Reviewerly/exHarmony huggingface dataset:
Blockchain: A Healthcare Perspective

Blockchain can be considered chain connecting blocks with a hash, grouped. Each contains its independent data and reference of the previous linked data. It creates a network controlled by the participants as to what they want to store and with whom they want to share. Blockchain technology can restructure the healthcare sector and upgrade the current situation, as it is secure, transparent, and reliable and stores real-time data. It can benefit healthcare by transforming the documentation of health records to electronic health records and remote patient monitoring sharing of these records to the concerned authorities (like government, hospitals, pharmaceutical industry, medical clinics, and insurance companies) using smart contracts. Blockchain provides an environment to keep medical data safe secure. It can keep data incorruptible, decentralized, and transparent log, allowing Blockchain’s extensive use for security applications. In this paper, the authors had discussed the working and features of Blockchain, implementation of Blockchain in healthcare, various Blockchain enabled healthcare system, blockchain model and EHR management, and various challenges in Blockchain.
Versatile feature learning with graph convolutions and graph structures

Graphs represent real world relationships, and graph embedding projects nodes in a graph to a latent space that can help simplify downstream tasks. Recent development of graph convolutions in deep learning significantly improves the performance of many learning tasks on graphs. Unfortunately, prior embedding methods either do not embed graphs with node features, or fail to produce high-quality embeddings for downstream learning tasks that result in large performance gap in comparison to direct learning on graphs.We present a versatile and effective embedding method, Conv2Vec, for embedding graphs with or without node features. It is based on graph convolutions with objective functions motivated by concepts and structures from classical graph algorithms. Conv2Vec produce high-quality embedding for both plain graphs and graphs with node features for downstream tasks.We evaluate the embeddings generated by Conv2Vec with a transductive node classification task. With the generated embeddings and very simple machine learning approaches, we are able to achieve accuracies similar to those achieved by direct learning with graph convolutions. Interestingly, if we strip the node features from the graph and thus learning an embedding has to rely entirely on the graph topology, node classification with our embedding significantly outperforms direct learning with various graph convolutions. This suggests that structures from classical graph algorithms may play an important role in learning on graphs.
Exploratory Investigation on a Naive Pseudo-labelling Technique for Liquid Droplet Images Detection using Semi-supervised Learning

Annotation are essential in using machine learning for object detection in images. However, good detection will require high quality annotations for supervised learning-based techniques. It is also known that manual annotation by human experts are laborious, tedious and error-prone. This problem is more so challenging in physical sciences images, such as liquid droplets or crystals. In this paper, we suggest a minimal effort approach to train YOLOv3 models using a relatively smaller set of training data for liquid droplet images. The proposed approach uses only minimal amount of annotations or annotated images, and naively build training data set using YOLOv3 models. The investigation compares two approach variants and discuss the results as an exploratory work in finding suitable semi-automatic image annotation techniques for object detection in liquid droplet images.
Zero-Cost, Arrow-Enabled Data Interface for Apache Spark

Distributed data processing ecosystems are widespread and their components are highly specialized, such that efficient interoperability is urgent. Recently, Apache Arrow was chosen by the community to serve as a format mediator, providing efficient in-memory data representation. Arrow enables efficient data movement between data processing and storage engines, significantly improving interoperability and overall performance. In this work, we design a new zero-cost data interoperability layer between Apache Spark and Arrow-based data sources through the Arrow Dataset API. Our novel data interface helps separate the computation (Spark) and data (Arrow) layers. This enables practitioners to seamlessly use Spark to access data from all Arrow Dataset API-enabled data sources and frameworks. To benefit our community, we open-source our work and show that consuming data through Apache Arrow is zero-cost: our novel data interface is either on-par or more performant than native Spark.
Privacy Leakage Avoidance with Switching Ensembles

We consider membership inference attacks, one of the main privacy issues in machine learning. These recently developed attacks have been proven successful in determining, with confidence better than a random guess, whether a given sample belongs to the dataset on which the attacked machine learning model was trained. Several approaches have been developed to mitigate this privacy leakage but the tradeoff performance implications of these defensive mechanisms (i.e., accuracy and utility of the defended machine learning model) are not well studied yet. We propose a novel approach of privacy leakage avoidance with switching ensembles (PASE), which protects against current membership inference attacks and does that with very small accuracy penalty, while requiring acceptable increase in training and inference time. Instead of using disjoint subsets for training the classifiers as in the current state-of-the-art PATE approach, PASE uses significantly overlapping subsets for training the classifiers in the ensemble. The consequence of that distinction is in moving from significantly reduced training sizes (and, correspondingly, reduced accuracy) for individual classifiers to insignificantly reduced training sizes (and, correspondingly, insignificantly reduced accuracy) for individual classifiers. We test our PASE method, along with the the current state-of-the-art PATE approach, on three calibration datasets and analyze their tradeoffs.
The Blockchain University: Disrupting “Disruption”?

This paper explores the promise of disruption of higher education offered by latest platform technologies—a combination of mobile applications for connecting teachers and students and blockchain technology for secure transactions of information and money. We start with a brief examination of several generations of technological disruptions arriving from the Silicon Valley with a special focus to educational technology. Showing that these disruptions are primarily focused to furthering capitalist mode of production, we question whether the latest disruption could provide different results. Advertised as “Uber for students, Airbnb for teachers”, the Woolf University offers the seductive promise of radical transformation of higher education based on cooperative principles. Our analysis, which is based on early ideas about the development of the Woolf University, indicates that it has the potentials to offer cooperative learning to students, cooperative employment to academic workers, all the while retaining highest quality of teaching and learning modelled after ancient scholastic principles. On that basis, we conclude that the Woolf University, together with other adaptations of blockchain technology for educational purposes, does offer a lot of potential for fundamental disruption of higher education and should be closely watched in the times to come.
Electronic Credential Full Life Cycle State Management Mechanism

With the rapid development of electronic commerce, electronic invoice is gradually replacing paper invoice, playing an unprecedented role in the process of commodity transactions. However, at present, e-invoices are only electronically issued and verified, and e-invoices need to be printed into paper invoices in subsequent stages such as reimbursement and archiving, which cannot realize electronification of the entire lifecycle. At the same time, there are security issues such as issuing or reimbursing the same invoice multiple times in the process of e-invoice circulation, lacking of effective supervision and management over the entire lifecycle. Aiming at the above problems, an entire lifecycle state management mechanism for electronic invoices was proposed. By designing different status information for e-invoices at different stages and migration rules, the safe circulation and supervision of the entire lifecycle of e-invoices were realized. Security analysis shows that the mechanism effectively solves the security problem of issuing or reimbursing the same invoice multiple times in the process of e-invoice circulation.
An Effective Implicit Multi-interest Interaction Network for Recommendation

Data features in real industrial recommendation scenarios are high-dimensional, diverse and sparse. Rich feature interaction can improve the model effect and bring practical benefits. Factorization machines (FMs) can perform explicit second-order feature interactions, while deep neural networks (DNNs) can perform implicit non-linear feature interactions. A series of models integrating FMs and DNNs are used to perform diverse feature interactions. However, most of the previous work performed feature interaction without considering the diverse interests of users. In reality, users often have multiple preferences and interests, which are implicitly included in the features and need to be effectively extracted. In this paper, we propose an implicit multiple interest network (IMIN), taking into account the importance of interest. Specifically, the model constructs the implicit multiple interests of the user and the item through the implicit multi-interest layer, and realizes the interest alignment between the user and the item through the interest alignment layer. We further use the interest interaction and aggregation layer to construct rich interest feature interactions. In addition, we introduce an auxiliary loss in the model optimization part to ensure the difference of interest. We conducted comprehensive and rich experiments on three real-world data sets. Experimental results show that IMIN performs better than other competitive models, which proves the effectiveness of the model.
Human Face Recognition Using Eigenface, SURF Method

One such complicated and exciting problem in computer vision and pattern recognition is identification using face biometrics. One such application of biometrics, used in video inspection, biometric authentication, surveillance, and so on, is facial recognition. Many techniques for detecting facial biometrics have been studied in the past three years. However, considerations such as shifting lighting, landscape, the nose being farther from the camera, the background being farther from the camera creating blurring, and noise present renders the previous approaches bad. To solve these problems, numerous works with sufficient clarification on this research subject have been introduced in this paper. This paper analyzes the multiple methods researchers use in their numerous researches to solve different types of problems faced during facial recognition. A new technique is implemented to investigate the feature space to the abstract component subset. Principle component analysis (PCA) is used to analyze the features and uses speed up robust features (SURF) technique, eigenfaces, identification, and matching is done, respectively. Thus, we get improved accuracy and almost similar recognition rate from the acquired research results based on the facial image dataset, which has been taken from the ORL database.
Effects of Image Compression on Image Age Approximation

In-field sensor defects are at the core of temporal image forensics, as a temporal order among pieces of evidence can be established by knowing their onset time. A characteristic of these single pixel defects is that they appear as point-like image noise. Since sensor defects exhibit noise-like properties, they are vulnerable to image compression. For this reason, it is important to evaluate the effect of image compression on the defect based image age approximation techniques available. In this paper, we assess the robustness of these age approximation methods with respect to four different compression techniques (i.e., ‘JPEG’, ‘JPEG 2000’, ‘JPEG XR’ and ‘Better Portable Graphics’) and different compression strengths. Since the approximation techniques considered require the defect locations to be known in advance, we assess the effect of image compression on the defect detection methods proposed in the context of image age approximation.
Exploiting Visual Context and Multi-grained Semantics for Social Text Emotion Recognition

Social text is a kind of user-generated data on social media, which can reflect the opinions and emotions of netizens in their social activities. The study about emotion recognition for social text could help us clarify netizens’ emotional position on certain issues, products, or services to support opinion monitoring, marketing management and so on. But with the continuous improvement of information technology, the mode of emotional expression has become increasingly complex and diverse. For the sake of concise and comprehensive implications, social texts are usually very short and noisy, which are often accompanied with visual context and pictorial multi-grained semantics. Therefore, traditional research strategies based on plain text analysis have great limitations faced with those contents. To this end, we in this article focus on the semantic and emotional uncertainty in social texts through taking into account the textual content of social text and its related non-text information to improve emotion semantic representations for social text emotion recognition. Specifically, to realize the visual context modeling, we first leverage the visual information associated with social text as the contextual supplement to enhance the emotional semantics of short social text. Then, we model the semantics of social text with different granularity to fully mine the limited information of social text. After that, with the help of visual context enhancement and multi-grained semantic mining, we are capable of alleviating the limited expression of social text and its uncertainty of semantics and emotions. This could represent the emotion semantics of social text effectively for its emotion recognition. Finally, extensive experiments on the real multi-modal dataset demonstrate that our proposed method has promising results with high efficiency.
Community Environmental Detection and Community Outdoor Space Health Promotion Based on Cloud Resource Scheduling Mechanism

In the context of rapid economic growth, people's living standards are improving day by day, and China's urbanization process is also gradually accelerating. Urban renewal is a very complex project. The renewal and expansion of most cities in the country usually involve demolishing and rebuilding. This way of urban renewal only pays attention to the renewal efficiency of cities and towns, and ignores the health needs of residents, which will make community residents lack a sense of security, pleasure, comfort, and belonging to the community, and greatly reduce the quality of life and physical fitness of community residents. Building a healthy city is an important goal of today's society, and community is the basic unit of a city, so it is urgent to build a health-promoting community. We should pay attention to and study the social environment, the external space environment of residential areas, and the interaction between them, so as to make develop them in a healthy direction and create a healthy and positive community. Therefore, it is an urgent task to improve the community residents' attention to the above problems and build community outdoor space, which has very important practical significance. In this paper, we use the cloud resource scheduling mechanism and the Internet of things to study the healthy role of community outdoor space planning. Through the transplantation of embedded Linux system, the development of communication interface and the external acquisition sensing device, we collect the information of working status and running status, and transmit it to the database built in the cloud through the 4G module on the motherboard. Then, HTML + java script + CSS + JSON + Ajax technology is used to write the display interface and logical relationship, so that the data can be displayed on the mobile terminal and PC, which can provide a useful technical exploration for outdoor space planning. The community Internet of Things technology realizes the intelligent community environment detection, and the community cloud resource scheduling mechanism combined with the community Internet of Things can obtain the residents' help information in time and allocate the community outdoor resources in real time, which can not only meet the health needs of community members but also be conducive to the sustainable development of a health-promoting community. In addition, the cloud resource scheduling mechanism and the Internet of things also provide new ideas for the study of the health promotion research of community outdoor space planning.
A Technical Support to Enrich Existing Software Development Courses, with the Additional Topic of Secure Coding

In a world of ubiquitous computers and interconnected systems, security is crucial and secure coding is an essential prerequisite for making systems secure. In order to achieve this, secure coding must not be bolt-on in teaching, but an integral part of any software development education. The complete redesign of software development courses can be difficult and time consuming to implement. In this paper, we present ESprESSO-IT which is short for “Enforcing SafE and Secure SOftware – In Teaching”, a funded research project by the municipal department MA 23 of Vienna and a solution to integrate secure coding into existing courses without relying on a retrospective leaning approach, whereby students learn the topic of secure programming after they may have already internalized insecure code implementations.
Optimal Design of the Frame of Baja Racing Car for College Students

Based on the competition rules of Baja Competition of China Society of Automotive Engineering in 2020, this paper designs, analyzes and optimizes the frame structure of Baja racing project. The mechanical properties of the frame are analyzed and verified by the finite element analysis method, and the structure and material properties of the frame are optimized on the basis of mechanical analysis. The mechanical performance of the frame obtained by optimization analysis is more superior, and the lightweight of the frame is realized at the same time. The optimized frame can effectively ensure the safety of drivers, improve race performance, and can be promoted from the theoretical and conceptual level to the level of practical application value, which has a certain reference value for the design of participating students.
Automating the Classification of Requirements Data

This paper proposes a pilot approach based on the comparative analysis of supervised Machine Learning models coupled with basic Natural Language Processing concepts for classifying Functional and Non-Functional Requirements from huge collections of data relevant to the Requirements Engineering (RE) phase within software development. The publicly available PROMISE Software Engineering Repository dataset is used in the execution of this approach. Non-Functional Requirements are further classified into subclasses based on attributes they address since they are not directly related to the core functions of the concerned software. This overall research initiative helps to make the RE phase more efficient and reduces human effort in software development. It leverages Big Data in Software Engineering.
ASSESSING OVERHEAD LINE'S SUSCEPTIBILITY TO STORM WIND DAMAGE USING OPEN DATA

Wind and storm are one of the leading causes of supply interruptions especially in forested countries. Long and wide scale interruptions can cause large monetary losses and hazards to the society. Thus, decreasing the amount and length of interruptions is a high priority for distribution system operators (DSOs). Security of supply can be improved by cabling or other investments to network infrastructure. However especially in rural areas with a declining popu-lation more cost-effective options should be considered, investments targeted to areas benefitting the most. Having up to date and accurate data of the network environment is key in accomplishing this. DSOs often have only rough classifications of their network environments and they are not frequently updated. However there exists a wealth of open data that is both accurate and updated more regularly, which can be used to more accurately categorise line segments based on their susceptibility to wind and storm damage. This paper focuses on developing a method to accurately classify overhead line section&apos;s susceptibility to storm wind damage by combining forest data and DSO interruption data. The categorization results corresponded to ex-pectations and DSO&apos;s internal data, however the sample size was fairly small.
Revocable Data Sharing Methodology Based on SGX and Blockchain

Data sharing methodology has recently been an active research area due to the development of information technology. As blockchain gets popular, decentralized storage mode becomes a favorable method for data sharing. Moreover, non-repudiation, confidentiality, revocability and fine-grained access are sometimes indispensable in practice. In light of these requirements, we propose a solution by combining decentralized ciphertext-policy attribute-based encryption (CP-ABE) and Software Guard eXtension (SGX) with blockchain. In our framework, the use of blockchain makes shared data publicly accessible and undeniable. To ensure confidentiality and fine-grained access control, we take advantage of decentralized CP-ABE to encrypt data. SGX is utilized as a key management service for the decentralized CP-ABE, making our data sharing methodology revocable without updating ciphertext. Overall, our methodology achieves privacy protection, revocability and decentralized fine-grained access. In addition, we perform experiments on Ethereum, and the results demonstrate that our approach is feasible.
Forensic Verification of Handwritten Documents using Secure Multi Party Computation

The recent advance in the use of image processing techniques has benefitted many areas including forensic and digital verification in cybercrime detection. Image processing techniques are used for producing digital evidence in criminal activities including counterfeiting of currency notes, cheques, fake government documents, wills, official reports and certificates. In this article, we find the solution to help document verifier or examiner to find out and differentiate a fake document from the original document. However, we need to secure all the documents that are used for verification to maintain data security. Therefore, we use Secure Multi-Party Computation to preserve the documents securely. The expected solution focusses on image processing tools for verification of computer manipulated documents and capable of detecting altered written documents preserving the documents integrity all through the process. The objective of this article is to build an ImageNet model to recognize the fake document with an accuracy of greater than 97%, and to keep the false negative rate less than 5%. The accuracies achieved by ResNet50 and VGGNet16 using SMPC are 97.62% and 93.88% respectively in verifying the unauthorized document given by the author.