Reviewerly/exHarmony huggingface dataset:
Optimizing Image Processing with CNNs through Transfer Learning: Survey

The field of image processing has been revolutionized by Convolutional Neural Networks (CNNs), which exhibit exceptional capability in feature extraction and accurate image classification. However, training CNNs requires large volumes of annotated data and significant computational resources. Considering these challenges, transfer learning has emerged as a promising approach to reducing the dependence on labeled data and computational resources. Transfer learning involves utilizing knowledge gained from a source task to improve the training process for a target task. This technique has demonstrated considerable benefits; however, it also possesses certain limitations. Consequently, this survey explores the advantages and constraints of transfer learning and the various factors that influence its effectiveness in optimizing image processing using CNNs. Additionally, the survey investigates the most recent advancements and research in the field of transfer learning specifically for image processing with CNNs. In summary, this comprehensive analysis highlights the significance of transfer learning in the context of optimizing image processing with CNNs, providing unique insights into this rapidly evolving domain.
The Difficulties of the Students of IAI As’adiyah Sengkang in Mastering English

This research objective is to describe the difficulties of the students in mastering English Preliminary observations in IAI As’adiyah Sengkang indicate that the English ability of the students is varied. The researcher limited this research in the second semester students of English Education Program of IAI As’adiyah Sengkang as participants of the research. The researcher focused in the difficulties of the students in mastering English. There are Two Difficulties discussed. They are the difficulties of the students in speaking and the difficulties of students in grammar. The researcher used descriptive qualitative research in this research. The technique of collecting the data in this research is in the form of interviews. The interviews provided questions that on the difficulties of students in mastering English. Interview is the technique in getting evidence deeply to students. The result of the research proves that most of the students in the second semester of English education program of IAI As’adiyah Sengkang liked learning English. They have many reasons why they like learning English. Besides that, they consider that English speaking is difficult to be learnt. They have two main problems. Those are pronunciation and vocabulary. Most of students also have problems in learning grammar. They are difficult to understand the formula of the structure. They also have problems in understanding the tenses.
DESIGN OF ANDROID-BASED MUNA LANGUAGE DICTIONARY APPLICATION USING EXTREME PROGRAMMING METHOD

Muna is one of the languages spoken by the Muna tribe in Southeast Sulawesi, Indonesia, which belongs to the Austronesian language family. Over time, Muna language began to be rarely used. Muna language learning in education also began to be rarely used and there are almost no Muna language lessons anymore, because people prefer to use foreign languages rather than using their own local languages. As a result, many people in the capital of Muna Regency do not understand or know the vocabulary of the Muna language. One way to preserve it is by utilizing existing technology through applications that can help search vocabulary as a substitute for conventional dictionaries that are large in size that can be carried and viewed at any time. Therefore, this research aims to preserve the Muna language through the design of the Muna language dictionary application by applying the Extreme Programming method. Extreme Programming method is a software development approach that emphasizes collaboration, flexibility, and responsiveness to change. The Muna language dictionary application will be designed with various features that make it easier for users to understand and use the Muna regional language. The Muna regional language dictionary application has also been tested using black box testing, where all the features in this application run well and in accordance with what is needed. Index Terms— Muna language, application, dictionary, Extreme programing.
Tree-Based Structural Representation and Difference Computation for PDF Documents

Abstract Purpose : The goal of this paper is to introduce a method that uses the structured representation of two PDF documents to compute differences between them. Methods : Included in this work there are three main contributions. The first one is a method for parsing PDF documents and representing them with a tree structure. The second one is our algorithm for computing differences between two PDF documents by leveraging their tree structure. The third one is a method for generating pairs of original and modified synthetic PDF documents. Results : Our system for parsing and representing PDF documents is able to detect the true structure of a PDF document with high accuracy when tested on synthetic PDFs. Our tree-based difference computation approach, when tested on synthetic documents, is able to capture structural differences and improve computational complexity significantly compared to the performance of state of the art alternative approaches. Conclusion : Tree-based PDF document representations allow for efficient, accurate, and comprehensive computation of differences between documents.
Tabular-based self-supervised learning approach for encrypted traffic classification

Encrypted traffic classification (ETC) plays an important role in network management. In most research, the statistical features, transformed traffic images, or text are used for classification. However, the statistical features' design is time-consuming and labor-intensive, and the transformed traffic data lack spatial or semantic features. Considering that the headers of traffic packets have a uniform structure and are independent of each other, traffic data are most similar to tabular data. Thus we propose a data processing approach to convert packet headers into traffic tables in which each field is viewed as a column (feature). In addition, traffic data are hard to label in real traffic environments, and each field contributes differently to the classification. Therefore, a self-supervised learning algorithm, SubTab, is used as the baseline network to reduce the reliance on labeled data and assign different weights to different fields. To the best of our knowledge, this is the first time that the ETC problem is solved from the tabular domain. Experimental results on two real-world datasets, ISCX VPN-nonVPN and the self-collected dataset SHU-ET, demonstrate that our method surpasses state-of-the-art methods based on traffic images or text and proves that traffic tables are more suitable for ETC problems. In addition, our method achieves a great performance with only 10% of labeled data and reduces the reliance on labeling data.
A Cnn Model for Improved Image Denoising With an Attention Guided Feature Selection

Abstract Deep Convolutional Neural Networks, or DCNNs, have undergone numerous modifications to enhance their capabilities in image processing, including image restoration, but there is still room for improvement. They have been improved in a number of ways, including reducing information loss, increasing feature utilization , and reducing computational complexity. This study presents a network structure known as CNN with attention (CNWATT2) that can preserve detail and edge information while also making the denoised image easier to view. Multiple features from the input image are extracted and fed into a forward network structure by the CNWATT2 using convolutional kernels of varying sizes. It is made up of two CNNs, and an attention module is added to the output of each CNN so that it can choose the features that affect the model before the concate-nation operation (attention-guided concatenation) to combine these features into the final feature map. The feature selection mechanism is enhanced by incorporating an attention model at the output of each CNN model. The CNWATT2 denoising method outperforms recently used denoising models DnCNN, FFD-NET IRCNN, and BRDNET in terms of both objective and subjective quality and its ability to effectively remove Gaussian noise from both color and grayscale images.
DeepSearch: A deep learning based document retrieval system

Abstract Deep learning has enabled machines to be used for many tasks, including computer vision, image recognition, natural language processing, self-driving cars, and predicting the contingencies of certain business transactions. Effective application of deep learning techniques can help develop intelligent applications. Many web-based tools get their information from generic search engines such as Google but also specialized search engines such as PubMed (for retrieving information on biomedical publications). In this study, we evaluate the performance of different deep learning architectures for the task of document retrieval. Our goal was to identify the optimal architecture for the document retrieval task. We therefore compare the deep architecture of feed-forward neural networks with recurrent neural networks, in particular the long short-term memory neural networks. We observe that the local context is not sufficient as it does not provide reliable predictions, which are, however, provided by long distance dependencies where long short-term memory neural networks are used. We first describe the algorithms used for our evaluation and then compare their performance. Our dataset contained two publicly available document sets from the 20-Newsgroup and BBC, respectively.
Super Fantasy Zone, Magic Sword, Karnov... (Supplement III:14)

(Supplement to the history of computer science and technology)
Analysis of the Factors in Influencing Students’ Willingness to Communicate in the Implementation Curriculum 2013 in English at Eleventh Grade of MAS Yati Kamang Mudiak

This research was due to discover Analysis of Students’ Factors in Influencing Willingness to Communicate in the Implementation Curriculum 2013 in English At Eleventh Grade of MAS Yati Kamang. This under graduated thesis aimed to find out dominant factors that influence students’ Willingness to Communicate (WTC) in the Implementation Curriculum 2013 at Eleventh grade of MAS Yati Kamang in Academic Year 2021/2022. It had been done at Eleventh Grade of MAS Yati Kamang because some students appeared to be hesitant to share their ideas. The other students seem nervous and not confident when the teacher asked students to raise their hands. Some students saw to avoid when the teacher ask them to go in front of the classroom using English. When the teacher asked one of students why the students were afraid come to the front of the class, and the students answered because they were afraid of being laughed by their friends and the students were afraid of being wrong. Hence, the researcher research about what were Students’ Factors in Influencing Willingness to Communicate in the Implementation Curriculum 2013 in English At Eleventh Grade of MAS Yati Kamang. The design of this research was qualitative research design. The key informants of the research were the Eleventh Students of MAS Yati Kamang, that consist of was 18 students. This research used observation checklist and interview to collect the data. The item of observation checlist was the item of environmental, individual dimension, linguistic dimension that consist of 22 items. In analyzing the data, the researcher used data reduction, data display, and drawing conclusion. Based on the results of the research, the researcher find out of the main Students’ Factors in Influencing Willingness to Communicate in the Implementation Curriculum 2013 in English At Eleventh Grade of MAS Yati Kamang. So, it concluded factors that influence students’ willingness to communicate was individual dimension. Hence, it indicated that individual dimension was factor dominant that supported students’ willingness to communicate.
A COMPREHENSIVE REVIEW ON DATA STREAM MINING TECHNIQUES FOR DATA CLASSIFICATION; AND FUTURE TRENDS

Data Mining is a developing interdisciplinary control managing Data Reclamation and Data Stream Mining techniques, whose subject is gathering, overseeing, processing, breaking down, and visualizing the huge volume of organized or unstructured data. Data stream mining indicates how to look at Unknown patterns from a massive amount of data over algorithms. It has experienced quick improvement with significant progress in math, statistics, data science, and computer science domains. Data streams are commonly generated by various sources such as sensor networks, social media feeds, financial transactions, online retail, network traffic, and many other applications. The gathered data could be additionally utilized for various purposes, for example, execution assessment, irregularity discovery, change identification, or issue finding of the operating systems. This data stream analysis is done using different data stream mining techniques. This paper provides a broad overview of the distinct approaches used for data stream mining. Initially, we studied the different techniques of data stream mining. Next, we discuss the different clustering and classification techniques and their benefits. Then we examine the evaluation of different data stream mining techniques results that some techniques are feasible for real-time data streams and some of not. This study provides a complete understanding of techniques and their benefits. The studies done so far need to be sufficiently exhaustive for data mining techniques, so future work is needed to assess which technique is feasible for real-time data streams.
Experimental demonstration of scalable quantum key distribution over a thousand kilometers

Abstract Secure long distance communications protecting the transmitting of sensitive information vital for business and advanced technologies from being accessed by unauthorized individuals, is one of the major challenges of modern life. The existing means of transmitted data protection are vulnerable to upcoming quantum computer attacks. Yet, remarkably, the quantum mechanics that engendered a threat to secure communications offers a guaranteed protection against the malicious attacks allowing to develop novel quantum key distribution (QKD). The major problem of the long-distance transmission arises due to essential signal decay in optical channels occurring at distances about a hundred kilometers. We resolve this problem by an original Terra Quantum QKD (TQ-QKD) protocol using semiclassical pulses containing enough photons for random bit encoding and exploiting erbium amplifiers to retranslate photon pulses and, at the same time, ensuring that at the chosen pulse intensity only a few photons can go outside the channel even at distances of about a hundred meters.As a result, an eavesdropper is incapable to utilize the lost part of the signal. A central component of the TQ-QKD protocol is the end-to-end loss control of the fiber-optic communication line. Our control precision reduces the degree of the signal leak below the detectable level, depriving an eavesdropper any possibility to efficiently utilize the lost part of the signal. Our work experimentally demonstrates the implementation of the TQ-QKD protocol allowing for a fully secure quantum key distribution over 1079 kilometers. Further refining of the transmitting scheme's components will expand the secure transmission scale over the global distances.
Efektifitas Kombinasi Media dalam Penyisihan BOD dan TSS Limbah Cair Rumah Makan Menggunakan Constructed Wetland dengan Tanaman Cyperus Papyrus

The restaurant business can have an impact on increasing liquid waste which can contaminate the receiving water body. One of the treatment methods that can be applied as further treatment is a constructed wetland system with continuous flow. The aim of the research was to determine the amount of BOD and TSS removal and the effect of pH and temperature on the research. Preliminary tests conducted showed levels of BOD = 1,562 mg/L and TSS = 192 mg/L. The research reactor consisted of 3 units, filled with wastewater with a debit of 7 ml/minute and 5 Cyperus papyrus stems planted in a variety of planting media, the reactor containing clay growing media (R1), the reactor containing sand growing media (R2), and (R3). containing clay planting medium combined with sand media. The reactor used continuous flow for 4 days, starting with a 7-day acclimatization test. The parameters to be tested in this study are pH, temperature, BOD, and TSS. The results showed that constructed wetlands with Cyperus papyrus plants could remove the best BOD on day 1 in a reactor with clay media of 66%. In the constructed wetland test results with the Cyperus papyrus plant the best ability to remove TSS was achieved on day 2 in sand media (R2) which could remove TSS levels of 84.3%.
A Study on Self-Confidence Impact of ELF Students’ Speaking

Self-confidence plays a crucial part in speaking English, and many students are still less confident when speaking, which causes them to not engage more in speaking activities. This research aimed to know how self-confidence works in students speaking to ninth-grade students, specifically at SMP Negeri 9 Palu, and the factors that contributed to their lack of confidence. For the topic, 30 students were selected as the sample and given twelve questions from the questionnaire and five interview questions. The research method was a descriptive qualitative method. The results that the researcher has done are self - confidence indeed works on students’ speaking. Students’ lack of confidence is caused by some factors, which are afraid of making mistakes when they speak, they still have lack of vocabulary, lack of grammar, feel of under pressure every time the teacher asks them to speak, shyness, nervous, feel of uncomfortable, not enjoying when they attempt to speak, insecurities of their friends who have the good ability more than them. This study is expected to contribute to developing students’ speaking ability and solve students’ problems by knowing their speaking and what factors contribute to their unconfident. Moreover, this study also may help teachers treat students so they can participate actively in class without being constrained with self–confidence.
The Practice and Application of Computer-Aided Translation Technology in English Teaching

English teaching is essential to computer-aided translation technology (CATT). It can improve students’ learning efficiency and cultivate their comprehensive ability. It can also promote China’s cultural dissemination and international exchange. This paper mainly introduces the characteristics of computer-aided translation technology and how to effectively apply it to real life to analyze and study its significant impact on our daily work. Then this paper designs an English teachingassisted translation model based on computer-aided technology. Then the performance of the English teaching model is tested by simulation. The test results show that the accuracy of computer-aided translation is more than 92%, and the translation time is within 10s. This shows that the model can meet users' needs and provide constructive suggestions and opinions for English teachers to improve English teaching.
Problematika Pembelajaran Menulis Permulaan pada Siswa Sekolah Dasar

Writing lessons are offered in elementary schools from grade one to grade VI. Students start learning how to write in grades I and II. Therefore, the process of learning to write at this level is known as early writing lessons, but in grades III, IV, V, and VI it is known as early writing learning. The goal of this study is to find a solution to the problem that low grade students are experiencing with the need to write. The current study includes SDN 112/I Perumnas in grades one and two. Qualitative descriptive is the employed technique. Information that was gathered through surveys, interviews, observations, and documentation In data analysis, quantitative analysis is used. The findings reveal challenges faced by first grade students, including (a) writing letters, words, and simple sentences; (b) writing a follow-up letter; (c) write sentences correctly; (d) can distinguish between the letters b and d (often confused); (e) find it difficult to imitate or copy the written form given by the teacher; and (f) find it difficult to arrange words into simple sentences. In contrast, the two students in the class came together to discuss the topic of (a) letters, words, and a short-term calendar.
Cautious Bayesian Optimization: A Line Tracker Case Study

In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We incorporate a prior model (transfer learning) used for the mean of the Gaussian processes, a semi-parametric Kernel, and acquisition function optimization under chance-constrained requirements. In this way, experimental fine-tuning of a performance objective under experiment-model mismatch can be safely carried out. The methodology is illustrated in a case study on a line-follower application in a CoppeliaSim environment.
Sant nok fyller Kjersti 70 år, men ...

Norwegian has a productive class of sentence adverbials, disjuncts of the form adjective + nok ‒ [A nok] for short. Examples include sant nok ‘true (enough)’, paradoksalt nok ‘paradoxically (enough)’, and tåpelig nok ‘foolishly (enough)’. Three structural analyses have been proposed for such constructions in the Scandinavian languages. The first has [A nok] as an adjective phrase, with an adjective head and nok as a modifier, i.e. two words. In the second and third proposal, [A nok] is considered to be one word, viz. an adverb. The proposals differ with respect to word formation type. One considers [A nok] expressions to be compounds, which implies that nok is seen as an adverbial root. The other considers them to be derivatives, with an adverbial suffix -nok. This article studies the grammatical and phonological properties of [A nok] disjuncts in order to determine what kind of constructions they represent. It is argued that they are not single words, but phrases, viz. adverb phrases. nok is an adverbial head taking an adjectival complement.
UNSUPERVISED ANOMALY DETECTION OF IMAGES BY AUTO-ENCODER

In the realm of computer vision, image anomaly detection has always been an important research direction. Despite various anomaly detection approaches based on deep neural networks have been developed successively, the progress of end-to-end unsupervised image anomaly detection is still limited. This paper proposes a novel unsupervised anomaly detection automatic adaptive dynamic clustering model named UAD-ADC that can performs end-to-end outlier detection on images without available labels. Specifically, UAD-ADC employs a deep auto-encoder to learn the feature representation of images and combine with data augmentation to improve the quality of learned features. To detect abnormal images, we propose an effective surrogate supervision method and construct a discriminative model based on a deep clustering method. In particular, an iterative optimization method combined with self-paced learning is adopted to select reliability samples to train the discriminant model. Finally, an efficient anomaly scoring strategy is designed to evaluate images to improve anomaly detection performance. Extensive experiments under different outlier ratios on image benchmarks demonstrate that UAD-ADC significantly improves anomaly detection performance compared to the state-of-the-art unsupervised anomaly detection methods.