Reviewerly/exHarmony huggingface dataset:
IMPROVING THE ABILITY TO WRITE REVIE TEXTS THROUGH LEARNING MODELS COOPERATIVE INTEGRATED READING AND COMPOSITIO (CIRC) CLASS XI STUDENTS STATE SENIOR HIGH SCHOOL 1 SARADAN

One of the goals of learning Indonesian is that students can write. Writing is an activity of expressing ideas that are in the mind which are obtained through reading, speaking, and listening activities which are described in a word to become a sentence. One of the abilities that students must have in learning Indonesian is writing review texts. The review text is one of the texts that is learned in class XI at the senior high school level. Writing review texts has not obtained optimal results following the minimum school completeness criteria. So we need a learning model that can facilitate students, namely the cooperative integrated reading and composition (CIRC) learning model. The purpose of this study was to obtain results of improving the ability to write review texts through the cooperative integrated reading and composition (CIRC) learning model. SMAN 1 Saradan became the research location. As many as 28 students in class XI MIPA 3 in the 2022/2023 school year were the subjects of the study. Classroom Action Research (PTK) is a research design that goes through four research steps namely planning (plan), action (action), observation (observation), and reflection through two cycles. Tests and observations become instruments in research. Data analysis was carried out through a qualitative approach by calculating the class average value using the percentage of classical completeness. Students who get a KKM score of ≥75 are declared complete. The results of the study were that the ability to write review texts without going through cooperative integrated reading and composition (CIRC) at SMAN 1 Saradan was 82.2, meaning that there were 23 whose grades did not exceed the KKM. While the results of the study were able to write review texts through cooperative reading and composition (CIRC) in cycle I 67.9% or 19 students in the less category. Whereas after being given cycle II it showed a result of 3.6% or 1 student in the less category. Based on the results of the study it was concluded that students' ability to write review texts through cooperative integrated reading and composition (CIRC) can be increased.&#x0D;
SVP: Safe and Efficient Speculative Execution Mechanism through Value Prediction

Speculative execution attacks such as Spectre and Meltdown exploit the wrong execution patch to leak private data. In current state-of-the-art defense strategies, executions of all memory accesses that use speculatively-loaded addresses are blocked, resulting in high overhead. Our key observation is that these blocked memory accesses can be executed without operand-dependent hardware resource usage through value prediction. Therefore, we propose a novel hardware defense framework, named Speculative Value Prediction (SVP), to safely and efficiently execute the potentially unsafe memory accesses earlier. We build SVP on the cycle-accurate Gem5 simulator and its performance improvement is positively correlated with the coverage of value predictors. Experiments show that when using the value predictor with 30%/60%/100% coverage, SVP outperforms the state-of-the-art defense mechanism STT in the Spectre model by 21.5%/50.3%/107.7% respectively, and in the Futuristic model by 28.7%/55.4%/105.7% respectively.
PERANCANGAN SISTEM INFORMASI AKUNTANSI KAS KECIL (PETTY CASH) DI PT. KOPRIMA SANDYSEJAHTERA

Petty cash is a company-provided fund for operational activities with a relatively small nominal amount. When processing petty cash at PT. Koprima Sandysejahtera still uses Microsoft Excel, which makes the company's performance less effective in managing petty cash. Therefore, the authors designed a web-based cash accounting information system with the programming language PHP and My SQL as a database. The research method used is qualitative. Data and information were obtained through observation, interviews, documentation, and literature research. The software development method used is the waterfall model, where the phases from system analysis to system maintenance are sequential and cannot be done randomly. With the existence of a computerized system, the company's performance in terms of petty cash management is expected to become more effective.
Design and implementation of Jiangxi earthquake monitoring management platform

Since the implementation of the "Tenth Five-Year Plan" digital seismic observation network project, the daily duty of the seismic network of Jiangxi Province Seismological Bureau has changed greatly from the past, and the workload has increased obviously. In this paper, the design and implementation of the operation and management platform of the seismic network of Jiangxi Province Seismological Bureau are realized by means of information technology, which effectively improves the operation and service quality of the network.
The Impact of Cloud Computing on IT Spending: A Comparative Analysis

This study investigates how cloud computing affects business IT expenditures. This study's goal is to determine whether or not cloud computing has raised or decreased IT spending. We carried out a side-by-side comparison of businesses that have embraced cloud computing and those that have not. According to the study's results, organizations that have adopted cloud computing have seen a significant decrease in their IT spending.
Jordanian Women Following-up Online Stores’ Ads

Objectives: This study aimed to identify the reasons for Jordanian women's engagement with electronic store advertisements, explore the habits and usage patterns of Jordanian women regarding electronic store advertisements, investigate the motives behind Jordanian women's interest in electronic store advertisements, examine the types of electronic stores followed by Jordanian women (local, regional, international), determine the most significant goods and services that Jordanian women seek through electronic stores, analyze the factors that capture the attention of Jordanian women towards e-store advertisements, and understand the purchasing behavior of Jordanian women in relation to e-store ads.&#x0D; Methodology: This study falls within the scope of descriptive research and relies on the survey method. A questionnaire was used as a tool to collect data from a sample of (450) Jordanian women in the northern Jordanian region. The objective of the study was to gather information and answer the research objectives.&#x0D; Results: The study revealed several findings. Firstly, the highest percentage of Jordanian women indicated that they follow electronic store advertisements 1 to less than 5 times a day. Additionally, the study found that communication through social networking sites was the primary channel for electronic store ads. Specifically, Ali Baba store ads were the most followed by Jordanian women. The use of images and graphics in electronic advertisements emerged as the most influential factors in attracting attention, generating interest, and creating a desire among Jordanian women. Moreover, the study showed that the purchasing behavior of Jordanian women influenced by electronic advertisements was moderate. Furthermore, no statistically significant differences were found between the reasons for Jordanian women's follow-up to e-store advertisements and their purchasing behavior based on variables such as income level, age, and educational achievement.&#x0D; Conclusion: The purchasing behavior influenced by Jordanian women's follow-up to electronic advertisements was moderate.
Public Use of Telemedicine Communication Technology and Its Relationship to Healthcare Quality: A Comparative Study between the Egyptian and Saudi Public

Objectives: The research aims to identify the public's use of communication technology for the provision of remote medical services and its relationship to the quality of healthcare.&#x0D; Methods: The two researchers used the field survey method and a comparative approach to identify similarities and differences in the study sample. The study was conducted with a sample of 400 individuals from the Egyptian public. The survey newspaper tool was employed, and various statistical techniques were utilized, including the Pearson correlation coefficient and the t-test.&#x0D; Results: The findings of the study indicate a statistically significant correlation between the public's use of communication technology for providing remote medical services and the quality of healthcare. Additionally, a statistically significant correlational relationship exists between the role of communication technology in providing telemedical services and the development of public usage, along with its contribution to generating interest in these services.&#x0D; Conclusions: It is crucial to emphasize the public's use of communication technology for providing telemedicine services in order to achieve the Sustainable Development Goals (SDGs) and the global commitment to attaining universal health coverage by 2030.
Disentangling high-level factors and their features with conditional vector quantized VAEs

Two recent works have shown the benefit of modeling both high-level factors and their related features to learn disentangled representations with variational autoencoders (VAE). We propose here a novel VAE-based approach that follows this principle. Inspired by conditional VAE, the features are no longer treated as random variables over which integration must be performed. Instead, they are deterministically computed from the input data using a neural network whose parameters can be estimated jointly with those of the decoder and of the encoder. Moreover, the quality of the generated images has been improved by using discrete latent variables and a two-step learning procedure, which makes it possible to increase the size of the latent space without altering the disentanglement properties of the model. Results obtained on two different datasets validate the proposed approach that achieves better performance than the two aforementioned works in terms of disentanglement, while providing higher quality images.
Modeling and Performance Analysis of Cloud-Based Active Distribution Networks Based on EdgeCloudSim

In order to realise the performance + analysis of the cloud-based active distribution network, this paper proposes the modeling and performance analysis method of the cloud-based active distribution network. Firstly, based on the task processing requirements of the active distribution network, the corresponding task modeling method based on microservices is proposed. Then, the cloud-based active distribution network architecture modeling is proposed, and the corresponding dynamic resource allocation process is realised. In addition, through the professional edge computing simulation software EdgeCloudSim, the modeling of the cloud-based active distribution network is realised. The modeling includes specific scenarios, resource allocation and two task spatio-temporal logics. Task delay and resource load rate are as performance metrics. Finally, with microservices as the research granularity, the performance differences of different task spatio-temporal logics are analysed.
Jensen-Shannon Divergence of Quantum Hashing Algorithms Based on Quantum Walk

A hashing algorithm is typically a “black box” that garbles input data and makes it unreadable. Hashing algorithms are one-way programs. Consequently, the input text cannot be unscrambled and decoded easily by anyone else. Hashing protects data so even if someone gains access to your server where the hash is stored, the items stored remain unreadable. Recently quantum walks have shown promise as a new means to develop effective hashing algorithms. We studied a quantum walked based hash algorithm on a circle using Jensen-Shannon(JS) divergence. We found that the minimum JS divergence decreases with an increase in message length. This leads to a high potential of collisions when the hashing algorithm is run on noisy quantum computers. Furthermore, we found that the JS divergence only increases with length of circle till the length of the circle, $$m =n+1$$ , where n is the bit length, and is even and m is odd or $$m =n+2$$ when n is odd.
IMPROVING STUDENTS’ SPEAKING AND WRITING SKILLS BY USING FISHBOWL TECHNIQUE FOR THE TWELFTH GRADE OF SMK TELKOM SANDHY PUTRA BANJARBARU

This research purposes to improve student’s speaking and writing skills for the Twelfth Grade students of SMK Telkom Sandhy Putra Banjarbaru by using Fishbowl Technique. The number of subjects were 13 students who had been taught English Speaking and Writing based on the curriculum in academic year 2015/2016. The research was carried out by using Classroom Action Research (CAR) implemented by the English teacher is the researcher itself. The action was implemented into two cycles. The data were collected from the scores of pre-test, cycle one and cycle two. The result of observations and questionnaire were also calculated to give additional informations. The implementation uses Fishbowl Technique focused on : (1) the benefit of using Fishbowl as the way of technique (2) to improve the ability of the students in speaking and writing skills. A setting room is used to support implementation of modeling in this research. The results showed that students’ speaking and writing skills by using Fishbowl technique could improve the students’ ability in speaking and writing. The significant improvement of students’ achievement in speaking and writing could be seen from the increasing average score for speaking achievement from Cycle 1 to Cycle 2 that was from 67,84 to 78,00 and for writing achievement that was 70,30 to 80,61.
Language and political commitment: a pragmatic analysis of President Muhammadu Buhari’s june 12 inaugural democracy day speech

Political discourses all over the world and among scholars have assumed different linguistic impetus. Language and political intrigues are inseparable concepts among the electorates and the elected political office holders in any society. Hence, this research work was conducted to identify the speech act features of President Muhammadu Buhari’s June 12 inaugural democracy day speech. The paper explored the pragmatic functions of locution, illocution and perlocutionary acts in the speech. It adopted the theoretical framework of Speech Act as propounded by J.L. Austin (1962) and modified by John Searle in his classification, (1969). It further looked at specific illocutionary acts that were used in the speech; they include assertives (31%), directives (10%), expressives (11%), commissives (46%) and declaratives (02%). Thus, the analysis proved further that the illocutionary acts performed by Mr. President showed the preponderance use of commissive and assertives acts which were mostly used to convince, assert, explain, persuade and command the listeners to certain actions. This goes a long way in showing commitment to building a better nation. The result showed that politicians rely extensively on statements or speeches using commissive acts to achieve their political ambitions before, during and after electioneering campaigns. Needed to be said at this juncture, is the fact that illocutionary acts play significant roles in political speeches across the world
CA-Net: Context Aggregation Network for Nuclei Classification in Histopathology Image

Abstract Accurately classifying nuclei in histopathology images is essential for cancer diagnosis and prognosis. However, due to the touching nuclei, nucleus shape variation, background complexity, and image artifacts, end-to-end nucleus classification is still difficult and challenging. In this manuscript, we propose a context aggregation network (CA-Net) for nuclei classification by fusing global contextual information which is critical for classifying nuclei in histopathology images. Specifically, we propose a multi-level semantic supervision (MSS) module focusing on extracting multi-scale context information by varying three different kernel sizes, and dynamically aggregating the context information from high to low level. Furthermore, we employ the GPG and SAPF modules in encoder and decoder networks to exact and aggregate global context information. Finally, the proposed network is verified on a mainstream nuclei classification image datasets (PanNuke) and achieves an improved global accuracy of 0.816. Our proposed MSS module can be easily transferred into any UNet-liked architecture as a deep supervision mechanism.
THE APPLIED OF FUNCTIONAL DATA ANALYSIS TO COMPARISON 100 TIMES SIMULATED MONTHLY RAINFALL USING SOME TWO PARAMETERS DISTRIBUTION

Three probability models of mothly rainfall such as Gamma, Weibull and Log Normal distribution are evaluated in terms of their ability to reproduce the mean statistics derived from 100 times simulation of monthly rainfall in the Pekanbaru City, Indonesia. One of the important studies is to investigate and understand the simulate mean monthly rainfall patterns that occur throughout the year. To identify the pattern, it requires a rainfall curve to represent monthly observation of rainfall received during the year. Functional data analysis (FDA) methods are capable to convert discrete data into a function that can represent the rainfall curve and as a result, try to describe the hidden patterns of the rainfall. This study is focused on investigating 100 curve average monthly rainfall simulatated by three different quantile functions using the FDA. The mean and standard deviation of FDA for average monthly precipitation are obtained. Through these two statistics a the confidence interval curves of the mean curve are presented represent 95% pointwise confidence intervals. In this study, most of the monthly average rainfall from the actual data were around the FDA mean and the monthly average rainfall was within the FDA confidence interval. In this study, 100 times monthly rainfall simulations using the quantile function of the gamma and log normal distributions found that the mean FDA can capture most of the mean monthly rainfall from historical data, and within the FDA interval. The contradictory results shown by the monthly rainfall simulations using the Weibull distribution, most of the monthly average rainfall historical data cannot be captured by the FDA mean and are outside the FDA confidence interval. Based on the Mean Absolute Error (MAE) value of the average monthly rainfall of historical data and the monthly average of the FDA, it can be concluded that the gamma distribution can produce simulated rain better than the log normal distribution.
A GNN-based Network for Tissue Semantic Segmentation in Histopathology Image

Abstract Segmentation of different tissue regions in pathological images hold an significant position diagnosis and prognosis of cancer. Although the convolutional neural network(CNN) and transformer which treat the image as a grid or sequence structure have been widely employed in this task, which is difficult to capture irregular and complex targets flexibly. So it is still a challenging task. At this paper, we employ a GNN-based segmentation method for pathological images which adopts the encoding-decoding structure. We first represent the input image as a graph structure which consists of a number of patches viewed as nodes and connect the nearest neighbors to build a graph. We also introduce ViG block to build a hierarchical pyramid architecture which consists of grapher module with graph convolution and FFN module with two linear layers. In addition, we utilize a pyramid CNN architecture decoder to aggregate graph information with multi-scales. The proposed method reaches 75.68% and 87.72% mean Dice on BCSS and LAUD-HistoSeg datasets respectively demonstrate the effectiveness.
Advanced Keylogger with Keystroke Dynamics

Keyloggers are software programs that are used to record and monitor keystrokes made on a computer keyboard. They are often used by hackers to illegally access personal details, including passwords, debit and credit card details, and other sensitive information. Keystroke dynamics is a biometric technique that uses the unique patterns and rhythms of a person's keystrokes to identify them. By combining keyloggers with keystroke dynamics, it is possible to create a more secure and accurate form of user authentication. Traditional keyloggers capture only the keystrokes, but not the entire activity of the user like system and application details. The goal is to create a software that is similar to a keylogger yet is far more sophisticated. The keystrokes entered into a keyboard-based system will be saved by the software. The software also provides information on the system's current application that the user is on. Additionally, the program logs data, like public and private IP address, MAC Address, and User information. Together with these characteristics, the program will also examine the users' typing patterns and be able to recognize anyone using the system based on their typing pattern. The keyboard input is intercepted by the keylogger software, which records all keystrokes and stores them in a log file. The data is typically encrypted and stored securely to prevent unauthorized access. The keylogger can then transmit the log file to a remote server, where the data can be analyzed and used for various purposes.
Fast Hash Image Retrieval by Hierarchical Polarized Network

Nowadays, the amount of data in image retrieval databases is increasing, which faces great challenges in retrieval time and storage cost. Therefore, image retrieval methods based on hash have attracted extensive attention. These methods can map high-dimensional features of images into low-dimensional hash codes to reduce retrieval time and storage costs. In this paper, a hierarchical polarized network HPN is proposed to learn hierarchical and accurate hash codes. The network generate accurate superclass features and particular subclass features for each image. Hierarchical retrieval is used to speed up the retrieval time and enhance retrieval accuracy. And we use a differentiable multilevel polarization loss function to optimize the network. Polarization loss can push each channel in the network outputs away from zero, and we do not need to use pairs or triples of labels, which makes the process of learning hash codes simple and efficient. The strategies of step-by-step update, adaptive update and ternary assignment are used to further improve the discrimination of hash codes. Extensive experiments on CIFAR100, miniImageNet and DeepFashion three public datasets show that our proposed hierarchical polarized network HPN outperforms the existing image retrieval methods based on hash and hierarchy in accuracy and speed.
Comparison of text sentiment analysis based on traditional machine learning and deep learning methods

Due to the rapid development and widespread use of Internet technology, the Internet has provided a convenient platform for individuals to express their emotions and view comments on microblogs. The collection of user feedback has resulted in a vast amount of data resources, including information on public sentiment. Such data is crucial for the platform's operation and direction, making sentiment analysis of these data sources important for social development. In this paper, we utilize various methods, such as support vector machine (SVM) and recurrent neural network (RNN), including Bayesian, CNN, LSTM, BERT, among others, to analyze the sentiment of online commentary text represented quantitatively as vectors. We evaluate the performance of each model based on accuracy, recall, and F1 score. Our experimental results demonstrate that Bert+CNN outperforms other models in terms of accuracy, recall, and F1 verification. Additionally, we find that LSTM achieves the highest recall, while GRU exhibits the highest F1 score.