Reviewerly/exHarmony huggingface dataset:
Video Copyright Protection

Illegal distribution of digital data is a common danger in the film industry, especially with the rapid spread of the Internet, where it is now possible to easily distribute pirated copies of digital video on a global scale. The Watermarking system inserts invisible signs to the video content without changing the content itself. The aim of this paper is to build an invisible video watermarking system with high imperceptibility. Firstly, the watermark is confused by using the Arnold transform and then dividing into equal, non-overlapping blocks. Each block is then embedded in a specific frame using the Discrete Wavelet Transform (DWT), where the HL band is used for this purpose. Regarding the method of selecting the host frames, the chaotic map (tent map) was used to choose a number of frames which are greater or equal to the number of blocks of the watermark. The host frame selection method makes the discovery of the watermark information by illegal means very complicated. The experimental results show that the proposed method can produce excellent transparency with robustness against some attacks where the average_ PSNR reaches to 72.806.
Lesson Study in an Inclusive Mathematics Classroom using Multicommunications Media: A Ring Theory Course

Lesson Study in an Inclusive Mathematics Classroom using Multicommunications Media: A Ring Theory Course. Objective: This study aims to describe learning ring theory courses by a lesson study using multicommunication media in inclusive classes of Muhammadiyah Makassar University. Methods: This study uses an experimental research design by analyzing and directly assisting students in learning. The analysis of this activity refers to plan, do, and see stages. The subjects in this study were students of the 2017 class of Muhammadiyah Makassar University, totaling 28 students, one of whom was a deaf student. Findings: The results show that at the beginning of the performance of lesson study without video, deaf students were still less actively involved in discussing with their group friends, while in the performance of learning using multi communication media, namely in the form of learning videos accompanied by running text and sign language, deaf students seemed more active than previous learning without video media. Conclusion: It is concluded that the use of multi-communication media through the implementation of lesson study has a positive impact on learning and helps students understand the material, especially students who are deaf. Keywords: lesson study, multicommunication, inclusive education. Abstrak: Lesson Study di Kelas Matematika Inklusi menggunakan Media Multikomunikasi: Mata Kuliah Ring Theory. Tujuan: Penelitian ini bertujuan untuk mendeskripsikan pembelajaran ring theory dengan menerapkan lesson study menggunakan media multi komunikasi di kelas inklusi Universitas Muhammadiyah Makassar. Metode: Penelitian ini menggunakan desain penelitian eksperimen dengan menganalisis dan mendampingi siswa secara langsung dalam pembelajaran. Analisis kegiatan ini mengacu pada tahapan plan, do, dan see. Subjek dalam penelitian ini adalah mahasiswa Universitas Muhammadiyah Makassar angkatan 2017 yang berjumlah 28 mahasiswa, salah satunya adalah mahasiswa tunarungu. Temuan : Hasil penelitian menunjukkan bahwa pada awal pelaksanaan lesson study tanpa video, siswa tunarungu masih kurang aktif berdiskusi dengan teman satu kelompoknya, sedangkan pada pelaksanaan pembelajaran menggunakan media multikomunikasi yaitu berupa video pembelajaran yang disertai dengan video pembelajaran. dengan menjalankan teks dan bahasa isyarat, siswa tunarungu tampak lebih aktif dibandingkan dengan pembelajaran sebelumnya tanpa media video. Kesimpulan: Disimpulkan bahwa penggunaan media multikomunikasi melalui pelaksanaan lesson study memberikan dampak positif dalam pembelajaran dan membantu siswa dalam memahami materi, khususnya siswa tunarungu. Kata kunci: lesson study, multikomunikasi, pendidikan inklusi. DOI: http://dx.doi.org/10.23960/jpp.v13.i2.202308
Evaluación de herramientas de software libre, para el sistema operativo Windows, en la adquisición de evidencias de la memoria RAM

The objective of this article, is to evaluate free software tools of the Windows operating system, for the acquisition of evidence of RAM memory, using the ISO/IEC 25010 standard that defines characteristics and measures of quality of use of a product. The above takes into consideration the technical form for RAM dump analysis and the use of the specific tools DumpIt, FTK Imager, Windows Forensic Toolchest (WFT), OS Forensic and RamCapturer.&#x0D; In order to guarantee the usefulness of the selected tools, it is contemplated to define, classify, identify, obtain, analyze and interpret results obtained with the Volatility tool, and thus be able to detect which instrument recovers more, the most efficient one that affects the evidence less. The latter is of utmost importance, since a very subtle alteration could change the HASH obtaining large scale problems in a trial.
Buenas prácticas en informática forense para el procesamiento de evidencia digital o información electrónicamente almacén

This article responds to the need to build good practices in computer environments and scenarios for the processing of digital evidence, which strengthens the scientific technical capacities of the researcher and / or public or private expert, to obtain positive results against the treatment of the element probative material and / or physical evidence [1] found at the scene, which due to its digital or electronic nature requires special packaging. Likewise, the practitioner, through the application of an adequate methodology, perfect the packaging procedure and submitting it to the established chain of custody protocol [2], regardless of the place where it is collected, the principles of sameness, authenticity and originality. It is expected that the reader will be able to identify the materials that they can use for the manufacture of a container whose utility is destined to the packaging of digital evidence or information stored electronically [3].
Attendance monitoring of masked faces using ResNext-101

SARC virus, Coronavirus, Ebola and bird flu have all caused pandemics in the last few decades. Most of these diseases spread through the air when someone coughs, sneezes or even talks. The government makes citizens wear masks. Furthermore, all academic activities are conducted in virtual mode as a result of this predicament, making taking attendance of pupils difficult while they are wearing masks on their faces. To overcome this issue, the proposed work will track a student’s attendance while using ResNext-101 in a virtual classroom setting. ResNext-101, a deep learning technique, is used on masked faces in this work and it is a good model for accurately detecting masked faces. By using the Gaussian data augmentation approach, the outcome reveals a level of accuracy of 51.70 percent with a loss of 1.9452.
Development of an algorithm for determining the specialization of an educational program

The authors propose definition algorithm of students’ educational program specialization in the field of IT technologies that is based on the methodology of educational data ranking. The algorithm has shown the importance of data mining methods for solving educational problems. This algorithm can be considered as a prototype of a recommender system, so students will be able to make their own decisions: follow the recommendations or make their choice based on other factors that are dominant for them.
CREATION OF QUESTIONNAIRE KEYWORDS WITH THE CISE METHOD FOR KMS USER SATISFACTION EVALUATION

This study aims to obtain user satisfaction factors for a knowledge management system so that a questionnaire can be made for evaluation or measurement. The SECI method is used with the CISE sequence which consists of four knowledge creation steps, namely C-combination, I-nternalization, S-socialization, and ending E-externalization. The stage begins with literature studies and then modifications are made with the selection, addition, and incorporation of existing models. From understanding and analyzing several models, discussions or brainstorming with colleagues were then carried out so that a final model was obtained to compile a list of keywords and statements as a questionnaire based on indicators related to knowledge management and the satisfaction of knowledge management system users. The results obtained there are eight user satisfaction factors divided into technical aspects (knowledge quality, knowledge sharing, system quality, service quality) and social aspects (management support, system use, perceived usefulness, user attribute). Five attributes were obtained from each of these factors as keywords resulting in 40 questionnaire statements. It can be concluded that the factors of user satisfaction knowledge management system can be built from the satisfaction of information system users in general by adding distinctive factors including the creation, transfer, and documentation of knowledge.
Bert-BiLSTM-CRF based financial entity identification

With the rapid development of information technology, artificial intelligence technology and the financial industry began to deeply integrate up. Algorithmic trading, credit card fraud detection and a series of other new technologies being applied to the financial industry all require a large amount of data support. However, due to the increasing amount of online financial data, it is difficult for the majority of investors and financial industry practitioners to obtain the required information in a timely manner. Entity recognition technology, as the basis of natural language processing, can quickly extract effective information from the massive financial texts and can provide effective help for investors and financial industry practitioners. In this paper, we propose a neural network model based on Bert-BiLSTM-CRF, which is applied to recognize financial entities. Through experimental analysis, the model achieves more than 95% of all indicators. Compared with the conventional model, the model has superior performance.
Research on named entity identification method based on medical field

With the development of internet technology, more and more scholars are applying computer technology to research in the medical field. In this paper, we will investigate the named entity recognition method. Under the study of BERT-CRF model, we propose a named entity recognition model based on multi-task pre-training model with adversarial learning and network sharing and apply it to entity recognition in medical field with the aim of improving the accuracy of entity recognition in medical field. The model introduces multi-task joint learning and adversarial learning modules to improve the entity boundary effect and solve the noise problem of word boundary information, while achieving the purpose of information enhancement. On the CMeEE (Chinese Medical Entity Extraction) dataset, the model showed a significant improvement in accuracy, recall, and F1 score.
CL-SRGAN: generative adversary network equipped with curriculum learning for image super-resolution

Single image super-resolution is an approach to optimize the image stripe structure and improve the image quality. Recently, it developed rapidly based on convolution neural network, specially designed for this task, which becomes a hot topic of research and have shown remarkable result. Recently, many models have been developed based on Generative Adversarial Networks (GAN) and display enormous superiority compared with traditional deep learning methods. In GANs settings, adversarial loss pushes the generated image to natural image manifold with the help of a discriminator and at the same time trains discriminator to better discriminate the real image from those fake images generated by generator. In this course of confrontation, the generator is excellently trained and have achieved outstanding performance in the image super-resolution task. However, the traditional SRGAN image super-resolution reconstruction algorithm has slow training convergence speed. Moreover, excessive high-frequency texture sharpening leads to distortion of some details, which has a negative impact on the reconstructed image. In this work, curriculum learning algorithm is implemented to solve these problems and thus originally propose CL-SRGAN method, which is designed to help SRGAN achieve better performance on image resolution task. In the final experiment, CL-SRGAN has made an effective breakthrough in processing image reconstruction.
Downward-Growing Neural Networks

A major issue in the application of deep learning is the definition of a proper architecture for the learning machine at hand, in such a way that the model is neither excessively large (which results in overfitting the training data) nor too small (which limits the learning and modeling capabilities of the automatic learner). Facing this issue boosted the development of algorithms for automatically growing and pruning the architectures as part of the learning process. The paper introduces a novel approach to growing the architecture of deep neural networks, called downward-growing neural network (DGNN). The approach can be applied to arbitrary feed-forward deep neural networks. Groups of neurons that negatively affect the performance of the network are selected and grown with the aim of improving the learning and generalization capabilities of the resulting machine. The growing process is realized via replacement of these groups of neurons with sub-networks that are trained relying on ad hoc target propagation techniques. In so doing, the growth process takes place simultaneously in both the depth and width of the DGNN architecture. We assess empirically the effectiveness of the DGNN on several UCI datasets, where the DGNN significantly improves the average accuracy over a range of established deep neural network approaches and over two popular growing algorithms, namely, the AdaNet and the cascade correlation neural network.
A Resource-Adaptive Routing Scheme with Wavelength Conflicts in Quantum Key Distribution Optical Networks

Quantum key distribution (QKD) has great potential in ensuring data security. Deploying QKD-related devices in existing optical fiber networks is a cost-effective way to practically implement QKD. However, QKD optical networks (QKDON) have a low quantum key generation rate and limited wavelength channels for data transmission. The simultaneous arrival of multiple QKD services may also lead to wavelength conflicts in QKDON. Therefore, we propose a resource-adaptive routing scheme (RAWC) with wavelength conflicts to achieve load balancing and efficient utilization of network resources. Focusing on the impact of link load and resource competition, this scheme dynamically adjusts the link weights and introduces the wavelength conflict degree. Simulation results indicate that the RAWC algorithm is an effective approach to solving the wavelength conflict problem. Compared with the benchmark algorithms, the RAWC algorithm can improve service request success rate (SR) by up to 30%.
Deep Classification with Linearity-Enhanced Logits to Softmax Function

Recently, there has been a rapid increase in deep classification tasks, such as image recognition and target detection. As one of the most crucial components in Convolutional Neural Network (CNN) architectures, softmax arguably encourages CNN to achieve better performance in image recognition. Under this scheme, we present a conceptually intuitive learning objection function: Orthogonal-Softmax. The primary property of the loss function is to use a linear approximation model that is designed by Gram–Schmidt orthogonalization. Firstly, compared with the traditional softmax and Taylor-Softmax, Orthogonal-Softmax has a stronger relationship through orthogonal polynomials expansion. Secondly, a new loss function is advanced to acquire highly discriminative features for classification tasks. At last, we present a linear softmax loss to further promote the intra-class compactness and inter-class discrepancy simultaneously. The results of the widespread experimental discussion on four benchmark datasets manifest the validity of the presented method. Besides, we want to explore the non-ground truth samples in the future.
Research on the application of artificial intelligence in the library sector

This study examines the literature on AI and libraries, examines the significant roles AI has played recently in industries related to libraries, and briefly describes relevant technical functions and their application characteristics in the library field. It begins with six key technologies: OCR, data mining, natural language processing, face recognition, knowledge mapping, and machine learning, and then makes a thorough analysis of each. Detailed analysis and summary of the results achieved in the practical application of AI, an analytical overview of the business functions related to AI in the library field on the development and reform of libraries and the current application status of various technologies, and the problems that libraries may encounter in the practical implementation of AI-related technologies are pointed out.
Semantic matching method-based question and answer classification algorithm research

A deep learning multi-classification algorithm is proposed to address the problem that machines cannot accurately understand the semantic differences of the same words in different sentences in the current research on semantic matching question-and-answer classification algorithms. The HanLP word separation algorithm is used to classify the problem first, then the words commonly used in the sentences are replaced according to the correspondence in the dictionary library, and finally the replaced data are imported into the BERT model for encoding and learning. It was verified that the combination of the HanLP word classification algorithm and the BERT model allowed the machine to understand the user's input test to a greater extent and with a higher accuracy rate than other methods. When applied to real-world scenarios, this method will make the user experience more efficient and convenient. With the rapid development of AI, this method will also help in the exploration of natural language.
Aspect-level sentiment analysis model fused with GPT and multi-layer attention

With the popularization of the Internet and the rapid development of artificial intelligence, the requirements for the accuracy of sentiment analysis on text data have been continuously improved, and aspect-level sentiment analysis has emerged as the times require. This paper analyzes and combines the research results of aspect-level sentiment analysis in recent years and proposes an aspect-level sentiment analysis model integrating GPT and multi-layer attention. (GPT and Multi-Layer Attention Network, GPT-MAN). The model first combines GPT and aspect coding to obtain a new word vector representation method, and then inputs the word vector with rich semantics into the network with a multi-layer attention mechanism, Sentiment Analysis Accuracy. In the experiment, the GPT-MAN model is analyzed from the selection of word embedding model and the optimization of hyperparameters using the datasets of Restaurant, Laptop and Twitter, and the effectiveness of this method is verified. And in comparison, with different models, the accuracy of the model in the three datasets increased by 2.57, 3.34, and 1.19 percentage points, respectively.
An active learning application on loan default prediction: based on forest classifier model

Under the global pandemic, the application of loans in various forms increases under the influence of economic gloom. Small loan agencies in regions with undeveloped credit systems often neglect background checks because it is costly and time-consuming. Lack of loan approval standards increases the occurrence of defaults. Ideally, a checker program that can make accurate default predictions with a small amount of data contributes to this dilemma. This paper shows a random forest classifier model can achieve 93.2% accuracy incorporating an active learning strategy with only 50 labeled data. Compared to the standard classifier model, the active learning strategy improves the efficiency by 7% with 50 labeled data and increases as much as 14.5% when provided with only 20 labeled data. The source data used in this paper comes from a dataset provided by Univ. AI simulates real-life personal financial records in India.
Intelligent recording live broadcast of practical training based on AI technology

At present, the use of artificial intelligence in the front line of teaching has begun to take shape. Artificial Intelligence technology (AI) has triggered a new wave, and the innovation of science and technology has brought about the diversification of teaching methods. Fragmentary learning is everywhere all the time, online and offline are organically integrated, and knowledge breakpoints inside and outside the classroom are seamlessly linked up. Our classroom is changing with each passing day. This paper introduces AI technology to carry out the research of intelligent recording live broadcast of practical training, aiming at the problem of high picture stutter rate in the current process of recording live broadcast of practical training, which affects the subjective experience. Aiming at the practical training courseware with poor compatibility, the courseware is converted into H5 form courseware. On this basis, the data in the courseware is encoded and decoded. Through the real-time processing of guidance and control, the synchronization of video and audio recording, playing, and switching is realized. By using AI technology, live subtitle bars are automatically output. Through the comparative analysis of the application effect of the new recording live broadcast method, it is concluded that this method can effectively reduce the picture stutter rate and bring a better subjective experience to users.
A traffic image semantic segmentation algorithm based on UNET

In recent years, the traffic image semantic segmentation plays a crucial role in automatic driving. The result of semantic segmentation will directly affect the car's understanding of the external scene. Thus, a semantic segmentation algorithm based on UNET network model is proposed for getting better results in traffic images segmentation. To prove the effectiveness of the proposed algorithm, highway driving dataset is used on the experiments. The experimental results show that the proposed network can achieve high precision image semantic segmentation in complex road scenes, and the segmentation accuracy is greatly improved compared with other network models.
Feature point matching algorithm based on metric learning

In order to solve the problem that the quality of feature point matching and the computational efficiency cannot be achieved simultaneously, this paper proposes twin network feature point matching algorithm based on metric learning. Features and feature descriptors of image blocks is extracted through twin networks, and similarity measure loss function is used to complete feature matching in this paper. The results of network training and testing on HPatches dataset show that the algorithm is helpful to improve the accuracy and matching efficiency of feature matching point pairs.