Reviewerly/exHarmony huggingface dataset:
Enhancement and Segmentation of Medical Images Using AGCWD and ORACM

&lt;p&gt;Images that are obtained in the real world in low contrast are inappropriate for human eyes to read the medical images. Enhancement and segmentation have an important role to play in digital image processing, pattern recognition, and the computer vision. Here, this paper presents an effective way of changing histograms and improving contrast in digital images. Segmentation is done on AGCWD enhanced images. Histogram equalization is an important technique for contrast enhancement. Nevertheless, modern Histogram Equalization commonly results in unnecessary contrast enhancement, which in turn offers an un-natural presence to the processed image and produces visual artifacts. We present an automated transformation technique that helps boost dimmed image brightness by gamma correction and weighted distribution, commonly known as Adaptive Gamma Correction Weighted Distribution (AGCWD). The contrast enhancement level can be modified using this technique; noise robustness, white or black stretching, and the protection of medium brightness can be easily integrated into the optimization process. Finally, a contrast enhancement algorithm with low complexity is introduced. All the process of enhancement will be done during the process of pre-processing the image. Later, in post-processing, we introduce a specific level set method known as ORACM for better segmentation of an enhanced AGCWD image, and it is compared with the traditional level set method.&lt;/p&gt;
Rédei permutations with cycles of the same length

Let $${\mathbb {F}}_{q}$$ be a finite field of odd characteristic. We study Rédei functions that induce permutations over $$\mathbb {P}^1({\mathbb {F}}_{q})$$ whose cycle decomposition contains only cycles of length 1 and j, for an integer $$j\ge 2$$ . When j is 4 or a prime number, we give necessary and sufficient conditions for a Rédei permutation of this type to exist over $$\mathbb {P}^1({\mathbb {F}}_{q})$$ , characterize Rédei permutations consisting of 1- and j-cycles, and determine their total number. We also present explicit formulas for Rédei involutions based on the number of fixed points, and procedures to construct Rédei permutations with a prescribed number of fixed points and j-cycles for $$j \in \{3,4,5\}$$ .
Bitcoin Adoption as a New Technology for Payment Mechanism in a Tourism Collaborative Network

Bitcoin is designed to operate as a fine mechanism of Collaborative Network (CN). The Tourism Industry is a particular sector in which this emerging technology, as a new mechanism of payment based on peer to peer and autonomous characteristics, could have a significant impact on growth and helps to reach untapped regions. Given its revolutionary nature, Bitcoin is not yet penetrated in this industry, while the main issue is the number of players who are connected from service providers to tourists, agencies, and suppliers. These heterogeneous players need to use Bitcoin simultaneously when compatibility is the major problem. This means that the Technology Adoption/Acceptance are affected by the behavior of the Tourism Collaborative Network (TCN). The aim of this work is to develop a Technology Acceptance/Adaptation model to evaluate the situation of Bitcoin in TCN and identify factors affecting Bitcoin acceptance/adoption in the context of CN as well.
Accounting for pandemics and economic crises management: Moral hazard, blockchain, and smart-contracts

During economic crises, sovereign states and central banks support the general economy and firms with a range of emergency measures, such as the allocation of subsidies to enterprises and citizens. The sudden availability of money without the need for any consideration by the recipient leads the way to inappropriate conduct known as moral hazards, such as diversion or improper use of the financial resources received. The moral hazard arises from the individual tendency to rational behavior when in the presence of information asymmetry, inadequate controls, or favorable contractual positions. To reduce moral hazard, and to preserve the intentionality of the states, information asymmetry must be reduced. Avoiding moral hazard is particularly important in cases such as the COVID-19 pandemic, but also during economic crises and other emergency situations. This paper conceptualizes a relevant topic for the economy and accounting fields of study because information tends to be naturally asymmetrical. Traditional accounting is limited by the fact that some accounting practices or techniques can be used to reduce the effect of the pandemics on the economic performance of organizations. In our study we propose a way to reduce the natural subjectivity in accounting and reporting, using blockchain and smart contracts technologies, as a solution to information asymmetry during crises (such as economic ones or pandemics)
An upper bound on the number of compatible parameters in simultaneous quantum estimation

Simultaneous estimation of multiple parameters is required in many practical applications. A lower bound on the variance of simultaneous estimation is given by the quantum Fisher information matrix. This lower bound is, however, not necessarily achievable. There exists a necessary and sufficient condition for its achievability. It is unknown how many parameters can be estimated while satisfying this condition. In this paper, we analyse an upper bound on the number of such parameters through linear-algebraic techniques. This upper bound depends on the algebraic structure of the quantum system used as a probe. We explicitly calculate this bound for two quantum systems: single qubit and two-qubit X-states.
Designing of an Immaculate Smart City with Cloud Based Predictive Analytics

The major objective of a smart city is to provide ripest and rightest services to its residents with minimal human intervention.The data collected from various sensors, receptors and receivers can be stored in a reliable, cost effective and manageable cloud storage.Predictive analytics such as predictive modelling and machine learning can be used to analyze the latest and past data from the cloud to forecast the near and far future.The predictive analytics will immediately send notifications and the appropriate actions to be taken to the concerned system so that the residents can get equable life.Waste management such as sewage management and trash management are one of the major exertions in a smart city.In this paper, predictive analytics is applied on the cloud data collected from various sensors attached on the waste management resources such as trash bins and sewage pipes.Firstly, group of trash bins and sewage pipes are attached with radio wave sensors which in turn connected to an IoT enabled receiver to provide cost effective data collection.Secondly, the data from the receivers are stored in a cloud.Thirdly, predictive analytics using K-means clustering are proposed for applying on the data in the cloud.Finally, actions like redirecting the trash collection truck and sewage clearing robot based on the priority can be done automatically and long-term actions can be directed to the concerned authorities with notifications and alerts to take necessary actions.In this paper, designing of an immaculate smart city is proposed which can be achieved by developing IoT based trash collection and sewage cleaning system.For analysis purpose synthetic data is considered where levels for garbage cleaning bins at different time and liquid levels of drainage pipes at different time are represented with different numbers.
Conditioned Natural Language Generation using only Unconditioned Language Model: An Exploration

Transformer-based language models have shown to be very powerful for natural language generation (NLG). However, text generation conditioned on some user inputs, such as topics or attributes, is non-trivial. Past approach relies on either modifying the original LM architecture, re-training the LM on corpora with attribute labels, or having separately trained `guidance models' to guide text generation in decoding. We argued that the above approaches are not necessary, and the original unconditioned LM is sufficient for conditioned NLG. We evaluated our approaches by the samples' fluency and diversity with automated and human evaluation.
Source Base of Virtual Historical Reconstructions of Historical and Cultural Heritage Objects: Problem Setting

Virtual historical reconstructions being an actively developing branch of historical information science are characterized by a peculiar approach to source base formation and study. New complexes of sources used pose several methodological problems of source studies. The first one is the place of scientific and technical documents in classical classifications of source studies. The second one is the place of documents which were created electronically or digitized in source studies classifications. The third problem is the consideration of complex source studies methods (analysis and synthesis) in relation to specific features of different source types and their informational capabilities used to solve virtual historical reconstruction problems.&amp;nbsp; The author proposes original solutions to the indicated problems based on the analysis of classification schemes of source studies existing in Russian historiography as well as the basic principles of identifying the types of historical sources proposed by L.N. Pushkareva, I.D. Kovalchenko, S.O. Sсhmidt, O.M. Medushevskaya, etc. She also specifies electronic documents (both digitized and initially electronic ones) within the framework of studying the possibilities to "embed" electronic documents into existing classifications of source studies as well as formulates new approaches to the methodology of source analysis highlighting a new stage in the development of source criticism: multidisciplinary source studies.
Composite Fermions as Deformed Oscillators: Wavefunctions and Entanglement

Composite structure of particles somewhat modifies their statistics, compared to the pure Bose- or Fermi-ones. The spin-statistics theorem, so, is not valid anymore. Say, п-mesons, excitons, Cooper pairs are not ideal bosons, and, likewise, baryons are not pure fermions. In our preceding papers, we studied bipartite composite boson (i.e. quasiboson) systems via a realization by deformed oscillators. Therein, the interconstituent entanglement characteristics such as entanglement entropy and purity were found in terms of the parameter of deformation. Herein, we perform an analogous study of composite Fermi-type particles, and explore them in two major cases: (i) “boson + fermion” composite fermions (or cofermions, or CFs); (ii) “deformed boson + fermion” CFs. As we show, cofermions in both cases admit only the realization by ordinary fermions. Case (i) is solved explicitly, and admissible wavefunctions are found along with entanglement measures. Case (ii) is treated within few modes both for CFs and constituents. The entanglement entropy and purity of CFs are obtained via the relevant parameters and illustrated graphically.
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks

The problem of adversarial examples has shown that modern Neural Network (NN) models could be rather fragile. Among the more established techniques to solve the problem, one is to require the model to be {\it $ε$-adversarially robust} (AR); that is, to require the model not to change predicted labels when any given input examples are perturbed within a certain range. However, it is observed that such methods would lead to standard performance degradation, i.e., the degradation on natural examples. In this work, we study the degradation through the regularization perspective. We identify quantities from generalization analysis of NNs; with the identified quantities we empirically find that AR is achieved by regularizing/biasing NNs towards less confident solutions by making the changes in the feature space (induced by changes in the instance space) of most layers smoother uniformly in all directions; so to a certain extent, it prevents sudden change in prediction w.r.t. perturbations. However, the end result of such smoothing concentrates samples around decision boundaries, resulting in less confident solutions, and leads to worse standard performance. Our studies suggest that one might consider ways that build AR into NNs in a gentler way to avoid the problematic regularization.
An Alternative Paradigm for Developing and Pricing Storage on Smart Contract Platforms

Smart contract platforms facilitate the development of important and diverse distributed applications in a simple manner. This simplicity stems from the inherent utility of employing the state of smart contracts to store, query and verify the validity of application data. In Ethereum, data storage incurs an underpriced, non-recurring, predefined fee. Furthermore, as there is no incentive for freeing or minimizing the state of smart contracts, Ethereum is faced with a tragedy of the commons problem with regards to its monotonically increasing state. This issue, if left unchecked, may lead to centralization and directly impact Ethereum's security and longevity. In this work, we introduce an alternative paradigm for developing smart contracts in which their state is of constant size and facilitates the verification of application data that are stored to and queried from an external, potentially unreliable, storage network. This approach is relevant for a wide range of applications, such as any key-value store. We evaluate our approach by adapting the most widely deployed standard for fungible tokens, i.e., the ERC20 token standard. We show that Ethereum's current cost model penalizes our approach, even though it minimizes the overhead to Ethereum's state and aligns well with Ethereum's future. We address Ethereum's monotonically increasing state in a two-fold manner. First, we introduce recurring fees that are proportional to the state of smart contracts and adjustable by the miners that maintain the network. Second, we propose a scheme where the cost of storage-related operations reflects the effort that miners have to expend to execute them. Lastly, we show that under such a pricing scheme that encourages economy in the state consumed by smart contracts, our ERC20 token adaptation reduces the incurred transaction fees by up to an order of magnitude.
Students' Technological Literacy to Improve Academic Writing and Publication Quality

Writing is an academic culture to be developed in higher education environment.Along with its development, the distribution of the academic works goes beyond geographic barriers, hence, making it easier for researchers to explore and obtain material for literature review.However, these conveniences are not in line with the students' ability to publish their academic writing.Thus, this research aims at identifying ways to improve students' technological literacy to increase the number of academic papers in reputable publications and reporting the collaboration results in increasing the number of student publications.Action Research was chosen as the research design, which was done by involving 63 students of a university in Sumedang Regency, consisting of 23 female students and 40 male students coming from various regions in West Java, Indonesia.The findings showed that the students' weaknesses in writing were in its originality, cited references that were not representative and credible, and the inconsistency between in-text citation and reference list.Then, this research employed seven critical steps to publish academic writing: (1) Introduce and use of technological literacy, (2) search of journals for publication, (3) writing assistance, (4) article editing, (5) similarity check, (6) journal submission, and (7) final revision assistance.These steps were found to increase the number of student publications by 84.12%.Hence, higher education institutions in Indonesia are suggested to increase the publication numbers to enhance technology information-based academic culture.
Projecting an ultra-strongly-coupled system in a non-energy-eigenbasis with a driven nonlinear resonator

In the ultra-strong coupling regime of a light-matter system, the ground state exhibits non-trivial entanglement between the atom and photons. For the purposes of exploring the measurement and control of this ground state, here we analyze the dynamics of such an ultra-strongly-coupled system interacting with a driven nonlinear resonator acting as a measurement apparatus. Interestingly, although the coupling between the atom and the nonlinear resonator is much smaller than the typical energy scales of the ultra-strongly-coupled system, we show that we can generate a strong correlation between the nonlinear resonator and the light-matter system. A subsequent coarse- grained measurement on the nonlinear resonator significantly affects the light-matter system, and the phase of the light changes depending on the measurement results. Also, we investigate the conditions for when the nonlinear resonator can be entangled with the ultra-strongly coupled system, which is the mechanism that allows us to project the ground state of the ultra-strongly coupled system into a non-energy eigenstate.
Typestates to Automata and back: a tool

Development of software is an iterative process. Graphical tools to represent the relevant entities and processes can be helpful. In particular, automata capture well the intended execution flow of applications, and are thus behind many formal approaches, namely behavioral types. Typestate-oriented programming allow us to model and validate the intended protocol of applications, not only providing a top-down approach to the development of software, but also coping well with compositional development. Moreover, it provides important static guarantees like protocol fidelity and some forms of progress. Mungo is a front-end tool for Java that associates a typestate describing the valid orders of method calls to each class, and statically checks that the code of all classes follows the prescribed order of method calls. To assist programming with Mungo, as typestates are textual descriptions that are terms of an elaborate grammar, we developed a tool that bidirectionally converts typestates into an adequate form of automata, providing on one direction a visualization of the underlying protocol specified by the typestate, and on the reverse direction a way to get a syntactically correct typestate from the more intuitive automata representation.
Along the cow path

Technical communication and user experience studies traditionally uphold Western onto-epistemological distinctions between technical users and objects. Recent calls for the inclusion of cultural approaches to technical communication, however, have asked scholars to consider the influence cultural knowledge has on communication design. This article takes up these calls by reading technical documentation through new materialist and Indigenous ways of knowing. Using a prominent Jewish cemetery in Gainesville, Florida as a case study, this article treats technical artifacts and subjects as co-constitutive, arguing for the cultural and material agency of technical documentation design in mediating and shaping user experience.
Improvement of Neural Reverse Dictionary by Using Cascade Forward Neural Network

A reverse dictionary maps a description to the word specified by the description. The neural reverse dictionary (NRD) learns to map word embeddings for an input definition into an embedding of the word defined by the definition using neural networks. Such a function encodes phrasal semantics and bridges the gap between them and lexical semantics. However, previous NRD has a limitation in accuracy due to its insufficient capacity. To solve this problem, we used novel combinations of neural networks, which are effective in neural machine translation and image processing, with sufficient capacities. We found that, an LSTM output adjustment by using a multi-layer fully connected network with bypass structures (CFNN) was more effective for reverse dictionary tasks than using more complicated LSTM. BiLSTM+CFNN was comparable to the commercial system OneLook Reverse Dictionary in some metrics, and noised biLSTM+CFNN which we tuned by a noising data augmentation outperformed OneLook Reverse Dictionary in almost all metrics. We also examined the reasons for the success of biLSTM+CFNN and revealed that a bypass structure of the CFNN and balance in the capacity of LSTM and the CFNN contribute to the improved performance of the NRD.
On some graph-based two-sample tests for high dimension, low sample size data

Testing for equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few decades, several graph-based two-sample tests have been proposed in the literature, which can be used for data of arbitrary dimensions. Most of these test statistics are computed using pairwise Euclidean distances among the observations. But, due to concentration of pairwise Euclidean distances, these tests have poor performance in many high-dimensional problems. Some of them can have powers even below the nominal level when the scale-difference between two distributions dominates the location-difference. To overcome these limitations, we introduce some new dissimilarity indices and use them to modify some popular graph-based tests. These modified tests use the distance concentration phenomenon to their advantage, and as a result, they outperform the corresponding tests based on the Euclidean distance in a wide variety of examples. We establish the high-dimensional consistency of these modified tests under fairly general conditions. Analyzing several simulated as well as real data sets, we demonstrate their usefulness in high dimension, low sample size situations.
NLSC: Unrestricted Natural Language-Based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we claim that the effort to developing service descriptions, request translations, and service matching could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 36% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.