Reviewerly/exHarmony huggingface dataset:
An Analysis of Instructional Design Model and Students’ English Achievement

Teaching English in Indonesia should get attention to face the era of globalization. The initial survey shows that students still face problems or difficulties in learning English, especially productive skills. So, the study aimed to investigate the kinds of instructional design models (IDM) conducted by lecturers, to expose the stages of IDM, and to describe the learning achievement of the students’ English productive skills. The subjects of this study were lecturers who taught English productive skills, namely speaking and writing skills, and the fourth semester students who had programed these courses. The procedures were (1) a preliminary survey of learning problems, (2) designing research instruments, (3) validating instruments, (4) collecting data through instruments, and (5) analyzing and interpreting the results of data analysis. The instrument was a questionnaire that aimed to obtain information from lecturers, while the test aimed to measure the achievement of students’ English productive skills. The results showed that the lecturers used several kinds of IDM with various stages, and the students’ English achievement was in good classification for speaking skills and fair for writing skills. Therefore, the lecturers were considerably required to choose the kinds and various stages of IDM in their teaching and learning processes.
PrivSketch: A Private Sketch-Based Frequency Estimation Protocol for Data Streams

Local differential privacy (LDP) has recently become a popular privacy-preserving data collection technique protecting users' privacy. The main problem of data stream collection under LDP is the poor utility due to multi-item collection from a very large domain. This paper proposes PrivSketch, a high-utility frequency estimation protocol taking advantage of sketches, suitable for private data stream collection. Combining the proposed background information and a decode-first collection-side workflow, PrivSketch improves the utility by reducing the errors introduced by the sketching algorithm and the privacy budget utilization when collecting multiple items. We analytically prove the superior accuracy and privacy characteristics of PrivSketch, and also evaluate them experimentally. Our evaluation, with several diverse synthetic and real datasets, demonstrates that PrivSketch is 1–3 orders of magnitude better than the competitors in terms of utility in both frequency estimation and frequent item estimation , while being up to $$\sim $$ 100 $$\times $$ faster.
Parallel Pattern Enumeration in Large Graphs

Graphlet enumeration is a fundamental problem to discover interesting patterns hidden in graphs. It has many applications in science including Biology and Chemistry. In this paper, we present a novel approach to discover these patterns with queries, in a parallel database system. Our solution is based on an efficient partitioning strategy based on randomized vertex coloring, that guarantees perfect load balancing and accurate graphlet enumeration (complete and consistent). To the best of our knowledge, our work is the first to provide an abstract and efficient database solution with queries to enumerate both 3-vertex and 4-vertex patterns on large graphs.
Bitwise Algorithms to Compute the Transitive Closure of Graphs in Python

The transitive closure (TC) of a graph is a core problem in graph analytics. There exist many High Performance Computing (HPC) and database solutions to solve the TC problem for "big graphs". However, they generally require the graph to fit in main memory and they require converting into specific binary file formats. To solve such limitations, this paper presents a novel solution to solve TC within the Python library ecosystem, combining HPC techniques and database system algorithms. We introduce two complementary algorithms removing HPC memory limitations: (1) an algorithm that efficiently converts edges into bit vectors and (2) a database-oriented, bit-vector, highly parallel matrix algorithm, which processes the graph in blocks. An experimental evaluation shows our solution provides better performance than state-of-the-art Python libraries.
Meningkatkan Kemampuan Membaca Permulaan Anak Usia Dini dengan Media Kartu Huruf Kelompok B TK Negeri Pembina Mataram

This classroom action research aims to determine the improvement of Early Childhood Reading Skills with Media Cards for Group B Kindergarten Negeri Mataram Pembina Odd Semester for the 2021/2022 Academic Year. The subjects of this study were 15 students of group B of the State Pembina Mataram Kindergarten, consisting of 8 female students and 7 male students. This research uses action research in two cycles consisting of four stages, namely: design, activity and observation, reflection. The results based on research and discussion can be opened that the initial reading ability in children aged 5-6 years TK Negeri Pembina Mataram can be improved using picture word cards media. This can be proven from the results of increasing the original reading ability. In the initial conditions the average achievement was 70.56% and an increase of 13.34% so that in Cycle I it reached 71.11% and in Cycle I to Cycle II it increased by 33.33% so that in Cycle II it reached a score of 100 %.
The Effectiveness of Using Dialogue Journal Writing to Enhance Students’ Writing Skills at Lembaga Yasin alSys

This research investigated how the effectiveness of using the Dialogue Journal writing (dialogue journal writing) toward writing skills in recount text at Lembaga Kursus Yasin alSys students. This study concerned on find out the effectiveness of dialogue journal writing and explaine the five aspects of writing skill as proposed by Anderson, i.e. grammar, vocabulary, mechanics, fluency and form. This research is classified as experimental study which was done with the participation of 26 students class as the object of the study. The data obtained during the research were in quantitative and qualitative forms. The qualitative data were acquired from the field notes, interview transcripts, and photographs. The quantitative ones were collected from the results of the students’ pre-test and post-test and also from the students’ texts written in their dialogue journal writing books which were analyzed to get the mean scores to be compared and then determine the gain scores . In regard to the actions performed in two cycles, the use of dialogue journal writing was effective in improving the students’ skills in recount texts based on the following evidences. First evidence, the researcher found that from the data description demonstrated 0.004 score in Asymp Sig (2-tailed), because the Asymp Sig (2-tailed) score is lower than 95% the confidence interval that is 0.05, then the data is significant. Second evidence, from six male and female responses consisted lowest to highest sampling toward dialogue journal writing was found that there were five aspects appeared, i.e grammar, vocabulary, mechanic, fluency, and form. Following the result of this research, it could contribute to language teaching especially in writing sill.
Atmospheric Electricity: Sources, Dynamics, and Impacts on the Environment

This study is a review of the sources, dynamics and impacts of atmospheric electricity on the environment.Atmosphere ions are responsible for a weak flow of atmospheric electricity.These atmospheric ions are generated from different sources such as cosmic rays, radioactive gases, air radiolysis, lightning, waterfall, corona discharge, combustion and radioactive effluent from nuclear tests.These ions have different mobility depending on their sizes and the kind of charges they carry.The potential gradient between the atmospheric air and the earth's surface causes ions to move from the atmosphere, which is the positively charged region to the earth surface, which is the negatively charged region.The electric field in the air near the earth surface is 100 V/m.Negative small ions have higher mobility than positive small ions.High concentration of positive air ions poses a serious threat to human health, but negative ions promote the well-being of humans and living organisms.
Performance bounds of complex-valued nonlinear estimators in learning systems

The widely linear estimator offers theoretical and practical advantages over the strictly linear counterpart for the processing of the generality of complex signals. When applying the widely linear technique to a nonlinear estimator of learning systems, the resulting augmented nonlinear estimator also empirically produces performance advantages over the standard (strictly) nonlinear estimator. However, the theoretical justification for this observation is still lacking in the literature. To this end, we establish performance bounds of the augmented nonlinear estimator over the strictly nonlinear one in this paper. By expanding the analytic nonlinear function with the first-order Taylor formula, we obtain the closed form of the MSE difference △MSE and that of the complementary MSE difference △CMSE for the two estimators. By jointly considering the MSE and the complementary MSE, it is theoretically established that △MSE>0 always holds for noncircular input signals, and MSE performance advantages always exist in both the real and imaginary channels over its strictly nonlinear counterpart except for the jointly circular case. The simulation results validate our theoretical analysis.
UGC quality assessment: exploring the impact of saliency in deep feature-based quality assessment

The volume of User Generated Content (UGC) has increased in recent years. The challenge with this type of content is assessing its quality. So far, the state-of-the-art metrics are not exhibiting a very high correlation with perceptual quality. In this paper, we explore state-of-the-art metrics that extract/combine natural scene statistics and deep neural network features. We experiment with these by introducing saliency maps to improve perceptibility. We train and test our models using public datasets, namely, YouTube-UGC and KoNViD-1k. Preliminary results indicate that high correlations are achieved by using only deep features while adding saliency is not always boosting the performance. Our results and code will be made publicly available to serve as a benchmark for the research community and can be found on our project page.
Span prompt dense passage retrieval for Chinese open domain question answering

Dense passage retrieval is a popular method in information retrieval recently, especially in open domain question answering. It aims to retrieve related articles from massive passages to answer the question. Retriever can increase retrieval speed with less loss of accuracy compared to other methods. However, the pretrained language models used in recent research are often ineffective in semantic embedding, which will reduce accuracy. In addition, we find that contrastive learning will diverge the representation space, and Siamese models with independent parameters on both sides will decrease generalization performance. Therefore, we propose span prompt dense passage retrieval (SPDPR) based on span mask prompt tuning and parameter sharing in Chinese open-domain dense retrieval. This model can generate more efficient representation embeddings and effectively counteract the separation tendency between positive samples. We evaluate the effectiveness of SPDPR in DYKzh, as well as two Chinese datasets. SPDPR surpasses all SOTAs implemented in DYKzh and achieves a competitive result in other datasets.
Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5

The images captured using UAVs during inspection often contain a great deal of small targets related to transmission lines. These vulnerable elements are critical for ensuring the safe operation of these lines. However, due to various factors such as the small size of the targets, low resolution, complex background, and potential target aggregation, achieving accurate and real-time detection becomes challenging. To address these issues, this paper proposes a detection algorithm called P2-ECA-EIOU-YOLOv5 (P2E-YOLOv5). Firstly, to tackle the challenges posed by the issues of complex background and environmental interference impacting small targets, an ECA attention module is integrated into the network. The module effectively enhances the network’s focus on small targets, while concurrently mitigating the influence of environmental interference. Secondly, considering the characteristics of small target size and low resolution, a new high-resolution detection head is introduced, making the network more sensitive to small targets. Lastly, the network utilizes the EIOU_Loss as the regression loss function to improve the positioning accuracy of small targets, especially when they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P (precision) of 96.0% and an average accuracy (mAP) of 97.0% for small-target detection in transmission lines.
Research on non-smooth signal identification algorithm for electronic communication in a two-way relay network

Abstract To better allow accurate identification of non-smooth signals in two-way relay networks. In this paper, an algorithm based on the two-way relay network system model, the RLS algorithm, is proposed. The algorithm introduces the recursive least squares principle algorithm to the basic cost function of nonsmooth signal identification, optimizes the signal identification model by exponential weighting, and uses the time-varying variance matrix and the optimal identification data separation matrix to make real-time estimates of the new cost function for obtaining nonsmooth signals. And a new index, the PI index, is introduced to define whether the non-stationary signal is stable and reliable. Through two sets of comparison tests, we can learn that the RLS algorithm based on the two-way relay network system is better than other algorithms in the identification of non-stationary signals. It shows that the RLS algorithm can better identify and manage the information flow in a two-way relay network, make the non-stationary signal more stable, and further improve the convergence and convergence speed of the non-stationary signal to provide a better information interaction experience for both sides of electronic communication.
Exact/Partial Cipher-text Retrieval on the Encrypted Cloud Database using SCKHA Algorithm

Searchable encryption techniques are intelligent tools that attempt to solve the challenge of querying stored data on untrusted cloud servers while preserving data confidentiality.This paper proposes simple, powerful, and practical Searchable Symmetric Encryption (SSE) scheme which capable of executing SQL (Structural Query Language) queries over encrypted cloud databases.Our scheme improves the efficiency of the search over encrypted data by using the SCKHA (Symmetric Cipher based on Key Hashing Algorithm) algorithm instead of using the bloom filter technique which causes the problem of the false positives.We built a complicated index structure for the encrypted data by combining the encryption of the data unit using Advanced Encryption Standard (AES) scheme with the encryption of SCKHA scheme for the same data to ensure data security.We also proposed a complete framework for implementing our construction, validating its practicality to achieve exact and partial search over encrypted SQL databases.Furthermore, we performed experiments on adult data sets and analyzed the experimental results in terms of computation time for index generation, trapdoor generation and average search.Experiments showed that the taken time for generating the trapdoor and the keyword search in our scheme is faster than other schemes.We also analyzed the security of our scheme and evaluated its performance experimentally and theoretically.Finally, we provide features for evaluating single, conjunctive, and disjunctive queries.
User Experience Design for Nutrition Information Applications using Design Thinking

A healthy diet serves as one of the aspects of one's fitness and understanding caloric value. The present study aims to design a user experience for mobile nutrition apps using Augmented Reality technology. Solutions are designed using design thinking stages, including empathize, define, ideate, prototype, and testing. Data related to reasons, frustration, goals, and target users are collected at the empathize stage. At the define stage, the empathy map and Personas are created. A prospective user journey map and information architecture are generated in the ideating stage. Wireframe and mockups are generated in the prototype stage. Finally, usability testing (i.e., task scenarios) evaluates the learnability (100%) and efficiency (0.067 goals/sec) aspects, and SUS assesses the satisfaction aspect. The satisfaction aspect score was 85, indicating a high acceptability level, B grade scale, and excellent adjective rating.
Exploring the Impact of Integrated STEAM Education in Early Childhood and Primary Education Teachers

In recent years, Integrated STEAM education has been positioned as an appropriate educational approach to face the challenges of today’s society. Nevertheless, to implement this approach in the classroom requires trained and productively teachers. From this perspective, the objective of this article, is to analyse the impact of integrated STEAM education on teachers through their perceptions in a group of 31 in-service Chilean teachers teaching both in early childhood and primary education, with the aim to obtain a diagnosis for the future design of training processes in STEAM. To obtain data, the adaptation of an instrument that evaluates teachers’ perceptions of integrated STEM education has been validated. Specifically, three dimensions of this instrument have been analysed: familiarity, attitudes, and confidence of teachers about this educational approach. The results show that: (1) Familiarity: only 6% of the participants consider themselves very familiar with the approach; (2) Attitude: 97% of the participants declare themselves to be very willing or moderately willing to learn more about STEAM; (3) Confidence: the data shows, in general, a moderate level of confidence to implement this approach. In relation to the impact of STEAM integrated education in the Chilean teacher’s perceptions, it can be noted that despite the familiarity of the participants is very low, the scenario seems encouraging, because they show a very positive attitude towards this approach, together with a moderate confidence to implement it. Even though these early findings are encouraging, they also show that the teacher’s formation is fundamental, because its impact in the teachers, reverberates directly in a positive impact on the students. Consequently, it is necessary to promote the professional development of teachers in STEAM through training programs that improve knowledge about the approach and deliver the necessary tools to implement it in the classroom.
Innovative Research on the Application of the OBE Concept Cheerleading Teaching in the Smart Campus Environment

With the continuous development of information technology, a smart campus environment has become an important direction of modern education development. Based on OBE (Outcome-Based Education), this study discusses how to apply a smart campus environment to cheerleading teaching. By designing an intelligent teaching system and combining the characteristics of cheerleading teaching, the personalized and independent teaching process is realized. The results show that the application of the OBE concept in cheerleading teaching in a smart campus environment can improve students' learning interest and motivation and promote their all-around development.
Text Enhancement: Scene Text Recognition in Hazy Weather

Although deep learning-based methods for scene text recognition have achieved remarkable results on conventional datasets, recognizing text images under adverse weather conditions with poor visibility remains challenging. To address this problem, we propose a text image enhancement network that can be embedded into a scene text recognizer in a pluggable manner. This network comprises multiple sets of digital image processing (DIP) units, which are composed of differentiable filters whose parameters are estimated by a parallel sequential residual block (PSRB). The PSRB is a novelty designed module for extracting the sequence information of text image in a parallel manner. The whole network can be trained end-to-end with a text-image enhancement module and a recognition module. Extensive experiments on synthetic and real-world benchmarks demonstrate that our approach significantly improves the performance of popular text recognizers in adverse weather conditions.
FTDNet: Joint Semantic Learning for Scene Text Detection in Adverse Weather Conditions

In recent years, convolutional neural network (CNN)-based scene text detection methods have been extensively studied and obtained successful results in public datasets. However, scene text detection in adverse weather conditions suffers from poor visibility. In this paper, we use a multi-task learning approach to resolve this issue. We construct a foggy text detection network (FTDNet) composed of dual subnetworks: a text detection subnetwork and a visibility enhancement subnetwork. We employ DBNet as the text detection subnetwork, which shares the feature extraction layers for both two subnetworks. And we design a feature visibility enhancement (FVE) module for visibility enhancement subnetwork. In order to enable joint learning of multi-task networks, a novelty loss function called the mask dehazing loss is applied. This method achieved state-of-the-art results in terms of detection on both synthetic datasets and real-to-world datasets.
DSS: Synthesizing Long Digital Ink Using Data Augmentation, Style Encoding and Split Generation

As text generative models can give increasingly long answers, we tackle the problem of synthesizing long text in digital ink. We show that the commonly used models for this task fail to generalize to long-form data and how this problem can be solved by augmenting the training data, changing the model architecture and the inference procedure. These methods use contrastive learning technique and are tailored specifically for the handwriting domain. They can be applied to any encoder-decoder model that works with digital ink. We demonstrate that our method reduces the character error rate on long-form English data by half compared to baseline RNN and by 16% compared to the previous approach that aims at addressing the same problem. We show that all three parts of the method improve recognizability of generated inks. In addition, we evaluate synthesized data in a human study and find that people perceive most of generated data as real.
Network Public Opinion Monitoring Method Based on Machine Learning

To ensure the public safety of social networks, this paper designs and implements a public opinion monitoring system based on Sina Weibo. A design scheme based on machine learning and word segmentation technology is proposed. Jieba Chinese word segmentation tool is used to realize text preprocessing, text classification and tendency analysis. When classifying according to the emotional weight of emotional words in the text, LSTM model is used to improve the analysis efficiency and accuracy of machine learning. At the same time, the web page library update strategy for the public opinion analysis system is formulated to ensure the freshness of the local web page library. The experimental test analysis shows that such scheme can classify the emotional tendency of microblog content, and find out the news with high attention and strong emotion of netizens, and make the corresponding trend prediction, which achieves the purpose of public opinion monitoring.