Reviewerly/exHarmony huggingface dataset:
Security Notions for the VeraGreg Framework and Their Reductions

Homomorphic encryption enables computations with encrypted data, however, in its plain form, it does not guarantee that the computation has been performed honestly. For the Fully Homomorphic Encryption (FHE), a verifiable variant emerged soon after the introduction of FHE itself, for a single-operation homomorphic encryption (HE), particular verifiable variant has been introduced recently, called the VeraGreg Framework. In this paper, we identify a weakness of List Non-Malleability as defined for the VeraGreg framework-an analogy to the classical Non-Malleability-and define a stronger variant, which addresses the weakness and which we show not to be strengthenable any more. Next, we suggest a decomposition of the abstract VeraGreg framework, introduce novel notions of security for the resulting components and show some reductions between them and/or their combinations. We conjecture that VeraGreg achieves the strongest (and desirable) security guarantee if and only if its building blocks achieve certain, much more tangible properties. Finally, we suggest a simplification to the original VeraGreg instantiation, which now relies on hardness of particular kind of the famous Shortest Vector Problem for lattices.
Attribute Based Encryption For Secure Access In Cloud Using Block chaining

Multi-user system for access control to datasets stored in an untrusted cloud environment. Cloud storage like any other untrusted environment needs the ability to secure share information. Our approach provides an access control over the data stored in the cloud without the provider participation. The main tool of access control mechanism is ciphertext-policy attribute-based encryption scheme with dynamic attributes. Using a blockchain based decentralized ledger, our system provides immutable log of all meaningful security events, such as key generation, access policy assignment, change or revocation, access request. We propose a set of cryptographic protocols ensuring privacy of cryptographic operations requiring secret or private keys. Only ciphertexts of hash codes are transferred through the blockchain ledger. The prototype of our system is implemented using smart contracts and tested on Ethereum blockchain platform.
Requirements Engineering for Stakeholders of Factory Conversion: LoD Visualization of a Research Factory via AR Application

Restructuring in factories is becoming increasingly important to maintain the competitiveness of a factory. In the planning and construction of a factory building, many different project participants turn a construction project into a highly complex task. In the research factory Arena2036 of the University of Stuttgart, in addition to the original research areas of the mobility of the future the building itself is also regarded as an object of research. A method is proposed that uses elements of requirements engineering and enables the user to make a structured decision about the type of a digital building model and its LoD. Requirement templates enable a quick identification of requirements of all stakeholders. The method is suggested to find out which information is important for the stakeholders and what type of digital building model, in which LoD is required for the conversion. Based on this an Augmented Reality application is developed to support the exchange of technical planners during a factory restructuring. The exchange between technical planners is to be streamlined by the application and planning phases shortened. The experiences of using the application are presented and it is proposed as a first step towards a didactic concept in the Arena2036.
Reed–Muller and Kerdock Codes

In Chapter 6, we studied Reed–Solomon codes, codes whose codewords are the evaluation of polynomials in one variable of degree at most k − 1 at the elements of \({\mathbb F}_q \cup \{\infty \}\). Reed–Solomon codes are short length codes, where the length n is bounded by q + 1, and only useful when we take the field to be large. The alternant codes which we constructed from generalised Reed–Solomon codes in Chapter 7 allowed us to construct codes over small fields and we put this to good use. In this chapter we will consider another generalisation of Reed–Solomon codes, codes whose codewords are the evaluation of polynomials in many variables. This again allows us to construct linear codes over small fields and we will restrict our attention, for the most part, to binary linear codes. It will turn out that these codes are not asymptotically good. Nevertheless, they are an important class of codes which are widely implemented due to the availability of fast decoding algorithms. One example of such a decoding algorithm is the majority-logic decoding algorithm that we will study here. We will then go on and construct Kerdock codes which are certain subcodes of the second-order Reed–Muller codes. These codes can give examples of non-linear codes with parameters for which no linear code exists.
Are the CDC’s Corona Virus Statistics Fraudulent? An Accounting and Legal Analysis

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Mapping and Generating Adaptive Ontology of Decision Experiences

Decision-making is shared by many disciplines. In computer science decision-making systems aim to substitute or support people for making decisions. The systems generally need to acquire as many as possible data to provide possible options for any decision-making. The possible options are usually obtained by modeling situations data. However, situation data is becoming tremendous along with daily life changes and it is becoming more and more difficult to model and restore those situation data. However as human, when the situation data is lacking, we still can make appropriate decisions based on our "decision experiences". To learn how decisions are made adaptively by humans, this paper propose a method to characterize a decision-making process for a finite number of people only based on individual's actions without modeling any situation data. Then the characterization problem is formulated as a one-dimensional decision-making process and experimented as a number guessing game. The experimental results show the feasibility of the proposed method in mapping and generation of an adaptive ontology structure of decision experiences for experimental participants.
Freely improvised and non-academic electroacoustic music by urban folks

We discuss history and characteristics of the evolving global networks of anarchic communities of independent experimental musical artists that over the past half century have continued to flourish outside of established cultural institutions and with but incidental, ad hoc, financial funding.As a case-study related to our own practice we give an in-depth look into the International Headphone Festival Le Placard (1998-2013), a series of ephemeral (headphones only) concert-sites and laboratories for (not only) sound art and electronic music, that was highly innovative and influential also in the worlds of academic and commercial music, though -being the result of collective creative efforts, transmitted in (contemporary equivalents of) the fundamentally aural tradition that is typical of artistic folk modes-this is an influence destined to remain anonymous, hence uncredited. Improvised electroacoustic music, folklore, DIY, circuit-bending, artist networksWhile these Californian folks were having their regular Saturday night live improvised electronic music jams at the Scope, largely unaware, it seems, of the long-time schemed and far grander conspiracies that rule their universe from inside deeply hidden layers, master circuit bender, silicon
Blockchain Cryptography: Part 1

Cryptology is the science of encrypting and decrypting information and the methods employed to those ends. Cryptography (from the Greek “kryptós,” meaning secret or hidden, and “gráphein,” meaning writing) is a subset of cryptology that describes the creation of methods for encrypting information so it cannot be understood by unauthorized parties. Steganography refers to methods for disguising the communication channel over which cryptographically encrypted messages are sent.
Quantifying the contrast of objects in a complex image

This paper addresses the problem of no-reference quantitative assessing the overall contrast in an image. The purpose of this work is to increase the accuracy of quantifying the contrast for complex images. To this end, this paper proposes a new approach to assessing the overall contrast of objects in an image, which based on the analysis of the contrast value of simple images at various given levels of adaptation. Based on this approach, new techniques proposed to quantify the summary contrast of objects of complex multi-element images using the linear and weighted contrast kernels. The proposed technique is parameter-free, simple to implement, computationally low-costs, and can be recommended for image quality assessment in realtime mobile applications.
Countries: The role of Mobile Banking & Financial Services Development in Africa

Using a specific database on mobile banking and financial services across countries, we document the questionable pro poor and inclusive growth in developing countries and show the importance of mobile financial and banking development for workforce productivity growth and inequality reduction. This paper uses several econometric techniques to investigate mobile finance and banking stylized facts with a focus on its real impact in Africa. The mobile banking index is constructed using the principal component analysis method. The statistical analysis reveals that there is a positive link between mobile banking development and inclusive growth. The estimation of three econometric models using different dependent variables and estimation techniques mainly show a positive impact of mobile finance & banking development on both workforce productivity growth and inequality reduction. These main findings suggests that policies to boost mobile finance and banking development in Africa should be viewed as measures that would bear fruits in the medium to long run.
A Multifaceted Approach for Organising Patterns for the Internet of Things: A High-level Theoretical Discourse

The Internet of Things (IoT) consists of several patterns that address a variety of problems encountered in the design and construction of reference architectures. Essentially, these patterns represent a set of conventional solutions and technologies that are used in the IoT paradigm to create ambient intelligence. However, in order for the IoT practitioners to use these patterns effectively, a catalogue that groups and classifies them in a systematic manner is required. Such a catalogue must facilitate easy and quick retrieval of the pattern, and clearly describe the intended purpose of each pattern. At the moment, no such catalogue exists, and the reviewed catalogues in the literature are either cumbersome or address these issues inadequately. This makes it difficult for users and the IoT practitioners to easily choose the relevant pattern for the given problem. Due to the large number of the IoT patterns, it is difficult to organise them in a manner that facilitate systematic classification. In this study, we propose a methodical, fivestep approach by which we can arrange these patterns for easy identification. The nature of this paper is theoretical, thus we focus on exploring some linguistic theories and algebraic structures that make up the core essence of this multifaceted process.
Bayesian nonparametric estimation of bandwidth using mixtures of kernel estimators for length-biased data

Kernel density estimation has been applied in many computational subjects. In this paper, we propose a density estimation procedure from a Bayesian nonparametric perspective using Dirichlet process prior for the length-biased data under an unknown kernel function. In this situation, the kernel within the Dirichlet process mixture model will be approximated by the kernel density estimator. We present a Bayesian nonparametric method for finding the bandwidth parameter in the kernel density estimation using a Markov chain Monte Carlo approach. Then, this approach is used to the simulated and real data set. Finally, we compare the proposed bandwidth estimation with the other estimations like cross-validation and Bayes based on the mean integrated squared error criterion.
Residual and Ensemble Learning on Locally Derived Features for Face Recognition

In this paper, an efficient approach toward face recognition is presented. The proposed model is invariant to pose, expression, scale, illumination, and translation with the application of different techniques and implementation of their algorithms. The images are first preprocessed using different techniques like CS-LBP, XCS-LBP, CS-LDMP, etc. to remove noise and make it illumination invariant. In one implementation, these are fed as input to different descriptors. The keypoints detected are invariant to lighting condition. A wide variety of descriptors are applied to the detected keypoints in order to compare the quality of features computed. These descriptors are quantized to a single vector, representing the most salient features of the facial image. The vectors are then fed into the stacking classifier. In the other method, these preprocessed images are directly fed as input into the residual network. A comparison of the results by these two pipelines is studied on two benchmark face datasets, namely FACES94 and Grimace.
Autonomous Vehicles in Urban Traffic

This paper describes the implication of autonomous vehicles on urban transport system. Levels of vehicles autonomy and basic technologies as well as some impacts of autonomous vehicles on urban transport is presented. Also, there are some consequences of autonomous and connected vehicles on road infrastructure. Some implication of deployment of autonomous and connected vehicles for transport system of Republic of Croatia are highlighted.
Analytical Techniques for Describing User Preferences: Justification for (and Extension Of) the Matrix Factorization Technique

In the ideal world, we should be able to ask each user’s opinion about each alternative, but for large systems, with many possible alternatives, this is not realistic. Therefore, we need to extrapolate the user’s preferences based on partial information that we can elicit from the user.
When Trust is not Enough to Mobilize Blockchains

The Mobilization Decision theory provides an insight into why mobilization occurs and the factors that result in mobilization. In the last decade, blockchain technology has been touted as a technology that facilitates trust between unknown parties. Trust is at the core of all human interaction, be it commercial or social. However, the global adoption of blockchain is low. At a global scale, relatively few organizations have mobilized blockchain to either handle or support their organizational processes. In order to understand why this is the case, the mobilization decision theory is used to explain why the global adoption of blockchain is low and what needs to change to facilitate its widespread adoption at a global scale.
An early sound film pioneer: Eric M. C. Tigerstedt’s patents and patent applications 1912–1920

The legacy of the Finnish inventor and engineer Eric Magnus Campbell Tigerstedt (1887–1925) is not very widely known, even among the Finnish public. Nevertheless, Tigerstedt’s short yet prolific life touched and crossed several cultural and national boundaries: he was born to a Swedish-speaking aristocratic family in Russia’s Grand Duchy of Finland, but he studied in Germany, worked in Denmark and died in the United States at the age of 37. During his ill-fated career, Tigerstedt managed to create around 70 novel electrical devices and methods, which received over 100 patents from all over the world. Many of his inventions were aimed at creating a functioning and commercially viable sound film technology, including various amplifiers, loudspeakers and microphones. Even inventions such as the Cryptographone and an electronic hearing aid can be seen as side products of his ultimate dream of recording and reproducing synchronised sound with film image. This article and its attached documents introduce Eric Tigerstedt’s work on the ‘speaking image’ and the different approaches he used: sound on film both as variable density and as variable area photography, synchronisation of sound on disk with film projection, and recording sound – and even image – through magnetisation on a steel wire or steel band.
DART: Diversity-Enhanced Autonomy in Robot Teams

This paper defines the research area of Diversity-enhanced Autonomy in Robot Teams (DART), a novel paradigm for the creation and design of policies for multi-robot coordination. While current approaches to multi-robot coordination have been successful in structured, well understood environments, they have not been successful in unstructured, uncertain environments, such as disaster response. The reason for this is not due to limitations in robot hardware, which has advanced significantly in the past decade, but in how multi-robot problems are solved. Even with significant advances in the field of multi-robot systems, the same problem-solving paradigm has remained: assumptions are made to simplify the problem, and a solution is optimized for those assumptions and deployed to the entire team. This results in brittle solutions that prove incapable if the original assumptions are invalidated. This paper introduces a new multi-robot problem-solving paradigm which relies on a diverse set of control policies that work together synergistically to make multi-robot systems more resilient in unstructured and uncertain environments.
Smart Vehicular Management System Using Blockchain for Internet of Connected Vehicles

Smart Vehicular Systems prevail at improving the efficiency of vehicular systems; however, the aftereffects of an efficient system are often negated. This chapter focuses on developing a system that identifies a vehicle based on its unique ID using RFID readers. It makes an attempt to integrate the big hub technologies Blockchain and the Internet of Things to develop a viable Smart Vehicular System. The Internet of Things concept is used to maintain a log of the vehicles identified in various parking lots along with the time duration in the corresponding parking space. Blockchain is used to make automatic and secure payments towards parking based on the unique IDs of the vehicle and that of the parking area. The aim of a Smart Contract is to achieve a common parking fee irrespective of places, such as malls, theaters, hospitals, etc., and to reduce waiting time for parking and payment during peak hours. The Smart Vehicular System becomes a part of the Digital India Movement by reducing usage of physical currency notes and promotes the ecosystem by issuing e-receipts. Blockchain and manual transactions based on estimated data provided by the Ministry of Road and Transport were analyzed, and the transaction timing graph was plotted.
L-CNN: An Improved Convolutional Neural Network to Capture Long-Distance Dependencies

The convolutional neural network has already made great achievements in the field of natural language processing; however, since the CNNs could not capture long-distance dependencies very well, it does not achieve further achievements. Here, we design a CNN-based neural network for sentiment analysis tasks, we call it L-CNN, which is more capable than common CNNs in capturing long-distance dependence in sentences. L-CNN is a simple CNN-based neural network which also contains a local context layer and global context layer to help it capture long-distance dependencies. By the help of the context layers, L-CNN can bring an increase about two percentage points on the results for the task of sentiment analysis than the common CNN network without this mechanism.
LPI target tracking involving IEKF

We report on strategies and algorithms to track aerial targets with Low Probability of Interception (LPI). We especially focus on the IEKF which is well adapted to maneuvering targets and EKF in log-spherical coordinates to estimate the distance to a target with angle-only measurements.
Analytical Techniques Help Enhance the Results of Data Mining: Why Filtering Out Higher Harmonics Makes It Easier to Carry a Tune

This chapter provides a third (and final) example of how analytical techniques can help enhance the results of data mining.
Process Analytics Model Approach in Healthcare

Process analytics involves the relationship between the doctor, diagnostic centers and patient. The primary advantages of using process analytics in healthcare are expert guidance, global medical assistance, and possible alternate treatment mechanisms. The secondary advantages are the analysis of the same type of disease complications and the creation of a disease-based healthcare data repository. This chapter focuses on the process model-based approach for healthcare analytics. The two emerging techniques Big data and IoT are needed to be incorporated with the process model for storing and analyzing the healthcare data. The first category assists administrators with identifying areas to streamline operations and concretely increase savings. Research and development are crucial aspects of healthcare, providing new innovative solutions and treatments that can be properly tracked, measured, and analyzed.
Emergence of Middleware to Mitigate the Challenges of Multi-Cloud Solutions onto Mobile Devices

Cloud computing has invaded our lives so beautifully, that it is time to make cloud more productive. What makes Cloud computing novel is the possibility of an almost immediate resizing of resources on a pay-as-you-go usage model. With companies moving forward with various cloud initiatives, cloud consumers are discouraged from relinquishing their control of the infrastructure to the cloud providers. The rivalry among cloud providers to stay competitive in the market makes it necessary to lock-in its customers, and therefore customers cannot migrate easily to another due to non-interoperable APIs (Application Programming Interface), portability and migration issues. In addition, today’s cloud users are mobile devices and consuming a cloud service onto mobile device poses another set of risks. One way to handle this complexity is to devise an intermediary which can take care of the heterogeneity at the cloud and mobile level, and ensure a multi-cloud deployment of application by taking advantage of the best features from different vendors simultaneously. Thus, this paper is a sincere initiative to understand the problem beneath multi-cloud solutions and their embrace for the mobile world. Hence, the following paper begins with a broad coverage of existing work, gives an outline of a multi-cloud middleware, and discusses existing issues with API heterogeneity which is the prime point of concern in the vendor lock-in issue.
Non-distracting Feedback in Artificial Intelligence Supported Learning

The introduction of sensory information into computer-supported learning results in a manifold of available data to be collected. In this publication we investigate how this “more in data” does not inevitably lead to a “more in distraction” at the interface. This becomes possible by analyzing intentional and non-intentional actions of the user or the environmental conditions to detect – using artificial intelligence – for instance, if the learner is unconcentrated, tired, or over-challenged. Relying on this analysis within a learning session, a user model can therefore be dynamically updated. This offers the possibility for a direct feedback loop to reflect the learner’s status, progress, etc. but also raises novel challenges that need to be addressed, namely, when to intervene, how much feedback needs to be provided, and how to represent the required information in order to reduce the distraction and thus to improve learning success.