Reviewerly/exHarmony huggingface dataset:
Understanding the Introduction of Design Thinking as a Change Process

When Design Thinking is introduced into software development projects, it is often done in an unstructured way and with a tendency to formulate goals only vaguely. It mostly is unclear at which points within the software development process the desired changes should be implemented. Moreover, the implementation of changes is often not initiated by the management but by an employee and testing on a project is conducted in a non-systematic way. Usually, such test projects fail and consequently does the introduction of Design Thinking. This contribution is not about the technical aspects of integrating Design Thinking into a software development process. It is rather intended as a contribution toward understanding the implementation of Design Thinking into software development as a change process, enabling us to deal with challenges, that may arise during the implementation itself in a more targeted way.
A 3d Visual Security (3dvs) Score to Measure the Visual Security Level of Selectively Encrypted 3d Objects

Today, 3D objects are becoming more commonly used across many domains. However, it is necessary to secure them during their transmission over networks or when archiving them in the cloud. Encryption is a smart solution to protect 3D objects while remaining format compliant. While there exist many methods proposed for 3D quality evaluation, very few have been developed to evaluate the visual security level of encrypted 3D objects. In this paper, we propose an efficient metric, called 3D Visual Security (3DVS) score, to evaluate the visual security level of selectively encrypted 3D objects. First we present a new dataset composed of selectively encrypted 3D objects that have all been evaluated by more than 50 observers in terms of visual security. Secondly, we propose a model to determine the security parameters according to a desired security level. Finally, we detail our proposed 3DVS score which is based on full reference 3D metrics and serves to measure the visual security level of selectively encrypted 3D objects.
A Generalized Net Model of the Normal Heart Functioning

Within the last 20 years a lot of Generalized Net (GN) models of the ways of functioning of the different systems and organs in human body and models, related to description of biomedical processes in the living organisms were constructed. In this paper a GN model of the normal heart functioning is developed. It gives possibilities to evaluate heart’s parameters and to analyze its behavior and functioning. The GN model can be detailed and complicated and also it can be extended to accommodate some disorders of the cardiovascular system.
A Quantum-Inspired Model for Bit-Serial SIMD-Parallel Computation

Bit-serial SIMD-parallel execution was once commonly used in supercomputers, but fell out of favor as it became practical to implement word-level operations directly in MIMD hardware. Word-level primitive operations simplify programming and significantly speed-up sequential code. However, aggressive gate-level compiler optimization can dramatically reduce power consumed in massively-parallel bit-serial execution without a performance penalty. The model described here, Parallel Bit Pattern Computing, not only leverages gate-level just-in-time optimization of bit-serial code, but also uses a quantum-inspired type of symbolic execution based on regular expressions to obtain a potentially exponential reduction in computational complexity while using entirely conventional computer hardware.
Weblogmusic

Weblogmusic (http://weblogmusic.org) is a web-based platform for performers to improvise in asynchronous virtual ensembles and for audiences to experience these performances in ways that reflect the impact of technological mediation on human interaction, the human values and sensibilities in mediated experiences, and the power of mediation as an expressive element in art. These born-digital performances only exist in the moment of playback. The turn-based, time-shifted format compels performers and audiences to probe their senses of presence and authenticity. It is designed to exploit the unpredictable aspects of networked communication and embrace them as a distinct expressive dimension in composition, to probe the values at work in any kind of human connection. This is especially pertinent given the global shift to videocalls during the COVID-19 pandemic and the resulting void of live performances.
The Bus Control System Based on Intelligent Security Design

Abstract Due to traffic congestion and the changeable internal and external driving environment, it is difficult for bus drivers to respond quickly to the surrounding environment in an emergency, resulting in traffic accidents. This paper designs an intelligent and efficient active security system. The security monitoring system includes abnormal emotion processing system and trajectory offset processing system. The two systems are applied to image acquisition unit, image processing unit, vehicle trajectory determination unit, data storage unit and data transmission unit. The trajectory offset processing system and abnormal emotion processing system are used to analyze and process the data of various sensors, upload the data to the platform, and realize multi department cooperative processing.
Computation-in-Memory Accelerators for Secure Graph Database: Opportunities and Challenges

This work presents the challenges and opportunities for developing computing-in-memory (CIM) accelerators to support secure graph databases (GDB). First, we examine the database backend of common GDBs to understand the feasibility of CIM-based hardware architectures to speed up database queries. Then, we explore standard accelerator designs for graph computation. Next, we present the security issues of graph databases and survey how advanced cryptographic techniques such as homomorphic encryption and zero-knowledge protocols can execute privacy-preserving queries in a secure graph database. After that, we illustrate possible CIM architectures for integrating secure computation with GDB acceleration. Finally, we discuss the design overheads, useability, and potential challenges for building CIM-based accelerators for supporting data-centric calculations. Overall, we find that computing-in-memory primitives have the potential to play a crucial role in realizing the next generation of fast and secure graph databases.
Ontology Creation

Neural networks are like the models of the brain and nervous system. It is highly parallel and processes information much more like the brain than a serial computer. It is very useful in learning information, using and executing very simple and complex behaviors, applications like powerful problem solvers and biological models. There are different types of neural networks like Biological, Feed Forward, Recurrent, and Elman. Biological Neural Networks require some biological data to predict information. In Feed Forward Networks, information flows in one way. In Recurrent Networks, information flows in multiple directions. Elman Networks feature Partial re-currency with a sense of time.
Digital Ecosystem of Personal Development in the Process of Ecological and Economic Training of Managers

The current state of the ecological and economic situation, the presence of global problems determine the need to change the values of the developing society regarding the socio-cultural results of human activity in terms of ecohumanistic criteria for its assessment. This circumstance is due to the social educational order, which is associated with the education and training of young people who are able to think economically in accordance, but at the same time have a sufficiently high level of culture of positive relationships with the environment. Against the background of the high dynamism of the spread of information and communication technologies for the implementation of positive creative processes, the problem of optimal management of the economy and natural resource potential is becoming increasingly relevant, therefore, a professional manager, being a subject of the management subsystem, must be ready to balance the components of the socio-ecological and economic system. The ongoing ecological and economic crisis requires the development of new approaches to solving emerging problems that are adequate to the current state of social development. The design of the digital ecosystem of the educational space in the process of ecological and economic training of managers with an emphasis on their personal development creates favorable conditions for the transition to the internal locus of control, self-control of the student's personality, which, in contrast to the traditional external control of the educational system, is expressed in the ability to reflect and make balanced ecological and economic management decisions and bear full responsibility for them.
A Lightweight Phishing Website Detection Algorithm by Machine Learning

With the rapid development of the Internet, phishing websites now show the characteristics of short life cycle and low construction cost, which leads to a large amount of data brought by the detection of phishing websites for URL (uniform resource locator). It will also lead to increased retrieval time and decreased detection speed. In order to deal with diverse, complex and hidden phishing websites, this paper proposes a lightweight framework for detecting phishing websites. We first choose the faster Minhash signature to match URLs. On one hand, similarity detection is employed if the websites is suspicious. On the other hand, based on machine learning, the phishing websites can be finally determined by intention detection without similar sites.
Study on Optimization Method of Hidden Layer Nodes and Training Times in Artificial Neural Network

Artificial neural network, as the basis of the continuous development of the current field of artificial intelligence, contains many types of algorithms, but most of them contain hidden layer. By understanding the prediction accuracy of the previous RBF neural network, it is easy to affect its own application efficiency and numerical changes due to the unsuitable central nodes of the randomly selected hidden layer. Therefore, under the background of new era, in order to better show the effectiveness of the application of artificial neural network, based on the understanding on the basis of application of RBF neural network, using the neighbor spread AP clustering algorithm to carry on the improvement and optimization, and after the new optimization method and its modeling process, according to the simulation analysis judgment finally optimization method is scientific and effective.
Blockchain as a platform to digitally transform the electricity sector

Abstract The possibilities of using blockchain in the electric power sector are analyzed based on a study of key blockchain projects and startups initiated by the world’s leading electric power companies, defining the type of blockchain platform, as well as the goals and contribution of each project to improving energy efficiency. The key types of blockchain projects in the electric power industry have been identified. The authors came to the conclusion that in the Russian Federation, despite the large number of startups, including pilot blockchain projects, the demand for such developments is constrained by the insufficient development of the regulatory framework in the field of electricity and tariff regulation. Studying foreign experience of legislative regulation and practical application of blockchain in the electric power industry will make a significant breakthrough in achieving the goals set by the concept of “Digital Transformation 2030”
Super Resolution of MR via Learning Virtual Parallel Imaging

Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, it is often clinically challenging to obtain high-quality MR images. Super-resolution (SR) is potentially promising to improve MR image quality without any hardware upgrade. Instead of the classical SR reconstruction method that enhance the spatial resolution via utilizing the spatial information itself, in this work, we propose a novel SR method via learning channel information in virtual parallel imaging. We use auxiliary variable technology to make the channel number of network output to be equal to the network input, increasing the number of channels information to achieve SR reconstruction. Compared with state-of-the-art SR methods, the present approach is advantageous in generating more details with higher resolution.
Using Handle system to provide persistent identifiers for diploma

One of the essential factors for recruiters in a selection process is study background. It is, therefore, crucial to verify the authenticity of diplomas. Currently, it's easy to make a fake diploma with some software. Some websites can also generate one in a few minutes. You just need to give your information and pay some money. Between lies on the resume, fake documents, buying diplomas, producing real fake certificates, recruiters have a hard time dealing with candidates who come forward. This paper proposes to combine the handle system with blockchain technology to avoid this scourge. The handle system will provide to each diploma delivered by an institution a persistent identifier and will use blockchain technology to store and secure the data. The system will give all graduated students a unique identifier to check quickly and indefinitely their diplomas and grades.
BFR-RetinaNet: An Improved RetinaNet Model for Vehicle Detection in Aerial Images

Vehicle detection in aerial images has been applied in many fields and attracted more and more scholars’ attention. In the task, the objects are multi-directional and arranged densely, the background information is complex, and the scale of the object is different. To achieve better detection performance, an improved detection model BFR-RetinaNet is proposed, which is based on the single-stage object detection model RetinaNet. BFR-RetinaNet optimizes vehicle positioning by adding a directional anchor box regression branch. Simultaneously, the model introduces a balanced feature pyramid structure to enhance the extraction of features and reduce the interference of complex backgrounds. The experimental results on the aerial dataset show that the precision, recall, and average precision of the proposed model have been improved to varying degrees. It achieves 86.2% Precision, 98.4% Recall, and 90.8% Average Precision (AP), which is 7.96, 0.45, and 0.58 points higher than R-RetinaNet.
Towards Practical Robustness Evaluation and Robustness Enhancing

Deep neural networks (DNNs) have been widely applied on various machine learning tasks and have achieved significant performance across multiple domains. However, it well known that DNNs suffer from severe adversarial vulnerability. Thus it raises great concernswhen DNNs are adopted to safety-critical tasks. These concerns boost the area of adversarial machine learning, which mainly fo-cus on evaluating model robustness through adversarial attacks and gain reliable model performance through adversarial defenses.Among this board topic, my research work focus on a practical perspective. Specifically, there are three subtopics: (1) Enhancing robustness performance for adversarial learning from feature perspective. (2) Standardized and Reliable evaluation for black box attacks under different settings. (3) Building user-friendly adversarial learning tools to help evaluate model robustness. In this research statement, we will mainly focus on these three topics and we will take this opportunity to share our contribution to the relate problems.
A Comparative Analysis of Multiple Label Image Classification Techniques

Multiple label image classification deals with the problem where each image contains a single instance but it is associated with multiple number of object labels. Multiple object label classification problem has attracted significant amount of interest from many people and successfully applied in many image object labelling and object detection, prediction tasks. Since the past ten years, significant number of research development has been done in this machine learning domain. Several conventional neural network approaches applied on multiple object label learning and prediction based on dependency between label of the image object instance, co-existing labels and ranking of multiple labels in the image. This study aims to provide a detailed review analysis on multiple labels learning domain with emphasis on statistical machine learning, object detection and semi supervised multiple label learning using conventional machine learning algorithms and deep learning algorithms. At first, the basics of multiple object label classification such as definition and metrics for evaluation are discussed. Second, many multiple label image classification methods are shortlisted based on common term along with their performance analysis and discussions. Third, several related approaches for classification and setups are discussed and finally open research problems identified and few proposed solutions in the field of multiple label image learning are given for future references.
Adoption of Blockchain to Support the National Health Insurance Implementation in South Africa

The National Department of Health aims to provide good quality healthcare to all citizens and especially those who cannot afford proper healthcare through the implementation of the National Health Insurance (NHI). This will be done by pooling funds to establish a social solidarity fund for all citizens in need of healthcare regardless of their socio-economic status. The South African healthcare sector currently makes use of skewed healthcare financing systems, which makes it difficult to have a convergent healthcare scheme such as the NHI. Therefore, it is proposed that for the implementation of the NHI in South Africa, a new healthcare financing system, is developed based on artificial intelligence (AI) and blockchain algorithms with embedded deep learning capabilities. It is anticipated that the proposed healthcare financing system will help build resilience in South Africa's healthcare financing system.
The long-tail data-driven financial recommender systems

Long-tailed financial recommender systems, are attracting attention for their many applications in online services. Such systems demonstrate their ability to better accommodate the interests of network users and offer greater promise for better meeting their financial service needs. In this article, the authors discuss some approaches to using the information contained in the long tail of requests from web users interested in obtaining online financial services. This article answers the question of why user tail queries are starting to play an important role in financial recommendation systems? And how they correspond to the interests of social media users and how they are taken into account and participate in the development of recommendations by financial intermediaries. A hypothesis is put forward: Long-tail data (LTD) as a way to improve the accuracy of financial recommendations (R) generated by a generative adversarial network (GAN). For this, the most typical consumer data (ND) included in normal distributions are processed by generative filters (GM) and corrected by the discriminator model (DM). ND → GM; LTD → DM → GM → R. Long-tail data improves the accuracy of financial advice, expands the client base, and increases the availability of financial services.
Categorizing npm Packages by Analyzing the Text Information in Software Repositories

To prevent JavaScript developers from reinventing wheels, npm ecosystem provides numerous third-party libraries for developers to realize relevant functionalities. Npm displays the tags provided by the creators for these packages to help developers find suitable ones. However, not all creators have the habit of tagging their packages, and thus npm cannot provide tag information of a lot of packages for developers to help them understand the package functionalities effectively. Considering that many tags are unrelated to the functionality of packages, we propose a method to find out the tags that are important to distinguish the functionality categories of packages and assign them to untagged packages for assisting developers in the process of retrieving the packages. Firstly, we analyze the attribute of existing tags in npm to establish category tags (functionality categories). Then, we further mine the readme of tagged packages to generate keywords for each category tag. Finally, our method identifies category tags for untagged packages by measuring the similarity between their readme and the keywords of category tags. The evaluation demonstrates that our approach has a good performance in assigning category tags to untagged packages.
The automatic approach for scientific papers dating

The paper investigates automatic methods of docu-ment date estimation. The document date is essential metadata for many applied tasks, such as event detection or internet search. Methods for estimating the dates of newspaper articles are well known. In this paper, we propose to predict the publishing year for scientific papers. We present a new dataset based on open access articles from eLIBRARY.RU. It contains articles written in Russian and English. The article introduces a method of dating documents based on the raw document text content. The computational experiment compares different solutions for document date estimation. We also analyze how prediction depends on the topic for different methods.
Learning Success Factor in Open Distance Learning using Fuzzy AHP

Open distance learning (ODL) method is a learning process that uses technology and the Internet to deliver lectures and share educational content with no barriers in terms of time or place. However, there are other challenges in employing the ODL method effectively, such as having trouble explaining the subject online; hence, lecturers must take the initiative to ensure that students are more satisfied with the new process. Fuzzy Analytic Hierarchy Process (AHP) used to determine the important factor and sub-factor that influences students' satisfaction towards open distance learning and rank them according to influence level. The findings derived show that Fuzzy AHP is a method that can help experts to make decisions under complicated situations and rank all the factors and sub-factors with precision.
Designing A Secure Integration Model For EGovernance Platforms

E-governance has become an important element of good governance in Namibia and elsewhere in the world. Countries including Namibia has embraced this phenomenal tool by employing e-government systems that provide, amongst other benefits, the governance, and the interoperability of external systems from various entities with a common goal. The communication in the e-governance platform and many data exchange systems alike, such as health information exchange systems, usually depends on the interoperability of the platforms achieved with platform-independent architectures and protocol such as Representational State Transfer (REST), Simple Object Access Protocol (SOAP) or Application Programming Interface (APIs), which is an important building block for developing distributed applications, published as web services over an Intranet or the Internet. However, in most interoperability or integrations of systems, the interfacing component of the integration has been regarded as the most likely to be the weak link in terms of vulnerabilities [4]. Protocols such as SOAP commonly used for web-service integration, are prone to several vulnerabilities such as SOAP Injection and WSDL disclosure/enumeration that could make SOAP powered web-service integration vulnerable if not mitigated. This paper adopts the Design Science paradigm to design a secure integration model to mitigate vulnerabilities presented by the SOAP-powered and any other web-service integration protocols, to achieve secure integrations of heterogeneous systems in e-governance platforms.