Reviewerly/exHarmony huggingface dataset:
The role of artificial intelligence in achieving entrepreneurship: A applied study on Al-Nasr Factory in Ibn Majid General Company

Summing up the body of research on artificial intelligence and its potential future paths reveals two dimensions of the rising relevance of AI for entrepreneurship study. In the first place, practical uses of AI become a separate field of study. Dependency on AI-driven research methodologies, including big data approaches or AI-driven prediction techniques, is the second dimension. These AI-based techniques give academics fresh and useful perspectives on entrepreneurs and entrepreneurial endeavors that are challenging to evaluate using conventional techniques. We come to the conclusion that, in order to preserve the fundamental research standards and scientific rigor that safeguard the field's health, standing, and influence, it is imperative that the field embrace AI as a subject and a research methodology with more zeal. The main objective of this study is to develop entrepreneurial business using artificial intelligence study on artificial intelligence is booming in many fields of business and management study, since the changes it is witnessing have a profound impact on business and the economy as a whole. In the realm of entrepreneurship research, however, artificial intelligence has gotten very little attention., Even Nevertheless, a large number of entrepreneurship scholars concur that artificial intelligence will probably have a significant and disruptive impact on entrepreneurship research. Based on the study's findings, the researcher recommended a number of things, including more research on artificial intelligence and entrepreneurship.
Bilateral Supervision Network for Semi-supervised Medical Image Segmentation

Massive high-quality annotated data is required by fully-supervised learning, which is difficult to obtain for image segmentation since the pixel-level annotation is expensive, especially for medical image segmentation tasks that need domain knowledge. As an alternative solution, semi-supervised learning (SSL) can effectively alleviate the dependence on the annotated samples by leveraging abundant unlabeled samples. Among the SSL methods, mean-teacher (MT) is the most popular one. However, in MT, teacher model’s weights are completely determined by student model’s weights, which will lead to the training bottleneck at the late training stages. Besides, only pixel-wise consistency is applied for unlabeled data, which ignores the category information and is susceptible to noise. In this paper, we propose a bilateral supervision network with bilateral exponential moving average (bilateral-EMA), named BSNet to overcome these issues. On the one hand, both the student and teacher models are trained on labeled data, and then their weights are updated with the bilateral-EMA, and thus the two models can learn from each other. On the other hand, pseudo labels are used to perform bilateral supervision for unlabeled data. Moreover, for enhancing the supervision, we adopt adversarial learning to enforce the network generate more reliable pseudo labels for unlabeled data. We conduct extensive experiments on three datasets to evaluate the proposed BSNet, and results show that BSNet can improve the semi-supervised segmentation performance by a large margin and surpass other state-of-the-art SSL methods.
On the Weights of Linear Codes with Prescribed Automorphisms

The number of nonzero weights of a linear code is essential in coding theory as it unveils salient properties of the code, such as its covering radius. In this paper, we establish two upper bounds on the number of nonzero weights of a linear code with prescribed automorphism. Our bounds are applicable for almost all linear codes and tighter than previously known bounds. Examples confirm that our bounds are sharp on numerous occasions. In addition, we give an infinite family of linear codes that attain our bounds with equality.
UAV-Enabled Federated Learning in Dynamic Environments: Efficiency and Security Trade-off

Unmanned aerial vehicles (UAVs) can be deployed as flying base stations to provide wireless communication and machine learning (ML) training services for ground user equipments (UEs). Due to privacy concerns, many UEs are not willing to send their raw data to the UAV for model training. Fortunately, federated learning (FL) has emerged as an effective solution to privacy-preserving ML. To balance efficiency and wireless security, this paper proposes a novel secure and efficient FL framework in UAV-enabled networks. Specifically, we design a secure UE selection scheme based on the secrecy outage probability to prevent uploaded model parameters from being wiretapped by a malicious eavesdropper. Then, we formulate a joint UAV placement and resource allocation problem for minimizing training time and UE energy consumption while maximizing the number of secure UEs under the UAV's energy constraint. Considering the random movement of the eavesdropper and UEs as well as online task generation on UEs in practical application scenarios, we present the long short-term memory (LSTM)-based deep deterministic policy gradient (DDPG) algorithm (LSTM-DDPG) to facilitate real-time decision making for the formulated problem. Finally, simulation results show that the proposed LSTM-DDPG algorithm outperforms the state-of-arts in terms of efficiency and security of FL.
Dark Side of the Web: Dark Web Classification Based on TextCNN and Topic Modeling Weight

The Dark Web is an internet domain that ensures user anonymity and has increasingly become a focal point for illegal activities and a repository for information on cyberattacks owing to the challenges in tracking its users. This study examined the classification of the Dark Web in relation to these cyber threats. We processed Dark Web texts to extract vector types suitable for machine learning classification. Traditional methods utilizing the entirety of Dark Web texts to generate features result in vectors including all words found on the Dark Web. However, this approach incorporates extraneous information in the vectors, diminishing learning effectiveness and extending processing duration. The research aimed to optimize the classification process by selectively focusing on keywords within each class, thereby curtailing word vector dimensions. This optimization was facilitated by leveraging the anonymity characteristic of the Dark Web and employing topic-modeling-based weight generation. These methods enabled the creation of word vectors with a constrained feature set, enhancing the distinction of Dark Web classes. To further improve classification performance, we integrated TextCNN with topic modeling weights. For validation, we employed two datasets and compared the performance of the model with other text classification algorithms, where the proposed model demonstrated superior effectiveness in Dark Web classification.
Automatic Detection of Abnormal Drone Flight State by Measuring Changes in Images Captured by a Drone-mounted Camera

While in flight, drones can be adversely affected by strong gusts of wind. Currently, drones are equipped with hardware such as accelerometers and speed sensors to mitigate these effects. However, these measures may be inadequate, and intense disturbances can lead to crashes. For instance, a remotely piloted drone blown off course by wind may become uncontrollable, and any delay in correcting its trajectory could result in an accident. Therefore, in this study, we analyze images captured by cameras mounted on drones during autonomous flight to detect unusual movements. This enables the control center to promptly address anomalies, independent of the already equipped internal sensors for anomaly detection, thus enhancing the rapid detection of flight anomalies. Additionally, it acts as a system backup in case these equipped sensors for anomaly detection become non-functional. In this paper, we assess the drone's speed and direction by analyzing changes in successive images taken by the onboard camera. Based on these assessments, we determine the normalcy of the flight state. For this, we introduce a novel method for detecting abnormal flight conditions, utilizing the observed variations in speed and direction. Extensive experimental testing demonstrates the effectiveness of our proposed method in identifying abnormal flight conditions during drone operations.
Context-Aware Civil Unrest Event Prediction Using Neutrosophic-Aspect-Based Sentiment Analysis, PSO, and Hierarchical LSTM

Civil unrest is among the important hurdles in the countries’ progress as it deteriorates gross domestic product (GDP), international relations, foreign direct investment (FDI), globalization, public opinion, tourism, and businesses. Due to civil unrest a variety of serious problems, viz. loss of life/injury, resources, political stability, and human rights occur. Recently, few researchers have given insights on the prediction of occurrences of civil unrest events by using hypothesis testing and some basic machine/deep learning models. Important factors such as people’s emotions/sentiments, contextual information, and civil unrest events feature’ importance are ignored presently. For the first time, the proposed work overcomes all these research gaps by hybridizing the neutrosophic set, aspect-based sentiment analysis, particle swarm optimization (PSO), and hierarchical long short-term memory (hierarchical LSTM). Neutrosophic set along with aspect-based sentiment analysis has been used to get the sentiment and features’ importance. The resulting features’ weights have been optimized using PSO. For a more comprehensive understanding of the input sequence and feature weights, hierarchical LSTM has been used. Doing so obtained results that are more accurately improved for civil unrest events prediction. The performance of the proposed model has been evaluated and compared with state of art methods. Experimentation and evaluation show the proposed model outperforms the baseline methods by 3% to 15%on the standard datasets in terms of accuracy.
Attention-Bridged Modal Interaction for Text-to-Image Generation

We propose a novel Text-to-Image Generation Network, Attention-bridged Modal Interaction Generative Adversarial Network (AMI-GAN), to better explore modal interaction and perception for high-quality image synthesis. The AMI-GAN contains two novel designs: an Attention-bridged Modal Interaction (AMI) module and a Residual Perception Discriminator (RPD). In AMI, we mainly design a multi-scale attention mechanism to exploit semantics alignment, fusion, and enhancement between text and image, to better refine details and context semantics of the synthesized image. In RPD, we design a multi-scale information perception mechanism with our proposed novel information adjustment function, to encourage the discriminator to better perceive visual differences between the real and synthesized image. Consequently, the discriminator will drive the generator to improve the visual quality of the synthesized image. Besides, based on these novel designs, we can design two versions, a single-stage generation framework (AMI-GAN-S), and a multi-stage generation framework (AMI-GAN-M), respectively. The former can synthesize high-resolution images because of its low computational cost; the latter can synthesize images with realistic detail. Experimental results on two widely used T2I datasets showed that our AMI-GANs achieve competitive performance in T2I task.
Pembelajaran Kontekstual sebagai Strategi Pengembangan Literasi Manusia

This study aims to analyze how contextual learning is implemented in the Kemuhammadiyahan course and how it affects human literacy abilities. The method used is quantitative and descriptive qualitative, that carried out by exploring student responses as research objects by filling out questionnaires by all students who take part in the study, totaling 228 students. Data is analyzed by validity test, reliability test, and effectiveness test. The results showed that the contextual learning strategies by real work lecture methods or project-based learning can help students learn to communicate with various types of people and community groups, learn to cooperate with friends and the community, learn to interact and adapt to various cultures, moral and ethical values in society, as well as think creatively and solutely, to improve students' human literacy skills.
DETEKSI BANJIR AREA PERKOTAAN BERBASIS CITRA DIGITAL CONVOLUTIONAL NEURAL NETWORK (VGG19)

Geographically and demographically, Indonesia has natural conditions that have the potential for floods disaster. There are at least 16,771 islands and 65,017 rivers that fill the archipelago. Unfortunately, the ever-increasing urban population accompanied by a lack of awareness and preparation for protecting the environment has resulted in a higher risk of flooding in urban areas. This study utilizes digital imagery to detect flood conditions in urban areas. In terms of access, digital images are available in urban CCTV monitoring systems as well as office areas, housing, and from people who have smartphones. The detection method used in this study is the VGG19 model which consists of 16 convolution layers and 3 standard classification layers. All convolution layers are divided into 5 blocks followed by a MaxPooling layer for each block to reduce the resolution of the input image. In the last layer, SoftMax layer is used to estimate the probability between flood labels and normal conditions. There are 4 parameters that were optimized during the VGG19 model training process, namely Batch Size, Learning Rate, Dropout and Epoch (training repetition). To test the proposed model, public datasets are used, namely the Roadway Flooding Image Dataset and Road Vehicle Images Dataset. The best flood detection results (or normal conditions) achieve the accuracy of 98.78%. As for the other three performance metrics, namely precision, recall and F1-score, they reach 99%. These results are generated by the VGG19 model with a Batch Size parameter of 20, a Learning Rate of 1e-5 (0.00001), 50% Dropout and 100 Epoch. The achievement values of the four metrics can be considered quite good, so that the VGG19 model has the opportunity to be developed for flood detection applications in order to monitor urban flood conditions.
PERENCANAAN BLUEPRINT JARINGAN KOMPUTER BERBASIS INTER-VLAN ROUTING PADA SMKN 3 PUJUT

The lack of access to the internet network in schools is a factor in the slow teaching and learning process, because the lack of wireless signal coverage also causes the school community to only gather at one point. In an effort to deal with this problem, researchers built an inter-vlan network blueprint with wireless hotspots, in order to improve network performance and network security. In the process of building an inter-vlan network blueprint with a mikrotik hotspot, using the ppdioo method, among others, prepare preparation, plan plan, design design, implement, operate, optimize optimization. From the results of this study, in building a blueprint for an inter-vlan network with a mikrotik hotspot, we get the appropriate results from the initial plan. Which can improve network performance and expand access to internet network coverage.
RANCANG BANGUN SISTEM PENERIMAAN DOKUMEN PADA PT. REASURANSI INDONESIA UTAMA

In the Administration at PT. Reinsurance Indonesia Utama letter is an important means of important and confidential information related to the agency contained in it. Timeliness in receiving letters must be carried out quickly and precisely. Because of that, a better and structured mail receipt information system process is needed to be faster in searching for existing data and in the process of making reports. This letter receipt system has the following capabilities: This reception system is run on a computer operational network in the administration section of PT. Reinsurance Indonesia Utama, administrative officers can access the system by entering the correct admin login, can change, search, and save the letter. The research methods used are observation, interviews, literature studies, systems analysis, proposed system design, system testing, and system implementation. The result of this design is to produce an application that can manage letters according to the specified flow, and solve existing problems.
EXPLANATORY DATA ANALISIS UNTUK MENGEVALUASI PENELUSURAN KATA KUNCI VIDEO PEMBELAJARAN DI YOUTUBE DENGAN PENDEKATAN MACHINE LEARNING

The purpose of this study was to find correlations related to the variable number of impressions, likes, subscribers, and comments on each learning video keyword search on YouTube. This research uses quantitative methods and experiments with secondary data sources. Exploratory Data Analysis in machine learning using several libraries in Python programming produces image visualizations that provide information related to the dataset that has been processed, such as boxplot graphs, histograms, line plots, and correlation graphs. Exploratory Data Analysis with machine learning that we have done finds results on boxplot graphs on five variables showing a whisker more elongated upwards which states positive data results. The difference in this histogram chart is in the duration variable. On the line plot graph, we find the keywords learning videos learning mathematics have the advantage of four variables and the keywords of accounting learning videos one variable. &#x0D; Exploratory Data Analysis using the correlation head map in the seaborn library shows that the like and comment variables strongly correlate with a value of 1. Duration variables have a low and negative correlation with other variables. The subscribers variable has a high correlation with the like variable 0.95. Thus, several indicators need to be considered in making learning videos, such as content or content of innovative and creative learning videos, so that the number of likes and comments becomes high. The length of time in learning videos does not affect the number of likes, subscribers, and comments.
PERANCANGAN SISTEM INFORMASI PERPUSTAKAAN PADA MTS NEGERI 3 KOTA JAMBI

Library information systems in this age of technology are indispensable in providing services and meeting every demand of the staff and management. The integration of databases, the use of electronic equipment, these computerized processes are often referred to as library automation. Library automation is a process of managing libraries using information technology. This automation process then presents the concept of a library system. Currently there are several schools whose libraries still use manual systems. Where the process of managing data on borrowing and returning books is still applied manually so that it experiences obstacles such as errors, difficulties in finding data and making reports to the head of the library. The purpose of this study is to produce a design of a library information system to facilitate the management of data in the library. This library information system is built with the waterfall method with five stages of applicable methods, namely software needs analysis, system design, program creation, system testing, and maintenance with PHP programming language and MySql as the database. System modeling is created using use case diagrams, activity diagrams, and class diagrams. Conclusions that can be drawn in this study include, this study produced a library information system that can manage data automatically and can print reports needed by the head of the library .The system can also automatically calculate the fines to be paid by members who are late in returning books.
KORELASI GEJALA PENYAKIT FLU PADA ANAK BALITA DENGAN MENGGUNAKAN ALGORITMA SEMUT

Influenza is one of the most common illnesses suffered by toddlers. Knowing the symptoms that appear most quickly, parents can provide appropriate first aid to their toddlers. A graph is a field of mathematics used to find the fastest pathways in a path based on the starting point to the endpoint. The graph used is a weighted graph with weights taken from the moderate pain suffered by toddlers, in which the range of a pain scale is 0 – 10. Then, using the ant algorithm to determine the distance from symptoms that often appear. The results obtained from pheromone evaporation of the ant algorithm are Fever (P1), Headache (P2), Weakness (P7), Vomiting (P8), and Diarrhea (P9). The pheromones taken as pathways were high pheromone values P1–P2 (0.0905), P2–P7 (0.0874), P7–P8 (0.0811), and P8–P9 (0.0810). Ant algorithm can identify flu symptoms in children under five and explain the relationship between the symptoms.
The Effect of the Flipped Classroom Model to Improve Critical Thinking and Reading Comprehension for Fifth-Grade Students

This paper is motivated by the condition of students who lack Literacy and Critical Reasoning in the learning process and learning outcomes that are still many under KKM because teachers are less familiar with several methods and have yet to use the Flipped Classroom Model. This study aims to improve Reading Comprehension and Critical Thinking using the Flipped Classroom model. This research was conducted in class V of SDN Cibabat Mandiri 1 Cimahi City. This study uses quantitative research methods with the Quasi Experiment Design model using observation, interview, pretest, posttest, and documentation assisted by SPSS 29.0 to calculate the data obtained. With a population of 74 students. The technique used is collecting data on this test method before and after treatment. A live question-and-answer technique was used to find out the test results from the study. After that, samples were taken for the Experimental class, namely the VA class with 37 students and the VB class as the control class with 37 students, so a total of 74 students. Based on the Hypothesis Test results, there is a viable data variant after treatment. The data collection techniques used were observation, interview, pretest, and posttest. The results showed a significant influence on the use of flipped classrooms to improve the reading comprehension and critical thinking skills of grade V students of SDN Cibabat Mandiri 1 Cimahi City. Based on the results of the independent sample T-test, Sig was obtained. (2-tailed) Alternatively, (Significance Two Side-p) of 0.001 &gt; 0.05 means Ho is rejected, and Ha is accepted. This is supported by the average score of the experimental class of 81.16 and the control class of 72.64.
Sistem Informasi Arus Kas Berbasis Multiuser Pada PT Xpresindo Logistik Utama Semarang

PT. Xpresindo Logistik Utama Semarang Branch is a service company engaged in the expedition/delivery of goods located at Jalan Abdulrahman Saleh number 89a Semarang. Cash data processing system at PT. Xpresindo Logistik Utama Semarang Branch is simple/single, so it cannot be accessed simultaneously by other interested parties (in this case is marketing which requires an amount of money for each truck).&#x0D; There is still a difference between the report on the amount of road money contained in the cash flow report and the report on the amount of road money in revenue (marketing reports), this is because if there is additional road money or other costs (unloading, mell, etc.) -other) cannot be automatically added to the income statement data, and this greatly affects the accuracy of calculating the amount of profit earned by the company.&#x0D; The research method used is the System Development Life Cycle (SDLC) which includes planning, analysis, design, implementation and use. The new system created using MySQL Server database and Microsoft Visual Basic 6.0 Multiuser-based information system is expected to increase work productivity because it is more effective and efficient because it can be accessed by many interested parties without being limited by distance and time.