Reviewerly/exHarmony huggingface dataset:
Fuzzy evaluation model of urban resilience for COVID-19 prevention and control based on TOPSIS

At present, the novel coronavirus variant strain has broken out in some cities in China. After prevention and control, the epidemic situation is basically in the final stage of control. To assess the resilience of cities in different regions, this paper established a COVID-19 resilience assessment model based on TOPSIS and fuzzy mathematics. Firstly, according to the concept of urban resilience, three first-level indicators are selected, namely elasticity, resilience and adaptation. Then 17 second-level indicators are selected to quantify the three first-level indicators. Finally, based on the results of the model, possible approaches to improve resilience in COVID-19 prevention and control are proposed.
Evaluation of Various Defense Techniques Against Targeted Poisoning Attacks in Federated Learning

Federated Learning (FL) allows individual clients to train a global model by aggregating local model updates each round. This results in collaborative model training while main-taining the privacy of clients' sensitive data. However, malicious clients can join the training process and train with poisoned data or send artificial model updates in targeted poisoning attacks. Many defenses to targeted poisoning attacks rely on anomaly-detection based metrics which remove participants that deviate from the majority. Similarly, aggregation-based defenses aim to reduce the impact of outliers, while L2-norm clipping tries to scale down the impact of malicious models. However, oftentimes these defenses misidentify benign clients as malicious or only work under specific attack conditions. In our paper, we examine the effectiveness of two anomaly -detection metrics on three different aggregation methods, in addition to the presence of L2-norm clipping and weight selection, across two different types of attacks. We also combine different defenses in order to examine their interaction and examine each defense when no attack is present. We found minimum aggregation to be the most effective defense against label-flipping attacks, whereas both minimum aggregation and geometric median worked well against distributed backdoor attacks. Using random weight selection significantly deteriorated defenses against both attacks, whereas the use of clipping made little difference. Finally, the main task accuracy was directly correlated with the BA in the label-flipping attack and generally was close to the MA in benign scenarios. However, in the DBA the MA and BA are inversely correlated and the MA fluctuates greatly.
Deep learning-based student's concentration analysis system

Students&apos; concentration status is not only an evaluation method for students&apos; acceptance of courses but also an important reference factor for the quality of teachers&apos; teaching. This paper proposes a deep learning-based student concentration analysis system. We use the methods and models of deep learning to analyze the concentration of students from two aspects of fatigue state and distraction behavior. The main contents are as follows: input each frame of the image obtained in the video stream into the third-party face recognition module Dlib and the retrained YOLOv5 series network model. The system mainly includes two modules: the human fatigue detection module detects the positions of the eyes and mouth through the key points of the face and calculates the opening and closing degrees of the eyes and mouth, and the constructor calculates the fatigue value of the student and gives a threshold to determine whether the student is in a fatigued state. The human behavior recognition module uses the YOLOv5 series of target detection models retrained after modifying the weights to identify objects other than faces that appear in the incoming images and judge students&apos; behaviors according to the objects that appear.
Development of an Internet-Based Information/DataResource Platform during the COVID-19 Pandemic Using Hierarchical Clustering

there is heavy usage of Internet-based information resource platformsdue to the COVID-19 pandemic. If it can present the search results as a list of groupsof related terms and context, that would be ideal. An additional enhancement would be to display information in the search results in a classified manner. Therefore, the research is proposing an effective and efficient information retrieval system. The methodology adopted follows the conventional Information Retrieval (IR) system design except that Cosine similarity and hierarchical clustering were introduced for search result ranking. The Average Precision of the proposed system was compared with that of flat clustering such as K-means. The result of the evaluation shows that hierarchical clustering improved in precision, being10% better than flat clustering result retrieval result. This led to greater usability, userexperience,and efficiency of the system.
Determination of multi-modal dimension metric learning with application to web image retrieval

Many real-world applications, like multimedia retrieval, confront the difficulty of determining the distance between any two items on Multi-modal data. According to most current Dimension Metrics Learning (DML) techniques, distance metrics may be learned using just one feature type or an aggregated feature space where many features are simply connected. Even though DML has been extensively researched. This study proposes a new framework for online learning, as well as a new classroom learning system for online Multi-modal dimension metric learning (OMDML), that is both efficient and scalable. This paper proposes a low-rank OMDML calculation to reduce the expensive cost of DML on high-dimensional component space, which reduces the computational cost while maintaining extremely competitive or much higher learning accuracy. With the purpose of determining whether or not multi-modular image recovery calculations can be successfully implemented, a large number of experiments are carried out. In most datasets tested, the suggested approach consistently outperforms alternative state-of-the-art algorithms, according to extensive experimental results
Collection-CAM: A Faster Region-Based Saliency Method Using Collection-Wise Mask Over Pyramidal Features

Due to the black-box nature of deep networks, making explanations of their decision-making is extremely challenging. A solution is using post-hoc attention mechanisms with the deep network to verify the decision basis. However, those methods have problems such as gradient noise and false confidence. In addition, existing saliency methods either have limited performance by using only the last convolution layer or suffer from large computational overhead. In this work, we propose the Collection-CAM, which generates an attention map with low computational overhead while utilizing multi-level feature maps. First, the Collection-CAM searches for the most appropriate form of the partition through bottom-up clustering and clustering validation process. Then the Collection-CAM applies different pre-processing procedures on the shallow feature map and final feature map to overcome the false positiveness when applied without distinction. By combining collection-wise masks according to their contribution to the confidence score, the Collection-CAM completes the attention map generation process. Experimental results on ImageNet1k, UC Merced, and CUB dataset and various deep network models demonstrate that the Collection-CAM not only can synthesize a saliency map with a better visual explanation but also requires significantly lower computational overhead compared to those of region-based saliency methods.
REVIEW OF CLOUD COMPUTING CRYPTOGRAPHY

The delivery of computing services over the internet, as opposed to storing data on a local memory device or a proprietary disc drive, is known as cloud computing. Servers, storage, databases, networking, and software are some examples of computing services. The primary justification and major benefit of using the cloud are the user’s ability to store data there and access it from any location at any time, as well as the low cost of all its services. Despite this, because the data stored in the cloud is not directly maintained by the customer, security has always been a major concern with cloud computing. The data owners are unlikely to be aware of the route their data is taking when they upload or store data using a cloud computing service. The user is unaware of whether or not a third party is gathering, processing, and accessing their information. Numerous cryptography algorithms have been proposed to address security concerns. This paper discussed different cryptography algorithms that are present in the previous work with a focus on the fundamentals of cloud computing.
Petrographic and geochemical characteristics of dolomites in the Devonian Shogram Formation, Karakorum Ranges, North Pakistan

Excellent outcrops of Devonian carbonate rocks are present in the Karakorum Ranges of North Pakistan that are highly dolomitized.The Devonian Shogram Formation comprises both matrix and cement dolomite.Petrographic studies revealed the texture of matrix dolomite as fine-grained anhedral dolomite (D1), mediumgrained subhedral to anhedral dolomite (D2), mediumgrained euhedral dolomite (D3) and coarse-grained anhedral dolomite (D4).The saddle cement dolomite phase of hydrothermal origin precipitated in vugs and fractures.The origin of various dolomitization phases has been interpreted based on petrographic associations, elemental and stable isotopes signatures of each phase.The first two phases of dolomitization are affiliated with the compaction flow of overlying shales based on evidence of low Fe and Mn content and isotopically light signatures of δ 18 O.The increase in burial resulted in the third phase of dolomite showing signatures of redox conditions and high depleted δ 18 O compared to the original marine signatures.The final phases of matrix dolomite and saddle dolomite cement are associated with tectonic activity in the Karakorum region, which brought the deep-source Mg-rich fluids along the Reshun Fault.
Desain Sistem Informasi Monitoring Berbasis Website Untuk Menilai Perkembangan Siswa

One of the objectives of the assessment is monitoring, which aims to see whether the implementation of the learning assessment has been in accordance with the assessment plan or not. In the era of the industrial revolution 4.0, technology is needed to facilitate the assessment process so that it can be carried out by various parties. The busyness of parents is one factor in the need for a tool to monitor the development of children at school. This study aims to produce a website-based monitoring information system design in assessing student development. The method used is a research and development method, with a 4-D model, namely definition, design, development, and dissemination. This article only discusses two stages, namely the definition stage and the design stage. At the definition stage, some information was collected regarding problems in monitoring student assessments. Data was collected using a questionnaire method given to high school students and teachers, as well as interviews with several parents of high school students. The results of the definition stage indicate that a student development monitoring system is needed that can be accessed by parents from home. At the design stage, a website-based monitoring information system design is designed that contains seven main views, namely: Login, Home, Student Identity, Grade List, Monitoring, Settings, and Logout. Keywords: Assisted information system, monitoring, website, student learning outcomes
Performance evaluation of the particle swarm optimization for clustering based on different parameter selection

Particle Swamp Optimization (PSO) is an effective method for solving a wide range of problems. However, the most existing PSO algorithms easily trap into local optima when solving complex multimodal function optimization problems. In this paper, we explain the importance of PSO algorithm’s general purposes to optimize strategy which has various parameters that decide its conduct and viability in advancing a given issue. This study gives a rundown of the best selections of parameters for different advancement situations which should enable the specialist to accomplish better outcomes with less exertion. In this paper, we define an important in the PSO algorithm, the parameters and how to apply this algorithm to different type of datasets online and offline. Repeat Consumption Matrices Dataset has been tested with PSO algorithm to define the number of clusters using selection of parameters. The results are then having been compared with the Genetic Algorithm (GA) where the algorithms have been tested based on the number of cluster and DB index for each of the data. Based on the results, it is shown that the PSO algorithm gives good and efficient results compared to GA algorithm. The output of the research is a PSO-based clustering algorithm that can be used in data mining by providing accurate and robust results in clustering, which can be realized in web search engines and automatic doc-ument organization.
Urgensi Layanan Penelusuran Untuk Mahasiswa Skripsi Di Perpustakaan Perguruan Tinggi

The Final Student (S1 level) of UIN Saifuddin Zuhri, has the final assignment in research /thesis. The academic library provides services related to student information about thesis assignments. This research aims to find out the views of students in semester seven and above about the importance of special library services for final-level students—research methods through descriptive qualitative approaches, with data retrieval through interviews and documents. This research will be interviews conducted in two stages; first, in Five students researching their thesis and students who have just graduated; Second, the question is open to 99 students in semester seven and above. The results showed that the first stage, information retrieval service as a unique service for thesis students, is very needed. However, some students do not encounter obstacles in completing their thesis but still consider if information retrieval service is necessary. The second stage, open question answers to 99 students, showed that special services for thesis students were indispensable. Student complaints include difficulty finding referrals and a lack of knowledge about compiling a good thesis. In conclusion, the college library has demanded that final-level students hold special services for thesis students. The services provided should be with sufficient officers, in this case, adjusted to the number of students who are thesis.
Upaya Meningkatkan Hasil Belajar Materi IPS Indahnya Kebersamaan melalui Metode Diskusi Siswa Kelas IV SD Negeri 44 Mataram

The purpose of this study was to determine the increase in learning outcomes of social studies material The Beauty of Togetherness through the Discussion Method for Class IV students of SD Negeri 44 Mataram in the Academic Year of 2021/2022. This research is a classroom action research. The subjects in this study were 28 fourth grade students at SD Negeri 44 Mataram with 10 male students and 18 female students. Data collection techniques used are observation, documentation, and tests. Observation is used to collect data on interest in learning. The test is used to measure learning achievement in Mathematics. The data analysis used is descriptive qualitative and descriptive quantitative. The results showed that: 1) The data on student learning outcomes in the Social Sciences material The Beauty of Togetherness using discussion mode increased from the initial data obtained classical completeness was 57.14%, increased to 67.86% in the first cycle, to 82.14% in the second cycle. to 100% in cycle III. So student learning outcomes in social studies material The Beauty of Togetherness using the discussion method carried out by the teacher is very good; 2) Teaching and learning activities carried out by teachers have increased from a score of 26 (72.23%) in cycle I, to 29 (80.56) in cycle II, to 32 (88.89) in cycle III; 3) Student learning activities, it can be described that the average percentage in the first cycle is 66.67%. However, in the second cycle, student learning activities increased to 77.78% in the third cycle, and to 86.11%. So the student learning activities in the Social Sciences material The Beauty of Togetherness is good.
Bootstrapping Joint Entity and Relation Extraction with Reinforcement Learning

Extracting entities and relations for types of interest from text is important for knowledge graph construction. Previous methods of entity and relation extraction rely on human-annotated corpora and adopt an incremental pipeline, which require abundant human expertise and are vulnerable to errors cascading. In this paper, we present ROTATE, a novel approach for jointly bootstrapping entity and relation extraction with reinforcement learning. The bootstrapping process of ROTATE consists of three bootstrapping sub-processes, which extract head entity, tail entity, and relation, respectively. Each sub-process starts with a few seed instances, then generates patterns and expands the seed set to start the next iteration. In particular, we propose a joint pattern scoring strategy in which the scores of patterns in each bootstrapping sub-process also take the seed information of the other two sub-processes into account. Moreover, we introduce reinforcement learning to solve the semantic drift problem in the bootstrapping process by formulating the seed expansion problem as a sequential decision making problem, and design a reward function that considers both seed quality and quantity. Experimental results on a collection of sentences from news articles confirm the effectiveness of our approach.
CRP Levels, Thrombocyte Index and Coagulation Status with TEG Profile in ITP Patients

Examinations related to Immune Thrombocytopenia (ITP) cases have been widely demonstrated in diagnosing and monitoring hemostasis status. The increased MPV and PDW have a high sensitivity to the diagnosis of ITP and CRP is one of the causes of ITP. Monitoring of bleeding in ITP still uses conventional examination. Thromboelastography (TEG) is a global hemostasis assessment tool that assesses real-time overall hemostasis. TEG profiles especially MA and R-time can be used for case assessment of ITP patients. This research describes the correlation between CRP levels, platelet index and coagulation status with TEG profiles (MA and R-time) in ITP patients. This research used analytical observational with cross sectional approach. A sample of 32 respondents aged &gt;18 years with a platelet count &lt;150,000/mm3. Then the respondents measured EDTA blood samples for platelet index (MPV and PDW) and CRP levels, in the citrate blood sample 3.2% measured coagulation status (aPTT and PT) as well as examining the TEG profile, especially R-time and MA. Correlation test uses the Pearson test for data that is normally distributed, while the Spearman test is for data that are not normally distributed. There is a correlation between CRP levels, platelet counts, MPV and MA TEG (p=0.002, p=0.000, p=0.001), there is no correlation between PDW and MA TEG (p=0.06), there is a correlation between aPTT and R-time TEG (p=0.001) and there is no correlation between PT and R-time TEG (p=0.667). Overall, there is a correlation between conventional variables and the TEG profile in ITP patients.
Penentuan Pembukaan Gerai dengan Menggunakan Analytic Hierarchy Process Empat Layer

In the neighborhood, there is a slew of flavored drink shops. Bubble drinks, sometimes known as "boba drinks," are currently popular across various demographics, particularly among teenagers and young people. The sweet taste of the drink and the range of options drive demand for this boba drink, which is growing increasingly popular. In addition to its presentation, this beverage producer offers customization options for beverage taste, sugar level, drink glass size, and even ice cube quantity to satisfy consumer preferences. Several criteria, such as pricing, purchasing power, and location, must be considered while deciding on store opening These elements can still be subdivided. Therefore the AHP (Analytical Hierarchy Process) method is appropriate for determining store openings. The findings of this study demonstrate that, among numerous alternative locations, location G has the highest value, with a weight of 0.2236.
AN ANALYSIS OF FINAL TEST QUALITY ON EFL STUDENTS

The purpose of this research is to determine the quality of the final test in terms of difficulty level, discrimination leves, distractor effectiveness, validity, and reliability for senior high school students in second grade at SMA Negeri 3 Parepare. This research used a quantitative design with content analysis. The investigation was conducted at SMA Negeri 3 Parepare. The subject was 134 English final test form the second grade. However, the researcher took 58 students from three classes to serve as test subjects, with the sample for this research being retrieved utilizing a random selection group technique. They were XI MIPA 1, XI MIPA, and XI IPS 1. Based on the overall analysis of the 30 test items, it can be concluded that the final test for senior high school at the second grade SMA Negeri 3 Parepare was of medium difficulty (p), meaning that the test was neither too easy nor too tough. On the discrimination (D) scale, there were 6 (20%) items with outstanding classification, 1 (3,33%) item with adequate classification, and 23 (76,6%) items with good classification. Distractor effectiveness is likewise weak, since 12 (40%) of the items have very poor distractors. On the validity 9 (30%) items were invalid and need to be replaced. However, 21 (70%) items were valid. The final test for senior high school at the second grade SMA Negeri 3 Parepare is reliable since the reliability index 0,972, it was higher than the table value of critical of product moment it has shows that the final test was reliable.
Design of Optimal Fuzzy Controller for Heat Exchanger Temperature

The heat exchanger system (HES) is widely used in petroleum industries as well as in chemical applications because it may maintain the process of controlling the pressure or temperature of a liquid.In addition, HES is often complicated, and expensive devices.The heat exchanger types used in simpler applications are inexpensive but nonlinear, and their dynamic response is very weak; Therefore, it is quite difficult to be modelled, and their dynamics operation is hard to control.In this work, several methods are applied to the heat exchanger model and its control to select the appropriate model and control unit in complex working conditions.The problems that affect the dynamic parameter setting adopted by the traditional incremental proportional integrated derivative (PID) and the intelligently designed control systems which combine a fuzzy controller that adapts to a PID controller that has a better performance are studied.The traditional PID controller is always built using MATLAB-Simulink, and two fuzzy logic controllers are designed.One of the designs is based on the sine-cosine algorithm (SCA), and another uses particle swarm optimization (PSO).Results show that the sine-cosine algorithm (FLC_SCA) controller has excellent response and strength compared to PID and particle swarm dependent optimisation (FLC_PSO) is suitable for complex time-delay systems.
Investigating Keyword Method in Teaching English Vocabulary for Junior High School with Special Needs Student

This study aimed to see whether the Keyword Method could be used to teach English vocabulary for special needs student. This research used descriptive qualitative method as the research design. Throughout the research, quiz and test scores improved. The Keyword Method has been proven to be an effective teaching tool through extensive research. It improves the concreteness and meaning of newly acquired information, and the Keyword Method connects newly learned information to previously stored knowledge.