Reviewerly/exHarmony huggingface dataset:
Impact of applying super resolution to low resolution face images on the performance of deep neural networks

Abstract Face images captured in unconstrained environment differ in various aspects such as expression, illumination, resolution, occlusion, pose etc. which makes face recognition task difficult. The face images captured by the camera from a distance will have low resolution and lack many finer details that makes face recognition a challenging task. Super resolution (SR) is a process of generating high resolution (HR) images from one or more images. In this work, we apply super resolution to low resolution (LR) images of faces to find the impact on the deep models performance. To achieve this, we create dataset with face images captured in unconstrained environment. Later we designed a CNN model with eight layers and trained on the dataset created. Our deep model with low memory requirement and less parameters achieves an accuracy of 99.75% on test dataset and outperforms fine-tuned VGGFace by a small margin. The performance of our deep neural network and fine-tuned VGGFace was observed on low resolution images pre and post-super resolution. The deep neural network-based model available in OpenCV, SRGAN super resolution model and INTER_CUBIC interpolation are used to generate HR images. The HR images generated by OpenCV, SRGAN are better than INTER_CUBIC interpolation. The results show that HR images generated by applying SR to low resolution face images improve the image quality in terms of Mean squared error (MSE), Structural similarity index measure (SSIM) and Peak to signal noise ratio (PSNR). However, the results indicate that improvement in the image quality does not significantly improve performance of deep model.
Research on Application of Blockchain Technology in Higher Education in China

The rise of new technology usually has a great impact on education. Blockchain, as an emerging technology, has developed rapidly in recent years and has become a hot topic of education researchers. This technology is initially applied in the field of higher education in China now. In view of this, this paper first introduces the concept of blockchain technology, and expounds the characteristics and types of its application in the field of education in detail. Then combined with the existing typical application cases of blockchain technology in higher education field, the impact and value of blockchain technology on higher education are discussed, and possible challenges are analyzed. It is expected that this paper can provide reference for the innovation of comprehensive and deep integrated education concept and education mode based on blockchain in China.
Dynamic Adaptive Chain Model of Knowledge Graph Representation Learning

Knowledge graph representation learning models are mostly used for static data. When the data changes, the models cannot be adjusted dynamically as the data changes. The data is constantly changing in actual usage scenarios. However, most representation learning models focus on prediction accuracy and ignore the dynamic change of knowledge graph. A trained representation learning model is closed, but the facts in the real world are constantly changing. The knowledge graph of the real world should be open to realize the description of reality. This paper proposes a dynamic adaptive chain transfer model aim to deal with changing knowledge graph data. Our model realizes the dynamic increase and decrease of triples without retraining. We designed an experimental method to verify the validity of the model. Experimental results show that our model can achieve dynamic data changes and keep the performance of the original model.
Research on Image Caption of Children's Image Based on Attention Mechanism

Image Caption refers to a technique in which the computer uses neural networks to identify the image content and output text statements that conform to people's reading habits by studying the object categories, attributes, relationships among objects, etc. This paper builds an Image Caption network model with Attention Mechanism. The model first uses convolution neural network ResNet50 to extract image features, encoding image information, and then weighted image features through Attention Mechanism. Finally, the three-layer stacked LSTM network is used to decode the image features and output the description statements. Also, in this paper, Smooth L1 is used as a loss function of the Attention Mechanism to solve the problem of gradient explosion caused by excessive gradient and strengthen the training effect. Because the whole process of Image Caption is like making the machine "talking about pictures ", this paper applies this technology to early childhood education in order to help children" talking about pictures "purpose.
Real Time Hand Gesture Recognition Applied for Flight Simulator Controls

Dynamic hand gesture recognition is a desired human-computer interactions for flight simulator controls, which needs powerful enough to satisfy the requirements of high classification accuracy, fast response time. In this paper we presents a hand gesture recognition system that based on RGB camera to identify motion, hand and gesture in a sequential way. Frame difference is designed as a motion detection modality and a lightweight hand detection neural network is utilized to activate the gesture classifier. To train the system to recognize designed gestures, large gesture JESTER dataset is employed to train and test the proposed deep learning neural networks, which includes 2D ConvNets, 3D ConvNets and a fully connected layer. Experimental results show that the offline gesture classification accuracy on the JESTER dataset is 95.96% and online recognition algorithm runs on average 164 fps in the presence of hand. According to the questionnaire results after the subjects used our flight simulator system, most subjects expressed satisfaction with our gesture recognition system.
Image mosaicing for visual feedback in ground robot navigation

Nowadays, computer vision is an essential part of modern autonomous mobile robots. Fisheye cameras are employed to capture large scenes with a single camera, but the hard radial distortion limits the accuracy of measurements. In this research, a vision system with multiple low-distortion cameras to capture large flat scenes from different viewpoints is proposed. This system applies a homography-based image mosaicing method and linear image interpolation. The obtained results show that the proposed system is useful for visual navigation of ground mobile robots.
Hierarchical Fuzzy Graph Attention Network for Group Recommendation

Human's group activities have contributed to the development of group recommender systems. The group recommender system can provide personalised services for various online user groups through analysing groups' preferences. However, current group recommendation methods have failed to exploit complex relationships among users, groups and items when extracting groups' preferences. Meanwhile, most previous works are based on crisp techniques, which result in rigid preference profiling. Benefiting from the development of graph attention networks, this paper represents the complex relationships among users, groups and items as various graphs, including user-/group-item graph, user-group graph and user-user graph, and proposes a hierarchical fuzzy graph attention network (HGAT-F) to enhance fuzzy profiling for both groups and items. Experiments results on real world datasets show that HGAT-F has enhanced group recommendation than previous works.
Research on Tracking Robot Based on Depth Camera

In order to solve the problem of automatic following of mobile robot in unstructured environment, an autonomous following method based on depth camera was proposed. A mobile platform based on ROS operating system and equipped with depth camera is designed. According to the depth-space characteristics of the depth camera, the 3D depth data and the coordinate information of the object are obtained. Secondly, according to the RGB mode of the depth camera, the specific target recognition and following problem is solved. The target is calibrated well in advance to reduce a lot of time spent in the later stage of target recognition and calibration, and the flexibility of following is improved by pasting the calibration objects on the target that needs to be followed. The improved artificial potential field algorithm is used to solve the unreachable problem of the target point and complete the path planning, so as to complete the autonomous obstacle avoidance following task.
Sentiment Analysis on Micro-Blogs

Sentimental analysis has been growing among the researcher and in business due to ongoing importance in many domains such as feedback system, review system, individualâ€™s sentiment etc. Due to availability of large corpus of texts as of now the supervised models are now started to yield good result. But text is not quite straight forward it is needed to cleaned and prepared for further processing. In this research we aim to create a different model so as to develop and predict sentiments on different categories of micro-blogs obtained on social-media. The approach we to tried here is to construct many existing theories and approaches and then apply the proposed model. We used pre-trained GloVe model for word representation which is basically an unsupervised learning algorithm trained on many text corpuses.The Proposed Model mentioned above is build using CNN, Dense Network and unidirectional LSTM and GRU cells of multiple layers. The output obtained is categorical ranged between 0-10.This model is than used as a back-end to evaluate summarized sentiment of posts with sentiments of each micro-blogs from the respective post. The fetching and transferring of data done with help of REST-API.This type of Learning-Model and Fetching technique enhances the accessibility and availability of service.
Ensuring Source Code Propagation Procedures for a Large-Scale Software Development Scenario: An Experience Report

Currently, software development demands continuous effort to manage source code changes, produced by several teams constantly. Usually Version Control Systems (VCSs) are used to manage these changes. It is very important to define and follow policies and procedures that helps avoiding error insertions while performing changes on software products, although in a large-scale scenario, with teams spread out globally, it is also difficult to verify and ensure that those procedures are correctly applied.
BILPAS: Blockchain-Based Indoor Location Paging and Answering Service

Now indoor Location-Based Services (LBSs) have numerous applications built on indoor navigation. However, current indoor navigation can only meet the needs of finding a certain point of interest (POI), it can not help to find a certain person. Therefore, we propose Location Paging and Answering Service (LPAS) for finding out someone in an indoor environment. However, it may face serious problems of privacy disclosure of the clients' indoor location data. To ensure security, privacy and convenience, we leverage blockchain to model the indoor environment, and build Blockchain-Based Indoor LPAS (BILPAS) by embedding our three-way-handshake Diffie-Hellman Key Agreement (DHKA) procedures. Our BILPAS can automatically establish a secure and privacy communication tunnel between any two users, greatly liberating participants from unnecessary self-determining interactions. We present the proof-of-concept prototype and implement it on Hyperledger Fabric. Then, we conduct the security and privacy analysis and evaluate the time overhead. The experiment results validate the high privacy, security and convenience of our BILPAS.
Improving Entity Linking by Encoding Type Information into Entity Embeddings

Entity Linking (EL) refers to the task of linking entity mentions in the text to the correct entities in the Knowledge Base (KB) in which entity embeddings play a vital and challenging role because of the subtle differences between entities. However, existing pre-trained entity embeddings only learn the underlying semantic information in texts, yet the fine-grained entity type information is ignored, which causes the type of the linked entity is incompatible with the mention context. In order to solve this problem, we propose to encode fine-grained type information into entity embeddings. We firstly pre-train word vectors to inject type information by embedding words and fine-grained entity types into the same vector space. Then we retrain entity embeddings with word vectors containing fine-grained type information. By applying our entity embeddings to two existing EL models, our method respectively achieves 0.82 $$\%$$ and 0.42 $$\%$$ improvement on average F1 score of the test sets. Meanwhile, our method is model-irrelevant, which means it can help other EL models.
Machine-learning for physics simulation anomaly detection

Multi-physics hydrodynamic direct numerical simulations (DNS) are often computationally intensive, requiring significant computational resources to complete. For simulations requiring thousands of processors, the probability of anomalies occurring during a simulation is not insignificant. Since these simulations often run for a long time without human validation, such undetected anomalies can be costly. We will present results of our application of ML based techniques for early anomaly detection to hydrodynamics simulations. By treating the intermediate output of hydrodynamic simulations as images or videos, we could borrow ML techniques from computer vision for the task of anomaly detection. We generated a training dataset using CLAMR, a cell-based adaptive mesh refinement application which implements the shallow water equations. Modifications were done to the application to obtain a wider range of experiments for our dataset. By varying the mesh resolution, domain size, and the initial state of the simulation, we generated a range of experiments who’s states can be learned using computer vision techniques. Additionally, those same experiments could be run with anomalies injected during the simulation so our models could be trained to differentiate between nominal and anomalous simulation states. We also present ML models using PetaVision, a neuromorphic computing simulation toolkit, as well as other ML frameworks, and demonstrate that they can predict the state of a simulation at a succeeding time step based on the state of the DNS given results from a number of preceding time steps. We will further compare the relative performance of these approaches to early anomaly detection and potential next steps to applying these techniques to more complex, multi-physics DNS applications.
Deep Integration Navigation Technique Based on Strong Tracking UKF Algorithm

In order to improve precision and reliability of deep integration system of Inertial Navigation System (INS) and Global Satellite Navigation System (GNSS), an adaptive strong tracking unscented Kalman filter algorithm was adopted, which utilize suboptimal multiple fading factors to achieve parameter adaptation task for various dynamic characteristics, and improve algorithm precision and real-time ability simultaneously. Simulation experiment are provided for illustrating the effective improvement in navigation estimation accuracy. The results show that STUKF algorithm is better than single UKF and STF, which can alternate traditional algorithm for INS/GNSS deep integration.
Overview of Distant Supervised Relation Extraction

Relation extraction is a fundamental task in natural language processing, aiming at extracting relational triples from plain text. However, there are fewer instances in the manually constructed dataset to meet the learning needs of relation extraction models. Distant supervision approach has attracted the interest of numerous researchers due to its ability to construct large datasets at a low cost. Nevertheless, there are certain problems with distant supervision approach due to overly strong assumptions. In this paper, we introduce three main problems in distant supervised relation extraction: the noise labeling problem, the triple-overlapping problem, and the long-tail data distribution problem. Furthermore, we summarize current solutions and evaluation strategies and then summarize future research challenges.
Age Estimation in Juveniles using Convolution Neural Network

Age estimation models have been developed by many but no one has been able to build it with much accuracy and precision. This becomes even more challenging while estimating age of juveniles. This project aims to find a solution to this challenging problem. Juvenile age estimation can be used in multiple domains. It can be used majorly to stop criminal activities affecting children and teenagers. Faces of the subjects are captured and then the images are analysed using neural networks. The project is based mainly on concepts of machine learning like computer vision and convolution neural networks. This project has enormous scope as the algorithm applied can be continuously optimised as per requirements. Convolution Neural networks are highly used for tasks involving image analysis and identification. Computer vision deals with how the computer perceives and visualises the input provided to it. The fundamental concepts used in any machine learning based image analysis are Computer vision and Convolution Neural networks. These fundamentals have been developed by researchers across the world but still have some improvements to be done. With accurate dataset and proper optimisation of algorithms it is possible to create an age estimation model more precise and accurate than the existing ones.
Mining Consumer Services Based on User Preference with Associative and Process Mining

A customer makes a decision based on sentiments when using services. These sentiments drive the selections in services that are correlated with each other. It is hard to grasp the sentiments of consumers when many combinations of options are available. This paper proposed a method to extract preferred services based on consumer behavior with strong correlation values between options in a service. The approach is by mining the association rules that correspond to the business process model. We first formalized a problem for extracting the preferred service model. Then, we proposed an extraction method by pruning the associative rules with more substantial relation using Cook’s distance and visualizing consumer behavior with a process model. Finally, we illustrated the approach of the proposed method and showed that we could extract preference with a higher correlation value compared to the conventional method.
Employee Satisfaction and Job Performance in the Accommodation Sector: Basis for Human Resource Plans

Purpose: It is impossible to meet the guest's needs when employees come and go, and it is costly to find new employees to recruit. The study aims to find ways of retaining employees while also investigating why they leave their jobs.&#x0D; Design/Method/Approach: A descriptive-correlational method of research in a quantitative design was used to determine correlation between variables. The study was highly accepted in terms of a Cronbach α = .988 which indicates an excellent index of reliability. Survey questionnaires served as a tool to draw facts about employees’ satisfaction and employees’ level of job performance in the accommodation sector.&#x0D; Findings: The study revealed the existing relationship between employee satisfaction and job performance in the accommodation sector. Based on the research findings, employee satisfaction is an important aspect that affects job performance and organizational success. Hence, the accommodation sector may leap at a chance of increasing employee satisfaction since it is the best predictor of job performance in the accommodation sector.&#x0D; Theoretical Implications: This paper expanded the body of research on employee satisfaction in relation to Herzberg’s Theory: (1) hygiene factors such as work environment, wages and benefits, and job security if they are not present at work will lead to dissatisfaction; (2) motivator factors such as career and personal work will increase employee satisfaction; (3) employee satisfaction mediates relationship with job performance.&#x0D; Originality/Value: This research can act efficiently through an enhanced human resource plan providing an optimal balance of human capital leading to increased productivity and retained quality employees.&#x0D; Research Limitations/Future Research: This research opens avenues for future research on dynamic capabilities in the hotel sector.&#x0D; &#x0D; Paper type: Theoretical
Mass online course in mathematics for school students: the problem of content organization

The article summarizes the experience of forming the content of online courses in mathematics for school students. The main approaches to the organization and presentation of materials and tasks are studied.
Discovering Tactical Memory From Observed Human Performance in Machine Learning

This article describes an investigation for composing a representation of significant past events that are retained in memory by an observed human actor. These memories influence that actor's performance of a task or making a decision. More specifically, we seek to infer which aspects of the environment and which events have significant effect on an observed actor's future decisions. We introduce a new memory modeling algorithm, memory composition learning, which processes traces of an observed actor's performance, and from these, composes a set of memory features that describe important events in his/her memory that affected these actions. These memory features are subsequently used to produce memory-enhanced traces that can be used by machine learning algorithms to learn memory-influenced behaviors. We implemented a prototype of our approach and evaluated it in two simulated domains, one with synthetic memory-influenced vacuum cleaner agents and one involving human subjects controlling a lawn mower that required memory-influenced behaviors. Results show that our approach is able to discover intuitive representations of tactical memory from observed behavior in both domains, and that these memory representations contributed to improved machine learning of human behavior.
Towards an Ontology-based approach to the “new normality” after COVID-19: the Spanish case during pandemic first wave

The impact of the pandemic caused by COVID-19 has been immense in all fields of human activity.In most of the affected countries, the authorities have decreed a series of legal measures to try to stop the growth of the disease and the number of people affected by it.These legal measures involved, in most cases, restrictions on the free movement of people and on work and trade activities, new hygiene procedures, and social distancing.In the particular case of Spain, the rapid evolution of the pandemic led to the declaration of a so-called state of alarm and a period of confinement in private homes.The novelty of the situation and the large number of official decrees approved may have led to confusion among the Spanish population in some cases.In this context, in this paper we present an ontological approach that could be the germ of a knowledge base on the measures approved by the Spanish Government in relation to the pandemic during the so-called first wave (March-June 2020).This ontology has been developed in OWL (Ontology Web Language) for its possible compatibility with semantic web-based applications and allows the consultation of statements related to various aspects of daily life during this period.