Reviewerly/exHarmony huggingface dataset:
Data Placement Strategy of Data-Intensive Workflows in Collaborative Cloud-Edge Environment

With the continuous development and integration of mobile communication and cloud computing technology, cloud-edge collaboration has emerged as a promising distributed paradigm to solve data-intensive workflow applications. How to improve the execution performance of data-intensive workflows has become one of the key issues in the collaborative cloud-edge environment. To address this issue, this paper built a data placement model with multiple constraints. Taking deadline and execution budget as the core constraints, the model is solved by minimizing the data access cost of workflow in the cloud-edge clusters. Subsequently, an immune genetic-particle swarm hybrid optimization algorithm (IGPSHO) is proposed to find the optimal replica placement scheme. Through simulation, compared with the classical immune genetic algorithm (IGA) and particle swarm optimization (PSO), the IGPSHO has obvious advantages in terms of workflow default rate, time-consuming ratio, and average execution cost when the workflow scale is large.
Defending Machine Reading Comprehension against Question-Targeted Attacks

Machine reading comprehension (MRC) is used in open-domain question answering (QA) to obtain precise replies from retrieved passages. Meanwhile, pre-trained language models (PLM) have achieved human-level accuracy in various MRC benchmarks thanks to the success of deep learning. However, adversarial attack task has shown that models are easily misled towards small perturbations. In this paper, a model-agnostic adversarial framework, named DQAAT, is proposed to defend against question-targeted attacks. Considering the attacks are diverse from sentence-level to word-level, we generate multi-level adversarial samples for adversarial training, where a locator for finding vulnerable cues based on reinforcement learning is applied to improve attack effects. Experiments on 7 representative PLMs show that DQAAT can defend MRC model against various attacks while maintaining a high level of standard accuracy.
Evolutionary Multitasking with Search Direction Alignment

Evolutionary Multitasking (EMT) aims to tackle multiple tasks simultaneously with knowledge transfer for an improved overall performance. Unfortunately, the performance is vulnerable to negative transfer, when the involved tasks share a low similarity. Some domain adaptation (DA) techniques have been designed to enhance the similarity between tasks by the means of inter-task individual pairing or distribution alignment, but they do not explicitly exploit the relation between the search directions of different task populations. To this end, this study proposes a novel DA technique based on search direction alignment (SDA), which derives the inter-task transformation by determining and aligning the evolutionary directions of different task populations. After that, SDA is embedded into a pioneer EMT algorithm, i.e., MFEA, forming the SDA-MFEA. In SDA-MFEA, with the help of SDA, different task populations can possess the same evolutionary trend and thus positive knowledge transfer is more likely to occur during the optimization process. Extensive experiments conducted on a widely-used multitasking benchmark suite show that SDA-MFEA outperforms the canonical MFEA and four state-of-the-art DA-based MFEA variants, indicating the effectiveness of SDA.
System Information And Application For Official Marriage Trials For Polri &amp; PNS Members At Polda Bengkulu

One of the stages for forming a family within the Police environment, is that each prospective bride and groom will undergo a marriage coaching session. At the Bengkulu Regional Police level, this official marriage trial is under the auspices of the Human Resources Bureau (HR). Currently, the management of files and scheduling of service marriage sessions within the Bengkulu Police is still being done manually by physically collecting the required files and processing them by the admin. The purpose of this practical work is to design and build a system that can assist in the process of applying for official marriage hearings for members of the Police &amp; Civil Servants at the Bengkulu Regional Police. The system development method in this study uses the waterfall model. Process design on the system using DFD (Data Flow Diagram), database design using ERD (Entity Relation Diagram) and database design using MySQL.The result of this research is an information system and application for official marriage hearings for members of the National Police &amp; Civil Servants at the Bengkulu Regional Police which can assist in the process of filing for official marriage hearings. And conduct testing of the system using the blackbox method.
Post-Quantum Secure Identity-Based Proxy Blind Signature Scheme on a Lattice

Blind signatures have been widely applied when privacy preserving is required, and the delegation of blind signature rights and a proxy blind signature (Proxy-BS) become necessary when the signer cannot sign. Existing Proxy-BS schemes are based on traditional cryptographically hard problems, and they cannot resist quantum attacks. Moreover, most current Proxy-BS schemes depend on public key infrastructure (PKI), which leads to high certificate storage and management overhead. To simplify key management and resist quantum attacks, we propose a post-quantum secure identity-based proxy blind signature (ID-Proxy-BS) scheme on a lattice using a matrix cascade technique and lattice cryptosystem. Under the random oracle model (ROM), the security of the proposed scheme is proved. Security shows that the proposed scheme assures security against quantum attacks and satisfies the correctness, blindness, and unforgeability. In addition, we apply the ID-Proxy-BS scheme on a lattice to e-voting and propose a quantum-resistant proxy e-voting system, which is resistant to quantum attacks and achieves the efficiency of e-voting.
THE REASONS AND METHODS FOR TEACHING PRONOUNCIATION WITHOUT IMITATION

We teach pronunciation using 'listen and repeat' exercises because that's how it's always been done, and because common sense seems to say that it should work even if it doesn't.The generally poor results we get in the classroom are usually ascribed to listening problems.Instead, they are more likely to be the result of two basic misconceptions: the widespread notions (1) that speech sounds (and timing phenomena) can be learnt by imitation, and (2) that children do this when learning L1.From a theoretical perspective these notions are both dubious.With respect to the second, not only is there no evidence that children learn to pronounce this way, but evidence is accumulating that they do not.It is more likely that their bootstrap into the sound system is the imitative exchanges in infancy where their caregivers reflect their utterances back to them, reformulated into L1syllables.This basic paradigm can be reworked for the classroom, and has been successfully applied for many years by teachers using Gattegno's Silent Way.His approach has been significantly enhanced over that period and can be applied in conjunction with non-Silent Way approaches to grammar etc, and with intermediate and advanced students.
Research on Blended Learning Method for College Programming Courses Based on Case-Based Instruction—Taking the Construction of Intelligent Robot Project Based on C51 Microcontroller as an Example

As a fundamental compulsory course, C language programming is characterized by its numerous and intricate syntax rules, deep theoretical basis, diverse and abstract knowledge points.When using the traditional teaching mode, students generally find the course abstract and difficult to learn, resulting in poor teaching effectiveness.To address the above problems, this paper establishes a task-driven "theory-practice integration" project-based teaching system, with the construction of an intelligent robot project based on C51 microcontroller as the carrier.Through project-based learning, knowledge on the peripheral interface characteristics, internal structure principles, application design methods, and C language programming of the microcontroller are taught to students, so that they can learn by doing and do by learning, completely breaking the traditional teaching methods and system structures, and stimulating students' interest in learning, enthusiasm and initiative, thereby improving the classroom teaching efficiency.
PENGEMBANGAN MEDIA PEMBELAJARAN BAHASA INGGRIS BAGI KELOMPOK PELAYANAN ANAK DAN REMAJA JEMAAT IMANUEL BUHUN, DESA LETBAUN, KECAMATAN SEMAU – KABUPATEN KUPANG

Realizing the importance of English in this modern era, the community service team has developed a learning program in a practical and interesting way that in learning English. Working with religious institutions was one of the methods to support government programs. The program was held in Letbaun Village, Semau District in Kupang Regency. The problem for most Sunday school children in this area was their lack of interest in learning English and limited access to telecommunication networks, so they were more guided by manual learning resources or textbooks. The underlying thing was the lack of vocabulary and grammar in English. The team conducted practical, creative, and fun English learning materials and videos to attract interest in learning English. The book was equipped with pictures and vocabulary relating to general English materials such as greetings, self-introduction, parts of the body, and names of days and months, also provided some Bible-themed materials (God’s creation, the ten plagues in Egypt, the Lord's Prayer in English, the name of the books in English). The book was provided with the aims, directions for Sunday School Teachers, and exercises. There were still several things that have not been achieved such as evaluation of learning outcomes. To complete this activity, the team planned an evaluation to be carried out in the coming semesters regarding the use of products.Keywords : bible-themed, community service, grammar, Sunday school, vocabulary
Pengembangan Media Pembelajaran Poster Menggunakan Smartphone Untuk Meningkatkan Pemahaman Sejarah Siswa Pada Pembelajaran Sejarah Kelas X IPS 5 SMA Negeri 15 Batam

This research produces a learning tool or media which is to improve students' understanding of history in learning history with indicators that students are capable of interpreting, exemplifying, classifying, summarizing, concluding, differentiating, and explaining. This development research uses the ADDIE model with the stages of Analysis, Design, Development, Implementation, Evaluation. The validation results of material experts with an average score of 4.53, question experts with an average score of 4.6, media experts with an average score of 4.83 in the ''Very Eligible'' category, while for the assessment of class X IPS 5 students of SMA Negeri 15 Batam as a user obtained a value with an average score of 4.65 ''Very Eligible'' used in the learning process. The results of the tests carried out by students at the pretest with a value of 57.8 and continued with the posttest with a result of 85.7. That way it shows students can recall the material that has been studied and answer correctly the questions given by the teacher. The Normalized Gain value was 68.9 in the "Effective Enough" category. From this information, it shows that the use of posters to increase students' understanding of history is ''Quite Effective'' used in history learning.
Hybrid photonic deep convolutional residual spiking neural networks for text classification

Spiking neural networks (SNNs) offer powerful computation capability due to its event-driven nature and temporal processing. However, it is still limited to shallow structure and simple tasks due to the training difficulty. In this work, we propose a deep convolutional residual spiking neural network (DCRSNN) for text classification tasks. In the DCRSNN, the feature extraction is achieved via a convolution SNN with residual connection, using the surrogate gradient direct training technique. Classification is performed by a fully-connected network. We also suggest a hybrid photonic DCRSNN, in which photonic SNNs are used for classification with a converted training method. The accuracy of hard and soft reset methods, as well as three different surrogate functions, were evaluated and compared across four different datasets. Results indicated a maximum accuracy of 76.36% for MR, 91.03% for AG News, 88.06% for IMDB and 93.99% for Yelp review polarity. Soft reset methods used in the deep convolutional SNN yielded slightly better accuracy than their hard reset counterparts. We also considered the effects of different pooling methods and observation time windows and found that the convergence accuracy achieved by convolutional SNNs was comparable to that of convolutional neural networks under the same conditions. Moreover, the hybrid photonic DCRSNN also shows comparable testing accuracy. This work provides new insights into extending the SNN applications in the field of text classification and natural language processing, which is interesting for the resources-restrained scenarios.
HKG: A Novel Approach for Low Resource Indic Languages to Automatic Knowledge Graph Construction

Knowledge graph (KG), a visual representation of text data as a semantic network, holds enormous promise for the development of more intelligent robots. It leads to significant potential solutions for many tasks like question answering, recommendation, and information retrieval. However, this area is confined to using English text only. Since low-resource languages are now being used in the world of AI, it is necessary to develop a semantic network for them as well. In this research work, the authors provide state-of-the-art techniques for automatic knowledge graph construction for the Hindi language, which is still unexplored in ontology. Constructing a knowledge graph faces several hurdles and obstacles in the linguistic domain, primarily when it deals with the Hindi language. With an emphasis on the Indian perspective, this research intends to introduce a novel approach ‘HKG’ for knowledge graph construction framework for Hindi. It also implements the LSTM model to evaluate the accuracy of newly constructed knowledge graphs and compute different evaluation metrics such as accuracy and F1-score. This knowledge graph evaluates the accuracy of 87.50 using Doc2Vec word embedding with a train-test split of 7:3.
Detection of Outlier in Time Series with Application to Dohuk Dam Using the SCA Statistical System

Outliers are data points or observations that stand out significantly from the rest of the group in terms of size or frequency.They are also referred to as "abnormal data".Before fitting a forecasting model, outliers are often eliminated from the data set, or if not removed, the forecasting model is altered to account for the presence of outliers.The first scenario covered in the study is the detection of outliers when the parameters have been established.Second, where there are unidentified parameters.This article mentions a number of causes for outlier correction and detection in time series analysis and forecasting.For the objective of the study, a time series of the volume of water entering the Dohuk dam reservoir in Dohuk city was used.The study arrived at the following conclusions after conducting their research: first, whenever the critical value increased, the value of residual standard error (with outlier adjustment) increased.Second, the quantity of outlier values dropped each time the critical value was raised.Third, forecasts with outlier correction perform better than forecasts without outlier adjustment when outliers are present.
The Development of Science Teaching Materials Based on Android Mobile to Improve Critical Thinking Skills in Junior High School Students

The development of science teaching materials based on android mobile is based on the need for teaching materials that are suitable for 21st-century learning which is synonymous with the use of technology, information, and communication. This research aims to know the validity, practicality, and effectiveness of science teaching materials based on android mobile in improving students' critical thinking skills. The manufacture of teaching materials also aims to improve the low critical thinking skills of junior high school students. The research conducted is development research with the ADDIE development model. The subject of the study was a grade 7A student of SMPN 2 Maesan, Bondowoso. This study used instruments in the form of validation sheets, practicality sheets, and critical thinking skill tests. Data analysis techniques used in research consist of validity test analysis, practicality test analysis, and effectiveness test analysis. The results showed that (1) the percentage of validity of science teaching materials based on android mobile is 92% of the average of three validators, (2) the percentage of the practicality of science teaching materials based on android mobile is 86%, (3) the effectiveness score of science teaching materials based on android mobile 86%, (4) the effectiveness score of science teaching materials based on android mobile is 0.69 obtained from the N-gain test and 75% from the student response questionnaire so that it can be concluded that science teaching materials based on android mobile are valid, practical and effective for improving the critical thinking skills of junior high school students.
Research on Customer Experience of Hotel Robot Service in Hangzhou through the Lens of Experience Economy Model

This study aims to reveal the key dimensions of customer experience of hotel robot service in China through the lens of Experience Economy Model.Qualitative research was conducted by analyzing 10,024 customer reviews from four typical hotels in Hangzhou.The study extended the traditional dimensions of "entertainment," "escapism," "esthetics," and "education" in the Experience Economy Model to include the dimensions of "facilities," "emotions," and "efficiency."The study not only provides a more complete and systematic description of customer experience of hotel robot service but also has important empirical significance for the application of Experience Economy Model.
Target Tracking with Non-standard Measurements

Multi-target tracking is the process of estimating the number of targets and their states based on imperfect sensor measurements contaminated by noises, missed detections and false alarms. The key challenges lie in the uncertainty of the number of targets and the uncertainty of the correlation between the measurement set and targets. The multi-target tracking is expected to filter out the influence of these three aspects in order to obtain an accurate estimation of true target states. A Bayesian solution to this problem requires a model describing the association between measurements and the underlying (unknown) target states. The vast majority of traditional trackers (such as the JPDA or MHT tracker) use the so-called standard measurement model: each target generates at most one measurement and each measurement originates from at most one target at a given time, which is the well-known point target model. In fact, in addition to traditional trackers, the RFS-based multi-target tracking algorithms introduced in the previous chapters are also based on the standard measurement model. This model assumption greatly simplifies the development of multi-target trackers and is also consistent with most real-world situations. However, in some special cases, it is an unrealistic representation of the real measurement process, typically manifested in extended target tracking and target tracking with merged measurement.
Probability Hypothesis Density Filter

Although the particle multi-target filter introduced in the previous chapter provides a general solution for the multi-target Bayesian recursion, due to the combinatorial complexity of multi-target Bayesian recursion, the computational load is too heavy. Hence, this filter is typically only suitable for relatively ideal scenarios where the number of targets is small or the signal to noise ratio is high for example.
Cardinalized Probability Hypothesis Density Filter

Although the PHD filter enjoys the significant advantage of low computational workloads, it potentially assumes that the posterior multi-target distribution approximately conforms with the Poisson distribution.
Multi-Bernoulli Filter

Unlike the PHD and CPHD filters, which are respectively the first- and second-order moment approximations of the multi-target Bayesian recursion, the multi-Bernoulli (MB) filter is the probability density approximation of the complete multi-target Bayesian recursion.
Recognition of Offline Handwritten Gujarati Consonants Having Loop Feature Using GHCIRS

The Gujarati language has a vast character set that includes consonants, vowels, numerals, and conjunct consonants. The features of the characters make them different from each other. There are a total of 34 consonants in the Gujarati language which makes language efficient for oral and written communication. Few consonants have a feature like one or two loops with curves or vertical lines; whereas others have features like vertical lines and curves only. A total of eight (8) consonants have feature like either one or two loops. In this research paper the researcher has taken into consideration the said eight (8) joint and dis-joint consonants. The researchers here aim to identify these eight (8) character consonants. A total of 10,400 samples written by different individuals have been collected for the eight curved feature Gujarati consonants. The accuracy achieved after applying Gujarati Handwritten Character Recognition (GHCIR) system to the sample data set is 94%, which is a benchmark in the field of offline handwritten Gujarati character recognition. The paper also shows the performance analysis for the GHCIR system using the F1 score and Kappa Coefficient.
Meningkatkan Aktivitas dan kemampuan Literasi (Baca) Menggunakan Teknik Bermain pada Siswa Kelas III SDN 1 Sawah Lama Bandar Lampung TP 2019/2020

The purpose of the study is in line with the formulation of the problem, so this study aims to increase literacy learning activities and outcomes using Playing Techniques for Class III students of SD Negeri 1 Sawah Lama Bandar Lampung in the 2019/2020 school year. The method used is CAR with planning, implementation, observation and reflection steps carried out in 3 cycles. The research subjects were the third grade students of SD Negeri 1 Sawah Lama Bandar Lampung totaling 34 students. The results showed that the application of playing techniques could increase the activity of learning Indonesian language literacy materials for Class III students of SD Negeri 1 Sawah Lama Bandar Lampung for the 2019/2020 school year.