Reviewerly/exHarmony huggingface dataset:
Multi-features Based Multi-layer Perceptron for Facial Expression Recognition System

Facial Expression Recognition (FER) is a complex research topic in the computer vision field for the last decades. Several approaches and methods have been used to resolve the recognition problem. However, traditional and deep learning-based methods achieve improved performance but are limited to their features limiting the recognition rate. Therefore, the latest strategy is developed for the FER system based on multi-features extraction using the traditional method and deep learning method to reduce the intra-class variation and raise the inter-class dissimilarity. Here a Multi-Feature based MLP (MF-MLP) Classifier is proposed to concentrate on the facial appearance detection problem. First, the Multi-Feature Extraction method uses the LBP and ResNet-50 to extract the multi-features, texture features and high-level visual concepts from the images. Second, the Multi-Layer Perceptron Neural Network is proposed with the Fusion function, which fuses the output vectors generated by MLP to produce the final output vector representing the probability distribution of the expressions. Finally, the proposed MF-MLP Classifier model is skilled in an end-to-end fashion by using the fusion function to fine-tune the MLP. Extensive experiments have been conducted on both constrained datasets (CK+, JAFFE) and unconstrained datasets (FER2013) to demonstrate the effectiveness of the proposed MF-MLP model.
Deep Clustering using Dirichlet Process Gaussian Mixture

Deep clustering is an emerging topic in deep learning where traditional clustering is performed in deep learning feature space. However, clustering and deep learning are often mutually exclusive. In the autoencoder based deep clustering, the challenge is how to jointly optimize both clustering and dimension reduction together, so that the weights in the hidden layers are not only guided by reconstruction loss, but also by a loss function associated with clustering. The current state-of-the-art has two fundamental flaws. First, they rely on the mathematical convenience of Kullback-Leibler divergence for the clustering loss function but the former is asymmetric. Secondly, they assume the prior knowledge on the number of clusters is always available for their dataset of interest. This paper tries to improve on these problems. In the first problem, we use a Jensen-Shannon divergence to overcome the asymmetric issue, specifically using a closed form variant. Next, we introduce an infinite cluster representation using Dirichlet process Gaussian mixture model for joint clustering and model selection in the latent space which we called deep model selection. The number of clusters in the latent space are not fixed but instead vary accordingly as they gradually approach the optimal number during training. Thus, prior knowledge is not required. We evaluate our proposed deep model selection method with traditional model selection on large class number datasets such as MIT67 and CIFAR100 and also compare with both traditional variational Bayes model and deep clustering method with convincing results.
Development and optimization of immersive virtual drilling equipment teaching environment

Abstract In view of the phenomenon of scene jumps, slow model refresh speed and fixed visual direction during virtual roaming, in the virtual drilling equipment teaching platform developed based on Unity 3D, the Cardinal spline curve interpolation algorithm is used to optimize the roaming path and improve the smoothness of the roaming. It uses rendering optimization algorithm to improve the fluency of the scene and adjusts the visual angle of observation through orientation change technology algorithm for design and optimization. The results show that the optimized drilling equipment teaching platform improves the comfort of roaming learning, the scene loading speed is increased by 28.73%, and the visual orientation is more convenient to observe the experimental objects. On the whole, it provides students with a comfortable and realistic teaching environment.
Multi-Supervised Encoder-Decoder for Image Forgery Localization

Image manipulation localization is one of the most challenging tasks because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned. Unlike many existing solutions, we employ a semantic segmentation network, named Multi-Supervised Encoder–Decoder (MSED), for the detection and localization of forgery images with arbitrary sizes and multiple types of manipulations without extra pre-training. In the basic encoder–decoder framework, the former encodes multi-scale contextual information by atrous convolution at multiple rates, while the latter captures sharper object boundaries by applying upsampling to gradually recover the spatial information. The additional multi-supervised module is designed to guide the training process by multiply adopting pixel-wise Binary Cross-Entropy (BCE) loss after the encoder and each upsampling. Experiments on four standard image manipulation datasets demonstrate that our MSED network achieves state-of-the-art performance compared to alternative baselines.
Attribute Structured Knowledge Distillation

Knowledge distillation aims at transferring sufficient knowledge from one cumbersome teacher network to another compressed student network. Most previous knowledge distillation methods mainly focus on mimicking the teacher's output of each instance or inter-instance relations from the teacher to the student, while neglecting the attribute structured relations at an instance level. In this paper, we propose a novel Attribute Structured Knowledge Distillation (ASKD) to transfer attribute-level structured relations. It models two types of structured relations, including inter-region structure and inter-class structure, which captures informative knowledge from the teacher model. Inter-region structure captures local feature relations, while interclass structure focuses on cross-class relations. Transferring such attribute-level structure from the teacher to the student would make the student focus on more informative knowledge of the teacher. Extensive experiments on public datasets, including CIFAR-100 and TinyImageNet, demonstrate the superiority of our method over the state-of-the-art methods under similar architecture and different architecture teacher-student pairs.
Ensuring Communication and Information Security of Energy Storage Systems

In the present work, the use of the “dark fiber” service to build the communication subnet of a network for charging electric vehicles has been proposed. The advantages and disadvantages of the possible implementation of the proposal for the construction of a communication network through dark fiber are listed.
An Experimental Stack Overflow Chatbot Architecture Using NLP Techniques

In computer programming, it is very common to come across faults or bugs that may result in unintended outcomes or none at all. To get the program to function correctly so as to obtain the desirable results, debugging is vital. In some scenarios, errors are noticeable, obvious, and easy to understand, while in others, with only a cryptic code present, it sometimes becomes tedious to debug. While there are several community question answering (CQA) websites such as Stack Overflow, it is noted that there is a general difficulty in finding the appropriate post pertaining to user’s query, especially for beginners. In this paper, we have proposed and implemented a task-specific chatbot model that is also capable of handling general conversations. The bot uses the community question answering website Stack Overflow’s posts to be capable of answering programming related queries. For implementation of the proposed model, a custom Stack Overflow general dialog dataset has been used. An intent classifier classifies the user’s input, and if the user's query is unique to a task, the task-specific classifier classifies the programming language for which the question is being asked. Long short-term memory (LSTM), a powerful Recurrent Neural Network (RNN) deep learning model, has been utilized to achieve this classification. Additionally, word embeddings enable transformation of the dataset and users’ queries into word vectors to facilitate similarity-based fetching. The experimental analysis indicated that the chatbot could identify user motives with 99.15% accuracy and the question programming language with 82.77% accuracy.
The Effects of Technological Disruption on Suppliers and Customers: Evidence from Blockchain Laws

Real-world contractual agreements between firms are often incomplete, leading to suboptimal investment and loss of value in supply-chain relationships. To what extent can blockchain technology help alleviate problems arising from contractual incompleteness? We examine this issue by exploiting a quasi-natural experiment based on the staggered adoption of U.S. state laws that increased firms’ in-state ability to develop, adopt, and use blockchain technology. We find that, after exposure to a pro-blockchain law, firms with greater asset specificity exhibit more positive changes to Tobin’s Q, R&D, and blockchain-related innovation. Also, such firms appear to rely less on vertical integration, form more strategic alliances, and shift their emphasis to less geographically proximate customers. Overall, our results suggest that blockchain technology can help firms remedy constraints and inefficiencies arising from contractual incompleteness.
WLAMr-DDH: Weighted Laterals With Augmentation Mask for Discriminative Deep Hashing for Face Image Retrieval

We propose WLAMr-DDH which is an efficient convolutional neural network based approach for deep semantic hashing for face image retrieval. The proposed model is a substantial improvement upon the end-to-end deep hash learning in the classification framework, where the model learns to generate hash codes and hash functions in a single stage. It combines the strong representation capabilities obtained from the proposed weighted lateral connections for multi-scale feature representation with our novel augmentation mask layer. The augmentation mask is a simple yet effective way to obtain weighted saliency maps at the final convolution layer which makes the prominent features to have more contribution in the final output. Also, with weight normalization via re-parametrization, which decouples the direction from magnitude, faster convergence is achieved. Results obtained from the proposed framework on publicly available datasets shows substantial improvements and outperforms several state-of-the-art methods by good margin. The approach is generic and WLAM block can be easily plugged in a multitude of image retrieval and hashing applications.
Region Awareness for Identifying and Extracting Text in the Natural Scene

Understanding text that appears in a natural scene is essential to a wide range of applications. This issue is still challenging in the community of document analysis and recognition because of the complexity of the natural scene images. In this paper, we propose a new method to effectively detect text regions by identifying the location of characters. The mainstay of our work is to concentrate on designing a network for text detection and a network for text recognition. For text detection, the proposed method directly predicts characters or text lines that appear in the full scene images, and the approach is able to work for text with arbitrary orientations and quadrilateral shapes. To do that, our model produces the score of character position and the score of character similarity. These scores are utilized to group each character into a single object. For the text recognition phase, the detected text is fed into a second network which is used to extract the features from the text images and to map the features to a sequence of characters. The experiments are performed on public datasets, and the obtained results show that the proposed approach gives competitive performance compared to state-of-the-art approaches.
IMPLEMENTATION OF DIGITAL LITERACY COMPETENCY AT SENIOR HIGH SCHOOL IN JAKARTA

The development and implementation of information and technology in education, give an impact to literacy. The implementation of information and technology with literacy is known as digital literacy. Digital literacy not only talks about the skill or competency of using technology as a teacher in the learning process, but how school as a formal education institution can help the students to have digital literacy competency since this skill is needed by the students and teachers who are imposed by the technology as an impact of the pandemic. Schools not only have a responsibility to educate students about digital literacy competency in the learning process, but in all the school aspects must support the implementation of digital literacy. This research wants to explore how digital literacy competency has been implemented at senior high school level in Indonesia. Therefore, this descriptive qualitative research objective is to explore the digital literacy competency implemented at Citra Kasih School in Indonesia. The data collection techniques used in this research were observing and interviewing the principal by asking questions that had passed the validity test. Following the digital literacy competency which was set by Indonesian Ministry of Education and Culture (2017), this research explored the implementation of digital competency into three bases, class based, school environment based, and community based. From the research, it can be concluded that Citra Kasih Senior High School has applied three bases of digital literacy competency indicator at school. In the class based, Citra Kasih Senior High School has attended digital literacy training, applied and utilized digital literacy, and understood how to use digital media and the internet. Based on the school environment Sekolah Citra Kasih High School has implemented digital literacy in reading references, book borrowing frequencies, school activity, school policy. School service, and information presentation. However, on community based, Citra Kasih Senior High School has implemented digital literacy in facilities and infrastructure and in the involvement of parents and institutions.
Pattern Structures for Knowledge Processing and Information Retrieval

In this work, we consider an application of pattern structures to the task of semantic information retrieval. We review the existing techniques of lattice-based information retrieval and introduce a novel approach to defining similarity of documents and search queries. This approach is based on the combination of the well-known embedding technique and pattern structures to provide a coherent assessment of document similarity. The experimental evaluation performed on a subset of users’ reviews collected from Amazon demonstrates the effectiveness of the combination of pattern structures with the introduced modifications compared to other lattice-based techniques.
An extensive review of data security infrastructure and legislature

Abstract The last few decades have witnessed technology progress with leaps and bounds leading to the creation and global adoption of different types of digital devices and platforms that can make personalized recommendations to an individual. One of the consequences of the ubiquity of these devices is the daily generation of data in large quantities. This data includes the sensitive data of an individual as well as multinational organizations and therefore, it must be always kept confidential to prevent its theft and misuse by malicious parties. However, the large volumes of data generated make it difficult to create a robust security solution to safeguard the data from different types of cyberattacks. The paper contributes to the data security industry by consolidating numerous data security algorithms that change with the infrastructure that the data is amid and also outlines the regulations surrounding it. This review paper also aims to highlight the benefits and drawbacks of the security algorithms proposed by researchers to encourage further discussion and consideration of the algorithms for their potential implementation in appropriate domains and also inspire them to develop more powerful and robust security algorithms to cover the drawbacks of the existing ones.
Reasearch on Application of Blended Teaching Model in the Course of University computer foundation in military academies

The traditional blended teaching model mostly relies on students' autonomous learning ability in their spare time, not suitable for personnel training in military academies, also cannot meet the needs of cultivating high-quality practical talents according to the uniqueness of teaching activities in military academies. In view of the situation, the paper proposes a new platform-based blended teaching model, which is based on pad class and virtual training. Firstly, from the perspective of technology for implementation, in the platform-based blended teaching model, all applications supporting blended teaching should be unified application access standards, and the internal data sharing mechanism can realize, which could achieve the quality and efficiency analysis of teaching effect based on big data. Furthermore, from the perspective of function realization, the teaching activities are divided into several pad class models scientifically, and the learning methods are divided into fine-grained again. The learning characteristics of military cadets are fully considered, and the enthusiasm and initiative of cadets are improved in their limited learning time. Taking the course of "university computer foundation" as an example for practical application, this model has operability in military academies, and satisfy the demand of the actual teaching practice.
LSTM sentiment classification model based on one kind of dynamic loss weighting function

In Sentiment classification, the polarity of commentary text is unbalanced, which leads to the bias of the classification model. The Classification model based on the neural networks usually weight the loss function, but the effect of improving the performance of the model is limited. To solve this problem, we propose LSTM sentiment classification model based on the dynamic weighted loss function. Based on the LSTM text classification model, this model adjusts the weighting values of different categories in the loss function. According to the prediction error rate of different categories in the training process, the model can effectively improve the accuracy of a few classes while taking into account the recall rate of most categories. The proposed model has been tested on data sets of hotel reviews and electronic product reviews. The results show that the F1 values of a few classification results can reach 81.2% and 91.7%. Compared with other weighted loss methods, the proposed model has a significant improvement.
The Joy of Dressing Is an Art: Outfit Generation Using Self-attention Bi-LSTM

Fashion represents one's personality, what you wear is how you present yourself to the world. While in traditional brick & mortar stores, there is staff available to assist customers which results in increased sales, online stores rely on recommender systems. Proposing an outfit with-respect-to the desired product is one such type of recommendation. This paper describes an outfit generation framework that utilizes a deep-learning sequence classification based model. While most of the literature related to outfit generation is regarding model development, the segment describing training data generation is still not mature. We have proposed a novel approach to generate an accurate training dataset that uses the latent distance between positive and random outfits to classify negative outfits. Outfits are defined as a sequence of fashion items where each fashion item is represented by its respective embedding vector obtained from the Bayesian Personalised Ranking- Matrix Factorisation (BPR-MF) algorithm which takes user clickstream activity as an input. An outfit is classified as positive or negative depending on its Goodness Score predicted by a Bi-LSTM model. Further, we show that applying Self-Attention based Bi-LSTM model improved the performance (AUC), relevance (NDCG) by an average 13%, 16% respectively for all gender-categories. The proposed outfit generation framework is deployed on Myntra, a large-scale fashion e-commerce platform in India.
The construction of a business English cross-cultural communicative competence training system taking into account the clustering algorithm of large data sets

The construction of a business English cross-cultural communicative competence training system that takes into account the clustering algorithm of large data sets can not only improve cross-cultural communicative competence, but also has a traction effect on the cultivation of cross-cultural communicative competence. This article has carried out the research on the promotion of domestic and foreign cultural communication competence, and briefly summarized the system construction of business English cross-cultural competence in the analysis of the main large data set clustering algorithms. At this level, through the operation of combining theory and practice, this paper constructs a large data set clustering from the inside out, the metaphysical and the metaphysical, from the perspective of mutual interaction and interaction, and the integration of knowledge and action. Algorithmic business English cross-cultural communication ability training system.
Improving the energy efficiency of sorting centers by identifying objects and digit-letter information with neural networks

The article is devoted to the development and analysis of methods of identifying dynamic objects. A neural network with the architecture of SSD MobileNetV2 has been developed to solve the problem of detecting baggage tags and barcodes. Several approaches are considered to solve the problem of identifying digital-letter information: Tesseract, SSD InceptionV2, OpenCV and a convolutional neural network. The efficiency of the methods on real images was checked. It was concluded that electricity consumption can be reduced by 49.43%.
The Innovation Strategy for Citrus Crop Prediction Using Rough Set Theory

The agri-food system of the world is undergoing a radical change in relation to the future scenario with the reconfiguration of the production factors. The future of agriculture, following the other sectors, will be that of an innovative agriculture based on digitization. The future outlook indicates that a “digital agricultural revolution” will be the change that would allow to have the quantities of food for the needs of the whole world. Predictive analysis is a tool that would provide the best use of production, reduce waste, and satisfy food needs. The process uses heterogeneous data, often large in size, in models capable of generating clear and immediately usable results to more easily achieve this goal, such as reducing material waste and inventory, and to obtain a finished product that meets specifications. The proposed theoretical model represents a first modeling to make usable the innumerable amount of data that, in the future, the agri-food system, through digital transformation, will be able to provide, to which it will be necessary to give an adequate response in methodological and operational terms.