Reviewerly/exHarmony huggingface dataset:
Digital dilemmas in the (post-)pandemic state: Surveillance and information rights in South Korea

Drawing on South Korea’s response to COVID-19, this article examines how the digital measures that were implemented by the nation state during the pandemic intensified the dilemma between public safety and information rights. South Korea’s highly praised handling of COVID-19 raises the question of how far digital technology can infiltrate everyday life for the sake of public safety and how citizens can negotiate the rapid digital transformation of a nation state. The South Korean government’s digital measures during the pandemic involved the extensive use of personal data; however, citizens were not allowed sufficient participation in the flow of information. By critically examining the South Korean case, this article reveals that the government coped with the pandemic through digital surveillance as a way to avoid physical lockdown, and in so doing, projected its desire for transition to a digitally advanced state while facilitating nationalism through a digital utopian discourse.
Detection of Brain Tumor Using K-Nearest Neighbor (KNN) Based Classification Model and Self Organizing Map (SOM) Algorithm

Knowledge discovery is also known as Data mining in databases, in recent years that technique plays a major role in research area. Data mining in healthcare domain has noteworthy usage in real world. The mining method can enable the healthcare field for the enhancement of institutionalization of its administrations and become quicker with best in class technologies. Innovation utilization isn't restricted to basic leadership in undertakings, yet spread to different social statuses in all fields. In this paper a novel approach for the detection of brain tumor is proposed. The novel approach uses the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used. Discrete wavelet transform (DWT) is used for transforming input image data set, in which RGB color of input data image has been converted into gray scale. Then it has been classified using KNN after that the error avoiding algorithm has been carried out. This will help to differentiate tumor cells and the normal cells. The presence of tumor in brain image is detected using parametric analysis by simulation.
Optimizing the decoy-state BB84 QKD protocol parameters

Abstract Quantum key distribution (QKD) protocols allow for information theoretically secure distribution of (classical) cryptographic key material. However, due to practical limitations the performance of QKD implementations is somewhat restricted. For this reason, it is crucial to find optimal protocol parameters, while guaranteeing information theoretic security. The performance of a QKD implementation is determined by the tightness of the underlying security analysis. In particular, the security analyses determines the key-rate , i.e., the amount of cryptographic key material that can be distributed per time unit. Nowadays, the security analyses of various QKD protocols are well understood. It is known that optimal protocol parameters, such as the number of decoy states and their intensities, can be found by solving a nonlinear optimization problem. The complexity of this optimization problem is typically handled by making a number of heuristic assumptions. For instance, the number of decoy states is restricted to only one or two, with one of the decoy intensities set to a fixed value, and vacuum states are ignored as they are assumed to contribute only marginally to the secure key-rate. These assumptions simplify the optimization problem and reduce the size of search space significantly. However, they also cause the security analysis to be non-tight, and thereby result in sub-optimal performance. In this work, we follow a more rigorous approach using both linear and nonlinear programs describing the optimization problem. Our approach, focusing on the decoy-state BB84 protocol, allows heuristic assumptions to be omitted, and therefore results in a tighter security analysis with better protocol parameters. We show an improved performance for the decoy-state BB84 QKD protocol, demonstrating that the heuristic assumptions typically made are too restrictive. Moreover, our improved optimization frameworks shows that the complexity of the performance optimization problem can also be handled without making heuristic assumptions, even with limited computational resources available.
Experimental study on the dynamic behaviour of drones designed for racing competitions

Drones designed for racing usually feature powerful miniaturised electronics embedded in fairly light and strong geometric composite structures. The main objective of this article is to analyse the behaviour of various models of racing drones and their geometrical structures (airframes). Two approaches have been made: (i) an analysis of the information collected by a set of speed and time sensors located on an indoor race track and using a statistical technique (box and whiskers diagram) and (ii) an analysis of the know-how (flight sensations) of a group of racing pilots using a series of technical interviews on the behaviour of their drones. By contrasting these approaches, it has been possible to validate numerically the effects of varying the arm angles, as well as lengths, on a test race track and relate the geometry of these structures to racing behaviour.
Average quantum coherence of pure-state decomposition

We study the average quantum coherence over the pure state decompositions of a mixed quantum state. An upper bound of the average quantum coherence is provided and sufficient conditions for the saturation of the upper bound are shown. These sufficient conditions always hold for two and three dimensional systems. This provides a tool to estimate the average coherence experimentally by measuring only the diagonal elements, which remarkably requires less measurements compared with state tomography. We then describe the pure state decompositions of qubit state in Bloch sphere geometrically. For any given qubit state, the optimal pure state decomposition achieving the maximal average quantum coherence as well as three other pure state decompositions are shown in the Bloch sphere. The order relations among their average quantum coherence are invariant for any coherence measure. The results presented in this paper are universal and suitable for all coherence measures.
High-level ETL for semantic data warehouses

The popularity of the Semantic Web (SW) encourages organizations to organize and publish semantic data using the RDF model. This growth poses new requirements to Business Intelligence technologies to enable On-Line Analytical Processing (OLAP)-like analysis over semantic data. The incorporation of semantic data into a Data Warehouse (DW) is not supported by the traditional Extract-Transform-Load (ETL) tools because they do not consider semantic issues in the integration process. In this paper, we propose a layer-based integration process and a set of high-level RDF-based ETL constructs required to define, map, extract, process, transform, integrate, update, and load (multidimensional) semantic data. Different to other ETL tools, we automate the ETL data flows by creating metadata at the schema level. Therefore, it relieves ETL developers from the burden of manual mapping at the ETL operation level. We create a prototype, named Semantic ETL Construct (SETLCONSTRUCT), based on the innovative ETL constructs proposed here. To evaluate SETLCONSTRUCT, we create a multidimensional semantic DW by integrating a Danish Business dataset and an EU Subsidy dataset using it and compare it with the previous programmable framework SETLPROG in terms of productivity, development time, and performance. The evaluation shows that 1) SETLCONSTRUCT uses 92% fewer Number of Typed Characters (NOTC) than SETLPROG, and SETLAUTO (the extension of SETLCONSTRUCT for generating ETL execution flows automatically) further reduces the Number of Used Concepts (NOUC) by another 25%; 2) using SETLCONSTRUCT, the development time is almost cut in half compared to SETLPROG, and is cut by another 27% using SETLAUTO; and 3) SETLCONSTRUCT is scalable and has similar performance compared to SETLPROG. We also evaluate our approach qualitatively by interviewing two ETL experts.
ML-CB: Machine Learning Canvas Block

Abstract With the aim of increasing online privacy, we present a novel, machine-learning based approach to blocking one of the three main ways website visitors are tracked online—canvas fingerprinting. Because the act of canvas fingerprinting uses, at its core, a JavaScript program, and because many of these programs are reused across the web, we are able to fit several machine learning models around a semantic representation of a potentially offending program, achieving accurate and robust classifiers. Our supervised learning approach is trained on a dataset we created by scraping roughly half a million websites using a custom Google Chrome extension storing information related to the canvas. Classification leverages our key insight that the images drawn by canvas fingerprinting programs have a facially distinct appearance, allowing us to manually classify files based on the images drawn; we take this approach one step further and train our classifiers not on the malleable images themselves, but on the more-difficult-to-change, underlying source code generating the images. As a result, ML-CB allows for more accurate tracker blocking.
Selective network discovery via deep reinforcement learning on embedded spaces

Abstract Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream learning tasks given resource collection constraints are of great interest. In this paper, we formulate the task-specific network discovery problem as a sequential decision-making problem. Our downstream task is selective harvesting, the optimal collection of vertices with a particular attribute. We propose a framework, called network actor critic (NAC), which learns a policy and notion of future reward in an offline setting via a deep reinforcement learning algorithm. The NAC paradigm utilizes a task-specific network embedding to reduce the state space complexity. A detailed comparative analysis of popular network embeddings is presented with respect to their role in supporting offline planning. Furthermore, a quantitative study is presented on various synthetic and real benchmarks using NAC and several baselines. We show that offline models of reward and network discovery policies lead to significantly improved performance when compared to competitive online discovery algorithms. Finally, we outline learning regimes where planning is critical in addressing sparse and changing reward signals.
The Norwegian Ministry of Education and Research’s action plan for digitalization in primary and secondary education and training: appraisal and critique

This paper reviews and analyzes the Norwegian Ministry of Education and Research's Action Plan for Digitalization in Primary and Secondary Education and Training (AP20).Policy papers are often intended for administrative levels, but will also be perceived as guidance and recommendations for educators.The purpose of this paper is to examine the kind of support and guidelines AP20 gives to educators.This is done by partly investigating the plan's action steps and priorities.However, AP20 will also be compared with other Ministry documents, in order to look for inner consistency across documents.Finally, AP20's suggestions, action steps, and view of digitalization, will be briefly evaluated in respect to how digitalization naturally occurs and materializes in current and future-oriented educational practices, to determine its ecological validity.The analysis revealed that AP21 is somewhat ambiguous as to its purpose and intended audience.It comes across as rather instrumental: addressing infrastructure, resources, and skills.While this can be explained as being due to its primary aims, scope and audience, it cannot escape the larger educational and pedagogical perspectives, including epistemologies, to be relevant for policies and practices, administration and epistemic work.Thus, there is a need for clarification in future versions.
BLOCKCHAIN AND THE DECENTRALISATION OF THE CYBERSECURITY INDUSTRY

This paper aims to analyse the potential impact that blockchain technology will have on the cybersecurity industry. We will look at different applications of blockchain technology in the field of cybersecurity, as well as a new trend of decentralisation of cybersecurity services. Blockchain is precisely a cybersecurity architecture that enables the decentralisation of processes and business models, which could have a direct consequence on cybersecurity services, as well as on the industry itself. Cybersecurity companies will have to adapt to a new ecosystem in which the blockchain technology will enable crowdsourcing of cybersecurity services. Keywords: Blockchain, Cybersecurity, Disruptive Technology, Technology Innovation, Technology Strategy
A hybrid differential evolution based on gaining‑sharing knowledge algorithm and harris hawks optimization

Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator "rand/1" of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.
A holistic approach for cross-platform software development

Cross-platform development solutions can help to make software available on different devices and platforms. But these are normally restricted to preconfigured platforms and consider that each individual solution is equal or similar to each other. As a result, developers have to resort to native development and build individual solutions, one for each device/platform, that cooperate to deliver the desired global functionality. This article presents an approach that takes advantage of existing solutions and have support for extending and including new platforms, and distributing functionality across devices. The approach is based on a general-purpose language that raises the abstraction level in order to keep the software free from platform details. Automatic transformations produce executable code that can be properly divided and deployed separately into different platforms. The proposed approach was evaluated in four ways. In the first evaluation, an existing cross-platform system was recreated using the approach. The second and third evaluations was conducted with expert and novice developers, who tested the approach in practice. The fourth evaluation introduced support for cross-platform testing. Results have brought evidence supporting the following main contributions: use of a single environment, the ability to reuse similar concepts between platforms and the potential to reduce costs.
Automatic image segmentation based on label propagation

This article introduces an automatic approach for the segmentation of coloured natural scene images based on graphs and the propagation of labels originally designed for communities detection in complex networks. Images are initially pre-segmented with super-pixels, followed by feature extraction using colour information of each super-pixels. The resulting graph consists of vertices which represent super-pixels, whereas the edge weights are a measure of similarity between super-pixels. The resulting segmentation corresponds to the propagation of labels among the vertices. In this article, three strategies for propagating labels have been formulated: (i) iterative propagation (ILP), (ii) recursive propagation (RLP) and (iii) a weighted recursive propagation (WRLP). The experiments have shown that the proposed methods, when compared to other state-of-the-art methods, produce better results in terms of segmentation quality and processing time.
Hybrid Algorithm Based on Chicken Swarm Optimization and Genetic Algorithm for Text Summarization

The daily massive flow of information requires automated summarization methods to extract the most important information.Manual summarization of large text documents is very complicated and time-intensive for human beings.Numerous methods rate all of them decadently based on the sentence scoring that labels ratings for input sentences.The higher-rated sentences are used as a part of the summary.In an extractive-based automated text summary, locating the relevant sentences is an essential problem for the researchers.Therefore, to deal with such problems, evolutionary algorithms are applied as a solution.This paper presents a hybrid approach (CSOGA) based on the effectiveness and convergence to the solution of a (CSO) chicken swarm optimization and a (GA) genetic algorithm for text summarization to ensure the optimal solution.The evaluations of the proposed algorithms are done on the standard dataset from CNN / Daily Mail and are measured by the Recall-Oriented Understudy for Gisting Evaluation (ROUGE).The performance of the proposed method is then compared with other methods.The results show that the new approach hybrid (CSOGA) has the best performance on text summarization quality.The proposed method was capable of generating a better accuracy than other algorithms on the ROUGE-1, ROUGE-2 and ROUGE-L.The highest increase in the accuracy of the proposed method was in ROUGE-1 with a rise of 4.4%, ROUGE-2 with a rise of 12.01%, and ROUGE-L with a rise of 9.8% comparing with the highest accuracy of the other extractive models.
Fully Constant-Size CP-ABE with Privacy-Preserving Outsourced Decryption for Lightweight Devices in Cloud-Assisted IoT

In recent years, ciphertext-policy attribute-based encryption (CP-ABE) has been recognized as a solution to the challenge of the information privacy and data confidentiality in cloud-assisted Internet-of-Things (IoT). Since the devices in cloud-assisted IoT are generally resource-constrained, the lightweight CP-ABE is more suitable for the cloud-assisted IoT. So how to construct the lightweight CP-ABE for the cloud-assisted IoT to achieve the fine-grained access control and ensure the privacy and confidentiality simultaneously is a prominent challenge. Thus, in this paper, we propose a constant-size CP-ABE scheme with outsourced decryption for the cloud-assisted IoT. In our scheme, the ciphertexts and the attribute-based private keys for users are both of constant size, which can alleviate the transmission overhead and reduce the occupied storage space. Our outsourced decryption algorithm is privacy-protective, which means the proxy server cannot know anything about the access policy of the ciphertext and the attributes set of the user during performing the online partial decryption algorithm. This will prevent the privacy from leaking out to the proxy server. And we rigorously prove that our scheme is selectively indistinguishably secure under the chosen ciphertext attacks (IND-CCA) in the random oracle model (ROM). Finally, by evaluating and implementing our scheme as well as other CP-ABE schemes, we can observe that our scheme is more suitable and applicable for cloud-assisted IoT.
Multilingual Phonetic Dataset for Low Resource Speech Recognition

Phone Recognition is one of the most important tasks in the field of multilingual speech recognition, especially for low-resource languages whose orthographies are not available. However, most speech recognition datasets so far only focus on high-resource languages, there are very few datasets available for low-resource languages, especially datasets with detailed phone annotation. In this work, we present a large multilingual phonetic dataset, which is preprocessed and aligned from the UCLA phonetic dataset. The dataset contains around 100 low-resource languages and 7000 utterances in total. This dataset would provide an ideal training/evaluation set for universal phone recognition.
DETERMINATION OF LOCAL BACKGROUND CONTENT OF SOME MACRO- AND MICROELEMENTS IN THE SOILS OF THE PERM REGION

According to the V.I. Vernadsky law, chemical elements are distributed unevenly in natural objects. Knowledge of the content of chemical elements in a particular area helps in solving various environmental problems. As a result of economic activity, there occurs anthropogenic transformation of the natural environment, including changes in the geochemical properties of landscapes. For an objective assessment of the anthropogenic impact when studying various territories, it is necessary to take into account the background content of macro- and microelements. Since there is a constant anthropogenic impact on the natural environment, the data on background content must be updated. The article presents the results of the content study conducted for some macro- and microelements (Sr, Pb, As, Zn, Ni, Co, Fe2O3, MnO, Cr, V, TiO2) in landscapes in the Perm region. To determine the content of these elements, the method of X-ray fluorescence analysis was applied. As a result, the geochemical specialization of the region and its natural areas (Northern Urals, Western Urals, Middle Taiga, South Taiga, Mixed Coniferous-broad-leaved Forests and Kungur forest-steppe) was determined, geochemical series were constructed, accumulating and dispersing elements were identified. The local background content of the studied elements was determined for each natural area and also the Perm region as a whole. The data obtained can be used to assess the anthropogenic impact on the natural environment.
The Efficiency of Single Truth: Triple-entry Accounting

We conduct a gap analysis to identify shortcomings in current accounting systems, juxtaposing the application of blockchain-inspired triple-entry accounting (TEA). The application of TEA remediates a number of issues or “pain points” in the accounting domain, from multi-party logistic nightmares and billing inefficiencies to “creative accounting.” An inventory of current practice further reveals that, although not intended to serve as an ultimate accounting tool in most cases, DLT has been used to develop a quasi or full TEA application in many separate instances. DLT developers are, with innovative TEA applications, breaking new ground in the accounting world, especially in the logistics and supply chains sectors. Anecdotal evidence suggests that the efficiency effects may well surpass the primary DLT-based TEA input costs, in particular given the addition of single truth: a common, shared ledger with IoT oracles between parties potentiates an agreed understanding of the facts. A TEA system thus reduces friction between parties, and thus lowers costs related to reconciliation and risk management.