Reviewerly/exHarmony huggingface dataset:
Image Enhancement Using GAN (A Re-Modeling of SR-GAN for Noise Reduction)

The introduction of neural networks and deep learning models in the field of image processing brought significant progress and achievements. These CNN networks are used in many ways in image processing for classification, segmentation and region of interest detection. They are mainly used in real-time applications like face detections, YOLO, resolution enhancement, etc. The major attention of this paper will be in image enhancement which has wide applications in fields like satellite images, surveillance, etc. The resolution enhancement has been solved in the past using arithmetic operations and has addressed the issue to some extent. The techniques like nearby neighbor and bicubic interpolation (Gao in, Opt. Express 19:26,161–26,173, 2011, [1]) were able to enhance images but were not much reliable in the real applications. With the introduction of GAN (Zhang et al., in [2]) into image processing, the problem of image resolution has been tackled to a certain level. The methods such as EDSR ( Lim et al., in [3]), SR-GAN (Ledig et al., in [4]) and VDSR (Kim et al., [5]) have introduced the CNN for addressing this issue. The SR-GAN model is currently the state of art in the field of image resolution enhancement and delivers a PSNR of 29.40 on Set5 dataset and 25.16 on BSD100 dataset. In this work, we are going to take the method of SR-GAN and address the problem that are left unsolved in the SR-GAN method and improves the PSNR and SSIM score with minor changes in the methodology.
THE EFFECTIVENESS OF JOHNNY’S GRAMMAR WORD CHALLENGE ON STUDENTS’ GRAMMAR ACHIEVEMENT AT THE TENTH GRADE OF SMAN 1 TULUNGAGUNG

ABSTRACTZamsari, Dia Noor Farahin. Students Registered Number. 17203163065.2020.The Effectivenessof Johnny Grammar Word Challenge Application on Students’ Grammar Achievement at The Tenth Grade of SMAN 1 Tulungagung. Thesis. English Education Department. Faculty of Tarbiyah and Teacher Training. State Islamic Institute (IAIN) of Tulungagung. Advisor : Dr. Sukarsono, M.Pd.Keywords: effect, Johnny Grammar Word Application, students’ grammar achievement.Learning English means learning skills, namely listening, speaking, reading and writing. To integrate for those skills there are components that also must be learnt by students, one of them is Grammar. When the student wants to master the English skills they must be mastered about grammar first.In this technology era, the teacher can uses an application to enhance the students’ grammar. One of them is Johnny Grammar Word Challenge Application. Johnny Grammar Word Challenge Application is an application of questions about vocabulary, grammar and daily English spelling with different levels of participation. Johnny Grammar’s Word Challenge is a quiz for English learners to test common vocabulary, spelling and grammar that appears in everyday English. This application can be applied outside and inside of the classroom. Not only it, but also the students can play this game everywhere and anytime.The research question of this study is “Is there any significant difference on Students’ Grammar Achievement after using Johnny Grammar Word Challenge Application?”. Based on the research question, the purpose of this study is to find the significance difference on students’ grammar achievement after using Johnny Grammar Word Challenge Application.In this research, the researcher used quantitative approach with Pre- experimental design with one group of Pre-Test and Post-Test. The population of this study was 10th grade students of SMAN 1 Tulungagung in academic year 2019/2020. The sample of the study was X IPA 1 which is consisted of 32 students and it was selected by purposive sampling technique.The research instrument is test that consists of 25 questions. The data was analyzing by using SPSS 16.0 version. The result of this research indicate that there is significant difference scores on students’ grammar achievement especially in Irregular verb after being used by Johnny Grammar Word Challenge Application.The result of this study showed that there is a significant difference between the results of Pre-Test and Post-Test. The mean value before giving treatment or in Pre-Test is 14.6250. Meanwhile, the mean value after giving treatment or in Post-Test is 19.6875. Sig. Value (two-tailed) is 0.000 and it was divided by 2 because the researcher used one tailed, the Sig. Value is 0.000 : 2 =0.Because of 0 is smaller than the significance level (α) = 0.05. So, the alternative hypothesis (Ha) is accepted and the null hypothesis (H0) is rejected. In conclusion, Johnny Grammar Word Challenge is effective to enhance students’ grammar achievement.
Cloud Based Online Examination Portal using Docker

Online examination portal is a cloud-based web application, which allows any industry or institute to arrange, conduct and manage examinations via an online environment. It can be done through the Internet/Intranet /Local Area Network environments. Some of the problems faced during manual examination systems are the delays occurred in result processing, filing poses a problem, and filtering of records is difficult. The chance of loss of records is high; also record searching is difficult. Maintenance of the system is also very difficult and takes lot of time and effort. Online examination is one of the crucial parts for online education system. It is efficient, fast enough and reduces the large amount of material resource. It proposes a plan to build a systematic, integrated, intelligent and extensible web application on the cloud computing platform such as Google cloud, Amazon web services by using the container-based Docker and Django framework.Cloud security means the set of control-based technologies which are designed to maintain the security and protect the information, data security and all the applications associated with it.
Algorithmic Instabilities of Accelerated Gradient Descent

We study the algorithmic stability of Nesterov's accelerated gradient method. For convex quadratic objectives, Chen et al. (2018) proved that the uniform stability of the method grows quadratically with the number of optimization steps, and conjectured that the same is true for the general convex and smooth case. We disprove this conjecture and show, for two notions of algorithmic stability (including uniform stability), that the stability of Nesterov's accelerated method in fact deteriorates exponentially fast with the number of gradient steps. This stands in sharp contrast to the bounds in the quadratic case, but also to known results for non-accelerated gradient methods where stability typically grows linearly with the number of steps.
CODE SWITCHING AND MIXING USED IN VLOG “NEBENG BOY”

Language plays an important role in society’s life. In society, language is used for communication. Language also became an important role at the process of teaching and learning. social background of teacher and student can influent in the usage of code switching and mixing. In communication, there are formal language and informal language. Formal language is used in formal situations, while informal language is more common in spoken language and in informal situations. Basically, to determine a context uses formal or informal language is by noticing the selected vocabulary. One of the characteristics of informal is the usage of code mixing used by members of certain group for internal communication that may not be understood by members of other groups. In this study, the writer discusses code switching and mixing used in Vlog because vlog is a type of video which is popular among teenagers nowadays. The researcher aim to conduct this research in order to be a model of teaching and learning. Therefore, improving knowledge about code is important to develop the ability in the usage of code switching and mixing.The formulations of research problems are: 1) What are the types of code switching and mixing used in vlog nebeng boy? 2) What factors influence code switching and mixing used in vlog nebeng boy? The purposes of this study are: 1) to find out the types of code switching and mixing used in vlog nebeng boy 2) to find out the factors of the usage code switching and mixing in vlog nebeng boy.The research design of this study is content analysis with qualitative approach. The data source of this study is taken from nine video blogs (vlogs) made by selected American teenage vloggers. The criteria to select the vloggers are: 1) Indonesian citizen, 2) bilingual or multilingual 3) have at least 10.000 viewer on the YouTube video, there are six types of code switching and mixing. The writer also uses some theories related to slang language and its definition that is Hoffman theory.
THE STUDENTS’ PERCEPTION OF USE E-LEARNING FOR ENGLISH TEACHING AT STATE ISLAMIC SENIOR HIGH SCHOOL (MAN) 1 POLEWALI MANDAR

ABSTRACTSyafaat. Student Registered Number. 17203163002. 2020. The Students’ Perception of Use E-Learning for English Teaching at State Islamic Senior High School (MAN) 1 Polewali Mandar Thesis. English Education Department. Faculty of Tarbiyah and Teacher Training. State Islamic Institute (IAIN) of Tulungagung. Advisor: Dr. Hj. Nanik Sri Rahayu,M.Pd.Keywords: E-Learning; PerceptionE-learning is an essential part of today’s education. E-learning may be used to refer to this modern type of learning including virtual learning, web-based learning and computer-based learning. Besides, e-learning is emerging as a modern technology that may have to some advantages over traditional teaching methods including cost-effectiveness, flexibility to time and place, and accessibility to instructional information. This research aimed to describe the students' perceptions of use e-learning in the appropriateness of usability and the appropriateness of ease to use for English teaching at State Islamic Senior High School (MAN) 1 Polewali Mandar. This research generally answered the question What are the students’ perceptions toward the use of E- Learning for English Teaching?.Research method of this study was using a descriptive approach with survey design. The population of this study was all the eleventh-grade religion students of MAN 1 Polewali Mandar. The sample of this study was used purposive sampling technique and got XI Religion 1, XI Religion 2, and XI Religion 3 class which the total consist of 110 students, where they all consist of 51 male students, and 59 female students.The data was collected through a questionnaire. The researcher analyzed the data by using SPSS 17.0 to find out the descriptive statistic such as frequency, percentage, and mean score. There were 57 students of eleventh-grades religion at MAN 1 Polewali Mandar participated in this research, where they all consist of 21 male students, and 36 female students.The result of this research was presented descriptively in order to reveal the students' perception. The result showed that the students’ perceived the e- learning web-based (e-learning-man1polman.com) to be useful in improving their understanding, independence, performance, self-discipline, motivation to learn, and interaction with each together and with the teacher. Moreover, the students has positive response toward the e-learning web-based module was ease to use. This implies that the inclusion of technology in education at the school in beneficial.
Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy.

Generating goal-oriented questions in Visual Dialogue tasks is a challenging and longstanding problem. State-Of-The-Art systems are shown to generate questions that, although grammatically correct, often lack an effective strategy and sound unnatural to humans. Inspired by the cognitive literature on information search and cross-situational word learning, we design Confirm-it, a model based on a beam search re-ranking algorithm that guides an effective goal-oriented strategy by asking questions that confirm the model’s conjecture about the referent. We take the GuessWhat?! game as a case-study. We show that dialogues generated by Confirm-it are more natural and effective than beam search decoding without re-ranking.
Do Different Tracking Tasks Require Different Appearance Models

Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and benchmarks has fragmented the problem in a multitude of different experimental setups. As a consequence, the literature has fragmented too, and now the novel approaches proposed by the community are usually specialised to fit only one specific setup. To understand to what extent this specialisation is actually necessary, in this work we present UniTrack, a unified tracking solution to address five different tasks within the same framework. UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple heads to address individual tasks and that do not require training. We show how most tracking tasks can be solved within this framework, and that the same appearance model can be used to obtain performance that is competitive against specialised methods for all the five tasks considered. The framework also allows us to analyse appearance models obtained with the most recent self-supervised methods, thus significantly extending their evaluation and comparison to a larger variety of important problems. Code available at this https URL.
SPARQLing Database Queries from Intermediate Question Decompositions

To translate natural language questions into executable database queries, most approaches rely on a fully annotated training set. Annotating a large dataset with queries is difficult as it requires query-language expertise. We reduce this burden using grounded in databases intermediate question representations. These representations are simpler to collect and were originally crowdsourced within the Break dataset (Wolfson et al., 2020). Our pipeline consists of two parts: a neural semantic parser that converts natural language questions into the intermediate representations and a non-trainable transpiler to the SPARQL query language (a standard language for accessing knowledge graphs and semantic web). We chose SPARQL because its queries are structurally closer to our intermediate representations (compared to SQL). We observe that the execution accuracy of queries constructed by our model on the challenging Spider dataset is comparable with the state-of-the-art text-to-SQL methods trained with annotated SQL queries. Our code and data are publicly available (https://github.com/yandex-research/sparqling-queries).
LibGuides: 4th Grade Social Studies Resources: Symbols

This guide will serve as a resource for 4th grade students to discover necessary resources to complete their 4th grade Social Studies projects.
The practice research of flexible management in college education management

Under the background of education reform, the idea of "people-oriented" is deeply rooted in people's hearts, which makes it difficult for the traditional rigid management to meet the needs of students.Flexible management, as a new management mode compared with rigid management, attaches importance to the guidance of students and stimulates students' consciousness of self-management and restraint, which has strong effectiveness and operability.Therefore, this paper analyzes the advantages of flexible management, and puts forward specific application countermeasures, in order to comprehensively improve the actual effect of university education management.
Summarization of German Court Rulings

Historically speaking, the German legal language is widely neglected in NLP research, especially in summarization systems, as most of them are based on English newspaper articles. In this paper, we propose the task of automatic summarization of German court rulings. Due to their complexity and length, it is of critical importance that legal practitioners can quickly identify the content of a verdict and thus be able to decide on the relevance for a given legal case. To tackle this problem, we introduce a new dataset consisting of 100k German judgments with short summaries. Our dataset has the highest compression ratio among the most common summarization datasets. German court rulings contain much structural information, so we create a pre-processing pipeline tailored explicitly to the German legal domain. Additionally, we implement multiple extractive as well as abstractive summarization systems and build a wide variety of baseline models. Our best model achieves a ROUGE-1 score of 30.50. Therefore with this work, we are laying the crucial groundwork for further research on German summarization systems.
TranslateLocally: Blazing-fast translation running on the local CPU.

Every day, millions of people sacrifice their privacy and browsing habits in exchange for online machine translation. Companies and governments with confidentiality requirements often ban online translation or pay a premium to disable logging. To bring control back to the end user and demonstrate speed, we developed translateLocally. Running locally on a desktop or laptop CPU, translateLocally delivers cloud-like translation speed and quality even on 10 year old hardware. The open-source software is based on Marian and runs on Linux, Windows, and macOS.
Bandits using Fuzzy to fill Knapsacks: Smallholder Farmers Credit Scoring

Consumer lending is an exercise where risk scoring takes the form of a typical decision making problem. For smallholder farmers, the credit scoring becomes specifically more challenging with the data gaps and outliers in data. Added to that, the process must be as cost-effective as possible while providing as accurate results as possible. This paper uses data obtained from smallholder farmers in Kakamega County in Kenya to set up an experiment of credit scoring as a Bandit with Knapsack problem with Fuzzy Unordered Rule Induction Algorithm (FURIA) being used as the exploit-explore algorithm and Fuzzy Analytical Hierarchical Process (FAHP) used to determine the ranking and consistency of the FURIA rules. The experiment returns a consistency ratio of 0.000529 which is significantly less than the 0.10 threshold. In this regard, the paper proposes the use of FURIA to reduce the regret in Bandit with Knapsack (BwK) as a technique for smallholder credit scoring.
Heterogeneous Dynamic Hybrid Algorithm in Cloud Computing to Load Balance to Improve Cloud Server Speed Efficiency

Load balancing is important in distributed operations in distributed environments and systems, and since cloud computing has grown and expanded rapidly in the recent period, and with the increase in its expansion, customers’ requirements for more services and better results have increased, balancing the burden in cloud computing has become an important and expanding research topic for researchers and scholars in the field of cloud computing. In this paper, a group of algorithms used in balancing the burdens in cloud computing are discussed. Where a hybrid algorithm is designed to distribute burdens in cloud computing. The complexity of the proposed algorithm is O(n). Experiments proved that the proposed hybrid burden distribution algorithm in cloud computing is more efficient in performance compared to the PSO algorithm. The performance was better when the number of user requests was 200, which is 1.02 times. The hybrid algorithm works effectively with more orders and has a profit rate of 2.03%. The results of the experiments showed that the average execution time of the proposed algorithm for burden distribution is reduced by 4.73% compared to the PSO algorithm.
MFAQ: a Multilingual FAQ Dataset

In this paper, we present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages. Although this is significantly larger than existing FAQ retrieval datasets, it comes with its own challenges: duplication of content and uneven distribution of topics. We adopt a similar setup as Dense Passage Retrieval (DPR) and test various bi-encoders on this dataset. Our experiments reveal that a multilingual model based on XLM-RoBERTa achieves the best results, except for English. Lower resources languages seem to learn from one another as a multilingual model achieves a higher MRR than language-specific ones. Our qualitative analysis reveals the brittleness of the model on simple word changes. We publicly release our dataset, model, and training script.
Grounding inductive biases in natural images:invariance stems from variations in data

To perform well on unseen and potentially out-of-distribution samples, it is desirable for machine learning models to have a predictable response with respect to transformations affecting the factors of variation of the input. Invariance is commonly achieved through hand-engineered data augmentation, but do standard data augmentations address transformations that explain variations in real data? While prior work has focused on synthetic data, we attempt here to characterize the factors of variation in a real dataset, ImageNet, and study the invariance of both standard residual networks and the recently proposed vision transformer with respect to changes in these factors. We show standard augmentation relies on a precise combination of translation and scale, with translation recapturing most of the performance improvement -- despite the (approximate) translation invariance built in to convolutional architectures, such as residual networks. In fact, we found that scale and translation invariance was similar across residual networks and vision transformer models despite their markedly different inductive biases. We show the training data itself is the main source of invariance, and that data augmentation only further increases the learned invariances. Interestingly, the invariances brought from the training process align with the ImageNet factors of variation we found. Finally, we find that the main factors of variation in ImageNet mostly relate to appearance and are specific to each class.
The Use of Language Learning apps in Learning Foreign Languages : Benefits and Challenges

Recently the use of language learning apps such as Rosetta Stone and Duolingo has gained popularity among foreign language learners, especially for those who want to learn foreign languages for general purposes. In this presentation, the presenters are going to report the results of 10 graduate students who used various language learning apps using their mobile over a period of ten weeks. The study aims to investigate learners’ attitudes toward foreign language learning apps and examine whether there is any significant difference in their attitudes depending on various factors. For the data collection, the questionnaire survey was administered to the participants and they were also asked to submit their progress reports (Weeks 2, 6, and 11). Then, their reports were analyzed using the NVivo (version 12). Due to the small sample size and short length of the participants’ use of the apps, the results may be preliminary in drawing any substantial conclusive findings, but will still give us some insights as to what we can do to help these language learners in the future