Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF E-COMIC LEARNING MEDIA FOR REDUCTION AND OXIDATION MATERIAL BASED ON FLIP PDF PROFESSIONAL ON THE DISCOVERY LEARNING

This research and development aims to produce a valid, practical, and effective e-comic reduction and oxidation reaction based Flip PDF Professional. The development of this e-comic refers to the ADDIE development model which includes the analysis stage, the design stage, the development stage, the implementation stage, and the evaluation stage. The subjects in this study were 2 media experts and material experts, 1 people of chemistry teacher, and 30 students of class X MIPA 2 SMA Negeri 14 Makassar. The instruments used for validity are validation sheets by media experts and material experts, practicality, namely observation sheets on the implementation of e-comic, teacher and student response questionnaires, and learning outcomes tests. The results of this study are: (1) the validity of the digital comic from the media expert is 3.72 and the material expert is 3.75 which is in the very valid category with a consistency test between validators are 100% which is in very high criteria, (2) the average percentage of e-comic practicality is 97%, where the percentage of practicality based on the questionnaire responses of teachers and students to digital comic are 100% and 90,55% in the very practical category, 3) e-comic meet the effective criteria (≥85%) based on the achievement of class completeness with percentage 86.67%. Based on these data, it is concluded that the e-comic reduction and oxidation reaction based Flip PDF Professional developed with the ADDIE model is valid, practical, and effective for use in learning
Indonesian Sign Language (BISINDO) Recognition Using Accurate and Fast Dynamic Time Warping Learning Model

Sign language recognition problem should be represented as a time series classification model with high accuracy.In the previous studies, Indonesian sign language (BISINDO) had been modeled with one of stochastic time series classification model, i.e.Hidden-Markov Model (HMM), but has low accuracy.In other studies, BISINDO had been recognized with high accuracy but using an unrepresentative model (non-time series classification model), i.e. the modified Generalized Linear Vector Quantization (mGLVQ) model with mode function.In this paper, we tried to use a deterministic time series classification model, named Accurate and Fast Dynamic Time Warping (AF-DTW) model.AF-DTW model is a modified form of Dynamic Time Warping (DTW) model.It is not only to improve the accuracy of DTW but also to accelerate the finding of optimal warping path.The output results showed that AF-DTW has a much higher accuracy than HMM, although it is not as accurate as mGLVQ.
Colour image steganography through channel transformation approach

The interest in information security strategies is expanding due the quick expansion in the use of media content and transmission over the web employments. Accordingly, there is a need of special embedding procedure for steganography. The creator presents an original reversible data hiding (RDH) algorithm for colour pictures that further develops the inserting execution by applying a channel transformation function and a versatile expectation blunder extension prediction error expansion structure. The proposed calculation will bring the first colour picture with no information misfortune from the inserted picture (which is unique in addition to target picture). The evaluation is done here at the zero for the given base place for the histogram that marginally changes the pixel esteems for inserting the information. It can insert more information when contrasted with the majority of the current data hiding calculations. A hypothetical confirmation and various examinations state that the embedding capability of the proposed model which consistently is further noteworthy in comparison with other reversible data hiding techniques. The calculation has been applied to a wide scope of various colour pictures effectively. Some exploratory outcomes are introduced to show the legitimacy of the calculation.
Background subtraction challenges in motion detection using Gaussian mixture model: a survey

&lt;p&gt;Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation.&lt;/p&gt;
Analysis of new differential evolution variants to solve multi-modal problems

Differential evolution algorithm requires diversified population to solve multi- modalproblems. DE supports non-distributed population. Differential evolution algorithm (DE) versions include differential evolution algorithm with best selection method and species evolution (DESBS) and differential evolution algorithm with hierarchical fair competition modal (HFCDE). This article analyzed the efficiency of HFCDE and DESBS to solve the multi-modal s’ problems. HFCDE and DESBS support non-distributed population structured. HFCDE starts with set of feasible solution then it distributed them in the different hierarchy. HFCDE provides the fair competition. DESBS is another semi distributed, differential evolution algorithm.It starts with set of feasible solutions (population). Later it identifies the niches and create the sub-groups within population. Both HFCDE and DESBS have outperformed the other variant of state-of-art variants of DE. Here, the performance of DESBS and the HFCDE are rigorously tested on the multimodal problems. The success of DESBS over HFCDE in multi-modal difficulties managed to overcome the phenomena of elitism to resolve the complex problems, it has been observed that DESBS performs better than HFCDE in complex multi-modal scenarios.
Governance Best Practices and CSR Policies

Achieving governance through creating effective and productive organizations defined by ethics, integrity, best practices, and transparency at a global level aims to foster business growth and socioeconomic development in countries with human dignity. Education is key to achieving these goals, as it promotes entrepreneurship and social welfare. As a result, human flourishing emerges, and digitalization processes foster organizations toward quality-related strategies. These growth-related goals are significantly desirable in developing nations, predominantly rural areas, as social change and economic development are needed to benefit societies and firms. Socioeconomic objectives are achieved only when corruption practices are reduced and cooperation strengthens communities and firms. This chapter analyzes how organizations foster social change, welfare, economic development, and corporate growth through the combination of governance, best practices, and CSR policies.
A Study on the Framework for the Construction of Children's Science Museum Based on Local Identity

The goal of this study is to analyze the current status of children's science centers and government-supported project cases, to identify the problems of children's science centers, and to establish a framework and provide guidelines for the establishment of a theme for children's science centers based on local identity, and the necessary approach to construction and business planning. The research method was conducted through three research methods: local discussions, fieldwork, and expert consultation, and detailed results were obtained. First, the current status of children's specialized science museums and government-supported projects were reviewed and analyzed, approach directions were suggested, and detailed problems were presented. Second, we presented a process for selecting local science and technology themes for the construction of children's science centers. Third, it presented a framework and guidelines for building a children's science center and setting up a business and operation plan. This study can serve as a framework and guideline for building and operating children's science centers that can identify and access local science and technology. It can also be used to revitalize local science and culture in areas experiencing population decline.
Exploring gender-wise, sector-wise, and grade-wise difference among secondary school students’ reading habits

&lt;span lang="EN-US"&gt;This study was conducted to examine students’ perceptions regarding reading habits and to explore gender-wise, sector-wise, and grade-wise differences in secondary school students’ reading habits. The students (N=538) who participated in this cross-sectional survey were selected through a non-proportional stratified random sampling technique from district Lahore. The researchers developed a questionnaire comprised of four subscales (perceptions, purpose, preferences, and problems in reading) to measure students reading habits. They ensured the validity of the instrument from experts and calculated the reliability that was Cronbach’s alpha=0.802. Data were analyzed by using different statistical techniques (mean, standard deviation, and independent samples t-tests). The results of descriptive statistics indicated that students gave more preferences to reading than perceptions about reading, the purpose of reading, and problems in reading. However, the least contributing factor was problems in reading. Whereas the findings of independent samples t-tests showed a significant difference in students’ perceptions about reading habits based on gender and class. However, an insignificant difference was found in students’ perception of reading habits based on sector-wise (public and private) schools. Books of students’ interests may be provided in libraries to encourage them to read. Moreover, teachers may also arrange more reading activities to enhance students’ reading skills.&lt;/span&gt;
FSC-based data fusion sensitivity assessment scheme

In recent years, with the large amount of data generated, data fusion and sharing have become more and more important. However, the expansion of the scope of data fusion and sharing has led to the increasing problem of data leakage risk. In this paper, we improve the FSC fusion algorithm to evaluate the sensitivity change during data fusion from three aspects: data set, data items and data values, so that the fused data sensitivity is more accurate, and later the fused data is anonymized for sharing. The algorithm reduces information loss and provides better privacy protection.
Cointegration identification with metric learning

Cointegration is an important topic for time series analysis, especially in finance pair trading and hedging area. Cointegration is a kind of structure in which a linear combination of two (or more) time series is stationary. Traditional way to identify cointegration is to use the OLS estimator, firstly run a regression and secondly run a unit root test on residuals. But such method is easy to lead to ambiguous and unstable result. Therefore, we developed a dimensionality reduction model based on automatically calculated common factors and adopted the Metric Learning method to find a method that can quickly reduce the dimensionality and test the cointegration relationship of stock pairs.
Research on blockchain scalability based on sharding strategy

The emergence of cryptocurrencies has promoted the development of blockchain technology. However, due to the low performance and poor scalability of the blockchain, it is difficult to apply the blockchain technology to production. Analysis of its essential reason is mainly caused by the distributed consensus protocol. Distributed consensus protocols provide data transparency, integrity, and immutability in a decentralized and untrusted environment, but good security greatly sacrifices scalability. In order to improve the performance and scalability of the system. This paper first improves the Byzantine consensus protocol and improves the throughput of a single shard; on this basis, an efficient shard formation protocol is designed, which can safely assign nodes to shards. This paper relies on trusted hardware (SGX) to achieve consensus and sharding protocol performance improvements. Second, we design a transaction protocol that ensures transaction security and flexibility even when the transaction coordinator is malicious; finally, our research is extensively evaluated on local clusters and on Google Cloud Platform. The results show that the consensus and shard formation protocol in this paper outperforms other advanced solutions in scale and can well scale the blockchain system through sharding and consensus formation protocol. More importantly, the scalable blockchain system based on the sharding strategy proposed in this paper achieves high throughput and can handle Visa-level workloads.
Research on digital watermarking algorithm based on discrete cosine transform

Hidden digital watermark is one of the important means of anti-counterfeiting to maintain copyright. By extracting the hidden digital watermark from images, audio and video through specific codes and algorithms, it can provide very strong and powerful evidence to prove one's copyright ownership. The idea is that the image is regarded as a two-dimensional matrix, and then according to the two-dimensional matrix to expand the corresponding operation, at the same time select the appropriate point, and according to the appropriate point in the digital watermark image, digital watermark embedded operation. In this paper, DCT is used to insert digital watermark data, and DCT reverse transformation method is used to realize the extraction function of digital watermark.
Entity extraction based on the parts of speech attention mechanism

Entity extraction is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations, persons...), which is a very important and fundamental problem in natural language processing. On the research of entity extraction, numerous models ignore the learning of grammatical structure. Considering the shortcomings of previous models, this paper first proposes the PALC (POStag-Attention-LSTM-CRF) model, which adds POS (part of speech) features to entity extraction. Specially, PALC fuses POS features with other features through a multi-layer bidirectional LSTM network and attention mechanism to improve the effect of entity extraction. The experimental results show that the accuracy of the PALC model in this paper on the CONLL03 dataset can be 90.65%, on the CONLL03 dataset can be 84.86%, and on OntoNote 5.0 English dataset can be 86.99%.
Ti-SSE: a time interval based sequential recommendation model via stochastic shared embeddings

Sequential recommendation systems attempt to use the sequence of user interactions to predict users' next behavior according to their recent based on their most recent actions. Markov chains, recurrent neural networks and self-attention have been widely developed because of their traditional ability to capture the dynamics of sequential patterns. However, most of these models make a simplified assumption that they treat the interaction history as an ordered sequence and ignore the time interval between each interaction. In other words, their model is a time series without considering the actual time interval. In this paper, we try to explicitly model the interactive timestamp in a sequence model framework and explore the impact of different time intervals on the prediction of the next project. We propose the model Ti-SSE which combines TiSASRec and SSE-PT model. Thus, we avoid the shortcomings of the traditional sequential recommendation models that ignore the absolute time intervals while the Stochastic Shared Embeddings (SSE) regularization makes our model far from overfitting due to excessive use of self-attention blocks. The results on several datasets show that our model Ti-SSE has advantages over the previous models.
Research and implementation of dynamic gesture recognition system based on ZYNQ

At present, gesture has become an important channel of human-computer interaction, and gesture recognition has been widely used in various fields. In this paper, the dynamic gesture recognition technology is studied from algorithm and system implementation for portable devices which require high real-time performance. The algorithm mainly uses the region of interest extraction based on face recognition, skin color detection based on HCrCg color space and gesture motion track marking based on scanline seed filling algorithm. The system is implemented by Xilinx ZYNQ, and a SOPC system architecture based on ARM Cortex-A9 hard core and ARM Cortex-M3 soft core and FPGA is proposed. The scanline seed filling algorithm with long running time is designed as a hardware accelerator to improve the running speed. Through the test of the prototype, the recognition accuracy can reach 95.75% in a simple background and 90.83% in a complex background. The average running time of the system is only 0.68 seconds, which is more than 30% faster than using pure software method. The system has good performance in recognition accuracy and running speed.
An improved algorithm for elliptic curve digital signature

With the progress of the times, the concept of the quantum computer has been put forward. Encryption algorithms developed based on the discrete logarithm problem are also facing more and more challenges, However, the Elliptic Curve Digital Algorithm (ECDSA) used by bitcoin is an encryption algorithm developed based on the discrete logarithm problem. Therefore, the author wants to design a method that can prevent the password from being quickly cracked by the quantum computer and can also be compatible with the original encryption algorithm system to reduce the maintenance cost of updating the encryption system. For the two points of appeal, the author thought of improving the original encryption algorithm ECDSA. In this work, secondary random encryption on cQ through the randomness of signature is performed to improve ECDSA. The improved ECDSA encryption algorithm is not an asymmetric algorithm. This is a combination of two encryption algorithms. The combination of asymmetric and symmetric algorithms makes it difficult for quantum computers to crack. If the attacker does not know which algorithm to use, he may be forced to conduct violent attacks, which greatly increases the difficulty of cracking the original algorithm. At present, the author has realized the improvement of ECDSA, and can re-encrypt the original asymmetric ECDSA encryption algorithm so that it does not belong to the asymmetric algorithm. In this way, the security of the huge cryptocurrency market can be further guaranteed, and the algorithm can be greatly compatible with the cryptocurrency system using ECDSA as the encryption algorithm.
Image harmonization with spatial feature interaction and back-projection upsample

Without any processing, the synthetic image visually unrealistic due to the established differences in the appearance of the foreground and background. In view of this situation, the task of image harmonization arises at the historic moment, and its purpose is to adjust the foreground appearance of a synthesized image to be closer to the background, thereby eliminating local visual differences. However, due to the limitation of the spatial feature interaction range in the feature extraction process, the global appearance transfer effect is not good. Therefore, to solve this problem, we propose an enhanced spatial feature interaction module. Meanwhile, we propose a back-projection up sampling module, which refines the reconstruction error during the reconstruction up sampling process and better restores the details of the reconstruction foreground. Our experiments on a public dataset, iHarmony4, show that the method effectively generates synthetic images with consistent overall appearance and enhanced detail.
Wild animal recognition based on effective-class-balanced softmax loss

Wild animal recognition is important for wild animal protection. Because the number of different wild animals is different in the wild. The wild animal image dataset collected in field by using camera trap is a typical long tail dataset. This paper proposes an Effective-Class-Balanced Softmax Loss (ECBSL) to solve the long tail problem of self-built wild animal dataset. Firstly, a new cross entropy loss function is obtained by using pointwise mutual information instead of conditional probability for modeling. Then the improved effective number of samples calculation method is used to approximately calculate the prior probability distribution of different animal species. Finally, the effectiveness of ECBSL is proved by experiments. Experiments on the self-built wild animal dataset show that the proposed method improves the recognition accuracy of the tail classes and the whole dataset. The comparison experiments with other methods show that the proposed method is superior to other methods.
Bilingual teaching resource recommendation method for international trade practice based on knowledge graph and neural network

Aiming at the problem that the traditional bilingual teaching resource recommendation technology does not take into account the differences of different learners' learning style, knowledge level and learning mode, and does not take into account the individuation. This paper proposes a bilingual teaching resource recommendation method for international trade practice based on knowledge graph and neural network. Firstly, we correlate the content to be learned with the resources, use the existing knowledge mapping technology, and then use Word2Vec to quantify its behavior and characteristics, and the recommendation model is embedded. Finally, BiLSTM and Attention are used to extract features, dig learners' implicit feedback information, and achieve the purpose of personalized recommendation for learners. Experimental results show that the proposed model is more effective than the traditional recommendation algorithm, and effectively improves the effect of bilingual teaching resources recommendation.
Design and implementation of web log analysis system under the industrial Internet background

Under the background of the booming industrial Internet, data acquisition and real-time calculation of industrial equipment have become the key issues of many industrial manufacturing enterprises. Therefore, this paper studies the design and implementation of web log analysis system. In this paper, a real-time industrial equipment big data acquisition and calculation system based on SparkStreaming, HBase and Hive is constructed. On the equipment big data acquisition technology, k-means algorithm in data mining technology is used to classify and integrate the data. Based on Spark framework, the parallel calculation of K-means algorithm is realized, which solves the problem of low efficiency of industrial big data analysis. Experiments show that the development of this system can effectively improve the efficiency of collecting and analyzing big data logs of industrial equipment, and further improve the timeliness and expansibility of transmission.