Reviewerly/exHarmony huggingface dataset:
Comparison of Semi-structured Data on MSSQL and PostgreSQL

The present study intends to compare the performance of two Data Base Management Systems, specifically Microsoft SQL Server and PostgreSQL, focusing on data insertion, queries execution and indexation. To simulate how Microsoft SQL Server performs with key-value oriented datasets, we use a converted TPC-H lineitem table. The dataset is explored in two different ways, first using the key-value-like format and second in JSON format. The same dataset is applied to PostgreSQL DBMS to analyze performance and compare both database engines. After testing the load process on both databases, performance metrics (execution times) are obtained and compared. Experimental results show that, in general, inserts are approximately twice times faster in Microsoft SQL Server because they are injected as plain text without any type of verification, while in PostgreSQL, loaded data includes a validating process, which delays the loading process. Moreover, we did additional indexation tests, from which we concluded that in general, data loading performance degrades. Regarding query performance in PostgreSQL, we conclude that with indexation, queries become three or four percent faster and six times faster in Microsoft SQL Server.
Interactional Freedom and Cybersecurity

We have become accustomed to the news of more and more cunning attacks to real-world systems, and equally accustomed to try to fix them even though further attacks may come. I discuss how to tackle and ultimately resolve this tedious and infamous attack-fix-loop practice by distilling out five paradigms to achieve cybersecurity: democratic, dictatorial, beautiful, invisible and explainable security. While each of these has distinctive features, various combinations, at some rate, of them may coexist, with the final aim of improving the way security measures account for the human element. Towards the end of the paper, I conjecture how the paradigms could be used to improve the ultimate security measure of our times, a Security Operation Centre. May I remark that many of the observations made below derive from my personal and current understanding and would require a number of experiments to be fully confirmed.
Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters

Post-disaster sector damage data is data that has features or criteria in each case the level of damage to the post-natural disaster sector data. These criteria data are building conditions, building structures, building physicals, building functions, and other supporting conditions. Data on the level of damage to the post-natural disaster sector used in this study amounted to 216 data, each of which has 5 criteria for damage to the post-natural disaster sector. Then the 216 post-disaster sector damage data were processed using Principal Component Analysis (PCA) to look for labels in each data. The results of these labels will be used to cluster data based on the value scale of the results of data normalization in the PCA process. In the data normalization process at PCA, the data is divided into 2 components, namely PC1 and PC2. Each component has a variance ratio and eigenvalue generated in the PCA process. For PC1 it has a variance ratio of 85.17% and an eigenvalue of 4.28%, while PC2 has a variance ratio of 9.36% and an eigenvalue of 0.47%. The results of the data normalization are then made into a 2-dimensional graph to see the visualization of the PCA results data. The result is that there is 3 data cluster using a value scale based on the PCA results chart. The coordinate value (n) of each cluster is cluster 1 (n<0), cluster 2 (0 ≤n <2), and cluster 3 (n≥2). To test these 3 groups of data, it is necessary to conduct trials by comparing the original target data, there are two experiments, namely testing the PC1 results with the original target data, and the PC2 results with the original target data. The result is that there are 2 updates, the first is that the distribution of PC1 data is very good in grouping the data when comparing the distribution of data with PC2, because the variance ratio and eigenvalue values of PC1 are greater than PC2. While second, the results of testing the PC1 data with the original target data produce good data grouping, because the original target data which has a value of 1 (slightly damaged) occupies the coordinates of cluster 1 (n<0), while the original target data which has a value of 2 (damaged moderately) occupies cluster 2 coordinates (0 ≤n <2), and for the original target data the value 3 (heavily damaged) occupies cluster 3 coordinates (n≥2). Therefore, it can be concluded that PCA, which so far has been used by many studies as feature reduction, this study uses PCA for labeling unsupervised data so that it has an appropriate data label for further processing.
Motivation of Young Generation to Create A Generation of Excellent Achievement and Character in Lampung Province

The low sense of enthusiasm in the work of the younger generation makes the next generation of the nation will be hindered from achieving their goals in the future. Various factors make it difficult for the younger generation to work and find their identity by finding and honing their life skills. The current globalization and the lack of love for reading and writing literacy are one of the biggest causes for the younger generation to find it difficult to achieve a generation of excellence, achievement, and character. The purpose of this study is to find out the efforts that can be made to increase the enthusiasm of the younger generation and to know the efforts that can be made to compile and achieve success tips in achieving goals for a better generation. This research was conducted by a survey of the younger generation in Lampung Province with a qualitative approach, in terms of literature study analysis or case study. Research has conducted in 35 locations consisting of schools, organizations, Islamic boarding schools, and groups of youth communities (young generation). The result of this study is that the usual motivation to raise enthusiasm or motivation is to apply the friendship facilitator method, achievement motivation training (AMT), and Training Within Industry (TWI). Success tips that can arouse the enthusiasm of the younger generation to achieve their goals are to record plans and set goals, always believe in abilities, always thirst for experience and knowledge, network, and maintain self-optimism.
PENGEMBANGAN MEDIA PEMBELAJARAN PENGOLAHAN DAN PENYAJIAN MAKANAN INDONESIA DENGAN APLIKASI LECTORA PADA SISWA KELAS XI JASA BOGA SMKN 4 KOTA JAMBI

The research intended to produce a Lectora Inspire-based learning media and recognize its effectivity on students of Cookery XI Class at SMKN 4 Kota Jambi. It was a type of developing research. The subject was students of Cookery XI Class at SMKN 4 Kota Jambi. The model of the research development referred to ADDIE model by Lee And Owen, which consists of 5 stages, namely: Analysis, Design, Development, Implementation and Evaluation. The method of data collection was observation, interview, questionnaire/ interview guide and documentation. The assessment of video propernesswas validated by media and content experts. The sample of the small class was 15 students and the big class was 30. The technique of data analysis was descriptive statistic. The result of the development research was a Lectora Inspire-based learning media properly used by students of Cookery XI Class not only in term of its suitability with the syllabus and lesson plan but also content and media aspects. The validation given by validators from content and media experts stated the properness in the scale of “Very Agree”. It was concluded that the Lectora Inspire-based learning media was usable for learning media. Based on the validation result, the media was tested to a small scale class and reached 91,42% of presentation result which was categorized in the scale of “Very Agree”. It was then tested in a field test with presentation result 89,79% which was categorized in the scale of “Very Agree”. As the result, it was clearly stated that the learning media of Preparation and Presentation of Indonesian Food was properly used as learning media for students of Cookery XI Class at SMKN 4 Kota Jambi.
White‐nose Syndrome Research and Conservation Grants (DOI)

Federal Grants & ContractsVolume 47, Issue 12 p. 4-4 Grants Alerts White-nose Syndrome Research and Conservation Grants (DOI) First published: 14 May 2023 https://doi.org/10.1002/fgc.33032Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume47, Issue1218 May 2023Pages 4-4 RelatedInformation
A method for mitigating the impact of burst feature variations with available bandwidth on application category estimation

There are many problems in setting appropriate quality requirements for communication flows. If flows that require low latency or high reliability are treated the same as other flows, these flows are susceptible to congestion. Therefore, we are studying technologies to effectively utilize network resources by identifying flows and setting appropriate quality requirements. When identifying flows, there is a problem that the traffic behavior changes due to available bandwidth limitations, and the identification accuracy decreases. In this paper, we explain the cause of the problem and show our improved application category estimation method mitigating the impact of available bandwidth fluctuations.
Early Detection of Breast Cancer using An Improved Deep-Learning Model

Deep learning algorithms are now being employed to undertake the inquiry, and preliminary results have been assessed as encouraging. Because they rely on comparably small breast pathology imaging datasets and an overoptimistic version of the softmax-cross entropy loss, popular convolutional neural networks (CNNs) and their derivatives are prone to overfitting in their classifiers. This is because their classifiers are prone to overfitting. Furthermore, these CNNs and their variants employ an unduly pessimistic application of the softmax-cross entropy loss. To perform computations, these CNNs use a modified version of the overconfident softmax-cross entropy loss approach. This is the outcome of the union of these two factors. AlexNet, VGGNet, and GoogleNet are a few examples of these networks. This is because both occurrences result in the loss of the overconfident softmax-cross entropy. This is the root of the problem. We present the AlexNet-BC model to alleviate overfitting and improve classification accuracy. It is a cutting-edge diagnostic paradigm for breast illness. This allows us to ensure the accuracy of our forecasts. This allows us to penalize overly pessimistic low-entropy output distributions while also ensuring that our forecasts appropriately represent uniform distributions. It also allows us to ensure the consistency of our output distributions. The proposed method will next be tested using datasets provided by BreaKHis, IDC, and UCSB in comparison to existing methodologies considered state-of-the-art. This will be accomplished by contrasting the proposed method with other strategies considered cutting-edge. The studies' findings show that the proposed approach outperforms what are now considered cutting-edge technologies across a wide range of zoom levels. Because of the high level of consistency and generalizability that it delivers, it has the potential to be a valuable tool in histopathological clinical computer-aided diagnosis systems. The worth of a possibility may be seen just by the fact that it is a possibility.
Solder: Retrofitting Legacy Code with Cross-Language Patches

Internet-of-things devices are widely deployed, and suffer from easy-to-exploit security issues. Due to code and platform reuse, the same vulnerability oftentimes ends up affecting a large installed base. Because patch deployments tend to be focused on server-side vulnerabilities, client software in large codebases such as Apache may remain largely unpatched, and hence, vulnerable. This problem is exacerbated by the prevalent use of some of these large codebases in IoT deployments, making their use more widespread.In this paper, we address this issue of leaving latent vulnerabilities in legacy codebases. We propose Solder, a framework to patch or retrofit legacy C/C++ code by replacing any target function with a newly-implemented one in a safe language such as Rust. This allows application users to freely patch their software in a safe language whenever a patch becomes available, without the need for waiting on developers to release an official patch. When dealing with mission-critical systems, it may be infeasible to wait the months it takes, on average, for patches to be released. Internally, Solder performs this function swapping on LLVM bitcode, and can either target source code directly to generate the IR, or if source is unavailable, can lift a binary to LLVM bitcode before running the analyses and then recompiling back to binary.Evaluation on 5 popular codebases shows that Solder produces a valid patched binary that is 2.3% smaller, on average, and mitigates 11 known exploitable vulnerabilities, while introducing limited overhead and no new bugs.
Understanding the Impact of Quantum Noise on Quantum Programs

Quantum computing is expected to introduce the next era of computing speed and power, and its software - quantum program is gaining increasing research interest in the software engineering community. A significant characteristic of quantum computing is the existence of noise. Unlike classical computers where the output of a program is usually deterministic, the execution of a quantum program may be affected by quantum noise. Such a difference may cause difficulties or misunderstandings for developers shifting from classical programming to quantum programming. To understand the impact of quantum noise on quantum programs and its implications for software developers, we conduct a series of studies with real-world quantum programs and quantum computing environments. Specifically, we first measure and analyze the noise in a real quantum computer by testing it with a basic quantum program. We find that a non-neglectable amount of quantum noise generally exists in real quantum computers. Then we investigate the robustness of quantum programs against different quantum noises by testing 18 real-world quantum programs and 50,000 randomly generated quantum circuits in simulated and real environments. We observe that quantum noise can significantly influence the correctness of quantum programs, and different quantum circuit structures show diverse sensitivity patterns under the same noise. Based on the observations, we build a machine learning model to predict the fidelity of a quantum program under certain quantum noise. The model achieves a small average fidelity prediction error, meaning the impact of noise can be precisely estimated statistically.
Sistem Absensi Mahasiswa Berbasis Pengenalan Wajah Menggunakan Algoritma YOLOv5

The development of information technology is accompanied by advances in the fieldArtificial Intelligence, one of which is the facial recognition systemmachine learning ordeep learning. When humans see an image or video, we can recognize and find objects of interest in an instant. The goal of object detection is to replicate this intelligence using computers. The student attendance system at Bina Insan Lubuklinggau University is still done manually so it has many deficiencies such as attendance data or signatures that can be manipulated by others, causing fraud. there is a risk of losing data, in the process of inputting data. So that in this study a Student Attendance System was created at the Faculty of Engineering, Bina Insan University Using the YOLO v5 Algorithm. It is hoped that this will be used to obtain appropriate attendance results and there will be no more fraud and the risk of inputting attendance data errors, especially at Bina Insan University, Lubuklinggau City. In this study, 5 class datasets were used in the form of 5 students at the Faculty of Engineering, Bina Insan University. The accuracy results in detecting faces are approximately 80%. From a dataset of approximately 1500 images, with a photo size of 640x640pixel, 16 batch withepoch as many as 100 models will be evaluated by taking into account the values of mAP, Precision, Recall. In this study, the model gets 99% mAP, 99% precision, and 99% recall, with this, the system created is good enough
Quaternion-based graph convolution network for recommendation

Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings. However, GCN is vulnerable to noisy and incomplete graphs, which are common in real world, due to its recursive message propagation mechanism. In the literature, some work proposes to remove the feature transformation during message propagation, but making it unable to effectively capture the graph structural features. Moreover, they model users and items in the Euclidean space, which has been demonstrated to have high distortion when modeling complex graphs, further degrading the capability to capture the graph structural features and leading to sub-optimal performance. To this end, in this paper, we propose a simple yet effective Quaternion-based Graph Convolution Network (QGCN) recommendation model. In the proposed model, we utilize the hyper-complex Quaternion space to learn user and item representations and feature transformation to improve both performance and robustness. Specifically, we first embed all users and items into the Quaternion space. Then, we introduce the quaternion embedding propagation layers with quaternion feature transformation to perform message propagation. Finally, we combine the embeddings generated at each layer with the mean pooling strategy to obtain the final embeddings for recommendation. Extensive experiments on three public benchmark datasets demonstrate that our proposed QGCN model outperforms baseline methods by a large margin.
Research on the Construction and Preliminary Application of Digital Teaching Resource Database

The school establishes a leading group through investigation and research, The division of labor is in place for work arrangement, the hardware is combined with the school network to build facilities for follow-up, the teachers establish and constantly update the information concept, and initially establish the school digital teaching resource library platform. In addition, the formulation, supervision and implementation of the resource library program, as well as training, have promoted the initial application of the school’s digital teaching resource library.
APLIKASI VIRTUAL TOUR FAKULTAS TEKNIK BERBASIS ANDROID MOBILE

UMPAR at the age of 18 has walked through two phases, namely; the pilot phase, and the development phase.. The high school, namely STKIP Muhammadiyah Parepare at that time, fostered three study programs, namely the Mathematics Education, English Education and Non-Formal Education (PLS) study programs. The effort to transform into a university was initiated by Drs. Said Amir Anjala, M as well as the First Rector.&#x0D; Unity supports all file formats, especially common formats such as all formats from artapplications. Unity is compatible with 64 bit versions, can operate on Mac Os x, Windows and Can produce games for mac, windows, Wii, iphone, ipad and Android.&#x0D; The research method used is Literature Study and Observational Research. In making this application using the C # programming language.&#x0D; The need for making a virtual tour application in taking pictures using the google camera application. This research produces games that can be played offline on all computers with low specifications and also on Android smartphones.
Ruffle: Rapid 3-Party Shuffle Protocols

Secure shuffle is an important primitive that finds use in several applications such as secure electronic voting, oblivious RAMs, secure sorting, to name a few. For time-sensitive shuffle-based applications that demand a fast response time, it is essential to design a fast and efficient shuffle protocol. In this work, we design secure and fast shuffle protocols relying on the techniques of secure multiparty computation. We make several design choices that aid in achieving highly efficient protocols. Specifically, we consider malicious 3-party computation setting with an honest majority and design robust ring-based protocols. Our shuffle protocols provide a fast online (i.e., input-dependent) phase compared to the state-of-the-art for the considered setting. To showcase the efficiency improvements brought in by our shuffle protocols, we consider two distinct applications of anonymous broadcast and secure graph computation via the GraphSC paradigm. In both cases, multiple shuffle invocations are required. Hence, going beyond standalone shuffle invocation, we identify two distinct scenarios of multiple invocations and provide customised protocols for the same. Further, we showcase that our customized protocols not only provide a fast response time, but also provide improved overall run time for multiple shuffle invocations. With respect to the applications, we not only improve in terms of efficiency, but also work towards providing improved security guarantees, thereby outperforming the respective state-of-the-art works. We benchmark our shuffle protocols and the considered applications to analyze the efficiency improvements with respect to various parameters.
Towards Sentence Level Inference Attack Against Pre-trained Language Models

In recent years, pre-trained language models (e.g., BERT and GPT) have shown the superior capability of textual representation learning, benefiting from their large architectures and massive training corpora. The industry has also quickly embraced language models to develop various downstream NLP applications. For example, Google has already used BERT to improve its search system. The utility of the language embeddings also brings about potential privacy risks. Prior works have revealed that an adversary can either identify whether a keyword exists or gather a set of possible candidates for each word in a sentence embedding. However, these attacks cannot recover coherent sentences which leak high-level semantic information from the original text. To demonstrate that the adversary can go beyond the word-level attack, we present a novel decoder-based attack, which can reconstruct meaningful text from private embeddings after being pre-trained on a public dataset of the same domain. This attack is more challenging than a word-level attack due to the complexity of sentence structures. We comprehensively evaluate our attack in two domains and with different settings to show its superiority over the baseline attacks. Quantitative experimental results show that our attack can identify up to 3.5X of the number of keywords identified by the baseline attacks. Although our method reconstructs high-quality sentences in many cases, it often produces lower-quality sentences as well. We discuss these cases and the limitations of our method in detail