Reviewerly/exHarmony huggingface dataset:
Construction of Data Driven Decomposition Based Soft Sensors with Auto Encoder Deep Neural Network for IoT Healthcare Applications

The architecture of IoT healthcare is motivated towards the data-driven realization and patient-centric health models, whereas the personalized assistance is provided by deploying the advanced sensors. According to the procedures in surgery, in the emergency unit, the patients are monitored till they are stable physically and then shifted to ward for further recovery and evaluation. Normally evaluation done in ward doesn’t suggest continuous parameters monitoring for physiological condition and thus relapse of patients are common. In real-time healthcare applications, the vital parameters will be estimated through dedicated sensors, that are still luxurious at the present situation and highly sensitive to harsh conditions of environment. Furthermore, for real-time monitoring, delay is usually present in the sensors. Because of these issues, data-driven soft sensors are highly attractive alternatives. This research is motivated towards this fact and Auto Encoder Deep Neural Network (AutoEncDeepNN) is proposed depending on Health Framework in the internet assisting the patients with trigger-based sensor activation model to manage master and slave sensors. The advantage of the proposed method is that the hidden information are mined automatically from the sensors and high representative features are generated by multiple layer’s iteration. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Hierarchical Extreme Learning Machine (HELM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). It is found that the proposed AutoEncDeepNN method achieves 94.72% of accuracy, 41.96% of RMSE, 34.16% of RAE and 48.68% of MAE in 74.64 ms.
Open Calls and Pilot Projects

Abstract We describe the two ELG open calls for pilot projects, the objective of which was to demonstrate the use and the advantages of ELG in providing basic LT for applications and as a basis for more advanced LT-based modules or components useful to industry. Our main goal was to attract SMEs and research organisations to either contribute additional tools or resources to the ELG platform (type A pilot projects) or develop applications using Language Technologies available in the ELG platform (type B pilot projects). We start with the detailed description of the submission and evaluation processes, followed by a presentation of the open call results. Afterwards we describe the supervision and evaluation of the execution phase of the projects, as well as lessons learned. Overall, we were very satisfied with the setup and with the results of the pilot projects, which demonstrate an enormous interest in ELG and the Language Technology topic in general.
Dynamic model construction and correlation analysis of college students’ academic performance

With the increase of college students year by year, their academic performance has become one of the key directions of the majority of scholars. To find out college students’ academic performance, we take the student academic performance caused by the mutual influence between the dynamic change of research direction, building mathematical model, through the study of college students in all academic examination, was conditioned in college students, and must be rebuilt effect between college students, to determine the existence of the equilibrium point, This paper also introduces and analyses the stability of the variables similar to the basic regeneration number in mathematical epidemiology, and gives the corollary of the existence of the latter branch. Considering the random influence of campus atmosphere on students and the uncertainty and lag of the influence mechanism among students, a time-delay stochastic model is constructed based on the mathematical model. Then, the quantitative analysis of these influences was carried out, and the relationship between them was found, and due efforts were made for the in-depth study of the academic performance of college students.
Interoperable Metadata Bridges to the wider Language Technology Ecosystem

Abstract One of the objectives of the European Language Grid is to help overcome the fragmentation of the European Language Technology community by bringing together language resources and technologies, information about them, Language Technology consumers, providers and the wider public. This chapter describes the mechanisms ELG has put in place to build interoperable bridges to related initiatives, infrastructures, platforms and repositories in the wider Language Technology landscape. We focus on the different approaches implemented for the exchange of metadata records about, in a generic sense, resources and exemplify them with the help of four use cases through which the ELG catalogue has been further populated. The chapter presents the protocols used for the population processes as well as the adaptations of the ELG metadata schema and platform policies that proved necessary to be able to ingest these new records.
Fraud Detection During Financial Transactions Using Machine Learning and Deep Learning Techniques

Credit card fraud detection is a popular challenge in online transactions. Due to stolen cards that are used in unauthorized transactions by fraudsters, credit card fraud may occur. Also, a fraudster may utilize credit card credentials for his own purpose. As we are dealing with credit card issues, it is vital to build an effective credit card fraud detection system. Nowadays, deep learning and machine learning techniques are introduced in order to lessen human effort and time. In this paper, we present different experiments and findings related to the detection of credit card counterfeit detection. Here, Convolutional Neural Network(CNN), Convolutional Neural Network(CNN) with Gated Recurrent Units(GRU) and Adaptive Boosting (AdaBoost) algorithms are compared. We apply an oversampling technique, Synthetic Minority Oversampling Technique (SMOTE) to overcome the imbalance issue of our dataset. Most notably, Convolution Neural Network achieves very high AUC-ROC, accuracy, precision, and recall among all of them. In terms of correctness, this result is better than previously published work.
3D Gesture and Finger Interaction for Floating Handwriting Recognition

This paper develops a floating gesture finger interaction system in three-dimensional space and combines text recognition to write and recognize text in the floating space of three-dimensional axis. By using the information generated by the RGB and depth cameras, the obtained RGB and depth information is used to detect moving objects, and image analysis is performed to track the finger part of the hand to achieve finger control, and further combine with text recognition system to design a forward-looking user interface.
Measuring Bug Reporter's Reputation and Its Effect on Bug Resolution Time Prediction

In software projects, bug reports remain open and get updates from team members during the related bug's lifetime. It is an important task to predict when a bug would be resolved so that managers plan timeline and allocate team resources accordingly. Prior works show that reporter information is the most effective feature for predicting resolution time. Previous work only considers bug reporting activity to define reporter reputation and misses other activities. In this paper, we propose a new reputation calculation method that considers all activities of a reporter within a bug tracking system. We collected bug reports of Chromium and WebRTC projects and calculated reputations of each bug reporter within the dataset using our weighted, activity based calculation method as well as other methods from previous work in order to compare performance. We trained a Doc2Vec model to utilize textual information in bug reports to build a base model for comparing different reputation methods. Bug reports are classified into two categories as FAST and SLOW according to their resolution time. Stochastic Gradient Descent (SGD) and Extreme Gradient Boost (XGB) classifier algorithms are employed. XGB classifier resulted with 55 % - 72 % F -Scores for FAST and SLOW respectively. The results also show that data characteristics of project affects the effectiveness of the reputation calculation method on bug resolution time prediction.
Real-time Image Stabilization Method Based on Low Altitude Fixed Wing UAV

The existing electronic image stabilization techniques are mainly based on feature point matching, which is difficult to solve the problem that the low altitude fixed wing UAV Using strapdown camera platform collects fewer image feature points. To solve this problem, a real-time image stabilization method based on attitude angle information is proposed in this paper. Firstly, the attitude angle information collected by IMU and the image information collected by visual sensors are aligned in real time. Then, the attitude angles of pitch, roll and yaw are filtered differently, and the corresponding basic matrix is calculated according to the processed angular information. Finally, affine transformation is used to stabilize the original image sequence, and a smooth image sequence is obtained. The experimental results show that the peak-signal-to-noise ratio of the image sequence after image stabilization is increased by more than 5%. The algorithm is suitable for low altitude fixed wing UAV platform. For images with less feature point information, it can also eliminate jitters. At the same time, it has strong robustness to large maneuvers of aircraft.
BiLSTM-based with Word-weight Attention for Chinese Named Entity Recognition

Natural language processing is a hot research area in recent years. Named entity recognition is a fundamental task in natural language processing. However, Chinese named entity recognition usually suffers from weak relationship between related words and sentences resulting in recognition errors. In order to clarify the weight of different words in sentences and strengthen the dependence between character and words, we propose a named entity recognition model LSTM-WWAT based on bidirectional long-term memory network (BiLSTM) and word-weight attention(WWAT). Firstly, we add word semantic information into the character vector of the embedding layer by matching the dictionary. Secondly, we use the BiLSTM to extract the context dependent features of characters and related words. Then, the model import the hidden vector into WWAT and depend on sentences features to strengthen the word weight, so that the output will be closer to the entity annotation we want. Finally, Random Conditional Field (CRF) is used to decode the optimal coding sequence as the result of named entity recognition. Experimental results show that, compared with baseline models, our model achieves significant improvements.
FedADSN: Anomaly detection for social networks under decentralized federated learning

With the rapid development of distributed machine learning, federated learning (FL) has attracted plentiful attention and has become a paradigm. Compared with general machine learning, its outstanding advantage is that it can greatly lock the privacy of user data, allowing data to be calculated without leaving the local. We break through the data fortress in social networks with FL. However, it can be enormously difficult to identify a wholly trustworthy server in a social network, which is an obvious deficiency of the FL. Therefore, the decentralized FL is imperative to eliminate the inherent evil of server. In this article, we borrow the core idea of FL and propose an anomaly detection for decentralized federated learning under social network (FedADSN) to detect users with anomalous behaviours. We incorporate Graph Anomaly Detection (GAD) into decentralized FL framework to find malicious attackers in social networks. Our experiments can be performed to calculate and find outliers for malicious users, which demonstrates the feasibility of our framework and claims our algorithms are the state of the art.
Transformers for Object Detection in Large Point Clouds

We present TransLPC, a novel detection model for large point clouds that is based on a transformer architecture. While object detection with transformers has been an active field of research, it has proved difficult to apply such models to point clouds that span a large area, e.g. those that are common in autonomous driving, with lidar or radar data. TransLPC is able to remedy these issues: The structure of the transformer model is modified to allow for larger input sequence lengths, which are sufficient for large point clouds. Besides this, we propose a novel query refinement technique to improve detection accuracy, while retaining a memory-friendly number of transformer decoder queries. The queries are repositioned between layers, moving them closer to the bounding box they are estimating, in an efficient manner. This simple technique has a significant effect on detection accuracy, which is evaluated on the challenging nuScenes dataset on real-world lidar data. Besides this, the proposed method is compatible with existing transformer-based solutions that require object detection, e.g. for joint multi-object tracking and detection, and enables them to be used in conjunction with large point clouds.
Matrix Neural Network with Kernel Activation Function and Its Online Combined Learning

In the paper, we introduce a matrix neural network with a kernel activation function that is meant to process information in real-time mode. The distinctive feature of the system is during learning not only synaptic weights but also the architecture of the system tunes. At the same time, there are such criteria to be minimized as mean square error and empirical risk one. The proposed system combines the advantages of support vector machines, probabilistic neural networks, and radial basis function networks that allow tuning the proposed system under conditions of a small training dataset.
Evaluation of a Virtual Reality Learning Enviroment testbed and Non-Verbal Cue Integration

Virtual Reality (VR) has the potential to, not only match the modalities of interpersonal communication for distance-learning but, reconfigure the learning process altogether. To this end it is important to examine VR communication parameters such as non-verbal cue (NVC) transferability and impact. In this study we present preliminary usability and realism evaluations by 32 participants for a Virtual Reality Learning Environment (VRLE) testbed that will facilitate a variety of relevant future experimentations. As presented here, the VRLEs supports a high-fidelity professor avatar featuring a number of NVCs.
Herstory: an AR storytelling application presenting women's heroic lives in public space

Augmented Reality (AR) storytelling for cultural heritage sites is witnessing a new wave of scholarship and experimentations underpinning core issues of the discipline alongside the development of enabling technologies. A promising research direction points to the interactive narrative design depending on the aims and desired outcomes of each project. The aim of the application “Herstory” is to design a compelling narrative out of fragmented stories and evidence that interprets and presents women's heroic lives in public space. In this paper, we discuss the design and implementation of an AR storytelling application and the critical steps required to create an appropriate experience for the user through a cohesive narrative that unfolds in a place of executions.
CRONUS: Fault-isolated, Secure and High-performance Heterogeneous Computing for Trusted Execution Environment

With the trend of processing a large volume of sensitive data on PaaS services (e.g., DNN training), a TEE architecture that supports general heterogeneous accelerators, enables spatial sharing on one accelerator, and enforces strong isolation across accelerators is highly desirable. However, none of the existing TEE solutions meet all three requirements. In this paper, we propose CRONUS, the first TEE architecture that achieves the three crucial requirements. The key idea of CRONUS is to partition heterogeneous computation into isolated TEE enclaves, where each enclave encapsulates only one kind of computation (e.g., GPU computation), and multiple enclaves can spatially share an accelerator. Then, CRONUS constructs heterogeneous computing using remote procedure calls (RPCs) among enclaves. With CRONUS, each accelerator’s hardware and its software stack are strongly isolated from others’, and each enclave trusts only its own hardware. To tackle the security challenge caused by inter-enclave interactions, we design a new streaming remote procedure call abstraction to enable secure RPCs with high performance. CRONUS is software-based, making it general to diverse accelerators. We implemented CRONUS on ARM TrustZone. Evaluation on diverse workloads with CPUs, GPUs and NPUs shows that, CRONUS achieves less than 7.1% extra computation time compared to native (unprotected) executions.
Who owns the address? A performant block storage model for hybrid cloud

Rapidly growing data volumes stimulate the migration of block storage from on-premise to hybrid cloud. Hybrid cloud block storage is composed of both on-premise and remote cloud storage. Unfortunately, the heterogeneity of on-premise clients (e.g., laptops, mobile devices, etc.) with various operating systems encumber the hybrid cloud architecture design. What's worse, the demanding fault-tolerance and latency requirements pose challenges to the block storage system. Under the premise of strong consistency and availability, there are two mainstream models of hybrid storage systems: the low latency but low throughput “active-standby” model where only one node can provide reads and writes, and the high throughput but high latency “active-active” model where all nodes can provide reads and writes. To address the dilemma between throughput and latency, we design a novel storage model called the “logical-active” model, which partitions the input data into shards in terms of the block address ranges and selects one owner node called “logical-active” for each shard. A unique “logical-active” node manages the creation and deletion of the shard and provides reads and writes for the clients, while one or more borrower nodes called “logical-standby” provide only reads after synchronization with the “logical-active” node. In this way, the “logical-active” model achieves low latency close to the “active-standby” model and high throughput close to the “active-active” model.
A Data Availability Modeling Approach Towards Cloud Storage Systems Based on Client Perspective

System data availability is a key indicator to measure the ability of a cloud storage system to provide continuous service. Usually, users could only get the data availability of a cloud storage system through measurement, meaning that they could not know whether the system works normally unless they have read and written data on it. To improve data availability, the common method in the industry is to use higher performance physical devices or design more complex organizational architectures. However, no matter how the service side is designed, if the client side does not have the ability to switch service points, the whole cloud storage system is not highly available. In this paper, we propose a data availability modeling approach towards cloud storage systems based on client perspective. Initially, we discuss traditional availability computing models and summarize the necessary conditions for a cloud storage system to realize high availability. Furthermore, combined with specific scenarios, we model two different modes of switching service nodes on the client side. Finally, we quantitatively analyze the optimal ratio of the detection requests and the read-write buffer on the premise of ensuring the storage networks performance.
Review of User Experience and User Interface Design for Children’s Educational Apps

A good user interface and user experience are the cornerstones for developing an application or website that can be used effectively. Naturally, students who are adjusting to the present pandemic situation need time to change their learning techniques, particularly if those approaches are entirely digital. The PRISMA approach, which included a thorough evaluation of the literature, was the principal tactic employed by the authors of this study. The PRISMA research methodology was chosen by the author because it permits reporting of the number of reviews and meta-analyses that were carried out while producing this study and also covers broad issue themes. For a website or program to be well-liked by users, UI/UX is well-known to be crucial. The appearance of a website or application without UI/UX will be uninteresting and confusing. The goal of this study is to identify a user interface and a user experience in children’s educational applications.
Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos

The Cosmos uses an Inter-Blockchain Communication protocol (IBC), which achieves interoperability among heterogeneous blockchains, and horizontal scaling. In Cosmos, inter-chain transactions show lower performance than intra-chain transactions. As the inter-chain transaction rate increases, the entire network's transaction processing speed per second (TPS) decreases. Detail performance evaluations on Cosmos are essential for improving interoperability and designing with the trade-offs between scalability and compatibility. Therefore, this paper proposes a new approach to measure and analyze the inter-chain message relay time in Cosmos. Our results focus on the performance of inter-chain in terms of communication between different chains.
Monitoring System 4.0 on Horticultural Plantation Land Based on The Internet of Things

The success of horticultural cultivation comes from climate and environmental factors. Climate change affects the number and quality of plants. The increasing risk of pests and plant diseases is due to weather anomalies. Therefore, this research aims to develop a system that can monitor microclimate conditions in plants and IoT control sensor devices via an Android-based smartphone. The research stages start with planning, requirement analysis, designing, system implementation, testing, and lastly feedback. The system is built using the Flutter Framework which produces Android-based applications. The development of this system was designed using the Scrum method in the development process and the UCD (User-Centered Design) approach for the usability evaluation process with users. The system was tested using Black Box Testing and Usability Testing. The black box test results are successful for all tested features. Usability testing was carried out using the Post-Study System Usability Questionnaire (PSSUQ) method using a 7-point Likert scale by testing the system on 16 respondents who were divided into 8 respondents for each user group, namely farmer and public respondents. The results of usability testing obtained an overall score for each user group, where the overall average value for the public respondent is 5.90 and the overall average value for farmers is 6.06. Based on these results, it can be concluded that the system is feasible to use for users since it provides good usability, information, and user interface for monitoring microclimate conditions.