Reviewerly/exHarmony huggingface dataset:
Quantum Computing: What, Why, Who

This paper provides an introductory reading about Quantum Computing (QC). It describes what is called QC, how does it work and the benefits it provides, naming some famous algorithms. It includes a review on the fields of application that QC is expected to fulfil, and the status of the QC ecosystem. The paper content has been presented in a plenary session at IEEE Chilecon 2019. It has been intended to motivate professionals and students to work on QC.
Tracking the Chemical Evolution of Iodine Species Using Recurrent Neural Networks

We apply recurrent neural networks (RNNs) to predict the time evolution of the concentration profile of multiple species resulting from a set of interconnected chemical reactions. As a proof of concept of our approach, RNNs were trained on a synthetic dataset generated by solving the kinetic equations of a system of aqueous inorganic iodine reactions that can follow after nuclear reactor accidents. We examine the minimum dataset necessary to obtain accurate predictions and explore the ability of RNNs to interpolate and extrapolate when exposed to previously unseen data. We also investigate the limits of our RNN by evaluating the robustness of the training initialization on our dataset.
Brave New World

The article focuses on the crucial changes that science as an established social institution and an epistemological enterprise is undergoing, the key one is the loss of its monopoly on the production of socially useful knowledge and gradual transformation into something new, which, due to institutional and cultural reasons, we continue to call ‘science’. We suppose that the most appropriate conceptualization of the new phenomenon, which is replacing science as an institution, is “technoscience”, since the technical component in scientific practices has now taken a dominant position and technology production has become more important than fundamental knowledge. Technoscience has at least two sources: 1) capitalization of scientific activity that has led to classical science has been replaced with technoscience developing on first-priority funded applied research; 2) theorization and autonomy of the techno sphere, which have resulted in instrumentalization of all levels of knowledge production as well as in technological / symbolic construction of reality and tangled ontology of technoscientific objects. We discuss both of these sources, with particular attention being paid to such trends as epistemic strategies transformation, modified reality, social sciences and humanities conformation to technoscience norms, and knowledge bearers egalitarianization. A crucial transformation of both science itself and its position in society breaks inevitably a demarcation line that separates scientific knowledge from other types of knowledge while promotes the replacement of scientific theory with discourses. Apparently, in “technoscience” an ethos of its own is being formed, where interaction with the “external environment” (with other social spheres) is crucial. In this context, scientific activity is becoming more and more transepistemic, transinstitutional practice, and accordingly ceases to be guided by the classical scientific ethos determined by the goals and objectives of academic community itself.
The clash of empires: regulating technological threats to civil society

This paper examines the regulation of technology platform companies providing a platform for user-generated media content while playing an increasingly dominant role in the global flow of news and information. In doing so, platform companies play a crucial role in modern civic life, by deciding which content will reach users, engage the public's attention, and be deemed credible. It is therefore crucial that we choose means of regulation that foster democratic values and robust civic engagement. In this paper we focus on the regulation of ‘computational propaganda', including misinformation and ‘fake news', the rise of synthetic media and so-called ‘deep fakes', and novel forms of algorithmic injustice, such as the manipulation of search engine results and their effect on elections. We argue that many existing regulations fall short in that they adopt an approach that views regulation as a battle between two competing powers, or ‘empires’ – that of the regulatory state versus the big tech companies. Accordingly, they approach regulation as a means of redistributing power between these two players, while discounting the end user, and they often involve unjustified restrictions of free speech through the imposition of content controls.
Usability testing for oppression

This study examines a document produced by the United States Department of Homeland Security handed out to immigrant parents during the "Family Separation Policy" crisis of 2018. The article examines whether such a document could be ethically tested for usability. Ultimately, the text argues that by the standards of the Belmont Report and the best practices in usability research, such a document would be extremely difficult (if not impossible) to test ethically. It argues that, while usability testing is an excellent tool for exploring how users interact with texts that can have life-changing consequences, it may also be used as a tool to perpetuate injustice and marginalize potential users.
Handling Vectorial Functions by Means of Their Graph Indicators

We characterize the ANF and the univariate representation of any vectorial function as parts of the ANF and bivariate representation of the Boolean function equal to its graph indicator. We show how this provides, when F is bijective, the expression of F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> and/or allows deriving properties of F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . We illustrate this with examples and with a tight upper bound on the algebraic degree of F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> by means of that of F. We characterize by the Fourier-Hadamard transform, by the ANF, and by the bivariate representation, that a given Boolean function is the graph indicator of a vectorial function. We also give characterizations of those Boolean functions that are affine equivalent to graph indicators. We express the graph indicators of the sum, product, composition and concatenation of vectorial functions by means of the graph indicators of the functions. We deduce from these results a characterization of the bijectivity of a generic (n,n)-function by the fact that some Boolean function, which appears as a part of the ANF (resp. the bivariate representation) of its graph indicator, is equal to constant function 1. We also address the injectivity of (n,m)-functions. Finally, we study the characterization of the almost perfect nonlinearity of vectorial functions by means of their graph indicators.
Blockchain and records management: disruptive force or new approach?

Purpose Archival repositories rely on the authenticity of records. The potential manipulation of records and information present a unique challenge to archival repositories and their mission to collect, maintain and provide access to the authentic records. The purpose of this paper is to outline the technical aspects of blockchain and describe potential records management and archival implications of the technology. The implications to consider include data management, records existing on a blockchain and records disposition. Design/methodology/approach The approach included reviews of technical literature and attendance at blockchain conferences, workshops and seminars. The literature review focused on general studies, technical publications, use cases and nascent applications for managing records created and stored on a blockchain. The approach is intended to answer the questions: what basic information do records managers need to understand blockchain? How will blockchain technology impact records management? Does blockchain shift toward decentralized records management? Findings Implemented properly, blockchain technology can immutably record transactions and provide independent verification and authentication of digital records. Given the ease with which electronic records can sometimes be manipulated, the promise of certainty and third-party verification has drawn sizeable interest and investment. This paper shows records exist on blockchains and must be managed appropriately. The distributed structure inherent in blockchains is a shift from centralized records management and will challenge the approach and assumptions of the profession. Records managers must determine recordkeeping requirements before developing a business application using blockchain and the fixed nature of blockchains means that these considerations must be included at the outset. Originality/value This proposed paper began as a blockchain white paper produced by the US National Archives for the federal records management community. It has been re-focused as a general review paper for the Records Management Journal to explain blockchain and show how it may be used to address broader records and information management concerns such as authenticity, veracity, and trustworthiness.
DKDR: An Approach of Knowledge Graph and Deep Reinforcement Learning for Disease Diagnosis

Using artificial intelligence to solve medical problems has been an intriguing yet challenging topic. In recent years, with the availability of electronic medical records (EMRs), many researchers have focused on diagnose diseases by mining EMRs. They apply machine learning algorithms to train EMRs, and modify models to improve the accuracy of diseases diagnosis. However, these studies input the patient characteristics into the model at once, without multiple online interactions. Or, put another way, although those methods have performed well in the laboratory, they are not suitable for a real consultation environment. In the real world, doctors often guide patients to describe the condition step by step, and then combine the checkup value to diagnose the disease. In this paper, we simulate this process and propose a novel system called DKDR, which combine a knowledge graph and deep reinforcement learning to diagnose disease. The medical knowledge graph is built by crawling 100K web pages, which help users improve the description of disease characteristics. We use q-learning to find the combination of symptoms in the best diagnosis and use convolutional neural networks (CNN) to train each strategy. Finally, we experiment on real medical datasets and synthetic medical datasets. DKDR finds the best combination of symptoms for diagnosing disease. The diagnostic accuracy rates for pneumonia, hyperlipidemia and obesity are 89%, 90% and 92%, respectively.
Special Education: Teaching Geometry with ICTs

Geometry is a very important field of mathematics which is crucial for the understanding of space. From an early age, children observe objects that cor-respond to geometric shapes and learn the position they occupy in space (above, below, etc.). It is a demanding lesson that requires imagination and combinational thought. With the introduction of technology in education, ge-ometry became more accessible to students, because through it pupils can now visualize figures, study and process them.
Reconsidering creativity: copyright protection for works generated using artificial intelligence

Reconsidering creativity: copyright protection for works generated using artificial intelligence Niloufer Selvadurai, Niloufer Selvadurai Emails: niloufer.selvadurai@mq.edu.au; rita.matulionyte@mq.edu.au Search for other works by this author on: Oxford Academic Google Scholar Rita Matulionyte Rita Matulionyte Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice, Volume 15, Issue 7, July 2020, Pages 536–543, https://doi.org/10.1093/jiplp/jpaa062 Published: 14 May 2020 Article history Received: 19 March 2020 Revision received: 30 March 2020 Accepted: 03 April 2020 Published: 14 May 2020
Teaching-Learning-Based Optimization Enhanced With Multiobjective Sorting Based and Cooperative Learning

Teaching-learning-based optimization (TLBO) algorithm has been shown to be an effective optimization algorithm. However, it is easily trapped into local optima when the global optimal solution of the function to be optimized is at the original dot or around the original dot. This paper presents a novel TLBO variant by incorporating multiobjective sorting-based mechanism and cooperative learning strategy to alleviate this problem. Taking advantages of multiobjective optimization in maintaining good population diversity, several teachers are selected based on non-dominated sorting, so as to guide learners to learn more effectively. In addition, the proposed algorithm adopts cooperative learning, including learning within and between groups, to improve the search ability of the algorithm. Experimental and statistical analyses are performed on CEC2014 benchmark functions. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison with other variants of TLBO and other state-of-the-art optimization algorithms.
A new method of multi color image encryption

A new method of multi color image encryption based on fractional discrete Fourier transform (FrDFT) and 3D Arnold chaotic map (ACM) is proposed in this paper. In this method, first three color images are converted into Bayer color images then these Bayer images are taken as red, green and blue components of a true color image. These three Bayer images are horizontally combined into a single complex image matrix then half of the part of this single matrix is considered as real part and other half part is considered as imaginary part and apply 2D FrDFT. After applying 2D FrDFT separate the image matrix into three components and apply 3D ACM on each of these components and combining these three components we get encrypted image. Decryption of the proposed method is same as encryption but in reverse order. Experimental results are done with various images. Key space, key sensitivity analyses are done for the proposed method. Entropy, histogram, MSE, PSNR and correlation analyses are given. These analyses and experimental results show that our method is a strong enough against these statistical attacks.
Evaluation model of students learning outcome using k-means algorithm

Abstract The progress of students learning outcomes have to evaluated and make monitor. Student learning outcomes were indicated by score of the exam. Educational institutions can determine the ability of students’ knowledge and competences based on student exam scores. For this reason, the goals of this research is develops an evaluation model of student learning outcomes. The model was developed using k-means algorithm and it has functions to analyze the student learning outcomes. The model was tested by conducted using 50 data exam of students, rapid manner software, and has implemented by the Python 3.0 programming language. The testing the evaluation model of student learning outcomes that were developed resulted: there are 3 clusters of student learning outcomes that are good, satisfying, and lacking. The tested results also showed that 40% of students had good learning outcomes, 44% of students were satisfactory, and 16% of other students had poor learning outcomes. The results of testing the student learning evaluation model can be used by the parties concerned to make decisions related to work programs or formulate steps that need to be taken to improve student learning outcomes.
Speech Command Recognition using Artificial Neural Networks

Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Googleâ€™s TensorFlow and AIY teams is used to implement different Neural Network models which include Convolutional Neural Network and Recurrent Neural Network combined with Convolutional Neural Network. The combination of Convolutional and Recurrent Neural Network outperforms Convolutional Neural Network alone by 8% and achieved 96.66% accuracy for 20 labels.
An Efficient Overflow Handling Technique for Histogram Shifting based Reversible Data Hiding

Reversible data hiding (RDH) is an active area of research in the field of information security. For the last few decades variety of approaches were introduced for the purpose of reversible data hiding in digital images. In general, the reversible data hiding techniques can be broadly classified into three categories: histogram shifting based RDH, lossless compression based RDH and difference expansion based RDH. There are many algorithms available in the literature based on the histogram shifting approach. One of the major issues in the histogram shifting based approach is overflow or underflow during the histogram shifting process. In this paper, we propose an efficient technique to handle the overflow or underflow by embedding some additional marking bits in the image during the embedding of the secret message bits. The proposed scheme ensures the exact recovery of the original image and it helps to reduce the overhead of overflow or underflow handling in histogram shifting based RDH scheme. All the experimental study is carried on the standard image data set (USC-SIPI image data set).
Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding

We propose a novel data hiding method in an audio host with a compressive sampling technique. An over-complete dictionary represents a group of watermarks. Each row of the dictionary is a Hadamard sequence representing multiple bits of the watermark. Then, the singular values of the segment-based host audio in a diagonal matrix are multiplied by the over-complete dictionary, producing a lower size matrix. At the same time, we embed the watermark into the compressed audio. In the detector, we detect the watermark and reconstruct the audio. This proposed method offers not only hiding the information, but also compressing the audio host. The application of the proposed method is broadcast monitoring and biomedical signal recording. We can mark and secure the signal content by hiding the watermark inside the signal while we compress the signal for memory efficiency. We evaluate the performance in terms of payload, compression ratio, audio quality, and watermark quality. The proposed method can hide the data imperceptibly, in the range of 729–5292 bps, with a compression ratio 1.47–4.84, and a perfectly detected watermark.
Threshold Ring Signatures: New Definitions and Post-quantum Security

A t-out-of-N threshold ring signature allows t parties to jointly and anonymously compute a signature on behalf on N public keys, selected in an arbitrary manner among the set of all public keys registered in the system. Existing definitions for t-out-of-N threshold ring signatures guarantee security only when the public keys are honestly generated, and many even restrict the ability of the adversary to actively participate in the computation of the signatures. Such definitions do not capture the open settings envisioned for threshold ring signatures, where parties can independently add themselves to the system, and join other parties for the computation of the signature. Furthermore, known constructions of threshold ring signatures are not provably secure in the post-quantum setting, either because they are based on non-post quantum secure problems (e.g. Discrete Log, RSA), or because they rely on transformations such as Fiat-Shamir, that are not always secure in the quantum random oracle model (QROM). In this paper, we provide the first definition of t-out-of-N threshold ring signatures against active adversaries who can participate in the system and arbitrarily deviate from the prescribed procedures. Second, we present a post-quantum secure realization based on any (post-quantum secure) trapdoor commitment, which we prove secure in the QROM. Our construction is black-box and it can be instantiated with any trapdoor commitment, thus allowing the use of a variety of hardness assumptions.
Easing ĐApp Interaction for Non-Blockchain Users from a Conceptual Modelling Approach

Blockchain decentralized applications (ÐApp’s) are applications which run on blockchains nodes. Thus, to interact directly with this sort of applications, users need to have a blockchain address, wallet, and knowledge about how to make transactions to interact with ÐApp’s. Therefore, the knowledge required to use a ÐApp can easily make users to desist when trying to interact with them. To tackle this issue, we propose a software architecture that will be in the middle of the user and the ÐApp, thus making users initially unaware of the fact that they are interacting with a ÐApp. This is achieved by analyzing the relationship between ÐApps and Apps by using UML modelling. Next, based on the previous analysis, we created a middleware for users to interact with a ÐApp in the same manner they do with a traditional web app, i.e., by using usernames, passwords and user interface elements instead of addresses, private keys or transactions. To put the developed middleware into practice, we developed a ÐApp that makes use of it. This ÐApp registers the time control of workers from companies by using blockchain to store the data in a secure and non-modifiable manner. Finally, we performed an experiment, thus demonstrating that a ÐApp that implements the proposed middleware would improve its usability for users with no experience with blockchain.
Towards Message-Driven Ontology Population - Facing Challenges in Real-World IoT

Large-scale Internet-of-Things (IoT) environments as being found in critical infrastructures such as Intelligent Transportation Systems (ITS) are characterized by (i) massive heterogeneity of data, (ii) prevalent legacy systems, and (iii) continuous evolution of operational technology. In such environments, the realization of crosscutting services demands a conceptual IoT representation, most promising, in terms of a domain ontology. Populating the ontology’s A-Box, however, faces some challenges, which are not sufficiently addressed by now. In this respect, the contribution of this short paper is three-fold: Firstly, in order to point out the complexity of addressed real-world IoT environments, we identify prevalent challenges for (semi-)automatic ontology population by means of a real world example. Secondly, in order to address these challenges, we elaborate on related work by identifying promising lines of research relevant for ontology population. Thirdly, based thereupon, we sketch out a solution approach towards message-driven ontology population.
The Effects of Randomness on the Stability of Node Embeddings

We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i.e., the random variation of their outcomes given identical algorithms and networks. We apply five node embeddings algorithms—HOPE, LINE, node2vec, SDNE, and GraphSAGE—to assess their stability under randomness with respect to their performance in downstream tasks such as node classification and link prediction. We observe that while the classification of individual nodes can differ substantially, the overall accuracy is mostly unaffected by the geometric instabilities in the underlying embeddings. In link prediction, we also observe high stability in the overall accuracy and a higher stability in individual predictions than in node classification. While our work highlights that the overall performance of downstream tasks is largely unaffected by randomness in node embeddings, we also show that individual predictions might be dependent solely on randomness in the underlying embeddings. Our work is relevant for researchers and engineers interested in the effectiveness, reliability, and reproducibility of node embedding approaches.