Reviewerly/exHarmony huggingface dataset:
Target Vectors of Life Self-determination of the University Economic Programs’ Graduates

The article presents the theoretical foundations and empirical results of studying the target vectors of life self-determination of graduates of the university’s economic programs. A review of modern publications on the issues of life self-determination of graduates of economic programs of the university confirms the relevance and significance of the study for the development of regional human resources. Empirical data was collected by the method of questioning 427 graduates of economic programs of the leading universities of the Stavropol Territory, carrying out training in this direction. The processing of the questionnaire database was carried out in the SPSS program (version 21). Conclusions are made; they characterize the target vectors of life self-determination of graduates of economic programs of the university and the importance of professional activity in achieving target settings. We propose the directions of further research of the issues of life self-determination of modern youth and the influence of social institutions of modern society on the development of life strategies.
Activity Classification from First-Person Office Videos with Visual Privacy Protection

With the advent of wearable body cameras, human activity classification from First-Person Videos (FPV) has become a topic of increasing importance for various applications, including life-logging, law enforcement, sports, workplace, and health care. One of the challenging aspects of FPV is its exposure to potentially sensitive objects within the user’s field of view. In this work, we developed a visual privacy-aware activity classification system focusing on office videos. We utilized a Mask-RCNN with an Inception–ResNet hybrid as a feature extractor for detecting and later blurring out sensitive objects (e.g., digital screens, human face, paper) from the videos. We incorporate an ensemble of Recurrent Neural Networks (RNNs) with ResNet, ResNext, and DenseNet-based feature extractors for activity classification. The proposed system was trained and evaluated on the FPV office video dataset which is a subset of the BON (Tadesse et al. Bon—egocentric vision dataset for office activity recognition [1]) dataset and includes 18 classes made available through the IEEE Video and Image Processing (VIP) Cup 2019 competition. On the original unprotected FPVs, the proposed activity classifier ensemble reached an accuracy of $$85.078\%$$ with precision, recall, and F1-scores of 0.88, 0.85, and 0.86, respectively. The performances were slightly degraded on privacy-protected videos, with accuracy, precision, recall, and F1-scores at $$73.68\%$$ , 0.79, 0.75, and 0.74, respectively.
Applied YOLOv5 and Context Constraint for Real-time and High Accuracy Human Detection

Detecting human in images or videos has been of interest to the computer vision research community for many years. This problem is also widely applied in areas such as security, tracking people, estimating posture, etc. In particular, there have been many impressive results in detecting people using CNNs as Fast R-CNN, VGG, SSD, Mask R-CNN, Mobilenet, etc. However, these studies are often applied on computers with high GPU speed, which makes building systems to detect, identify and track people expensive. The YOLOv5 was born with the purpose of running on low-cost, low-profile computers and training online from user data. In this paper, we propose the combination of YOLOv5 and context constraints for fast and accurate detection of people in images. The result of this study is a prepossessing step of 2D and 3D human pose estimation, evaluated and compared with other methods on the Human 3.6M dataset. For the evaluation we also mark the person with a bounding box on the test data of the Human 3.6M dataset (Protocol #1 - Subject 9, Subject 11). The person detection accuracy is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AP_{50}=99.78\%,AP_{55}=99.38\%,AP_{60}=98.42\%,AP_{65}= 97.07\%,AP_{70}=94.16\%$</tex> , respectively and the processing time is 55 Hz on the GTX 970 GPU 4G. Experimental results is available.
Erroff: A Tool to Identify and Correct Real-word Errors in Sinhala Documents

Sinhala is a low-resource Indo-Aryan language used by approximately 16 million people, mainly in Sri Lanka. Because of the complexity of the Sinhala language, detection of spelling errors is not so easy. A real-word error happens when a word is in the vocabulary but is not valid in the context in which it appears. Checking for real-word errors in a sentence is more difficult than checking for non-word errors, which are not in the vocabulary. We present the implementation of a neural-network based system for identifying real-word errors and non-word errors in Sinhala. We prepared a candidate list of real-word errors. Further, we have selected a suitable model and trained it using several different datasets. Thus, this paper sets a new baseline for the detection and correction of real-word errors in Sinhala documents. Our product, source code, candidate error list, training datasets, and evaluation dataset are publicly released.
KCL: A Declarative Language for Large-Scale Configuration and Policy Management

In recent years, the diversification, complexity, immediacy, and scale of delivery and operational requirements have increased exponentially. For example, delivering and managing complex service mesh and various cloud-native technologies, supporting a variety of operations on infrastructures, such as database, load balancer, dynamic configuration, etc, configuring monitoring for all types of applications, and arranging a range of variety of services and applications to regions across data centers or public clouds. These are just a glimpse of the leopard, and it is actually difficult to list them all. By summarizing and abstracting, we propose the KCL declarative language, development mechanism, and consistent workflow. Through the language model and constraint capabilities, we can improve the large-scale efficiency and liberate multi-team collaborative productivity of operational development and operation systematically while ensuring stability for large-scale configuration and policy management. KCL helps users of various roles to complete operational development and operation tasks in a simple, scalable, stable, efficient, divided-and-conquered manner. To date, the KCL has been used in more than 800 projects, and the average configuration writing and distributing time is shortened from more than 25 days to 2 days.
Analysis Of Cloud Computing Security Threats and Countermeasures

The demand for Cloud Computing (CC) is increasing at a higher growth rate because of the emerging technologies like machine learning, artificial intelligence, big data analytics, IoT, mobile supercomputing, serverless computing, and the industrial need for reducing operational costs. Cloud computing features such as customized offerings, pay-as-you-go or pay-per-use model, availability, seamless scalability, and flexibility have accelerated the demand for cloud services. Despite these many benefits, security is the top challenge in the adoption of cloud services. However, the recent research studies lack the gap between analyzing potential security threats and an exhaustive list of countermeasures proposed for mitigating the threats. The main objective of this paper is to provide a comprehensive view of cloud security threats and to identify potential (more vulnerable) threats. Further, the taxonomy of proposed solutions is provided for data security and privacy on the basis of analysis of research papers in the last decade.
Design and Application of Personalized Recommendation Module for English Writing Marking System Under Theme Model

In order to complete the personalized recommendation module design of English writing marking system and ensure that the design results can be put into use, this paper will carry out relevant research under the theme model. This paper first introduces the basic concept of topic model and model selection, and then introduces the personalized recommendation module design scheme based on probability topic model. Through research, personalized recommendation module can provide help for English writing, achieve accurate recommendation, and is more conducive to the improvement of users’ writing ability.
FEW-Shot Cross-Sensor Domain Adaptation Between SAR and Multispectral Data

In this paper, we present a novel few-shot cross-sensor domain adaptation technique between SAR and multispectral data for LULC classification. Cross-sensor, such as SAR and multispectral, domain adaptation is a long standing challenge in remote sensing. Due to scarcity of large annotated dataset for every domain, it is desirable to have a method that enables cross-domain training with limited supervisory signal in that domain. We address this problem in this paper with a novel few-shot domain adaptation technique. We leverage large corpus of annotated multispectral dataset to improve performance for SAR based LULC classification. We propose a novel Feature Domain Alignment (FDA) loss function to align higher dimension features between multispectral and SAR domain. We validate our approach in publicly available DFC2020 dataset and achieve 78% overall LULC classification accuracy using only 5% annotated SAR samples.
CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation

To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either task-unrelated or task-specific training signals from unlabeled data. We show that these two approaches, at the two extreme ends of the task-specificity spectrum, are suboptimal for the task performance. Utilizing too little task-specific training signals causes underfitting to the ground-truth labels of downstream tasks, while the opposite causes overfitting to the ground-truth labels. To this end, we propose a novel Class-Agnostic Semi-Supervised Learning (CA-SSL) framework to achieve a more favorable task-specificity balance in extracting training signals from unlabeled data. CA-SSL has three training stages that act on either ground-truth labels (labeled data) or pseudo labels (unlabeled data). This decoupling strategy avoids the complicated scheme in traditional SSL methods that balances the contributions from both data types. Especially, we introduce a warmup training stage to achieve a more optimal balance in task specificity by ignoring class information in the pseudo labels, while preserving localization training signals. As a result, our warmup model can better avoid underfitting/overfitting when fine-tuned on the ground-truth labels in detection and segmentation tasks. Using 3.6M unlabeled data, we achieve a significant performance gain of $$4.7\%$$ over ImageNet-pretrained baseline on FCOS object detection. In addition, our warmup model demonstrates excellent transferability to other detection and segmentation frameworks.
A Component-level Attention based Adaptive Graph Convolutional Network

Graph neural networks have attracted more and more attention in the task of learning node representations. Currently, most graph neural networks are usually applied to assortative graphs. They can't perform well in disassortative graphs, where adjacent nodes tend to have different class labels. However, previous studies have shown that it is effective for nodes to gather different information from their neighbors not only in disassortative graphs but also in assortative graphs. Based on this insight, this paper proposes a novel Component-level Attention Adaptive Graph Convolutional Network (CAAGCN) to collect different information more efficiently. Firstly, the model collects dissimilar features among nodes by introducing a component-level attention mechanism. One attention component learns the importance of neighbor nodes and the other regulates the proportion of the similar characteristics of neighbor nodes. Secondly, in order to collect different features between nodes more effectively, we optimize the input function during the attention learning process and preprocess the node features. It can effectively alleviate over-smoothing. Finally, extensive experiments on six networks verify the effectiveness of the method in node classification task.
A Mining approach for Automatic Processing of Regulatory Document

This paper presents a proposal for automatically detecting the structure of regulatory documents, tagging management and text segmentation in units able to be processed as entries in a database. The information stored in that way must be adequate to be efficiently managed by a chatter bot named PTAH, which aims to answer user questions in Natural Language. As part of this work, the usage, main difficulties and characteristics of managing the information in this context is explained. Then, there is a step by step derivation using the proposed method with a small test set of 27 documents. Finally, the results are analyzed and evaluated. The proposal is flexible and robust, based on simple processing, clustering and a rule-based automatic algorithm.
Malaysian Ethnicity Classification based on Face Images using Deep Learning

A face processing system will bring more benefits to numerous applications in surveillance systems, and image or video analysis if the ethnicity information is considered as part of the input data. This will also bring a challenge in face processing studies. Ethnicity information is one of the human characteristics that play a critical role in biometric recognition. Existing face processing approaches usually include two stages: (i) collecting features from face images; and (ii) using the extracted features as the input in a classifier. This paper tackles ethnicity classification based on face images by utilizing a deep learning model. In this project, we address the problem by extracting features and classifying them concurrently using the Convolutional Neural Network (CNN). The proposed method evaluates the classification of Malaysia ethnicity: Malay, Chinese, and Indian. Our dataset is comprised of Google Images and profile images collected from selected participants. The dataset is then annotated with the ethnic group information based on somatic facial features which a human use to distinguish the ethnicity categories. An average of 70% prediction accuracy is reported.
Neural Machine Translation for English-Assamese Language Pair using Transformer

Machine translation is a computer-based translation process that receives a set of words from a particular humanreadable language as input and outputs a second set of words in the intended human-readable language. Machine translation has assisted linguists and sociologists all across the world. A machine translation model may be statistical, rule-based, or based on neural networks. The Neural Machine Translation (NMT) model was introduced in response to the numerous drawbacks of rule- based and statistical-based machine translation models. This study adds a parallel corpus of Assamese and English and develops the NMT system, a transformer model with a self- attention mechanism. For translation from English to Assamese, the system received a higher bilingual evaluation understudy (BLEU) score of 7.6, and for translation from Assamese to English,a BLEU score of 23.2.
Blockchain Application Evaluation

As blockchain applications spread across a wide range of industries, the evaluation of blockchain applications is playing an increasingly important role in deciding which projects to invest in, develop, or cultivate. In this chapter, a comprehensive hierarchy evaluation model for blockchain applications is proposed based on the ecosystem model presented in Chap. 3 . Totally 16 factors respectively in technical, business and social aspects are examined and the blockchain-specific characteristics and suggestions for the evaluation are analyzed. Finally, a method on design and implementation of blockchain application evaluation based on the evaluation model are discussed for the reference of stakeholders such as investors, government sectors, blockchain supplier and users.
Learning a Typhoon Bayesian Network Structure from Natural Language Reports

Given the huge toll caused by natural disasters, it is critically important to develop an effective disaster management and emergency response technique. In this article, we investigate relationships between typhoon-related variables and emergency response from natural language (NL) reports. A major challenge is to exploit typhoon state information for typhoon contingency plan generation, especially from unstructured text data based on NL input. To tackle this issue, we propose a novel framework for learning typhoon Bayesian network structures (FLTB), which can extract typhoon state information from unstructured NL, mine inter-information causal relationships and then generate Bayesian networks. We first extract information about typhoon states through NL processing (NLP) techniques, and then analyze typhoon reports by designing heuristic rules to identify causal relationships between states. We leverage these features to improve the learned structures and provide user-interaction mechanisms to finalize Bayesian networks. We evaluate the performance of our framework on real-world typhoon datasets and develop the Bayesian networks based typhoon emergency response systems.
A Curvature based Isometric Feature Mapping

Unsupervised metric learning consists in building adaptive distance functions without knowledge of the class labels to improve pattern classification. Usually, this process can be accomplished by manifold learning algorithms, through nonlinear dimensionality reduction. In this paper, we propose a curvature based Isometric Feature Mapping, a method that uses differential geometric concepts to build an intrinsic distance function that measures the variations of the local tangent spaces along shortest paths in the KNN graph, motivated by the Frenet-Serret equations and the notion of curvature. Experimental results with several real world datasets show that the proposed method is capable of producing classification performance comparable to other state-of-the-art dimensionality reduction based metric learning techniques, such as t-SNE and UMAP.
Resampling Imbalanced Data and Impact of Attribute Selection Methods in High Dimensional Data

The class Imbalance problem in a classification dataset is challenging the machine learning community in terms of performance for minority data the poor representation of minority class affects the performance of the classification algorithm, which is evident through various assessment metrics. This paper deals with High dimensional Imbalanced data set and the effectiveness of feature selection on Imbalanced data. Imbalance on high dimensional data set leads to sub optimal performance of the classifier. More over handling dealing with high dimensional imbalance data, poses additional technical challenges that would result in overly fitting classifiers. Several approaches have been suggested in literature. In this paper different feature selection methods were explored and with the selected features Random under sampling technique are applied. The classification results reveal that feature selection and sampling is paramount to achieve the best possible results from high-dimensional imbalanced data. The results depict that sampling done with subset features yields good classification performance.
Adopting Mobile Augmented Reality as Cultural Heritage Souvenir: A Design Framework Exploration

In recent years, Augmented Reality (AR) technology has been widely used in creating cultural heritage souvenirs. However, these designs of mobile AR cultural heritage souvenirs are subjected to various limitations. For an instant, there are limited guidelines for designers on how to create cultural heritage souvenirs. This paper proposes a new framework for the implementation of mobile AR. The Vision in Design framework and the Creative intelligence method are applied to the new framework. To demonstrate the design framework, we utilized Yunnan cultural heritage souvenirs as an example. Finally, we reflected on the design framework and process. This framework will serve as a guide for designers to create cultural heritage souvenirs.
Automatic Item Generation with Reading Passages: A Systematic Literature Review

Automatic Item Generation (AIG) is a system to generate items/questions automatically with a model using computer technology. AIG can help teachers to provide questions in education assessments more efficiently. Currently, AIG is already implemented in various educational subjects. Based on the author’s knowledge, there are no review papers that discuss AIG with reading passages. Here we conduct a systematic literature review to provide a comprehensive review of AIG, especially AIG with reading passages. The research objective is to know about AIG approaches datasets, evaluations, and challenges. We use S phases: planning and managing the review, defining the research question, identifying inclusion and exclusion criteria, search strategy, screening titles and abstracts, selecting full-text paper, data extraction, analysis, and synthesis. This research reviewed 30 relevant papers between 201S and 2022. We found that various approaches have been implemented. An approach is not driven by what kind of language will be used in the generated question. On the other hand, dataset selection is always based on the question language and question type that will be generated. Before generated questions are used in assessment, it is necessary to evaluate the question quality. Despite the success stories above, AIG still has some challenges for future development.
The ADI Game: Exploring Futures of Graphic Design and Artificial Intelligence in AI-Driven Autonomous Brands

Artificial Intelligence (AI) is poised to impact creative disciplines, such as graphic design, and this is prompting researchers to rethink the role of the designer and consider a plethora of questions regarding the ethical use of AI, algorithms and data. Recent research suggests that this rapid development and fusion of AI and graphic design could enable designers and researchers to anticipate future trends and explore new opportunities associated with data-driven innovation and AI-fuelled creativity. This paper explores the potential impact of AI on graphic designers, including how they may deploy AI as a graphic design automated system in branding rather than simply as another tool to make traditional designs. In order to address this subject, this paper presents the ADI card game, which has been tested with participants in a co-design workshop conducted in Saudi Arabia, a country undergoing a transformation in terms of technology and innovation under the government's Vision 2030. The findings illustrate the possibilities of developing self-governing brands through gameplay, which will pose new challenges and opportunities for future research.