virtual viewpoint rendering algorithm based on layered image mapping to address the issues of overlap and artifacts in virtual views. According to the idea of layered mapping, the overlapping phenomenon caused by the mismapping of background pixels to the foreground region in the process of spatial mapping is avoided. We use dual viewpoint complementarity to fill holes in the left and right virtual viewpoint foreground, and then fuse them with their respective backgrounds within the frame to avoid overlapping holes filled by background pixels and interference between the front and background pixels.
A Shared Synapse Architecture for All-Optical Spiking Neural Networks

Silicon photonics is a promising technology for the development of neuromorphic hardware and Spiking Neural Networks (SNN). These architectures rely on wavelength division multiplexing (WDM) and precise calibration of microring resonators (MRR). Implementing larger neural network models requires an increasingly larger number of MRRs and this makes the calibration process complex and untenable. We propose a shared synapse architecture to reduce the number of MRRs required to perform synaptic weighting. This architecture reduces the number of required MRRs by half. The attenuation on each phase-change material (PCM) cell is derived from the pre-trained weights of the model without the need for retraining. To this end, a flow for assigning wavelengths to the inputs and finding the desired attenuation for each waveguide with PCM cell is introduced. Our simulation tests suggest similar weighting accuracy compared with the baseline model despite using fewer resources.
A Necessary and Sufficient Condition to Generate Representative Clip for Edge-Constrained Clustering of Layout Pattern Classification Problem

Pattern classification problem is to classify the given clips into the smallest number of clusters, where each cluster has a representative clip. Each cluster should satisfy that every clip in the cluster satisfy the given constraint with the representative clip of the cluster. Most of researches for the problem select some clips as representatives, but Takkala proposed a proposition to generate a representative clip to make the number of clusters smaller. In this paper, we first show that the proposition of Takkala is sometimes incorrect, and we proposed a simple method to generate a representative clip from given clips for “ edge-constrained clustering (ECC)” if there exists. Then, we show that the method satisfy a necessary and sufficient condition.
Bionic Algorithm Based on Plant Physiological Characteristics

In recent decades, research on bionic algorithms based on plant physiological features has been carried out. The main purpose of this study is to develop a new method for controlling and monitoring plants, which can be used as a substitute or supplement to classical methods. In particular, the use of sensors capable of measuring plant physiological parameters enables us to control plants with minimal effort and low cost. The research of bionic algorithms based on plant physiological characteristics is about using plants as the source of new algorithms, which are not known to us. The main purpose of this study is to find out whether it is possible to use plants and their biochemical reactions to create artificial intelligence (AI). In addition, we want to study what kind of AI can be created with the help of these algorithms. This will be done by studying the behavior of plants and how they respond to different stimuli (such as light, temperature, humidity, etc.). This information can be used to create an artificial intelligence system that mimics the way plants react. In order for this type of research project to work, it must first determine what information is needed.
Energy Trading in the Internet of Energy using Ethereum Smart Contracts and Smart Energy Meters

The emergence of the Internet of Energy (IoE) has paved the way for decentralized energy trading, which involves the exchange of energy among prosumers (both producers and consumers) in a peer-to-peer (P2P) fashion. Smart energy meters, which can measure the energy consumption and production of prosumers, play a critical role in IoE-based energy trading. However, the lack of trust and transparency among prosumers and energy traders is a major barrier to the widespread adoption of P2P energy trading. In this context, Ethereum smart contracts can provide a solution by enabling transparent and secure execution of energy trading agreements among prosumers. This paper proposes an energy trading system that leverages Ethereum smart contracts and smart energy meters to enable P2P energy trading in the IoE. This work describes the architecture of the system and the implementation details of the smart contracts used for energy trading. This paper has presented a case pseudo code for an efficient and secure approach to trade energy based on Ethereum. It moved most of the processing and storage off-chain (in opposition to several existing solutions) to minimize the cost of using Blockchain, in terms of gas paid to process and store data related to smart. This was done while keeping the trading process as secure as when all processing and storage are performed on-chain. It also made use of stable coins to overcome the exchange rate instability of cryptocurrencies.
A Machine Learning based Decision Support System to Predict the Presence of Cervical Cancer

Cancer occurring in cervix, a part connecting the uterus and vagina, is one of the frequently happening cancer types among gynecological cancers following uterine cancer. The symptoms generally are experienced once the patient progresses to stage 1b cervical carcinoma on the canal (cervix uteri) joining the uterus and vagina which is clinically observable lesion. The Pap-Smear test is used for early-stage detection technique. Identification of the precancerous or cancerous cells on the individual’s cervix is done by the Papanicolaou test. Pap-Smear produces false-positive/false-negative results. Machine learning algorithms could provide us with an accurate diagnosis by conducting classification, prediction, and estimation. The ensemble technique incorporates four algorithms using the concept of machine learning. The latter provides the highest accuracy of 99% so that it enables the healthcare practitioners to make informed decisions. The proposed work aims to develop an intelligent decision support system which can aid medical practitioners in carrying out informed medical procedures. The system when containerized could make way for portability and easy deployment.
Development of an Indoor Navigation System for Mobile Robots Using Sensor Fusion and Path Planning Algorithms

This paper proposes an indoor navigation system for a mobile robot in large buildings and complexes using sensor fusion and path planning techniques. The proposed system consists of two main components: a mobile robot and a backend server. The mobile robot is equipped with sensors, including an inertial measurement unit (IMU), Wi-Fi, and BLE, to estimate its position and orientation in the environment. The robot is controlled using an Arduino microcontroller, which receives commands from the backend server and sends motor commands to the robot's motors. The backend server integrates information from the sensors and uses sensor fusion algorithms to estimate the robot's position and update the route information. The server also uses the MATLAB path planning algorithm to calculate the most efficient path for the robot and provides real-time updates to the robot's controller. The implementation of the MATLAB path planning algorithm and the Arduino four-wheeled robot provides a cost-effective and practical solution for indoor navigation.
Remote Gesture Recognition with CNN-3D Using MQTT Protocol

The presented model has been proposed for solving the problem of multi class gesture recognition in a memory optimized method. In this model CNN-3D deep neural network model is used along with SoftMax classifier for predicting at least five recognizable gestures. In general convolution 3D will perform complex 3D dimensional analysis that is 2D image recognition and 1D sequence recognition. The trained model will have a generalized behavior and can also be used as pretrained model for classification problems. This model can be an optimized solution for many consumer electronics industries that demand an action for recognized human gestures. In Consumer electronics like smart TV a human gesture like thumb up can be mapped to separate functions