Reviewerly/exHarmony huggingface dataset:
An Intelligent System for Detecting Fake Materials on the Internet

There has been a significant rise in internet usage in recent years, which has led to the presence of data theft and the diversity of counterfeit materials. This has resulted the proliferation of cybercrimes and the theft of personal data via social media, e-mail, and phishing websites that are similar to the websites commonly used to grab user data details like that of a credit card or login ID. Phishing, a prevalent form of cybercrime, poses a danger to online security through the theft of personal information, and with the emergence of the COVID-19 virus, which has led to people and organizations being drawn towards the Internet and many people and companies being forced to work remotely, it has led to an increase in the existing phishing threats. Previously, hackers took advantage of the situation to infiltrate the devices of people and companies in numerous ways, which caused huge financial losses and damage to organizations. Based on previous results and research, Machine Learning (ML) is selected by researchers as an efficient method for identifying malicious software web pages from original web pages. This paper presents 30 characteristics of websites, which are analyzed using a correlation matrix to determine the relationship between variables. Feature selection is performed through a wrapper method and Extra Tree Classifiers (ETC) to identify the top-ranked characteristics (Features) for website classification. To evaluate web pages, various machine learning techniques such as Random Forest Tree (RF), Multilayer Perceptron (MLP), Decision Tree (DT), and Support Vector Machine (SVM) are used. The results of monitoring indicate that MLP, a deep neural network, outperforms all other techniques in terms of performance.
Accuracy of Prostate Volume Measurement on 2d Transabdominal USG Modality Using the Gradient Vector Flow (GVF) Segmentation Application Measurement Technique

In the field of radiology, supporting examinations to evaluate the prostate include examination with X-rays, Magnetic Resonance Imaging (MRI), and ultrasonography (USG). Of all imaging examinations that have been carried out throughout the world, it is estimated that around 25% use ultrasound. The general objective of this research is to analyze the design of the Gradient Vector Flow (GVF) segmentation algorithm on 2D ultrasound images to measure prostate volume in the adult age group. This study created a Gradient Vector Flow (GVF) segmentation application for the prostate organ to calculate the prostate volume calculation. RnD research with an experimental approach was carried out by creating a Gradient Vector Flow (GVF) segmentation design application, tested on 15 DICOM-based ultrasound images, measuring and calculating prostate volume using caliper measurement on ultrasound equipment and Gradient Vector Flow segmentation application. Measurements were made by an expert (sonographer) as an observer with data analysis using SPSS statistics. Analysis of ultrasound image of prostate volume using caliper measurement technique and Gradient Vector Flow (GVF) segmentation is similar to a p-value of 0.950 (&gt; 0.05). Measurement with caliper measurements has similarities shown by the mean rank value of Gradient Vector Flow (GVF) of 15.60 with a mean rank value of caliper measurement of 15.40. GVF application measurement on transabdominal ultrasound prostate volume measurement is similar to caliper measurement and can be used to get more accurate results on objects with less clear edge characteristics. However, the caliper measurement method can be used more efficiently.
Supersensitive phase estimation for hybrid interferometer using balanced homodyne detection

Abstract We have theoretically investigated the phase sensitivity of the hybrid interferometer with two coherent beams using the method of balance homodyne detection. The measurement device is a nonlinear–linear hybrid interferometer consisting of an optical parametric amplifier and a beam splitter. We prove that the phase sensitivity can beat the shot noise limit in the optimal conditions. Furthermore, we derive the quantum Cramér–Rao bound of the hybrid interferometer. The effects of transmission loss and detection loss on the measurement accuracy are discussed. The detection accuracy of this scheme is compared with that of the conventional SU (1,1) interferometer. Our results show that this scheme outperforms the conventional SU (1,1) interferometer scheme at high parametric strength. This scheme can be implemented with existing experimental techniques and will have important applications in quantum precision measurements.
Compositional analysis

Abstract Health outcomes are often multi-faceted as many environmental variables have synergistic impacts. By integrating data on chemical agents from the lithosphere, hydrosphere, and atmosphere, it will be possible to note the roles each play, and which are having the most demonstrable effect on long term cognitive health outcomes. This presentation will showcase an integrated and compositional approach taken to model the effects that chemical agents in each sphere are having on long term cognitive health outcomes. Firstly, chemical agent data will be extracted using circular buffers. Secondly, the relationships that chemical agents share across the environmental spheres are validated through correlation (complex heatmap/clustering) and dimension reduction (PCA) techniques. Thirdly, cognitive health outcome variables are integrated into this framework, to note which chemical agents and health variables show correlations. Lastly, a forward-selection algorithm like selbal is demonstrated to indicate the level of significance that each health outcome shares with each chemical agent(s). Through the integration of these techniques, it will be notable which chemical agents and environmental sphere is having the most significant effect on long term cognitive health outcomes.
A systematic review of real-world contact tracing strategies at the onset of the COVID-19 pandemic

Abstract Background Contact tracing (CT) represented one of the core activities for the prevention and control of COVID-19 in the early phase of the pandemic, before the availability of effective vaccines. Poor evidence is available on the real-world implementation of CT strategies and adoption of CT digital tools during the initial phase of the emergency. Methods We aimed to provide a comprehensive picture of the organizational aspects of CT activities during the first wave of the pandemic, through the identification and description of CT strategies used in different settings during the period March-June 2020 and of digital tools adopted in the early phase. A systematic review of published studies describing organizational models of COVID-19 CT strategies and digital tools used in real-world settings was conducted. PubMed, Embase and Cochrane Library were searched. Results We retrieved a total of 1,637 studies, 30 of which met all our inclusion criteria. Seven studies target the general population, 10 studies described CT activities carried out in specific population subgroups, 13 described the use of CT apps in real world settings. Main activities implemented to rapidly scale-up CT activities included decentralization of CT, involvement of trained non-public health resources (e.g. University students, civil servants), use of digital tools for CT management, inter-agency collaboration, community engagement. Digital apps proved to be a useful supplementary component in CT strategies, however their acceptability was hampered by privacy, equity and effectiveness's concerns. Conclusions Despite the lack of data on CT effectiveness, available evidence provides indication on coordination mechanisms and use of human and technical resources needed for the rapid scale up of CT activities. Further research on real world implementation of CT strategies during the COVID-19 pandemic would be needed to contribute to a more robust evidence-making process. Key messages • Decentralization of CT activities, involvement of trained non public health professionals, intersectoral collaboration were the key components of early COVID-19 CT strategies. • The use of smartphone apps and other digital tools proved to be a substantial support for CT activities. However, privacy, ethics and equity issues must be clearly addressed.
Semi-supervised learning and bidirectional decoding for effective grammar correction in low-resource scenarios

The correction of grammatical errors in natural language processing is a crucial task as it aims to enhance the accuracy and intelligibility of written language. However, developing a grammatical error correction (GEC) framework for low-resource languages presents significant challenges due to the lack of available training data. This article proposes a novel GEC framework for low-resource languages, using Arabic as a case study. To generate more training data, we propose a semi-supervised confusion method called the equal distribution of synthetic errors (EDSE), which generates a wide range of parallel training data. Additionally, this article addresses two limitations of the classical seq2seq GEC model, which are unbalanced outputs due to the unidirectional decoder and exposure bias during inference. To overcome these limitations, we apply a knowledge distillation technique from neural machine translation. This method utilizes two decoders, a forward decoder right-to-left and a backward decoder left-to-right, and measures their agreement using Kullback-Leibler divergence as a regularization term. The experimental results on two benchmarks demonstrate that our proposed framework outperforms the Transformer baseline and two widely used bidirectional decoding techniques, namely asynchronous and synchronous bidirectional decoding. Furthermore, the proposed framework reported the highest F1 score, and generating synthetic data using the equal distribution technique for syntactic errors resulted in a significant improvement in performance. These findings demonstrate the effectiveness of the proposed framework for improving grammatical error correction for low-resource languages, particularly for the Arabic language.
The Implementation of “wordwall” web-based games as instructional media to improve Arabic vocabulary mastery of 8th grade students at SMP Nurul Huda Modung

In digital era, learning is not enough just to be carried out with conventional media. Today's students are close to various applications and websites, so it is necessary to apply them in the learning process. One of the innovative instructional media today is an educational web-based game named “Wordwall”. This application is a learning web-based game in which there are many types of games that students can play.&#x0D; This study aims to find out how to learn Arabic vocabulary using the web-based game "Wordwall" and what are the advantages and disadvantages of "Wordwall" in learning Arabic vocabulary for eighth graders at Nurul Huda Junior High School. Modung District, Bangkalan Region.&#x0D; The methodology in this research is descriptive qualitative, to describe the conditions of the research subjects and reveal the facts in the field. This research took place at Nurul Huda Junior High School Modung, Bangkalan Region, specifically eighth graders. The method of data collection was by interview, observation and documentation.&#x0D; As the results, this educational game brings many advantages, and disadvantages as well. The advantages are: the students can study enthusiastically and happily, because they can play and learn a lot of new vocabulary at the same time. This web-based game is also flexible; it can be used by students of any level. On the other hand, there are also disadvantages of “Wordwall”. Among them are: for basic users, there are only 5 game templates. If you want more, users have to pay 54,000 rupiah per month. The drawback of a free account is that sometimes the font changes suddenly and sometimes errors occur. Besides those things, learning takes a very long time online, and it requires a lot of internet quota.
Improving resource recycling based on deep learning

The manual sorting of recyclable garbage has caused several issues such as the wastage of human resources and low resource utilization. To solve this problem, an improved Single Shot Multibox Detector (SSD) deep learning approach has been developed for recyclable garbage detection. To reduce the number of parameters and make the model easier to deploy and apply, a lightweight network called RepVGG has been chosen to replace the VGG16 network in the SSD. Additionally, the auxiliary convolutional layer structure of the SSD has been modified to further reduce the number of parameters. Additionally, the SK module has been integrated to adaptively adjust the size of the receptive field and enhance the detection accuracy. Experimental results of Waste Classification data set from Kaggle website have demonstrated that the improved SSD model has better detection accuracy and real-time performance, with an accuracy of 95.23%, which is 4.33 percentage points higher than the original SSD, and a detection speed of up to 64 FPS. This algorithm can be better applied in industry.
A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset

Batch process monitoring datasets usually contain missing data, which decreases the performance of data-driven modeling for fault identification and optimal control. Many methods have been proposed to impute missing data; however, they do not fulfill the need for data quality, especially in sensor datasets with different types of missing data. We propose a hybrid missing data imputation method for batch process monitoring datasets with multi-type missing data. In this method, the missing data is first classified into five categories based on the continuous missing duration and the number of variables missing simultaneously. Then, different categories of missing data are step-by-step imputed considering their unique characteristics. A combination of three single-dimensional interpolation models is employed to impute transient isolated missing values. An iterative imputation based on a multivariate regression model is designed for imputing long-term missing variables, and a combination model based on single-dimensional interpolation and multivariate regression is proposed for imputing short-term missing variables. The Long Short-Term Memory (LSTM) model is utilized to impute both short-term and long-term missing samples. Finally, a series of experiments for different categories of missing data were conducted based on a real-world batch process monitoring dataset. The results demonstrate that the proposed method achieves higher imputation accuracy than other comparative methods.
Development of a Knowledge-Based Multimodal Deep Learning System for Automatic Breast Lesion Segmentation and Diagnosis in MG/DMR Images

Deep learning networks (DLNs) rely on labeled training datasets as their fundamental building blocks. While various databases exist worldwide, there is currently no domestic solution available in our country. This project aims to create a domestic database by automatically segmenting breast lesions in MG/DMR images based on their types and developing a knowledge-based multimodal DL-based integrated computer-aided diagnosis system to analyze the images, thereby providing the system with continuous learning capability. Different brands of devices exist for MG/DMR, necessitating the multimodal operation of image processing/artificial intelligence algorithms. To achieve this goal, the network was trained first, and then prelearned data were transferred to enable the training of data from different networks once accurate results are obtained. The developed system has the potential to enable the automatic detection of breast lesions, ensuring fast and high diagnostic accuracy. Additionally, it might also facilitate the retrospective analysis of patients' periodic check-up results.
The Differential Privacy Framework for Convolutional Neural Network using PATE, ADLM and SGD models

The use of CNNs raises concerns over privacy, especially when dealing with sensitive data, therefore, differential privacy has emerged to protect the privacy of data providers while allowing data analysis and model training. Differential privacy is a mathematical framework that protects sensitive data by adding random noise to the training data. In this study, we propose a differential privacy framework for CNNs using the PATE(Private Aggregation of Teacher Ensembles), ADLM(Adaptive Laplace Mechanism), and SGD(Stochastic Gradient Descent) models. We experimentally demonstrate that the proposed framework achieves better accuracy while providing strong privacy guarantees in image recognition tasks.
Research on Cross-Chain Consensus Models in Complex Trust Environments

With the application of blockchain technology in the fields of energy big data, government affairs and data sharing, the types of blockchain adapted to various scenarios have gradually flourished, and the ledgers operate in isolation from each other, forming data silos and causing challenges for interchain data access and consistent collaboration. Most of the current cross-chain mechanisms based on blockchain are notary mode and relay mode, which cannot be dynamically switched according to their respective organizations' trust degree, making it difficult to meet the requirements of cross-chain data access between blockchain systems in complex trust environments. Based on this issue, a cross-chain consensus model based on trust establishment is proposed. The scheme constructs a blockchain security trust model based on the network state, blockchain system security, node data consistency and malicious node rate during blockchain operations, combines fuzzy theory and probability distributions to determine the trust components of each system, and uses those trust components and their weights to determine the system's trust value. The cross-chain consensus scheme is designed based on this model, which can be flexibly switched between the notary model and the committee relay model to satisfy cross-chain interconnection and mutual access between systems with different trust relationships. The simulation results show that the proposed model and scheme have good performance in terms of stability and performance.
Improving Bipartite Networks Embedding with Partially Available Vertex Content

Most interactive networks in the real world are bipartite networks, the research and analysis of bipartite networks has very high application value. Network embedding aims to represent nodes in a network as low-dimensional dense vectors, while retaining structural information and characteristics in the original network to the greatest extent. Most of the existing bipartite network only focus on structural characteristics, ignoring the effective content information on the vertex in the network. Different from the existing work, the problem studied in this paper has the following two characteristics: (1) There exists content information on vertices in bipartite networks; (2) One type of vertices has partially content information in bipartite networks. To solve this problem, this paper proposes a novel bipartite network embedding with partially available vertex content boosted, namely BEAVC, which can effectively use the limited content information of vertices to enhance the structure-only representation. The experimental results show that the embedding algorithm compensated by vertex information can better characterize nodes.
DLPriv: Deep Learning Based Dynamic Location Privacy Mechanism for LBS in Internet-of-Vehicles

In the Internet of Vehicles (IoV), location-based service providers rely on collecting and analyzing user-generated trajectory data to deliver high-quality experiences anytime and anywhere. However, the utilization of data mining technologies raises concerns regarding the potential inference of personal privacy information, posing security risks to user privacy. Existing studies typically use differential privacy to obfuscate actual trajectories, albeit at the cost of reduced data utility. To address this challenge and strike a balance between privacy and data utility, we propose DLPriv, a dynamic location privacy mechanism. DLPriv leverages a Long Short-Term Memory (LSTM) network to predict a driver's next location, which in turn determines the level of data obfuscation. This mechanism considers two optimization objectives, trajectory privacy and utility, and enables fine-grained control over privacy levels. Experimental results demonstrate that our proposed mechanism improves data utility by 37% compared to existing work, while providing the same level of privacy.
Teaching a Robot to Toss Arbitrary Objects with Model-Based Reinforcement Learning

In this paper, we investigate the use of ModelBased Reinforcement Learning (MBRL) for pick-and-throw tasks, which is a variation of the pick-and-place task. In pick-and-throw applications, objects are thrown instead of placed to increase time efficiency and enlarge the workspace reachable by the robot. The effectiveness of analytical methods for this task is limited by the complex system dynamics and the disturbances due to communication and control. Recently, the application of data-driven solutions, in particular Model-Free Reinforcement Learning (MFRL), has attracted attention. In this paper, we explore the use of a MBRL algorithm to solve the pick-and-throw task. Experiments carried out in simulation show the potentialities of our approach, in terms of accuracy and data efficiency, both with respect to analytical methods and a MFRL approach.
Combining 3D Planning and Control Barrier Functions for Safe Motion of Quadrotor UAVs Among Obstacles

This paper proposes the combination of a fast motion planner, which generates potentially unfeasible trajectories for the center of mass of an underactuated UAV, with a tracking controller integrating a Control Barrier Function. In this way avoidance of both stationary and moving obstacles is guaranteed even when the UAV deviates from the collision-free trajectory issued by the planner, due to its unfeasibility. Simulations in a dynamic and cluttered environment validate the method.
Blockchain-Enabled Cross-Layer Radio Frequency Fingerprinting Identification with Machine Learning for IIoT

Motivated by rising voices of light weight high security for the Industrial Internet of Things (IIoT), in this article, we propose a cross-layer authentication scheme by taking advantage of both the radio frequency fingerprinting (RFF) and the blockchain trust. Due to the mobility of terminal devices in the IIoT network, the wireless terminal devices often move from one region to another region, which causes traditional RFF identification scheme to not be able to be adopted. In this article, the RFFs of the wireless terminal devices that are easy to invade are extracted and learned by the first access edge device. Then the learned model is shared with other edge devices by aiding with blockchain. This way, IIoT network identifies wireless terminal nodes with high security by using the unique structure of the transmitter waveform without adding any burden while repetitive training is avoided. Besides, an evaluation metric has been proposed to evaluate the performance of robust RFF identification in practical mobile scenarios. Lastly, this article further presents several research directions for RFF identification in practical mobile scenarios.