Reviewerly/exHarmony huggingface dataset:
A convex multi-class model via distance metric learning based class-to-instance confidence

To make the overlapping classes more separable, distance metric learning (DML) can learn a distance metric based on both the data features and labeling information of instances. However, most of the existing DML methods are considered as the k NN DML methods and adopt the k NN model to predict instances. The drawback of k NN DML is that all training instances need to be stored and accessed to predict a test instance, and the classification performance is influenced by the setting of nearest neighbor number k . To overcome these problems, there are several DML methods construct the non- k NN multi-class model to predict the instances. But, all of them are non-convex and the convex non- k NN DML method has not been explicitly proposed. In this paper, we propose a convex non- k NN DML model for multi-class classification problem, called DML-based class-to-instance (C2I) confidence (DM-C2IC). Specifically, we learn a specific Mahalanobis distance to make the overlapping classes more separable and then put forward a DML-based C2I confidence model based on it. To capture the correlations between different classes, the DML-based C2I confidence model is trained with the goal that for each instance the DML-based confidence of its corresponding class is larger than the DML-based confidence of every other class. An iterated approach is developed to optimize DM-C2IC, which can converge to the global optimum. Moreover, to further boost the classification performance, a kernel version of DM-C2IC is introduced. Extensive experiments show that the proposed DM-C2IC method outperforms the existing k NN DML methods. • A novel convex multi-class model via DML-based C2I confidence (DM-C2IC) is proposed. To further boost the classification performance, DM-C2IC is extended to the kernel space. • DM-C2IC is a non-kNN method for multi-class DML problems and only needs to store the DML-based C2I confidence model of which the size is Kd. More importantly, DM-C2IC can avoid the influence of the nearest neighbor number k. • A generalized block coordinate descent optimization approach is developed for DM-C2IC. The optimization approach can train and update the DML-based C2I confidence model and the specific Mahalanobis distance metric alternately. • Extensive experiments are conducted to evaluate the performance of the DM-C2IC model. The better results indicate that DM-C2IC achieves explicitly accuracy improvements than the existing kNN DML methods.
Eight-port homodyne detector: The effect of imperfections on quantum random-number generation and on detection of quadratures

The eight-port homodyne detector is an optical circuit designed to perform the monitoring of two quadratures of an optical field, the signal. By using quantum Bose fields and quantum stochastic calculus, we give a complete quantum description of this apparatus, when used as quadrature detector in continuous time. We can treat either the travelling waves in the optical circuit, either the observables involved in the detection part: two couples of photodiodes, postprocessing of the output currents. The analysis includes imperfections, such as not perfectly balanced beam splitters, detector efficiency, electronic noise, phase and intensity noise in the laser acting as local oscillator; this last noise is modelled by using mixtures of field coherent states as statistical operator of the laser component. Due to the monitoring in continuous time, the output is a stochastic process and its full probability distribution is obtained. When the output process is sampled at discrete times, the quantum description can be reduced to discrete mode operators, but at the price of having random operators, which contain also the noise of the local oscillator. Consequently, the local oscillator noise has a very different effect on the detection results with respect to an additive noise, such as the noise in the electronic components. As an application, the problem of secure random number generation is considered, based on the local oscillator shot noise. The rate of random bits that can be generated is quantified by the min-entropy; the possibility of classical and quantum side information is taken into account by suitable conditional min-entropies. The final rate depends on which parts of the apparatus are considered to be secure and on which ones are considered to be exposed to the intervention of an intruder. In some experimentally realistic situations, the entropy losses are computed.
Security approaches for electronic health data handling through the Semantic Web: A scoping review

Integration of health information systems are crucial to advance the effective delivery of healthcare for individuals and communities across organizational boundaries. Semantic Web technologies may be used to connect, correlate, and integrate heterogeneous datasets spread over the internet. However, when working with sensitive data, such as health data, security mechanisms are needed. A scoping review of the literature was undertaken to provide a broad view of security mechanisms applied to, or along with, Semantic Web technologies that could allow its use with health data. Searches were conducted in the most relevant databases for the scope of this work. The findings were classified according to the main objective and features presented by each solution. Twenty-six studies were included in the review. They introduced mechanisms that addressed several security attributes, such as authentication, authorization, integrity, availability, confidentiality, privacy, and provenance. These mechanisms support access control frameworks, semantic and functional interoperability infrastructures, and privacy compliance solutions. The findings suggest that the application and use of Semantic Web technologies is still growing, with the healthcare area being particularly interested. The main security mechanisms for Semantic Web technologies, the key security attributes and properties, and the main gaps in the literature were identified, helping to understand the technical needs to mitigate the risks of handling personal health information over the Semantic Web. Also, this research has shown that complex and robust solutions are available to successfully address several security properties and features, depending on the context that the electronic health data is being managed.
Spin-wave quantum computing with atoms in a single-mode cavity

We present a method for network-capable quantum computing that relies on holographic spin-wave excitations stored collectively in ensembles of qubits. We construct an orthogonal basis of spin waves in a one-dimensional array and show that high-fidelity universal linear controllability can be achieved using only phase shifts, applied in both momentum and position space. Neither single-site addressability nor high single-qubit cooperativity is required, and the spin waves can be read out with high efficiency into a single cavity mode for quantum computing and networking applications. We describe how to establish linear quantum processing using a lambda scheme in a rubidium-atom system and calculate the expected experimental operational fidelities due to fundamental and technical errors. We derive efficient methods to achieve linear controllability in both a single-ensemble and dual-ensemble configuration. Finally, we propose to use the spin-wave processor for continuous-variable quantum information processing and present a scheme to generate large dual-rail cluster states useful for deterministic computing.
LiDAR Integration with ROS for SLAM Mediated Autonomous Path Exploration

Path exploration is a significant problem domain of point-to-point robot navigation. Even the availability of robust sensors has created evolving computational challenges for mobile robot navigation (MRN) specially in GPS denied indoor environments (IE). This paper presents the theoretic and experimental analysis of path exploration challenges and possibilities in an indoor environment by systematic integration of LiDAR with ROS platform for constructing assessable SLAM. The algorithmic solutions have been tested with customized differential drive structure robot platform (CUBOT). This also studies the mapping of the concerned trajectory and environment localization. Consumer grade 2D RP LiDAR is used here as the percept for the edging obstacle periphery, and the associated tangential data is used to construct the localization and mapping. Significant analysis is done on visual representation of LiDAR data in ROS platform for performance evaluation of the path exploration and planning algorithms. This evaluated result would clearly serve as reference to researchers for selecting appropriate SLAM algorithm with an associated memory representation of already explored path. It would also facilitate further combination with optimized global and local path planning techniques to achieve a desired goal by autonomous mobile robot in constrained indoor environment.
Introduction to Blockchain

As life sciences research organizations explore methods to facilitate patient-centered and innovative technologies, they are increasingly exploring distributed ledger technologies (“blockchain”) to address many of these needs. Blockchain is demonstrating the potential to transform life sciences research, allowing more data capabilities and innovation. Blockchain-based applications vary from audit trails for provenance to integrating remote devices to managing data for decentralized trials. As blockchain is emerging in life sciences, there are questions about the benefits and drawbacks of these technologies. This chapter introduces basic information about the common characteristics of blockchain technologies and the features they add to life sciences research. This chapter also addresses some of the uses of blockchain and lays the groundwork for the real-world applications, benefits, and drawbacks described in future chapters.
Blockchain-Based Scalable Network for Bioinformatics and Internet of Medical Things (IoMT)

The major problem in today’s data creation and monetization is that the data creators (individual people trading, traveling, and interacting on social media) are not the data aggregators (the Googles, Facebooks, and Amazons of the world). As such, the full potential of the personal data value in the age of informatics has yet to fully materialize. This leads to constant conflict within the data ecosystem regarding who has the right to own and monetize data; the creators or aggregators. It has also led to a protracted debate on data sovereignty and expanded legislation for data privacy that we deal with every day when we navigate any website. The holy-grail solution for such a problem is vertical integration, i.e., integrating the data value chain by combining and ensuring that data creators and aggregators are the same in the data value stack. Until recently, this was deemed technologically impossible because individuals in society cannot be their own bank, e-commerce platform, their own search engine, and their own social media. However, the advent of miniaturized sensors driven by advancements in device engineering and miniaturization ushered in a new age of multifunctional sensors, often called the Internet of Things (IoT). In particular, the distributed miniaturized devices that measure the biological attributes of individuals are called the Internet of Medical Things (IoMT). This chapter describes an end-to-end ecosystem that offers a solution to this problem and the commercial pilot model it has implemented utilizing the nascent but promising blockchain technology.
Life Sciences Intellectual Property Through the Blockchain Lens

Intellectual property (IP) refers to a broad range of intangible assets derived through creations of the mind. Intangible assets are not physical and, as such, are distinguishable from other assets like real estate and personal property. Intangible assets can be owned by individuals or entities and encompass everything from inventions, to expressive works, to designations for goods and services. With the recent emergence of blockchain, life sciences organizations are realizing that blockchain can be an attractive tool to manage assets while protecting and enforcing IP rights. This chapter provides an overview of the different facets of IP protection for blockchain in life sciences and explains how life sciences organizations can utilize blockchain technologies to help procure, maintain, and enforce their IP.
Supporting Smart Mobility in Smart Cities Through Autonomous Driving Buses: A Comparative Analysis

As more and more people are moving to bigger cities, housing is becoming less and less available and cities are becoming more congested due to the high number of people and their cars. This leads to increased traffic, congestion, air pollution and degradation of quality of life. One approach to deal with this problem is to operate autonomously driving buses through artificial intelligence and internet of things. This paper compares four projects with autonomous driving buses. The aim is to find out, what added value such projects bring to smart cities. Findings indicate that well-implemented autonomous buses can reduce air and noise pollution, reduce congestion, and improve safety. In the paper, we identify enablers and challenges for autonomous driving from the four cases. While artificial intelligence and internet of things pose the biggest challenges in terms of scalability and interoperability, these disruptive technologies are also the biggest enablers, as these make the deployment of autonomous driving buses possible in the first place. The new data from the internet of things open up new possibilities for planning and operating autonomous busses.
The Art of Ethics in Blockchain for Life Sciences

Blockchain is one of the emerging technologies with profound societal and economic disruptive potential. It can also act as a catalyst for a new era where boundaries between physical, biological, and digital worlds become increasingly blended. This impact will likely trigger a complex cascade of adaptive changes in how we live, work, and educate future generations. Although ethics and moral values have been in existence for centuries, the digital era and rapid large-scale adoption of emerging technologies such as blockchain are posing novel digital ethics challenges that need to be addressed from a philosophical, legal, and self-sovereignty perspective. This chapter highlights how we can design proactive digital ethics programs in life sciences that mitigate potential negative consequences of blockchain deployments. Further, design thinking methodology combined with ethics principles can assist with building a human-centered blockchain ecosystem in the life sciences industry that will protect human rights. Specific digital ethics nuances related to various domains within life sciences as well as cultural or socioeconomic differences that can impact our blockchain ethical design frameworks will be addressed, and topics for future research will be suggested.
Blockchain Adoption in Life Sciences Organizations: Socio-organizational Barriers and Adoption Strategies

The goal of this chapter is to reveal socio-organizational barriers for blockchain adoption in life sciences organizations and delineate the strategies that managers and executives can undertake to facilitate adoption. By drawing on the literature on innovation adoption and organizational culture and studying real-time blockchain adoption, this chapter describes the following socio-organizational barriers for blockchain adoption in life sciences organizations: (1) negative stereotypes of blockchain technology, (2) perceived technological complexity of blockchain, (3) highly institutionalized nature of life sciences organizations, and (4) lack of ecosystem mindset. The chapter also reveals strategies that can facilitate adoption: (1) holistic evaluation of blockchain life sciences use cases, (2) framing blockchain adoption as aligned with the innovative and safety-driven culture in life sciences organizations, (3) unobtrusive implementation, and (4) acting swiftly when the innovative culture gains strength. These insights draw attention to the overlooked socio-organizational aspects of blockchain adoption in life science and offer practical insights to make blockchain adoption a reality.
Cybersecurity Considerations in Blockchain-Based Solutions

Blockchain technology has a reputation for providing a higher level of assurance and security than other information systems. However, many design decisions, implementation realities, limitations, and trade-offs can create underlying vulnerabilities in blockchain-based solutions that are exploitable by malicious attackers. This chapter discusses some of the most common vulnerabilities in blockchain-based solutions that can arise in the context of life sciences research. For each of these vulnerabilities, mitigating strategies are proposed to address the identified risk. These mitigating strategies reduce the likelihood and impact of the occurrence and, thereby, bring the cybersecurity risk to an acceptable level. Like any other information system, securing a blockchain-based solution requires a holistic, contextual, and risk-based approach that investigates all possible attack vectors and contextually evaluates their potential harm.
Coping with the Opportunities and Challenges of Smart Policing: A Research Model

The paper aims to examine how police managers cope with the threats and opportunities associated with the implementation of smart policing applications. Smart policing has reshaped the practices of police managers by offering opportunities to improve decisions with data-driven approaches. These new approaches change the traditional ways in which police managers exercise discretion. Using the theoretical lens of coping theory, we develop the foundations for a research model to explain how smart policing stimulates problem-focused and emotion-focused strategies depending on managers’ perceptions of control and discretion. Future empirical studies using the model can inform more broadly on our understanding of how public sector employees respond to data-driven technologies and automated decision-making.
Optimalisasi Algoritma C4.5 dalam Menganalisis Indikasi Penyebab Penyakit Feline Immunodeficiency Virus (FIV) pada Kucing

The health of a cat is an aspect and a major concern for someone who maintains it. Cats can be exposed to many diseases, one of which is Feline Immunodeficiency Virus. Feline Immunodeficiency Virus, also known as feline aids, is a disease that attacks the cat's immune system. This virus causes a decrease in the cat's immune system so that cats become vulnerable and susceptible to disease. This disease is difficult to detect because the symptoms are almost invisible. This study aims to explore new knowledge stored in the data list of cats suffering from FIV so that the cause of the disease can be known and how to solve it. The data used in this study were 50 data obtained from the Animal Hospital of West Sumatra with attributes of Body Weight (BB), Body Temperature (ST), Breathing (PN), Appetite (NM), Vomiting (MN), Dental Condition (KG), Feather Condition (KB), Skin Condition (KK) and yield. The data is processed using the C4.5 Algorithm method based on the symptoms experienced by the cat. After processing, the data will produce the entropy value and gain value for each symptom, then the right decision tree is made for cats with FIV disease. The results of this study are new knowledge about FIV disease in cats in the form of a decision tree from the symptoms they experience. The results show that there are 21 rules with the chosen determinant attribute, namely the Skin Condition (KK) attribute with a gain value of 0.398683. This research is useful for the community, especially those who keep cats so that they can be used as a reference in knowing the symptoms experienced by cats if there is an indication of FIV disease.
Vocabulary-Based Classification and Contact-Induced Formation of Neologisms in Two Standard Varieties of Karelian

Abstract This paper investigates the lexical similarities and formation of neologisms of two written standard varieties of Karelian, North and Livvi Karelian, spoken in the Republic of Karelia, Russia. Firstly, a naïve Bayes statistical model was generated to classify North and Livvi Karelian newspaper texts automatically. Secondly, the word formation strategies of neologisms from the classified newspaper texts were studied. The strategies between the two varieties were compared in terms of the code-copying framework. The results from the automatic classification and the investigation of neologisms show that the standards differ in lexicon and phonology, but the strategies of forming neologisms are similar: the most common strategy is to form words by language-internal means, and the other strategies are selective and global copying from Finnish, Russian, and English. The similar strategies in both standards suggest similar language planning.
Overview and Comparison of the Main Approaches to the Implementation of Contact Tracing Mechanisms in the COVID-19 Pandemic

The spread of the COVID-19 pandemic poses new challenges and threats to the international community. In particular, today the number of people infected with coronavirus infection already exceeds 80 million, almost 2 million died from the pandemic, and these terrible statistics are not final. Under these conditions, quarantine restrictions are almost the only effective means of counteracting the effects of the epidemic by reducing the rate of virus spread. However, quarantine restrictions have extremely serious consequences for the world economy, they have already led to a significant reduction in production and, consequently, jobs in almost all sectors of the economy. Therefore, the development and implementation of the latest information technologies for contact tracking in the context of the COVID-19 pandemic is an extremely important and urgent task. The purpose of this article is to research and substantiate various information technologies (such as Bluetooth, Wi-Fi, GPS, etc.) in contact tracking tools to prevent the spread of infectious diseases. We analyze different approaches to the implementation of contact tracing mechnisms, highlight their advantages and disadvantages as well as opportunities for improvement. Also we analyze examples of real applications of the advanced countries functioning at the state level.
Bregman Divergencies, Triangle Inequality, and Maximum Likelihood Estimates for Normal Mixtures

The concepts of distance and angle and their algebraic realizations in the form of a scalar product are known to lead to kernel versions of sophisticated clustering algorithms. Here the more recently utilized Bregman Divergencies are treated. They possess all the properties of a metric apart from satisfying the triangle inequality. However, they can be suitably modified. En passant an apparent gap in a former paper is eliminated by exploiting an old proof concerning (conditionally) positive definite kernels. In addition an explicit isometric embedding of the modified Bregman Divergence in a Reproducing Kernel Hilbert Space is described. On a practical level recalling some basic facts on normal mixtures the well known connection between the parameter estimation problem for these mixtures and clustering algorithms is shown to hold in this abstract setting as well thus providing a more flexible approach to maximum likelihood estimates.