Reviewerly/exHarmony huggingface dataset:
Duolingo Mobile Application for English Listening Skill Improvement of Vocational School Students

Duolingo application in mobile assisted language learning can help students flexibly to learn English listening skills by its game-based features. Meanwhile, listening skill as one of significant language aspects for oral communication purpose is highly required for professional need by vocational senior high school. Therefore, this research was intended to find out the progress of students’ English listening skill in SMK Analis Kesehatan Nasional Surakarta using Duolingo and students’ perceptions toward the mobile application. This study used quantitative research with pre-experimental design and its data collection used pre-test, post-test, and close-end questionnaire. The findings show that scores of students’ pre-test and post-test presented significant improvement of students’ English listening skill. The average students’ pre-test score was 73.25 while the average students’ post-test score was 83.3. It was supported by students’ perception that 90% students gave positive perception to the usage of Duolingo mobile application in their English learning process.
The Influence of Vocabulary Self-Collection Strategy on the Vocabulary Mastery

The objective of the study was to find out there was a significant influence of using vocabulary self-colection strategy on the vocabulary mastery of the Seventh Grade Students at SMP YKPP Pendopo. The method was pre-experimental. The population of this study was the seventh grade students of SMP YKPP Pendopo with totaling 105 students. The sample selected by using convenience method with the total 31 students. The data collected by mean of tests, namely pretest and posttest. The data analyzed by Matched t-test. The score of tobtained was 0.0083 1697 at the significant level p0.05 for two tailed and degree of freedom was 30, t table was 1.697. It indicated that the used of vocabulary self-collection strategy on the vocabulary mastery of the seventh grade students at SMP YKPP Pendopo was significant influence.
IMPLEMENTASI GATED RECURRENT UNIT (GRU) UNTUK PREDIKSI HARGA SAHAM BANK KONVENSIONAL DI INDONESIA

Advances in technology, information and technology at this time are growing rapidly, a lot of human work is facilitated by technology. Technology has also developed in investment instruments, especially in stock investments. Previously, when there was no technology, it would be very difficult to predict the stock price of state-owned banks in the future for ordinary people who do not understand fundamental or technical analysis. However, with technology, especially in the field of Deep Learning, it will be very possible to predict future stock prices without having to understand fundamental or technical analysis. In this study, stock price predictions of state-owned banks for the next 30 days were made using the Gated Recurrent Unit (GRU) model on stocks of state-owned banks in Indonesia, namely BRI, BNI, BTPN, and Mandiri bank shares. The data used uses historical close price data on the stock for 5 years with a total of 1260 data. From the results of the research for test data, the smallest RMSE value was found in BTPN bank shares of 23,91164 followed by BRI bank shares of 264,1475, BNI bank of 427,7984 and the largest RMSE value in BMRI bank shares amounting to 907,4804
Electrical Engineering Specialty Construction Based on “Four-dimensional Practical Teaching, Multimedia Integration, System Reconfiguration” Multi-Innovation Methods

Based on the new demand of national strategic development and the new situation of international competition, the construction of first-class universities and first-class majors and the realization of the higher education connotative development shall be accelerated. In the meantime, new technologies are emerging in the field of electrical engineering, the cross integration of disciplines and specialties is more extensive, and the demand for talents in enterprises become more and more diverse. Therefore, electrical engineering major in our university focus on the goal of National Engineering Education Certification and first-class major construction, proposed “Four-dimensional practical teaching, Multimedia integration, System reconfiguration” multi-innovation methods to conduct major construction.
To Block or Not to Block: Accelerating Mobile Web Pages On-The-Fly Through JavaScript Classification

The increasing complexity of JavaScript in modern mobile web pages has become a critical performance bottleneck for low-end mobile phone users, especially in developing regions. In this paper, we propose SlimWeb, a novel approach that automatically derives lightweight versions of mobile web pages on-the-fly by eliminating the use of unnecessary JavaScript. SlimWeb consists of a JavaScript classification service powered by a supervised Machine Learning (ML) model that provides insights into each JavaScript element embedded in a web page. SlimWeb aims to improve the web browsing experience by predicting the class of each element, such that essential elements are preserved and non-essential elements are blocked by the browsers using the service. We motivate the core design of SlimWeb using a user preference survey of 306 users and perform a detailed evaluation of SlimWeb across 500 popular web pages in a developing region on real 3G and 4G cellular networks, along with a user experience study with 20 real-world users and a usage willingness survey of 588 users. Evaluation results show that SlimWeb achieves a 50% reduction in the page load time compared to the original pages, and more than 30% reduction compared to competing solutions, while achieving high similarity scores to the original pages measured via a qualitative evaluation study of 62 users. SlimWeb improves the overall user experience by more than 60% compared to the original pages, while maintaining 90%-100% of the visual and functional components of most pages. Finally, the SlimWeb classifier achieves a median accuracy of 90% in predicting the JavaScript category.
IDENTIFIKASI PENERIMAAN BEASISWA DIPA MENGGUNAKAN METODE BACKWARD CHAINING

Scholarships are generally a provision of fees for education for students who are still actively attending lectures at a tertiary institution. With the scholarship so that it can help students to finance their needs during college. With the backward chaining method can facilitate the campus bureaucrats in determining students who are entitled to receive scholarships. Backward Chainning is a model in reverse of hypotheses, a potential or conclusion that must be proven by facts that support the hypothesis. The backward chaining model in receiving scholarships takes some data from existing variable criteria such as active lecture certificates, student cumulative achievement indexes, attaching TOEFL certificate, identity card, and curriculum vitae. Next make a decision tree from the data variables that exist and finally is implemented. The results of this study have an accuracy of 95%, which can be recommended to get the scholarship and this method can facilitate the determination of scholarship acceptance. The backward chaining method is very effective and accurate in determining DIPA scholarship recipients with an accuracy rate of 95%, making it easier for the North Sumatra state Islamic university community to select students who are eligible to receive.
Little Complexities

Digital photography All over, I see and hear "save the bees!" followed by a myriad of agreement, but not much in-depth thought or attention. The problems we face must be addressed on both global and local scales, and often the smallest scale analysis is the most crucial and overlooked. Look at the little complexities in nature, because we can learn from them.
Multiview Clustering with Self Representation and Structural Constraints

Multi-view clustering, which divides objects into multiple clusters with high intra-cluster and low inter- cluster similarity for all perspectives, is of enormous significance for uncovering the mechanisms of systems. Multi-view data effectively model and characterise the underlying complex systems. Because they only consider the shared characteristics of objects or their correlation, current algorithms are criticised for their subpar performance because they ignore the heterogeneity and structural constraints of different views. A brand-new network-based method called Multi-view Clustering with Self-representation and Structural Constraints (MCSSC), which combines matrix factorization with low-rank representation of various perspectives, is presented to address these issues. In particular, a network is built for each view to reduce heterogeneity from multi-view data, converting the multi-view clustering problem into the multi-layer networks clustering problem. The MCSSC factorises network-related matrices by projecting them into a shared space and simultaneously trains an affinity graph for objects in multiple views with self-representation in order to extract the shared properties of multiple views. The structural constraint is applied to the affinity graph, where the clusters are identified, to aid with clustering. Numerous tests show that MCSSC performs noticeably better than the state-of-the-art in terms of accuracy, indicating the superiority of the suggested method
CHANGING SECURITY WITH EVERY SPIRAL

The Microsoft SDL integrates security and privacy considerations at every stage of development, assisting programmers in creating highly secure software while also addressing security compliance needs and lowering development costs (“[1]”). The software model: Spiral is crucial to this lifecycle's perspective. Microsoft has proposed the Microsoft Security Development Lifecycle (Microsoft SDL), a software development method based on the spiral model, to assist developers in producing software or applications while lowering security concerns, addressing security flaws, and even lowering development and maintenance costs. Training, requirements gathering, design, implementation, testing, deployment, and reviews are the seven phases that make up the process which is further explained in twelve different methodologies of the lifecycle model created by them. KEYWORDS: Microsoft, Software Engineering, Spiral Mode.
Akuntansi Sebagai Alat Perencanaan

This study aims to help stakeholders who use manual bookkeeping and introduce simple accounting as a planning concept in the future with Excel tools as program assistants. This study uses a research design. Analysis is used for system development in business processes, problem identification, and information needs analysis. The results achieved are the concept of recording purchases, receivables and cash disbursements to overcome the problems that exist in the company. The conclusion of the study is that the use of the Excel program can accelerate the financial process and minimize efficiency as well as the basis for further accounting programming as a planning tool in companies. Accounting is applied to basically all aspects of strategic management, so that accounting functions as a tool in strategic management.
Model Fingerprinting with Benign Inputs

Recent advances in the fingerprinting of deep neural networks are able to detect specific instances of models, placed in a black-box interaction scheme. Inputs used by the fingerprinting protocols are specifically crafted for each precise model to be checked for. While efficient in such a scenario, this nevertheless results in a lack of guarantee after a mere modification of a model (e.g. finetuning, quantization of the parameters).In this paper we propose fingerprinting scheme (coined FBI) that are resilient to significant modifications of the models. These modifications are viewed and modeled as variants. We demonstrate that benign inputs, that are unmodified images, are sufficient material for efficient fingerprinting. We leverage an information-theoretic approach to achieve a success rate of 95.2%. It is experimentally validated over an unprecedented set of more than 1,000 neural networks, while demonstrating performance improvements over a state-of-the-art fingerprinting method. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Wassertein Gan Synthesis for Time Series with Complex Temporal Dynamics: Frugal Architectures and Arbitrary Sample-Size Generation

Generating surrogate data using Deep Neural Network (DNN) has become a classic task in image processing, while DNN time series synthesis is less often considered. The present work addresses issues related to the DNN synthesis of time series, with complex, scalefree time nonreversible temporal dynamics, using Wassertein Generative Adversarial Network. Instead of proposing yet another overperforming architecture, it discusses, first, synthesis quality quantitative assessment and, second, architecture designs that both reduce, for the Generator, the number of trainable parameters by a factor of 10000 (compared to state-of-the-art architectures), at no expense in performance cost, and permit to generate time series of size longer than that of the training set, without retraining. This works can thus be considered a contribution towards sustainable Artificial Intelligence.
Deep Double Self-Expressive Subspace Clustering

Deep subspace clustering based on auto-encoder has received wide attention. However, most subspace clustering based on auto-encoder does not utilize the structural information in the self-expressive coefficient matrix, which limits the clustering performance. In this paper, we propose a double self-expressive subspace clustering algorithm. The key idea of our solution is to view the self-expressive coefficient as a feature representation of the example to get another coefficient matrix. Then, we use the two coefficient matrices to construct the affinity matrix for spectral clustering. We find that it can reduce the subspace-preserving representation error and improve connectivity. To further enhance the clustering performance, we proposed a self-supervised module based on contrastive learning, which can further improve the performance of the trained network. Experiments on several benchmark datasets demonstrate that the proposed algorithm can achieve better clustering than state-of-the-art methods.
Dual-graph co-representation learning for knowledge-Graph Enhanced Recommendation

Knowledge graphs can help improve the performance of recommender systems by mitigating sparsity and cold-start problems. However, existing approaches usually suffer from problems of domain distribution matching and cycle consistency for co-representation learning, as the representations of items from the recommendation domain and entities from the knowledge graph domain are heterogeneous and cannot consistently or effectively transfer information between domains. Moreover, previous works simply propagate in one of user-item graph and knowledge graph, and ignore the topology information of the other graph. We design a dual-graph framework, named DGCR, with two graph neural networks propagating in user-item graph and knowledge graph respectively to extract topology information from both graphs. We also propose Cycle Transfer Unit, shared in every layer as the key to DGCR to model the universal rule of domain transfer. Cycle Transfer Unit can further alleviate the problems of bias between domains and information loss during transfer.
The Algebraic Weak Factorisation System for Delta Lenses

Delta lenses are functors equipped with a suitable choice of lifts, and are used to model bidirectional transformations between systems. In this paper, we construct an algebraic weak factorisation system whose R-algebras are delta lenses. Our approach extends a semi-monad for delta lenses previously introduced by Johnson and Rosebrugh, and generalises to any suitable category equipped with an orthogonal factorisation system and an idempotent comonad. We demonstrate how the framework of an algebraic weak factorisation system provides a natural setting for understanding the lifting operation of a delta lens, and also present an explicit description of the free delta lens on a functor.
Step restriction for improving adversarial attacks

We propose an algorithm to automatically restrict the step size in the iterative optimization process with an application to adversarial attacks on speaker verification models. The proposed algorithm dynamically determines a subspace with a restriction radius r to which the Taylor approximation is applied at each iteration and then solves a linear problem within the subspace by using the projected gradient method. In experiments, we demonstrate adversarial attacks on three speaker verification models: i-vectors, SE-ResNet-34, and ECAPATDNN. We show that the degree of adversarial perturbations generated by the proposed algorithm is smaller than that generated by the conventional attack method.
Teaching Informatics: Rendering Data from Multiple Sources using Web Services

Teaching Informatics is a sub-field of informatics which focuses on imparting education using technology. During the pandemic the education system was in a thrust to convert all the materials into digital format which is referred to as the Digital Learning Object irrespective of the type of the file. The objective is to preserve the content and not the file type. In order to manage these learning objects, Digital Repository could be designed in accordance with the curriculum. In order to achieve this the latest software technology called, web services can be adapted. As the content can be accessed globally these services has to be designed with the feature of localization. Based on the kind of content designed by the user the appropriate tool for localization can be adopted.
UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works usually adopt end-to-end models and has strong dependency on Pseudo Paired Data and Original Paired Data. But when only pre-training on Pseudo Paired Data, previous models have negative effect on correction. While fine-tuning on Original Paired Data, the source side data must be transcribed by a well-trained ASR model, which takes a lot of time and not universal. In this paper, we propose UCorrect, an unsupervised Detector-Generator-Selector framework for ASR Error Correction. UCorrect has no dependency on the training data mentioned before. The whole procedure is first to detect whether the character is erroneous, then to generate some candidate characters and finally to select the most confident one to replace the error character. Experiments on the public AISHELL-1 dataset and WenetSpeech dataset show the effectiveness of UCorrect for ASR error correction: 1) it achieves significant WER reduction, achieves 6.83% even without fine-tuning and 14.29% after fine-tuning; 2) it outperforms the popular NAR correction models by a large margin with a competitive low latency; and 3) it is an universal method, as it reduces all WERs of the ASR model with different decoding strategies and reduces all WERs of ASR models trained on different scale datasets.
Dynamic Alignment Mask CTC: Improved Mask CTC With Aligned Cross Entropy

Because of predicting all the target tokens in parallel, the non-autoregressive models greatly improve the decoding efficiency of speech recognition compared with traditional autoregressive models. In this work, we present dynamic alignment Mask CTC, introducing two methods: (1) Aligned Cross Entropy (AXE), finding the monotonic alignment that minimizes the cross-entropy loss through dynamic programming, (2) Dynamic Rectification, creating new training samples by replacing some masks with model predicted tokens. The AXE ignores the absolute position alignment between prediction and ground truth sentence and focuses on tokens matching in relative order. The dynamic rectification method makes the model capable of simulating the non-mask but possible wrong tokens, even if they have high confidence. Our experiments on WSJ dataset demonstrated that not only AXE loss but also the rectification method could improve the WER performance of Mask CTC.
APGP: Accuracy-Preserving Generative Perturbation for Defending Against Model Cloning Attacks

Well-trained Deep Neural Networks (DNNs) are valuable intellectual properties. Recent studies show that adversaries only with black-box query access can steal the functionality of DNNs by using knowledge distillation (KD) techniques. In this paper, we propose a novel formulation to defend against model cloning attacks. Then we implement our defense as a plug-and-play generative perturbation model, dubbed as Accuracy-Preserving Generative Perturbation (APGP). Our method is the first to effectively defend against KD-based model cloning without damaging model accuracy. Numerous experiments demonstrate the effectiveness of our defense across different datasets and DNN model cloning attacks, and the advances compared to existing methods.
Improving Contrastive Learning of Sentence Embeddings from AI Feedback

Contrastive learning has become a popular approach in natural language processing, particularly for the learning of sentence embeddings. However, the discrete nature of natural language makes it difficult to ensure the quality of positive and negative sample pairs generated through data augmentation methods. Although supervised contrastive learning can produce more accurate sample pairs with human feedback labels, it still lacks fine-grained training signals. In this paper, we propose to improve \textbf{C}ontrastive \textbf{L}earning of sentence embeddings from \textbf{AI} \textbf{F}eedback \textbf{(CLAIF)}. Our method utilizes AI feedback from large pre-trained language models (LLMs) to construct sample pairs with fine-grained sample similarity scores to improve contrastive learning. Besides, we combine human feedback and AI feedback to provide better supervision signals for supervised contrastive learning of sentence embeddings. Experimental results show that our method achieves state-of-the-art performance on several semantic textual similarity (STS) and transfer learning tasks compared to other unsupervised and supervised contrastive learning methods.
Robust Knowledge Distillation from RNN-T Models with Noisy Training Labels Using Full-Sum Loss

This work studies knowledge distillation (KD) and addresses its constraints for recurrent neural network transducer (RNN-T) models. In hard distillation, a teacher model transcribes large amounts of unlabelled speech to train a student model. Soft distillation is another popular KD method that distills the output logits of the teacher model. Due to the nature of RNN-T alignments, applying soft distillation between RNNT architectures having different posterior distributions is challenging. In addition, bad teachers having high word-error-rate (WER) reduce the efficacy of KD. We investigate how to effectively distill knowledge from variable quality ASR teachers, which has not been studied before to the best of our knowledge. We show that a sequence-level KD, full-sum distillation, outperforms other distillation methods for RNN-T models, especially for bad teachers. We also propose a variant of full-sum distillation that distills the sequence discriminative knowledge of the teacher leading to further improvement in WER. We conduct experiments on public datasets namely SpeechStew and LibriSpeech, and on in-house production data.