Reviewerly/exHarmony huggingface dataset:
Mixing Transactions with Arbitrary Values on Blockchains

Due to the transparency of blockchain, adversaries can observe the details of a transaction, and then utilize the amount as a unique quasi-identifier to make deanonymization. Nowadays, to obscure the linkages between receivers and senders within a transaction on the blockchain, mixing services are widely applied in many real applications to enhance cryptocurrencies' anonymity. The basic idea of mixing services is to hide an output within several other outputs in a transaction such that adversaries cannot distinguish them by their amounts since they are purposely selected to have the same amount. For a set of original outputs with different amounts, mixing services need to decompose them into a set of decomposed outputs, where any decomposed output has some other decomposed outputs with the same amount. Since the transaction fee is related to the number of outputs, we are motivated to decompose original outputs into a minimal set of decomposed outputs, which is challenging to guarantee the privacy-preserving effect at the same time. In this paper, we formally define the anonymity-aware output decomposition (AA-OD) problem, which aims to find a c-decomposition with a minimum number of decomposed outputs for a given original output set. A c-decomposition guarantees that for any original output <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$o$</tex> , there are at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c$</tex> of all decomposed outputs with an amount of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> coming from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$o$</tex> . We prove that the AA-OD problem is NP-hard. Thus, we propose an approximation algorithm, namely Boggart <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Boggart is a magical creature in J. K. Rowling's Harry Potter series who can shift his shape and no one knows what it looks like., to solve the AA-OD problem with a (2/c + 3)-approximation bound on the number of decomposed outputs. We verify the efficiency and effectiveness of our approach through comprehensive experiments on both real and synthetic data sets.
An Optimized Parallel Hybrid Architecture for Cryptocurrency Mining

One of the requirements for mining cryptocurrency (Crypto) is that the secured ledger of the blockchain must be updated. However, updating the secured ledger requires that the miner develop and solve complex mathematical equations in higher orders hexadecimal 64-digit solution called a hash. In addition to this challenge, the mining processes of cryptocurrency are both resource and cost-intensive. The resources required include, but not limited to mining software, hardware, power (energy usage), CPU or compute cycles, and NP-hard problem. Apart from these numerous challenges that are associated with mining cryptocurrency, the amount of speed that is required to mine a single block is core. Cryptocurrency mining speed requirement is significantly important because only miners that can have the fastest mining device are most likely to get the reward (profit) from competing for a block. In this paper, we designed and implemented a model to speed up mining process which is capable of giving miners an advantage to arrive at a block earlier. The novelty of our architecture is that our design is based on high performance computing paradigm where we achieve processor speed up by parallelizing the number of processors p. We experimented by varying p = 4, 8, 16. Our experimental results where we used the MC6800 simulated on Easy68k emulator demonstrate feasibility of our proposed model and prove that speed was an essential key to cryptocurrency mining. Keywords: Cryptocurrency, Blockchain, Architecture, Mining, Speedup, Bitcoin. CISDI Journal Reference Format Allenotor, D. &amp; Oyemade, D. A. (2021): An Optimized Parallel Hybrid Architecture for Cryptocurrency Mining. Computing, Information Systems, Development Informatics &amp; Allied Research Journal. Vol 12 No 1, Pp 95-104 DOI - https://doi.org/ 10.22624/AIMS/CISDI/V12N1P10. Available online at www.isteams.net/cisdijournal
Fuzzy cognitive mapping and photovoice: A pilot of a novel participatory methodology for addressing equity in community resilience research

This manuscript introduces a novel participatory mixed methods approach that combines fuzzy cognitive mapping (FCM) and photovoice. This pilot is designed to explore the possibilities for this mixed methodology to address two key gaps in the community resilience literature: (1) narrow focus on disaster-based or acute resilience and (2) lack of historically marginalized voices. This work was conducted in a rural, Black, Southern community. This pilot study suggests that while the proposed method does allow for the prioritization of marginalized voices and for a systems level approach to non-disaster resilience building; the FCM/photovoice combination presents limitations in interpretation for non-discrete decision-making. This method can be overwhelming for participants. Further research is needed to refine the protocol for its use in multiple decision-making processes within larger local phenomenon. Additionally, the combination of narrative storytelling and empirical analysis presents a unique opportunity for informing policy. This work is part of a larger project focused on the linkages between food environment, health, and general community resilience (Mann et al., 2020). It is important to emphasize that the purpose of this article is merely to present this method as an avenue for studying community resilience. A more robust analysis of the data collected as part of this study has been published elsewhere in more detail (Mann et al., 2020).
Interception of an aerial manoeuvring target using monocular vision

Abstract In this work, a vision-based guidance algorithm for interception of an aerial manoeuvring target is proposed. Vision-based aerial engagement in 3D space in an outdoor environment is challenging due to uncertainty in estimating the target location. The vision-based guidance algorithm proposed here generates the desired velocity commands for the interceptor drone based on the orientation of the target pixel in the image plane. The desired velocity at the centre of gravity of the interceptor is calculated such that the velocity at the camera centre is aligned towards the line joining the target pixel and the camera centre. It is shown that the proposed guidance strategy forces the target pixel towards the centre of the image plane and leads to low miss distance. The algorithm developed is first tested in the ROS-Gazebo platform and then implemented in field experiments.
Distributed robust cooperative tracking against multiple maneuvering targets based on random finite set

Abstract This paper considers the distributed multi-target tracking problem with mobile sensor network. Based on the random finite set and consensus methods, a distributed multi-target tracking algorithm is designed by local filtering stage and consensus fusion stage. In the local filtering stage, the state and measurement information of multi-target are described by random finite set. Each node in the sensor network obtains the state estimates of multi-target individually by applying the probability hypothesis density filtering algorithm. To reduce the computational complexity, the Jacobian matrix and Hessian matrix are used to calculate the priori probability density. With the local neighboring interactions, the estimate results of multi-target system are obtained after the consensus fusion stage. Besides, the stochastic boundedness of the estimation error of the distributed multi-target tracking algorithm is proved by constructing a stochastic process. In the final, a simulation example is given to verify the multi-target tracking performance of the proposed algorithm.
CheetahKG: A Demonstration for Core-based Top-$k$ Frequent Pattern Discovery on Knowledge Graphs

Knowledge graphs capture the complex relationships among various entities, which can be found in various real world applications, e.g., Amazon product graph, Freebase, and COVID-19. To facilitate the knowledge graph analytical tasks, a system that supports interactive and efficient query processing is always in demand. In this demonstration, we develop a prototype system, CheetahKG, that embeds with our state-of-the-art query processing engine for the top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> frequent pattern discovery. Such discovered patterns can be used for two purposes, (i) identifying related patterns and (ii) guiding knowledge exploration. In the demonstration sessions, the attendees will be invited to test the efficiency and effectiveness of the query engine and use the discovered patterns to analyze knowledge graphs on CheetahKG.
Quantum metrology with multimode Gaussian states of multiple point sources

A unified theoretical framework is developed for measuring multimode Gaussian states of multiple point sources. The spatial and temporal mode functions are found analytically by aid of the circular symmetry of the object plane and aperture, as well as by the rectangular spectral density function of the background. They relate the modal operators and statistical moments to the locations of the point sources through the prolate spheroidal functions. An experimentally straightforward implementation is proposed, which consists of a nonabsorptive polarization- and phase-modulating spatial light modulator (SLM), three lenses, and a polarization beam splitter. Interestingly, it is found that the spatial modal annihilation operators are related to the modal field operators in the image plane by a simple complex-valued scale factor. The quantum Fisher information matrix and quantum Cram\'er-Rao bound (QCRB) are also discussed. The numerical calculation of QCRB is completed for the case where the spatially multimode aperture fields are prepared by the spatial propagation from temporally single-mode thermal states, coherent states, or quadrature squeezed states of the point sources in the object plane. The results show that the Rayleigh limit can always be surpassed by means of increasing the mean photon rate or the squeeze parameter.
Computing the longest common almost-increasing subsequence

In this paper, we revisit the problem of computing longest common almost increasing subsequence (LCAIS) where, given two input sequences, the goal is to compute a common subsequence that is 'almost' increasing. Here the concept of an almost increasing subsequence offers an interesting relaxation over the increasing condition. This problem has been studied in the literature albeit with some constraints. Here, we present a number of a number of algorithmic approaches to solve the problem more generally and efficiently.
Optimization of English Learning Mode under the Influence of Artificial Intelligence Translation

With the development of social science and technology, artificial intelligence has been applied to many fields, and artificial intelligence translation has provided great help for language learners. This paper analyzes the necessity of artificial intelligence translation for English learning, explores the influence of artificial intelligence translation on English learning, and proposes optimized English learning modes which provide help to the people involved.
Distance learning in Croatian and Serbian music schools

This paper presents research that aimed to determine how teachers employed in Croatian and Serbian music schools coped with distance learning during the COVID-19 pandemic. The random sample was represented by 589 teachers working in music schools, of which 369 were from Croatia and 220 were from Serbia. The results showed that teachers from both countries successfully dealt with the implementation of distance music education. The results also showed that the youngest teachers are the most confident in their abilities and knowledge for using electronic devices in distance learning. Teachers believe that students cannot progress in distance learning as much as they can in traditional teaching. Technological difficulties (poor internet, poor audio and visual transmission) are the most common problem faced by teachers in distance learning. Also, most teachers stated that they would keep some of the distance learning elements in their future work. The results of this research can serve as a guide in achieving the necessary changes in music teaching which, with more extensive integration of information and communication technologies (ICT) in the teaching process, would provide a better and more efficient transition of teachers and students to a virtual environment.
A New Automatic Hyperparameter Recommendation Approach Under Low-Rank Tensor Completion Framework

Hyperparameter optimization (HPO), characterized by hyperparameter tuning, is not only a critical step for effective modeling but also is the most time-consuming process in machine learning. Traditional search-based algorithms tend to require extensive configuration evaluations for each round to select the desirable hyperparameters during the process, and they are often very inefficient for the implementations on large-scale tasks. In this paper, we study the HPO problem via meta-learning (MtL) approach under the low-rank tensor completion (LRTC) framework. Our proposed approach predicts the performance for hyperparameters of new problems based on their previous performance so that the underlying suitable hyperparameters with better efficiency can be attained. Different from existing approaches, the hyperparameter performance space is instantiated under tensor framework that can preserve the spatial structure and reflect the correlations among the adjacent hyperparameters. When some partial evaluations are available for a new problem, the task of estimating the performance of the unevaluated hyperparameters can be formulated as a tensor completion (TC) problem. Toward the completion purpose, we develop an LRTC algorithm utilizing the sum of nuclear norm (SNN) model. A kernelized version is further developed to capture the nonlinear structure of the performance space. In addition, a corresponding coupled matrix factorization (CMF) algorithm is established to render the predictions solely depend on the meta-features to avoid additional hyperparameter evaluations. Finally, a strategy integrating LRTC and CMF is provided to further enhance the recommendation capacity. We test recommendation performance with our proposed methods for classical SVM and the state-of-the-art deep neural networks such as vision transformer (ViT) and residual network (ResNet), and the obtained results demonstrate the effectiveness of our approaches under various evaluation metrics by comparing with the baselines commonly used for MtL.
HET-KG: Communication-Efficient Knowledge Graph Embedding Training via Hotness-Aware Cache

With the popularization and application of Artificial Intelligence technology, knowledge graph embedding methods are widely used for a variety of machine learning tasks. However, most of the current knowledge graph embedding models are trained with a large number of parameters and high computational time complexity. This becomes a main obstacle to apply these existing models to large-scale knowledge graphs. To address this challenge, we propose HET-KG, a distributed system for training knowledge graph embedding efficiently. HET-KG can reduce the communication overheads by introducing a cache embedding table structure to maintain hot-embeddings at each worker. To improve the effectiveness of the cache mechanism, we design a prefetching algorithm and a filtering algorithm for adaptively selecting hot-embeddings, and provide two kinds of hot-embedding table construction strategies. To address the issue of inconsistency between the local cached hot-embeddings and the global embeddings, we also develop a hot-embedding synchronization algorithm for dynamically updating the cache embedding table, which can guarantee the inconsistency bounded within a given threshold. Finally, extensive experiments are conducted on three knowledge graph datasets FB15k, WN18, and Freebase-86m. The experimental results show that HET-KG achieves 3.7x and 1.1x speedup over the state-of-the-art systems PyTorch-BigGraph and DGL-KE, respectively.
Health Assistant Using Natural Language Processing

Virtual Assistants take care of patients’ needs as well as maintain their health records. The demand for AI is increasing rapidly in Health factors to maintain the big records. Our Virtual Assistant helps you by a user interface by which you talk it with your disease so that it understands your disease by your symptoms and provide you medicine for a specific disease, maintain your health record and perfect diet by machine learning algorithms also if you want it makes your appointment with the doctor your specific area by which you contact with your doctor. NLP makes an interface by which virtual Assistants work on human data.
Application of Ontologies in the Enterprise—Overview and Critical Analysis

For many years, it was claimed that semantics should provide foundation of knowledge management in the enterprise. Today, it is easy to realize that this vision did not materialize. The aim of this work is to critically analyse the state of the art of use of semantic technologies in the enterprise and an attempt at diagnosing key problem(s).
Decoding of Generalized Concatenated Codes Over the One-Lee Error Channel for the McEliece Cryptosystem

The code-based McEliece cryptosystem is a promising candidate for post-quantum cryptography. The sender encodes a message, using a public scrambled generator matrix, and adds a random error vector. In this work, we consider q-ary codes and restrict the Lee weight of the added error symbols. This leads to an increased error correction capability and a larger work factor for information-set decoding attacks. In particular, we consider codes over an extension field and use the one-Lee error channel, which restricts the error values to Lee weight one. For this channel model, generalized concatenated codes can achieve high error correction capabilities. We discuss the decoding of those codes and the possible gain for decoding beyond the guaranteed error correction capability.
4-adic complexity of quaternary cyclotomic sequences and Ding-Helleseth sequences with period pq

In this paper, we consider two kinds of quaternary sequences, i.e., quaternary classical cyclotomic sequences with period q where q is an odd prime, and quaternary Ding-Helleseth generalized cyclotomic sequences with period pq where p is odd prime distinct from q. Then, using the generalized "Gauss periods", we derive 4-adic complexity of these sequences for any p,q and the results show that they have high symmetric 4-adic complexity.
Generalized Longest Repeated Substring Min-Entropy Estimator

The min-entropy is a widely used metric to quantify the randomness of generated random numbers, which measures the difficulty of guessing the most likely output. It is difficult to accurately estimate the min-entropy of a non-independent and identically distributed (non-IID) source. Hence, NIST Special Publication (SP) 800-90B adopts ten different min-entropy estimators and then conservatively selects the minimum value among ten min-entropy estimates. Among these estimators, the longest repeated substring (LRS) estimator estimates the collision entropy instead of the min-entropy by counting the number of repeated substrings. Since the collision entropy is an upper bound on the min-entropy, the LRS estimator inherently provides overestimated outputs. In this paper, we propose two techniques to estimate the min-entropy of a non-IID source accurately. The first technique resolves the overestimation problem by translating the collision entropy into the min-entropy. Next, we generalize the LRS estimator by adopting the general Rényi entropy instead of the collision entropy (i.e., Rényi entropy of order two). We show that adopting a higher order can reduce the variance of min-entropy estimates. By integrating these techniques, we propose a generalized LRS estimator that effectively resolves the overestimation problem and provides stable min-entropy estimates. Theoretical analysis and empirical results support that the proposed generalized LRS estimator improves the estimation accuracy significantly, which makes it an appealing alternative to the current-standard LRS estimator.
On One-Bit Quantization

We consider the one-bit quantizer that minimizes the mean squared error for a source living in a real Hilbert space. The optimal quantizer is a projection followed by a thresholding operation, and we provide methods for identifying the optimal direction along which to project. As an application of our methods, we characterize the optimal one-bit quantizer for a continuous-time random process that exhibits low-dimensional structure. We numerically show that this optimal quantizer is found by a neural-network-based compressor trained via stochastic gradient descent.
From Generalisation Error to Transportation-cost Inequalities and Back

In this work, we connect the problem of bounding the expected generalisation error with transportation-cost inequalities. Exposing the underlying pattern behind both approaches we are able to generalise them and go beyond Kullback- Leibler Divergences/Mutual Information and sub-Gaussian measures. In particular, we are able to provide a result showing the equivalence between two families of inequalities: one involving functionals and one involving measures. This result generalises the one proposed by Bobkov and Götze that connects transportation-cost inequalities with concentration of measure. Moreover, it allows us to recover all standard generalisation error bounds involving mutual information and to introduce new, more general bounds, that involve arbitrary divergence measures.
Entanglement-Assisted Quantum Error-Correcting Codes over Local Frobenius Rings

In this paper, we provide a framework for constructing entanglement-assisted quantum error-correcting codes (EAQECCs) from classical additive codes over a finite commutative local Frobenius ring. We give a formula for the minimum number of entanglement qudits required to construct an EAQECC from an additive code over a finite Galois ring. This significantly extends known results for EAQECCs over finite fields.
Construction of the Visual Cryptography Scheme with the Maximum Relative Difference under a Strong General Access Structure

Visual cryptography schemes are used for secret sharing of digital images. In this paper we develop a new simple construction of a visual cryptography scheme with the maximum relative difference for an arbitrarily given strong access structure. Our construction is based on finding the optimal rational-valued solution to a certain linear programming problem which is closely related to the maximization of the relative difference. Validity of the construction is theoretically proved. Several examples are also given for demonstrating usefulness of our construction.