Reviewerly/exHarmony huggingface dataset:
Using NAS as a tool to explain neural network

Explainability is the only way to establish the interaction between human and AI. Model explainability is necessary to verify whether the behavior of the model meets human expectations. It can establish trust relationship with users and simplify the transition from manual process to automatic process.Looking for appropriate mathematical tools to model the expression ability and training ability of deep neural network, and gradually transforming the parameter based deep learning based on empiricism into the deep learning based on the quantitative guidance of some evaluation indicators, is a new topic we need to face in the research of artificial intelligence.We think that the neural network search (NAS) technology in automatic machine learning can be used as a tool to help people to further solve the "black box" problem of artificial intelligence.
Detection of Stop Sign Violations From Dashcam Data

In this article we present a novel machine learning pipeline for automatic detection of stop sign violations from dashcam videos, Inertial Measurement Units (IMU) and Global Positioning System (GPS) data. We developed a two-step approach, including a detector (Stop Sign Detector) capable of identifying stop signs presence, position, and size within video frames, followed by a classifier (Stop Violation Classifier) that assesses the presence of violations along with a severity score. The Stop Sign Detector is a deep convolutional neural network (CNN) for image classification, which leverages the information contained in its deeper layer feature maps in order to extract estimates of position and size of the detected stop signs. The Stop Violation Classifier fuses the information provided by the Stop Sign Detector with IMU/GPS data to assess the presence and severity of a stop sign violation. The proposed approach has been tested on several thousands of real-world videos, recorded from US vehicles, in all kinds of weather conditions, times of the day and environments. Our method achieves an area under the precision-recall curve of 94% with a required computational time of 2.4 seconds to process a 16-second video entirely on CPU.
Identification of Key Audience Groups Based on Maximizing Influence

As the development of social networks has promoted scientific research in various fields, the Sina Weibo platform has become popular with users because of its real-time information and has become a new communication method and research tool for public opinion topics. The key audience groups of Weibo topics have an important influence on public opinion, so the research on key audience groups of Weibo hot topics has become one of the main contents of public opinion analysis. The research focuses on the characteristics of Weibo social platforms and the relationship between participants and forwarders in popular topics on Weibo. On the one hand, the main improvement’s overreliance on algorithms makes it impossible to achieve the greatest impact on scalability. By combining data driving with machine learning, some practical improvements are made. On the other hand, to improve the quality of the seed set for maximizing the impact of propagation impact prediction, a multi-dimensional Weibo audience user influence maximization measurement algorithm maximize your influence (MYI) is proposed. The experimental results show that the MYI algorithm can more fully identify the main audience groups in information dissemination and play an extremely important role in guiding public opinion.
Research on Transfer Learning Technology in Natural Language Processing

In the field of natural language processing, traditional machine learning methods rely on a large amount of labeled data to obtain better results and require training data and test data to satisfy the assumption of independent and identically distributed. Application of transfer learning technology provides a good solution to the problems above and promotes the research progress of natural language processing. This paper studied the application of transfer learning in natural language processing, made the definition of transfer learning, classified the transfer learning in natural language processing, and prospected the development of transfer learning in natural language processing.
Multi-Scene Virtual Try-on Network Guided by Attributes

Existing image-based virtual try-on methods often require personal photos, causing privacy violations. We thus propose a novel Multi-Scene Virtual Try-on Network (MSVTON), which generates try-on images while only giving clothes images and attribute texts consisting of view angle, skin color, etc. With our model, various scene try-on images can be created by manipulating the attributes. In order to improve semantic consistency and visual realism of the generated images, MSVTON decomposes the virtual try-on process into two stages. MS-VTON consists of Scene Learning Network (SLN) and Content Learning Network (CLN). SLN learns the semantics of try-on scenes and generates coarse try-on images in the first stage, and CLN refines minutiae such as clothes textures, etc. in the second stage. Experiments show that the FID score of MSVTON reaches 9. S, which outperforms the existing image-toimage translation methods.
Short-term Power Generation Prediction of Photovoltaic Panels Based on Meteorological Parameters and Support Vector Machine

On the basis of fully considering the impact of photovoltaic (PV) panels performance and actual operating state parameters on the power generation efficiency, a multi-variable short-term PV power generation prediction method based on support vector machine (SVM) is proposed in this paper. As a supervised learning model, SVM has outstanding advantages in data analysis and pattern recognition. Meanwhile, in order to obtain accurate and reliable data sources, an online monitoring system is established in the PV power station of Jilin Province. Actual monitoring data from PV monitoring system are used to train and validate of model, the simulation results show that the proposed model has higher prediction accuracy.
Bi-Level Attention Model With Topic Information for Classification

This paper proposes a bi-level attention neural network model (TBAM) that incorporates topic information. This model is suitable for a variety of classification situations and has proven effectiveness in sentiment classification and text classification. Compared with other advanced neural networks that are also used in classification, it has achieved better classification results. The TBAM model has three characteristics: (1) The model reflects the hierarchical structure of the document; (2) The model uses the attention mechanism at the vocabulary and sentence levels respectively, so that it can pay attention to the more important and the less important when constructing the document representation. content. (3) In the vocabulary level, by introducing potential topic information into the semantic representation of the vocabulary level to improve the effect of vocabulary representation, the topic information is regarded as domain information, which makes up for the ignorance of the domain information by the classification model, and increases the model domain adaptability. Through testing on the public data sets CCF-BDCI and THUCNews, the model shows good results on the three indicators of Precision, Recall, and F1.
Improvement to Naive Loss Functions with Outlier Identifier

We present a new Loss function, Loss with Outlier Identifier (LOI), a technique that produces a more robust calculation of prediction loss in Machine Learning fields and limits training time and procedures to minimum extend. LOI is designed based on the advantages of several well-known algorithm while compensating their disadvantages through interdisciplinary techniques. We show that by add two free parameters that do not require extra training, LOI is ensured to be continuous and derivable at all points and thus can be minimized through normal Gradient Descent algorithm. This function can be used to provide a more reliable loss for model training and thus produce a better model overall.
Implementing an Intelligent Collaborative Agent as Teammate in Collaborative Writing: toward a Synergy of Humans and AI

This paper aims at implementing a hybrid form of group work through the incorporation of an intelligent collaborative agent into a Collaborative Writing process.With that it contributes to the overall research gap establishing acceptance of AI towards complementary hybrid work.To approach this aim, we follow a Design Science Research process.We identify requirements for the agent to be considered a teammate based on expert interviews in the light of Social Response Theory and the concept of the Uncanny Valley.Next, we derive design principles for the implementation of an agent as teammate from the collected requirements.For the evaluation of the design principles and the human teammates' perception of the agent, we instantiate a Collaborative Writing process via a web-application incorporating the agent.The evaluation reveals the partly successful implementation of the developed design principles.Additionally, the results show the potential of hybrid collaboration teams accepting non-human teammates.
Regression analysis of clustered interval-censored failure time data with cure fraction and informative cluster size

Clustered interval-censored failure time data occur in many areas and many methods have been proposed for their analysis. In practice, in addition to the clustering and interval censoring, such data may also present two other issues, cure fraction and informative cluster size, and it does not seem to exist an approach that can deal with all of these issues together. To address this, we present a within-cluster resampling method and in the approach, the multiple imputation procedure is applied for estimation of unknown parameters. To assess the performance of the proposed method, a simulation study is conducted and suggests that it works well in practical situations. Also the method is applied to a set of real data that motivated this study.
Design of Vocational Education Self-Evaluation System Based-on Semantic Web Ontology

Abstract Educational institutions need to carry out regular self-evaluations to improve quality. These activities include evaluation and reflection on internal and external conditions in order to formulate future work programs. Self-evaluation needs to be supported by complete data relating to the condition of the institution. One of the problems in implementing self-evaluation is collecting data that is spread across various systems, either manual, computer, or online. In practice, data integration requires a long time and accuracy. One way to solve this problem is to develop a self-evaluation system using semantic web technology. This paper will discuss the semantic technology-based study program self-evaluation system. The method used is system development, which consists of 5 stages, namely 1) analysis, 2) design, 3) development, 4) implementation and 5) evaluation. This paper focuses on the analysis and design of self-evaluation systems based on semantic web ontology. The results showed that the concept of semantic web technology could be implemented in a self-evaluation system that has been developed and results in better performance.
Authentication of Facial Images with Masks Using Periocular Biometrics

Due to COVID-19 pandemic, wearing a mask rapidly becomes a new social norm that people in the society should comply. Although it is altruistic behavior preventing all people from serious infections, it brings about hassles for individuals. For example, when a face is partly covered with a mask, identification using a face recognition is going to be malfunctioning. To this end, we propose a novel computational framework that enables the personal authentication with a partial face image, a face covered with a mask. For the experiments, we constructed the datasets of facial images containing the periocular regions only, extracted from full facial images covered with the mask. Given the datasets, we trained our framework, a variant of a Siamese network, with various configuration of hyper-parameters. As a result, RMSprop optimizer with the learning rate 1 $$\times $$ 10–5 trained from periocular datasets showed the highest accuracy for the personal authentication. Next, we conducted a comparative experiment with our proposal and the model trained with datasets containing the full facial regions. When testing with the periocular region images, our proposal is superior in the authentication accuracy to that of the model trained with the full facial regions. This result raises the optimistic expectation that in the era of COVID-19, facial images covered with mask can still be used for the authentication using face recognition at a nearly same level of accuracy. This means that people can use the face recognition applications without taking off the mask, which provides the safe circumstances against the infections.
Modified Ant Colony Placement Algorithm for Containers

Container is an evolving lightweight virtualization innovation that attempts to perfectly capture a function and its library dependencies to be executed seamlessly at the operating system level without pre-installations or s/w setup. Placement of containers at the appropriate platform is essential in the utilization optimization of resources in cloud infrastructures. Efficient resource utilization can be achieved only when the containers are optimally mapped to VMs. Poor placement may cause a bottleneck in the cloud if VMs are loaded heavily and this may affect the response time of a given set of tasks. The ant colony optimization technique was used to schedule tasks and containers on VMs and PMs in the cloud. The disadvantage of typical ACO is its tendency to schedule tasks to the most used (high pheromone intensity) node. If the node is carrying a big load it will have an issue of overhead. By tracking preceding scheduling, this hassle could be solved by lowering the processing time and tracking load on each VM. With concerning the challenges and difficulty of the container placement, this paper proposes Modified Ant Colony Optimization Technique (MACO) for the placement of containers. The new proposal takes into consideration the scheduling history to enhance the scheduling decision. The results of MACO are compared with the basic Ant Colony Optimization technique (ACO) and First Come First Serve algorithm (FCFS). The experimental results show that the MACO is better than FCFS and the basic ACO in terms of response time and throughput.
Visualizing Big Data with Mixed Reality

The visualization helps understand the data. It is a technique to show outliers, noise and worthy data with the help of charts, graphs, plots, and various other techniques. The size of data is changing with time and becoming big data. The visualization of big data is becoming a challenge. This paper explores various state-of-the-art techniques and implements these techniques on Unity3D as a virtual reality (VR) application. The paper concludes that VR, AR and MR visualization techniques are better techniques to understand the big data with a 3D visualization and real time interaction.
LITERATURE SURVEY ON ARCHIVED KUMAMOTO PREFECTURAL DOCUMENTS OF THE JULY 28, 1889 KUMAMOTO EARTHQUAKE

The authors have been performed literature surveys on July 28, 1889 Kumamoto earthquake taking an opportunity of the occurrence of 2016 event in Kumamoto Prefecture. In the previous paper, reconnaissance reports and newspapers at that time were surveyed. In this paper, more literature surveys including archived Kumamoto prefectural documents were added. Processes of collecting damage data and summing up were traced using those official documents. Damage statistics appeared in various reports were also discussed based on their origins.
Joint compressive autoencoders for full-image-to-image hiding

Image hiding has received significant attention due to the need of enhanced multimedia services such as multimedia security and meta-information embedding for multimedia augmentation. Recently, deep learning-based methods have been introduced that are capable of significantly increasing the hidden capacity and supporting full-size image hiding. However, these methods suffer from the necessity to balance the errors of the modified cover image and the recovered hidden image. In this paper, we propose a novel joint compressive autoencoder (J-CAE) framework to design an image hiding algorithm that achieves full-size image hidden capacity with small reconstruction errors of the hidden image. More importantly, our approach addresses the trade-off problem of previous deep learning-based methods by mapping the image representations in the latent spaces of the joint CAE models. Thus, both visual quality of the container image and recovery quality of the hidden image can be simultaneously improved. Extensive experimental results demonstrate that our proposed method outperforms several state-of-the-art deep learning-based image hiding techniques in terms of imperceptibility and recovery quality of the hidden images while maintaining full-size image hidden capacity.
Reversible Database Watermarking Based on Random Forest and Genetic Algorithm

The advancing information technology is playing more and more important role in data mining of relational database. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The transfer and sharing of databases cause the copyright-related security threats. Database watermarking technology can effectively solve the problem with copyright protection and traceability, which has been attracting researchers' attention. In this paper, we proposed a novel, robust and reversible database watermarking technique, named histogram shifting watermarking based on random forest and genetic algorithm (RF-GAHCSW). It greatly improves the watermark capacity by means of histogram width reduction and eliminates the impact of the prediction error attack. Meanwhile, random forest algorithm is used to select important attributes for watermark embedding, and genetic algorithm is employed to find the optimal secret key for the database grouping and determine the position of watermark embedding to improve the watermark capacity and reduce data distortion. The experimental results show that the robustness of RF-GAHCSW is greatly improved, compared with the original HSW, and the distortion has little effect on the usability of database.
AR Learning Platform for Mentally Differently Abled Children

Learning has always been a challenge for Mentally Differently Abled children as the present school system cannot cater to their educational needs. Several studies have shown that using tech-aided tools has helped such children and made them eager to learn more. AR is capable of providing a superior learning platform due to its features like interaction and advanced spatial visualization. Therefore, incorporating Augmented Reality with educational contents would increase its effectivity and would also be able to cater the Mentally Differently Abled Children. In this paper, an Augmented Reality based Learning Platform is proposed which would have virtual scenes that transform a normal book into a multi-scene immersive experience specifically for the mentally differently abled children. It would be a multi-sensory platform that links text, image, sound and movement in a format which promises increased concentration as well as a better understanding in children with mental disabilities.
Localization properties of a discrete-time 1D quantum walk with generalized exponential correlated disorder

We study the dynamics of the 1D Hadamard quantum walk featuring generalized exponential correlated phase disorder. We report the existence of distinct dynamical regimes and discuss the prospect of a judicious tuning of the strength of localization of the walker via the degree of correlation. In particular, we unveil that when the typical correlation length is smaller than the chain size, the maximum spreading of the quantum wavepacket is achieved when the underlying disorder displays Gaussian correlations. Our work provides a framework for investigating the weakening of Anderson localization due to correlated disorder and may also find applications in the context of quantum information processing.
Asymptotic Properties of a Class of Criteria for Best Model Selection

The paper investigates the asymptotic convergence of some typical criteria for model selection from a given data sample. A range of known criteria are generalized into a special class joining two different groups based on both explicit and implicit implementing the trade-off between model accuracy and complexity. Criteria of the first group contain various explicit penalty terms for the model complexity whereas those from the second group are based on the sample division into two parts which is typical for the GMDH criteria. Definitions of asymptotic characteristics of the criteria are given and analyzed as well as the fact of consistency of such generalized class of criteria under some sufficient conditions regarding input vectors is proved.
Research on the construction of conceptual hierarchical model based on air pollution

Abstract In recent years, the problem of air pollution has become more and more serious, attracting close attention of mankind. The ontology system is constructed for the semantics of related words of air pollution to realize the sharing and standardization of semantics. Humans’ comments on the air conditions of the living environment are taken as corpus, and words related to air pollution are extracted from the corpus text to construct the ontology of air pollution. Divide the levels according to the attributes of air pollutants, and build and enrich the ontology system. This paper builds an ontology model based on air pollutants, divides hierarchical structure on the basis of concepts.
Active Learning Enhanced Sequence Labeling for Aspect Term Extraction in Review Data

Analyzing reviews with respect to each aspect gives better understanding as compared to overall opinions and this requires the aspect terms and their corresponding opinions to be extracted. Supervised models for aspect term extraction require large amount of labeled data. Aspect annotated data is scarcely available for use and the cost of manual annotation of the entire data is huge. This study proposes a way of using Active Learning to select a highly informative subset of the data that needs to be labeled, to train the supervised model. The identification of aspect terms is defined as a sequence labelling problem with the help of BiLSTM network and CRF. The model is trained on publicly available SemEval (2014–16) datasets for restaurant and laptop reviews. The results show a 36% and 42% reduction in annotation cost for restaurants and laptops respectively, with negligible effect on the model's performance. A significant difference in cost is observed between active learning guided sampling and random sampling approaches.