Reviewerly/exHarmony huggingface dataset:
KCNet: Kernel-Based Canonicalization Network for Entities in Recruitment Domain

Online recruitment platforms have abundant user-generated content in the form of job postings, candidate, and company profiles. This content when ingested into Knowledge bases causes redundant, ambiguous, and noisy entities. These multiple (non-standardized) representation of the entities deteriorates the performance of downstream tasks such as job recommender systems, search systems, and question answering. Therefore, making it imperative to canonicalize the entities to improve the performance of such tasks. Recent research discusses either statistical similarity measures or deep learning methods like word-embedding or siamese network-based representations for canonicalization. In this paper, we propose a Kernel-based Canonicalization Network (KCNet) that outperforms all the known statistical and deep learning methods. We also show that the use of side information such as industry type, url of websites, etc. further enhances the performance of the proposed method. Our experiments on 351,600 entities (companies, institutes, skills, and designations) from a popular online recruitment platform demonstrate that the proposed method improves the overall F1-score by 23% compared to the previous baselines, which results in coherent clusters of unique entities.
Bidirectional Edge-Enhanced Graph Convolutional Networks for Aspect-based Sentiment Classification

Aspect-based sentiment classification aims to predict the sentiment polarity of an aspect term in a sentence. It has been verified that syntactic dependent trees, especially integrating with graph convolutional networks (GCN) can provide crucial syntactic features for sentiment classification. However, it can not consider the dependency label information between the aspect and context in the sentence, which contains rich semantic information. In this paper, we propose a bidirectional edge-enhanced graph convolutional networks (BE-GCN), which combines the syntactic structure and the dependency label information effectively. Specifically, we design an edge-enhanced module to dynamically update the dependency edge according to dependency relation and contextual information. Then the dependency edge can update the word representation reversely, thereby selectively outputting sentiment features according to the given aspect. Comparison experiments demonstrate the effectiveness of our model using dependency label information and syntactic structure.
The Impact of Exponent Variable on the Performance and Effectiveness of FCM Algorithm for Ontology Construction in Structured Knowledge Management

Data Clustering is an effective mechanism for the clustering of similar data objects. Fuzzy logic based or soft data clustering technique is very suitable for the formation of data clusters semantically when a data object is the member of more than one clusters unlike hard data clustering techniques. Fuzzy exponent variable is one of the most important variable in fuzzy logic based clustering technique. This variable has significant impact on the performance and effectiveness of the clustering results. An experimental evaluation is performed to analyze the impact of fuzzy exponent variable in this study. The experiments are performed on textual web document dataset through Fuzzy C-Means soft clustering technique. The results retrieved with various values of fuzzy exponent variable, are analyzed and presented to represent the performance and effectiveness.
Hierarchical Attention Transformer Networks for Long Document Classification

Profiting from the pre-trained language representation models like BERT, the recently proposed document classification methods have obtained considerable improvement. However, most of these methods usually model the document as a sequence of text and omit the structure information, which appears obviously in long document composed of several sections with assigned relations. For this purpose, we propose a novel Hierarchical Attention Transformer Network (HATN) for long document classification, which extracts the structure of the long document by intra- and inter-section attention transformers, and further strengths the feature interaction by two fusion gates: the Residual Fusion Gate (RFG) and the Feature Fusion Gate (FFG). The proposed method is evaluated on three long document datasets and the experimental results show that our approach outperforms the related state-of-the-art methods. The code will be available at https://github.com/TengfeiLiu966/HATN
Multilevel Categorization of Bengali News Headlines using Bidirectional Gated Recurrent Unit

Text Classification is one of the popular tasks of Natural Language Processing. Like many other languages, Bengali is also gaining popularity in this field and the most famous work here is the categorization of different unlabelled news articles into some predefined categories like National, International, IT, and so on. This study focuses on classifying Bengali News Headlines instead of news articles from different online newspapers. It categorizes News Headlines into six different classes using Bidirectional Gated Recurrent Unit. The proposed model got an accuracy of 97% on training data and 84% on validation data which is quite satisfactory compare with the performances of other existing models in this field.
Applying TOGAF-based Cloud Adoption Framework

Despite of its flexibility and scalability, many organizations still encountered challenges to successfully implement cloud solution. Migrating from legacy to cloud solution apparently required rigorous communication and coordination with cloud provider. TOGAF-based cloud adoption framework was designed to facilitate both the enterprise architecture and business strategy. This paper demonstrated how this framework was applied in a real case of cloud call center initiative. The step-by-step approach in this framework has assisted the project team to assess the extent of preparation effort, perform cloud solution selection process, and estimate the benefits to business.
Comparison of The Use of Bigrams and Stopword Removal for Classification Using Naive Bayes (Case Study on Sentiment Analysis of By.U Internet Users)

With the rapid growth of the number of youth, several providers have launched digital service provider innovations. By.U as a pioneer has been serving this segment for the past year, and its presence has received various responses from netizens. This response, if researched, can improve services and lead to other innovations. However, no research addresses this. Therefore, a final project was written regarding Sentiment Analysis on the provider by.U. In this reearch, a classification model was made with 3804 data using Naive Bayes Classifier and TF-IDF with Bi-Grams. The comparison was made by eliminating one of the preprocessing steps: stopword removal. The comparison found that TF-IDF and Bi-Grams without the application of stopword removal had the best performance values. The best performance value is obtained in the split validation scenario 90:10 with accuracy 86.88%, Precision 88.24%, recall 83.33%, and f1-score 85.71%.
Adaptive Coarse-to-Fine Interactor for Multi-Scale Object Detection

Scale variation is one of the key challenges of object detection. Multi-level feature fusion is presented to alleviate the problems, e.g., Feature Pyramid Network (FPN) and its extended methods. However, the input features fed into these methods and the interaction among features from different levels are insufficient and rigid. To fully exploit the features of multi-scale objects and enhance the feature interaction, we propose a novel and effective framework called Adaptive Coarse-to-Fine Interactor (ACFI). Specifically, ACFI consists of three cascaded components: Multi-Resolution Fusion (MRF), Fine-Grained Interaction (FGI), and Edge-aware Enhancement (EAE). MRF adaptively extracts multi-level features from multi-resolution images and multi-stage features, and then these features are fed into FGI to have a fine-grained interaction utilizing bottom-up guidance. After that, EAE further refines the features obtained by FGI, and enhances the detailed edge information and suppresses the redundant noise. After the coarse-to-fine process, we can obtain powerful multiscale representations of various objects. Each component can be embedded into any backbones, separately. Experimental results show the superiority of our method and verify the effectiveness of each proposed module.
Implementation of Dewblock Clients on a Mobile Platform

Dewblock is a blockchain system based on dew computing and cloud-dew architecture. The goal of Dewblock was to implement a generic client architecture comparable to a typical blockchain system but with sufficiently low memory usage to be deployed on a personal computer or mobile phone, providing functionality consistent with a full node. In order to provide the features of a full node, the dewblock client uses the mechanisms of a cloud-dew architecture while maintaining a standard peer-to-peer architecture for internode communication. In this paper, a mobile application will be discussed, which implements Dewblock allowing it to run on any Android mobile phone. The primary intent was to prove the deployability of Dewblock on mobile platforms, and while Android was chosen here for expediency, the general principles involved can be applied to other platforms, such as iOS or personal computers.
A Comparative Investigation of Eye Fixation-based 4-Class Emotion Recognition in Virtual Reality Using Machine Learning

Research on emotion recognition that relies purely on eye-tracking data is very limited although the usability of eye-tracking technology has great potential for emotional recognition. This paper proposes a novel approach for 4-class emotion classification using eye-tracking data solely in virtual reality (VR) with machine learning algorithms. We classify emotions into four specific classes using VR stimulus. Eye fixation data was used as the emotional-relevant feature in this investigation. A presentation of 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> videos, which contains four different sessions, was played in VR to evoke the user’s emotions. The eye-tracking data was collected and recorded using an add-on eye-tracker in the VR headset. Three classifiers were used in the experiment, which are k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). The findings showed that RF has the best performance among the classifiers, and achieved the highest accuracy of 80.55%.
Rapid development of a novel and open‐access mixed reality resource for dental education

The authors declare that there is no conflict of interest.
Analysis of Systematic Reform of Future Teaching in the Age of Artificial Intelligence

Artificial intelligence has developed rapidly in recent years, which not only brings convenience to people’s lives, but also plays an important role in education. In the past, teachers mainly relied on relevant teaching materials to prepare lessons, but now teachers can use artificial intelligence technology to prepare lessons, which reduces the burden of preparing lessons for teachers. Artificial intelligence, referred to as AI for short, is a branch of computer science, and it is a discipline that studies how to make computers simulate people’s thinking and intelligent behavior. The application of artificial intelligence in education can promote education equality and improve teaching efficiency. For example, in some remote mountain schools, educational resources are scarce. The application of artificial intelligence can integrate and share educational resources to the network platform to achieve education equality. Then, this paper analyzes and explores how to change the teaching methods in the future. Starting from the current situation of the development of artificial intelligence in China and combining with the current teaching, it continuously analyzes and studies the teaching methods incorporating artificial intelligence to promote the systematic reform and development of future teaching.
Software Process Metrics in Agile Software Development: A Systematic Mapping Study

Software process metrics are being used for improving agile processes in software companies. This helps a lot in managing customer satisfaction and the growth of the company. Different studies have stated different process metrics and practitioners are using various metrics in terms of software process improvement. However, it is difficult to find out the best-suited metrics for every company as per the project nature. A systematic mapping study is used to identify potential process improvement metrics and finding the best metrics in agile development. We have found only 13 papers on process metrics. Finally, we have classified them into three categories assuming their impacting factors. This study provides an overview of agile software development process metrics that will help the practitioners to adopt efficient process metrics in Global Software Development (GSD).
Building a modified block matching kernel based on Wave Atom transform for efficient image denoising

The block-matching and 3D filtering strategy is a recent adequate solution for different problems related to low-pass image filter; however, this technique presents some drawbacks. In fact, handling noise with variable density and average or Gaussian noise with high densities may limit the performance of this approach. To overcome these limitations and increase the performance of the original BM3D approach, we propose in this work a modified BM3D technique based on wave atom transform with adaptive threshold called BM3D-AT. Experimental results and simulations revealed that the proposed technique is more efficient in removing different types of noise from different images, hence achieving outstanding performances.
Efficient Parameter Based Online Object Tracking

Traditional object tracking approaches solve the problem as a supervised learning task. Due to variations in video frames over time, the appearance model of the object is updated for each video frame. Using density or mixture based model to represent target object is a common technique in Bayesian approaches. However, in histogram based representation where the data is proportional, Gaussian distribution is not the proper choice. In this paper, we propose a framework for feature representation on the simplex manifold for proportional data utilizing the histogram representation of the target object in initial frame. Extracted density features are concatenated with histogram based features to get a better representation of the target object. A set of parameter vectors determine the appearance features of the target object in the subsequent frames. To precisely model proportional data, we employ five appropriate distributions; namely, Dirichlet, Generalized Dirichlet, Scaled Dirichlet, Beta-Liouville and Inverted Dirichlet distributions. We present experimental results to show that the proposed method, which incorporates both generative and discriminative features, has an improved tracking performance.
Human Visual System Model-Based Optimized Tone Mapping of High Dynamic Range Images

High dynamic range (HDR) image and video technology can provide significant picture quality improvement over the standard dynamic range (SDR). However, when HDR content is represented on an SDR display, dynamic range compression may result in image quality deterioration. To address this problem, we propose an optimized human visual system (HVS) response model-based tone-mapping algorithm to preserve the perceptual responses between the HDR image and its tone-mapped image. First, we measure the HVS response differences using a 2D histogram when an HDR image is displayed on an HDR device and when its tone-mapped image is displayed on an SDR device. Then, we formulate an optimization problem to minimize the differences. By efficiently solving the optimization problem, we obtain an optimal tone-mapping curve. Experimental results on actual displays demonstrate that the proposed algorithm provides superior image quality compared with conventional algorithms in terms of both subjective and objective evaluations.
Face Attribute Estimation with HMAX-GCNet Model

With the development of biomedicine, more and more computational models inspired by biological vision system have emerged. The HMAX model (Hierarchical Model and X) based on the visual pathway of the cerebral cortex is one of the classic calculation models. The model has achieved remarkable results in several coarse-grained recognition tasks. In this paper, the use of this model in fine-grained attribute prediction is studied. We propose a new image patch extraction method consisting of the distribution characteristics of face attributes. Graph convolutional neural networks are used to learn the relationship between attributes, which is embedded in the HMAX features. Compared with traditional HMAX, our prediction model performs better on face attribute estimation, with an improvement of 3%.
Student Break Behavior Recognition Dataset

With the explosive growth of classroom video data, it is possible to utilize artificial intelligence technology to recognize students' break behavior, which is very beneficial for personalized teaching. However, the lack of a dataset specifically for student break behavior may prevent its development. Therefore, this paper constructs a dataset of student break behavior. The dataset collected 64 break videos from Beijing Normal University. After processing, 1,538 video samples containing 9 student break behavior categories were obtained. Because we collect real video data, the student behavior is spontaneous, which makes the dataset representative and realistic. In addition, we also analyzed the dataset. Finally, it provides a baseline for the dataset and compares it with the current mainstream dataset. The results show that our dataset is feasible and reliable.
Attribution Modeling for Deep Morphological Neural Networks using Saliency Maps

Mathematical morphology has been explored in deep learning architectures, as a substitute to convolution, for problems like pattern recognition and object detection. One major advantage of using morphology in deep learning is the utility of morphological erosion and dilation. Specifically, these operations naturally embody interpretability due to their underlying connections to the analysis of geometric structures. While the use of these operations results in explainable learned filters, morphological deep learning lacks attribution modeling, i.e., a paradigm to specify what areas of the original observed image are important. Furthermore, convolution-based deep learning has achieved attribution modeling through a variety of neural eXplainable Artificial Intelligence (XAI) paradigms (e.g., saliency maps, integrated gradients, guided backpropagation, and gradient class activation mapping). Thus, a problem for morphology-based deep learning is that these XAI methods do not have a morphological interpretation due to the differences in the underlying mathematics. Herein, we extend the neural XAI paradigm of saliency maps to morphological deep learning, and by doing, so provide an example of morphological attribution modeling. Furthermore, our qualitative results highlight some advantages of using morphological attribution modeling.
Privacy Protection Model for Blockchain Data Sharing Based on zk-SNARK

In the era of big data, data sharing and communication play a crucial role. The blockchain data sharing model based on ciphertext policy attributed-based encryption (CP-ABE) is an existing solution to data sharing. However, it puts the access policy and attributes directly on the blockchain, so every one in the blockchain can access these access policies and attributes, which will cause privacy leakage. To solve this problem, a privacy protection model based on zk-SNARK is proposed in this paper. The blockchain double-chain structure was applied in this model, and the fine-grained access control of data sharing was realized based on CP-ABE scheme. At the same time, zk-SNARK technologies were adopted to protect sensitive access policies and sensitive attributes from disclosure, effectively defending the privacy of users when data sharing in blockchain.
Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health Records

Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient Electronic Health Records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes.
Scaring People is Not Enough

Fear appeals have been used for thousands of years to scare people into engaging in a specific behavior or omitting an existing one. From religion, public health campaigns, political ads, and most recently, cybersecurity, fear appeals are believed to be effective tools. However, this assumption is often grounded in intuition rather than evidence. We know little about the specific contexts within which fear appeals may or may not work. In this study, we begin to examine various components of a fear appeal within the context of password hygiene. A large-scale randomized controlled experiment was conducted with one control and three treatment groups: (1) fear only; (2) measures needed and the efficacy of such measures, and (3) fear combined with measures needed and the efficacy of such measures. The results suggest that the most effective way to employ a fear appeal within the cybersecurity domain is by ensuring that fear is not used on its own. Instead, it is important that information on the measures needed to address the threat and the efficacy of such measures is used in combination with information about the nature of the threat. Since many individuals that enter the information technology profession become the de facto security person, it is important for information technology education programs to distill in students the inadequacy of fear, on its own, in motivating secure actions.