Reviewerly/exHarmony huggingface dataset:
Construction of Blockchain Technology Audit System

Blockchain technology is one of the ten most avant-garde innovative technologies that are most likely to change the road of innovation in 2016, and perhaps the most significant invention since the emergence of the Internet. Blockchain has the advantages of “decentralized”, “Distributed Accounting”, “open and transparent”, “tamper proof”, and these advantages will reshape the audit industry, and the requirements for audit professionals will also change. This paper is committed to building an audit professional curriculum system to cultivate students with audit professional foundation and blockchain technology, In order to cultivate the compound audit talents needed by enterprises to discover value and transfer value under the background of blockchain technology.
Adaptive color correction and detail restoration for underwater image enhancement

Underwater images have different color casts due to different attenuation conditions, such as bluish, greenish, and yellowish. In addition, due to floating particles and special illumination, underwater images have problems such as the lack of detail and unnecessary noise. To handle the above problems, this paper proposes a new, to the best of our knowledge, three-step adaptive enhancement method. For the first step, adaptive color correction, the three channels are adjusted based on the intermediate color channel, which is calculated by considering the positional relationship of the histogram distribution. For the second step, denoise and restore details, we first transform the space to hue, saturation, value (HSV), a detailed restoration method based on the edge-preserving decomposition that restores the lost detail while removing the influence of some noise. For the third step, we improve the global contrast. Still in the HSV space, a simple linear stretch strategy is applied to the saturation channel. Experiments on the standard underwater image enhancement benchmark data set have proved that our method yields more natural colors and more valuable detailed information than several state-of-the-art methods. In addition, our method also improves the visibility of underwater images captured by low-light scenes and different hardware cameras.
Cycle or Minkowski

Knowledge graph (KG) embedding aims to encode entities and relations into low-dimensional vector spaces, in turn, can support various machine learning models on KG related tasks with good performance. However, existing methods for knowledge graph embedding fail to consider the influence of the embedding space, which makes them still unsatisfactory in practical applications. In this study, we try to improve the expressiveness of the embedding space from the perspective of the metric. Specifically, we first point out the implications of Minkowski metric used in KG embedding and then make a quantitative analysis. To solve the limitations, we introduce a new metric, named Cycle metric, based on the oscillation property of the periodic function. Furthermore, we find that the function period has a significant influence on the expressiveness of the embedding space. Given a fully trained model, the smaller the period, the better the expressive ability. Finally, to validate the findings, we propose a new model, named CyclE by combining Cycle Metric and the popular KG embeddings models. Comprehensive experimental results show that Cycle is more appropriate than Minkowski for KG embedding.
Phase estimation of local Hamiltonians on NISQ hardware

Abstract In this work we investigate a binned version of quantum phase estimation (QPE) set out by Somma (2019 New J. Phys. 21 123025) and known as the quantum eigenvalue estimation problem (QEEP). Specifically, we determine whether the circuit decomposition techniques we set out in previous work, Clinton et al (2021 Nat. Commun. 12 1–10), can improve the performance of QEEP in the noisy intermediate scale quantum (NISQ) regime. To this end we adopt a physically motivated abstraction of NISQ device capabilities as in Clinton et al (2021 Nat. Commun. 12 1–10). Within this framework, we find that our techniques reduce the threshold at which it becomes possible to perform the minimum two-bin instance of this algorithm by an order of magnitude. This is for the specific example of a two dimensional spin Fermi-Hubbard model. For example, we estimate that the depolarizing single qubit error rate required to implement a minimum two bin example of QEEP—with a <?CDATA $5\times5$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>5</mml:mn> <mml:mo>×</mml:mo> <mml:mn>5</mml:mn> </mml:math> Fermi-Hubbard model and up to a precision of <?CDATA $10\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>10</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> —can be reduced from 10 −7 to 10 −5 . We explore possible modifications to this protocol and propose an application, which we dub randomized quantum eigenvalue estimation problem (rQEEP). rQEEP outputs estimates on the fraction of eigenvalues which lie within randomly chosen bins and upper bounds the total deviation of these estimates from the true values. One use case we envision for this algorithm is resolving density of states features of local Hamiltonians.
Reduce food crop wastage with hyperledger fabric-based food supply chain

Food is the utmost important thing for every living being. The quality and safety of food has become a crucial factor in the food industry. Most of the customers tend to pay more attention to food safety and seek to get food from verifiable resources. To improve this trustworthiness Distributed Ledger Technology (DLT) - based Food Supply Chain (FSC) plays a vital role because of its traceability. There are multiple actors involved throughout the journey of FSC and with the high visibility of data in DLT, everyone can ensure trust. The transparency of data itself is a reason for some to opt-out because some of their private data can be exposed to others. Hyperledger Fabric (HF) based FSC can address that matter as it supports permissioned network solutions. Though there are a lot of solutions available in a similar kind of approach, whether the crops take their journey throughout the FSC without any wastage, is still questionable. This study focuses on reducing wastage of food crops as they take a long journey in their raw state and possible hazards are high. It discusses farmers' behavior based on the Sri Lankan context and how it accompanies food crop wastage. Further, this paper ruminates the other possible crop wastage that can take place in FSC and how to eliminate it with the proper involvement of knowledgeable and authorized parties. Then, the study explores how all the parties can collaboratively join the FSC based on HF so that everyone can benefit. Finally, it concludes on how such design is effectively contributing to reducing food crop wastage in Sri Lanka (SL).
Forming the Competence of the Teacher of Educational Robotics

In the near future, the most popular professions will be those related to digital technologies: IT specialist, big data engineer, data architect, robot developer, operator of robotic systems. In this regard, the modern digital society needs professionals with knowledge in the field of artificial intelligence, physics, electronics, programming and design. In order for such demanded personnel to appear on the labor market, their training must begin at school, developing children's interest in engineering and technical professions. To solve this problem, a subject was introduced into schools aimed at the formation of technical and engineering knowledge of students - educational robotics. At the same time, there was a need to train teachers in this area. The article describes a meaningful model of training future teachers of educational robotics, in which the components of their professional competence are highlighted, the target and methodological components of training are presented, the organizational conditions for the formation of teachers' competence for the successful implementation of professional activities in the field of educational robotics are determined.
Local-dimension-invariant Calderbank–Shor–Steane codes with an improved distance promise

Quantum computers will need effective error-correcting codes. Current quantum processors require precise control of each particle, so having fewer particles to control might be beneficial. Although traditionally quantum computers are considered as using qubits (2-level systems), qudits (systems with more than 2-levels) are appealing since they can have an equivalent computational space using fewer particles, meaning fewer particles need to be controlled. In this work, we prove how to construct codes with parameters $$[[2^N-1,2^N-1-2N,\ge 3]]_q$$ for any choice of prime q and natural number $$N\ge 3$$ . This is accomplished using the technique of local-dimension-invariant (LDI) codes. Generally, LDI codes have the drawback of needing large local-dimensions to ensure the distance is at least preserved, and so this work also quadratically reduces this requirement by utilizing the structure of CSS codes, allowing for the aforementioned code family to be imported for any local-dimension choice.
A Device to Determine the Panel Orientation for Best Annual Solar Energy Generation at a Selected Location

Within the past few years, solar photovoltaic (PV) power generation has gained popularity along with sustainable concepts around the world. The low efficiency of PV systems could be identified as a leading drawback of energy conversion. Panel tilt angle and direction directly affect the amount of irradiance incident on solar panels, governing the amount of energy generated. Assuming ideal conditions, fixed panels are installed to face the equator, as used in common practice, while the tilt angle is kept equal to the latitude of the location. Due to the uniqueness of geographic and weather conditions in different locations, the irradiance patterns may deviate from the ideal. Thus, the optimum panel orientation may differ from conventional practise. Thus, the objective of this study was to introduce a universal method which can determine the location-specific optimum orientation of solar panels. The in-situ weather and irradiance data were captured for a period of time (approx. 30 weeks) at a given location. In addition, weather data at the location was collected for the past few years via relevant authorities. A mathematical model was developed to extrapolate the irradiance incident on various orientations using available data as a guideline for an average year. The orientation that is expected to capture the highest amount of irradiance was determined as the optimum orientation. Then, a case study was carried out following the above methodology at the department of mechanical engineering, University of Moratuwa. The analytical results showed that the overall efficiency of the PV system was increased by 0.4% compared to that obtained with the generic tilt (i.e. 7°) setup. The efficiency of the entire system could be increased without an additional cost if such analysis is conducted prior installation. The analysis would be more appropriate for large scale solar farms rather than domestic installations.
Graph-based Semi-Supervised Learning by Strengthening Local Label Consistency

Graph-based algorithms have drawn much attention thanks to their impressive success in semi-supervised setups. For better model performance, previous studies have learned to transform the topology of the input graph. However, these works only focus on optimizing the original nodes and edges, leaving the direction of augmenting existing data insufficiently explored. In this paper, we propose a novel heuristic pre-processing technique, namelyLocal Label Consistency Strengthening (ŁLCS), which automatically expands new nodes and edges to refine the label consistency within a dense subgraph. Our framework can effectively benefit downstream models by substantially enlarging the original training set with high-quality generated labeled data and refining the original graph topology. To justify the generality and practicality of ŁLCS, we couple it with the popular graph convolution network and graph attention network to perform extensive evaluations on three standard datasets. In all setups tested, our method boosts the average accuracy by a large margin of 4.7% and consistently outperforms the state-of-the-art.
RoBin: Facilitating the Reproduction of Configuration-Related Vulnerability

Vulnerability reproduction paves a way in debugging software failures, which need intensive manual efforts. However, some key factors (e.g., software configuration, trigger method) are often missing, so we can not directly reproduce the failure without extra attempts. Even worse, highly customized configuration options of programs create a barrier for reproducing the vulnerabilities that only appear under some specific combinations of configurations. In this paper, we address the problem mentioned above - reproducing the configuration-related vulnerability. We try to solve it by proposing a binary similarity-based method to infer the specific building configurations via the binary from crash report. The main challenges are as follows: precise compilation option inference, program configuration inference, and source-code-to-binary matching. To achieve the goal, we implement RoBin, a binary similarity-based building configuration inference tool. To demonstrate the effectiveness, we test RoBin on 21 vulnerable cases upon 4 well-known open-source programs. It shows a strong ability in pinpointing the building configurations causing the vulnerability. The result can help developers reproduce and diagnose the vulnerability, and finally, patch the programs.
Light-Weight Multi-channel Aggregation Network for Image Super-Resolution

Deep convolutional neural networks (CNNs) have been extensively applied on single image super-resolution (SISR) due to the strong representation. However, since SISR is an ill-posed problem, many CNN-based methods rely heavily on excessive parameters and high computation cost, which limit the practical application on devices with limited resources. In this paper, a light-weight yet effective multi-channel aggregation network (MCAN) is proposed to improve the performance of SISR while maintaining efficiency. Specifically, the network is built upon several efficient multi-channel aggregation blocks, each of which contains a multi-channel aggregation (MCA) module and a dilated attention (DA) module. The proposed MCA module reduces network parameters considerably, moreover, the channel split and concatenation operations ensure multi-channel interaction and enrich multi-scale features effectively. Furthermore, the DA module captures multiple spatial feature correlations using multi-scale dilated convolution for a larger receptive field. Experimental results on four publicly available datasets demonstrate the superiority of the proposed MCAN in terms of accuracy and model complexity.
Quantum entanglement in the one-dimensional anyonic Hubbard model

Issues related to quantum entanglement in systems of indistinguishable particles, as discussed in the information theoretic approach, are extended to anyonic statistics. Local and non-local measurements discussed in this framework are carefully analyzed in the two-site anyonic Hubbard model which provides a concrete case-study. The von Neumann entropy, the single-particle reduced density matrix, the pair correlation function, and the pseudo-momentum distribution function are worked out paying special attention to the dependence on the statistics parameter. Restrictions arising from superselection rules for anyons are outlined.
KG-RL: A Knowledge-Guided Reinforcement Learning for Massive Battle Games

In a multi-agent game, the complexity of the environment increases exponentially as the number of agents increases. Learning becomes difficult when there are so many agents. Mean field multi-agent reinforcement learning (MFRL) uses the average action of the neighbors to increase the input of the value network, which can be applied in the environment with hundreds of agents. However, inefficient exploration and slow convergence speed limit the performance of the algorithm. In this article, we propose a new Knowledge-Guided Reinforcement Learning (KG-RL) method, which can be divided into rule-mix and plan-extend. We use the rule-mix to encode knowledge into plans which can reduce redundant information and invalid actions in the state. And the plan-extend can combine the result of rule-mix with reinforcement learning to achieve more efficient joint exploration. Through experiments in Magent environment, we prove that the win rate of our proposed KG-RL is 22% higher than that of knowledge-based decision tree and 39% higher than that of MFRL. Thus, the KG-RL can perform well in massive battle games due to its high exploration efficiency and fast convergence.
Detection and Localization of Tampered Image using Hash Functions

In this fast-developing world digital dependency has taken up great heights. With its immense usage comes the concept of revealing false information of an image to feed the dreadful intention which is called nothing but Image Forgery. The duplicated or forged image is operated in a way that detecting the tampered regions can be an exhaustible task. The paper exactly deals with such alterations in the structure of the image. It offers a Hashing technique, which as best described, helps to detect and locate the tampered regions of the image. A comparison of various Hash Functions like MD5 and SHA-2, in particular, is put into effect with its implementation highlighting the desired tampered region of the image which is facilitated by the sliding window approach.
Optimization of Interval Type-2 Intuitionistic Fuzzy Logic System for Prediction Problems

Derivative-based algorithms have been adopted in the literature for the optimization of membership and non-membership function parameters of interval type-2 (T2) intuitionistic fuzzy logic systems (FLSs). In this study, a non-derivative-based algorithm called sliding mode control learning algorithm is proposed to tune the parameters of interval T2 intuitionistic FLS for the first time. The proposed rule-based learning system employs the Takagi–Sugeno–Kang inference with artificial neural network to pilot the learning process. The new learning system is evaluated using some nonlinear prediction problems. Analyses of results reveal that the proposed learning apparatus outperforms its type-1 version and many existing solutions in the literature and competes favorably with others on the investigated problem instances with low cost in terms of running time.
Automated Cargo Delivery in Low Altitudes: Concepts and Research Questions of an Operational-Risk-Based Approach

The project Automated Low Altitude Air Delivery–ALAADy is a study on the design of a heavy-lift transportation drone in the context of certification based on operational risks. This chapter is the opening of a book providing the results of the initial phase of the project within the years 2016 to 2019. Conceptually, in ALAADy, unmanned aircraft systems (UAS) are envisioned to provide a payload capacity of around one metric ton. Research questions on how the newly established EASA Specific Category of drone operation and its acceptable means of compliance, the Specific Operations Risk Assessment (SORA), influences the UAS are introduced and motivated in this chapter. Applying the SORA imposes limits to the operation to ensure safety. The project focus is investigating the impact on the UAS design if the operational limitations are maximized by restricting the flight over sparsely populated areas and below common air traffic only, while exploring large-scale UAS at the same time. The aspects considered include UAS design, aircraft configuration, system architecture, datalinks, safe autonomy, and airspace integration. The impact on the use-cases of such a UAS as well as the effect on the economical perspective are also addressed. Finally, this chapter introduces the need to design and implement scaled technology demonstrators and presents the overall project’s perspective to experimental analysis. One demonstrator is based on a manned micro light gyrocopter converted into a cargo drone. It enables payload transportation of around 150–200 kg and is applied to validate the conceptual part of the ALAADy project beyond simulation.
Locality-Constrained Collaborative Representation with Multi-resolution Dictionary for Face Recognition

Sparse learning methods have drawn considerable attention in face recognition, and there are still some problems need to be further studied. For example, most of the conventional sparse learning methods concentrate only on a single resolution, which neglects the fact that the resolutions of real-world face images are variable when they are captured by different cameras. Although the multi-resolution dictionary learning (MRDL) method considers the problem of image resolution, it takes a lot of training time to learn a concise and reliable dictionary and neglects the local relationship of data. To overcome the above problems, we propose a locality-constrained collaborative representation with multi-resolution dictionary (LCCR-MRD) method for face recognition. First, we extend the traditional collaborative representation based classification (CRC) method to the multi-resolution dictionary case without dictionary learning. Second, the locality relationship characterized by the distance between test sample and training sample is used to learn weight of representation coefficient, and the similar sample is forced to make more contribution to representation. Last, LCCR-MRD has a closed-form solution, which makes it simple. Experiments on five widely-used face databases demonstrate that LCCR-MRD outperforms many state-of-art sparse learning methods. The Matlab codes of LCCR-MRD are publicly available at https://github.com/masterliuhzen/LCCR-MRD.
Cooperative Coevolution with Two-Stage Decomposition for Large-Scale Global Optimization Problems

Cooperative coevolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems. However, CC with static decomposition method is ineffective for fully nonseparable problems, and CC with dynamic decomposition method to decompose problems is computationally costly. Therefore, a two-stage decomposition (TSD) method is proposed in this paper to decompose LSGO problems using as few computational resources as possible. In the first stage, to decompose problems using low computational resources, a hybrid-pool differential grouping (HPDG) method is proposed, which contains a hybrid-pool-based detection structure (HPDS) and a unit vector-based perturbation (UVP) strategy. In the second stage, to decompose the fully nonseparable problems, a known information-based dynamic decomposition (KIDD) method is proposed. Analytical methods are used to demonstrate that HPDG has lower decomposition complexity compared to state-of-the-art static decomposition methods. Experiments show that CC with TSD is a competitive algorithm for solving LSGO problems.
A Discrete Sparrow Search Algorithm for Robot Routing Problem

Arranging a reasonable route satisfying constraints for robots to serve is crucial for its application in hospital logistics. The problem was modeled as a single depot delivery problem with capacitated constraint. For solving the problem, a discrete sparrow search algorithm was proposed in this study with multiple enhancement strategies. Based on the original sparrow search algorithm, local search and genetic operators were combined to make it feasible for the aforementioned problem. In addition, the solution representation had also been ameliorated to fit the application. These defined operations hold a good balance between exploration and exploitation. Experiment results showed that our proposed approach gives a promising solution when compared to other novel heuristic methods.