Reviewerly/exHarmony huggingface dataset:
Online assessment solution without in-person proctoring

Abstract Online assessment became an important part of the assessment of students after March 2020 in the COVID-Time. Traditional face-to-face evaluations were no more possible, therefore the use of solutions like Google Suite Educational became mandatory. In this paper we explore the strengths and weaknesses of some applications for student evaluation. Also, we will present a Web-based evaluation software that we have developed and which allows to schedule questions at random with a given time for students to answer the question and without the possibility to come back to a previous question.
Research on the Method of Identifying Students' Online Emotion Based on ALBERT

Text sentiment analysis is a computer-based emotional classification of human language texts. At present, most text sentiment classification models are based on word vectors, and these models are difficult to solve the problem of polysemy. In order to effectively identify students' emotions through online comments, this paper constructs a text emotion classification model based on ALBERT and BiLSTM by using bidirectional transformers encoder. Through the contrast experiment, it is found that this model has better performance.
The Color Improvement of Underwater Images Based on Light Source and Detector

As one of the most direct approaches to perceive the world, optical images can provide plenty of useful information for underwater applications. However, underwater images often present color deviation due to the light attenuation in the water, which reduces the efficiency and accuracy in underwater applications. To improve the color reproduction of underwater images, we proposed a method with adjusting the spectral component of the light source and the spectral response of the detector. Then, we built the experimental setup to study the color deviation of underwater images with different lamps and different cameras. The experimental results showed that, a) in terms of light source, the color deviation of an underwater image with warm light LED (Light Emitting Diode) (with the value of Δa*2+Δb*2 being 26.58) was the smallest compared with other lamps, b) in terms of detectors, the color deviation of images with the 3×CMOS RGB camera (a novel underwater camera with three CMOS sensors developed for suppressing the color deviation in our team) (with the value of Δa*2+Δb*2 being 25.25) was the smallest compared with other cameras. The experimental result (i.e., the result of color improvement between different lamps or between different cameras) verified our assumption that the underwater image color could be improved by adjusting the spectral component of the light source and the spectral response of the detector. Differing from the color improvement method with image processing, this color-improvement method was based on hardware, which had advantages, including more image information being retained and less-time being consumed.
Predict Human Motion of Walk with Probability Distributions by Combining Machine Learning and Particle Filter

Avoiding collisions with humans during daily life is essential in human–robot interaction (HRI) environments. To this end, we propose a neural network (NN) to predict walking motions—the most frequent human motion in HRI. In a previous study, an NN was set up for predicting the walking position 0.5 s ahead by using skeletal coordinates with a low root mean square error. However, the prediction accuracy was found to be lower when predicting the four limbs, possibly because their motions had strong position uncertainty during walking. In the present study, a particle filter (PF) was used to predict a possible range, rather than the specific coordinates, of foot positions. The PF was used in three steps: initialization, sampling importance resampling, and moving the particle to predict. Once the initial distribution was set up, only the last two steps needed to be repeated frame-by-frame. The probability state distribution results of the feet were verified using three measures, and the reliability of these results was verified.
CCAP: Cooperative Context Aware Pruning for Neural Network Model Compression

In this paper, we propose a new cross-domain model compression technique to yield a compact target model. We utilize a Cooperative Context-Aware Pruning (CCAP) module to produce sparse attention maps. They are then used to transmit the source models’ parameters to the target model precisely. We also leverage a weight-regular loss to minimize the difference between the source models’ and the target models’ parameters. Our quantitatively empirical evaluation shows that our CCAP module plus the weight-regular loss achieves lower model complexity without having serious performance decreasing.
An Intellectual Property Data Access Control Method for Crowdsourced Testing System

In the crowdsourced testing system, due to the openness of crowdsourced testing platform and other factors, the security of crowdsourced testing intellectual property cannot be effectively protected. We proposed an attribute-based double encryption scheme, combined with the blockchain technology, to achieve the data access control method of the code to be tested. It can meet the privacy protection and traceability of specific intellectual property in the crowdsourced testing environment. Through the experimental verification, the access control method is feasible, and the performance test is good, which can meet the normal business requirements.
Interval-valued Data Ward's Hierarchical Agglomerative Clustering Method: Comparison of Three Representative Merge Points

This paper presents Ward Linkage Data Clustering on interval-valued data. Nowadays, clustering methods rely dissimilarity measures for interval-valued data uses representative point distance. Our work applies Ward&#x0027;s Hierarchical Agglomerative Clustering Method to interval-valued data based on the Range Euclidean Metric as a reliable alternative to be used to uncertainty quantification from interval-valued data. The range metric use makes possible different merge points be explored in the Hierarchical Cluster Analysis methods. We observe the remarkable impact of using three different merge points-infimum, supremum and midpoint of used range Euclidean distance on obtained clusters.
Object Permanence in Videos: DNN Performance vs. Human Ability

Object permanence, the idea that an object still exists when not in view, is a fundamental aspect of human cognition that typically develops in infancy. However, object permanence is often left out of machine vision models designed to identify objects in video. As machine vision technology becomes more widespread, it is important to understand the key differences between machine and human visual processing, and how we can bridge these gaps. In order to compare object permanence capabilities in humans and machine models, we created a custom video dataset of objects undergoing occlusion, including frames with partial and full occlusions. Human object detection performance on the dataset was assessed via survey, and compared with the SOTA object detection algorithm YOLOv3, as well as a novel recurrence-based model we call YOLO-LSTM. Although human performance degrades significantly when identifying object size and location under occlusion, human subjects almost always recognize that occluded objects still exist, while YOLOv3 shows linearly decreasing performance as objects become more occluded. YOLO-LSTM has a lower accuracy overall than YOLO-v3, but demonstrates more humanlike behavior.
Neural Local and Global Contexts Learning for Word Sense Disambiguation

Supervised Word Sense Disambiguation (WSD) has been one of the popular NLP topics, while how to utilize the limited volume of the sense-tagged data and interpret a diversity of contexts as relevant features remains a challenging research question. This paper focuses the problem and proposes a method for effectively leveraging a variety of contexts into a neural-based WSD model. Our model is Transformer-XL framework which is coupled with Graph Convolutional Network (GCNs). GCNs integrates different features from local contexts, i.e., full dependency structures, words with part-of-speech (POS), word order information into a model. By using hidden states obtained by GCNs, Transformer-XL learns local and global contexts simultaneously, where the global context is obtained from a document appearing with the target words. The experimental results by using a series of benchmark WSD datasets show that our method is comparable to the state-of-the-art WSD methods which utilize only the limited number of sense-tagged data, especially we verified that dependency structure and POS features contribute to performance improvement in our model through an ablation test.
Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems

Anomaly detection techniques constitute a fundamental resource in many applications such as medical image analysis, fraud detection or video surveillance. These techniques represent an essential step also for artificial self-aware systems that can continually learn from new situations. In this chapter, we present a semisupervised method for the detection of anomalies for this type of self-aware agents. The described method leverages the message-passing capability of Generalized Dynamic Bayesian Networks (GDBNs) to provide anomalies at different abstraction levels for diverse types of time-series data (i.e., both low-dimensional and high-dimensional). The detected anomalies could consequently be employed to enable the system to evolve by integrating the new acquired knowledge. To present a case study for the description of the anomaly detection method, we propose to use multisensory data from a semiautonomous vehicle performing different tasks in a closed environment.
Transreal Numbers and Sentient Logic

This chapter introduces the basic concepts of Total Semantics based on transreal numbers, a new field of mathematics in which division of zero is allowed. In the logic created from the transreals, it is possible to evaluate non-propositional objects such as sensations or feelings as having truth value. By covering all semantical possibility, transreal semantics enables a “poetical discourse” based on images and an immediate intuition of reality, an intuition that is both sensory and mental. This describes, and defines, a new type of epistemic operation—a sentient one—and leads to the possibility of developing a formal epistemic logic that assesses how we obtain or acquire knowledge from the perspective of sentient agents.
Applying VR/AR Technology in Product Advertising to Improve User Experience

Virtual Reality (VR) and Augmented Reality (AR) are technology trends that have achieved many achievements, attracting investment in research and development in countries around the world. Numerous industries use these technologies mostly for education, entertainment, and advertising purposes.In this article, we study VR/AR technology applied in ecommerce to improve the customer’s product experience. Customers can “touch” the product through virtual product simulation models, get more visual information about the product (instead of just viewing the product through images on the website or leaflets, posters) thus encouraging customers to make a purchase decision. We hope that this research will help customers have more positive experiences when choosing to shop for products. We expect that improving the product experience results in improvements to the perception people have of the brand.
Online Algorithms for the Interval Scheduling Problem in the Cloud: Affinity Pair Threshold Based Approaches

In the interval scheduling problem, jobs have known start and end times (referred to as job intervals) and must be assigned to processing nodes for their whole duration. Although the problem originally stems from the resource allocation demands of resident processes in operating systems, it found a renewed interest in the Cloud context, both in IaaS and SaaS, since reservations for virtual machines and services often have known activation intervals. A common objective of interval scheduling is to minimize busy time of machines which relates (among others) to minimizing the number of machines participating in the computation. As a consequence, bin packing techniques have been applied in the past. In this paper we tackle the online version of the problem, whereby future job arrivals are unknown. We propose novel algorithms that work as a pre-processing step to any bin packing scheme by offering recommendations that are enforced in all packing decisions. Job overlaps are used to characterize pairwise job affinity and subsequently provide threshold based job allocation recommendations. Thresholds are calculated using lower bound theoretical analysis upon two extreme workloads (sparse and dense). Experimental evaluation using real world workloads illustrates the merits of our approach against state-of-the-art algorithms.
Study on Named Entity Recognition in Chinese Literatures on Hypertension treatment

Chinese Medical literature research results are more accurate and representative than other medical texts. This paper studies the extraction method of named entity recognition in Chinese medical literatures on hypertension treatment. This paper proposes a Bi-directional Long Short-Term Memory-Conditional Random Fields (BiLSTM-CRF) model based on Attention mechanism for Chinese named entity recognition. BiLSTM-CRF is used as the model infrastructure while the Attention mechanism is used to learn the dependence of each word on the full text. In addition, the dictionary of literature keywords is built to improve the efficiency of recognition. Compared with the routine BiLSTM-CRF model, the recognition effect of the BILSTM-CRF model based on Attention mechanism was better. The value of Precision, Recall and F1 score were 84.6%, 87.9% and 86.2% respectively. The BiLSTM-CRF model based on Attention mechanism can effectively realize named entity recognition in Chinese medical literatures on hypertension treatment.
Dental EHR-infused Persona Ontologies to Enrich Dental Dialogue Interaction of Agents

The quality of patient-provider communication can predict the healthcare outcomes in patients, and therefore, training dental providers to handle the communication effort with patients is crucial. In our previous work, we developed an ontology model that can standardize and represent patient-provider communication, which can later be integrated in conversational agents as tools for dental communication training. In this study, we embark on enriching our previous model with an ontology of patient personas to portray and express types of dental patient archetypes. The Ontology of Patient Personas that we developed was rooted in terminologies from an OBO Foundry ontology and dental electronic health record data elements. We discuss how this ontology aims to enhance the aforementioned dialogue ontology and future direction in executing our model in software agents to train dental students.
An intermediary utility-based service search and structure organization approach in service-oriented MAS

In distributed service-oriented multiagent systems (SOMASs), agents cooperate autonomously to accomplish service discovery. In a SOMAS application, each agent plays two roles: (i) satisfying the service demands as a service provider, and (ii) forwarding the received queries as an intermediary agent. This study defines that the intermediary utility of an agent is positively related to the success rate of the service discovery in which it participates. If the intermediary utility of an agent is high (e.g., when the neighbors of the agent have abundant service resources), then the agent can better guide service searches. Based on this characteristic, this study proposes an intermediary utilities-based service search and structure organization approach to improve the efficiency of service discovery. In this approach, a service query has a higher probability of being forwarded to agents with higher intermediary utilities, which accelerates the service search. Additionally, the connectivity of the system structure can be preserved by the proposed approach, which ensures that the services can be accessed globally in the system. Extensive experiments demonstrate that the proposed approach can achieve better performance regarding the success rate of service discovery and average forwarding time of service queries comparing to previous approaches.
Research on Environmental Pollution Detection Method Based on Artificial Intelligence

With the improvement of the level of information technology, the application of artificial intelligence technology in environmental pollution detection has gradually attracted people's attention. The effective combination of information technology and pollution detection methods is the focus of research. Aiming at the problems of detecting pollution information and tracking pollution control information in the daily environment, an environmental pollution detection model based on artificial intelligence technology is proposed. First, analyze the environmental pollution detection business to build the framework of environmental detection; secondly, comb out all the links involving artificial intelligence technology in detail; finally, use the experimental analysis method to verify the practical applicability, and the experimental analysis results show that: based on manual The environmental pollution detection method of intelligent technology is more effective, has a larger carrying capacity, and can better ensure the follow-up effect in the later stage.
Approaches and Methods for Assessing the Information Security of Data Transmission Networks

The report examines approaches to assessing the information security of data transmission networks (DTN). The analysis of methods for quantitative assessment of information security risks is carried out. A methodological approach to the assessment of IS DTN based on the risk-oriented method is presented. A method for assessing risks based on the mathematical apparatus of the queening systems (QS) is considered and the problem of mathematical modeling is solved.
Enriching demand prediction with product relationship information using graph neural networks

Demand prediction is crucial for companies in the retail industry to increase their profit and customer satisfaction. Although recent studies show the success of state-of-art machine learning and deep learning models in demand prediction, enriching datasets using graph-based feature representations to improve demand forecasting models is still rare. In this study, we propose a demand forecasting model that forecasts demand with the usage of graph-based product embeddings. Unlike most of the existing methods, the sale information data is used to extract the relations and several relationships are utilized to construct graphs. Using the Node2Vec and GraphSAGE algorithms, five different embeddings are evaluated to reflect the different relationships of products. Extreme Gradient Boosting Regressor (XGBR) is preferred over other models because of the ability to handle high sparse data. In order to observe and compare the results of different models, we also implement Long Short Term Memory (LSTM). The performance is evaluated using a public retail dataset and the results show that the proposed model gives less error using Node2Vec graph-based embedding with XGBR.
Application of Augmented Reality Technology in Anesthesiology

This software, as the first virtual simulation teaching software that integrates advanced mixed reality technology with anesthesia theory learning and practical operation, intends to increase the interest, sense of reality, and immersion of traditional medical teaching through the integration of new technologies and emerging disciplines. In terms of software design, it aims to guide students by applying a correct cognitive process from theoretical knowledge, teaching video, teaching animation to basic test questions, simulation operation and finally virtual simulation operation, which will help to consolidate students’ learning and improve their memory of knowledge gradually.
Object Detection with Semi-supervised Adversarial Domain Adaptation for Real-Time Edge Devices

Object detection on real-time edge devices for new applications with no or a limited amount of annotated labels is difficult. Where traditional data-hungry methods fail, transfer learning can provide a solution by transferring knowledge from a source domain to the target application domain. We explore domain adaptation techniques on a one-stage detection architecture, i.e. YOLOv3, which enables use on edge devices. Existing methods in domain adaptation with deep learning for object detection, use two-stage detectors like Faster-RCNN with adversarial adaptation. By using a one-stage detector, the speed increases by a factor of eight. With our proposed method, we reduce by $$28\%$$ the changes in performance introduced by the gap between the source and target domains.
Classification of Dispersed Patterns of Radiographic Images with COVID-19 by Core-Periphery Network Modeling

In real world data classification tasks, we always face the situations where the data samples of the normal cases present a well defined pattern and the features of abnormal data samples vary from one to another, i.e., do not show a regular pattern. Up to now, the general data classification hypothesis requires the data features within each class to present a certain level of similarity. Therefore, such real situations violate the classic classification condition and make it a hard task. In this paper, we present a novel solution for this kind of problems through a network approach. Specifically, we construct a core-periphery network from the training data set in such way that core node set is formed by the normal data samples and peripheral node set contains the abnormal samples of the training data set. The classification is made by checking the coreness of the testing data samples. The proposed method is applied to classify radiographic image for COVID-19 diagnosis. Computer simulations show promising results of the method. The main contribution is to introduce a general scheme to characterize pattern formation of the data “without pattern”.
CRC-Model for Word Attributes Classification in Chinese Diagnostic Report

Chinese diagnostic report has enormous potential in doctor coaching and text structuring. With the development of natural language processing, the tasks on Chinese diagnostic report have attracted more and more scholars’ attentions. These tasks contain classifying words in texts as word attributes classification task. The deep learning methods, e.g., convolutional neural network (CNN) and recurrent neural network (RNN), can capture features through word embedding in diagnostic report and have shown a powerful performance to solve name entity recognition (NER) tasks. Therefore, this paper introduces a new method named convolution recurrent classification model (CRC-Model), which can achieve better performance than previous methods. The proposed method is sufficiently compared with three state-of-the-art NER models and two variants of the proposed method, which can obtain a total F1 of 94.33%. Meantime, in terms of training efficiency, the proposed method shortens the 25% training time compared with other methods. The experimental results and comparisons demonstrate that the CRC-Model does not only achieve better results than other methods, but also improve training efficiency in some extent.