Reviewerly/exHarmony huggingface dataset:
Knowledge Guidance Based Work Ticket Intelligent Generation of Electric Power Equipment Inspection

Electric power inspection work ticket is an important text document in the electric power inspection business. The current methods of generating work tickets have problems such as irregular manual filling, low efficiency, and poor knowledge library portability, etc. To solve the problems, this paper constructs the knowledge graph (KG) in the field of power inspection and proposes a knowledge-guided method for intelligently generating work tickets of electric power inspections. Firstly, the named entity of the component or part in the work order is recognized by applying a bidirectional long and short-term memory network (Bi-LSTM) and conditional random field (CRF). Secondly, the bi-encoder is applied for entity disambiguation to make the entity-mention with the description text of defect in the work order corresponds to the node of the component or part defect in the KG. Finally, the relevant paths of the target linked entity are inquired in the KG, and the semantic similarities based on the cosine distance between the text of work order and the texts of paths are calculated to select the optimal path, and the nodes under this path are filled in the slot to generate work tickets. This paper conducts simulation experiments and analysis results for named entity recognition, entity disambiguation, and semantic similarity comparison to verify the effectiveness of work ticket intelligent generation of electric power inspection.
Tracing Information for Agricultural Product and Identifying Key Regulatory Decisions towards Eco-Economics Sustainability

Information traceability of agricultural products is of vital importance in the supply chain in the ongoing wave of big data relying on a group of advanced technologies, such as blockchain. This paper investigates how to effectively supervise and manage the agricultural supply chain with regard to information traceability. Starting from establishing a system framework for tracing and querying information by applying BigchainDB, this paper combines Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) to identify and assess the key regulatory decisions for the government for both economic and ecological purposes. The final results obtained by DEMATEL and ANP are consistent. The vital role of the planting link in eco-economics sustainability is affirmed. Through analyzing the demands of supply chain practitioners, as well as both economic and ecologic views of key points, the qualification information is attached significance. And the preservation and storage processes also deserve attention.
Machine learning-based authorship attribution using token n-grams and other time tested features

Authorship Attribution is a process to determine and/or identify the author of a given text document. The relevance of this research area comes to the fore when two or more writers claim to be the prospective authors of an unidentified or anonymous text document or are unwilling to accept any authorship. This research work aims to utilize various Machine Learning techniques in order to solve the problem of author identification. In the proposed approach, a number of textual features such as Token n-grams, Stylometric features, bag-of-words and TF-IDF have been extracted. Experimentation has been performed on three datasets viz. Spooky Author Identification dataset, Reuter_50_50 dataset and Manual dataset with 3 different train-test split ratios viz. 80-20, 70-30 and 66.67-33.33. Models have been built and tested with supervised learning algorithms such as Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Decision Tree and Random Forest. The proposed system yields promising results. For the Spooky dataset, the best accuracy score obtained is 84.14% with bag-of-words using Naïve Bayes classifier. The best accuracy score of 86.2% is computed for the Reuter_50_50 dataset with 2100 most frequent words when the classifier used is Support Vector Machine. For the Manual dataset, the best score of 96.67% is obtained using the Naïve Bayes Classification Model with both 5-fold and 10-fold cross validation when both syntactic features and 600 most frequent unigrams are used in combination.
Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition

Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base and make full use of the advantages of Transformer with long-range dependency modeling to circumvent the disadvantages of CNNs limited to local receptive field. However, for multi-label images containing multiple objects from different categories, scales, and spatial relations, it is not optimal to use global information alone. Our goal is to leverage ViT's patch tokens and self-attention mechanism to mine rich instances in multi-label images, named diverse instance discovery (DiD). To this end, we propose a semantic category-aware module and a spatial relationship-aware module, respectively, and then combine the two by a re-constraint strategy to obtain instance-aware attention maps. Finally, we propose a weakly supervised object localization-based approach to extract multi-scale local features, to form a multi-view pipeline. Our method requires only weakly supervised information at the label level, no additional knowledge injection or other strongly supervised information is required. Experiments on three benchmark datasets show that our method significantly outperforms previous works and achieves state-of-the-art results under fair experimental comparisons.
The design of outpatient blood collection based on collaboration robots

At this stage, blood examination is mainly carried out by manpower. More than half of the blood collection processes of medical personnel is used for the treatment of blood collection tubes. Sometimes patients will take multiple blood samples at one time, which will also increase the workload of medical personnel. This project proposes a scheme to use the collaborative robot to carry out the pretreatment such as picking up and labeling of blood collection tubes, which can improve the processing efficiency of blood collection tubes and the work efficiency of medical personnel.
Exploring hidden semantics in neural networks with symbolic regression

Many recent studies focus on developing mechanisms to explain the black-box behaviors of neural networks (NNs). However, little work has been done to extract the potential hidden semantics (mathematical representation) of a neural network. A succinct and explicit mathematical representation of a NN model could improve the understanding and interpretation of its behaviors. To address this need, we propose a novel symbolic regression method for neural works (called SRNet) to discover the mathematical expressions of a NN. SRNet creates a Cartesian genetic programming (NNCGP) to represent the hidden semantics of a single layer in a NN. It then leverages a multi-chromosome NNCGP to represent hidden semantics of all layers of the NN. The method uses a (1+λ) evolutionary strategy (called MNNCGP-ES) to extract the final mathematical expressions of all layers in the NN. Experiments on 12 symbolic regression benchmarks and 5 classification benchmarks show that SRNet not only can reveal the complex relationships between each layer of a NN but also can extract the mathematical representation of the whole NN. Compared with LIME and MAPLE, SRNet has higher interpolation accuracy and trends to approximate the real model on the practical dataset1.
Construction of Legal Reporting Information Platform Based on Natural Optimization Algorithm

Natural optimization algorithms have attracted much attention from researchers because they can simulate or explain certain prediction processes. The traditional method of predicting the factor value of legal reporting information based on causal window has shortcomings caused by individual weak classifiers, so the prediction adaptability is poor. Aiming at the construction of the early warning model of legal reporting information, this paper proposes a semi-integrated natural optimization algorithm. The natural optimization algorithm uses the variance of the supporting area factor to characterize the smoothness of the factor neighborhood and uses the optimal threshold parameter for factor classification. It solves the capacity-distortion problem of the hidden algorithm of traditional legal reporting information. The experimental results show that the natural optimization algorithm has better performance. The classification error rate in the question is reduced to 0.137, which effectively promotes the practicability of classification prediction of legal reporting information.
Social Media as a Learning Tool: A Perspective on Formal and Informal Learning

Social networks have been recognized as a fertile learning environment, particularly in encouraging the exchange of knowledge. Students primarily use online communities and groups to read, exchange textbooks, make joint decisions, or share learning materials. Present work assesses the relationship between informal digital learning and formal education settings, with social media interventions. Applications of popular social media platforms have been explored in both formal and informal learning environments. Integration of social media platforms as a learning tool has been theorized by reviewing the contribution of social media and highlighting their specific outcomes.
Multi-instance positive and unlabeled learning with bi-level embedding

Multiple Instance Learning (MIL) is a widely studied learning paradigm which arises from real applications. Existing MIL methods have achieved prominent performances under the premise of plenty annotation data. Nevertheless, sufficient labeled data is often unattainable due to the high labeling cost. For example, the task in web image identification is to find similar samples among a large size of unlabeled dataset through a small number of provided target pictures. This leads to a particular scenario of Multiple Instance Learning with insufficient Positive and superabundant Unlabeled data (PU-MIL), which is a hot research topic in MIL recently. In this paper, we propose a novel method called Multiple Instance Learning with Bi-level Embedding (MILBLE) to tackle PU-MIL problem. Unlike other PU-MIL method using only simple single-level mapping, the bi-level embedding strategy are designed to customize specific mapping for positive and unlabeled data. It ensures the characteristics of key instance are not erased. Moreover, the weighting measure adopted in positive data can extracts the uncontaminated information of true positive instances without interference from negative ones. Finally, we minimize the classification error loss of mapped examples based on class-prior probability to train the optimal classifier. Experimental results show that our method has better performance than other state-of-the-art methods.
An Industrial Experience Report on Retro-Inspection

To reinforce the quality of code delivery, especially to improve future coding quality, one global Information and Communication Technology (ICT) enterprise has institutionalized a retrospective style inspection (namely retro-inspection), which is similar to Fagan inspection but differs in terms of stage, participants, etc. This paper reports an industrial case study that aims to investigate the experiences and lessons from this software practice. To this end, we collected and analyzed various empirical evidence for data triangulation. The results reflect that retro-inspection distinguishes itself from peer code review by identifying more complicated and underlying defects, providing more indicative and suggestive comments. Many experienced inspectors indicate defects together with their rationale behind and offer suggestions for correction and prevention. As a result, retro-inspection can benefit not only quality assurance (like Fagan inspection), but also internal audit, interdivision communication, and competence promotion. On the other side, we identify several lessons of retro-inspection at this stage, e.g., developers’ acceptance and organizers’ predicament, for next-step improvement of this practice. To be specific, some recommendations are discussed for retro-inspection, e.g., more adequate preparation and more careful publicity. This study concludes that most of the expected benefits of retro-inspection can be empirically confirmed in this enterprise and its value on the progress to continuous maturity can be recognized organization-wide. The experiences on executing this altered practice in a large enterprise provide reference value on code quality assurance to other software organizations.
“Three rates in one” matching model for lean management of the whole project

For the rough management of the whole process of the project, lean management is needed. First, take the power grid infrastructure project as the entry point, actively carry out cross-departmental data joint analysis, and build a “three rates in one” project investment statistical analysis system for the whole process; second, dig deeply into the internal relationship between project construction progress, investment completion, and recorded cost, and establish a theoretical curve model of the project as an analysis benchmark for the entire process of project execution; finally, determine the reasonable interval of the three-rate deviation of the project, carry out the cross analysis of the theoretical curve and the actual curve, accurately locate the abnormal progress, accurately analyze the abnormal root cause, and realize the project progress deviation dynamic warning.
Empowered Reading Circles and Transliteracy

For decades, reading circles have been important both as strategies to promote reading and as social environments for talking about what has been read. This chapter presents two educational experiences based on reading circles and empowered with the use of ICT in two private middle schools in Mexico: the enriched reading circle (ERC), a hybrid experience, and the online participatory reading circle (OPRC), a virtual experience. The second one was implemented in the context of physical distancing during the COVID-19 pandemic. The aim of both projects was to promote participatory reading and to develop transliteracy skills; this chapter presents some of the results.
Game AI using Reinforcement Learning

Artificial Intelligence (AI) is the way into the future. Many undertakings are currently overseen by an AI rather than a human; nonetheless, many tasks that are as yet overseen by people can be better done utilizing an AI. However, since the AI innovation isn’t cutting edge as yet, it is unimaginable for now. Thus, we desire to foster an AI prepared through computer games, and for it to master intricate and pragmatic abilities playing them. This is conceivable through the intellectual abilities needed to play computer games and their dynamic and tangled climate. Video games are exceptionally valuable since we can promptly investigate how the specialist performs by contrasting its score with different players. We can imagine video games as a microcosm of human capacity since they are so various and pervasive across human culture. In this way, they are extraordinarily significant to evaluate and demonstrate AI.
Learning to transfer knowledge from RDF Graphs with gated recurrent units

The Internet is a vital part of today’s ecosystem. The speedy evolution of the Internet has brought up practical issues such as the problem of information retrieval. Several methods have been proposed to solve this issue. Such approaches retrieve the information by using SPARQL queries over the Resource Description Framework (RDF) content which requires a precise match concerning the query structure and the RDF content. In this work, we propose a transfer learning-based neural learning method that helps to search RDF graphs to provide probabilistic reasoning between the queries and their results. The problem is formulated as a classification task where RDF graphs are preprocessed to abstract the N-Triples, then encode the abstracted N-triples into a transitional state that is suitable for neural transfer learning. Next, we fine-tune the neural learner to learn the semantic relationships between the N-triples. To validate the proposed approach, we employ ten-fold cross-validation. The results have shown that the anticipated approach is accurate by acquiring the average accuracy, recall, precision, and f-measure. The achieved scores are 97.52%, 96.31%, 98.45%, and 97.37%, respectively, and outperforms the baseline approaches.
Data Stewards, Curators, and Experts

Research data management practices continue to evolve as data become more connected, distributed, granular, and continuous. While keeping their mandate to serve the academic community, libraries are seeking to foster knowledge creation, learning, and information access in an ever more data-driven and online environment. In this chapter, the authors describe their perspectives that shape the library's mandate, roles, and technology. They detail specific roles that have been created to foster data engagement: a secure enclave data core, an institutional data catalog, scientific software management, bioinformatics and research reproducibility support, data integrity, and community engagement. A constant focus on customer service has shaped each role, built the library's reputation, and contributed to high-impact services.
Generating high resolution images using Latent Adversarial Generator

Realistic image generation is a tedious process and many formulations for this have already been proposed. In single image super-resolution, the image proximity to the training dataset from a comparatively low- resolution image is being predicted. This paper proposes to generate specimens of high-resolution images offered incredibly limited facts with a novel approach named Latent Adversarial Generator (LAG). Under this generative sampling architecture, employ the information (absurdly of very low- resolution) to lead what sort of patterns the structure should generate. The outcome of the proposed strategy is not a distinct image which associates to the input and yet instead a feasible group of relevant images sampled from the diverse of raw images. The low-resolution input image is being trained with IMLE (Im-plicit Maximum Likelihood Expansion), a mechanism of LAG to overcome mode collapse and improve training stability to generate samples of output images. After training the low-resolution image, 16X super-resolution images are being generated. Image decompression is applied to such images in order to achieve a collection of probable images as high-resolution images.
InDiP: Intelligent Digital Painter for Low Resolution Face Inpainting

In the recent years Artificial Intelligence (AI) has become a go to approach to solve any kind of real-world problems. In these pandemic times, there are many issues arising around the world. One of such issues is the identification of a masked person. It has become difficult to even recognize the very well-known persons due to masks, which has made the field of surveillance suffer a lot. The Object of this research is to solve this issue by reconstructing the face behind the mask using deep learning models. We are proposing a network called as Intelligent Design Painter (InDiP) with capabilities of reconstructing the face behind the mask at low resolutions. This would make the recognition of the masked persons easier. To achieve this target initially work has been done on reconstructing the general human images for fine tuning. After fine tuning the algorithm it has applied on masked faces to obtain the desired results. When checked the accuracy with the original image, the results seem satisfactory. In this approach a sequence Transformer is trained to forecast pixels based on data from a succession of 2D inputs without taking into consideration any of the 2D input structure. Even though the GPT-2 scale model has been trained on low-resolution ImageNet datasets without labels, the network is able to perform satisfactorily. So by using this model ONE can deduce the facial images of the masked people.
A resistance outlier sampling algorithm for imbalanced data prediction

Classification of imbalanced data is an important challenge in current research. Sampling is an important way to solve the problem of imbalanced data classification, but some traditional sampling algorithms are susceptible to outliers. Therefore, an iF-ADASYN sampling algorithm is proposed in this paper. First, based on the ADASYN algorithm, we introduce the isolation Forest algorithm to overcome its vulnerability to outliers. Then, a calculation method of anomaly index which can delete outliers accurately of minority data is presented. The experimental results of four UCI public imbalanced datasets show that the algorithm can effectively improve the accuracy of the minority class, and increase the stability. In the real thrombus dataset, the AUC value of the iF-ADASYN algorithm is more significant than that of SMOTE and ADASYN algorithms, and the recognition rate of patients with thrombosis increased by 20%. The iF-ADASYN algorithm obtains better resistance to outliers than the original ADASYN algorithm. Meanwhile, it improves the accuracy of minority class decision boundary region division.
Foreword to the Special Section on MIG 2021

• Motion in games, immersive environments and interactive applications. • Graphics and animation, game technology, robotics. • Computer vision, physics, psychology, and urban studies. • Motion control, physics based motion, crowd control and navigation.
Testing Context Aware Application and its Research Challenges

In the digital era, Context Aware Applications (CAA) are considered to be one of the emerging and prominent applications. These applications are self-adaptive, that can determine the situation and adapt accordingly to provide better service to the end user. Testing these applications is quiet challenging, due to its dynamic nature, as context keeps varying. To test such applications thoroughly, tester needs context information (context data) and sequence of actions performed by the end user. This makes test case development and execution difficult one. Traditional testing approach deals with static inputs and expected outputs which are fixed. However, requirements for CAA keeps changing based on the environment. Hence, test case development techniques used for traditional testing may not be sufficient for testing a CAA. This paper, gives an overview about the various CAA testing techniques that are available in literature, research challenges in testing such applications, which might help the researchers in this field.