Reviewerly/exHarmony huggingface dataset:
Blockchain-based Enterprise Architecture for Comprehensive Healthcare Information Exchange (HIE) Data Management

Timely clinical data exchange has been the primary function of Health Information Exchanges (HIEs). However, there have been growing needs for sharing public health related data among government entities. Because the primary stakeholders for public health data are different from clinical data, data are stored over multiple systems, making the retrieval of related public health data items a hard process. In such situations, blockchain technology may be used to connect and securely exchange those scattered data items. In this paper, we proposed an enterprise architecture (EA) for HIEs to adopt blockchain technology so they could store and exchange both clinical and other health related data for efficient general public healthcare management. With the proposed solution, HIEs can not only exchange relevant data items, but also securely manage, record, and transfer complete health records. In our approach, we have incorporated a Hyperledger Fabric (HLF) Network blockchain technology that utilizes Blockchain as a Service (BaaS) to enterprise architecture so that HIEs can manage comprehensive health data.
A Model for Text Line Segmentation and Classification in Printed Documents

In this paper, we propose a new model for text line segmentation and classification, which consists of convolutional and two-layer bi-directional long short-term memory (BiLSTM) networks. Trained on the synthetic text dataset, it performs excellently when predicting the real data. Without labelling every line on the real data, a generalized standard for evaluating the accuracy is proposed. We also propose a simplified IoU loss to improve the execution speed greatly. In the experiments, it achieves 98.1% line segmentation accuracy and 99.5% classification accuracy on the English fiction Pride and Prejudice by Jane Austen, and achieves 98.5% line segmentation accuracy and 99.7% classification accuracy on the The Secret Of Plato's Atlantis by John Arundell, outperforming the traditional methods. Furthermore, for 1024 × 724 input samples, it gets 2.95 FPS speed when using a Tesla K80 GPU. Index Terms—Text line segmentation, Text classification, Synthetic text, BiLSTM, Convolutional network.
On Repurposing Social Virtual Reality Platforms to Support Distributed Learning

We investigate repurposing existing virtual reality applications as teaching platforms. A pilot study conducted using six freely available social applications on the Oculus Quest platform investigates the practical issues involved in using these applications for distributed learning in a higher education context. Recordings and reports from collaborative sessions with each application yield insights into employing virtual reality as an environment to support teaching. First time use involves both adapting to the platform interface, but also ensuring mutual agreement on conventions, control and expressions. Several applications support limited importing and authoring of content. Addressing and locating rooms and participants is a challenge, while the opportunities associated with avatar representation and identity promise enhanced interactions between individuals beyond traditional teacher/student roles.
Hybrid Cryptography for Cloud Computing

In the present scenario, we come across millions and trillions of data in our daily lives which can be handled by a data center. Cloud is the data center that enables users to access the files and applications from almost any device and any geographical location as computing and storage take place on servers in the data center instead of the user device locally. It facilitates users with services like Software, Applications promptly without any hazard. Though the cloud has mesmerized the world with its advanced capabilities still there is safety concern involved in it because the cloud is shareable. All security components must ensure data security for every user. In this study report, a new security model using Hybrid Cryptography is designed as data in the cloud is vulnerable to issues like unauthorized data access, integrity violation, identity management, etc. Hybrid Cryptography comprises both symmetric and asymmetric algorithms. Encryption of communication requires Symmetric-key encryption whereas data exchange is taken care by the Public-key encryption technique. RSA algorithm deals with Authentication, blowfish algorithm ensures Data Confidentiality, and Secure Hash Algorithm-2 deals with Data Integrity. The present study concluded that advanced methods provide high data security over the internet and provide services on demand effectively without delay or error.
Lessons Learnt in the Implementation of Quantum Circuit Simulation Using Decision Diagrams

Decision diagrams have shown to be a suitable data-structure for tackling the complexity of the quantum world. Accordingly, there has been a lot of research on how to improve their efficiency for quantum circuit simulation as well as broadening their scope. However, there are several smaller yet still interesting aspects that emerge when (re-)implementing corresponding approaches. In this work, we cover these aspects, illustrate them with examples, back them by further experiments, and derive corresponding learnt lessons from these considerations. This eventually gives more detailed insights into the implementation of quantum circuit simulation based on decision diagrams and eventually offers some interesting lessons learnt that may help to use those implementations in a more effective fashion and/or to develop further improvements upon them.
Chinese Real-word Error Automatic Detection and Correction Based on Confusion Set and Generalization Model

With the development of informatization, there are many errors in Chinese texts, and one kind of error is called “Real-word Error” which like English Real-word Error. The wrong word itself is also a right word in dictionary which confirm by the specific context. While using data resource of corpus to proofread text, data Sparseness will bring the miscarriage of justice problem. In view of the above problem, this paper proposes a Chinese real word error detection and correction method based on confusion set and generalization model. The method constructs the Chinese word confusion sets and trigram, and combines N-gram model and Bayesian model to preliminarily screen the current word and further validate on the probability of the current word appearing in the context respectively. In addition, the method alleviates the problem of false positive caused by sparse corpus data by synonym generalizing the context features. The proposed method combines automatic error-detecting and automatic error-correction together. Experimental results show that the proposed method can effectively find Real-word Error in Chinese text, and has higher recall rate, detecting accuracy rate and correcting accuracy rate and can give the correction lists.
Improved Reservoir Computing by Carbon Nanotube Network with Polyoxometalate Decoration

Physical reservoir computing (RC) is a recently introduced framework for information processing using the complex dynamics of physical systems. In this paper, a physical reservoir based on a molecular network of polyoxometalate (POM) decorated single-walled carbon nanotubes (SWCNT) with PBMA composite is fabricated. By the lab-built hardware platform, we experimentally demonstrate its excellent performance in a time series prediction benchmark with large short-term memory capacity (MC), indicating SWCNT/POM network as a promising substrate for reservoir computing because abundant inner charge and discharge in junctions leading to special electron transport characteristics that make rich dynamic and high-dimensional mapping properties appear in the POM-decorated SWCNT composite structure.
Image extraction for vehicle theft detection using Neural Network

The invent of new technology has paved way for a new direction in parking and securing system for vehicle. One of the major issue of the general publics is vehicle theft which can be handled by the typical security system. By utilizing the strategy of face recognition and the face detection, the vehicle theft will be avoided. The face recognition system which can be used as biometric data measures, whose applicability is simpler and trust worthy than others like unique mark in finger print, iris checking, signature, and so on. This paper proposes a framework that utilizes the blend of methods like face recognition and detection using Neural Network.The face recognition is performed on live images with no application field as a primary concern and it refers to the mental cycle process by which people find and take care of appearances in a visual scene. Processes used in the framework are white balance rectification, skin like district segmentation, facial component extraction and face image extraction on a candidate's face. At that time, a face arrangement technique is utilized the Feedforward Neural Network to coordinate the framework. Framework is additionally equipped for identifying and perceiving different faces in live gained security framework for vehicle.
Data Interoperability and Repository for Oceanography Research

Large amount of oceanic data have been acquired over Indonesian waters by various institutions over the years. These collections are normally stored in the multiple institutions' archives, resulting in scattered storages. Indonesian National Oceanographic Data Center (InaNODC) aims to integrate these data collections using a portal that enables integrated access that allows data exchange between different formats for interoperability. InaNODC integrates data collections from Konsorsium Riset Samudera (KRS) - a collection of government institutions that conduct ocean research and operate research vessels. In this paper, we present the flow process of accessing data collection using CTD data from the Arafura Sea as an example of one of the data types stored in InaNODC. This shows the capability of InaNODC to do a query, extract, and display the intended data. In the future, InaNODC is designed to be a part of IODE-UNESCO as one of the oceanographic and marine data centers in compliance with international standards.
Dual Logic of Teacher Role Transformation Based on Artificial Intelligence

With the advent of the information education, and artificial intelligence as a factor has been integrated into the teaching of "information technology" teaching structure, the development of artificial intelligence, for example, machine learning makes learners learning process to produce massive text data mining is more accurate, and teachers also endowed with new connotation, present a new era characteristics. In the current situation of massive educational resources, artificial intelligence education has triggered the thinking about the role of teachers in traditional education, and promoted the practical shift and complex logic of the transformation of the role of teachers. Based on the analysis of artificial intelligence education, this study tries to break through the "experiential orientation" of teachers' role in traditional education, and tries to construct a dual logic teacher role of "innovation" and "humanity", so as to explore the profound changes that artificial intelligence education brings to the construction of new teacher role.
Content Engineering for State-of-the-art SEO Digital Strategies by Using NLP and ML

The evolution of how people consume content and how that content is processed by search engines attracted a lot of attention in the past years. Content is the key aspect in every phase of the customer journey and modern content strategies rely on more intelligent, structured content organization. At the same time, search algorithms are getting smarter every year, while also making it harder for online businesses to get traffic without investing in a quality website and a concrete ongoing optimization strategy in mind. This is especially important for large content platforms and businesses that increase their content efforts over time because these types of platforms and processes cannot rely on manual categorizations and monitoring. They become increasingly complex when using multiple metrics to evaluate performance. Therefore, our idea is to perform automatic content categorization by using the concept of semantic similarity between content articles. We would like to test the hypothesis that the use of NLP and ML approaches to organize the content into content hubs can lead to better website performance monitoring and improved understanding of the most profitable segments of the website. The final outcome can serve as a basis for better decision-making and further optimizations in the internal linking network.
Full-Reference And No-Reference Quality Assessment For Compressed User-Generated Content Videos

With the development of video capture devices and network technology, recent years have witnessed an exponential increase of user-generated content (UGC) videos in various sharing platforms. Comparing to professionally generated content (PGC) videos, these UGC videos are generally captured by amateurs using smartphone cameras in various life scenes and contains various in-capture distortions. Besides, these videos undergo multi stages that may affect the perceptual quality before finally viewed by end-users. Complex and diverse distortion types bring difficulties to objective quality assessment. In this paper, we present a data-driven video quality assessment (VQA) method for UGC videos based on a convolutional neural network (CNN) and a Transformer. Specifically, the CNN backbone is used to extract features from frames and the output is fed to the Transformer encoder for the prediction of quality score. The proposed method can be used for both full-reference (FR) and no-reference (NR) VQA with slight adaptations. Our method ranks first and second in MOS track and DMOS track of the challenge on quality assessment of compressed UGC videos [1], respectively.
A Cyber-Security Strategy for Internationally-dispersed Industrial Networks

Globalization implies geographically dispersed supply chains composed of facilities strategically located in several countries and regions of the world. These structures commonly involve several Operational Technology (OT) and Information Technology (IT) infrastructures and integration to enable accurate and useful information processing. Such integration (also called Cyber-Physical Systems) transforms the industry and facilitates massive data volumes' systematic transformation into valuable information. Security risks posed by such integration may be substantial and, depending on the size of the company, and the number of integration points, dealing with them could easily cost millions of dollars. With the main objective of studying available strategies to manage security risks in companies with dispersed supply chains, this paper reviews international cyber-security standards and regulations and proposes a more comprehensive strategy. The strategy includes IT services, optimized perimeter segregation, and data flow policies among OT and IT networks to balance a high level of protection and cost-effectiveness.
Technology deployment in physics lessons: Understanding optics better with Augmented Reality

Abstract The following article presents the results of a seminar on “Augmented Reality in Institutional Education”. There, together with STEM trainee teachers, an optics experimentation circle was developed for pupils in the 7th and 8th grade at the Gymnasium for the subject of “Nature &amp; Technology” or “Physics”. This includes (real) experiments (lunar phases / eclipses, refraction, refractive error, color mixing), which were enriched with Augmented Reality technology created by the students. By creating a learning scenario with the help of Augmented Reality at school, the real environment continues to be perceived by the students but is supplemented with visualizations of physical model concepts. For example, the refraction of laser light on a water surface in the real experiment was overlaid with a visualization of the physical ray model of light, so that both information can be perceived simultaneously. This offers the students the opportunity to link physical model concepts statically but also in their spatial or temporal change in conjunction with the real experiment and to actively interact with it at the same time. Even without linking to a real object, augmented reality makes it possible to visualize changing processes or three-dimensional movements of virtual objects and to integrate holograms into the real world. We hope that the experiments presented here can demonstrate the benefits of AR extensions to real experiments, so that more applications will find their way into STEM lessons in the future.
Project-based learning opportunities in a digital environment: research on the naming of tourist destinations

The study is devoted to a problem that has not been sufficiently studied in relation to the field of education, namely, the problem of project-based learning in the digital environment. To solve this problem, education is considered as a contemporary phenomenon that allows the use of IT technologies and thus expands the opportunities of students both in terms of their mobility and the access to the educational process from anywhere in the world, and in terms of attracting intellectual, creative and technical resources. It was supposed that project-based learning in the digital environment has considerable prospects for students to gain professional skills, develop creative ideas, and introduce research methods into the business environment. As an example, the sociolinguistic research in the field of tourism was used. The project study conducted by the students is due to the growing interest of Russians in traveling around the country, but at the same time it showed the unpreparedness of the advertising and information base and proved that appealing to the promotion of domestic tourism, specialists should understand that today it is necessary to transform approaches to advertising tours and excursion routes. Thus, the implementation of joint student projects in the digital environment, covering sociolinguistic research and further work of specialists in the tourism industry, carried out on their basis on information and advertising support for tourist and excursion activities, will allow implementing project-based training as an important part of the practice-oriented educational process. In such projects, interdisciplinary interaction and inter-university cooperation can be implemented.
Fast Approximation of the Top-k Items in Data Streams Using a Reconfigurable Accelerator

This paper presents a novel method for finding the top-k items in data streams using a reconfigurable accelerator. The accelerator is capable of extracting an approximate list of the topmost frequently occurring items in an input stream, which is only scanned once without the need for random-access. The accelerator is based on a hardware architecture that implements the well-known Probabilistic sampling algorithm by mapping its main processing stages to two custom systolic arrays. The proposed architecture is the first hardware implementation of this algorithm, which shows better scalability compared to other architectures that are based on other stream algorithms. When implemented on an Intel Arria 10 FPGA (10AX115N2F45E1SG), 50% of the FPGA chip is sufficient for 3000+ Processing Elements (PEs). Experimental results on both synthetic and real input datasets showed very good accuracy and significant throughput gains compared to existing solutions. With achieved throughputs exceeding 300 Million items/s, we report average speedups of 20x compared to typical software implementations, 1.5x compared to GPU-accelerated implementations, and 1.8x compared to the fastest FPGA implementation.
Asian Giant Hornets Recognition Using Deep Convolutional Neural Network

Abstract We trained 2 deep neural networks to identify whether the images with high resolution in the dataset provided by COMAP contain any Asian giant hornet. We divide the classification problem into two subproblems: feature extraction, and image classification. In order to reduce the impact of sample imbalance, we apply image flipping and Borderline-SMOTE methods for data augmentation first, and then divide the data into the training set and the validation set (testing set). Next, we utilize auto-encoder to collect key features of images and the testing loss dips to 0.0274 after training. Next, we establish the Classification-Net to solve the identification problem based on the features just extracted, and the accuracy in testing set reaches 0.8030. Finally, we summarize the main features of having negative labels from three aspects: species characteristics, subject definition and background softness.
Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning

We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by sampling triples of anchor, positive, and negative data points from RGB-depth images and their natural language descriptions. We show that our approach can benefit from, but does not require, post-processing steps such as Procrustes analysis, in contrast to some of our baselines which require it for reasonable performance. We demonstrate the effectiveness of our approach on two datasets commonly used to develop robotic-based grounded language learning systems, where our approach outperforms four baselines, including a state-of-the-art approach, across five evaluation metrics.
Service composition based on hierarchical state machine

There have been many research methods on Web service composition, and state machine is one of them. The service composition based on the state machine is easier to understand, more intuitive and clearer than other methods. However, as the number of states increases, the jump between states will also become complicated. In this regard, a solution to the service composition based on a hierarchical state machine is proposed, which uses the service state machine to transform into a service rule library, and finally obtains the hierarchical state machine by adding default states and hierarchical generation algorithms. The reachability and equivalence verification proves that the method is feasible. Secondly, in the process of Web service composition, there are situations where multiple sub-services meet the user's functional requirements. In this case, in order to meet other non-functional requirements of users, it is necessary to filter the sub-services in the set of candidate subservices. In this regard, an improved firework particle swarm algorithm is proposed. The good point set method is introduced to initialize the particle population, which improves the diversity of the particle population at the beginning of the algorithm, and uses the particle position instead of the fitness value to improve the explosion radius formula and expand the search range of the algorithm. Improve convergence speed. In order to verify the effectiveness of the proposed algorithm, the experimental data adopts the QWS data set, and compares the improved firework particle swarm algorithm with IDPSO and FWPSO algorithms. The experimental results show that the proposed improved firework particle swarm algorithm can solve Web service composition more effectively Optimization.