Reviewerly/exHarmony huggingface dataset:
Dual-Transformer Head End-to-End Person Search Network

Abstract Person Search mainly consists of two submissions, namely Person Detection and Person Re-identification (re-ID). Existing approaches are primarily based on Faster R-CNN and CNN (e.g., ResNet). While these structures may detect high-quality bounding boxes, they seem to degrade the performance of re-ID. To address this issue, this paper proposes a Dual-Transformer Head Network (DTHN) for end-to-end person search, which contains two independent Transformer heads, a box head for detecting the bounding box and extracting efficient bounding box feature, and a re-ID head for capturing high-quality re-ID features for the re-ID task. Specifically, after the image goes through the ResNet backbone network to extract features, the RPN proposes possible bounding boxes. The box head then extracts more efficient features within these bounding boxes for detection. Following this, the re-ID head computes the occluded attention of the features in these bounding boxes and distinguishes them from other persons or backgrounds. Extensive experiments on two widely used benchmark datasets, CUHK-SYSU and PRW, achieve state-of-the-art performance levels, 94.9 mAP and 95.3 top-1 score on the CUHK-SYSU dataset, and 51.6 mAP and 87.6 top-1 score on the PRW dataset, which demonstrates the advantages of this paper’s approach.
Comparison of distance metric in k-mean algorithm for clustering wheat grain datasheet

One of the data mining models is clustering, clustering models can be used to create groupings of data. Clustering is done by creating groups of data that are close to each other. The research was conducted by clustering wheat seed datasheets.Â The wheat grain datasheet contains various types of wheat data.Â The purpose of this research is to create a clustering model. The algorithm used is the K-means algorithm and a comparison is made with several distance Metric algorithms. The datasheet used was tested with the K-means algorithm and tested the clustering value (k) ranging from k = 2 to k = 6. Comparison of clustering results with K-means is also done by comparing with distance metric algorithms, namely Euclidean distance, Manhattan distance, and Chebychev distance.Â All testing processes are evaluated, and the evaluation is done to select many good groupings. The evaluation process is carried out using the Davis-Bouldin method. The results of the grouping that has been done, each seen Davis Bouldin evaluation. The evaluation value of Davis Bouldin is sought from the smallest value and if the evaluation result is negative, the value is solved. The research method used is Knowledge Discovery in Database (KDD). The results showed that the same datasheet and using the K-means algorithm and the same evaluation resulted in different evaluation values. The Euclidian, Manhattan, and Chebychev algorithms produce the best k value of 2, The conclusion of the wheat seed datasheet clustering research produces a value of k = 2
Implementation of the breadth-first search method on forward-chaining inferences to diagnose autism disorders in children

Autism is a type of mental disorder that can affect children as young as three years old, but can affect children from birth. This mental disorder is characterized by social relationships, culture, verbal and non-verbal communication, imagination, and a single attractive object. By using the Autism Disorder Diagnostic Expert System, we hope that most people will be able to attempt an autism diagnosis without an expert. This system works by answering all system questions. The "forward chaining" method is a way of drawing conclusions that starts with facts and tests hypotheses toward conclusions. To test the hypothesis, begin matching facts or statements with IF. The forward chaining method searches for a solution to a problem. The forward chaining inference and breadth first search methods were used to design this system, as well as Microsoft Access for database management systems and Visual Basic Language Programming. The expected outcome is that all users, particularly parents, will be able to easily assess the possibility of their child developing autism symptoms from an early age. The output of the system is whether there is a possibility of autism in a child based on the facts and symptoms given to the system.
Multivariate time series anomaly detection based on graph neural network and grated neural network

Multivariate time series anomaly detection aims to identify time periods in time series data that are abnormal or deviate from historical data patterns. The increasing dimension of multivariate time series data and the complex temporal dependence relationships make anomaly detection more difficult. Therefore, dealing with the temporal and spatial dependence of the time series data becomes the key to anomaly detection. In this paper, we propose an anomaly detection model GNN-GRUAD based on Graph Neural Network and Gate Recurrent Unit, which can obtain the graph structure of inter-feature dependencies without relying on a priori knowledge, and use it together with the temporal data as the input of the graph neural network to capture the data's spatial dependence relationships. The model also incorporates GRU to model the temporal dependencies of the data, to finally predict the data and achieve anomaly detection based on the prediction error. This paper compares with several other baseline models on the SWaT and WADI datasets, and the model in this paper achieves good anomaly detection performance with an improvement of 0.37% and 7.02% compared to the best baseline on the two publicly available datasets, respectively.
Hybrid text classification model based on graph convolution network and neural network

With the rapid development of graph neural network technology, its application in the field of natural language processing is more and more extensive, text classification is one of the important applications, everyday life will produce a large number of non-Euclidean text data, while the traditional classification methods in the graphic structure of text data has been a great challenge. Graph convolutional neural network(GCN) is considered to be able to model the structural attributes and node feature information of graphs well, and is gradually becoming a good choice for text classification of graph data. This paper proposes a text classification model based on graph convolution network and neural network local enhancement. On the basis of using GCN to extract features, Bi-LSTM method is used to balance the experimental results, enrich the feature information by capturing local information, integrate the attention mechanism, and fuse the evaluation values to improve the accuracy of classification. It is verified that this method has achieved better results than the existing classification methods in many classical data sets such as 20NG and OHSUMED.
Research on the pseudorandom sequence generator based on compertz map and piecewise map

For the purpose of improving the phenomenon of short period of the sequence result in using single chaotic map, this paper proposes a pseudorandom sequence generator scheme based on Compertz map and piecewise map. The sequence generated by our scheme passed the National Institute of Standards and Technology (NIST) randomness tests, compared with other schemes, the sequence generated by our scheme shows good randomness and statistical characteristics. In the aspect of application, the sequence generated by our scheme can be use as pseudorandom numbers and secret key. In terms of confidentiality, the seed key consist of the combination of control parameters of piecewise map, initial value of chaotic system, number of initial iteration rounds of chaotic map and left shift of key sequence. In the aspect of security, the secret key generating by our scheme will cost a lot for the attacker to crack.
Multi-label pedestrian attribute recognition model based on domain generalization

At present, there are distribution differences in the public data set of pedestrian attribute recognition, and the sampling scenarios are diversified, which leads to the reduction of the prediction accuracy of the pedestrian attribute recognition algorithm (PAR) in the cross-data set and the limitation of its application in the actual scene. For the appeal problem, this paper proposes a multi-source and multi-label pedestrian attribute recognition method based on domain generalization (DG). First, label multiple data sets as source domains through label alignment to determine the common attribute labels in each source domain. Secondly, add the countermeasure data enhancement module (ADM) to generate the data distribution of the target domain based on the difference of data distribution in the source domain. This model can predict the data in the unknown target domain, thus optimizing the performance of pedestrian attribute recognition (PAR) in cross-domain and actual scenarios. The model was tested on the public data sets PETA, RAP, PA-100K. Compared with HPNet, LGNet, PGDM, StrongBaseline and other models, the average recognition accuracy (mA) index and F1 value increased by 1.16% and 1.51% respectively, which proved the effectiveness of the model in pedestrian attribute recognition.
Implementasi Secure Storage Menggunakan Metode Full Disk Encryption dan Tamper Proof pada Cloud Storage

Currently, storage development has shifted to internet-based storage, or cloud storage, as online storage media. Cloud storage is becoming more popular among the public due to several advantages, including the fact that when the storage on personal computers runs out, users do not need to purchase new storage devices or incur additional costs for device maintenance. However, using such online storage can increase risks such as unauthorized access, data leakage, sensitive data, and privacy rights. This study proposes a prototype secure personal cloud storage system with physical database protection. The device has data encryption features that use full disk encryption to secure data on the server, VPN to secure communication between user and server, and tamper proof to physically secure the database. The test results show that the implementation of secure storage affects the performance of cloud storage, as evidenced by the decrease in data write speed from 19.87 Mb/s to 15.75 Mb/s after the implementation of LUKS full disk encryption. On the security side of data transmission, OpenVPN can secure the transmission between the user and the server. This is evidenced by sniffing testing using wireshark tools which shows that data transmission has been encapsulated by the OpenVPN application. In unit testing carried out on LUKS, it proved that the number of LUKS version 2 key slots corresponds to the reference, which is a maximum of 32 key slots. The last test proves that the tamper proof mechanism can work as expected. The tamper proof mechanism works when the device detects an indication of an attack on the physical device. The tests carried out were destroying the device casing using a hammer, opening the casing bolt, and a saw. The test results show that the device will perform damage to the SSD when the attack indication exceeds the tolerance value.
Seismic Soundoff: Uncovering the hidden history of Ghana

Cyril D. Boateng discusses his SEG field camp, “Investigating the slave trade in southeastern Ghana using integrated geophysical techniques.” He explains the concept behind “the archaeology of slavery” and describes the various geophysical investigations used across four communities. This conversation highlights the significant value that geophysics brings to a problem. It shows how SEG field camps are an invaluable tool for building the next generation of scientists and providing humanitarian benefits.
Pengembangan Augmented Reality Pengenalan Hewan Berdasarkan Jenis Makanannya untuk Siswa Sekolah Dasar

A less interesting learning process resulted in students feeling bored with the material presented. Therefore this study aims to create interesting learning media so that students are interested in learning the material optimally. The research method used by the author uses the Multimedia Development Live Cycle (MDLC) system development method developed by Luther. The Multimedia Development Live Cycle (MDLC) is carried out based on six stages, namely concept, design, material collection, assembly, testing, and distribution. This research produces a product in the form of an Augmented Reality application to classify animals based on the type of food for elementary school children. The results of the Black Box test can be concluded that the application can run well. The results of the feasibility test calculation obtained a score of 81.31% included in the very feasible category. The products used in this study were declared very suitable for use by elementary school children and used as learning media so that they could help students' understanding processes and foster students' interest and enthusiasm for learning.
IStructE's History Study Group: 50 years of visits, tours and lessons learned

Robert Thorne charts the progress of the IStructE's History Study Group as it celebrates its 50th anniversary.
Bibliometric Analysis of Neural Network in improving Computational Fluid Dynamics Education

The integration of neural networks into computational fluid dynamics (CFD) has led to significant advancements in fluid dynamics simulations, enhancing accuracy and effectiveness.This convergence presents a promising avenue for studying fluid systems and has the potential to revolutionize our understanding of their complex behaviours.To foster collaboration and drive research in this field, a comprehensive bibliometric review was conducted, examining the intersection of fluid dynamics education and neural networks.From a pool of 999 articles gathered from the Google Scholar database, 779 articles published between 2018 and 2023 were selected using the Publishing or Perish (PoP) software.These articles were analysed and categorized using VOSviewer, revealing three distinct clusters representing the primary research topics in computational fluid dynamics and neural networks.The findings of this study provide valuable insights into the current state of research in this emerging field, offering a comprehensive overview of key themes and their relationships.These insights can guide researchers and practitioners in identifying gaps, exploring collaborations, and advancing interdisciplinary approaches.Moving forward, further exploration of the identified clusters and associated keywords can deepen understanding of specific research topics, while investigations into methodologies and techniques employed in the articles can contribute to the development of advanced computational tools and frameworks for fluid dynamics simulations.Collaborative efforts and interdisciplinary approaches hold great potential for advancing our knowledge of fluid systems and driving progress in this exciting field.
Network Design Using Load Balancing Equal Cost Multi Path (ECMP) Method At Lexima Consultant Office

The development of technological advances also contributed to the advancement of Internet technology. Where the Internet is one of the needs that is considered important in all fields, this is evidenced by the number of Internet users from 2020 to 2023 having a significant increase. Lexima Consultant as one of the institutions engaged in information system creation services, of course, requires an internet connection. However, there are several obstacles faced, one of which is the absence of a load balancing and failover network system which sometimes makes the need for internet access with large bandwidth allocations to be disrupted and even cut off. Implementing the right technique at the Lexima Consultant office for sending data packets as the main factor supporting activities is certainly very much needed. In the research conducted, a load balancing technique was used using the ECMP method to optimize network performance and avoid internet network disturbances. From the research conducted, it can be concluded that the implementation of the load balancing method using the ECMP method has been able to divide connection paths and failover can run properly, so the possibility of disruption in the form of a network connection loss will be very small
Thrombocytopenia in Malaria in Children

Introduction:Malaria is a cause of high mortality and morbidity. In 2022 during August-September, there was a sudden increment in malaria cases among children in our hospital. We evaluated the role of platelet count to diagnose malarial infection in children. Methodology: This study included paediatric patients seen in August-September of 2022 in BCM Hospital, Khairabad, Sitapur for fever who had thrombocytopenia in their first complete blood count report. This research comprised 230 children with fever and thrombocytopenia. Results:It was found that a moderate to severe platelet count decline was 86.21 per cent sensitive and 56.14 per cent specific for malaria with high negative and positive predictive values. Conclusion: In conclusion, moderate to severe thrombocytopenia is a key indicator of malaria, hence all children with fever and thrombocytopenia should be tested for malaria. If moderate to severe thrombocytopenia is seen, further specialised tests such as repeated peripheral blood smears, ELISA for parasite-specific antigens, etc. should be done.
Methods of Identifying Vulnerabilities in the Information Security Incident Management Process

"The application of tools and methods to improve the effectiveness of the information security management system by detecting and neutralizing cyber attacks, monitoring and blocking DDoS attacks, Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), blocking TOR Exit Nodes, blocking Layer 7 Regular Expression attacks, web filtering, blocking malware attacks, ransomware, spammers and spam servers has become a sine-qua-non requirement in the management of information security incidents in order to minimize their impact on IT systems. The paper provides insight into the impact of integrating much-needed security requirements for information technology users in military systems. The collection of information was carried out on a statistical sample of 128 students from military academies in Romania, Bulgaria, Poland. The research methods are complemented by cross-sectional (questionnaire, survey, observation) and observational methods."
Education 5.0 Serving Future Skills for Industry 5.0 Era

This chapter addresses the relationship between Industry 5.0 and Education 5.0, outlines the future skills required for Industry 5.0, and illustrates how these two sectors interact with the Sustainable Development Goals (SDG) for modernizing Society 5.0. It places a strong emphasis on high-quality education and uses tailored learning to help employees and companies optimize for Industry 5.0. Additionally, it discusses the OECD Learning Compass 2030 and Future of Education and Skills 2030 reports, as well as the International Organization for Standardization (ISO) 21001:2018-ISO standard utilized for future educational management. The chapter emphasizes the need to improve the educational system while presenting various options for adopting personalized education.
Abstract: Deep-learning on Lossily Compressed Pathology Images

Digital whole slide imaging (WSI) systems allow scanning complete probes at microscopic resolutions, making image compression inevitable to reduce storage costs. While lossy image compression is readily incorporated in proprietary file formats as well as the open DICOM format for WSI, its impact on deep-learning algorithms is largely unknown.We compare the performance of several deep learning classification architectures on different datasets using a wide range and different combinations of compression ratios during training and inference.We use ImageNet pre-trained models, which is commonly applied in computational pathology. With this work, we present a quantitative assessment on the effects of repeated lossy JPEG compression for ImageNet pre-trained models.We showadverse effects for a classification task, when certain quality factors are combined during training and inference. This work was published on the International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis [1].
Abstract: Liver Tumor Segmentation in Late-phase MRI using Multi-model Training and an Anisotropic U-Net

Automatic liver tumor segmentation can support the planning of liver interventions, such as selective internal radiation therapy (SIRT). Most studies on deep learning-based liver tumor segmentation have focused on contrast-enhanced CT, however dynamic contrastenhanced MRI (DCE-MRI) can yield a higher sensitivity. In this work , we demonstrate the deep learning-based segmentation of liver tumors in the late hepatocellular phase of DCE-MRI. In particular, we employ an anisotropic 3D u-net architecture (aU-Net) and a multi-model training strategy: the training is started 16 times using different random weight initializations. After 5k, 10k, 20k and 40k iterations, the number of neural networks is reduced to the best performing half, based on validation data. This approach significantly improves the segmentation performance compared to a standard singlemodel training (mean Dice score 0.74 vs. 0.70), and is close to the inter-rater-agreement of three clinical experts (mean Dice score 0.78). Moreover, the aU-Net architecture alone improves the segmentation performance compared to our previous study using three 2D U-Nets working on orthogonal view directions (mean Dice score 0.65). In a qualitative rating, 66% of automatic segmentations from multi-model training are rated as good or very good, compared to 43% for the single-model training. However, the detection performance (F1-score 0.59) is still inferior to the inter-observer-agreement of the clinical experts (0.76). In summary, this study demonstrates that correctly detected liver tumors can be automatically segmented with high accuracy in late-phase DCE-MRI data, but the detection can still be improved, in particular of smaller lesions [1, 2].
Advanced Firestore

Since we completed a note-taking application, we are now more comfortable with SwiftUI and the Firebase console. It is time to create a new application, this time with more complexity: there will be a feed view like that of famous social media platforms such as Instagram, a search feature, and a profile section where you can view your posts, and there is the ability to sign in seamlessly with your Apple account.