Reviewerly/exHarmony huggingface dataset:
An Alert Notification Subsystem for AI Based Clinical Decision Support: A Protoype in NICU

The potential for recommendation systems integrated within clinical workflows for effective dissemination of vital information needed in decision making at the bedside is explored in this paper. Our premise is that by utilizing big data analytics platforms for processing high frequency physiological data from multiple patients, fused with clinical context, we can generate recommendations on patients detected as potential for onset of conditions, and that if such information is communicated on time to the appropriate health care providers could have an impact when making decisions on care of critically ill patients. To support this, we have designed and developed an alert notification subsystem that combines vast analytics to detect abnormal patient's physiology, determine who is on service at the bedside and then generate appropriate notification to that care provider during their schedule time in a hospital critical care unit.
A Dropout Style Model Augmentation for Cross Domain Few-Shot Learning

We focus on few-shot learning with cross-domain shift. While most existing few-shot learning assumed similar distributions between the source and target data, we aim to deal with the scenarios when we need to experience domain shift, the scenarios that fit to many real-world applications such as customization from lab-made toys to industrial products. More specifically, we consider the domain shift when we do not have much overlap between the source and the target domains, such as they do not have many overlapped classes or they do not have many common features. In fact, we experience significant improvement from existing work especially when we do not have much overlap between the source and the target domains. We propose a simple yet effective dropout-style method given a model that has been trained based on low-complexity concept from the source domain. The main idea is to sample several sub-networks by dropping neurons (or feature maps) to construct a bunch of models with diverse features for the target domain. Afterward, we choose the most suitable sub-networks to construct the ensemble for the target domain learning. The proposed method requires almost no external storage other than the original model for the source domain learning. As another advantage over other learning methods is the computation in the method is highly parallelizable for the purpose of efficiency. To evaluate the proposed method, we conduct experiments and show that the proposed method can be in cooperated with many metric-based few-shot learning base models, with consistent improvement under various settings.
A Proximal Alternating-direction-method-of-multipliers-based Nonnegative Latent Factor Model

A high dimensional and sparse (HiDS) matrix is commonly found in big data-related industrial applications. An alternating direction method of multipliers (ADMM)-based nonnegative latent factor (ANLF) model can efficiently extract latent patterns and knowledge from an HiDS matrix filled with nonnegative data. However, it suffers fluctuations near the local minimum, thereby leading to accuracy loss. To address this issue, this paper proposes a proximal alternating-direction-method-of-multipliers-based nonnegative latent factor (PANLF) model with the following ideas: a) building an augmented Lagrangian function for addressing nonnegativity constraints; b) incorporating proximal regularization into the learning scheme of decision parameters to fast search a local optimum, and c) adopting ADMM to split an optimization task into multiple subtasks where each one is solved based on the results of previously solved ones for fast convergence. Experimental results on four industrial-size HiDS matrices demonstrate that when compared with state-of-the-art models, PANLF possesses higher accuracy with comparable time and space complexity in predicting the missing data of an HiDS matrix.
Machine Learning-Based Algorithmic Approach for Enhanced Anomaly Detection in Financial Transactions

Governments all over the world aim to encourage digital money transaction to minimize administration cost, avoid customer’s physical present while purchasing, avoid money loss from theft, and to save time. So nowadays, people have started to use cashless transaction. Due to the increasing nature of cashless transaction, credit card becomes common nowadays, and many people have adapted to this payment mode. Customers are expanding on a regular basis as a result of the flexibility afforded by credit card transactions. Because of their ubiquity, credit card payments will not come without any risk. There are many types of credit card frauds, for instance, skimming, phishing, card lost or stolen, etc. and by using some machine learning algorithms, many fraud detection systems are available in recent years but the efficiency of those algorithms is not appreciable. Genetic algorithm, k-means algorithm, artificial neural network, logistic regression, SVM, etc. are few algorithms used to develop the system to detect credit card fraud. This paper makes use of an algorithm called isolation forest, which helps to detect the outlier data points in a better way. It performs well in large dataset, and also, it provides better results and accuracy when compared to SVM and local outlier factor.
A Secure VM Allocation Strategy Based on Tenant Behavior Analysis and Anomaly Identification

Cloud computing is gaining popularity due to prominent advantages of dynamic provisioning, economies of scale and low expenditures. However, co-resident attacks pose great threats to security and reliability of cloud infrastructure. Previous work has shown the effectiveness of secure virtual machine (VM) allocation strategies to defend against attacks and improve security. Unfortunately, existing approaches cannot distinguish potential malicious tenants before running VMs, so they adopt a reckless strategy of stacking VMs, which indirectly mitigates threats but fails to provide adequate security or balance workload. This paper presents an approach to reduce attack risk and balance workload by recognizing potential attackers before VM allocation and applying a secure allocation strategy to prevent malicious tenants from accessing normal ones. We analyze tenant behavior and VM usage data to identify potential attackers, assisted by machine learning methods. A new metric is proposed to measure co-resident attack risk and a novel risk-control VM allocation strategy is designed to minimize it. Implementation and evaluation on a dataset consisting of real-world VM workload demonstrate that our approach significantly outperforms existing approaches in minimizing the risk of co-resident attacks and balancing workload of datacenter as well as individual tenants.
Xplore Word Embedding Using CBOW Model and Skip-Gram Model

Word embedding has gained a lot of attention in recent years. To learn the systematic relation between words from a large junk of unlabeled text data is quite popular these days while using in Machine Translation, Question Answering or even in Text summarization. Computer understand only 0’s and 1’s now can understand text by simply using word embedding and making vectors in corpus i.e. word vector to represent them in numerical form which are used to perform linear algebra on the words which are now in numerical form in vectors. Word embedding is very effective tool in natural language processing (NLP), both CBOW (Continuous bag of words) and SKIP GRAM have increased the efficiency of word embedding along with it both models have reduced the training time. This paper is divided into two parts. In the first part we have discussed what is word embedding along with two models to implement word embedding, these models are continuous bag of word and skip gram. Our objective is to implement these two-model using shallow neural networks. In second part we have compared the efficiency of both the model while using different size of data sets.
Project Quality Management Challenges and Solutions in IT Project: A Systematic Literature Review

Project quality management (PQM) is an essential, critical, and expensive stage in a system development life cycle. However, many IT projects have functional defects that have an impact on project scope and budget. This underlies the research to find out the challenges and solutions in the implementation of PQM. Through the systematic literature review (SLR) method, studies related to the challenges and solutions of PQM implementation in IT projects were obtained. Based on the conducted SLR, 42 studies were obtained, then analyzed, and resulted in 12 challenges classified into six classifications by referring to PMBOK 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> edition. Furthermore, the most challenging results are the management classification, with the percentage reaching 43% of the entire studies. The management classification consists of two challenges. First, the lack of PQM consists of a review, evaluation/inspection, and assessment process. Second, the lack of the project manager's skill to manage the planning, monitoring, controlling, and project scheduling activities. The final step is mapping the solutions obtained based on the study and guidelines on the effective quality management Prince2.
Quality enhancement of compressed screen content video by cross-frame information fusion

In recent years, with the rise of various online learning platforms and game live broadcast industries, screen content video, a special type of video, is gradually emerging, and its traffic on the Internet is also increasing. Therefore, how to effectively enhance the quality of the screen content video has become an urgent problem to be solved. There exist a few successful compressed video enhancement algorithms. However, since there are a large number of areas with similar colors in the compressed screen content video, the traditional algorithms based on optical flow and deformable convolution cannot align the screen content video frames well. Specifically, for screen content videos containing animations and games, we propose a screen content video quality enhancement network based on the cross-fusion of multi-frame information. It includes a feature extraction module, a feature fusion module, an edge detail recovery module, and a reconstruction module. Our main contribution is the alignment-free quality enhancement framework based on cross-frame information fusion instead of traditional alignment based approaches. Through our experiments, the best results have been achieved on 13 screen content videos containing animations and games compressed by the SCC branch of HEVC/H.265.
An Evaluation of Meta-reasoning over OWL 2 QL

There has been increasing interest in enriching ontologies with meta-modeling and meta-querying for the past few years. Unfortunately, the Direct Semantics for OWL2 and SPARQL does not support meta-constructs in a satisfactory way: While meta-axioms (involving identifiers used both as a class and an individual) can be syntactically expressed using punning, different occurrences of the same identifier will be treated as different entities. For example, GoldenEagle used as an instance of EndangeredSpecies and also as a class containing individuals, will be treated as two separate, unrelated entities. Meta-queries (for example, asking for classes that also occur as individuals) are not allowed at all in SPARQL under the Direct Semantics Entailment Regime. To overcome this, a new semantic flavour for SPARQL, called Meta-modeling Semantics Entailment Regime (MSER), has been introduced. In previous work, Cima et al. have proposed a reduction from OWL 2 QL (a light-weight profile of OWL 2) and associated meta-queries in SPARQL to query answering over Datalog rules. In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. These tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set Programming). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies.
Session-based Recommendation via Contrastive Learning on Heterogeneous Graph

In this work, we propose a novel session-based recommendation model which can fully leverage the intriguing relationships among items. Firstly, a heterogeneous graph with diverse edges is constructed to capture semantic information among items. Meanwhile, two challenges involved in heterogeneous graph are addressed. One is the noisy or conflict knowledge introduced by meta-path based neighbors, and the other is "disconnected graph" which is incurred by sampling from disparate types of relationships. To alleviate these problems, a global-level contrastive learning model on heterogeneous graph is designed, while we also propose an adaptive subgraph sampling algorithm and a new adaptive edge perturbation policy to cope with the isolated node problem on augmentation. Finally, a local-level fine-tuning model is followed to predict users’ next behavior. Extensive experiments are performed on two real-world datasets demonstrating that the performance of our model is superior to the state-of-the-art methods.
Distortion-based Neural Network for Compression Artifacts Reduction in VVC

To reduce compression artifacts in video coding, prior knowledge from the codec is often utilized by deep learning based enhancement methods. However, most existing algorithms only take limited kinds of prior knowledge into consideration and directly feed it into neural networks, resulting in finite information obtained by the enhancement module. In this paper, a distortion-based neural network is proposed to better take advantage of features from the codec and further facilitate the quality of decoded videos. Firstly, a variety of prior knowledge is unitedly exploited to estimate the compression distortion. Secondly, an estimation module is also designed for the information obtained from the codec to improve the precision of estimation. With the accurately estimated distortion level, the enhancement module in the proposed method could reduce the corresponding artifacts by flexibly controlling the filtering strength. Moreover, the proposed method is integrated into VTM-7.1, achieving on average 5.92% BD-rate saving for Y component under All Intra configuration.
Evaluation of Comprehensive Ability of Accounting Applied Talents Based on GFAHP-Cloud Model

Through the objective and fair evaluation of the comprehensive ability of accounting talents cultivated in colleges and universities, on the one hand, it is beneficial to provide direction for cultivating accounting applied talents in colleges and universities, and on the other hand, it can also provide reference for enterprises to select talents. Firstly, this study constructs an evaluation index system for the comprehensive ability of accounting applied talents and selects 15 indicators from three aspects of knowledge level, ability level, and moral level. Secondly, the group fuzzy analytic hierarchy process is used to determine the index weight. Finally, a fuzzy comprehensive evaluation model based on the cloud model is constructed and tested in practice. This study hopes to make an objective and fair evaluation for accounting talents by quantitatively evaluating the comprehensive ability of accounting applied talents.
Multi Scale Attention Network for Crowd Counting

Reasonable management and control of extra crowded scenes have become a hot topic in recent years. Counting people from density map generated from the object location annotations is an effective way to analyze crowd information and control crowds in severely congested scenes. In this paper, we propose a novel end-to-end crowd counting method called MSANet for crowd counting. MSANet consists of the VGG16 backbone as the fronted part, two branches as the back-end part, including the attention map extractor to predict crowd states (means with people or not), and density map branch to regress the density map. What is more, to obtain high-resolution density map, we combine different scale maps from the front part to the back-end part. On the design of the loss function, to enhance the resolution of the predicted map and its structural similarity to ground truth, we proposed a new loss function for crowd counting. The test result based on the public dataset ShanghaiTech and Subway Crowd Counting Dataset supported by the Nanjing Metro demonstrates the effectiveness of our method.
Digital Twins by Physical Education Teaching Practice in Visual Sensing Training System

The paper expects to improve the efficiency and intelligence of somatosensory recognition technology in the application of physical education teaching practice. Firstly, the combination of induction recognition technology and the Internet is used. Secondly, through the Kinect sensor, bone data are acquired. Finally, the hidden Markov model (HMM) is used to simulate the experimental data. On the simulation results, a gait recognition algorithm is proposed. The gait recognition algorithm is used to identify the motion behaviour, and the results are displayed in the Web (World Wide Web) end built by the cloud server. Meantime, in view of the existing problems in the practice of physical education, combined with the establishment and operation of the Digital Twins (DTs) system, the camera source recognition architecture is carried out since the twin network and the two network branches share weights. This paper analyses these problems since the application of somatosensory recognition technology and puts forward the improvement methods. For the single problem of equipment in physical education, this paper puts forward the monitoring and identification function of the cloud server. It is to transmit data through Hypertext Transfer Protocol (HTTP) and locate and collect data through a monitoring terminal. For the lack of comprehensiveness and balance of sports plans, this paper proposes a scientific training plan and process customization based on Body Mass Index (BMI), analyses real-time data in the cloud, and makes scientific customization plans according to different students’ physical conditions. Moreover, 25 participants are invited to carry out the exercise detection and analysis experiment, and the joint monitoring of their daily movements is tested. This process has completed the design of a feasible and accurate platform for information collection and processing, which is convenient for managers and educators to comprehensively and scientifically master and manage the physical level and training of college students. The proposed method improves the recognition rate of the camera source to some extent and has important exploration significance in the field of action recognition.
Absorptive Capacity, Blockchain and Food Traceability: An Empirical Invetigation

Food holds a major role in human societies and involved from different essential actors, such as farmers, distributers, retailers, consumers, etc., which forms the food supply chain and argues as one of the most complex and challenge systems. Although traceability forms basis of its food supply chain, but it still faces some fundamental problems. Current research indicates that blockchain is a promising technology for handling these problems, in which various nodes (actors) are connected through the blockchain traceability system with efficiency, feasibility and trust. However, many mysteries are still existing in the blockchain food traceability system, such as the dynamic interactions among the nodes and the responses for the frequent contextual changes are still waiting for discover urgently. In this study adopted absorptive capacity, as the theoretical lens, for discovering the blockchain traceability system node’s capability for absorbing the blockchain as new knowledge, the interaction among the chain nodes, and the chain nodes’ absorption on the contextual changes. This study contributes to the IS literature by providing an account of the blockchain about absorptive capacity of these loose-connected chain nodes on the new knowledge, and responsibility of these chain nodes collectively on the frequent contextual changes?
Image Segmentation Based on Non-convex Low Rank Multiple Kernel Clustering

Subspace clustering has been widely used in image segmentation. These methods usually use superpixel segmentation to pre-segment image, while the superpixel segmentation method always divides image into superpixel blocks of similar shape and size, which results in poor segmentation and is time-consuming. In addition, the existing image segmentation methods based on subspace clustering don’t consider processing nonlinear structure data and complex noise. In order to solve the above problems, this paper proposes an image segmentation method (AMR_WT_NLMSC) based on non-convex low-rank multi-kernel clustering, which uses the adaptive morphological reconstruction seed segmentation (AMR_WT) for pre-segmentation, and designs non-convex low-rank multi-kernel subspace clustering (NLMSC) achieves the final segmentation. Experiments on real image datasets show that AMR_WT _NLMSC method has the more accurate segmentation effect.
Real-Time Prediction of Ocean Observation Data Based on Transformer Model

Ocean monitoring depends on the widely deployed oceanographic buoys and observation stations that integrate various types of ocean sensors. Those ocean sensors often work in harsh environments, so the data collected by the sensors are sometimes abnormal, which affects the accuracy of downstream applications, e.g., the ocean data assimilation and intelligent data mining. It is not realistic to conduct abnormal data detection (quality control) on huge amounts of increasing data only rely on human-based services. Hence, current research tendency is adopting AI models to realize automatic quality control, which largely depends on the accuracy of the AI model in ocean data modeling and prediction. Hence, in order to accurately model and predict the observation data, this paper explores and compares various sequential data modeling methods, which are widely adopted in time series data forecasting. We further proposes a Transformer model for accurate real-time prediction of those data. The experimental results show that our proposed method is better than the baselines both in single-step and multiple-step prediction experimental settings. As far as we know, this is the first work that applying transformer based model in ocean observation data intelligent analysis.
Application of Data Mining Technology Based on Data Center

Abstract Data mining technology refers to the use of mathematics, statistics, computer science and other methods to process a large amount of information to obtain useful conclusions and provide valuable decisions for people. With the rapid development and popularization of the Internet era and the more and more extensive application of computers in various fields, data mining technology has become a hot research field in today’s society. Based on the data center, this paper studies the data mining technology. Firstly, this paper expounds the definition of data mining, and studies the process of data mining and the steps of processing data. Then, this paper also designs and studies the framework of data mining, and tests the performance of the algorithm. Finally, the test results show that data mining technology can well meet the target requirements.